Applied Sciences

16 pages, 2652 KiB

Open AccessArticle

Research on Seismic Signal Analysis Based on Machine Learning

by Xinxin Yin, Feng Liu, Run Cai, Xiulong Yang, Xiaoyue Zhang, Meiling Ning and Siyuan Shen

Appl. Sci. 2022, 12(16), 8389; https://doi.org/10.3390/app12168389 - 22 Aug 2022

Cited by 11 | Viewed by 4081

In this paper, the time series classification frontier method MiniRocket was used to classify earthquakes, blasts, and background noise. From supervised to unsupervised classification, a comprehensive analysis was carried out, and finally, the supervised method achieved excellent results. The relatively simple model, MiniRocket, [...] Read more.

In this paper, the time series classification frontier method MiniRocket was used to classify earthquakes, blasts, and background noise. From supervised to unsupervised classification, a comprehensive analysis was carried out, and finally, the supervised method achieved excellent results. The relatively simple model, MiniRocket, is only a one-dimensional convolutional neural network structure which has achieved the best comprehensive results, and its computational efficiency is far stronger than other supervised classification methods. Through our experimental results, we found that the MiniRocket model could well-extract the decisive features of the seismic sensing signal. In order to try to eliminate the tedious work of making data labels, we proposed a novel lightweight collaborative learning for seismic sensing signals (LCL-SSS) based on the method of feature extraction in MiniRocket combined with unsupervised classification. The new method gives new vitality to the unsupervised classification method that could not be used originally and opens up a new path for the unsupervised classification of seismic sensing signals. Full article

(This article belongs to the Special Issue Intelligent Computing and Remote Sensing)

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20 pages, 6252 KiB

Open AccessArticle

SRMANet: Toward an Interpretable Neural Network with Multi-Attention Mechanism for Gearbox Fault Diagnosis

by Siyuan Liu, Jinying Huang, Jiancheng Ma and Jia Luo

Appl. Sci. 2022, 12(16), 8388; https://doi.org/10.3390/app12168388 - 22 Aug 2022

Cited by 8 | Viewed by 2463

Abstract

Deep neural network (DNN), with the capacity for feature inference and nonlinear mapping, has demonstrated its effectiveness in end-to-end fault diagnosis. However, the intermediate learning process of the DNN architecture is invisible, making it an uninterpretable black-box model. In this paper, a stacked [...] Read more.

Deep neural network (DNN), with the capacity for feature inference and nonlinear mapping, has demonstrated its effectiveness in end-to-end fault diagnosis. However, the intermediate learning process of the DNN architecture is invisible, making it an uninterpretable black-box model. In this paper, a stacked residual multi-attention network (SRMANet) is proposed as a means of feature extraction of vibration signals, and visualizing the model training process, designing Squeeze-excitation residual (SE-Res) blocks to obtain additive features with minimal redundancy and sparsity. This study recommends the use of the attention fusion unit to ensure the interpretability of the model and ultimately to obtain representative features. By feeding the output gradient of the attention layer back to the original signal, the key feature components in the time domain signal can be effectively captured. Finally, the interpretability, identification accuracy and adaptability of the model under different operating conditions are verified on 12 different fault tasks in the planetary gearbox. Full article

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14 pages, 6454 KiB

Open AccessArticle

Solving the Moment Amplification Factor of a Lateral Jet by the Unsteady Motion Experimental Method

by Fei Xue, Yunlong Zhang, Ning Cao and Liugang Li

Appl. Sci. 2022, 12(16), 8387; https://doi.org/10.3390/app12168387 - 22 Aug 2022

Cited by 1 | Viewed by 1535

Abstract

In this paper, unsteady motion tests of a lateral jet adjusting an air vehicle’s attitude are carried out. Curves of pitch moment amplification factors (K_M) for a lateral jet versus angle of attack (α) are obtained using a [...] Read more.

In this paper, unsteady motion tests of a lateral jet adjusting an air vehicle’s attitude are carried out. Curves of pitch moment amplification factors (K_M) for a lateral jet versus angle of attack (α) are obtained using a wind tunnel free-flight test technique with a jet and data processing method. This new method overcomes the disadvantage of previous experiments that can study only one unsteady characteristic. The free-flight test technique in the proposed method ensures that the test model can be coupled in real-time with multiple parameters (unsteady flow caused by the jet, unsteady air vehicle aerodynamic force, and unsteady air vehicle motion). This approach simulates an actual air vehicle’s complete jet test process and ensures more authentic and reliable test results. In the new data processing method, continuous data curves are fitted to discrete data points, making it easier to convert the angular displacement versus time curve into the pitch moment versus α curve to obtain K_M. The results show that when the pressure of the micro high-pressure gas cylinder is 2.0 MPa, K_M is below 1, indicating that the lateral jet does not significantly promote the pitching moment. When the gas cylinder pressure is 4.0 MPa and the angle of attack is 5° < |α| < 16°, K_M is greater than 1, and the lateral jet promotes the pitching moment. When 16° < |α| < 20°, K_M is less than 1, and the lateral jet does not significantly contribute to the pitching moment. It was further found that K_M decreases slowly with increasing α. When |α| > 30°, the influence of the jet on the pitching moment nearly disappears. Full article

(This article belongs to the Special Issue Structural Wind Engineering: Measurement, Simulation and Dynamic Analysis)

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23 pages, 9515 KiB

Open AccessArticle

Visual Servo Control of the Macro/Micro Manipulator with Base Vibration Suppression and Backlash Compensation

by Yaowen Zhang, Yechao Liu, Zongwu Xie, Yang Liu, Baoshi Cao and Hong Liu

Appl. Sci. 2022, 12(16), 8386; https://doi.org/10.3390/app12168386 - 22 Aug 2022

Cited by 6 | Viewed by 2387

Abstract

This study investigates the visual servo control of the space station macro/micro manipulator system. The proposed approach is based on the position-based eye-in-hand visual servo (PBVS) and takes advantage of the hardware sensors to overcome the macro manipulator’s base flexibility and joint backlash. [...] Read more.

This study investigates the visual servo control of the space station macro/micro manipulator system. The proposed approach is based on the position-based eye-in-hand visual servo (PBVS) and takes advantage of the hardware sensors to overcome the macro manipulator’s base flexibility and joint backlash. First, a vibration suppression approach based on the reaction force feedback control is proposed, the deflection forces are measured by the six-axis force/torque sensor at the base of the micro-manipulator, and damping is injected into the flexible base in the closed-loop control to suppress the base vibration. Second, the small changes of joint backlash are compensated based on the macro manipulator joint angles sensor and converted to the desired motion of the payloads. Finally, PBVS with the lag correction is proposed, which is adequate for the precise positioning of large payloads with significant low-frequency oscillations. Ground micro-gravity experiment implementation is discussed, simulations and experiments are carried out based on the equivalent 3-DOF flexible base manipulator system and the macro/micro manipulator ground facilities, and results demonstrate the effectiveness of the proposed control algorithm. Full article

(This article belongs to the Special Issue Active and Passive Approaches to Vibration Control in Flexible Mechanical Systems)

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17 pages, 5361 KiB

Open AccessArticle

Internal Curing Effect of Waste Glass Beads on High-Strength Cement Composites

by Sujeong Pyeon, Gyuyong Kim, Sangsoo Lee and Jeongsoo Nam

Appl. Sci. 2022, 12(16), 8385; https://doi.org/10.3390/app12168385 - 22 Aug 2022

Cited by 4 | Viewed by 2095

Abstract

High-strength concrete (HSC) uses binders and microfillers with ultrafine particles, such as silica fume. The resulting dense internal hydration structure rapidly decreases HSC humidity, causing shrinkage cracks and affecting internal hydration. Herein, the hydration degree inside high-strength cement composites (HSCCs) was examined using [...] Read more.

High-strength concrete (HSC) uses binders and microfillers with ultrafine particles, such as silica fume. The resulting dense internal hydration structure rapidly decreases HSC humidity, causing shrinkage cracks and affecting internal hydration. Herein, the hydration degree inside high-strength cement composites (HSCCs) was examined using waste glass beads (WGBs) as lightweight aggregates (LWAs). Moreover, unreacted hydrate reduction and hydrate formation tendencies were investigated. WGBs with particle sizes within 2.00–6.00 mm were added at ratios of 5%, 10%, and 20% after pre-wetting. The increased number of hydrates inside the specimens were examined under steam curing (80 °C) and room temperature curing (25 °C). The strength decreased as the WGB content increased. Thermogravimetric, X-ray diffraction, and Si nuclear magnetic resonance analyses revealed that the hydration degree of Si inside HSCCs changed when the content of pre-wetted LWAs changed. A visual inspection of the specimen cross-section and scanning electron microscopy–energy-dispersive X-ray spectrometry (SEM–EDS) analysis revealed the moisture trapped inside WGB pores and the hydration tendency. Under steam curing and room temperature curing, the paste contained different amounts of hydrates, depending on WGB content. Moreover, water-absorbed WGBs were continuously desorbed through SEM–EDS, and hydrates were present in WGB pores. Full article

(This article belongs to the Special Issue Advanced Fiber-Reinforced Cementitious Composites)

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26 pages, 8289 KiB

Open AccessArticle

A Mixing Process Influenced by Wall Jet-Induced Shock Waves in Supersonic Flow

by Ji Zhang, Daoning Yang, Yi Wang and Dongdong Zhang

Appl. Sci. 2022, 12(16), 8384; https://doi.org/10.3390/app12168384 - 22 Aug 2022

Cited by 2 | Viewed by 1709

Abstract

With the development of hypersonic air-breathing propulsion systems, such as the supersonic combustion ramjet (Scramjet) and rocket-based combined cycle (RBCC) engines, the mixing process of supersonic airstream with fuel in the engine combustor has been drawing more and more attention. Due to the [...] Read more.

With the development of hypersonic air-breathing propulsion systems, such as the supersonic combustion ramjet (Scramjet) and rocket-based combined cycle (RBCC) engines, the mixing process of supersonic airstream with fuel in the engine combustor has been drawing more and more attention. Due to the compressibility effects, the mixing process in a supersonic condition is significantly inhibited. In the present paper, the novel strategy of wall-jet induced shock waves (WJISW) is put forward to realize mixing enhancement. The interaction process between WJISW and the supersonic mixing layer is researched and the enhanced-mixing mechanism is revealed, employing large eddy simulation (LES) methods. The fine vortex structures of the flow field are well captured and presented, utilizing the numerical schlieren technique. Detailed visualization results indicate that WJISW in a low frequency condition can result in the ‘region action mode’ (RAM) never reported before. The drastic dynamic behaviors including growth, deformation, and distortion in the interaction region can undoubtedly promote the mixing of upper and lower streams. The Reynolds stress distributions along the streamwise x-direction suggest that more intense fluctuations can be achieved with a low frequency WJISW. Moreover, a sharp increase in mixing layer thickness can be realized in the interaction region. The dynamic mode decomposition (DMD) analysis results show that the mixing layer evolution process is dominated by the mode induced by WJISW, which leads to the coexistence of both large- and small-scale structures in the flow field. The entrainment process corresponding to large-scale vortices and the nibbling process corresponding to small-scale vortices can obviously promote mixing enhancement. It is suggested that the present proposed strategy is a good candidate for enhanced-mixing with application to Scramjet and RBCC combustors. Full article

(This article belongs to the Special Issue Intelligent Autonomous Decision-Making and Cooperative Control Technology of High-Speed Vehicle Swarms, Volume II)

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13 pages, 2294 KiB

Open AccessArticle

Rapid and Accurate PPA Prediction for the Template-Based Processor Design Methods

by Mingxin Tang, Libo Huang and Wei Chen

Appl. Sci. 2022, 12(16), 8383; https://doi.org/10.3390/app12168383 - 22 Aug 2022

Cited by 1 | Viewed by 1909

Abstract

The template-based chip design method aims to build rapidly. However, it still need synthesis and simulation flows to get the performance, power, and area (PPA) reports and find the proper parameters set in the design space exploration, which takes a long time. Therefore, [...] Read more.

The template-based chip design method aims to build rapidly. However, it still need synthesis and simulation flows to get the performance, power, and area (PPA) reports and find the proper parameters set in the design space exploration, which takes a long time. Therefore, a rapid and accurate PPA prediction method is proposed. At first, the PPA Prediction Model based on Multivariate Linear regression (ML-PM) is proposed to fit the multiple parameters’ influence on the PPA via the single parameter affection. Moreover, a Multivariate NonLinear regression Prediction Model (MNL-PM) based on Amdahl’s law is introduced to improve the accuracy of the PPA estimation. The empirical evaluation of the method shows that the PPA prediction for the template-based chip design methods can reach 98.60%, 99.19%, and 98.53% accuracy on performance, power, and, area separately, when compared with the PPA generated via the synthesis and simulation flows. Full article

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16 pages, 7289 KiB

Open AccessArticle

Plasma-Sprayed Flexible Strain Sensor and Its Applications in Boxing Glove

by Yongsheng Liao, Yue Cheng, Zhongyu Zhuang, Rongjun Li, Yuan Yu, Ruixue Wang and Zhiwei Jiao

Appl. Sci. 2022, 12(16), 8382; https://doi.org/10.3390/app12168382 - 22 Aug 2022

Cited by 2 | Viewed by 1848

Abstract

The most common and easy approach to fabricating flexible strain sensors is based on the deposition principle. To improve the design of the sensing layer pattern, the reproducibility of the process and the sensitivity of the sensor, a controllable low-temperature-plasma spraying method for [...] Read more.

The most common and easy approach to fabricating flexible strain sensors is based on the deposition principle. To improve the design of the sensing layer pattern, the reproducibility of the process and the sensitivity of the sensor, a controllable low-temperature-plasma spraying method for conducting nanoparticles was proposed. A flexible strain sensor was developed with multiwalled carbon nanotubes as the sensing layer and silica gel films as the substrate. The effects of plasma treatment on the cyclic stability and sensitivity of the sensor were examined and compared. The changes in the sensitivity of the sensor with the pattern parameters were also studied. The sensitivity of the sensor treated with low-temperature plasma was greatly improved (from 3.9 to 11.5) compared to that of an untreated sensor. In addition, pattern parameters significantly affected the rate of change in the resistance. A portable smart boxing glove prototype was developed using the prepared sensor and was then tested. The results showed that the smart glove could transmit and monitor a striking force of 49–490 N in real time with a sampling time, resolution, response time, and recovery time of 100 ms, up to 1.05 kg, 8 ms, and 150 ms, respectively. Full article

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14 pages, 1334 KiB

Open AccessArticle

Risk Analysis of Seaport Construction Project Execution

by Magdalena Kaup, Dorota Łozowicka, Karolina Baszak, Wojciech Ślączka and Agnieszka Kalbarczyk-Jedynak

Appl. Sci. 2022, 12(16), 8381; https://doi.org/10.3390/app12168381 - 22 Aug 2022

Cited by 5 | Viewed by 2920

Abstract

This article concerns the assessment of the level of risk at the stage of construction of a seaport, with particular emphasis on selected adverse incidents that can significantly affect the timeliness of the investment. In this article, the matrix method was used to [...] Read more.

This article concerns the assessment of the level of risk at the stage of construction of a seaport, with particular emphasis on selected adverse incidents that can significantly affect the timeliness of the investment. In this article, the matrix method was used to analyse and evaluate the level of risk, and statistical analysis and case studies were used to identify incidents occurring during the port construction project. This allowed the identification of incidents with the highest probability of occurrence during the port construction process and to determine their impact on environmental pollution and the timeliness and success of the investment. The risk analysis performed identified 15 typical incidents of technical nature. The determined risk level for these incidents is at a moderate level or lower, which can be considered acceptable. For all undesirable incidents the values of probability and loss levels have been averaged, because e.g., a fire can have an extremely different dimension and can cause a different scale of losses. Analysis presented in the paper indicate the need to develop procedures for proceeding during the implementation of significant technical tasks to minimize the level of risk of adverse incidents and their consequences. Full article

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14 pages, 2076 KiB

Open AccessArticle

Hypertension Detection Based on Photoplethysmography Signal Morphology and Machine Learning Techniques

by Lucian Evdochim, Dragoș Dobrescu, Stela Halichidis, Lidia Dobrescu and Silviu Stanciu

Appl. Sci. 2022, 12(16), 8380; https://doi.org/10.3390/app12168380 - 22 Aug 2022

Cited by 13 | Viewed by 2827

Abstract

In our modern digitalized world, hypertension detection represents a key feature that enables self-monitoring of cardiovascular parameters, using a wide range of smart devices. Heart rate and blood oxygen saturation rate are some of the most important ones, easily computed by wearable products [...] Read more.

In our modern digitalized world, hypertension detection represents a key feature that enables self-monitoring of cardiovascular parameters, using a wide range of smart devices. Heart rate and blood oxygen saturation rate are some of the most important ones, easily computed by wearable products that are provided by the photoplethysmography (PPG) technique. Therefore, this low-cost technology has opened a new horizon for health monitoring in the last decade. Another important parameter is blood pressure, a major predictor for cardiovascular characterization and health related events. Analyzing only PPG signal morphology and combining the medical observation with machine learning (ML) techniques, this paper develops a hypertension diagnosis tool, named the ANC Test™. During the development process, distinguishable characteristics have been observed among certain waveforms and certain types of patients that leads to an increased confidence level of the algorithm. The test was enchanted by machine learning models to improve blood pressure class detection between systolic normotensive and hypertensive patients. A total of 359 individual recordings were manually selected to build reference signals using open-source available databases. During the development and testing phases, different ML models accuracy of detecting systolic hypertension scored in many cases around 70% with a maximum value of 72.9%. This was resulted from original waveform classification into four main classes with an easy-to-understand nomenclature. An important limitation during the recording processing phase was given by a different PPG acquisition standard among the consulted free available databases. Full article

(This article belongs to the Special Issue Applications of Photoplethysmography (PPG) in Healthcare and Wellbeing)

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16 pages, 3362 KiB

Open AccessArticle

Evaluation of Performance of Polyacrylamide-Modified Compacted Clay as a Gas Barrier: Water Retention and Gas Permeability and Diffusion Characteristics

by Yu-Zhang Bi, Jia-Ming Wen, Hao-Liang Wu and Yan-Jun Du

Appl. Sci. 2022, 12(16), 8379; https://doi.org/10.3390/app12168379 - 22 Aug 2022

Cited by 5 | Viewed by 2236

Abstract

In this paper, the performance of a gas barrier that consisted of polyacrylamide (PAM)-modified compacted clayey soil was experimentally explored. The moisture content and water loss characteristics of the tested soils were adopted as indicative indices of water retention capacity (WRC). The gas [...] Read more.

In this paper, the performance of a gas barrier that consisted of polyacrylamide (PAM)-modified compacted clayey soil was experimentally explored. The moisture content and water loss characteristics of the tested soils were adopted as indicative indices of water retention capacity (WRC). The gas permeability (K_p) and gas diffusion coefficient (D_p) of the modified compacted clays were evaluated via gas permeability and gas diffusion tests. The test results showed that the moisture content of the modified compacted clay samples subjected to drying tests increased with increasing polyacrylamide content. K_p and D_p decreased with increasing PAM content. Compared with 0.2% PAM content, the K_p of the sample with 1.0% PAM was reduced by ten times, and the D_p was reduced to ~35%. Compared to the unmodified clay, the liquid limit of the PAM-modified clay increased by 45~55%. Comparison of the liquid limit tests between this study and previous studies revealed that the liquid limit ratio of the zwitterionic polyacrylamide (ZP)-modified soil was much higher than the other material-modified soils. The results of this study are useful to facilitate the application of modified compacted clays as gas barrier materials at industrial contaminated sites. Full article

(This article belongs to the Topic Sustainability in Buildings: New Trends in the Management of Construction and Demolition Waste)

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17 pages, 1961 KiB

Open AccessArticle

Call Failure Prediction in IP Multimedia Subsystem (IMS) Networks

by Amr Bahaa, Mohamed Shehata, Safa M. Gasser and Mohamed S. El-Mahallawy

Appl. Sci. 2022, 12(16), 8378; https://doi.org/10.3390/app12168378 - 22 Aug 2022

Cited by 4 | Viewed by 3525

Abstract

An explosion of traffic volume is the main driver behind launching various 5G services. The 5G network will utilize the IP Multimedia Subsystems (IMS) as a core network, same as in 4G networks. Thus, ensuring a high level of survivability and efficient failure [...] Read more.

An explosion of traffic volume is the main driver behind launching various 5G services. The 5G network will utilize the IP Multimedia Subsystems (IMS) as a core network, same as in 4G networks. Thus, ensuring a high level of survivability and efficient failure management in the IMS is crucial before launching 5G services. We introduce a new methodology based on machine learning to predict the call failures occurring inside the IMS network using the traces for the Session Initiation Protocol (SIP) communication. Predicting that the call will fail enables the operator to prevent the failure by redirecting the call to another radio access technique by initiating the Circuit Switching fallback (CS-fallback) through a 380 SIP error response sent to the handset. The advantage of the model is not limited to call failure prediction, but also to know the root causes behind the failure; more specifically, the multi-factorial root is caused by using machine learning, which cannot be obtained using the traditional method (manual tracking of the traces). We built eight different machine learning models using four different classifiers (decision tree, naive Bayes, K-Nearest Neighbor (KNN), and Support Vector Machine (SVM)) and two different feature selection methods (Filter and Wrapper). Finally, we compare the different models and use the one with the highest prediction accuracy to obtain the root causes beyond the call failures. The results demonstrate that using SVM classifier with Wrapper feature selection method conducts the highest prediction accuracy, reaching 97.5%. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence and Machine Learning in Wireless Communications and Networks)

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26 pages, 15102 KiB

Open AccessArticle

Experimental Development of Composite Bicycle Frame

by Milan Dvořák, Tomáš Ponížil, Viktor Kulíšek, Nikola Schmidová, Karel Doubrava, Bohumil Kropík and Milan Růžička

Appl. Sci. 2022, 12(16), 8377; https://doi.org/10.3390/app12168377 - 22 Aug 2022

Cited by 4 | Viewed by 3642

Abstract

This article focuses on the development of a carbon composite bicycle frame using various experimental methods of structural analysis. Two types of frame specimen were used. The complete frame specimen was tested in accordance with ISO test load cases with the addition of [...] Read more.

This article focuses on the development of a carbon composite bicycle frame using various experimental methods of structural analysis. Two types of frame specimen were used. The complete frame specimen was tested in accordance with ISO test load cases with the addition of an ergometer test in order to refine the operational strain envelope of such a frame. Resistive strain gauges and optical Fiber Bragg Grating (FBG) sensors were used for this analysis. The FBG sensors were embedded inside the head tube joints during the manufacturing process. The head connection was designed as a geometrically precise form–connection of wound composite tubes, reinforced with a wrap of high-strength unidirectional carbon tapes and carbon fabrics. Additional structural strength laboratory tests were conducted using simplified frame specimens, in order to evaluate the range of the limit case strain ranges. The digital image correlation method was used for the evaluation of the strain distribution in the head tube area. Resistive strain gauges were used for local strain analysis in critical areas. The acoustic emission method was used to detect structural defects before they could influence the stiffness response of the frame. It was found that the joints of the frame tubes are crucial for the strength and safety of the frame. Therefore, attention was also focused on the strengthening of the head tube joint, and on its experimental verification. A positive effect on the strength of the reinforced frame was found by doubling the thickness of the carbon fabric in the head tube joint area. Full article

(This article belongs to the Special Issue Experimental Mechanics, Instrumentation and Metrology II)

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15 pages, 6716 KiB

Open AccessArticle

Cyclic Behavior of L-Shaped RC Short-Limb Shear Walls with High-Strength Rebar and High-Strength Concrete

by Pinle Zhang, Jinyulin Wang and Junfang Gao

Appl. Sci. 2022, 12(16), 8376; https://doi.org/10.3390/app12168376 - 22 Aug 2022

Cited by 4 | Viewed by 1577

Abstract

Six RC short-limb shear walls with an L-shaped section, constructed with high-strength rebar and high-strength concrete, were loaded to destruction with pseudo-static loading. Experimental results were discussed and compared with L-shaped RC short-limb shear walls with high-strength horizontal rebar in detail. Different failure [...] Read more.

Six RC short-limb shear walls with an L-shaped section, constructed with high-strength rebar and high-strength concrete, were loaded to destruction with pseudo-static loading. Experimental results were discussed and compared with L-shaped RC short-limb shear walls with high-strength horizontal rebar in detail. Different failure modes were obtained, such as flexure-dominated failure for specimens with an aspect ratio of 2.8 and 2.15 and bending-shear failure for specimens with an aspect ratio of 1.75. With a decrease in the aspect ratio, ductility decreased, whereas with an increase in the axial compression ratio, the load-carrying capacity increased but ductility decreased accordingly. An obvious pinching effect was found in specimens with a smaller aspect ratio and a higher axial compression ratio. Using high-strength longitudinal rebar and high-strength concrete can obviously improve the lateral load-carrying capacity of walls; and using high-strength horizontal rebar can obviously improve the ultimate deformation capacity. The average ultimate drift ratios of HPLW and LW far exceeded the specification requirements of the Chinese GB50011-2010 code. Full article

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18 pages, 1738 KiB

Open AccessArticle

QoS/QoE in Flying Ad Hoc Networks Applied in Natural Disasters

by Jesús Hamilton Ortiz Monedero, José Luis Arciniegas Herrera, Juan Carlos Cuellar Quiñones, Carlos Andrés Tavera Romero and Bazil Taha Ahmed

Appl. Sci. 2022, 12(16), 8375; https://doi.org/10.3390/app12168375 - 22 Aug 2022

Cited by 1 | Viewed by 1857

Abstract

In this work, a group of mechanisms are exposed to provide quality of experience in flying ad hoc networks using a swarm of drones in a natural disaster service. End-to-end video traffic was analyzed. The metrics used to experimentally measure QoE/QoS are: delay, [...] Read more.

In this work, a group of mechanisms are exposed to provide quality of experience in flying ad hoc networks using a swarm of drones in a natural disaster service. End-to-end video traffic was analyzed. The metrics used to experimentally measure QoE/QoS are: delay, jitter and packet loss. The experience quality was evaluated before the disaster (C00), at the moment (B00) and after the disaster (I00). The methodology used to perform the design was experimental, and the NS simulator was used to evaluate the behavior of the swarm of drones connected through a flying ad hoc network. To perform data analysis, treatment and repetitions related to video traffic, the response surface methodology (MSR) was used, which is a set of mathematical techniques in order to optimize the obtained responses. The composite core design (DCC) was also used as it was the best fit to our experiment due to its flexibility. Since the quality of the experience was evaluated at three moments, the quality of services was also analyzed with three metrics. The main contributions of the research are a mathematical model of the quality of the experience based on the quality of the service; an experiment design using the end-to-end NS simulator; a methodology for the mathematical and statistical analysis of the data obtained; an algorithm that allows, from service quality metrics, to obtain the quality of the experience for end-to-end video traffic; and a proposal for future work for data analysis in a physical environment and applied to the environmental sector. Full article

(This article belongs to the Special Issue Analytics, Privacy and Security for IoT and Big Data)

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13 pages, 3435 KiB

Open AccessArticle

A Data-Driven Model of Cable Insulation Defect Based on Convolutional Neural Networks

by Weixing Han, Guang Yang, Chunsheng Hao, Zhengqi Wang, Dejing Kong and Yu Dong

Appl. Sci. 2022, 12(16), 8374; https://doi.org/10.3390/app12168374 - 22 Aug 2022

Cited by 2 | Viewed by 1878

Abstract

The insulation condition of cables has been the focus of research in power systems. To address the problem that the electric field is not easily measured under the operating condition of 10 kV transmission cables with insulation defects, this paper proposes a data-driven [...] Read more.

The insulation condition of cables has been the focus of research in power systems. To address the problem that the electric field is not easily measured under the operating condition of 10 kV transmission cables with insulation defects, this paper proposes a data-driven cable insulation defect model based on a convolutional neural network approach. The electric field data during cable operation is obtained by finite element calculation, and a multi-dimensional input feature quantity and a data set with the electric field strength as the output feature quantity are constructed. A convolutional neural network algorithm is applied to construct a cable data-driven model. The model is used to construct a cloud map of the electric field distribution during cable operation. Comparing the results with the finite element method, the overall accuracy of the data-driven model is 94.3% and the calculation time of the data-driven model is 0.025 s, which is 360 times faster than the finite element calculation. The results show that the data-driven model can quickly construct the electric field distribution under cable insulation defects, laying the foundation for a digital twin structure for cables. Full article

(This article belongs to the Special Issue Recent Advances in Artificial Intelligence, Machine Learning, and Deep Learning)

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19 pages, 1445 KiB

Open AccessReview

Holistic Approach for Artificial Intelligence Implementation in Pharmaceutical Products Lifecycle: A Meta-Analysis

by Konstantin A. Koshechkin, Georgiy S. Lebedev, Eduard N. Fartushnyi and Yuriy L. Orlov

Appl. Sci. 2022, 12(16), 8373; https://doi.org/10.3390/app12168373 - 22 Aug 2022

Cited by 4 | Viewed by 4179

Abstract

Recent developments in Digital Medicine approaches concern pharmaceutical product optimization. Artificial Intelligence (AI) has multiple applications for pharmaceutical products’ lifecycle, increasing development speed, quality of the products, and efficiency of the therapy. Here, we systematically review the overall approach for AI implementation in [...] Read more.

Recent developments in Digital Medicine approaches concern pharmaceutical product optimization. Artificial Intelligence (AI) has multiple applications for pharmaceutical products’ lifecycle, increasing development speed, quality of the products, and efficiency of the therapy. Here, we systematically review the overall approach for AI implementation in pharmaceutical products’ lifecycle. The published studies in PubMed and IEEE Xplore were searched from inception to March 2022. The papers were screened for relevant outcomes, publication types, and data sufficiency, and a total of 73 (1.2%) out of 6131 studies were retrieved after the selection. We extracted the data according to the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement. All Artificial Intelligence systems could be divided into multiple overlapping categories by implementation. For the 177 projects found, the most popular areas of AI implementation are clinical trials and pre-clinical tests (34%). In second place are novel small molecule design systems, with 33% of the total. The third most popular scope for AI implementation is target identification for novel medicines. More than 25% of the systems provide this functionality. It is interesting that most of the systems specialize in only one area (102 systems—57%). None of the systems provide functionality for full coverage of the lifecycle and function in all categories of the tasks. This meta-analysis demonstrated that Artificial Intelligence solutions in pharmaceutical products’ lifecycle could find numerous implementations, and none of the available market solutions covers them all. Full article

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14 pages, 788 KiB

Open AccessReview

Biophysical Controls That Make Erosion-Transported Soil Carbon a Source of Greenhouse Gases

by Rattan Lal

Appl. Sci. 2022, 12(16), 8372; https://doi.org/10.3390/app12168372 - 22 Aug 2022

Cited by 5 | Viewed by 2224

Abstract

Soil erosion is a selective process which removes the light fraction comprised of soil organic carbon (SOC) and colloidal particles of clay and fine silt. Thus, a large amount of carbon (C) is transported by erosional processes, and its fate (i.e., emission, redistribution, [...] Read more.

Soil erosion is a selective process which removes the light fraction comprised of soil organic carbon (SOC) and colloidal particles of clay and fine silt. Thus, a large amount of carbon (C) is transported by erosional processes, and its fate (i.e., emission, redistribution, burial, and translocation into aquatic ecosystems) has a strong impact on the global carbon cycle. The processes affecting the dynamics of soil C emission as greenhouse gases (i.e., CO_2, CH₄, N₂O), or its deposition and burial, vary among different stages of soil erosion: detachment, transport, redistribution, deposition or burial, and aquatic ecosystems. Specific biogeochemical and biogeophysical transformative processes which make erosion-transported carbon a source of C emission are determined by the type of erosion (rill vs. inter-rill in hydric and saltation erosion vs. air-borne dust in aeolian erosion), soil temperature and moisture regimes, initial SOC content, texture, raindrop-stable aggregates and water repellency, crusting, slope gradient, physiography and the slope-based flow patterns, landscape position, and the attendant aerobic vs. anaerobic conditions within the landscape where the sediment-laden C is being carried by alluvial and aeolian processes. As much as 20–40% of eroded SOC may be oxidized after erosion, and erosion-induced redistribution may be a large source of C. In addition, human activities (e.g., land use and management) have altered—and are altering—the redistribution pattern of sediments and C being transported. In addition to O₂ availability, other factors affecting emissions from aquatic ecosystems include sub-surface currents and high winds, which may also affect CH₄ efflux. The transport by aeolian processes is affected by wind speed, soil texture and structure, vegetation cover, etc. Lighter fractions (SOC, clay, and fine silt) are also selectively removed in the wind-blown dust. The SOC-ER of dust originating from sand-rich soil may range from 2 to 41. A majority of the C (and nutrients) lost by aeolian erosion may be removed by saltation. Even over a short period of three seasons, wind erosion can remove up to 25% of total organic C (TOC) and total N (TN) from the top 5 cm of soil. A large proportion of C being transported by hydric and aeolian erosional processes is emitted into the atmosphere as CO₂ and CH₄, along with N₂O. While some of the C buried at the depositional site or transported deep into the aquatic ecosystems may be encapsulated within reformed soil aggregates or protected against microbial processes, even the buried SOC may be vulnerable to future loss by land use, management, alkalinity or pH, the time lag between burial and subsequent loss, mineralogical properties, and global warming. Full article

(This article belongs to the Special Issue Soil Erosion: Dust Control and Sand Stabilization, Volume II)

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29 pages, 2344 KiB

Open AccessArticle

Generation of Controlled Synthetic Samples and Impact of Hyper-Tuning Parameters to Effectively Classify the Complex Structure of Overlapping Region

by Zafar Mahmood, Naveed Anwer Butt, Ghani Ur Rehman, Muhammad Zubair, Muhammad Aslam, Afzal Badshah and Syeda Fizzah Jilani

Appl. Sci. 2022, 12(16), 8371; https://doi.org/10.3390/app12168371 - 22 Aug 2022

Cited by 2 | Viewed by 1942

Abstract

The classification of imbalanced and overlapping data has provided customary insight over the last decade, as most real-world applications comprise multiple classes with an imbalanced distribution of samples. Samples from different classes overlap near class boundaries, creating a complex structure for the underlying [...] Read more.

The classification of imbalanced and overlapping data has provided customary insight over the last decade, as most real-world applications comprise multiple classes with an imbalanced distribution of samples. Samples from different classes overlap near class boundaries, creating a complex structure for the underlying classifier. Due to the imbalanced distribution of samples, the underlying classifier favors samples from the majority class and ignores samples representing the least minority class. The imbalanced nature of the data—resulting in overlapping regions—greatly affects the learning of various machine learning classifiers, as most machine learning classifiers are designed to handle balanced datasets and perform poorly when applied to imbalanced data. To improve learning on multi-class problems, more expertise is required in both traditional classifiers and problem domain datasets. Some experimentation and knowledge of hyper-tuning the parameters and parameters of the classifier under consideration are required. Several techniques for learning from multi-class problems have been reported in the literature, such as sampling techniques, algorithm adaptation methods, transformation methods, hybrid methods, and ensemble techniques. In the current research work, we first analyzed the learning behavior of state-of-the-art ensemble and non-ensemble classifiers on imbalanced and overlapping multi-class data. After analysis, we used grid search techniques to optimize key parameters (by hyper-tuning) of ensemble and non-ensemble classifiers to determine the optimal set of parameters to enhance the learning from a multi-class imbalanced classification problem, performed on 15 public datasets. After hyper-tuning, 20% of the dataset samples are synthetically generated to add to the majority class of each respective dataset to make it more overlapped (complex structure). After the synthetic sample’s addition, the hyper-tuned ensemble and non-ensemble classifiers are tested over that complex structure. This paper also includes a brief description of tuned parameters and their effects on imbalanced data, followed by a detailed comparison of ensemble and non-ensemble classifiers with the default and tuned parameters for both original and synthetically overlapped datasets. We believe that the underlying paper is the first kind of effort in this domain, which will furnish various research aspects to with a greater focus on the parameters of the classifier in the field of learning from imbalanced data problems using machine-learning algorithms. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence: Machine Learning, Data Mining and Data Sciences)

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22 pages, 8525 KiB

Open AccessArticle

Innovative Design of Novel Main and Secondary Arch Collaborative Y-Shaped Arch Bridge and Research on Shear Lag Effect of Its Unconventional Thin-Walled Steel Box Arch Ribs

by Qian Huang, Xiaoguang Wu, Hui Wei and Qida Chen

Appl. Sci. 2022, 12(16), 8370; https://doi.org/10.3390/app12168370 - 22 Aug 2022

Cited by 8 | Viewed by 5456

Abstract

The first main and secondary collaborative Y-shaped steel box arch bridge under construction in China is a rarely seen innovative practice among bridges already built at home and abroad, which is an attractive engineering research topic in the field of advanced bridge design [...] Read more.

The first main and secondary collaborative Y-shaped steel box arch bridge under construction in China is a rarely seen innovative practice among bridges already built at home and abroad, which is an attractive engineering research topic in the field of advanced bridge design and construction, and the investigation of this bridge has made a groundbreaking contribution. The structure of unconventional thin-walled steel box arch ribs is very novel, abandoning the traditional two-dimensional arch rib structure form and adopting the new structural mode of single–double combination and joint working of main and secondary arches. However, for this innovative design, many technical difficulties including innovative design details, mechanical behavior of thin-walled structures and construction methods still need to be pioneeringly explored and thoroughly researched. In this paper, the innovative design concept of unconventional thin-walled arch ribs for spatial Y-shaped steel box arch bridges is described, and a comparative analysis with the corresponding conventional single arch rib structure is carried out. Due to the limitations of the common conventional arch bridge research methods, a combined global and local finite element method is used to analyze the static and dynamic properties of the structure, and the shear lag effect of the thin-walled steel box arch ribs is studied in a pioneering and exploratory approach. In addition, the stress distribution of the bifurcated section of the arch ribs and the configuration of the diaphragm are analyzed in detail to verify the reasonableness, advantage and applicability of the innovative design. The results show that the main and secondary arch collaboration Y-shaped steel box arch bridge has reasonable structure and superior mechanical properties and has a greater value for promotion The design concept and analysis method are worthy of use as a reference for the aesthetical and mechanical design of similar spatial Y-shaped arch bridges in the future. Full article

(This article belongs to the Special Issue Advanced Technologies for Bridge Design and Construction)

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19 pages, 1459 KiB

Open AccessArticle

Influence Mechanism of Transportation Integration on Industrial Agglomeration in Urban Agglomeration Theory—Taking the Yangtze River Delta Urban Agglomeration as an Example

by Gongding Wei, Xueyan Li, Mingyuan Yu, Guangquan Lu and Zhiyu Chen

Appl. Sci. 2022, 12(16), 8369; https://doi.org/10.3390/app12168369 - 22 Aug 2022

Cited by 12 | Viewed by 2592

Abstract

This study selected the Yangtze River Delta urban agglomeration as the research area, combining it with the current situation of the transportation development of the Yangtze River Delta urban agglomeration to construct the urban agglomeration transportation integration index system and evaluate the development [...] Read more.

This study selected the Yangtze River Delta urban agglomeration as the research area, combining it with the current situation of the transportation development of the Yangtze River Delta urban agglomeration to construct the urban agglomeration transportation integration index system and evaluate the development status of the Yangtze River Delta urban agglomeration transportation integration. The study examined the influence mechanism of transportation infrastructure on industrial agglomeration. The results are as follows: (1) From 2011–2020, the Yangtze River Delta urban agglomeration’s transportation integration index showed a clear upward trend. (2) The integration level of local transportation played an important role in promoting local industrial agglomeration. Promoting industrial agglomeration in neighboring areas had a negative spillover effect on industrial agglomeration in this region. Developing transportation integration in other regions had an insignificant positive effect on the development of local industrial agglomeration. (3) Urban agglomeration transportation integration impacted regional industrial agglomeration, mainly through the “cost effect.” Thus, cities in the Yangtze River Delta in 2020 need to accelerate the construction of relevant transportation infrastructure so as to promote the integrated development of higher-quality transportation in the Yangtze River Delta urban agglomeration. Full article

(This article belongs to the Special Issue Applied Industrial Technologies Correlated to Advanced Decision-Making Techniques in Dynamic Industry 4.0 Sustainable Engineering Processes)

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37 pages, 2272 KiB

Open AccessReview

Review on Compressive Sensing Algorithms for ECG Signal for IoT Based Deep Learning Framework

by Subramanyam Shashi Kumar and Prakash Ramachandran

Appl. Sci. 2022, 12(16), 8368; https://doi.org/10.3390/app12168368 - 21 Aug 2022

Cited by 10 | Viewed by 5208

Abstract

Nowadays, healthcare is becoming very modern, and the support of Internet of Things (IoT) is inevitable in a personal healthcare system. A typical personal healthcare system acquires vital parameters from human users and stores them in a cloud platform for further analysis. Acquiring [...] Read more.

Nowadays, healthcare is becoming very modern, and the support of Internet of Things (IoT) is inevitable in a personal healthcare system. A typical personal healthcare system acquires vital parameters from human users and stores them in a cloud platform for further analysis. Acquiring fundamental biomedical signal, such as with the Electrocardiograph (ECG), is also considered for specific disease analysis in personal healthcare systems. When such systems are scaled up, there is a heavy demand for internet channel capacity to accommodate real time seamless flow of discrete samples of biomedical signals. So, there is a keen need for real time data compression of biomedical signals. Compressive Sensing (CS) has recently attracted more interest due to its compactness and its feature of the faithful reconstruction of signals from fewer linear measurements, which facilitates less than Shannon’s sampling rate by exploiting the signal sparsity. The most common biomedical signal that is to be analyzed is the ECG signal, as the prediction of heart failure at an early stage can save a human life. This review is for a vast use-case of IoT framework in which CS measurements of ECG are acquired, communicated through Internet to a server, and the arrhythmia are analyzed using Machine learning (ML). Assuming this use-case specific for ECG, in this review many technical aspects are considered regarding various research components. The key aspect is on the investigation of the best sensing method, and to address this, various sensing matrices are reviewed, analyzed and recommended. The next aspect is the selection of the optimal sparsifying method, and the review recommends unexplored ECG compression algorithms as sparsifying methods. The other aspects are optimum reconstruction algorithms, best hardware implementations, suitable ML methods and effective modality of IoT. In this review all these components are considered, and a detailed review is presented which enables us to orchestrate the use-case specified above. This review focuses on the current trends in CS algorithms for ECG signal compression and its hardware implementation. The key to successful reconstruction of the CS method is the right selection of sensing and sparsifying matrix, and there are many unexplored sparsifying methods for the ECG signal. In this review, we shed some light on new possible sparsifying techniques. A detailed comparison table of various CS algorithms, sensing matrix, sparsifying techniques with different ECG dataset is tabulated to quantify the capability of CS in terms of appropriate performance metrics. As per the use-case specified above, the CS reconstructed ECG signals are to be subjected to ML analysis, and in this review the compressive domain inference approach is discussed. The various datasets, methodologies and ML models for ECG applications are studied and their model accuracies are tabulated. Mostly, the previous research on CS had studied the performance of CS using numerical simulation, whereas there are some good attempts for hardware implementations for ECG applications, and we studied the uniqueness of each method and supported the study with a comparison table. As a consolidation, we recommend new possibilities of the research components in terms of new transforms, new sparsifying methods, suggestions for ML approaches and hardware implementation. Full article

(This article belongs to the Special Issue Research on Biomedical Signal Processing)

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13 pages, 1647 KiB

Open AccessFeature PaperArticle

A Novel Reference Governor for Disturbance Observer-Based Load Pressure Control in a Dual-Actuator-Driven Electrohydraulic Actuator

by Guisheng Zhao, Shaonan Chen, Yixiang Liu and Kai Guo

Appl. Sci. 2022, 12(16), 8367; https://doi.org/10.3390/app12168367 - 21 Aug 2022

Cited by 2 | Viewed by 1751

Abstract

In real-world applications, hydraulic pressure control performance is influenced by model uncertainties, the control bandwidths of valves and pumps, and deviations from the linear working region. To overcome the aforementioned obstacles, a novel reference governor for disturbance observer (DOB)-based load pressure control is [...] Read more.

In real-world applications, hydraulic pressure control performance is influenced by model uncertainties, the control bandwidths of valves and pumps, and deviations from the linear working region. To overcome the aforementioned obstacles, a novel reference governor for disturbance observer (DOB)-based load pressure control is proposed in this paper for a dual-actuator-driven electrohydraulic cylinder. First, a control-oriented model for load pressure control was developed. On the basis of this, a nonlinear DOB-based feedback controller, as well as a mid-range control architecture for the variable displacement pump and proportional valve, was fabricated so that the performance degradation caused by the pump’s slow responses and imprecise system parameters is suppressed. Specifically, this controller is augmented by a novel smooth reference governor, which modifies the load pressure command in the pressure transition periods to guarantee that the actuator’s constraints are not violated. Another merit of the novel reference governor is that it ensures a smooth trajectory transition, and therefore, unmodeled high-frequency plant dynamics will not be invoked. Case studies were carried out to verify the effectiveness of the proposed control approach. The study results show that the approach can significantly enhance the hydraulic system’s pressure tracking performance. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Mechatronics)

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24 pages, 10834 KiB

Open AccessArticle

Research on the Dynamic Damage Properties and Determination of the Holmquist–Johnson–Cook Model Parameters for Sandstone

by Shufeng Liang, Shijun Hou and Shuaifeng Wu

Appl. Sci. 2022, 12(16), 8366; https://doi.org/10.3390/app12168366 - 21 Aug 2022

Cited by 3 | Viewed by 2165

Abstract

During blasting in engineering construction, the surrounding rock becomes unstable and is damaged under the impacts of multiple low-amplitude stress waves. It is of great practical significance to understand the damage evolution characteristics and the attenuation of the mechanical properties of rocks subjected [...] Read more.

During blasting in engineering construction, the surrounding rock becomes unstable and is damaged under the impacts of multiple low-amplitude stress waves. It is of great practical significance to understand the damage evolution characteristics and the attenuation of the mechanical properties of rocks subjected to multiple stress waves. Single impact and repeated impact tests for sandstone were carried out using a split Hopkinson pressure bar (SHPB) loading system. The single impact test results showed that the sandstone materials were strain-rate-dependent, and the dynamic constitutive curve could be divided into four stages, namely the linear elastic stage, the new crack formation stage, the plastic strengthening stage and the unloading stage. The failure pattern mostly indicated splitting tensile failure, and the impact damage threshold was 45 J. The relationship between the damage and stress wave amplitude was D = 0.0029·exp\({\boxed{f_{()}}}\)(5.4127•σ/76.13) − 0.0504. The repeated impact test results showed that the dynamic compressive strength and the dynamic elastic modulus decreased, while the failure strain increased gradually as the number of impacts (n) increased. The sandstone specimen under repeated impacts had only one fracture surface compared with the single impact failure pattern. The cumulative damage presented the development form of ‘rapid rise–steady development–rapid rise’, and the damage evolution law could be expressed by D = 0.265 − 0.328·ln⁡⁡⁡\({\boxed{f_{()}}}\)(ln13.989/n). Finally, a set of methods to determine the Holmquist–Johnson–Cook (HJC) model parameters for sandstone was proposed based on a single impact test, repeated impact test, uniaxial compression test and triaxial compression test. The numerical simulation results of the SHPB test showed that the dynamic constitutive curves of sandstone were in good agreement with the experimental results. Full article

(This article belongs to the Special Issue Multiphysics Modeling for Fracture and Fragmentation of Geomaterials)

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7 pages, 1386 KiB

Open AccessCommunication

Graphene Passively Q-Switched Nd:YAG Laser by 885 nm Laser Diode Resonant Pumping

by Liwei Xu, Yingyi Li, Jun Cai, Wanli Zhao, Tongyu Liu, Tongyu Dai, Youlun Ju and Yu Ding

Appl. Sci. 2022, 12(16), 8365; https://doi.org/10.3390/app12168365 - 21 Aug 2022

Cited by 3 | Viewed by 2388

Abstract

A graphene passively Q-switched Nd:YAG laser experienced resonant pumping by an 885 nm laser diode (LD), as demonstrated in this paper. In the continuous-wave operation, the maximum average output power was up to 1.8 W with the absorbed pump power being 11.7 W, [...] Read more.

A graphene passively Q-switched Nd:YAG laser experienced resonant pumping by an 885 nm laser diode (LD), as demonstrated in this paper. In the continuous-wave operation, the maximum average output power was up to 1.8 W with the absorbed pump power being 11.7 W, and the slope efficiency was 51.2%. In the Q-switching operation, the maximum average output power was up to 639 mW with a pulse width of 2.06 μs at the repetition frequency of 102.7 kHz, while the slope efficiency and the beam quality factor M² were 25.3% and 1.25, respectively. Full article

(This article belongs to the Special Issue Advances in Middle Infrared (Mid-IR) Lasers and Their Application)

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23 pages, 786 KiB

Open AccessArticle

Power System Inertia Dispatch Modelling in Future German Power Systems: A System Cost Evaluation

by Henning Thiesen

Appl. Sci. 2022, 12(16), 8364; https://doi.org/10.3390/app12168364 - 21 Aug 2022

Cited by 4 | Viewed by 2570

Abstract

Increasing the share of grid frequency converter-connected renewables reduces power system inertia, which is crucial for grid frequency stability. However, this development is insufficiently covered by energy system modelling and analysis as well as related scientific literature. Additionally, only synchronous inertia from fossil [...] Read more.

Increasing the share of grid frequency converter-connected renewables reduces power system inertia, which is crucial for grid frequency stability. However, this development is insufficiently covered by energy system modelling and analysis as well as related scientific literature. Additionally, only synchronous inertia from fossil fuel-emitting power plants is represented, although renewable generators are a source of synthetic inertia, thus resulting in increased must-run capacities, CO₂ emissions and system costs. The work at hands adds an analysis of the future German power system considering sufficient inertia to the literature. Therefore, results of an novel open-source energy system model are analysed. The model depicts minimum system inertia constraints as well as wind turbines and battery storage systems as a carbon-dioxide-free source for a synthetic inertial response. Results indicate that integrating system inertia constraints in energy system models has a high impact on indicators such as system costs. Especially when investments in additional storage units providing an inertial response are necessary. With respect to researched scenarios, system cost increases range from 1% up to 23%. The incremental costs for providing additional inertia varies between 0.002 EURO/kg·m² and 0.61 EURO/kg·m². Full article

(This article belongs to the Special Issue Renewable Energy Systems: Optimal Planning and Design)

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18 pages, 1788 KiB

Open AccessArticle

Artificial Intelligence Synergetic Opportunities in Services: Conversational Systems Perspective

by Shai Rozenes and Yuval Cohen

Appl. Sci. 2022, 12(16), 8363; https://doi.org/10.3390/app12168363 - 21 Aug 2022

Cited by 7 | Viewed by 3237

Abstract

The importance of this paper is its discovery of the unused synergetic potential of integration between several AI techniques into an orchestrated effort to improve service. Special emphasis is given to the conversational capabilities of AI systems. The paper shows that the literature [...] Read more.

The importance of this paper is its discovery of the unused synergetic potential of integration between several AI techniques into an orchestrated effort to improve service. Special emphasis is given to the conversational capabilities of AI systems. The paper shows that the literature related to the use of AI in service is divided into independent knowledge domains (silos) that are either related to the technology under consideration, or to a small group of technologies related to a certain application; it then discusses the reasons for the isolation of these silos, and reveals the barriers and the traps for their integration. Two case studies of service systems are presented to illustrate the importance of synergy. A special focus is given to the conversation part of these service systems: the first case presents an application with high potential for integrating new AI technologies into its AI portfolio, while the second case illustrates the advantages of a mature application that has already integrated many technologies into its AI portfolio. Finally, the paper discusses the two case studies and presents inclusion relationships between AI capabilities to facilitate generating a roadmap for extending AI capabilities with synergetic opportunities. Full article

(This article belongs to the Special Issue Smart Services: Artificial Intelligence in Service Systems)

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16 pages, 2460 KiB

Open AccessArticle

Analysis of Malicious Node Identification Algorithm of Internet of Vehicles under Blockchain Technology: A Case Study of Intelligent Technology in Automotive Engineering

by Jing Chen, Tong Li and Rui Zhu

Appl. Sci. 2022, 12(16), 8362; https://doi.org/10.3390/app12168362 - 21 Aug 2022

Cited by 3 | Viewed by 1997

Abstract

False messages sent by malicious or selfish vehicle nodes will reduce the operation efficiency of the Internet of Vehicles, and can even endanger drivers in serious cases. Therefore, it is very important to detect malicious vehicle nodes in the network in a timely [...] Read more.

False messages sent by malicious or selfish vehicle nodes will reduce the operation efficiency of the Internet of Vehicles, and can even endanger drivers in serious cases. Therefore, it is very important to detect malicious vehicle nodes in the network in a timely manner. At present, the existing research on detecting malicious vehicle nodes in the Internet of Vehicles has some problems, such as difficulties with identification and a low detection efficiency. Blockchain technology cannot be tampered with or deleted and has open and transparent characteristics. Therefore, as a shared distributed ledger in decentralized networking, blockchain can promote collaboration between transactions, processing and interaction equipment, and help to establish a scalable, universal, private, secure and reliable car networking system. This paper puts forward a block-network-based malicious node detection mechanism. Using blockchain technology in a car network for malicious node identification algorithm could create a security scheme that can ensure smooth communication between network vehicles. A consensus on legal vehicle identification, message integrity verification, false message identification and malicious vehicle node identification form the four parts of the security scheme. Based on the public–private key mechanism and RSA encryption algorithm, combined with the malicious node identification algorithm in the Internet of Vehicles, the authenticity of the vehicle’s identity and message is determined to protect the vehicle’s security and privacy. First, a blockchain-based, malicious node detection architecture is constructed for the Internet of vehicles. We propose a malicious node identification algorithm based on the blockchain consensus mechanism. Combined the above detection architecture with the consensus mechanism, a comprehensive and accurate verification of vehicle identity and message authenticity is ensured, looking at the four aspects of vehicle identification, accounting node selection, verification of transmission message integrity and identification of the authenticity of transmission messages. Subsequently, the verification results will be globally broadcast in the Internet of Vehicles to suppress malicious behavior, further ensure that reliable event messages are provided for the driver, improve the VANET operation environment, and improve the operation efficiency of the Internet of Vehicles. Comparing the proposed detection mechanism using simulation software, the simulation results show that the proposed blockchain-based trust detection mechanism can effectively improve the accuracy of vehicle node authentication and identification of false messages, and improve network transmission performance in the Internet of Vehicles environment. Full article

(This article belongs to the Special Issue Novel Hybrid Intelligence Techniques in Engineering)

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13 pages, 1149 KiB

Open AccessArticle

Assessing the Phytochemical Profile and Potential of Traditional Herbal Infusions against Aldose Reductase through In Silico Studies and LC-MS/MS Analysis

by Thalia Tsiaka, Eftichia Kritsi, Dimitra Z. Lantzouraki, Paris Christodoulou, Diamantina Tsigrimani, Irini F. Strati, Vassilia J. Sinanoglou and Panagiotis Zoumpoulakis

Appl. Sci. 2022, 12(16), 8361; https://doi.org/10.3390/app12168361 - 21 Aug 2022

Cited by 8 | Viewed by 2484

Abstract

In the current market, there is a growing interest in traditional herbal nutraceuticals. Therefore, herbal formulations have re-emerged as products with sought-after nutraceutical and disease-preventing properties. The health-promoting effects of herbal bioactives are attributed to the active phytoconstituents of these plants. Thus, the [...] Read more.

In the current market, there is a growing interest in traditional herbal nutraceuticals. Therefore, herbal formulations have re-emerged as products with sought-after nutraceutical and disease-preventing properties. The health-promoting effects of herbal bioactives are attributed to the active phytoconstituents of these plants. Thus, the aim of the present study was to evaluate the putative nutraceutical effectiveness of the preparations of ten herbs (chamomile, purple coneflower, lemon verbena, pennyroyal, spearmint, oregano, marjoram, headed savory, sea buckthorn, and St. John’s wort) by combining in silico techniques and LC-MS/MS analysis. The binding potential of the selected phenolic compounds, according to literature and web databases, was investigated by using molecular target prediction tools. Aldose reductase (AR), an enzyme of polyol pathway which is related to hyperglycemic-induced pathologies, emerged as the most promising molecular target. The molecular docking results showed that rosmarinic acid, caftaric acid, naringenin, and quercetin presented the highest binding affinity. In a further step, the phytochemical profile of the examined infusions, obtained by LC-MS/MS analysis, revealed that the abovementioned compounds were present, mainly in the herbs of the Lamiaceae family, designating headed savory as the herbal infusion with possible significant inhibitory activity against AR. Full article

(This article belongs to the Special Issue Antioxidants in Natural Products II)

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17 pages, 4883 KiB

Open AccessArticle

A GIS Partial Discharge Defect Identification Method Based on YOLOv5

by Yao Lu, Zhibin Qiu, Caibo Liao, Zhibiao Zhou, Tonghongfei Li and Zijian Wu

Appl. Sci. 2022, 12(16), 8360; https://doi.org/10.3390/app12168360 - 21 Aug 2022

Cited by 15 | Viewed by 2743

Abstract

The correct identification of partial discharge types is of great significance to the stable operation of GIS. In order to improve the recognition accuracy and result of partial discharge, and to meet the requirements of real-time monitoring of GIS equipment, this paper proposes [...] Read more.

The correct identification of partial discharge types is of great significance to the stable operation of GIS. In order to improve the recognition accuracy and result of partial discharge, and to meet the requirements of real-time monitoring of GIS equipment, this paper proposes a GIS partial discharge defect recognition model based on YOLOv5. First, the GIS partial discharge simulation experiment is established to create the dataset of partial discharge PRPD map. Then, a YOLOv5-based GIS partial discharge defect recognition model is constructed, and different training methods are used to optimize the parameters of the model. By comparing with target detection models based on other deep learning methods, such as Faster-RCNN and YOLOv4, the YOLOv5 model discussed in the paper has significantly improved the recognition efficiency and recognition accuracy, in which mAP value is 95.89% and FPS is 28.89. In addition, the model can realize the distinction and identification of multiple PD types in a single PRPD map. At last, the YOLOv5-based GIS partial discharge defect identification model is applied to the test in a 500 kV substation. The model accurately determines the type of GIS partial discharge, which verifies the accuracy and validity of the model. Full article

(This article belongs to the Section Electrical, Electronics and Communications Engineering)

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