Applied Sciences

23 pages, 6339 KiB

Open AccessArticle

A Novel Deep Reinforcement Learning Approach to Traffic Signal Control with Connected Vehicles

by Yang Shi, Zhenbo Wang, Tim J. LaClair, Chieh (Ross) Wang, Yunli Shao and Jinghui Yuan

Appl. Sci. 2023, 13(4), 2750; https://doi.org/10.3390/app13042750 - 20 Feb 2023

Cited by 7 | Viewed by 4235

The advent of connected vehicle (CV) technology offers new possibilities for a revolution in future transportation systems. With the availability of real-time traffic data from CVs, it is possible to more effectively optimize traffic signals to reduce congestion, increase fuel efficiency, and enhance [...] Read more.

The advent of connected vehicle (CV) technology offers new possibilities for a revolution in future transportation systems. With the availability of real-time traffic data from CVs, it is possible to more effectively optimize traffic signals to reduce congestion, increase fuel efficiency, and enhance road safety. The success of CV-based signal control depends on an accurate and computationally efficient model that accounts for the stochastic and nonlinear nature of the traffic flow. Without the necessity of prior knowledge of the traffic system’s model architecture, reinforcement learning (RL) is a promising tool to acquire the control policy through observing the transition of the traffic states. In this paper, we propose a novel data-driven traffic signal control method that leverages the latest in deep learning and reinforcement learning techniques. By incorporating a compressed representation of the traffic states, the proposed method overcomes the limitations of the existing methods in defining the action space to include more practical and flexible signal phases. The simulation results demonstrate the convergence and robust performance of the proposed method against several existing benchmark methods in terms of average vehicle speeds, queue length, wait time, and traffic density. Full article

(This article belongs to the Special Issue Transportation Planning, Management and Optimization)

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17 pages, 537 KiB

Open AccessArticle

Estimating the Energy Savings of Energy Efficiency Actions with Ensemble Machine Learning Models

by Elissaios Sarmas, Evangelos Spiliotis, Nikos Dimitropoulos, Vangelis Marinakis and Haris Doukas

Appl. Sci. 2023, 13(4), 2749; https://doi.org/10.3390/app13042749 - 20 Feb 2023

Cited by 13 | Viewed by 2313

Abstract

Energy efficiency financing is considered among the top priorities in the energy sector among several stakeholders. In this context, accurately estimating the energy savings achieved by energy efficiency actions before being approved and implemented is of major importance to ensure the optimal allocation [...] Read more.

Energy efficiency financing is considered among the top priorities in the energy sector among several stakeholders. In this context, accurately estimating the energy savings achieved by energy efficiency actions before being approved and implemented is of major importance to ensure the optimal allocation of the available financial resources. This study aims to provide a machine-learningbased methodological framework for a priori predicting the energy savings of energy efficiency renovation actions. The proposed solution consists of three tree-based algorithms that exploit bagging and boosting as well as an additional ensembling level that further mitigates prediction uncertainty. The proposed models are empirically evaluated using a database of various, diverse energy efficiency renovation investments. Results indicate that the ensemble model outperforms the three individual models in terms of forecasting accuracy. Also, the generated predictions are relatively accurate for all the examined project categories, a finding that supports the robustness of the proposed approach. Full article

(This article belongs to the Section Energy Science and Technology)

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17 pages, 3162 KiB

Open AccessArticle

Visual Exploration of Cycling Semantics with GPS Trajectory Data

by Xuansu Gao, Chengwu Liao, Chao Chen and Ruiyuan Li

Appl. Sci. 2023, 13(4), 2748; https://doi.org/10.3390/app13042748 - 20 Feb 2023

Cited by 2 | Viewed by 2147

Abstract

Cycling—as a sustainable and convenient exercise and travel mode—has become increasingly popular in modern cities. In recent years, with the proliferation of sport apps and GPS mobile devices in daily life, the accumulated cycling trajectories have opened up valuable opportunities to explore the [...] Read more.

Cycling—as a sustainable and convenient exercise and travel mode—has become increasingly popular in modern cities. In recent years, with the proliferation of sport apps and GPS mobile devices in daily life, the accumulated cycling trajectories have opened up valuable opportunities to explore the underlying cycling semantics to enable a better cycling experience. In this paper, based on large-scale GPS trajectories and road network data, we mainly explore cycling semantics from two perspectives. On one hand, from the perspective of the cyclists, trajectories could tell their frequently visited sequences of streets, thus potentially revealing their hidden cycling themes, i.e., cyclist behavior semantics. On the other hand, from the perspective of the road segments, trajectories could show the cyclists’ fine-grained moving features along roads, thus probably uncovering the moving semantics on roads. However, the extraction and understanding of such cycling semantics are nontrivial, since most of the trajectories are raw data and it is also difficult to aggregate the dynamic moving features from trajectories into static road segments. To this end, we establish a new visual analytic system called VizCycSemantics for pervasive computing, in which a topic model (i.e., LDA) is used to extract the topics of cyclist behavior semantics and moving semantics on roads, and a clustering method (i.e., k-means ++) is used to further capture the groups of similar cyclists and road segments within the city; finally, multiple interactive visual interfaces are implemented to facilitate the interpretation for analysts. We conduct extensive case studies in the city of Beijing to demonstrate the effectiveness and practicability of our system and also obtain various insightful findings and pieces of advice. Full article

(This article belongs to the Special Issue New Insights into Pervasive and Mobile Computing)

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11 pages, 6856 KiB

Open AccessCommunication

Robot Assisted THz Imaging with a Time Domain Spectrometer

by Dominik Bachmann, Rolf Brönnimann, Luis Nicklaus Caceres, Sofie L. Gnannt, Erwin Hack, Elena Mavrona, Daniel Sacré and Peter Zolliker

Appl. Sci. 2023, 13(4), 2747; https://doi.org/10.3390/app13042747 - 20 Feb 2023

Cited by 1 | Viewed by 1795

Abstract

THz-Time domain spectroscopic imaging is demonstrated combining a robotic scanning method with continuous signal acquisition and holographic reconstruction of the object to improve the imaging resolution. We apply the method to a metallic Siemens star in order to quantify resolution and to wood [...] Read more.

THz-Time domain spectroscopic imaging is demonstrated combining a robotic scanning method with continuous signal acquisition and holographic reconstruction of the object to improve the imaging resolution. We apply the method to a metallic Siemens star in order to quantify resolution and to wood samples to demonstrate the technique on a non-metallic object with an unknown structure. Full article

(This article belongs to the Special Issue Recent Advances in the Technologies and Applications of Terahertz Imaging)

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15 pages, 4411 KiB

Open AccessArticle

Underwater Object Detection Method Based on Improved Faster RCNN

by Hao Wang and Nanfeng Xiao

Appl. Sci. 2023, 13(4), 2746; https://doi.org/10.3390/app13042746 - 20 Feb 2023

Cited by 24 | Viewed by 4378

Abstract

In order to better utilize and protect marine organisms, reliable underwater object detection methods need to be developed. Due to various influencing factors from complex and changeable underwater environments, the underwater object detection is full of challenges. Therefore, this paper improves a two-stage [...] Read more.

In order to better utilize and protect marine organisms, reliable underwater object detection methods need to be developed. Due to various influencing factors from complex and changeable underwater environments, the underwater object detection is full of challenges. Therefore, this paper improves a two-stage algorithm of Faster RCNN (Regions with Convolutional Neural Network Feature) to detect holothurian, echinus, scallop, starfish and waterweeds. The improved algorithm has better performance in underwater object detection. Firstly, we improved the backbone network of the Faster RCNN, replacing the VGG16 (Visual Geometry Group Network 16) structure in the original feature extraction module with the Res2Net101 network to enhance the expressive ability of the receptive field of each network layer. Secondly, the OHEM (Online Hard Example Mining) algorithm is introduced to solve the imbalance problem of positive and negative samples of the bounding box. Thirdly, GIOU (Generalized Intersection Over Union) and Soft-NMS (Soft Non-Maximum Suppression) are used to optimize the regression mechanism of the bounding box. Finally, the improved Faster RCNN model is trained using a multi-scale training strategy to enhance the robustness of the model. Through ablation experiments based on the improved Faster RCNN model, each improved part is disassembled and then the experiments are carried out one by one, which can be known from the experimental results that, based on the improved Faster RCNN model, [email protected] reaches 71.7%, which is 3.3% higher than the original Faster RCNN model, and the average accuracy reaches 43%, and the F1-score reaches 55.3%, a 2.5% improvement over the original Faster RCNN model, which shows that the proposed method in this paper is effective in underwater object detection. Full article

(This article belongs to the Section Robotics and Automation)

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25 pages, 12305 KiB

Open AccessArticle

Evaluation of Engineering Site and Subsurface Structures Using Seismic Refraction Tomography: A Case Study of Abydos Site, Sohag Governorate, Egypt

by Abdelbaset M. Abudeif, Gamal Z. Abdel Aal, Nessreen F. Abdelbaky, Ahmed M. Abdel Gowad and Mohammed A. Mohammed

Appl. Sci. 2023, 13(4), 2745; https://doi.org/10.3390/app13042745 - 20 Feb 2023

Cited by 2 | Viewed by 2569

Abstract

Because of the strategic importance of the Abydos archaeological site in Egypt as a source of wealth for Egyptian tourism, this study was concerned with carrying out geophysical measurements to detect subsurface succession and measure variations in the geotechnical engineering features of the [...] Read more.

Because of the strategic importance of the Abydos archaeological site in Egypt as a source of wealth for Egyptian tourism, this study was concerned with carrying out geophysical measurements to detect subsurface succession and measure variations in the geotechnical engineering features of the soils/rocks in order to protect this significant area. The findings will assist geologists and seismologists in collaborating with archaeologists for future site development, revitalization, and investment. The primary objectives of this work were to determine the subsurface lithology, evaluate the engineering geotechnical properties of soils/rocks, identify the layer thicknesses, and identify the site class by calculating V_s³⁰. To achieve these goals, seventeen (17) seismic refraction tomography (SRT) P- and S-wave measurements were executed in front of the Osirion location. SeisImager Software was used for the processing and interpretation of the outcomes. The results were the travel time–distance curves, which were used for building the 2D seismic models that exhibited the velocity and the depth of the layered models. These models were validated by our previous works using electric resistivity tomography and borehole data. The results indicated that this site consisted of three geoseismic subsurface layers. The first layer was the surface that was made up of wadi deposits, which were a mixture of gravel, sand, and silt and were characterized by incompetent to slightly competent materials. The second layer corresponded to the sand and muddy sand deposits of competent rock that was of fair to moderate quality. The third layer (clay deposits) had a higher velocity and was more compact and may be employed as a bedrock layer. The elastic moduli, V_s³⁰, petrophysical, and geotechnical properties of the three geoseismic layers were appraised as essential parameters. Integration of petrophysical and geotechnical parameters and elastic moduli revealed that the third layer was composed of competent clays, which were characterized by low values of porosity, void ratio, Poisson ratio, and stress ratio. It also had a high rigidity, Young’s and bulk moduli, concentration and material indexes, N-value, ultimate bearing capacities, and high density values, and vice versa for the first layer. The standard NEHRP site class was B (rocks). These parameters are ordinarily used as key indications and serve as significant inputs for any future work. Full article

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17 pages, 10440 KiB

Open AccessArticle

Gap and Force Adjustment during Laser Beam Welding by Means of a Closed-Loop Control Utilizing Fixture-Integrated Sensors and Actuators

by Klaus Schricker, Leander Schmidt, Hannes Friedmann and Jean Pierre Bergmann

Appl. Sci. 2023, 13(4), 2744; https://doi.org/10.3390/app13042744 - 20 Feb 2023

Cited by 3 | Viewed by 2487

Abstract

The development of adaptive and intelligent clamping devices allows for the reduction of rejects and defects based on weld discontinuities in laser-beam welding. The utilization of fixture-integrated sensors and actuators is a new approach, realizing adaptive clamping devices that enable in-process data acquisition [...] Read more.

The development of adaptive and intelligent clamping devices allows for the reduction of rejects and defects based on weld discontinuities in laser-beam welding. The utilization of fixture-integrated sensors and actuators is a new approach, realizing adaptive clamping devices that enable in-process data acquisition and a time-dependent adjustment of process conditions and workpiece position by means of a closed-loop control. The present work focused on sensor and actuator integration for an adaptive clamping device utilized for laser-beam welding in a butt-joint configuration, in which the position and acting forces of the sheets to be welded can be adjusted during the process (studied welding speeds: 1 m/min, 5 m/min). Therefore, a novel clamping system was designed allowing for the integration of inductive probes and force cells for obtaining time-dependent data of the joint gap and resulting forces during welding due to the displacement of the sheets. A novel automation engineering concept allowed the communication between different sensors, actuators and the laser-beam welding setup based on an EtherCAT bus. The subsequent development of a position control and a force control and their combination was operated with a real time PC as master in the bus system and proved the feasibility of the approach based on proportional controllers. Finally, the scalability regarding higher welding speeds was demonstrated. Full article

(This article belongs to the Special Issue Laser Material Processing and Thermal Joining Process)

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19 pages, 1852 KiB

Open AccessReview

A Comprehensive Review of Conventional, Machine Leaning, and Deep Learning Models for Groundwater Level (GWL) Forecasting

by Junaid Khan, Eunkyu Lee, Awatef Salem Balobaid and Kyungsup Kim

Appl. Sci. 2023, 13(4), 2743; https://doi.org/10.3390/app13042743 - 20 Feb 2023

Cited by 34 | Viewed by 4235

Abstract

Groundwater level (GWL) refers to the depth of the water table or the level of water below the Earth’s surface in underground formations. It is an important factor in managing and sustaining the groundwater resources that are used for drinking water, irrigation, and [...] Read more.

Groundwater level (GWL) refers to the depth of the water table or the level of water below the Earth’s surface in underground formations. It is an important factor in managing and sustaining the groundwater resources that are used for drinking water, irrigation, and other purposes. Groundwater level prediction is a critical aspect of water resource management and requires accurate and efficient modelling techniques. This study reviews the most commonly used conventional numerical, machine learning, and deep learning models for predicting GWL. Significant advancements have been made in terms of prediction efficiency over the last two decades. However, while researchers have primarily focused on predicting monthly, weekly, daily, and hourly GWL, water managers and strategists require multi-year GWL simulations to take effective steps towards ensuring the sustainable supply of groundwater. In this paper, we consider a collection of state-of-the-art theories to develop and design a novel methodology and improve modelling efficiency in this field of evaluation. We examined 109 research articles published from 2008 to 2022 that investigated different modelling techniques. Finally, we concluded that machine learning and deep learning approaches are efficient for modelling GWL. Moreover, we provide possible future research directions and recommendations to enhance the accuracy of GWL prediction models and improve relevant understanding. Full article

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12 pages, 1080 KiB

Open AccessArticle

The Temperature Interval of the Liquid–Glass Transition of Amorphous Polymers and Low Molecular Weight Amorphous Substances

by Migmar V. Darmaev, Michael I. Ojovan, Alexey A. Mashanov and Timur A. Chimytov

Appl. Sci. 2023, 13(4), 2742; https://doi.org/10.3390/app13042742 - 20 Feb 2023

Cited by 2 | Viewed by 1901

Abstract

We present calculation results of the temperature interval δT_g characterizing the liquid–glass transition in amorphous materials obtained on the basis of available data of the empirical parameters C₁ and C₂ in the Williams–Landel–Ferry (WLF) viscosity equation. We consider the unambiguous [...] Read more.

We present calculation results of the temperature interval δT_g characterizing the liquid–glass transition in amorphous materials obtained on the basis of available data of the empirical parameters C₁ and C₂ in the Williams–Landel–Ferry (WLF) viscosity equation. We consider the unambiguous dependence of the relative transition temperature interval δT_g/T_g on the fraction of the fluctuation volume f_g frozen at the glass transition temperature T_g utilizing Sanditov’s model of delocalized atoms. The parameter f = ΔV_e/V, which determines the molecular mobility characteristic of delocalized atoms in the liquid–glass transition region, is weakly dependent on the nature of most vitreous substances and can be found as f_g = 1/C₁. We show that the temperature interval δT_g is less than 1% of the T_g for most amorphous substances. This result conforms with Simon’s classical idea of a small temperature range in which the structure freezes. The structural relaxation time τ_g at T_g of polymers and chalcogenide glasses is also calculated. Full article

(This article belongs to the Special Issue Polymer Materials Chemistry: Design, Synthesis and Emerging Applications)

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16 pages, 2535 KiB

Open AccessArticle

Optimization of BP Neural Network Model for Rockburst Prediction under Multiple Influence Factors

by Chao Wang, Jianhui Xu, Yuefeng Li, Tuanhui Wang and Qiwei Wang

Appl. Sci. 2023, 13(4), 2741; https://doi.org/10.3390/app13042741 - 20 Feb 2023

Cited by 4 | Viewed by 2186

Abstract

Rockbursts are serious threats to the safe production of mining, resulting in great casualties and property losses. The accurate prediction of rockburst is an important premise that influences the safety and health of miners. As a classical machine learning algorithm, the back propagation [...] Read more.

Rockbursts are serious threats to the safe production of mining, resulting in great casualties and property losses. The accurate prediction of rockburst is an important premise that influences the safety and health of miners. As a classical machine learning algorithm, the back propagation (BP) neural network has been widely used in rockburst prediction. However, there are few reports about the influence study of different training sample sizes, optimization algorithms and index dimensionless methods on the prediction accuracy of BP neural network models. Therefore, 100 groups of typical rockburst engineering samples were collected locally and abroad, and considering the relevance, scientificity and quantifiability of the prediction indexes, the ratio of the maximum tangential stress of surrounding rock to the rock uniaxial compressive strength (σ_θ/σ_c), the ratio of the rock uniaxial compressive strength to the rock uniaxial tensile strength (σ_c/σ_t) and the elastic energy index (W_et) were chosen as the prediction indexes. When the number of samples was 40, 70 and 100, sixty improved BP models were established based on the standard gradient descent algorithm and four optimization algorithms (momentum gradient descent algorithm, quasi-Newton algorithm, conjugate gradient algorithm, Levenberg–Marquardt algorithm) and four index dimensionless methods (unified extreme value processing method, differentiated extreme value processing method, data averaging processing method, normalized processing method). The prediction performances of each improved model were compared with those of standard BP models. The comparative study results indicate that the sample size, optimization algorithm and dimensionless method have different effects on the prediction accuracy of BP models, which are described as follows: (1) The prediction accuracy value A of the BP model increases with the addition of sample size. The average value A_ave of twenty improved models under three kinds of sample sizes increases from A_ave (40) = 69.7% to A_ave (100) = 75.3%, with a maximal value A_max from A_max (40) = 85.0% to A_max (100) = 97.0%. (2) The value A and comprehensive accuracy value C of the BP model based on four optimization algorithms are generally higher than those of the standard BP model. (3) The improved BP model based on the unified extreme value processing method combined with the Levenberg–Marquardt algorithm has the highest value A_max (100) = 97.0% and value C = 194, and the prediction results of five engineering cases are completely consistent with the actual situation at the site, so this is the best BP neural network model selected in this paper. Full article

(This article belongs to the Special Issue Mining Safety: Challenges & Prevention)

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25 pages, 6986 KiB

Open AccessArticle

Process Optimization of Microwave-Assisted Extraction of Chlorophyll, Carotenoid and Phenolic Compounds from Chlorella vulgaris and Comparison with Conventional and Supercritical Fluid Extraction

by Ioulia Georgiopoulou, Soultana Tzima, Vasiliki Louli and Kostis Magoulas

Appl. Sci. 2023, 13(4), 2740; https://doi.org/10.3390/app13042740 - 20 Feb 2023

Cited by 23 | Viewed by 3950

Abstract

The production of bioactive products from microalgae biomass with efficient and environmentally friendly technologies is a field of great research interest. The present work focuses on the recovery of high-added value bioactive components from Chlorella vulgaris through microwave-assisted extraction (MAE) with aq. ethanol [...] Read more.

The production of bioactive products from microalgae biomass with efficient and environmentally friendly technologies is a field of great research interest. The present work focuses on the recovery of high-added value bioactive components from Chlorella vulgaris through microwave-assisted extraction (MAE) with aq. ethanol 90% v/v. The effect of extraction temperature (40–60 °C), duration (5–25 min), solvent-to-biomass ratio (20–90 mL_solv/g_biom), and microwave power (300–800 watts) was investigated regarding the extraction yield, extract’s chlorophyll, carotenoid and phenolic content, and antioxidant activity. MAE optimization at 60 °C, 300 watts, 14 min, and 22 mL_solv/g_biom led to 11.14% w/w yield, 63.36 mg/g_extr total chlorophylls, 7.06 mg/g_extr selected carotenoids of astaxanthin, lutein and β-carotene, 24.88 mg/g_extr total carotenoids, 9.34 mg_GA/g_extr total phenolics, and 40.49 mg_extr/mg_DPPH IC₅₀ (antioxidant activity indicator). Moreover, the conventional solid-liquid extraction (SLE) with aq. ethanol 90% v/v, the supercritical fluid extraction (SFE) with CO₂, as well as SFE with cosolvent addition (10% w/w ethanol), were also performed for comparison purposes. The results revealed that SLE presented the highest yield. However, the non-conventional methods of MAE and SFE led to extracts of competitive or even better quality under significantly shorter extraction duration. Full article

(This article belongs to the Special Issue Bioactive Compounds from Various Sources: Beneficial Effects and Technological Applications II)

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14 pages, 7893 KiB

Open AccessFeature PaperArticle

Comparative Analysis of Partially Renewable Composite Fuels Based on Peat, Lignite and Plant Oil

by Roman Egorov, Dmitrii O. Glushkov and Maxim Belonogov

Appl. Sci. 2023, 13(4), 2739; https://doi.org/10.3390/app13042739 - 20 Feb 2023

Cited by 1 | Viewed by 1449

Abstract

The inevitable depletion of exploited fossil fuel deposits motivates the investigation of every possibility of saving them. One of the ways to do that is to combine fossil fuels with renewable plant-derived fuels. This paper studies the specific aspects of the thermochemical conversion [...] Read more.

The inevitable depletion of exploited fossil fuel deposits motivates the investigation of every possibility of saving them. One of the ways to do that is to combine fossil fuels with renewable plant-derived fuels. This paper studies the specific aspects of the thermochemical conversion of composite fuels consisting of peat or lignite with rapeseed oil. It was shown that mixtures of peat or lignite with rapeseed oil can be successfully gasified when the temperature is higher than 700–800 °C. The self-sustaining combustion of these fuels does not support such high temperatures, and thus the process requires external heating. The obtained optimal component ratio for peat-oil and lignite-oil compositions is about 1:2 and 3:2, respectively. Such mixtures allow the most efficient usage of the oxidation heat during conversion. The high calorific value of such fuels is very close to that of rapeseed oil (38.5 MJ/kg), even for the lignite-oil composition with 40 wt% lignite. Lower overall prices of fossil fuels compared to pure renewable plant-derived fuels help reduce costs and save valuable fossil fuels. Full article

(This article belongs to the Special Issue Fuel Combustion Mechanisms, Characteristics and Emission Analysis)

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13 pages, 2401 KiB

Open AccessArticle

Biomechanical Analysis of Femoral Stem Features in Hinged Revision TKA with Valgus or Varus Deformity: A Comparative Finite Elements Study

by Edoardo Bori and Bernardo Innocenti

Appl. Sci. 2023, 13(4), 2738; https://doi.org/10.3390/app13042738 - 20 Feb 2023

Cited by 1 | Viewed by 1568

Abstract

Hinged total knee arthroplasty (TKA) is a valid option to treat patients during revision of an implant; however, in case of varus/valgus deformity, the force transmission from the femur to the tibia could be altered and therefore the performance of the implant could [...] Read more.

Hinged total knee arthroplasty (TKA) is a valid option to treat patients during revision of an implant; however, in case of varus/valgus deformity, the force transmission from the femur to the tibia could be altered and therefore the performance of the implant could be detrimental. To be able to evaluate this, the goal of this study was to investigate, using a validated finite element analysis, the effect of varus/valgus load configurations in the bones when a hinged TKA is used. In detail, short and long stem lengths (50 mm, and 120 mm), were analyzed both under cemented or press-fit fixation under the following varus and valgus deformity: 5°, 10°, 20°, and 30°. The main outputs of the study were average bone stress in different regions of interest, together with tibio-femoral contact pressure and force. Results demonstrated that changes in the varus or valgus deformity degrees induce a change in the medio-lateral stress and force distribution, together with a change in the contact area. The effect of stem length and cement do not alter the tibio-femoral contact biomechanics but its effect is mainly localized in the distal femoral region, and it is negligible in the proximal regions. Full article

(This article belongs to the Special Issue Advanced Imaging in Orthopedic Biomechanics)

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15 pages, 5270 KiB

Open AccessArticle

Thermomechanical Stresses in Silicon Chips for Optoelectronic Devices

by Claudia Mezzalira, Fosca Conti, Danilo Pedron and Raffaella Signorini

Appl. Sci. 2023, 13(4), 2737; https://doi.org/10.3390/app13042737 - 20 Feb 2023

Cited by 1 | Viewed by 1762

Abstract

The growing interest in improving optoelectronic devices requires continuous research of the materials and processes involved in manufacturing. From a chemical point of view, the study of this sector is crucial to optimize existing manufacturing processes or create new ones. This work focusses [...] Read more.

The growing interest in improving optoelectronic devices requires continuous research of the materials and processes involved in manufacturing. From a chemical point of view, the study of this sector is crucial to optimize existing manufacturing processes or create new ones. This work focusses on the experimental evaluation of residual stresses on samples that are intended to simulate part of the structure of an optoelectronic device. It represents an important starting point for the development of optoelectronic devices with characteristics suitable for future industrial production. Silicon chips, with a thickness of 120 μm, were soldered onto copper and alumina substrates, using different assembly parameters in terms of temperature and pressure. Using Raman spectroscopy, the stress evaluation was estimated in a wide temperature range, from −50 to 180 °C. Silicon chips soldered with AuSn alloy on copper substrates demonstrated at 22 °C a compressive stress, developed in the center of the assembly with a maximum value of −600 MPa, which reached −1 GPa at low temperatures. They present a stress distribution with a symmetric profile with respect to the central area of the chip. The silicon chip assembled on a ceramic substrate without pressure turned out to be extremely interesting. Even in the absence of pressure, the sample did not show a large shift in the Raman position, indicating a low stress. Full article

(This article belongs to the Section Applied Physics General)

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21 pages, 13258 KiB

Open AccessArticle

Identification Procedure for Design Optimization of Gravitational Electromagnetic Energy Harvesters

by Mirco Lo Monaco, Caterina Russo and Aurelio Somà

Appl. Sci. 2023, 13(4), 2736; https://doi.org/10.3390/app13042736 - 20 Feb 2023

Cited by 6 | Viewed by 1776

Abstract

Energy harvesting is a promising technique for supplying low-power devices as an alternative to conventional batteries. Energy harvesters can be integrated into Autonomous Internet of Things (AIoT) systems to create a wireless network of sensor nodes for real-time monitoring of assets. This paper [...] Read more.

Energy harvesting is a promising technique for supplying low-power devices as an alternative to conventional batteries. Energy harvesters can be integrated into Autonomous Internet of Things (AIoT) systems to create a wireless network of sensor nodes for real-time monitoring of assets. This paper shows a design and optimization methodology for gravitational vibration-based electromagnetic energy harvesters (GVEHs) of different sizes considering the design constraints of its real application. The configuration, analytical model, and electro-mechanical coupling of these devices are described in detail. A numerical model is developed in the Ansys Maxwell FEM environment to derive the non-linear stiffness and damping of the asymmetric magnetic suspension. Experimental laboratory tests on three harvester prototypes are compared to numerical results of dynamic simulations in MATLAB/Simulink for the validation of the proposed model through error estimation. The fully-parametric validated model is used to perform sensitivity analyses on the device’s mechanical characteristics of natural frequency and magnet equilibrium position by varying the fixed and moving magnets dimensions. The set of magnets composing the magnetic spring is chosen complying with the application design constraints of size and resonance frequency tuning. Coil parameters of length and number of turns are optimized for maximum output power generation. The optimized device simulated performances are compared to other devices in the literature in terms of NPD, a significant index that evaluates power density under different excitation amplitudes. The optimized harvester presents the highest NPD value of 2.61, achieving an improvement of 52% with respect to the best harvester amongst the three tested prototypes. Full article

(This article belongs to the Special Issue State-of-the-Art in Energy Harvesting for IoT and WSN)

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11 pages, 1188 KiB

Open AccessArticle

The Improvement and Application of the Geoelectrochemical Exploration Method

by Ming Kang, Huanzhao Guo, Wende Zhu, Xianrong Luo and Jianwen Yang

Appl. Sci. 2023, 13(4), 2735; https://doi.org/10.3390/app13042735 - 20 Feb 2023

Cited by 1 | Viewed by 1386

Abstract

The anionic and cationic species of elements from deeply buried deposits migrate to the near surface driven by various geological forces. The geoelectrochemical exploration method (GEM), derived from CHIM, consists of the application of an electric field to collect these active ions at [...] Read more.

The anionic and cationic species of elements from deeply buried deposits migrate to the near surface driven by various geological forces. The geoelectrochemical exploration method (GEM), derived from CHIM, consists of the application of an electric field to collect these active ions at the designated electrode. Prospecting effects have been investigated by researchers since the coming up of CHIM. However, the cumbersome technical equipment, complex techniques and low production efficiency have restricted its potential application in field geological survey. This paper presents the newly developed CHIM that is electrified by a low voltage dipole. The improved technique allows both anionic and cationic species of elements to be extracted simultaneously in an anode and in a cathode. Compared with the conventional CHIM method, the innovative techniques called dipole geoelectrochemical method are characterized by simple instrumentation, low cost and easy operation in field, and in particular enables simultaneous extraction of anionic and cationic species of elements, from which more information can be derived with higher extraction efficiency. The dipole geoelectrochemical method was proposed and applied in the experiments of the Yingezhuang gold ore from Zhaoyuan, Shandong Province, the 210 gold ore from Jinwozi, Xinjiang Province, and the Daiyinzhang gold polymetallic deposit from Wutaishan, Shanxi Province. There are clearly anomalies above the gold ore body, indicating the effectiveness and feasibility of the improved dipole geoelectrochemical method in both scientific research and mineral exploration. The results of anode extraction in several mining areas have shown good results, indicating that gold may be mainly negatively charged. In fact, many metal nanoparticles, clay minerals, or complexes of metal ions are negatively charged, so they migrate to the anode electrode and enrich. Full article

(This article belongs to the Special Issue New Advances and Illustrations in Applied Geochemistry)

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9 pages, 1468 KiB

Open AccessCommunication

Optimal Complex Morlet Wavelet Parameters for Quantitative Time-Frequency Analysis of Molecular Vibration

by Shuangquan Li, Shangyi Ma and Shaoqing Wang

Appl. Sci. 2023, 13(4), 2734; https://doi.org/10.3390/app13042734 - 20 Feb 2023

Cited by 2 | Viewed by 3467

Abstract

When the complex Morlet function (CMOR) is used as a wavelet basis, it is necessary to select optimal bandwidth and center frequency. However, the method to select the optimal CMOR wavelet parameters for one specific frequency is still unclear. In this paper, we [...] Read more.

When the complex Morlet function (CMOR) is used as a wavelet basis, it is necessary to select optimal bandwidth and center frequency. However, the method to select the optimal CMOR wavelet parameters for one specific frequency is still unclear. In this paper, we deeply investigate the essence of CMOR wavelet transform and clearly illustrate the time-frequency resolution and edge effect. Then, the selection method of the optimal bandwidth and center frequency is proposed. We further perform the quantitative time-frequency (QTF) analysis of water molecule vibration based on our method. We find that the CMOR wavelet parameters obtained by our method can not only meet the requirement of frequency resolution but also meet the limit of edge effect. Moreover, there is an uphill energy relaxation in the vibration of the water molecule, which agrees well with the experimental results. These results demonstrate that our method can accurately find the optimal CMOR wavelet parameters for the target frequency. Full article

(This article belongs to the Topic Theoretical, Quantum and Computational Chemistry)

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18 pages, 4975 KiB

Open AccessArticle

Trajectory Tracking of Autonomous Vehicle Using Clothoid Curve

by Jianshi Li, Jingtao Lou, Yongle Li, Shiju Pan and Youchun Xu

Appl. Sci. 2023, 13(4), 2733; https://doi.org/10.3390/app13042733 - 20 Feb 2023

Cited by 4 | Viewed by 2735

Abstract

This paper proposes a clothoid-curve-based trajectory tracking control method for autonomous vehicles to solve the problem of tracking errors caused by the discontinuous curvature of the control curve calculated by the pure pursuit tracking algorithm. Firstly, based on the Ackerman steering model, the [...] Read more.

This paper proposes a clothoid-curve-based trajectory tracking control method for autonomous vehicles to solve the problem of tracking errors caused by the discontinuous curvature of the control curve calculated by the pure pursuit tracking algorithm. Firstly, based on the Ackerman steering model, the motion model is constructed for vehicle trajectory tracking, Then, the position of the vehicle after the communication delay of the control system is predicted as the starting point of the clothoid control curve, and the optimization interval of the curve end point is determined. The clothoid control curves are calculated, and their parameters are verified by the vehicle motion and safety constraints, so as to obtain the optimal clothoid control curve satisfying the constraints. Finally, considering the servo system response delay time of the steering system, the steering angle target control value is obtained by previewing the curvature of the clothoid control curve. The field experiment is conducted on the test road, which consists of straight, right-angle turns and lane-change elements under three sets of speed limitations, and the test results show that the proposed clothoid-curve-based trajectory tracking control method greatly improved the tracking accuracy compared with the pure pursuit method; in particular, the yaw deviation is improved by more than 50%. Full article

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30 pages, 6905 KiB

Open AccessReview

A State-of-the-Art Review of Non-Destructive Testing Image Fusion and Critical Insights on the Inspection of Aerospace Composites towards Sustainable Maintenance Repair Operations

by Muhammet E. Torbali, Argyrios Zolotas and Nicolas P. Avdelidis

Appl. Sci. 2023, 13(4), 2732; https://doi.org/10.3390/app13042732 - 20 Feb 2023

Cited by 10 | Viewed by 6080

Abstract

Non-destructive testing (NDT) of aerospace structures has gained significant interest, given its non-destructive and economic inspection nature enabling future sustainable aerospace maintenance repair operations (MROs). NDT has been applied to many different domains, and there is a number of such methods having their [...] Read more.

Non-destructive testing (NDT) of aerospace structures has gained significant interest, given its non-destructive and economic inspection nature enabling future sustainable aerospace maintenance repair operations (MROs). NDT has been applied to many different domains, and there is a number of such methods having their individual sensor technology characteristics, working principles, pros and cons. Increasingly, NDT approaches have been investigated alongside the use of data fusion with the aim of combining sensing information for improved inspection performance and more informative structural health condition outcomes for the relevant structure. Within this context, image fusion has been a particular focus. This review paper aims to provide a comprehensive survey of the recent progress and development trends in NDT-based image fusion. A particular aspect included in this work is providing critical insights on the reliable inspection of aerospace composites, given the weight-saving potential and superior mechanical properties of composites for use in aerospace structures and support for airworthiness. As the integration of NDT approaches for composite materials is rather limited in the current literature, some examples from non-composite materials are also presented as a means of providing insights into the fusion potential. Full article

(This article belongs to the Section Materials Science and Engineering)

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13 pages, 704 KiB

Open AccessArticle

Determination of Benefits of the Application of CMMS Database Improvement Proposals

by Ladislav Stazić, Nikola Račić, Tatjana Stanivuk and Đorđe Dobrota

Appl. Sci. 2023, 13(4), 2731; https://doi.org/10.3390/app13042731 - 20 Feb 2023

Cited by 4 | Viewed by 2163

Abstract

Computerized maintenance management systems (CMMSs) are software packages that support or organize the maintenance tasks of assets or equipment. They are found in the background of any ship maintenance operation and are an important part of maintenance planning, spare parts supply, record keeping, [...] Read more.

Computerized maintenance management systems (CMMSs) are software packages that support or organize the maintenance tasks of assets or equipment. They are found in the background of any ship maintenance operation and are an important part of maintenance planning, spare parts supply, record keeping, etc. In the marine market, there are a number of CMMSs that are competing fiercely to program a better and more modern program that will capture the market, which has been accompanied by published analyses and scientific papers. At the same time, the quality of the data entered into CMMS databases is questionable, a fact that has been ignored in practice and scientific circles; until recently, there were no published analyses and there was no way to measure the quality of the data entered. This article presents two proposals for improving the quality of CMMS databases and calculates their potential benefits. By implementing the first proposal, the evaluation methodology for the ship’s Planned Maintenance System database, between 10% and 15% of databases will have significant financial or safety benefits. This measure will also have an impact on more than 40% of the other databases that can also be improved. The second proposal will have a smaller impact of only 4%. The overall benefit of these proposals is to improve more than 60% of the databases and will result in a significant increase in safety or financial savings. Full article

(This article belongs to the Special Issue Sustainability in Maritime Transport: Advances, Solutions and Pending Tasks)

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14 pages, 2919 KiB

Open AccessArticle

Characterization of the Superplastic Magnesium Alloy AZ31 through Free-Forming Tests and Inverse Analysis

by Gillo Giuliano and Wilma Polini

Appl. Sci. 2023, 13(4), 2730; https://doi.org/10.3390/app13042730 - 20 Feb 2023

Cited by 1 | Viewed by 1456

Abstract

This work proposes a simple procedure to characterize 1.0 mm thick sheets of superplastic magnesium alloy AZ31. The simplest mathematical function that models the behavior of a superplastic material is a power law between stress and strain rate with two parameters connected to [...] Read more.

This work proposes a simple procedure to characterize 1.0 mm thick sheets of superplastic magnesium alloy AZ31. The simplest mathematical function that models the behavior of a superplastic material is a power law between stress and strain rate with two parameters connected to the material: K and m. First, the parameter m (variable with the strain) was defined directly by carrying out free-forming experimental tests at constant pressure and using a simple expression taken from the analytical modeling of the free-forming process. In the second step, an inverse analysis was carried out through a finite element model (FEM) and based on a numerical–experimental comparison between the results of the dimensionless height–time (H–t) curve, which made it possible to identify the variation of the parameter K in the same strain range. Once the m and K parameters were evaluated, it was possible to simulate the free-forming tests at constant pressure in the pressure range used to characterize the material. The proposed procedure to estimate m and K parameters made it possible to best match the numerical with the experimental results in terms of the dimensionless height–time curve. The difference between the forming time estimated by FEM and that measured experimentally along the H–t curve was found to be less than 9%. Full article

(This article belongs to the Special Issue New Insights in Material Forming)

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21 pages, 10670 KiB

Open AccessArticle

The Influence of Dome Geometry on the Results of Modal and Buckling Analysis

by Urszula Radoń, Paweł Zabojszcza and Milan Sokol

Appl. Sci. 2023, 13(4), 2729; https://doi.org/10.3390/app13042729 - 20 Feb 2023

Viewed by 1920

Abstract

The main purpose of this paper is to compare the results of modal analysis for two types of domes. The first one is a low-rise Schwedler dome. The second one is a high-rise geodesic dome. The low-rise Schwedler dome is subjected to large [...] Read more.

The main purpose of this paper is to compare the results of modal analysis for two types of domes. The first one is a low-rise Schwedler dome. The second one is a high-rise geodesic dome. The low-rise Schwedler dome is subjected to large displacement gradients and should be designed according to geometrical nonlinear analysis. In the case of high-rise geodesic dome, linear analysis is sufficient. In the modal analysis, the mass of the bars of the supporting structures was modeled as evenly distributed, while the mass of the covering and roof equipment was concentrated in the nodes. Classic calculations have been enriched with modal analysis taking into account normal forces. Normal forces affect the vibration frequency of the structure. Commonly used modal analysis does not take into account the influence of normal forces. In order to approximate the actual working conditions of the structure, calculations performed in Autodesk Robot Structure Professional 2022 can be performed in accordance with the modal analysis, taking into account the applied normal forces in the modal analysis. Additionally, stability loss was verified using linear or geometrical nonlinear buckling analysis. The exigence of including normal forces in modal analysis for low-rise domes is the novelty and main message of the work. Full article

(This article belongs to the Special Issue Advanced Research in Seismic and Dynamic Engineering for Structures and Infrastructures)

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21 pages, 2857 KiB

Open AccessArticle

Shaping the Properties of Osmo-Dehydrated Strawberries in Fruit Juice Concentrates

by Hanna Kowalska, Magdalena Trusinska, Katarzyna Rybak, Artur Wiktor, Dorota Witrowa-Rajchert and Malgorzata Nowacka

Appl. Sci. 2023, 13(4), 2728; https://doi.org/10.3390/app13042728 - 20 Feb 2023

Cited by 7 | Viewed by 3138

Abstract

The growing interest in high-quality food leads to looking for new solutions in the production of natural fruit snacks. Osmotic dehydration is one of the processes, which can be used to obtain a minimally processed product as well as to give it specific [...] Read more.

The growing interest in high-quality food leads to looking for new solutions in the production of natural fruit snacks. Osmotic dehydration is one of the processes, which can be used to obtain a minimally processed product as well as to give it specific characteristics. Usually, a sucrose solution is used as an osmotic agent; however, the use of chokeberry, strawberry, or cherry juice concentrates can be beneficial in the process of the osmotic dehydration of fruits. The process of the dehydration of strawberries with the use of fruit juice concentrates (chokeberry, strawberry, or cherry) and a sucrose solution as a standard was carried out at a temperature of 30 °C for 3 h. The kinetics of the processes (weight reduction, water loss, and solid gain) were evaluated as well as physical (water activity, color parameters L*, a*, b*, ΔE, texture with maximum force and compression work, and structure) and chemical properties (dry matter content, total polyphenols content, total anthocyanin content, vitamin C, antioxidant activity with DPPH and ABTS radicals, spectral analysis with FTIR method, sucrose, glucose and fructose content, and thermal decomposition with TG analysis). The use of fruit juice concentrates positively influences the enrichment of the final product with bioactive compounds, such as anthocyanin and vitamin C. Strawberry and chokeberry juice concentrates have proven to be good hypertonic media for increasing the antioxidant activity of dehydrated fruit. Moreover, the use of fruit concentrates has a positive effect on the sugar profile of dehydrated strawberries. Full article

(This article belongs to the Special Issue Drying Technologies in Food Processing)

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20 pages, 5189 KiB

Open AccessArticle

A Hybrid Grey Wolf Optimization Algorithm Using Robust Learning Mechanism for Large Scale Economic Load Dispatch with Vale-Point Effect

by Tzu-Ching Tai, Chen-Cheng Lee and Cheng-Chien Kuo

Appl. Sci. 2023, 13(4), 2727; https://doi.org/10.3390/app13042727 - 20 Feb 2023

Cited by 9 | Viewed by 2241

Abstract

This paper proposes a new hybrid algorithm for grey wolf optimization (GWO) integrated with a robust learning mechanism to solve the large-scale economic load dispatch (ELD) problem. The robust learning grey wolf optimization (RLGWO) algorithm imitates the hunting behavior and social hierarchy of [...] Read more.

This paper proposes a new hybrid algorithm for grey wolf optimization (GWO) integrated with a robust learning mechanism to solve the large-scale economic load dispatch (ELD) problem. The robust learning grey wolf optimization (RLGWO) algorithm imitates the hunting behavior and social hierarchy of grey wolves in nature and is reinforced by robust tolerance-based adjust searching direction and opposite-based learning. This technique could effectively prevent search agents from being trapped in local optima and also generate potential candidates to obtain a feasible solution. Several constraints of power generators, such as generation limits, local demand, valve-point loading effect, and transmission losses, are considered in practical operation. Five test systems are used to evaluate the effectiveness and robustness of the proposed algorithm in solving the ELD problem. The simulation results clearly reveal the superiority and feasibility of RLGWO to find better solutions in terms of fuel cost and computational efficiency when compared with the previous literature. Full article

(This article belongs to the Special Issue Machine Learning for Energy and Industrial Datasets Forecasting: Volume II)

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18 pages, 4546 KiB

Open AccessArticle

Sep-RefineNet: A Deinterleaving Method for Radar Signals Based on Semantic Segmentation

by Yongjiang Mao, Wenjuan Ren, Xipeng Li, Zhanpeng Yang and Wei Cao

Appl. Sci. 2023, 13(4), 2726; https://doi.org/10.3390/app13042726 - 20 Feb 2023

Cited by 1 | Viewed by 3079

Abstract

With the progress of signal processing technology and the emergence of new system radars, the space electromagnetic environment becomes more and more complex, which puts forward higher requirements for the deinterleaving method of radar signals. Traditional signal deinterleaving algorithms rely heavily on manual [...] Read more.

With the progress of signal processing technology and the emergence of new system radars, the space electromagnetic environment becomes more and more complex, which puts forward higher requirements for the deinterleaving method of radar signals. Traditional signal deinterleaving algorithms rely heavily on manual experience threshold and have poor robustness. To address this problem, we designed an intelligent radar signal deinterleaving algorithm that was completed by encoding the frequency characteristic matrix and semantic segmentation network, named Sep-RefineNet. The frequency characteristic matrix can well construct the semantic features of different pulse streams of radar signals. The Sep-RefineNet semantic segmentation network can complete pixel-level segmentation of the frequency characteristic matrix and finally uses position decoding and verification to obtain the position in the original pulse stream to complete radar signals deinterleaving. The proposed method avoids the processing of threshold judgment and pulse sequence search in traditional methods. The results of the experiment show that this algorithm improves the deinterleaving accuracy and has a good against-noise ability of aliasing pulses and missing pulses. Full article

(This article belongs to the Special Issue Advances in Wireless Communications Using Machine Learning and Deep Learning Techniques)

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17 pages, 6056 KiB

Open AccessArticle

Research on the Path Planning of Unmanned Sweepers Based on a Fusion Algorithm

by Yongjie Ma, Peng Ping and Quan Shi

Appl. Sci. 2023, 13(4), 2725; https://doi.org/10.3390/app13042725 - 20 Feb 2023

Cited by 2 | Viewed by 1714

Abstract

Path planning is one of the key technologies for unmanned driving. However, global paths are unable to avoid unknown obstacles, while local paths tend to fall into local optimality. To solve the problem of unsmooth and inefficient paths on multi-angle roads in a [...] Read more.

Path planning is one of the key technologies for unmanned driving. However, global paths are unable to avoid unknown obstacles, while local paths tend to fall into local optimality. To solve the problem of unsmooth and inefficient paths on multi-angle roads in a park which cannot avoid unknown obstacles, we designed a new fusion algorithm based on the improved A* and Open_Planner algorithms (A-OP). In order to make the global route smoother and more efficient, we first extracted the key points of the A* algorithm and improved the node search structure using heap sorting, and then improved the smoothness of the path using the minimum snap method; secondly, we extracted the key points of the A* algorithm as intermediate nodes in the planning of the Open_Planner algorithm, and used the A-OP algorithm to implement the path planning of the unmanned sweeper. The simulation results show that the improved A* algorithm significantly improved the planning efficiency, the nodes are less computed and the path is smoother. The fused A-OP algorithm not only accomplished global planning effectively, but also avoided unknown obstacles in the path. Full article

(This article belongs to the Section Computing and Artificial Intelligence)

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11 pages, 1134 KiB

Open AccessArticle

Document-Level Event Role Filler Extraction Using Key-Value Memory Network

by Hao Wang, Miao Li, Jianyong Duan, Li He and Qing Zhang

Appl. Sci. 2023, 13(4), 2724; https://doi.org/10.3390/app13042724 - 20 Feb 2023

Viewed by 1579

Abstract

Previous work has demonstrated that end-to-end neural sequence models work well for document-level event role filler extraction. However, the end-to-end neural network model suffers from the problem of not being able to utilize global information, resulting in incomplete extraction of document-level event arguments. [...] Read more.

Previous work has demonstrated that end-to-end neural sequence models work well for document-level event role filler extraction. However, the end-to-end neural network model suffers from the problem of not being able to utilize global information, resulting in incomplete extraction of document-level event arguments. This is because the inputs to BiLSTM are all single-word vectors with no input of contextual information. This phenomenon is particularly pronounced at the document level. To address this problem, we propose key-value memory networks to enhance document-level contextual information, and the overall model is represented at two levels: the sentence-level and document-level. At the sentence-level, we use BiLSTM to obtain key sentence information. At the document-level, we use a key-value memory network to enhance document-level representations by recording information about those words in articles that are sensitive to contextual similarity. We fuse two levels of contextual information by means of a fusion formula. We perform various experimental validations on the MUC-4 dataset, and the results show that the model using key-value memory networks works better than the other models. Full article

(This article belongs to the Special Issue Natural Language Processing (NLP) and Applications)

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19 pages, 4002 KiB

Open AccessArticle

The Modular Gait Design of a Soft, Earthworm-like Locomotion Robot Driven by Ultra-Low Frequency Excitation

by Zhifeng Qi and Xiuting Sun

Appl. Sci. 2023, 13(4), 2723; https://doi.org/10.3390/app13042723 - 20 Feb 2023

Cited by 4 | Viewed by 1893

Abstract

In complex and extreme environments, such as pipelines and polluted waters, gait programming has great significance for multibody segment locomotion robots. The earthworm-like locomotion robot is a representative multibody bionic robot, which has the characteristics of low weight, multibody segments, and excellent movement [...] Read more.

In complex and extreme environments, such as pipelines and polluted waters, gait programming has great significance for multibody segment locomotion robots. The earthworm-like locomotion robot is a representative multibody bionic robot, which has the characteristics of low weight, multibody segments, and excellent movement performance under the designed gait. The body segment cell can realize large deformation under ultra-low frequency excitation. The multibody segment robot can locomote under ultra-low frequency excitation with appropriate shifts. In this paper, a modular gait design principle for a soft, earthworm-like locomotion robot is proposed. The driven modules defined by modular gait generation correspond to the peristaltic wave transmissions of the excitation in the robot for different modular gait modes. A locomotion algorithm is presented to simulate the locomotion of the earthworm-like robot under different locomotion gaits. Moreover, the locomotion speeds are obtained for different modular gait modes. The results show that locomotion speed is related to the original state of the body segments and modular gaits. As the initial actuated segments and driven modules (which correspond to the excitation frequency and shift) increase, faster movement speeds can be realized, which resolves the speed saturation of the earthworm-like robot. The proposed modular gait design method gives a new gait generation principle for the improvement of the locomotion performance of soft, earthworm-like robots. Full article

(This article belongs to the Special Issue Advanced Vibro-Acoustic Technology: Intelligent Algorithms, Smart Materials and Dynamics)

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18 pages, 6034 KiB

Open AccessArticle

by Yufei Li, Bo Li, Xidong Zhao, Simiao Cheng, Wei Zhang and Wei Tian

Appl. Sci. 2023, 13(4), 2722; https://doi.org/10.3390/app13042722 - 20 Feb 2023

Cited by 6 | Viewed by 2373

Abstract

The machining system based on an industrial robot is a new type of equipment to meet the requirements of high quality, high efficiency and high flexibility for large and complex components of aircraft and spacecraft. The error compensation technology is widely used in [...] Read more.

The machining system based on an industrial robot is a new type of equipment to meet the requirements of high quality, high efficiency and high flexibility for large and complex components of aircraft and spacecraft. The error compensation technology is widely used in robotic machining to improve the positioning accuracy of an industrial robot with the intention of meeting the precision requirements of aerospace manufacturing. However, the robot’s positioning accuracy decreases significantly when the orientation of the tool changes dramatically. This stems from the fact that the existing robot compensation methods ignore the uncertainties of Tool Center Point (TCP) calibration. This paper presents a novel regionalized compensation method for improving the positioning accuracy of the robot with calibration uncertainties and large orientation variation of the TCP. The method is experimentally validated through the drilling of curved surface parts of plexiglass using a KUKA KR2830MT robot. Compared with a published error compensation method, the proposed approach improves the positioning accuracy of the robot under the large orientation variation to 0.235 mm. This research can broaden the field of robot calibration technology and further improve the adaptability of robotic machining. Full article

(This article belongs to the Section Robotics and Automation)

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26 pages, 12961 KiB

Open AccessArticle

Multi-Robot Collaborative Flexible Manufacturing and Digital Twin System Design of Circuit Breakers

by Linghao Wang, Liang Shu and Hao Zhou

Appl. Sci. 2023, 13(4), 2721; https://doi.org/10.3390/app13042721 - 20 Feb 2023

Cited by 3 | Viewed by 3100

Abstract

Circuit breakers (CBs) are mainly designed to interrupt current flow when faults are detected and have been widely used in industrial applications. The existing CBs manufacturing method is semi-automatic and requires a lot of labor. To realize flexible manufacturing, a multi-robot cooperative CBs [...] Read more.

Circuit breakers (CBs) are mainly designed to interrupt current flow when faults are detected and have been widely used in industrial applications. The existing CBs manufacturing method is semi-automatic and requires a lot of labor. To realize flexible manufacturing, a multi-robot cooperative CBs flexible manufacturing system (CBFMS) is presented in this study. Aiming at the efficiency of the multi-robot cooperative CBFMS key units, a two-arm cooperation robot approach is proposed. The reinforcement learning algorithm is developed to optimize the manufacturing trajectory of the two-arm cooperation robot. To build and optimize the multi-robot cooperative CBFMS, a digital twin (DT) system describing all physical properties of the physical manufacturing plant is constructed for simulation. In the developed DT system, a kinematic control model of the collaboration robot is established. A real-time display of the robot’s trajectory, manufacturing status, and process manufacturing is provided by the data interaction with the physical cell flow between the units. Following this design, a synchronous mapping between the flexible manufacturing DT system of the CBs and the physical workshop is realized, which enables real-time monitoring and management of the physical production line. The experiments’ results show that the manufacturing efficiency, compared with traditional CBs production, is improved by 22%. Moreover, the multi-robot cooperative CBFMS can make process changes according to the production requirements, which can improve the stability of production. Full article

(This article belongs to the Topic Virtual Reality, Digital Twins, the Metaverse)

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