Search Results (417)

With the growing adoption of circular economy principles in manufacturing, efficient disassembly and reassembly of end-of-life (EOL) products has become a key challenge in smart factories. This paper addresses the Disassembly and Assembly Line Balancing Problem (DALBP), which involves scheduling robotic tasks across workstations while minimizing total operation time and accounting for directional switching time between disassembly and assembly phases. To solve this problem, we propose an improved reinforcement learning algorithm, IQ($\lambda$), which extends the classical Q($\lambda$) method by incorporating eligibility trace decay, a dynamic Action Table mechanism to handle non-conflicting parallel tasks, and switching-aware reward shaping to penalize inefficient task transitions. Compared with standard Q($\lambda$), these modifications enhance the algorithm’s global search capability, accelerate convergence, and improve solution quality in complex DALBP scenarios. While the current implementation does not deploy live IoT infrastructure, the architecture is modular and designed to support future extensions involving edge-cloud coordination, trust-aware optimization, and privacy-preserving learning in Industrial Internet of Things (IIoT) environments. Four real-world disassembly-assembly cases (flashlight, copier, battery, and hammer drill) are used to evaluate the algorithm’s effectiveness. Experimental results show that IQ($\lambda$) consistently outperforms traditional Q-learning, Q($\lambda$), and Sarsa in terms of solution quality, convergence speed, and robustness. Furthermore, ablation studies and sensitivity analysis confirm the importance of the algorithm’s core design components. This work provides a scalable and extensible framework for intelligent scheduling in cyber-physical manufacturing systems and lays a foundation for future integration with secure, IoT-connected environments. Full article

Spilled hydrocarbons released from oil pipeline accidents can result in long-term environmental contamination and significant damage to habitats. In this regard, evaluating actions in response to vulnerability scenarios is fundamental to emergency management and groundwater integrity. To this end, understanding the trajectories and their influence on the various parameters and characteristics of the contaminant’s fate through accurate numerical simulations can aid in developing a rapid remediation strategy. This paper develops a numerical model using the CactusHydro code, which is based on a high-resolution shock-capturing (HRSC) conservative method that accurately follows sharp discontinuities and temporal dynamics for a three-phase fluid flow. We analyze nine different emergency scenarios that represent the breaking of a diesel oil onshore pipeline in a porous medium. These scenarios encompass conditions such as dry season rupture, rainfall-induced saturation, and varying pipeline failure pressures. The influence of the spilled oil pressure and water saturation in the unsaturated zone is analyzed by following the saturation contour profiles of the three-phase fluid flow. We follow with the high-accuracy formation of shock fronts of the advective part of the migration. Additionally, the mass distribution of the expelled contaminant along the porous medium during the emergency is analyzed and quantified for the various scenarios. The results obtained indicate that the aquifer contamination strongly depends on the pressure outflow in the vertical flow. For a fixed pressure value, as water saturation increases, the mass of contaminant decreases, while the contamination speed increases, allowing the contaminant to reach extended areas. This study suggests that, even for LNAPLs, the distribution of leaked oil depends strongly on the spill pressure. If the pressure reaches 20 atm at the time of pipeline failure, then contamination may extend as deep as two meters below the water table. Additionally, different seasonal conditions can influence the spread of contaminants. This insight could directly inform guidelines and remediation measures for spill accidents. The CactusHydro code is a valuable tool for such applications. Full article

Unmanned Aerial Vehicles (UAVs) equipped with onboard cameras and deep-learning-based object detection algorithms are increasingly used in search operations. This study investigates the optimal flight parameters, specifically flight speed and ground sampling distance (GSD), to maximize a search efficiency metric called effective coverage. A custom dataset of 4468 aerial images with 35,410 annotated cardboard targets was collected and used to evaluate the influence of flight conditions on detection accuracy. The effects of flight speed and GSD were analyzed using regression modeling, revealing a trade-off between the area coverage and detection confidence of trained YOLOv8 and YOLOv11 models. Area coverage was modeled based on flight speed and camera specifications, enabling an estimation of the effective coverage. The results provide insights into how the detection performance varies across different operating conditions and demonstrate that a balance point exists where the combination of the detection reliability and coverage efficiency is optimized. Our table of the optimal flight regimes and metrics for the most commonly used cameras in UAV operations offers practical guidelines for efficient and reliable mission planning. Full article

This study proposes a current control strategy to enhance the control stability of an interior permanent magnet synchronous motor (IPMSM) under transient conditions, such as rapid acceleration or deceleration in electric vehicle (EV) applications. Conventional current control methods provide optimal steady-state current references corresponding to torque commands using a lookup table (LUT)-based approach. However, during transitions between these reference points, particularly in the field-weakening region at high speeds, the voltage limit may be exceeded. When the voltage limit is exceeded, unstable overmodulation states may occur, degrading stability and resulting in overshoot of the inverter input current. Although ramp generators are commonly employed to interpolate between current references, a fixed ramp slope may fail to ensure a sufficient voltage margin during rapid transients. In this study, a method is proposed to dynamically adjust the rate of change of the d-axis current reference in real time based on the difference between the inverter output voltage and its voltage limit. By enabling timely field-weakening before rapid changes in speed or q-axis current, the proposed strategy maintains control stability within the voltage limit. The effectiveness of the proposed method was verified through simulations based on real vehicle driving profiles and dynamometer experiments using a 38 kW class IPMSM for a hybrid electric vehicle (HEV), demonstrating reduced input DC current overshoot, improved voltage stability, and enhanced torque tracking performance under high-speed transient conditions. Full article

With the rapid development of technologies such as cloud computing, big data, and the Internet of Things (IoT), Software-Defined Networking (SDN) is emerging as a new network architecture for the modern Internet. SDN separates the control plane from the data plane, allowing a central controller, the SDN controller, to quickly direct the routing devices within the topology to forward data packets, thus providing flexible traffic management for communication between information sources. However, traditional Distributed Denial of Service (DDoS) attacks still significantly impact SDN systems. This paper proposes a novel dual-layer strategy capable of detecting and mitigating DDoS attacks in an SDN network environment. The first layer of the strategy enhances security by using blockchain technology to replace the SDN flow table storage container in the northbound interface of the SDN controller. Smart contracts are then used to process the stored flow table information. We employ the time window algorithm and the token bucket algorithm to construct the first layer strategy to defend against obvious DDoS attacks. To detect and mitigate less obvious DDoS attacks, we design a second-layer strategy that uses a composite data feature correlation coefficient calculation method and the Isolation Forest algorithm from unsupervised learning techniques to perform binary classification, thereby identifying abnormal traffic. We conduct experimental validation using the publicly available DDoS dataset CIC-DDoS2019. The results show that using this strategy in the SDN network reduces the average deviation of round-trip time (RTT) by approximately 38.86% compared with the original SDN network without this strategy. Furthermore, the accuracy of DDoS attack detection reaches 97.66% and an F1 score of 92.2%. Compared with other similar methods, under comparable detection accuracy, the deployment of our strategy in small-scale SDN network topologies provides faster detection speeds for DDoS attacks and exhibits less fluctuation in detection time. This indicates that implementing this strategy can effectively identify DDoS attacks without affecting the stability of data transmission in the SDN network environment. Full article

The exponential growth of autonomous systems demands robust security mechanisms that can operate within the extreme constraints of real-time embedded environments. This paper introduces SMART DShot, a groundbreaking machine learning-enhanced framework that transforms the security landscape of unmanned aerial vehicle motor control systems through seamless integration of adaptive timing correction and real-time anomaly detection within Digital Shot (DShot) communication protocols. Our approach addresses critical vulnerabilities in Electronic Speed Controller (ESC) interfaces by deploying four synergistic algorithms—Kalman Filter Timing Correction (KFTC), Recursive Least Squares Timing Correction (RLSTC), Fuzzy Logic Timing Correction (FLTC), and Hybrid Adaptive Timing Correction (HATC)—each optimized for specific error characteristics and attack scenarios. Through comprehensive evaluation encompassing 32,000 Monte Carlo test iterations (500 per scenario × 16 scenarios × 4 algorithms) across 16 distinct operational scenarios and PolarFire SoC Field-Programmable Gate Array (FPGA) implementation, we demonstrate exceptional performance with 88.3% attack detection rate, only 2.3% false positive incidence, and substantial vulnerability mitigation reducing Common Vulnerability Scoring System (CVSS) severity from High (7.3) to Low (3.1). Hardware validation on PolarFire SoC confirms practical viability with minimal resource overhead (2.16% Look-Up Table utilization, 16.57 mW per channel) and deterministic sub-10 microsecond execution latency. The Hybrid Adaptive Timing Correction algorithm achieves 31.01% success rate (95% CI: [30.2%, 31.8%]), representing a 26.5% improvement over baseline approaches through intelligent meta-learning-based algorithm selection. Statistical validation using Analysis of Variance confirms significant performance differences (F(3,1996) = 30.30, p < 0.001) with large effect sizes (Cohen’s d up to 4.57), where 64.6% of algorithm comparisons showed large practical significance. SMART DShot establishes a paradigmatic shift from reactive to proactive embedded security, demonstrating that sophisticated artificial intelligence can operate effectively within microsecond-scale real-time constraints while providing comprehensive protection against timing manipulation, de-synchronization, burst interference, replay attacks, coordinated multi-channel attacks, and firmware-level compromises. This work provides essential foundations for trustworthy autonomous systems across critical domains including aerospace, automotive, industrial automation, and cyber–physical infrastructure. These results conclusively demonstrate that ML-enhanced motor control systems can achieve both superior security (88.3% attack detection rate with 2.3% false positives) and operational performance (31.01% timing correction success rate, 26.5% improvement over baseline) simultaneously, establishing SMART DShot as a practical, deployable solution for next-generation autonomous systems. Full article

The energy generated from renewable sources has an intermittent nature since solar irradiation and wind speed vary continuously. Hence, their energy should be stored to be utilized throughout their shortage. There are various forms of energy storage systems while the most widespread technique is the battery storage system since its cost is low compared to other techniques. Therefore, batteries are employed in several applications like power systems, electric vehicles, and smart grids. Due to the merits of the lithium-ion (Li-ion) battery, it is preferred over other kinds of batteries. However, the accuracy of the Li-ion battery model is essential for estimating the state of charge (SOC). Additionally, it is essential for consistent simulation and operation throughout various loading and charging conditions. Consequently, the determination of real battery model parameters is vital. An innovative application of the red-billed blue magpie optimizer (RBMO) for determining the model parameters and the SOC of the Li-ion battery is presented in this article. The Shepherd model parameters are determined using the suggested optimization algorithm. The RBMO-based modeling approach offers excellent execution in determining the parameters of the battery model. The suggested approach is compared to other programmed algorithms, namely dandelion optimizer, spider wasp optimizer, barnacles mating optimizer, and interior search algorithm. Moreover, the suggested RBMO is statistically evaluated using Kruskal–Wallis, ANOVA tables, Friedman rank, and Wilcoxon rank tests. Additionally, the Li-ion battery model estimated via the RBMO is validated under variable loading conditions. The fetched results revealed that the suggested approach achieved the least errors between the measured and estimated voltages compared to other approaches in two studied cases with values of 1.4951 × 10⁻⁴ and 2.66176 × 10⁻⁴. Full article

Rainbow table attacks utilize a time-memory trade-off to efficiently crack passwords by employing precomputed tables containing chains of passwords and hash values. Generating these tables is computationally intensive, and several researchers have proposed utilizing parallel computing to speed up the generation process. This paper introduces a modification to the traditional master-slave parallelization model using the MPI framework, where, unlike previous approaches, the generation of starting points is decentralized, allowing each process to generate its own tasks independently. This design is proposed to reduce communication overhead and improve the efficiency of rainbow table generation. We reduced the number of inter-process communications by letting each process generate chains independently. We conducted three experiments to evaluate the performance of the parallel rainbow tables generation algorithm for four cryptographic hash functions: NTLMv2, MD5, SHA-256 and SHA-512. The first experiment assessed parallel performance, showing near-linear speedup and 95–99% efficiency across varying numbers of nodes. The second experiment evaluated scalability by increasing the number of processed chains from 100 to 100,000, revealing that higher workloads significantly impacted execution time, with SHA-512 being the most computationally intensive. The third experiment evaluated the effect of chain length on execution time, confirming that longer chains increase computational cost, with SHA-512 consistently requiring the most resources. The proposed approach offers an efficient and practical solution to the computational challenges of rainbow tables generation. The findings of this research can benefit key stakeholders, including cybersecurity professionals, ethical hackers, digital forensics experts and researchers in cryptography, by providing an efficient method for generating rainbow tables to analyze password security. Full article

Background/Objectives: The role of body composition in sports performance has been widely studied, particularly in soccer. Understanding how anthropometric characteristics impact movement efficiency and neuromuscular performance is crucial for optimizing player performance. This study examined the relationship between body composition and locomotor performance in elite soccer players. Methods: Thirty-six male soccer players from the Mexican National Team participated in the study, where body composition was assessed using surface anthropometry. Players underwent tests to measure countermovement jump (CMJ) performance, sprinting speed, maximum acceleration, and distance covered during two games of the CONCACAF Nations League quarterfinals. Correlation matrices were created to identify the most significant associations, followed by generalized linear models (GLMs) to associate body composition variables with performance metrics. Results: Anthropometric profile tables were created by playing position. Higher body fat percentage (%BF) was associated with lower performance. Specifically, higher %BF was associated with slower sprint speed (B = −0.74 m/s, p < 0.001) and shorter distance covered (B = −4.86 m/min, p < 0.001). Conversely, greater muscularity, reflected by corrected girth values for the thigh and calf, was associated with improved CMJ performance. Thigh corrected girth was positively associated with concentric mean force (B = 48.85 N, p < 0.001), and calf corrected girth was positively associated with peak power (B = 240.50 W, p < 0.001). These findings underscore the importance of low body fat and high lean mass for efficient movement. Conclusions: The results highlight the critical role of body composition in enhancing soccer performance, particularly for explosive movements like jumps, sprints, and accelerations. This study suggests that monitoring and optimizing body composition should be a central focus of nutrition, training, and conditioning strategies, adapted to the specific positional demands of professional soccer. Full article

As is already known, some currently stable landslides may have been activated in the past along a pre-existing sliding surface and reached the residual strength there, as a consequence of high-cumulative displacements. After a fairly long period of quiescence, these landslides can reactivate due to a temporary increase in destabilising forces capable of mobilising the residual strength along the same sliding surface again. Some recent studies have suggested that, under certain conditions, the strength mobilised upon reactivation may slightly exceed the residual value and then decay towards the latter as the displacement progresses. Regarding this matter, many previous studies have hypothesised that some geotechnical variables could affect the recovered strength more significantly: the length of the ageing time, the vertical stress, the stress history, and the speed with which the reactivation occurs. The aim of this research is to confirm whether such recovery of strength upon reactivation is possible and which geotechnical parameters have the greatest influence on the process. To this end, laboratory tests were carried out with the Bromhead ring shear apparatus on normally consolidated saturated samples of both natural soils and clays provided by industry (bentonite and kaolin). The coupling effect of the ageing time, the vertical stress, and the reactivation speed on the mobilised strength upon reactivation were investigated, starting from a pre-existing residual state of these samples. Within the limits of this research, the results seem to confirm that all three geotechnical variables are influential, with a greater impact on the reactivation speed and, subordinately, on the ageing time for long quiescence periods. Therefore, it is concluded that a quiescent landslide could show a reactivated strength slightly higher than the residual value if the destabilising action could arise with a certain rapidity. Conversely, if the destabilising action occurs very slowly, the mobilised strength could correspond to the residual value. The experimental results of this research may find some application in the design of strengthening works for a stable quiescent landslide that could experience a fairly rapid increase in destabilising actions, such as in the case of seismic stress, morphological modification of the slope, or a rising water table. Full article

When a CMOS (Complementary Metal–Oxide–Semiconductor) imaging system operates at a high frame rate or a high line rate, the exposure time of the imaging system is limited, and the acquired image data will be dark, with a low signal-to-noise ratio and unsatisfactory sharpness. Therefore, in order to improve the visibility and signal-to-noise ratio of remote sensing images based on CMOS imaging systems, this paper proposes a low-light remote sensing image enhancement method and a corresponding ZYNQ (Zynq-7000 All Programmable SoC) design scheme called the BGIR (Bilateral-Guided Image Restoration) algorithm, which uses an improved multi-scale Retinex algorithm in the HSV (hue–saturation–value) color space. First, the RGB image is used to separate the original image’s H, S, and V components. Then, the V component is processed using the improved algorithm based on bilateral filtering. The image is then adjusted using the gamma correction algorithm to make preliminary adjustments to the brightness and contrast of the whole image, and the S component is processed using segmented linear enhancement to obtain the base layer. The algorithm is also deployed to ZYNQ using ARM + FPGA software synergy, reasonably allocating each algorithm module and accelerating the algorithm by using a lookup table and constructing a pipeline. The experimental results show that the proposed method improves processing speed by nearly 30 times while maintaining the recovery effect, which has the advantages of fast processing speed, miniaturization, embeddability, and portability. Following the end-to-end deployment, the processing speeds for resolutions of 640 × 480 and 1280 × 720 are shown to reach 80 fps and 30 fps, respectively, thereby satisfying the performance requirements of the imaging system. Full article

Gear whine noise is governed not only by intentional microgeometry modifications but also by unavoidable pitch (indexing) deviation. This study presents a workflow that couples a tooth-resolved surface scan with a calibrated pitch-deviation table, both imported into a multibody dynamics (MBD) model built in MSC Adams View. Three operating scenarios were evaluated—ideal geometry, measured microgeometry without pitch error, and measured microgeometry with pitch error—at a nominal speed of 1000 r min⁻¹. Time domain analysis shows that integrating the pitch table increases the mean transmission error (TE) by almost an order of magnitude and introduces a distinct 16.66 Hz shaft order tone. When the measured tooth topologies are added, peak-to-peak TE nearly doubles, revealing a non-linear interaction between spacing deviation and local flank shape. Frequency domain results reproduce the expected mesh-frequency side bands, validating the mapping of the pitch table into the solver. The combined method therefore provides a more faithful digital twin for predicting tonal noise and demonstrates why indexing tolerances must be considered alongside profile relief during gear design optimization. Full article

This research aims to assess the contribution of artificial intelligence (AI)-driven digital twin technology in improving the predictive planning of European smart cities, particularly in Greece. It considers the effect of specific elements including simulation accuracy, real-time data processing, artificial intelligence tools, and system readiness on the urban planning process. Structured questionnaires were administered to 301 urban professionals working in smart cities across Greece, focusing on their perceptions of the impact of digital twin features on predictive urban planning effectiveness. Respondents were asked how crucial they found the different features of digital twins in actually improving predictive urban planning. Measurement data were described using the arithmetic mean, standard deviation, and coefficient of variation, while categorical data were described using frequency distribution tables and percentages. This study revealed that the simulation fidelity, available real-time data integration, artificial intelligence analytics, and results- oriented monitoring system maturity have a positive impact on the accuracy, speed, and flexibility of urban planning. Some of the respondents noted these features as very useful for the prediction of urban conditions and decision-making purposes. Nevertheless, some drawbacks related to the computational load and data flow were also revealed. AI-driven digital twins are useful for improving the effectiveness of urban planning. However, they encounter technical issues; therefore, seeking to focus on system maturity and data integration is necessary for their successful implementation. Cities should adopt advanced digital twin technologies and enhance the compatibility of data and maintain AI transparency for better urban planning results. Full article

This paper presents a mathematical and statistical analysis of the security–performance trade-off in the context of the IoT Cloud architecture implemented at UBT Smart City. Through detailed modeling and real-world measurement data collected before and after the deployment of advanced security measures—such as VPN configuration, Network Security Groups (NSGs), Route Tables, and DDoS Protection—we quantify the impact of security on system performance. We propose a mathematical framework to evaluate the propagation delay of telemetry data through the system and employ queueing theory (M/M/1 model) to simulate the behavior of critical data processing services. Additionally, we perform hypothesis testing and statistical comparison to validate the significance of the observed performance changes. The results show an average delay increase of approximately 19% following the implementation of security mechanisms, highlighting the inevitable trade-off between enhanced security and operational speed. Finally, we introduce a multi-objective cost-delay function that can guide the selection of optimal security configurations by balancing latency and cost, providing a valuable tool for the future optimization of secure IoT infrastructures Full article

The stochastic fluctuation characteristics of wind speed can significantly affect the control performance of train suspension systems. To enhance the running quality of trains in non-stationary wind fields, this paper proposes a semi-active control method for trains based on fuzzy rules of non-stationary wind fields. Firstly, a dynamic model of the train and suspension system was established based on the CRH2 (China Railway High-Speed 2) high-speed train and magnetorheological dampers. Then, using frequency–time transformation technology, the non-stationary wind load excitation and train response patterns under 36 common operating conditions were calculated. Finally, by analyzing the response patterns of the train under different operating conditions, a comprehensive control rule table for the semi-active suspension system of the train under non-stationary wind fields was established, and a fuzzy controller suitable for non-stationary wind fields was designed. To verify the effectiveness of the proposed method, the running smoothness of the train was analyzed using a train-semi-active suspension system co-simulation model based on real wind speed data from the Lanzhou–Xinjiang railway line. The results demonstrate that the proposed method significantly improves the running quality of the train. Specifically, when the wind speed reaches 20 m/s and the train speed reaches 200 km/h, the lateral Sperling index is increased by 46.4% compared to the optimal standard index, and the vertical Sperling index is increased by 71.6% compared to the optimal standard index. Full article