bluuebunny/crossref_metadata_2025 huggingface dataset:
Federated Reinforcement Learning Algorithm with Fair Aggregation for Edge Caching
Authors: Luo Zicheng , Li Xiaohan , Zou Demu , Bai Hao
Published: December 2024

Edge caching is employed to solve the challenge of massive data requests, ensuring the quality of user experience. However, existed edge caching algorithms often overlook issues related to user mobility, and privacy protection, non-identically and independently distributed (non-i.i.d.) characteristics of content requests among base stations. To tackle these challenges, this paper proposes Federated Reinforcement Learning Algorithm with Fair Aggregation for Edge Caching (FFA-PPO) algorithm. This paper primarily focuses on scenario of non-i.i.d. content requests in multi-base-station and multi-mobile-user network. We model this problem as a Markov Decision Process (MDP) problem and propose a federated reinforcement learning method to solve MDP problem. The goal is to minimize the content transmission latency of base stations. FFA-PPO algorithm resolves gradient conflicts by seeking the optimal gradient vector within a local ball centered at the averaged gradient which ensures model’s fairness. In conclusion, simulation results prove that the proposed FFA-PPO algorithm outperforms other baseline algorithms in terms of content transmission latency, model’s fairness.
Optimal Path-Following Control for Redundant Manipulators: A Multi-Objective Optimization Approach
Authors: Deeb Ali , Aakel Ammar , Khokhlovskiy Vladimir , Shkodyrev Viacheslav
Published: December 2024

This paper presents an optimal control framework tailored for redundant robotic manipulators, aiming to devise precise joint-space trajectories while minimizing control efforts. The core contribution is the formulation of trajectory planning as a multi-objective optimization problem, tackled through a Genetic Algorithm-based Model Predictive Control strategy, followed by Gradient Descent refinement. Moreover, we develop a strategy to apply the resulted high-level design of the joint-space trajectory into a dynamic time-series control strategy that respects the physical constraints of actuators in motion, speed, acceleration and jerk. Experimental results on a simulation model underscore the framework’s efficacy, demonstrating minimal positioning errors without the need for high computational resources for the trajectory design. Moreover, dynamical analysis of the actuators signals for the low-level phase demonstrates the ability of the overall framework to be applied on a real robotic manipulator.
Extending Stator Length for Mitigating Common-Mode Shaft Voltage in Inverter-Fed Synchronous Machines
Authors: Zhang Qi , Zhou Han , Wang Xiuhe , Sun Lingling , Gu Xinwei
Published: December 2024

As industrial technology continues to evolve, synchronous machines powered by inverters are widely implemented to provide enhanced control performance. However, the inverter-fed systems can lead to asymmetries in the three-phase voltages, generating common-mode voltage (CMV) in machines with Y-winding connections configuration. Influenced by the parasitic capacitive effects between the internal conductors of the machine, the CMV results in a potential difference between the shaft and the housing, known as the common-mode shaft voltage (CMSV). When the CMSV exceeds a certain threshold, it can cause the lubricating film of the bearings to break down. To mitigate the risk of bearing failure due to the CMSV, this paper proposes a method for extending stator length, with the advantages of simpler and more cost-effective. This paper first presents the mechanism and mathematical model of CMSV generation, followed by an analysis of potential suppression measures. Furthermore, a method of extending the stator lamination length to reduce the coupling capacitance between the stator windings and the rotor is proposed, thereby achieving suppression of shaft voltage. Finally, a three-dimensional finite element analysis (3D-FEA) software is used to analyze and evaluate the model with different degrees of extending the axial length of the stator or rotor lamination and the machine with different stator inner diameter to axial length ratios. The longer the extension of the stator lamination length, the greater the suppression capability for CMSV. For machines with a larger stator inner diameter to axial length ratio, this method exhibits a more pronounced effect on attenuating the CMSV. Conversely, extending the axial length of the rotor significantly increases the CMSV.
Comparative Analysis of Sine Wave and Square Wave Drives in Synchronous Reluctance Motor
Authors: Chen Tiansa , Liu Jinpeng , Wang Xiuhe
Published: December 2024

The synchronous reluctance motor (SynRM) is gaining attention in various industrial applications due to its high efficiency and rugged rotor structure. This paper presents a comparative study of the performance of SynRM when driven by sine wave and square wave signals. The analysis covers key performance metrics including efficiency, power factor, torque ripple, noise, etc. The study summarizes the advantages and disadvantages of each driving method. And the findings provide insights for selecting the appropriate driving method for SynRMs in different application scenarios, balancing performance, complexity, and cost.
Design and Application of a Robust Control System of an Autonomous Multi-Rotor Aircraft Using a Perturbation Estimator
Authors: Hassani Hamid , Mansouri Anass , Ahaitouf Ali
Published: December 2024

In this study, a robust finite-time (FnT) sliding mode attitude and position control is proposed for the precise tracking of an underactuated quadrotor unmanned aerial vehicle (QUAV) subjected to multiple sources of uncertainties, i.e., unmodeled dynamics, external perturbations, and parameter uncertainties. The proposed flight control system is based on the nonsingular integral terminal sliding control (NITSMC) to ensure rapid tracking of the targeted 3D trajectories while reducing the steady state-errors. The performances of the control system are enhanced through the introduction of a disturbance observer (DO) that estimates unknown external disturbances and enhances the system’s robustness. Stability analysis and FnT convergence are verified using the Lyapunov stability analysis. Finally, comparative simulations under different flight conditions are performed to assess the accuracy and robustness of the suggested controller. The obtained results highlight the improved performance of the suggested approach in terms of accuracy, rapidity, and robustness against wind disturbances.
Real-Time Assessment of a Single Neuron PID Combined with a PD Feedforward Controller Using a Quadrotor System
Authors: Alyoussef Fadi , Abdallah Ayman M.
Published: December 2024

Following fast-changing trajectories and rejecting harsh disturbances might be essential control requirements in drone applications. However, these requirements cannot be achieved using traditional PID controllers. Thus, a single- neuron PID combined with PD feedforward control is proposed to accomplish these control goals. Also, a real-time application using a quadrotor system is considered for testing the proposed controller. Real-time results and control performance measures in the form of the integral absolute error show that the proposed scheme, relative to the single neuron PID alone, significantly reduces the tracking error and provides faster disturbance rejection.
Research on Servo Parameter Optimization of FANUC System
Authors: Liu Ping , Yu Daoting , Lin Guoyong
Published: December 2024

Based on the analysis of the control structure of the current ring, speed ring and position ring of the Servo control of the FANUC system, combined with the principle of frequency response, taking the GRU28IIX40 gantry CNC machine as an example, the servo axis Y axis is adjusted through the Servo Guide software, and the amplitude frequency curve and phase frequency curve are observed using the frequency response curve to find the frequency of oscillation points. The proper HRV filter is selected to eliminate the resonance point and optimize the parameters. The debugging and verification show that the rigidity margin of servo system can be improved effectively and the stability of servo system can be enhanced by optimizing servo parameters.
Key Problems and Countermeasures of End Effector of Fruit and Vegetable Picking Robot
Authors: Jiang Wenhao , Luo Qiang , Xin Qi , Ye Songlin , Huang Peng , Sun Zhaoran
Published: December 2024

With the development of agricultural science and technology and the improvement of labor cost, fruit and vegetable picking robots have gradually become a research hotspot in the field of agricultural mechanization. As a key component of fruit and vegetable picking robot, the performance of end-effector directly affects the quality, speed and success rate of picking. In this paper, through the introduction of domestic and foreign fruit and vegetable picking end-effector gripping mode, driving mode, separation mode, etc., we analyze the existing problems of the end-effector, and look forward to the future research direction of the end-effector from the aspects of lightweight, modularization and so on.
Improved Decision Making in Mechanical Repairable Systems with Preventive Maintenance Through Bayesian Analysis
Authors: Wu Anxiong , Fang Chih-Chiang
Published: December 2024

As systems and their components age, they naturally deteriorate, making preventive maintenance a crucial strategy for maintaining system functionality and extending operational lifespan. Preventive maintenance not only restores the system but also enhances its overall health, effectively slowing the aging process. While significant research has focused on identifying optimal preventive maintenance policies for repairable systems, these decisions often encounter considerable uncertainty, compounded by the challenge of limited data. Therefore, maximizing the use of available information becomes essential. In this paper, we introduce a Bayesian decision model designed to optimize the number of preventive maintenance actions for systems maintained through periodic preventive maintenance policies. The system’s deterioration is modeled using a non-homogeneous Poisson process with a power-law failure intensity. The model assumes that after each preventive maintenance action, the system’s condition lies between “as good as new” (perfect repair) and “as good as old” (minimal repair). Failures occurring between maintenance intervals are addressed through minimal repairs. A numerical method and solution algorithm are provided to demonstrate the proposed approach for mechanical engineers in practice.
Resilience as a Driver of Industrial Manufacturing Systems
Authors: Agote-Garrido Alejandro , Martín-Gómez Alejandro Manuel , Lama-Ruiz Juan Ramón
Published: December 2024

Modern industrial environments have increased their complexity and dynamism due to the incorporation of enabling technologies such as Artificial Intelligence and the Internet of Things. This fact has highlighted the urgent need to deepen and integrate resilience into manufacturing systems. Organizations, composed of people, materials, and activities, must anticipate hazards rather than merely react to them, thereby enabling their survival and development. Resilience Engineering allows manufacturing systems to adjust before, during, and after disruptions, ensuring production continuity and safety under any circumstances. This study establishes an analysis of the main approaches used to integrate resilience into industrial systems, enhancing their ability to anticipate, withstand, and recover from disruptions. Based on systems engineering theory, the internal and external elements of manufacturing systems are considered and the key factors affecting their efficiency are identified. Finally, a model of a resilient manufacturing system is proposed, delving into its operation at the industrial facility level.
Copper Sleeve Bad Spot Detection Based on Watershed Segmentation
Authors: Xiao Pingping , Li Xiaoguang , Zhu Juan
Published: December 2024

The copper sleeve of the high-pressure oil circuit of the automobile engine is one of the key factors affecting the safety and reliability of the engine oil circuit system. Starting from solving the surface defect detection of the copper cap, a copper sleeve bad spot detection algorithm based on watershed segmentation is proposed. The watershed transform is used for image segmentation. Each local minimum value in the grayscale image is segmented to obtain an area. The watershed algorithm based on the mark image is used to obtain a better image segmentation effect, thereby determining the area of the bad spot in the image. The simulation experiment shows that the watershed segmentation detection algorithm can effectively detect the location of the bad spot from the target area and improve the detection accuracy.
Future-Driven Computational Modeling: Shaping the Next Era of Electroplating
Authors: Boutana Ilhem , Boultif Oussama , Zait Abdessalam
Published: December 2024

Electroforming is a manufacturing process that uses electrolysis to deposit metal onto a substrate, creating intricate metal components with exceptional precision. Commonly used in industries like jewelry, electronics, aerospace, and automotive, it excels in producing complex geometries and high-precision parts. Modeling electroforming simulates the physical and chemical interactions during metal deposition to optimize parameters and improve product quality. This study uses a 2D, time-dependent secondary current distribution model in COMSOL Multiphysics to validate copper and nickel electroforming, offering insights into process optimization and performance prediction.
Planning the Movement of Operating Cylinders of Backhoe Dredgers with Combined Trajectories
Authors: Trinh Van-Hai , Vu Duong , Dang Dinh-Vu
Published: December 2024

Replacing the bucket with a drum cutter for excavators placed on floating platforms for dredging tasks is an effective option in construction. Dredging excavators used for channel opening have characteristics and working conditions that differ from conventional excavators such as high working depth, non-visible operating position. Therefore, controlling the drum cutter is a crucial task during equipment operation. In this paper, we will address the problem of inverse kinematics to determine the movements of the hydraulic cylinders to ensure the desired trajectory of the cutter is achieved. Several combined trajectories, both with and without transition points, will be studied to examine the system kinematic response of the system. The research results provide important information for controlling the drum cutter to optimize dredging activities for maritime channels, thereby improving efficiency and reducing energy consumption.
Simulation Study on Typical Maneuvering Motion of Underwater Vehicles Under Ocean Currents
Authors: Wang Xuan , Yang Qing-song , Shen Yun-sheng , Li De-jun , Peng Chao , Liu Hao
Published: December 2024

In deep-sea navigation environments, underwater vehicles are often subject to interference from ocean currents. This article takes a certain underwater vehicle as the research object. Firstly, the six degree of freedom motion equation under the ocean current is given. Then, simulations are carried out on the straight sailing motion, turning motion, and Z-shaped motion under cross flow to study the performance of underwater vehicles under transverse ocean currents. The research results indicate that the motion of underwater vehicles has strong coupling and nonlinear characteristics, and the navigation is stable in the absence of ocean currents; Under the interference of lateral ocean currents, the navigation trajectory deviates, and the longitudinal speed and longitudinal inclination angle have little effect. The overall navigation is stable, and the presence of lateral ocean currents will not damage the stability of straight, turning, and Z-shaped movements, but will cause significant lateral displacement. Due to the asymmetric shape of the underwater vehicle, it will also cause small vertical displacement; This study can provide reference for the operation and control of underwater vehicles under ocean currents.
Model-Free Time-Delay Control Based Non-Singular Fast Terminal Sliding Mode for Trajectory Tracking of Robot Manipulators
Authors: Boudjedir Chems Eddine , El Hadef Loubna
Published: December 2024

This paper proposes a time delay control based non-singular fast terminal sliding mode control for trajectory tracking of robot manipulators (TDC-NFTSMC). A time Delay Estimation (TDE) method is employed to estimate the complex nonlinearity and provide the model-free structure. NFTSMC is used to compensate for the TDE error and to achieve fast tracking convergence. The asymptotic stability of the sliding surface is proved using the Lyapunov method. A numerical simulation is conducted on a Delta robot to demonstrate the efficacy of the proposed control scheme.
From Chaos to Control: Leveraging Multi-Expert Strategies for Predictive Accuracy in Autonomous Systems
Authors: Grigoriev D.A. , Musaev A.A. , Sokolov B.V.
Published: December 2024

This paper addresses the challenge of predicting chaotic behavior in automatic control systems and robotics, especially in unstable environments. Chaotic elements in observational data diminish the effectiveness of traditional statistical methods, necessitating novel predictive approaches. We propose a predictive framework based on a multi-expert data analysis model for control applications. Preliminary predictions are generated by software experts as weak classifiers, while a supervising expert consolidates these into a final decision. This approach resembles stacking algorithms used in ensemble decision-making. Our methodology enhances predictive accuracy in chaotic environments, leveraging the structural redundancy of multi-expert systems for improved robustness. Empirical results indicate that it strengthens decision-making in unpredictable scenarios, paving the way for future research on managing chaotic dynamics in automatic control and robotics.
Analysis of Aerodynamic Load Characteristics at the Connection of Windshield-Less High-Speed Train Carriages
Authors: Lin Yongjun , Zhang Yongchang , Yuan Ruixuan , Zhu Zhijie
Published: December 2024

The connection between carriages of high-speed trains is a critical area influencing aerodynamic performance. This study, based on computational fluid dynamics (CFD) simulation methods, analyses the flow field distribution and aerodynamic load characteristics at the connection of windshield-less high-speed trains operating at 300 km/h on a single-car straight track. The results show that the carriage connection generates numerous vortex structures, forming local high-pressure regions that lead to pressure fluctuations, which may cause stress concentration and affect the train’s aerodynamic stability and safety. Due to the influence of incoming flow velocity, the connections closer to the head car experience greater pressure fluctuations, with more pronounced flow separation and disturbances.
Analysis of the Influence of Asymmetrical and Nonlinear Load on the State of Insulation of Power Transformer Windings in Incomplete-Phase Modes of Electric Network Operation
Authors: Yundin Mikhail , Yundin Konstantin
Published: December 2024

The article presents the results of experimental studies of incomplete-phase non-sinusoidal modes of operation of the electric network. During the experiments, the features of the operating modes with a break in one phase of the transformer supply line and the unloading mode of one of the phases of a three-phase four-wire network were investigated. Based on the results of the studies, the levels of asymmetry and non-sinusoidality of currents and voltages on the 0.4 kV side of the power transformer, the effective values of electrical quantities, the k-factor were analyzed, and vector diagrams of currents and voltages for these modes were given. As a result, conclusions were made about the effect of incomplete-phase non-sinusoidal modes on the state of the insulation of the power transformer windings.
Multi-Time Scale Reactive Power and Voltage Optimization Control Based on Second-Order Cone Programming
Authors: Wang Huanyou , Xie Guangqi
Published: December 2024

With the large-scale integration of distributed generations (DGs) such as photovoltaics (PVs) and wind turbines (WTs) into the distribution network (DN), problems such as increased network loss, increased voltage offset, and voltage exceeding limit have arisen. This article establishes a dynamic reactive power optimization multi-objective model for active and reactive power coordination in DN, which includes various active management devices such as energy storage system, DGs, capacitor banks (CBs), and static var generations (SVGs). Through linearization and convex relaxation processing, transforming the original non convex nonlinear optimization model into an effectively solvable second-order cone programming (SOCP) model. The IEEE33 node system was used for numerical analysis to verify the optimization stability and computational efficiency of the proposed method.
A Dynamic Queue Adjustment Algorithm for Task Offloading in Vehicular Edge Computing Based on MADDPG
Authors: Xu Hao , Zhang BenHong , Hu Qiwei , Du Zhaoyang
Published: December 2024

Vehicular Edge Computing (VEC) has brought great convenience to people’s daily life. However, some problems still exist in VEC networks. Existing studies usually consider latency as an optimization objective, however, for some tasks, it is only necessary to ensure that they are completed within the deadline. For this reason, the urgency of the tasks is defined and, unlike the usual first-come-first-served waiting, the tasks are queued according to their urgency. Then, the optimization objective of the system is formulated and the offloading decision process is transformed into a Markov Decision Process(MDP),and our problem is solved by a deep reinforcement learning based algorithm. Simulation results show that the performance of our proposed algorithm is better than other algorithms.
Life Prediction Method for White LED Based on Covariance Matrix
Authors: Wang Haochen , Qu Changqi , Zhu Qingyu
Published: December 2024

With the rapid development of electronic products, it is crucial for designers and manufacturers to understand the lifespan information of new products before launching them on the market in order to provide high-quality products on a global scale. Therefore, life prediction has become a necessary procedure for manufacturers to evaluate the quality of their products. For high-power white LED, an efficient lifespan prediction method can not only shorten the reliability evaluation time of LED products, timely launch new products, and help manufacturers gain greater market share, but also provide users with clear understanding of product quality based on the reliability information provided by manufacturers, providing guidance for selecting appropriate products. Therefore, this article proposes a white LED lifespan prediction method based on covariance matrix. By studying its performance of lumen degradation and chromaticity shift, a mathematical model is established to help more accurately evaluate the lifespan of LED products.
Temperature Insensitive Optical Fiber Electrical Field Sensor
Authors: Chen Chen , Zhang Mengwei , Liang Xinyu , Zeng Xin , Sun Zheng
Published: December 2024

Electric field sensing technology is a fundamental measurement and monitoring technique that plays a crucial role in various fields. In this paper, we report on a compact fiber optic electric field sensor designed for the precise detection of transient electric field distributions. The sensor employs a reflective polarization-reciprocal optical path, which inherently mitigates temperature-induced birefringence interference without the need for additional optical elements, thereby facilitating a smaller electric field sensing probe. An experimental platform has been constructed, and the sensor’s response to electric fields of varying frequencies has been tested. It is found that the sensor exhibits favorable response characteristics to electric fields ranging from 50 Hz to microsecond pulse electric fields.
A Target Detection Method Based on Hyperspectral and SAR Decision Fusion Model
Authors: Deng Xianming , Zhu Fan , Wang Yujuan , Zhang Tiancai , Wang Yongfeng , Wang Lin
Published: December 2024

A target detection method combining hyperspectral and SAR decision fusion is proposed. This method fully combines the advantages of hyperspectral target detection based on extended spatial morphology and SAR target detection and recognition based on fusion neural network deep learning, and fully considers the normalization of recognition results, overcoming the problem of differences in resolution and image gray values between hyperspectral and SAR images, and performing pixel registration. The experimental results show that the SAR detection algorithm based on deep fusion neural network has an accuracy of 78.6%, the detection algorithm based on extended spatial morphology has an accuracy of 89.9%, and the detection algorithm based on hyperspectral and SAR decision fusion has an accuracy of 90.1%.
A Multi-Period Interval-Valued Fuzzy Portfolio Decision Model with Realistic Constraints
Authors: Zhang Qiansheng , Ou Yuanjun , Yin Zhiyun
Published: December 2024

Considering the uncertainty of stock data, this paper studies interval-valued fuzzy portfolio decision model with ESG and some realistic constraints. First, a screening process is employed to identify investable stocks based on their ESG ratings. Then, a multi-period portfolio decision model with short-selling constraint is established by maximizing terminal wealth and minimizing terminal risk. By using the weighted programming method the proposed portfolio model is transformed into a single-objective model to solve the optimal decision strategy. Finally, a numerical example of stock data from the Shenzhen Stock Exchange is given to illustrate the efficiency of the presented portfolio decision model.
A New Three Way Multi-Attribute Group Decision-Making Method with Probabilistic Dominance Relation Based on Interval-Valued Fermatean Hesitant Fuzzy Sets
Authors: Lei Siyue , Ma Xiuqin , Qin Hongwu , Niu Xuli , Ren Dong
Published: December 2024

Interval-value Fermatean hesitant fuzzy sets (IVFHFSs) are new data model for dealing with complex, uncertain information. However, the researches on IVFHFSs score functions (SFs) have low discrimination rate and existing multi-attribute group decision-making under IVFHFSs are scarce and have no ability to classify. Therefore, this paper establishes a new three-way multi-attribute group decision-making (3W-MAGDM) based on IVFHFSs. First, novel IVFHFSs SFs are proposed. Next, the objective conditional probabilities calculation method is derived by using the probabilistic dominance relation and the relative loss functions calculation method in three-way decision is developed. The subjectivity of 3W-MAGDM under IVFHFSs is greatly reduced. Finally, a new 3W-MAGDM framework based on IVFHFSs is constructed. The new approach has a high discrimination rate in SFs and it not only has a ranking function but also has a categorization function.
A Graph Neural Network-Based Community Search System over Dynamic Graph
Authors: Song Yixin , Zhou Lihua
Published: December 2024

Community search (CS), aiming to find a densely connected subgraph containing given query vertices, is an important research topic in social network analysis and is widely used in similar recommendation, team organization, friendship recommendation and other practical applications. The purpose of the CS system is to display searched community in a visual form to users. It can help users better understand and analyze networks, making better decisions. However, the exist CS systems are mostly designed for static graphs, they cannot capture the dynamic attributes and cannot intuitively display the dynamic changes of the community. In this paper, we develop a CS system over dynamic graph based on graph neural network (GNN), aiming to locate the community with cohesive attributes over dynamic graph and visualize the community to intuitively display the dynamic changes of vertices and the relationships between them. We design a GNN-based method to capture the dynamic changes of attributes and design a friendly front-end interface that visualizes the result community in the form of a timeline. It allows users to view the status of the result community at any snapshot and fine-tune the result community according to their own conditions.
Research on Crane Scene Recognition Technology Combining Digital Twins with Neural Networks
Authors: Su Wensheng , Xu Jia , Wang Xinren
Published: December 2024

In the fields of computer vision and intelligent recognition, with the increasing complexity and dynamism of crane scenes, how to use digital twins technology and artificial intelligence technology to improve the accuracy and efficiency of crane image recognition has become a research hotspot. Based on the combination of digital twin technology, convolutional neural networks, and long short-term memory networks (DTE-CNN-LSTM), an algorithm has been developed to achieve intelligent recognition and semantic understanding of complex crane operation scenes by introducing a virtual simulation environment for image feature optimization and processing. The research results indicate that the DTE-CNN-LSTM algorithm performs well in multiple crane scenarios. After optimization, the recognition accuracy of night cranes reached 100%, the recognition accuracy of rainy scenes increased to 95%, and the misclassification rate decreased to 5%. The recognition accuracy of obstacle has reached 98%, and the recognition accuracy of crane operation area has reached 90%, significantly reducing the misclassification rate. Overall, the DTE-CNN-LSTM algorithm achieved an accuracy rate of 96.70% and an F1 value of 96.64% in classification tasks for different crane scenarios. Its robustness and generalization ability in complex crane working environments have been verified, demonstrating high potential for application.
Investigating XGBoost Efficiency on Diverse Time-Series Data Through PSO Parameter Tuning
Authors: Huailuk Khwanchai , Khetkrathok Natthapon , Inthapong Pirapong , Kaennakham Sayan , Samattapapong Nara
Published: December 2024

This study investigates the effectiveness of Particle Swarm Optimization (PSO) in enhancing the performance of the XGBoost algorithm on three distinct types of time-series datasets: intermittent (Dataset-1), multi-seasonal (Dataset-2), and non-stationary (Dataset-3). Time-series data often exhibit complex patterns, making hyperparameter tuning crucial for improving model accuracy. In this research, two PSO variants, PSOv1 and PSOv2, were applied to optimize key hyperparameters, specifically the learning rate (LR) and Max Depth (MxD), in order to minimize the Root Mean Square Error (RMSE). Both PSOv1 and PSOv2 demonstrated significant improvements over the baseline XGBoost model, with PSOv2 showing faster and more stable convergence across all datasets. Notably, the PSO algorithms were particularly effective for multi-seasonal and non-stationary time-series, where the convergence rate was faster compared to intermittent data. These results highlight PSO’s ability to fine-tune hyperparameters for better generalization, reducing overfitting and improving performance on unseen data. This work underscores the importance of swarm intelligence algorithms in optimizing machine learning models for complex time-series forecasting tasks, paving the way for future research in hybrid optimization techniques and the exploration of additional hyperparameters.
Performance Assessment of Fourier Convolutional Neural Networks in Medical Image Analysis for Breast Cancer Diagnosis
Authors: Lowmunkhong Songkiat , Panphuech Anan , Janthawanno Patthra , Onjun Ratapong , Kaennakham Sayan
Published: December 2024

Breast cancer remains a major global health concern, and early diagnosis is crucial for improving patient outcomes. This study explores the performance of Fourier Convolutional Neural Networks (FCNNs) in comparison to traditional Convolutional Neural Networks (CNNs) like VGG16 for classifying breast cancer using ultrasound images. We evaluated the models on key performance metrics, including accuracy, precision, recall, and AUC-ROC, while also considering computational factors such as training time and memory consumption. Results show that VGG16 consistently delivers stable performance across different learning rates, while FCNN16 significantly outperforms VGG16 at a lower learning rate of 0.0001, achieving near-perfect classification metrics. These findings highlight the potential of FCNNs for breast cancer diagnosis, though further research is necessary to fully optimize this approach.
Analysis of Turn to Turn Short Circuit Fault of Marine Propulsion Motor
Authors: Chen Rui , Zhu Kefeng , Zhao Kaiqi
Published: December 2024

With the long development of the Marine industry, the diversity of the Marine power system is getting higher and higher, but at the same time, there are more and more types of faults, such as transformer faults and Marine propulsion motor faults, etc. This paper takes the relevant data of a 146-meter-long ship as an example to analyze the turn to turn short circuit fault of the propulsion motor by simulation. The simulation and analysis of ship integrated electric propulsion system by computer can comprehensively test the target system, provide data support for system design and modification, and ensure the stable and reliable operation of the system in harsh conditions. The use of pre-simulation has attracted attention in the international field of ship electric power system design.
Automated Detection of Abnormal Vibration in Connecting Rod of Vibrating Groove Based on DS Theory Integrating Image and Sound Signals
Authors: Li Xudong , Wen Wu , Guo Fei , Liu Minchang , Zhang Rongya , Jin Xin , Liu Yang , Han Yingjun , Hu Hua , Hu Wu , Ma Yaping , Bao Tingjie
Published: December 2024

Objective: Because of the aging of parts and mechanical structure, the vibration of connecting rod is easy to produce abnormal state, while the abnormal vibration states are difficult to detect in a timely manner,thus resulting in shutdown and material breakage during the tobacco processing process, or uneven tobacco screening, thereby reducing the quality of tobacco products . In order to solve this problem, an automatic detection technology based on Dempster-Shafter (DS) theory is proposed in this paper, so as to deal with the abnormal vibration of connecting rod in time. Methods: The image data inside the tobacco sieve vibrating groove were collected, and the Kanade-Lucas-Tomasi (KLT) algorithm was used to track the continuous frames of the image and extract the connecting rod feature points at adjacent moments of the tobacco sieve vibrating groove. A series of microphones are constructed to obtain the location of noise source signals, and the noise source signals are denoised using wavelet coefficients. DS theory was used to fuse the image features and sound signals, calculate the vibration state conflict factor of connecting rod, judge the vibration state of connecting rod according to the conflict factor, and complete the automatic detection of abnormal vibration of connecting rod in the vibration groove of tobacco screen. Results: The experimental results show that the proposed method can accurately identify the fault position of the connecting rod and classify the abnormal vibration level, and the results of abnormal vibration amplitude of the connecting rod in the vibrating groove of tobacco screening are highly consistent with the actual results. Conclusion: The vibration anomaly automated detection technology designed in this article can be used for the analysis of the state of connecting rod mechanical components in the tobacco industry and the diagnosis of faults.
PINAR: Population INfograms for Analysis and Research
Authors: Tartan Emre Öner
Published: December 2024

This study introduces PINAR (Population INfograms for Analysis and Research), a Streamlit-based web tool for visualizing and analyzing demographic data sourced from the Turkish Statistical Institute (TURKSTAT) using data mining techniques. PINAR integrates various demographic modules, including birth rates, population distribution by marital status, sex, and age groups as well as the most common baby names, and the most prevalent names and surnames over the years. A key feature of the tool is its ability to generate infograms which visualize population data on a map of Turkey, providing insights at both the provincial and district levels. The tool also presents population pyramids and race bar plots. The infograms support K-means and hierarchical clustering in the ‘the most common baby names’ and ‘the most common names and surnames’ modules, helping users uncover patterns and groupings for more detailed analysis. Users can explore demographic changes by selecting specific years or comparing data between two different years, with support for analyses based on absolute counts, ratios, and temporal changes. By revealing key demographic trends, PINAR offers enhanced insights for informed decision-making and academic research, making it a valuable tool for policymakers, researchers, and the public.
A Low-Cost and Real-Time Solution for Enhancing Construction Site Safety
Authors: Lau Chi-Yat , Yuen Man-Ching , Yung Chi-Wai , Kwong Yiu-Man , Lai Yuen-Wang , Wong Lawrence-James , Iltaf Shier-Bano , Xie Suet-Ying , Chan Yee-Shing , Chui Kin
Published: December 2024

Nowadays, AI is widely adopted in different areas. This project is dedicated to enhancing safety on construction sites, especially for high-altitude work. Among them, the authors noticed that many workers did not comply with construction site rules during the construction process, such as not wearing safety equipment or standing under high-altitude cranes. These are very dangerous behaviors. Furthermore, in alignment with the Hong Kong government’s Smart Site Safety System initiative, the authors have developed several models designed to monitor construction sites in real-time. These models are trained to detect various safety measures and potential hazards, ensuring that workers adhere to necessary safety protocols and thereby significantly reducing the risk of accidents caused by negligence. Project use the model to detect whether people on the construction site are safe and then give feedback. The authors hope to cooperate with government policies and reduce casualties.
Efficient wavelength rerouting in WDM single‐fiber and multi‐fiber networks with and without wavelength conversion
Authors: Mohan G. , Murthy C. Siva Ram
Published: December 2024

In this paper, we consider wavelength rerouting in circuit switched wavelength‐routing Wavelength‐Division Multiplexed (WDM) networks. We address the rerouting issue and propose rerouting algorithms for single‐fiber and multi‐fiber networks with and without wavelength conversion. The wavelength continuity constraint imposed by WDM networks leads to poor blocking performance. The blocking performance can be improved by wavelength rerouting and by providing wavelength conversion capability at the routing nodes. Wavelength rerouting rearranges certain existing lightpaths to accommodate a new connection request. The wavelength continuity constraint is relaxed at the routing nodes having wavelength conversion capability. Since the number of technologically feasible wavelength channels per fiber is very few (up to 16), multi‐fiber networks have become an attractive alternative which can support increased traffic with improved performance [3].

Recently, in [1], a rerouting scheme called ‘Parallel Move‐To‐Vacant–Wavelength Retuning (MTV–WR)’ with many attractive features such as shorter disruption period and simple switching control, and a rerouting algorithm, for this scheme, to minimize the weighted number of rerouted lightpaths have been proposed. The algorithm runs in polynomial time, but is not time optimal. We present here a time optimal rerouting algorithm to minimize the weighted number of rerouted lightpaths. We also present a rerouting strategy with the same computational complexity for the networks with sparse wavelength conversion. We discuss, how the parallel MTV–WR scheme can be extended to the networks with sparse wavelength conversion for rerouting sub‐lightpaths. We also discuss how this scheme can be modified for multi‐fiber networks, wherein a lightpath can be moved to a vacant wavelength on any vacant fiber in the bundle of fibers, while maintaining the route. We extend the rerouting algorithm for the single‐fiber networks to the multi‐fiber networks with the above modified rerouting scheme. The performance of the proposed algorithms are evaluated through extensive simulation. The results show that the proposed algorithms improve the blocking performance significantly.
Novel recursive multicast algorithms in multistage interconnection networks for ATM switches
Authors: Park Jaehyung , Yoon Hyunsoo
Published: December 2024

In this paper, we study the multicast communication in the self‐routing multistage interconnection network (MIN) for constructing asynchronous transfer mode (ATM) switch architectures. Many of these applications require multicast communications in addition to conventional point‐to‐point communications. Multicast communication which the same message is delivered from a source to an arbitrary number of destinations, is fundamental in supporting collective communication primitives including cable TV, teleconferencing, and video‐on‐demand services. This paper presents a novel approach to supporting multicast communication, on the basis of the restricted address encoding scheme which constructs a short fixed‐size multicast header and the recursive scheme that recycles a multicast packet one or more times through the network to reach at desired destinations. We also propose novel recursive multicast algorithms providing deadlock‐freedom in MIN‐based ATM switches. The emphasis is on evaluating the performance of these algorithms in terms of the number of recycling passes, the complexity of switching elements, and the number of links used. The proposed algorithms can be easily applicable to buffered MIN‐based ATM switches.
A practical framework for minimum‐delay routing in computer networks
Authors: Vutukury Srinivas , Garcia‐Luna‐Aceves J.J.
Published: December 2024

The conventional approach to routing in computer networks consists of using a heuristic to compute a single shortest path from a source to a destination. Single‐path routing is very responsive to topological and link‐cost changes; however, except under light traffic loads, the delays obtained with this type of routing are far from optimal. Furthermore, if link costs are associated with delays, single‐path routing exhibits oscillatory behavior and becomes unstable as traffic loads increase. On the other hand, minimum‐delay routing approaches can minimize delays only when traffic is stationary or very slowly changing.

We present a ‘near‐optimal’ routing framework that offers delays comparable to those of optimal routing and that is as flexible and responsive as single‐path routing protocols proposed to date. First, an approximation to the Gallager’s minimum‐delay routing problem is derived, and then algorithms that implement the approximation scheme are presented and verified. We describe the first routing algorithm based on link‐state information that provides multiple paths of unequal cost to each destination that are loop‐free at every instant. We show through simulations that the delays obtained in our framework for minimum‐delay routing are comparable to those obtained using Gallager’s algorithm for minimum‐delay routing. Also, we show that our framework renders far smaller delays and makes better use of resources than traditional single‐path routing.
The Adaptive Stochastic Rate Allocation algorithm for Available Bit Rate traffic management in ATM networks
Authors: Petrovic Slobodan S. , De Subrata K.
Published: December 2024

The Adaptive Stochastic Rate Allocation (ASRA) algorithm for ABR traffic management in ATM networks is presented. As the dynamics and parameters of the traffic flow process are poorly known, the adaptive feedback gain depends not only on the parameters' estimates, but is also modulated by current uncertainty of the parameters. Therefore, the control algorithm expresses its dual nature by simultaneously performing two functions: ceaseless estimation of parameters and the process control. The allowed cell rates of the ABR sources are dynamically shaped by the feedback messages based on the instantaneous queue size in the ATM switch output buffer. The ASRA scheme is evaluated via simulation testing in comparison with the ERICA and BECN schemes. The requirements tested included: utilization, queuing delay, queuing delay variance, and queue size. The results of a simulation study suggest that the ASRA scheme can provide for higher priority VBR traffic, while retaining almost the same average utilization, considerably shorter average queue length, average queuing delay and particularly queuing delay variance in the ATM switch output buffer. The ASRA scheme's fairness in max‐min sense along with fast transient response is demonstrated.
A scalable logical topology for optical networks
Authors: Jaekel A. , Bandyopadhyay S. , Roychoudhury S. , Sengupta A.
Published: December 2024

One of the approaches investigated for multihop lightwave networks is to consider regular graphs as the logical topology for a multihop network. Standard regular topologies are defined only for networks with the number of nodes satisfying some rigid criteria and are not directly usable for multihop networks. Only a few recent proposals (e.g., GEMNET) are regular and yet allow the number of nodes to have any arbitrary value. These networks have one major problem – node addition requires a major redefinition of the network. For example, in a multistar implementation, a large number of retuning of transmitters and receivers and/or renumbering nodes are needed for GEMNET. In this paper we present a new logical topology which has a low diameter but is not strictly regular. The interesting aspect of this topology is that it allows the network to be expanded incrementally involving a relatively small number of edge definitions/redefinitions. In this paper we have described our new topology and its properties. We have also implemented a routing scheme that ensures a low diameter and an algorithm for adding nodes to the network.
Practical traffic grooming scheme for single‐hub SONET/WDM rings
Authors: Li Xiang‐Yang , Liu Liwu , Wan Peng‐Jun , Frieder Ophir
Published: December 2024

In SONET/WDM networks, one fiber supports multiple wavelengths and each wavelength supports several low rate tributary streams. ‘Traffic grooming’ then is defined as properly using SONET Add/Drop Multiplexer to electronically multiplex and demultiplex required tributary traffic patterns with minimal resource cost (wavelengths and ADMs).

This paper studies traffic grooming problem in single hub SONET/WDM networks and extends existing results. We analyze the real deployments, generalize their results, and study the practical special cases. We prove that BLSR/2 would never be more expensive than UPSR under any traffic pattern. We also present the exact minimum costs of uniform traffic in both UPSR and BLSR/2. We give approximation algorithms for optimal grooming of non‐uniform traffic. Finally, we consider how to select the line speeds if there are two different line speeds available.