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Articles 1591 - 1620 of 17309
Full-Text Articles in Engineering
Path-Choice-Constrained Bus Bridging Design Under Urban Rail Transit Disruptions, Yiyang Zhu, Jian Gang Jin, Hai Wang
Path-Choice-Constrained Bus Bridging Design Under Urban Rail Transit Disruptions, Yiyang Zhu, Jian Gang Jin, Hai Wang
Research Collection School Of Computing and Information Systems
Although urban rail transit systems play a crucial role in urban mobility, they frequently suffer from unexpected disruptions due to power loss, severe weather, equipment failure, and other factors that cause significant disruptions in passenger travel and, in turn, socioeconomic losses. To alleviate the inconvenience of affected passengers, bus bridging services are often provided when rail service has been suspended. Prior research has yielded various methodologies for effective bus bridging services; however, they are mainly based on the strong assumption that passengers must follow predetermined bus bridging routes. Less attention is paid to passengers’ path choice behaviors, which could affect …
Optimization Of Customer Service And Driver Dispatch Areas For On-Demand Food Delivery, Jingfeng Yang, Hoong Chuin Lau, Hai Wang
Optimization Of Customer Service And Driver Dispatch Areas For On-Demand Food Delivery, Jingfeng Yang, Hoong Chuin Lau, Hai Wang
Research Collection School Of Computing and Information Systems
With the rapid development and popularization of mobile and wireless communication technologies, on-demand food delivery (OFD) platforms have been able to connect restaurants, customers, and drivers in real time, drastically changing dining and food delivery services. Motivated by the critical need for supply and demand management in the on-demand food delivery market, we focus on the optimization of customer service area and driver dispatch area for on-demand food delivery services. Specifically, for each restaurant, the platform needs to decide the (1) customer service area (CSA), i.e., the surrounding area within which customers can see the restaurant’s information and order food …
Image Processing Techniques For Water Droplet Penetration Time And Contact Angle Estimation, Sai Balaji Jai Kumar
Image Processing Techniques For Water Droplet Penetration Time And Contact Angle Estimation, Sai Balaji Jai Kumar
UNLV Theses, Dissertations, Professional Papers, and Capstones
Water droplet behavior on soil surfaces plays a critical role in numerous environmental processes, including soil erosion, hydrological dynamics, and ecosystem health. Accurate characterization of soil water repellency, quantified by parameters such as water droplet penetration time (WDPT) and contact angles (WDCA), is essential for informed decision-making in agricultural management, forestry practices, and land-use planning. Despite the significance of these parameters, challenges exist in reliably estimating them due to the complex and dynamic nature of soil-water interactions. This thesis address challenges in estimating WDPT and WDCA, by leveraging state-of-the-art image processing techniques and machine learning algorithms. The research focuses on …
Smart Airports: Artificial Intelligence–Enabled Internet Of Things Networks Using Blockchain Technology, Edwin Ongola
Smart Airports: Artificial Intelligence–Enabled Internet Of Things Networks Using Blockchain Technology, Edwin Ongola
Journal of Aviation Technology and Engineering
This article provides a perspective on how an internet of heterogeneous self-service airport terminal systems can be used for data collection, which is stored on a private or consortium blockchain depending on the ownership or operations of an airport or both. Such a setup would help to increase efficiency, reduce costs, and improve traveler experience at airport terminals. Moreover, it would allow airports to gather data directly from passengers as opposed to waiting to receive the same data from airlines. Subsequently, this data, now on a blockchain system, becomes a data source for other applications such as machine learning. In …
Personalized Driving Using Inverse Reinforcement Learning, Rodrigo J. Gonzalez Salinas
Personalized Driving Using Inverse Reinforcement Learning, Rodrigo J. Gonzalez Salinas
Theses and Dissertations
This thesis introduces an autonomous driving controller designed to replicate individual driving behaviors based on a provided demonstration. The controller employs Inverse Reinforcement Learning (IRL) to formulate the reward function associated with the provided demonstration. IRL is implemented through a dual-feedback loop system. The inner loop utilizes Q-learning, a model-free reinforcement learning technique, to optimize the Hamilton-Jacobi-Bellman (HJB) equation and derive an appropriate control solution. The outer loop leverages this derived control solution to generate parameters for the reward function, which are subsequently integrated into the HJB equation. The ultimate control policy is deduced from the final reward function obtained …
Network Intrusion Detection Based On Machine Learning Strategies: Performance Comparisons On Imbalanced Wired, Wireless, And Software-Defined Networking (Sdn) Network Traffics, Hi̇lal Hacilar, Zafer Aydin, Vehbi̇ Çağri Güngör
Network Intrusion Detection Based On Machine Learning Strategies: Performance Comparisons On Imbalanced Wired, Wireless, And Software-Defined Networking (Sdn) Network Traffics, Hi̇lal Hacilar, Zafer Aydin, Vehbi̇ Çağri Güngör
Turkish Journal of Electrical Engineering and Computer Sciences
The rapid growth of computer networks emphasizes the urgency of addressing security issues. Organizations rely on network intrusion detection systems (NIDSs) to protect sensitive data from unauthorized access and theft. These systems analyze network traffic to detect suspicious activities, such as attempted breaches or cyberattacks. However, existing studies lack a thorough assessment of class imbalances and classification performance for different types of network intrusions: wired, wireless, and software-defined networking (SDN). This research aims to fill this gap by examining these networks’ imbalances, feature selection, and binary classification to enhance intrusion detection system efficiency. Various techniques such as SMOTE, ROS, ADASYN, …
Efficient Deep Neural Network Compression For Environmental Sound Classification On Microcontroller Units, Shan Chen, Na Meng, Haoyuan Li, Weiwei Fang
Efficient Deep Neural Network Compression For Environmental Sound Classification On Microcontroller Units, Shan Chen, Na Meng, Haoyuan Li, Weiwei Fang
Turkish Journal of Electrical Engineering and Computer Sciences
Environmental sound classification (ESC) is one of the important research topics within the non-speech audio classification field. While deep neural networks (DNNs) have achieved significant advances in ESC recently, their high computational and memory demands render them highly unsuitable for direct deployment on resource-constrained Internet of Things (IoT) devices based on microcontroller units (MCUs). To address this challenge, we propose a novel DNN compression framework specifically designed for such devices. On the one hand, we leverage pruning techniques to significantly compress the large number of model parameters in DNNs. To reduce the accuracy loss that follows pruning, we propose a …
Detection And Classification Of Unauthorized Use Of Irrigation Motors In Agricultural Irrigation, Önder Ci̇velek, Sedat Görmüş, Hali̇l İbrahi̇m Okumuş, Orhan Gazi̇ Kederoglu
Detection And Classification Of Unauthorized Use Of Irrigation Motors In Agricultural Irrigation, Önder Ci̇velek, Sedat Görmüş, Hali̇l İbrahi̇m Okumuş, Orhan Gazi̇ Kederoglu
Turkish Journal of Electrical Engineering and Computer Sciences
The decarbonisation of electricity generation requires the real-time monitoring and control of grid components in order to efficiently and timely dispatch demand. This highly automated system, known as the Smart Grid, relies on smart or sensor-equipped distribution network components to optimise energy flow and minimise losses. However, energy theft, a major obstacle to efficient resource utilisation, poses a significant challenge to achieving this goal. This study proposes and evaluates a real-time telemetry and control system designed to mitigate energy theft in agricultural irrigation applications. The system increases energy efficiency by tracking the energy use in agricultural irrigation. The key challenge …
A New Approach: Ordinal Predictive Maintenance With Ensemble Binary Decomposition (Opmeb), Ozlem Ece Yurek, Derya Birant
A New Approach: Ordinal Predictive Maintenance With Ensemble Binary Decomposition (Opmeb), Ozlem Ece Yurek, Derya Birant
Turkish Journal of Electrical Engineering and Computer Sciences
Predictive maintenance (PdM), a fundamental element of modern industrial systems, employs machine learning to monitor equipment conditions, estimate failure probabilities, and optimize maintenance schedules. Its core objective is to enhance equipment reliability, extend lifespan, and minimize costs through data-driven insights by enabling efficient maintenance scheduling, reducing downtime, and optimizing resource allocation. In this paper, we propose a novel ordinal predictive maintenance with ensemble binary decomposition (OPMEB) method for the PdM domain, considering the hierarchical nature of class labels reflecting the machine's health status, including categories like healthy, low risk, moderate risk, and high risk. The proposed OPMEB method was validated …
Enrichment Of Turkish Question Answering Systems Using Knowledge Graphs, Okan Çi̇ftçi̇, Fati̇h Soygazi̇, Selma Teki̇r
Enrichment Of Turkish Question Answering Systems Using Knowledge Graphs, Okan Çi̇ftçi̇, Fati̇h Soygazi̇, Selma Teki̇r
Turkish Journal of Electrical Engineering and Computer Sciences
Recent capabilities of large language models (LLMs) have transformed many tasks in Natural Language Processing (NLP), including question answering. The state-of-the-art systems do an excellent job of responding in a relevant, persuasive way but cannot guarantee factuality. Knowledge graphs, representing facts as triplets, can be valuable for avoiding errors and inconsistencies with real-world facts. This work introduces a knowledge graph-based approach to Turkish question answering. The proposed approach aims to develop a methodology capable of drawing inferences from a knowledge graph to answer complex multihop questions. We construct the Beyazperde Movie Knowledge Graph (BPMovieKG) and the Turkish Movie Question Answering …
A Real-Time Embedded System Designed For Nilm Studies With A Novel Competitive Decision Process Algorithm, Sai̇d Mahmut Çinar, Rasi̇m Doğan, Emre Akarslan
A Real-Time Embedded System Designed For Nilm Studies With A Novel Competitive Decision Process Algorithm, Sai̇d Mahmut Çinar, Rasi̇m Doğan, Emre Akarslan
Turkish Journal of Electrical Engineering and Computer Sciences
This paper explores the determination of any load or load combination in a power system at any moment. This process requires measurements at the main electric utility service entry of a house, known as nonintrusive measurement. To accurately identify loads, total harmonic distortion, RMS, third harmonic currents, and power consumption are considered their fingerprints. Based on these fingerprints, an algorithm called the competitive decision process is developed and integrated into an embedded system. This algorithm has a two-level decision mechanism. In the first stage, the winner loads with the highest similarity scores from each feature are determined, and the loads …
Ensemble Learning For Accurate Prediction Of Heart Sounds Using Gammatonegram Images, Sinam Ashinikumar Singh, Sinam Ajitkumar Singh, Aheibam Dinamani Singh
Ensemble Learning For Accurate Prediction Of Heart Sounds Using Gammatonegram Images, Sinam Ashinikumar Singh, Sinam Ajitkumar Singh, Aheibam Dinamani Singh
Turkish Journal of Electrical Engineering and Computer Sciences
The analysis of heart sound signals constitutes a pivotal domain in healthcare, with the prediction of imbalanced heart sounds offering critical diagnostic insights. However, the inherent diversity in cardiac sound patterns presents a substantial challenge in predicting imbalanced signals. Many scientific disciplines have focused a great deal of emphasis on the problem of class inequality. We introduce an ensemble learning approach employing a convolutional neural network model-based deep learning algorithm to effectively tackle the challenges associated with predicting imbalanced heart sound signals. We use a Gammatone filter bank to extract relevant features from the heard sound signal. Our approach leverages …
Multi-Label Voice Disorder Classification Using Raw Waveforms, Gökay Di̇şken
Multi-Label Voice Disorder Classification Using Raw Waveforms, Gökay Di̇şken
Turkish Journal of Electrical Engineering and Computer Sciences
Automated voice disorder systems that distinguish pathological voices from healthy ones have been developed with the aid of machine learning methods. Both clinicians and patients can benefit from these systems as they provide many advantages, compared to the invasive techniques. These systems can produce binary (healthy/pathological) or multi-class (healthy/selected pathologies) decisions. However, multiple disorders might exist in an individual’s voice. Multi-label classification should be considered in such cases. By this time, only a single report is available on this topic, where hand-crafted features were used, and a data augmentation technique was utilized to overcome class imbalances. In this study, a …
A New Dynamic Classifier Selection Method For Text Classification, İsmai̇l Terzi̇, Alper Kürşat Uysal
A New Dynamic Classifier Selection Method For Text Classification, İsmai̇l Terzi̇, Alper Kürşat Uysal
Turkish Journal of Electrical Engineering and Computer Sciences
The primary objective of employing multiple classifier systems (MCS) in pattern recognition is to enhance classification accuracy. Dynamic classifier selection (DCS) and dynamic ensemble selection (DES) are two purposeful forms of multiple classifier systems. While DES involves the selection of a classifier set followed by decision combination, DCS opts for the choice of a single competent classifier, eliminating the necessity for classifier combination. As a consequence, DCS methods exhibit superior efficiency in terms of processing time and memory usage compared to DES methods. Moreover, a substantial performance gap exists between the performance of Oracle and both DES and DCS methods. …
Strategic Study On Leading Construction Of Modern Agricultural System By Technology Innovation, Yan Yan, Xurong Mei, Xiudong Wang
Strategic Study On Leading Construction Of Modern Agricultural System By Technology Innovation, Yan Yan, Xurong Mei, Xiudong Wang
Bulletin of Chinese Academy of Sciences (Chinese Version)
Technology innovation is the fundamental solution to build a modern agricultural system, and it is also an important support for building the large base, large enterprise, and large industry of modern agriculture and achieving the modernization of material equipment, technology, management, agricultural informatization, and sustainable resource utilization of agriculture. This paper explains the scientific concepts and logical relationships of the agricultural production system, industrial system and management system, which are the main components of a modern agricultural system. It emphasizes that ensuring food security, promoting industrial integration and upgrading, and cultivating leading enterprises are current key tasks. Nevertheless, from the …
Effective Position Intelligent Decision Method Based On Model Fusion And Generative Network, Liqiang Guo, Liang Ma, Hui Zhang, Jing Yang, Lianfeng Li, Yaqi Zhai
Effective Position Intelligent Decision Method Based On Model Fusion And Generative Network, Liqiang Guo, Liang Ma, Hui Zhang, Jing Yang, Lianfeng Li, Yaqi Zhai
Journal of System Simulation
Abstract: Military intelligence technology is currently the most dynamic frontier and the inevitable trend for the development of unmanned equipment in the future. Aiming at the dual requirements of reliability and real-time performance of unmanned platform autonomous decision-making in complex environments and the shortcomings of existing combat simulation technology based on rule reasoning in terms of dynamics and flexibility, a research method of principle analysis and experimental verification is adopted. Based on the shooting experiment dataset of an unmanned platform, the effective position recognition link of attack decision-making is transformed into a binary classification problem with imbalanced categories in the …
Digital Twin Modeling And Control Of Robots For Intelligent Manufacturing Scenarios, Ying Li, Lan Gao, Zhisong Zhu
Digital Twin Modeling And Control Of Robots For Intelligent Manufacturing Scenarios, Ying Li, Lan Gao, Zhisong Zhu
Journal of System Simulation
Abstract: The introduction of Industry 4.0 and the Made in China 2025 development policy has accelerated the transformation of the manufacturing industry from automation to intelligence. Industrial robots, as the representative equipment of intelligent manufacturing, will also become more intelligent. Based on digital twin technology, digital modeling, and simulation debugging are conducted for such problems as interference and collision, tedious operation, and low efficiency of industrial robot spot welding debugging in production. Process Simulate from TECNOMATIX software is utilized to digitally model the robot spot welding station and define its motion, and TIA Portal and S7-PLCSIM Advanced are applied to …
Research On Learnable Wargame Agent Driven By Battle Scheme, Yifeng Sun, Zhi Li, Jiang Wu, Yubin Wang
Research On Learnable Wargame Agent Driven By Battle Scheme, Yifeng Sun, Zhi Li, Jiang Wu, Yubin Wang
Journal of System Simulation
Abstract: To enable the agent to cope with complex battle scenarios and objectives in wargame, a learnable wargame agent architecture driven by a battle scheme is proposed. By analyzing the "attachment characteristics" and "loose coupling characteristics" of the agent to wargame system, the learnable requirements of the agent are obtained. In the design of the agent framework, battle schemes are used to reduce the learning range of the agent. The finite state machine corresponds to the knowledge of the operational phase in the battle scheme, and the decision-making space of the agent is determined according to the framework of the …
A Deep Fuzzy Classifier Based On Feature Transform And Reconstruction, Rui Yin, Wei Lu, Jianhua Yang
A Deep Fuzzy Classifier Based On Feature Transform And Reconstruction, Rui Yin, Wei Lu, Jianhua Yang
Journal of System Simulation
Abstract: To obtain a classifier with good classification accuracy and interpretability, a deep fuzzy classifier based on feature transform and reconstruction (FR-DFC) is proposed. In FR-DFC, several fuzzy systems (FT_FS) for feature transform and a multi-prototype fuzzy classification system (MPRFD_FS) are stacked together to realize the classification process of the model, based on the hierarchically stacked thought originated from deep learning. Specifically, the stacked FT_FSs explore the hidden features in the data by transferring data from the original data space to the high-level feature space. MPRFD_FS, on the other hand, implements classification based on multiple prototypes that characterize the distribution …
Adaptive Particle Swarm Optimization Algorithm Based On Trap Label And Lazy Ant, Wei Zhang, Yuefeng Jiang
Adaptive Particle Swarm Optimization Algorithm Based On Trap Label And Lazy Ant, Wei Zhang, Yuefeng Jiang
Journal of System Simulation
Abstract: Many existing strategies for improving particle swarm optimization (PSO) fall short in assisting particles trapped in local optima and experiencing premature convergence to recover optimization performance. In response, an adaptive particle swarm optimization algorithm based on trap label and lazy ant (TLLA-APSO) is proposed. Firstly, the trap label strategy dynamically adjusts particle velocities, enabling the particle swarm to escape from local optima. Secondly, the lazy ant optimization strategy is employed to diversify particle velocity and enhance population diversity. Finally, the inertia cognition strategy introduces historical position into velocity updates, promoting path diversity and particle exploration while effectively mitigating the …
Simulation Optimization Of Airport Baggage Import System Based On Multi-Objective Wolf Pack Algorithm, Yifei Tao, Xiaopeng Ding, Junbin Luo, Xiao Fu, Jiaxing Wu, Yirong Li
Simulation Optimization Of Airport Baggage Import System Based On Multi-Objective Wolf Pack Algorithm, Yifei Tao, Xiaopeng Ding, Junbin Luo, Xiao Fu, Jiaxing Wu, Yirong Li
Journal of System Simulation
Abstract: Aiming at the problems of long waiting time for passenger baggage import and high system energy consumption during the operation of the baggage import system in civil aviation airports, a simulation optimization framework for solving this problem is proposed by comprehensively considering the influence of key control parameters on the operation efficiency of the baggage import system in airports, including the virtual window control mode, the operation speed of the collection belt conveyor, the length of the virtual window and the number of check-in counters opened at the same time. By analyzing the actual operating conditions of the airport …
Task Analysis Methods Based On Deep Reinforcement Learning, Xue Gong, Pengfei Peng, Li Rong, Yalian Zheng, Jun Jiang
Task Analysis Methods Based On Deep Reinforcement Learning, Xue Gong, Pengfei Peng, Li Rong, Yalian Zheng, Jun Jiang
Journal of System Simulation
Abstract: In response to the high coupling of task interaction and many influencing factors in task analysis, a task analysis method based on sequence decoupling and deep reinforcement learning (DRL) is proposed, which can achieve task decomposition and task sequence reconstruction under complex constraints. The method designs an environment for deep reinforcement learning based on task information interaction, while improving the SumTree algorithm based on the difference between the loss functions of the target network and the evaluation network, achieving the priority evaluation among tasks. The activation function operation mechanism is introduced into the deep reinforcement learning network, followed by …
Modeling And Verification Of Cooperative Vehicle Infrastructure System At Unsignalized Intersection Based On Time Automata, Wei Liu, Qirui Xiao, Xinhai Chen, Chang Rao, Yu Zhang, Bosi Wang
Modeling And Verification Of Cooperative Vehicle Infrastructure System At Unsignalized Intersection Based On Time Automata, Wei Liu, Qirui Xiao, Xinhai Chen, Chang Rao, Yu Zhang, Bosi Wang
Journal of System Simulation
Abstract: Cooperative vehicle infrastructure system (CVIS) is one of the advanced solutions to enhance intersection vehicle passage safety. Due to the lack of clear specifications and standards regarding the dynamic timing and transition processes of system object state interaction in existing CVIS technologies, ensuring the safety of passage control logic is challenging. This study utilizes formal language to describe the functional logic of CVIS in unsignalized intersections, verifying the safety of system object state interaction and control logic to improve vehicle passage safety at unsignalized intersections. Simulations are conducted for scenarios including single-vehicle non-conflict, dual-vehicle conflict, and multi-vehicle conflict to …
Real-Time Non-Photorealistic Rendering Method For Black And White Comic Style In Games And Animation, Yan Hu, Lizhe Chen, Hanna Xie, Yuyao Ge, Shun Zhou, Xingquan Cai
Real-Time Non-Photorealistic Rendering Method For Black And White Comic Style In Games And Animation, Yan Hu, Lizhe Chen, Hanna Xie, Yuyao Ge, Shun Zhou, Xingquan Cai
Journal of System Simulation
Abstract: To address the issues of high resource consumption and lengthy workflow in general nonphotorealistic, this paper proposes a real-time non-photorealistic rendering method for black and white comic style in games and animation. A specialized lighting model is designed to highlight the main environmental light and the grayscale grading of diffuse reflection based on the analysis of the lighting model effect. The pre-processing of the scene is achieved by merging the various components of the lighting model. A screen space three-phase edge detection method is proposed to sequentially perform depth edge detection, normal edge detection, and color edge detection on …
Simulation System For Carrier-Based Aircraft Ammunition Support Scheduling, Zhe Liu, Jiafeng Chen, Junfei Ma, Songhua Ma
Simulation System For Carrier-Based Aircraft Ammunition Support Scheduling, Zhe Liu, Jiafeng Chen, Junfei Ma, Songhua Ma
Journal of System Simulation
Abstract: For inefficient scheduling of carrier-based aircraft ammunition support on the flight deck of aircraft carriers, a Unity3D-based 3D virtual demonstration method for carrier-based aircraft ammunition scheduling is proposed, and a 3D virtual simulation system for carrier-based aircraft ammunition scheduling on the flight deck of aircraft carriers is constructed. A problem model targeted at carrierbased aircraft ammunition transportation and loading scheduling process as well as processing rules for path determination, sequence constraint, and loading position compensation are established based on the defining system. An optimal solution for the scheduling process is realized by using the improved discrete grey wolf optimizer …
Uav Path Planning Based On Improved Harris Hawk Algorithm And B-Spline Curve, Zhifeng Huang, Yuanhua Liu
Uav Path Planning Based On Improved Harris Hawk Algorithm And B-Spline Curve, Zhifeng Huang, Yuanhua Liu
Journal of System Simulation
Abstract: Aiming at the global path planning problem of unmanned aerial vehicles (UAVs) in dynamic environments, this paper proposes an improved Harris Hawk optimization algorithm. To address the problem of insufficient search performance in the later stage of the algorithm, an adaptive chaos and core population dynamic partitioning strategy is proposed to improve the searchability of the algorithm in the later stage. The Harris Hawk update formula is modified, and the golden sine strategy is introduced to improve the search efficiency of the algorithm. Then, an adaptive dynamic cloud optimal solution perturbation strategy is integrated to improve the ability of …
Optimal Scheduling Of Vehicle-Network Interaction Based On Interval Stackelberg Game Of Virtual Power Plant, Weiliang Liu, Qianwen Yan, Qiliang Zhang, Shuai Liu, Changliang Liu, Jiayao Kang, Xin Wang
Optimal Scheduling Of Vehicle-Network Interaction Based On Interval Stackelberg Game Of Virtual Power Plant, Weiliang Liu, Qianwen Yan, Qiliang Zhang, Shuai Liu, Changliang Liu, Jiayao Kang, Xin Wang
Journal of System Simulation
Abstract: To better exploit the regulation potential of electric vehicles (EVs), resolve the conflicts of interest among the stakeholders in vehicle-to-grid (V2G) interactions, and overcome the uncertainty of distributed energy sources and load, this paper proposes a two-level optimization scheduling model for V2G interactions based on the interval Stackelberg game of a virtual power plant (VPP). The VPP aggregator is considered as the upper level, and the EV users as the lower level. The upper level model uses interval numbers to describe the uncertainty of sources and loads, with the aim of minimizing the operating cost of the VPP aggregator, …
Maglev Ball Control Algorithm Based On Levant Differentiator, Zhenli Zhang, Yongzhuan Wang, Yao Qin, Jie Yang
Maglev Ball Control Algorithm Based On Levant Differentiator, Zhenli Zhang, Yongzhuan Wang, Yao Qin, Jie Yang
Journal of System Simulation
Abstract: To solve the problem of unsatisfactory control effect of permanent magnet electromagnetic hybrid suspension system caused by signal mutation and noise interference, the control method ILevant- PID, the combination of an improved Levant differentiator and PID, is proposed. The proposed method combines the strong adaptability of PID control and the robust characteristic of Levant differentiator on input noise to solve the chattering problem of the system output. The simulated anneal-particle swarm optimization is utilized to solve the constraints of the ILevant-PID controller, such as multiple parameters and strong correlation. The simulation results show that compared with the traditional PID …
Real-Time Scheduling Method For Dynamic Flexible Job Shop Scheduling, Quan Jiang, Jingxuan Wei
Real-Time Scheduling Method For Dynamic Flexible Job Shop Scheduling, Quan Jiang, Jingxuan Wei
Journal of System Simulation
Abstract: A multi-objective dynamic flexible job shop scheduling problem model with machine breakdown and random jobs arrival is constructed to address the interference of dynamic events in manufacturing processing on the scheduling scheme, and a real-time scheduling method with multiobjective proximal policy optimization (MPPO) algorithm is proposed. The MPPO algorithm trains two agents, routing agent (RA) and sequencing agent (SA), for real-time scheduling and real-time processing of dynamic events. It employs a linear combination of weight vectors and reward vectors as reward signals and stores the agents' parameters for each weight vector to optimize multiple objectives. The required state information, …
Path Planning For Mobile Robot Based On Angle Search, Yaru Wang, Dexin Yao, Zengli Liu, Yi Peng
Path Planning For Mobile Robot Based On Angle Search, Yaru Wang, Dexin Yao, Zengli Liu, Yi Peng
Journal of System Simulation
Abstract: The angle search algorithm for angle-controlled robots is proposed to increase the path search speed and optimize the path length. The algorithm effectively finds a path in static surroundings by performing an efficient search in a specific dimensional range based on the position of the robot and the target point. Firstly, search angles are predetermined according to the characteristics of the environment in the grid map. Then, the estimated angle of the robot's surrounding grid is computed. Finally, a new extension point is chosen by comparing the estimated angle to the search angle, demonstrating the usefulness and viability of …