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Improved Prediction of Metamaterial Antenna Bandwidth Using Adaptive Optimization of LSTM

computers, Materials & Continua 2022年第10期73卷 865-881页

作者： Doaa Sami Khafaga Amel Ali Alhussan El-Sayed M.El-kenawy Abdelhameed Ibrahim Said H.Abd Elkhalik Shady Y.El-Mashad Abdelaziz A.Abdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Systems Engineering Faculty of Engineering at ShoubraBenha UniversityEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

The design of an antenna requires a careful selection of its parameters to retain the desired ***,this task is time-consuming when the traditional approaches are employed,which represents a significant *** the other hand,machine learning presents an effective solution to this challenge through a set of regression models that can robustly assist antenna designers to find out the best set of design parameters to achieve the intended *** this paper,we propose a novel approach for accurately predicting the bandwidth of metamaterial *** proposed approach is based on employing the recently emerged guided whale optimization algorithm using adaptive particle swarm optimization to optimize the parameters of the long-short-term memory(LSTM)deep *** optimized network is used to retrieve the metamaterial bandwidth given a set of *** addition,the superiority of the proposed approach is examined in terms of a comparison with the traditional multilayer perceptron(ML),Knearest neighbors(K-NN),and the basic LSTM in terms of several evaluation criteria such as root mean square error(RMSE),mean absolute error(MAE),and mean bias error(MBE).Experimental results show that the proposed approach could achieve RMSE of(0.003018),MAE of(0.001871),and MBE of(0.000205).These values are better than those of the other competing models.

关键词： Metamaterial antenna long short term memory(LSTM) guided whale optimization algorithm(Guided WOA) adaptive dynamic particle swarm algorithm(AD-PSO)

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ForPKG-1.0: A Framework for Constructing Forestry Policy Knowledge Graph and Application Analysis

arXiv

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arXiv 2024年

作者： Sun, Jingyun Luo, Zhongze College of Computer and Control Engineering Northeast Forestry University Harbin150040 China

A policy knowledge graph can provide decision support for tasks such as project compliance, policy analysis, and intelligent question answering, and can also serve as an external knowledge base to assist the reasoning process of related large language models. Although there have been many related works on knowledge graphs, there is currently a lack of research on the construction methods of policy knowledge graphs. This paper, focusing on the forestry field, designs a complete policy knowledge graph construction framework, including: firstly, proposing a fine-grained forestry policy domain ontology;then, proposing an unsupervised policy information extraction method, and finally, constructing a complete forestry policy knowledge graph. The experimental results show that the proposed ontology has good expressiveness and extensibility, and the policy information extraction method proposed in this paper achieves better results than other unsupervised methods. Furthermore, by analyzing the application of the knowledge graph in the retrieval-augmented-generation task of the large language models, the practical application value of the knowledge graph in the era of large language models is confirmed. The knowledge graph resource will be released on an open-source platform and can serve as the basic knowledge base for forestry policy-related intelligent systems. It can also be used for academic research. In addition, this study can provide reference and guidance for the construction of policy knowledge graphs in other fields. © 2024, CC BY.

关键词： Knowledge graph

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Path Planning Method for Large-Area Stamp-Based DLP Printing

Path Planning Method for Large-Area Stamp-Based DLP Printing

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Electronic Information engineering and computer Science (EIECS), 2021 International Conference on

作者： Xiaokuo Gao Yanling Guo Jian Li Yangwei Wang College of Computer and Control Engineering Northeast Forestry University Harbin Heilongjiang China

ISBN: (数字)9798331531409

ISBN: (纸本)9798331531416

To address the issues of low efficiency and difficulty in interval printing splicing in large-area stamping-based stereolithography, a print path planning method based on an improved ant colony algorithm is proposed. The approach involves adjusting the distribution of ant parameters, updating the pheromone mechanism, and incorporating a time evaluation function to specifically solve the optimal print path problem. Experimental results show that this method achieves shorter print path lengths compared to the Zigzag algorithm and more effectively reduces the number of interval prints and the difficulty of splicing compared to the original ant colony algorithm, thereby improving print efficiency and quality.

关键词： Printing computer science Cross layer design Splicing Optimization methods Path planning Stereolithography Synchronization

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Metaheuristic Optimization for Mobile Robot Navigation Based on Path Planning

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computers, Materials & Continua 2022年第11期73卷 2241-2255页

作者： El-Sayed M.El-kenawy Zeeshan Shafi Khan Abdelhameed Ibrahim Bandar Abdullah Aloyaydi Hesham Arafat Ali Ali E.Takieldeen Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Science Department College of Engineering and Computer ScienceMustaqbal UniversityBuraidahSaudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Mechanical Engineering Qassim UniversityBuraidah51452Saudi Arabia

Recently,the path planning problem may be considered one of the most interesting researched topics in autonomous *** is why finding a safe path in a cluttered environment for a mobile robot is a significant requisite.A promising route planning for mobile robots on one side saves time and,on the other side,reduces the wear and tear on the robot,saving the capital *** route planning methods for the mobile robot have been developed and *** to our best knowledge,no method offers an optimum solution among the existing *** Swarm Optimization(PSO),a numerical optimization method based on the mobility of virtual particles in a multidimensional space,is considered one of the best algorithms for route planning under constantly changing environmental *** the researchers,reactive methods are increasingly common and extensively used for the training of neural networks in order to have efficient route planning for mobile *** paper proposes a PSO Weighted Grey Wolf Optimization(PSOWGWO)*** is a hybrid algorithm based on enhanced Grey Wolf Optimization(GWO)with *** order to measure the statistical efficiency of the proposed algorithm,Wilcoxon rank-sum and ANOVA statistical tests are *** experimental results demonstrate a 25%to 45%enhancement in terms of Area Under Curve(AUC).Moreover,superior performance in terms of data size,path planning time,and accuracy is demonstrated over other state-of-the-art techniques.

关键词： Mobile robot swarm optimization robot route planning neural networks

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H-STEP: Heuristic Stable Edge Service Entity Placement for Mobile Virtual Reality Systems

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IEEE Transactions on Mobile Computing 2025年

作者： Chi, Xuejian Chen, Honglong Ni, Zhichen Sun, Haiyang Sun, Peng Yu, Dongxiao Shandong Provincial Engineering Research Center of Intelligent Sensing and Measurement and Control Technology College of Control Science and Engineering Qingdao266580 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China Shandong University School of Computer Science and Technology Qingdao266237 China

Virtual reality (VR) technology, as a latency-sensitive application, can achieve real-time response to enhance the user's quality of experience (QoE) on edge devices. However, edge servers, unlike internally managed cloud servers, are prone to hardware failures, software abnormalities, and network attacks. Most prior studies have focused on reducing service delay and improving user coverage through service entity (SE) placement, often neglecting the critical impact of edge server malfunctions on user QoE. In this work, we design a stable service entity placement framework that connects users on faulty servers to collaborative edge servers, ensuring seamless task completion. This framework presents two primary challenges: determining the grouping of collaborative edge services and the placement of SEs. To address these challenges, we introduce a heuristic stable service entity placement (H-STEP) scheme. This scheme first determines the grouping of collaborative edge servers using an iterative search algorithm and then places SEs on suitable edge servers via a fast non-dominated sorting genetic placement algorithm. This approach balances stability benefits with total cost, enhancing the system's economic benefits. We theoretically analyze the performance of H-STEP and derive the performance gap between H-STEP and the optimal scheme. Extensive real-data-driven simulations demonstrate that H-STEP's performance closely approximates that of the optimal scheme and surpasses existing schemes. © 2002-2012 IEEE.

关键词： Virtual environments

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Shared-control design for robot-assisted surgery using a skill assessment approach

Shared-control design for robot-assisted surgery using a ski...

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作者： Song, Kai-Tai Hsieh, Ping-Jui Institute of Electrical and Control Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan

In this article, a shared-control system with skill-based share weight allocation is proposed for a robot-assisted minimally invasive surgery (MIS) procedure. A convolution neural network (CNN) is trained for online skill assessment, and the result is used to generate the share weights of robot autonomy and the user remote control. The control system can ensure synchronization of the two commands from the surgeon and robot autonomy and combine them to determine the motion of the surgical instrument. In this work, a contour-tracking task is handled by the suggested shared controller to simulate a surgical cutting operation. Experimental results on a lab-built robotic platform are presented to show the effectiveness of the proposed method. Multiple contour-tracking experiments have been tested to compare the tracking performances of pure manual remote control and the proposed shared-control method. Experimental results show that the shared controller achieved 34.5% improvement in tracking accuracy in comparison with pure manual control. © 2021 Chinese Automatic control Society and John Wiley & Sons Australia, Ltd.

关键词： Remote control

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Semi-supervised Learning with Nearest-Neighbor Label and Consistency Regularization 1

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Zheng, Guolin Li, Zuoyong Hu, Wenkai Fan, Haoyi Ching, Fum Yew Yu, Zhaochai Chen, Kaizhi College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou450001 China School of Computer Sciences Universiti Sains Malaysia Penang11800 Malaysia Minjiang University Fuzhou350121 China

ISBN: (数字)9783031200991

ISBN: (纸本)9783031200984

Semi-supervised learning, a system dedicated to making networks less dependent on labeled data, has become a popular paradigm due to its strong performance. A common approach is to use pseudo-labels with unlabeled data for training, however, pseudo-labels cannot correct their own errors. In this paper, we propose a semi-supervised method that uses nearest neighbor samples to obtain pseudo-labels and combines consistency regularization for image classification. Our method obtains pseudo-labels by computing the similarity of the data distribution between the weakly-augmented version of the unlabeled data and the labeled data stored in the support set and combines the consistency of the strongly-augmented version and the weakly-augmented version of the unlabeled data. We compared with several standard semi-supervised learning benchmarks and achieved a competitive performance. For example, we achieved an accuracy of 94.02 % on CIFAR-10 with 250 labels and 97.50 % on SVNH with 250 labels. It even achieved 91.59 % accuracy with only 40 labels data in the CIFAR-10. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Benchmarking

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Adaptive Fault Detection Scheme Using an Optimized Self-healing Ensemble Machine Learning Algorithm

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CSEE Journal of Power and Energy Systems 2022年第4期8卷 1145-1156页

作者： Levent Yavuz Ahmet Soran AhmetÖnen Xiangjun Li S.M.Muyeen Electrical and Computer Engineering Department in Abdullah Gül University Kayseri 38080Turkey Department of Electrical and Computer Engineering College of EngineeringSultan Qaboos UniversityAl-KhoudMuscat123Oman Department of Electrical Engineering Qatar UniversityDoha 2713Qatar State Key Laboratory of Control and Operation of Renewable Energy and Storage Systems China Electric Power Rescarch InstituteBeijing 100192China

This paper proposes a new cost-efficient,adaptive,and self-healing algorithm in real time that detects faults in a short period with high accuracy,even in the situations when it is difficult to *** than using traditional machine learning(ML)algorithms or hybrid signal processing techniques,a new framework based on an optimization enabled weighted ensemble method is developed that combines essential ML *** the proposed method,the system will select and compound appropriate ML algorithms based on Particle Swarm Optimization(PSO)*** this purpose,power system failures are simulated by using the PSCA D-Python *** of the salient features of this study is that the proposed solution works on real-time raw data without using any pre-computational techniques or pre-stored ***,the proposed technique will be able to work on different systems,topologies,or data *** proposed fault detection technique is validated by using PSCAD-Python co-simulation on a modified and standard IEEE-14 and standard IEEE-39 bus considering network faults which are difficult to detect.

关键词： Decision tree(DT) ensemble machine learning algorithm fault detection islanding operation k-Nearest Neighbor(kNN) linear discriminant analysis(LDA) logistic regression(LR) Naive Bayes(NB) self-healing algorithm

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Multimodal Sentiment Recognition Based on Sentiment Fusion Transformer 5

Multimodal Sentiment Recognition Based on Sentiment Fusion T...

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5th IEEE International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2023

作者： Guan, Xinyu Wang, Yuan College of Intelligent Equipment Shandong University of Science and Technology Tai'an China School of Control and Computer Engineering North China Electric Power University Beijing China

ISBN: (纸本)9798350310603

Multimodal emotion recognition (MER), which relies on its role in processing and analysing comments posted on social media and identifying the corresponding target emotion states, has a very important position in education, social media and especially in the field of HCI. In order to solve the problem of emotion recognition for video, text and other forms of data in social media, we propose a Transformer with feature. The Transformer is able to take into account the combination of different feature relations and extract global keys, so it is introduced for long-range sequential context extraction, and the residual connections are placed after the Transformer to prevent information collapse. In addition, this paper uses a multi-headed attention mechanism to combine the extracted different feature laws, and finally the fused feature vectors are fed into the classifier to classify the sentiment and output it to achieve multimodal sentiment recognition in a multimodal context. © 2023 IEEE.

关键词： Emotion Recognition

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Multimodal Feature Fusion and Emotion Recognition Based on Variational Autoencoder 5

Multimodal Feature Fusion and Emotion Recognition Based on V...

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5th IEEE International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2023

作者： Wang, Yuan Guan, Xinyu School of Control and Computer Engineering North China Electric Power University Beijing China College of Intelligent Equipment Shandong University of Science and Technology Tai'an China

ISBN: (纸本)9798350310603

Multimodal emotion recognition is one of the mainstream frontier directions in the field of AI, and has potential significant application value and wide application in related application scenarios involving intelligent science and human-computer interaction. Since the connection of semantic information features between contexts is a factor that needs to be focused on in multimodal emotion recognition (ER), although the CNN model can predict the information contained in the time series, it does not fully consider. The special relationship between semantic information has certain limitations. The CMN uses the semantic information that GRU can store in the memory unit. However, the practical application of this model is only to dynamically monitor the emotional changes of both parties over a period of time. There are deficiencies in the dialogue process involved. The recognition model based on variational autoencoder and multi-modal feature fusion constructed in this paper extracts rich contextual semantic information through Bi-LSTM, and uses the attention mechanism to analyze the multi-modal features extracted by multiple speakers. Features are fused to obtain important features, which effectively solves the problems mentioned above. The MFF-VAE recognition model proposed in this article effectively combines the extended feature extraction of horizontal multiple participants with the complementarity and connection of vertical semantic information, providing the next level of research direction for further improving recognition accuracy in the field of multimodal emotion recognition. © 2023 IEEE.

关键词： Emotion Recognition

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