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6th International Conference on Electrical, control and Instrumentation engineering, ICECIE 2024

作者： Oya, Hidetoshi Asada, Daiki Nagai, Shunya Nakano, Kazushi Tokyo City University Department of Computer Science Tokyo Japan Graduate School of Integrative Science and Engineering Tokyo City University Tokyo Japan Niihama College Department of Electronics and Control Engineering Ehime Japan Graduate School of Informatics and Engineering The University of Electro-Communications Tokyo Japan

ISBN: (纸本)9798350380040

In this paper, we present a new LMI-based non-iterative design strategy for static output feedback controllers with L2 Gain Performance for linear systems. In our approach, on the basis of the well-known necessary and sufficient condition for the existence of the static output feedback control for linear systems, the non-iterative LMI-based design procedure for the proposed static output feedback controller is derived. In this paper, we show the LMI-based non-iterative design procedure for static output feedback controllers with L2 Gain Performance and the effectiveness of the proposed static output feedback control is presented through a simple illustrative example. © 2024 IEEE.

关键词： Feedback control

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Dynamic Multihead Attention for Enhancing Neural Machine Translation Performance 5th

Dynamic Multihead Attention for Enhancing Neural Machine Tra...

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5th International Conference on Trends in Computational and Cognitive engineering, TCCE 2023

作者： Pandey, Mrinal Pandey, Rashmikiran Nazarov, Alexey Department of Radio Engineering and Cybernetics Moscow Institute of Physics and Technology Moscow Russia Federal Research Center Computer Science and Control Russian Academy of Sciences Moscow Russia

ISBN: (纸本)9789819719228

The research paper delves into the realm of "Attention-Based Neural Machine Translation for Multilingual Communication", focusing on the revolutionary impact of attention mechanisms in enhancing cross-language understanding. Through dynamic allocation of focus to pertinent segments of source sequences, attention mechanisms have significantly improved the precision and coherence of translations, playing a pivotal role in multilingual communication. The paper underscores the importance of this technology in addressing the complexities of aligning words across diverse languages and maintaining grammatical accuracy, crucial for effective cross-lingual interactions. The study also presents a visual flowchart outlining the core components of attention-based NMT, encompassing the encoder, decoder, and attention context. Moreover, the paper extrapolates the future trajectory of attention-based NMT, identifying areas for continued exploration and innovation. Researchers are urged to refine attention models to cater to intricate linguistic structures, potentially unveiling novel mechanisms that strike a balance between capturing long-range dependencies and computational efficiency. Furthermore, the paper anticipates the integration of attention-based NMT with multimodal inputs, unlocking the potential for comprehensive communication by incorporating visual and auditory cues. These advancements hold the promise of revolutionising multilingual communication, shaping a future where language barriers are transcended in various domains, from global diplomacy to education and beyond. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Neural machine translation

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DialogNTM: Context Reconstruction in Multi-Turn Dialogue Generation using Neural Turing Machines

DialogNTM: Context Reconstruction in Multi-Turn Dialogue Gen...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Zhao, Haohao Liu, Meiling Zhou, Jiyun Fu, Kaiquan College of Computer and Control Engineering Northeast Forestry University Harbin China Lieber Institute Johns Hopkins University Baltimore United States South Dakota State University Department of Electrical Engineering and Computer Science BrookingsSD United States

ISBN: (纸本)9781665410205

In actual conversational scenarios, we can often determine which parts of the previous dialogue are more critical based on the current inquiry. However, the existing contextual modeling methods often encode the query sentence and the dialogue history in a unified manner, which fails to effectively highlight the inference effect of the query sentence. Moreover, these methods typically process the dialogue history only at the information extraction level, neglecting the treatment of the context itself. In this paper, we propose a novel conversational context modeling technique called DialogNTM. Based on the guidance of the query sentence, the technology can effectively eliminate redundant information by reconstructing the representation of the context. Specifically, we have tweaked the memory and input flow of the Neural Turing Machine (NTM) to encode contextual information in memory and guide the read, write, and erase operations of memory through query sentence. This design simulates the human brain's dynamic retrieval and renewal mechanism of previous memories when dealing with current problems. We have conducted extensive experiments on three publicly available datasets to verify the effectiveness of the DialogNTM model. Compared to the benchmark model, DialogNTM showed significant performance improvements ranging from 11% to 73% across multiple automated evaluation metrics (3.52% to 8.68% in absolute terms). © 2024 IEEE.

关键词： Turing machines

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A Formation control for Multi-Agent Systems for Event-Trigger-Based Variable Gain controllers 49

A Formation Control for Multi-Agent Systems for Event-Trigge...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Asada, Daiki Nakagawa, Takuya Hoshi, Yoshikatsu Oya, Hidetoshi Nagai, Shunya Graduate School of Integrative Science and Engineering Tokyo City University Tokyo Japan Tokyo City University The Department of Computer Science Tokyo Japan Niihama College Department of Electronics and Control Engineering Ehime Japan

ISBN: (纸本)9798350331820

This paper deals with a design problem for a new event-trigger-based variable gain controller which achieves consensus for multi-agent systems (MASs) with the leader-follower structure. The proposed variable gain controller consists of stabilizing state feedback laws, state feedback inputs for consensus, and compensation inputs with adjustable parameters, and is designed by taking relative positions into account explicitly. In this paper, we show that sufficient conditions for the existence of the proposed formation control system are given in terms of Linear Matrix Inequalities (LMIs). © 2023 IEEE.

关键词： Linear matrix inequalities

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Hierarchical Classification of Transversal Skills in Job Ads Based on Sentence Embeddings

arXiv

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arXiv 2024年

作者： Leon, Florin Gavrilescu, Marius Floria, Sabina-Adriana Minea, Alina-Adriana Faculty of Automatic Control and Computer Engineering Romania Faculty of Materials Science and Engineering "Gheorghe Asachi" Technical University of Iași Romania

This paper proposes a classification framework aimed at identifying correlations between job ad requirements and transversal skill sets, with a focus on predicting the necessary skills for individual job descriptions using a deep learning model. The approach involves data collection, preprocessing, and labeling using ESCO (European Skills, Competences, and Occupations) taxonomy. Hierarchical classification and multi-label strategies are used for skill identification, while augmentation techniques address data imbalance, enhancing model robustness. A comparison between results obtained with English-specific and multi-language sentence embedding models reveals close accuracy. The experimental case studies detail neural network configurations, hyperparameters, and cross-validation results, highlighting the efficacy of the hierarchical approach and the suitability of the multi-language model for the diverse European job market. Thus, a new approach is proposed for the hierarchical classification of transversal skills from job ads. © 2024, CC BY-NC-ND.

关键词： Embeddings

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Spatial data intelligence and city metaverse: A review

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Fundamental Research 2025年第3期5卷 1169-1193页

作者： Meng, Xiaofeng Li, Yong Liu, Ke Liu, Yu Yang, Bin Song, Xuan Liao, Guoqiong Wang, Senzhang Yu, Ziqiang Chen, Longbiao Pan, Xiao Lin, Yuming School of Information Renmin University of China Beijing 100872 China Department of Electronic Engineering Tsinghua University Beijing 100084 China Department of Information Sciences National Natural Science Foundation of China Beijing 100085 China Institute of Remote Sensing and Geographic Information Systems School of Earth and Space Sciences Peking University Beijing 100871 China School of Data Science and Engineering East China Normal University Shanghai 200062 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen 518055 China School of Information Management Jiangxi University of Finance and Economics Nanchang 330013 China School of Computer Science and Engineering Central South University Changsha 410017 China School of Computer and Control Engineering Yantai University Yantai 264005 China Department of Computer Science Xiamen University Xiamen 361000 China College of Information Science and Technology Shijiazhuang Tiedao University Shijiazhuang 050043 China

Spatial Data Intelligence (SDI) encompasses acquiring, storing, analyzing, mining, and visualizing spatial data to gain insights into the physical world and uncover valuable knowledge. These understandings and knowledge play a crucial role in connecting physical and virtual realms, such as in developing a City Metaverse (CM) aimed at enhancing and optimizing modern urban environments. The advancement of CM holds immense potential to benefit urban dwellers, making research on SDI an increasingly prominent area of focus. This paper contributes significantly by organizing the relevant research and technologies within a coherent framework. Firstly, we identify SDI technologies capable of collecting real-world information to construct a virtual CM. Subsequently, we delve into the technologies that can be compositely integrated with SDI to facilitate interaction with and management of actual cities from the virtual perspective. Additionally, we emphasize the effectiveness and potential of these methods in practical applications. Lastly, we conclude our survey by discussing emerging challenges associated with technological progress, the industrial chain, legal and regulatory aspects, and ethical and moral considerations. © 2024

关键词： Application prospect City metaverse Smart city Spatial data intelligence Virtual-real interaction

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Integration of ANN for Accurate Estimation and control in Wastewater Treatment 28

Integration of ANN for Accurate Estimation and Control in Wa...

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28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023

作者： Ţiru, Andreea Elena Vasiliev, Iulian Diaconu, Larisa Vilanova, Ramon Voipan, Daniel Ratnaweera, Harsha Dunǎrea de Jos University of Galaţi Department of Automatic Control and Electrical Engineering Galaţi Romania Autonomous University of Barcelona Department of Automation Systems and Advanced Control Research Barcelona Spain Dunǎrea de Jos University of Galaţi Department of Computer Science and Information Technology Galaţi Romania Ås Norway

ISBN: (纸本)9798350339918

The management of wastewater is a significant global concern that calls for innovative solutions to lessen its negative effects on the environment. Conventional techniques of treating wastewater need improvement in order to deal with newly discovered contaminants, which highlights the importance of providing precise estimates of process performance and resource requirements. The worsening water shortage situation requires a paradigm shift in which wastewater is viewed as a useful resource. It is possible to create an economy that is both sustainable and circular by treating and recycling wastewater, putting less pressure on freshwater supplies, and leaving as little of an environmental footprint as possible. This study investigates the use of Artificial Neural Networks (ANNs) as software estimators in the treatment of wastewater, with a particular emphasis on predicting ammonium concentrations in effluent. In order to deal with imbalanced time-series data, the research introduces innovative data pretreatment strategies. These techniques include a Sliding Window protocol, Data Normalization, and a K-Fold training scheme. This illustrates the potential of ANNs to revolutionize wastewater treatment procedures and drive developments in this field. The suggested method demonstrates higher performance when estimating pollutant concentrations, showing the ability of ANNs to do so. © 2023 IEEE.

关键词： Neural networks

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Joint Power Allocation and Task Scheduling for Data Offloading in Non-Geostationary Orbit Satellite Networks

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IEEE Transactions on Network and Service Management 2025年第3期22卷 2882-2896页

作者： He, Lijun Jia, Ziye Wang, Juncheng Lansard, Erick Han, Zhu Yuen, Chau China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China Xidian University State Key Laboratory of Integrated Services Networks Xi’an710071 China Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing211106 China Southeast University National Mobile Communications Research Laboratory Nanjing211111 China Hong Kong Baptist University Department of Computer Science Kowloon Tsai Hong Kong Nanyang Technological University School of Electrical and Electronics Engineering Jurong West 639798 Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

In Non-Geostationary Orbit Satellite Networks (NGOSNs) with a large number of battery-carrying satellites, proper power allocation and task scheduling are crucial to improving data offloading efficiency. In this work, we jointly optimize power allocation and task scheduling to achieve energy-efficient data offloading in NGOSNs. Our goal is to properly balance the minimization of the total energy consumption and the maximization of the sum weights of tasks. Due to the tight coupling between power allocation and task scheduling, we first derive the optimal power allocation solution to the joint optimization problem with any given task scheduling policy. We then leverage the conflict graph model to transform the joint optimization problem into an Integer Linear Programming (ILP) problem with any given power allocation strategy. We explore the unique structure of the ILP problem to derive an efficient semidefinite relaxation-based solution. Finally, we utilize the genetic framework to combine the above special solutions as a two-layer solution for the original joint optimization problem. Simulation results demonstrate that our proposed solution can properly balance the reduction of total energy consumption and the improvement of the sum weights of tasks, thus achieving superior system performance over the current literature. © 2004-2012 IEEE.

关键词： Geostationary satellites

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Adaptive Dynamic Dipper Throated Optimization for Feature Selection in Medical Data

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computers, Materials & Continua 2023年第4期75卷 1883-1900页

作者： Ghada Atteia El-Sayed M.El-kenawy Nagwan Abdel Samee Mona M.Jamjoom Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Ahmad Taher Azar Nima Khodadadi Reham A.Ghanem Mahmoud Y.Shams Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Faculty of Computers and Artificial Intelligence Benha UniversityBenha13518Egypt College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Civil Engineering University of Science and TechnologyMiami33101USA Department of Civil and Environmental Engineering Florida International UniversityMiamiUSA Oral Biology Department Faculty of Oral and Dental MedicineDelta University for Science and TechnologyGamasaEgypt Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt

The rapid population growth results in a crucial problem in the early detection of diseases inmedical *** all the cancers unveiled,breast cancer is considered the second most severe ***,an exponential rising in death cases incurred by breast cancer is expected due to the rapid population growth and the lack of resources required for performing medical *** recent advances in machine learning could help medical staff in diagnosing diseases as they offer effective,reliable,and rapid responses,which could help in decreasing the death *** this paper,we propose a new algorithm for feature selection based on a hybrid between powerful and recently emerged optimizers,namely,guided whale and dipper throated *** proposed algorithm is evaluated using four publicly available breast cancer *** evaluation results show the effectiveness of the proposed approach from the accuracy and speed *** prove the superiority of the proposed algorithm,a set of competing feature selection algorithms were incorporated into the conducted *** addition,a group of statistical analysis experiments was conducted to emphasize the superiority and stability of the proposed *** best-achieved breast cancer prediction average accuracy based on the proposed algorithm is 99.453%.This result is achieved in an average time of 3.6725 s,the best result among all the competing approaches utilized in the experiments.

关键词： Medical dataset breast cancer guided whale optimizer dipper throated optimizer feature selection meta-heuristics

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Multiobjective Multiverse Optimizer for Multirobotic U-Shaped Disassembly Line Balancing Problems

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第2期5卷 882-894页

作者： Qin, Shujin Zhang, Shancheng Wang, Jiacun Liu, Shixin Guo, Xiwang Qi, Liang School of Economics and Management Shangqiu Normal University Shangqiu476000 China School of Information and Control Engineering Liaoning Petrochemical University Fushun113001 China Monmouth University West Long Branch NJ07764 United States College of Information Science and Engineering Northeastern University Shenyang110819 China Department of Computer Science and Technology Shandong University of Science and Technology Qingdao266590 China

The development of technology accelerates the upgrade of products, which results in a significant number of obsolete products. This research aims to solve the multirobotic multiproduct U-shaped disassembly line balancing problem (M2UDP), in which different products are disassembled on a U-shaped model in a preset cycle time and assigned tasks to robots in each workstation reasonably. A linear mixed-integer model is established to maximize disassembly profit and minimize carbon emissions. An improved multiobjective multiverse optimizer (IMMO) that utilizes the sigmoid activation function in neural networks is proposed to find the optimal plan for the model. The improved algorithm is verified via a set of real-life instances and compared with three classical multiobjective optimization algorithms. The experimental results show that the proposed IMMO performs better than those peers in solving the M2UDP problems. © 2023 IEEE.

关键词： Profitability

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