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The Weapon Target Assignment in Adversarial Environments 1

2nd International Conference on Cognitive Computation and systems, ICCCS 2023

作者： Gao, Rui Zheng, Shaoqiu Wang, Kai Zhang, Longlong Zhu, Zhiyuan Yuan, Xiaohu College of Electronic and Information Engineering Southwest University Chongqing400715 China Key Lab of Information System Requirement Nanjing Research Institute of Electronics Engineering Nanjing210007 China Department of Computer Science and Technology Tsinghua University Beijing100084 China

ISBN: (数字)9789819708857

ISBN: (纸本)9789819708840

In context of the current weapon target assignment (WTA) problem, consideration of target capability is often disregarded. To address this issue, we propose an optimized model for weapon target assignment in complex environments. Our model provides a comprehensive description and conducts a detailed analysis of constraints and objective functions in the context of adversarial weapon target assignment (AWTA). To efficiently tackle the assignment problem, we employ the multi-particle swarm optimization algorithm (MOPSO). During the optimization process, we incorporate learning factors and inertia weights to update particle positions, seamlessly implementing the model with a set of adjustable parameters. Experimental results demonstrate that our model effectively captures the concept of adversarial scenarios. Furthermore, by utilizing the MOPSO optimization algorithm, we achieve a superior Pareto front even for small-scale adversarial scenarios, successfully resolving the adversarial WTA problem. Our research contributes an effective optimization approach to address the weapon task assignment problem and offers valuable inspiration for future investigations into multi-objective research. In conclusion, our study reveals the potential of multi-objective optimization research in the context of weapon target assignment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Multiobjective optimization

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A Novel and Accurate Method of Diagnosis via AI and ML for Smart HC system

A Novel and Accurate Method of Diagnosis via AI and ML for S...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： J Ruby Elizabeth Mathews Emmanuel E Aswini Shriram Pathak M. Bhuvaneswari Abhishek Choubey Department of Computer Science and Business System Nehru Institute of Engineering and Technology T.M.Palayam Tamil Nadu India Sathyabhama university of science and technology chennai Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering IES Insititute of Technology and Management IES University Bhopal M.P. India Department of Biomedical Engineering Karpagam Academy of Higher Education Coimbatore Sreenidhi institute of science and technology Yamnampet ghatkesar Hyderabad

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

Recent breakthroughs in the internet of Issues (IoT), computing through the cloud, and the use of AI have changed the standard healthcare system, making a case for smart health care. By combining key technologies including IoT and AI, there is a major improvement in medical services. The merging of IoT and AI offers multiple possibilities within the healthcare industry. This study piece presents a new artificial intelligence- and internet of things convergence-based framework for disease detection in smart health care systems, emphasizing especially on cardiovascular diseases and diabetes. The suggested model includes separate steps, including data collection, preparation, classification, and parameter modification. Utilizing Internet of Things devices like smartwatches along with sensors enables smooth collection of information, while AI methods harness this data for the detection of illnesses. The technique presents a new approach, applying to the Crowd Search Optimizer algorithm-based Flowed Long and Short-Term Memory (CSO-CLSTM) approach to disease detection. CSO is applied to improve both ‘weights’ while ‘bias’ factors of the CLSTM framework, while the isolation Fg (iForest) method is implemented to remove outliers. Through testing with health care information, the CSO-LSTM model got excellent accuracy ratings of 96.16% when 97.26% in identifying heart disease while diabetes, respectively. Consequently, the suggested CSO-LSTM model appears as a solid and reliable method of disease evaluation in digital healthcare systems, showing its potential for broad application.

关键词： Accuracy Smart healthcare Merging Medical services Data collection Data models Diabetes

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Multimodal Joint Prediction of Traffic Spatial-Temporal Data with Graph Sparse Attention Mechanism and Bidirectional Temporal Convolutional Network

SSRN

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SSRN 2024年

作者： Zhang, Dongran Yan, Jiangnan Polat, Kemal Alhudhaif, Adi Li, Jun Guangdong Provincial Key Laboratory of Intelligent Transportation System School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen518107 China Department of Electrical and Electronics Engineering Faculty of Engineering Bolu Abant Izzet Baysal University Bolu Turkey Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj11942 Saudi Arabia

Traffic flow prediction plays a crucial role in the management and operation of urban transportation systems. While extensive research has been conducted on predictions for individual transportation modes, there is relatively limited research on joint prediction across different transportation modes. Furthermore, existing multimodal traffic joint modeling methods often lack flexibility in spatial-temporal feature extraction. To address these issues, we propose a method called Graph Sparse Attention Mechanism with Bidirectional Temporal Convolutional Network (GSABT) for multimodal traffic spatial-temporal joint prediction. First, we use a multimodal graph multiplied by self-attention weights to capture spatial local features, and then employ the Top-U sparse attention mechanism to obtain spa-tial global features. Second, we utilize a bidirectional temporal convolutional network to enhance the temporal feature correlation between the output and input data, and extract inter-modal and intra-modal temporal features through the share-unique module. Finally, we have designed a multimodal joint prediction framework that can be flexibly extended to both spatial and temporal dimensions. Extensive experiments conducted on three real datasets indicate that the proposed model consistently achieves state-of-the-art predictive performance. © 2024, The Authors. All rights reserved.

关键词： Forecasting

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Efficient and Robust Freeway Traffic Speed Estimation under Oblique Grid using Vehicle Trajectory Data

arXiv

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arXiv 2024年

作者： He, Yang An, Chengchuan Jia, Yuheng Liu, Jiachao Lu, Zhenbo Xia, Jingxin The Intelligent Transportation System Research Center Southeast University Nanjing211189 China The School of Computer Science and Engineering Southeast University Nanjing211189 China The Department of Civil and Environmental Engineering Carnegie Mellon University PittsburghPA15213 United States

Accurately estimating spatiotemporal traffic states on freeways is a significant challenge due to limited sensor deployment and potential data corruption. In this study, we propose an efficient and robust low-rank model for precise spatiotemporal traffic speed state estimation (TSE) using low-penetration vehicle trajectory data. Leveraging traffic wave priors, an oblique grid-based matrix is first designed to transform the inherent dependencies of spatiotemporal traffic states into the algebraic low-rankness of a matrix. Then, with the enhanced traffic state low-rankness in the oblique matrix, a low-rank matrix completion method is tailored to explicitly capture spatiotemporal traffic propagation characteristics and precisely reconstruct traffic states. In addition, an anomaly-tolerant module based on a sparse matrix is developed to accommodate corrupted data input and thereby improve the TSE model robustness. Notably, driven by the understanding of traffic waves, the computational complexity of the proposed efficient method is only correlated with the problem size itself, not with dataset size and hyperparameter selection prevalent in existing studies. Extensive experiments demonstrate the effectiveness, robustness, and efficiency of the proposed model. The performance of the proposed method achieves up to a 12% improvement in Root Mean Squared Error (RMSE) in the TSE scenarios and an 18% improvement in RMSE in the robust TSE scenarios, and it runs more than 20 times faster than the state-of-the-art (SOTA) methods. © 2024, CC BY-NC-ND.

关键词： Matrix algebra

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Analysis of Determination of Items Ordering Patterns by Using Apriori Method 4

Analysis of Determination of Items Ordering Patterns by Usin...

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4th International Conference on Cybernetics and Intelligent system, ICORIS 2022

作者： Riza, Bob Subhan Putra, Hendra Nusa Zamsuri, Ahmad Efrizoni, Lusiana Defit, Sarjon University of Potensi Utama Information System Medan Indonesia STIKES Dharma Landbouw Department of Medical Record Padang Indonesia University of Lancang Kuning Informatics of Engineering Pekanbaru Indonesia STMIK Amik Riau Informatics of Engineering Riau Indonesia University of Putra Indonesia YPTK Computer Science Padang Indonesia

ISBN: (纸本)9781665453950

This study is entitled Analysis of the Determination of Ordering Patterns of Items Using Apriori Method Case Study of Universitas Potensi Utama. The research conducted by the author uses data mining and uses the Apriori method which divides arranged items into two so that the more detailed types of items are often ordered so that it can help inventory to increase the stock of items needed by the staff of Universitas Potensi Utama. Ordering items is one of the activities carried out at the Universitas Potensi Utama which is usually used to process work activities in the office. The test results using a priori algorithm and a system built using 2 item sets showed 94% confidence results that have met the needs in determining the pattern of ordering items based on the tendency of ordering items carried out by each department. © 2022 IEEE.

关键词： Data mining

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Multimodal Joint Prediction of Traffic Spatial-Temporal Data with Graph Sparse Attention Mechanism and Bidirectional Temporal Convolutional Network

arXiv

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arXiv 2024年

Traffic flow prediction plays a crucial role in the management and operation of urban transportation systems. While extensive research has been conducted on predictions for individual transportation modes, there is relatively limited research on joint prediction across different transportation modes. Furthermore, existing multimodal traffic joint modeling methods often lack flexibility in spatial-temporal feature extraction. To address these issues, we propose a method called Graph Sparse Attention Mechanism with Bidirectional Temporal Convolutional Network (GSABT) for multimodal traffic spatial-temporal joint prediction. First, we use a multimodal graph multiplied by self-attention weights to capture spatial local features, and then employ the Top-U sparse attention mechanism to obtain spatial global features. Second, we utilize a bidirectional temporal convolutional network to enhance the temporal feature correlation between the output and input data, and extract inter-modal and intra-modal temporal features through the share-unique module. Finally, we have designed a multimodal joint prediction framework that can be flexibly extended to both spatial and temporal dimensions. Extensive experiments conducted on three real datasets indicate that the proposed model consistently achieves state-of-the-art predictive performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Urban transportation

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Enhancing Power Grid Stability: Design and Integration of a Fast Bus Tripping system in Protection Relays

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IEEE Transactions on Consumer Electronics 2024年

作者： Ghiasi, Mohammad Wang, Zhanle Mehrandezh, Mehran Alhelou, Hassan Haes Ghadimi, Noradin University of Regina Faculty of Engineering and Applied Science ReginaSKS4S 0A2 Canada School of Engineering CambridgeMA United States Monash University Department of Electrical and Computer System Engineering Australia Islamic Azad University Young Researchers and Elite Club Ardabil Branch Ardabil Iran

This article introduces a novel method for efficiently and promptly operating protection relays within a power system, with a specific emphasis on adaptive overcurrent (OC) protection in a power grid. The approach utilizes SEL751 and SEL751A relays that communicate through the IEC-61850 GOOSE protocol, establishing connections using intelligent electronic devices (IED) at several locations in the system. The power system, consisting of transformers, bus bars, and feeders, employs protection relays to identify OC faults based on time and current parameters. Relay configurations are tested in a case study at the Omicron Lab involving a 138/25kV substation. The test encompasses various fault scenarios on the low-voltage (LV) and high-voltage (HV) sides, incorporating different current values and fault times, utilizing the chosen time-overcurrent U3 (very inverse) curve. The proposed approach exhibits the ability to promptly remove faults in transformers and substations at both the HV and the LV levels. Furthermore, the experimental results closely match the theoretical calculations, validating the effectiveness of the proposed approach in the protection of AC grids. Additionally, built-in cybersecurity measures in SEL relays ensure secure and reliable operation, mitigating the risks of cyber-related relay failures. © 1975-2011 IEEE.

关键词： Relay protection

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Impact of the Continuous Evolution of Gene Ontology on the Performance of Similarity Measures for Scoring Confidence of Protein Interactions

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SN computer science 2020年第6期1卷 351页

作者： Paul, Madhusudan Anand, Ashish Pyne, Saptarshi Department of Computer Science and Engineering IIT Guwahati Guwahati India Department of Computer and System Sciences Visva-Bharati Santiniketan India

Gene ontology (GO) is a comprehensive resource for the properties of gene products and their relationships. A similarity measure can be defined between two gene products by utilizing GO, and the corresponding similarity score can be treated as a likelihood to interact between them physically. However, GO is being updated regularly by the addition of new terms and removal/merging of obsolete terms. Therefore, the similarity score of interaction may differ from one instance of GO to another. In this paper, we systematically study the impact of the continuous evolution of GO on the performance of similarity measures for the task of scoring confidence of protein–protein interactions (PPIs). We find that the performance of a similarity measure gets affected due to the continuous evolution of GO. We further observe that the degree of robustness of a similarity measure is highly influenced by the particular setting we consider. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Gene ontology (GO) Protein–protein interaction (PPI) Similarity measures

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Utilization of Computational Intelligence in the Development of a Health Monitoring system for Induction Machines

Utilization of Computational Intelligence in the Development...

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International Conference on Futuristic Technologies (INCOFT)

作者： J.N Singh Brijraj Singh Solanki Department of Computer Science and Engineering Galgotias University Greater Noida Uttar Pradesh India Department of Computer Sciences School of Computer & System Sciences Jaipur National University Jaipur India

Technology advancement makes significant contributions to the healthcare system. By automating the patient monitoring task, smart health monitoring will reduce human effort while improving patient work flow management. Computational intelligence plays a vital part in intelligent failure diagnosis and prediction since it is one of the most potent data processing methods for resolving challenging nonlinear issues. The continued use of computational intelligence in machinery condition monitoring and problem diagnosis has, however, only been briefly covered in a few thorough papers. This paper describes the creation of an induction machine health monitoring system. It is briefly explained how artificial neural networks work and how they might be used for machine diagnostics. It is covered the general situation of designing intelligent systems based on neural networks. The effectiveness and accuracy of the proposed intelligent system for identifying machine health are demonstrated.

关键词： Vibrations Fault diagnosis Condition monitoring Induction motors Smart healthcare Stator windings Synchronous motors

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Implementation of Soft Computing-Based Optimization Strategies to Enhance Power system Stability

Implementation of Soft Computing-Based Optimization Strategi...

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International Conference on Futuristic Technologies (INCOFT)

作者： Gautam Kumar Mukesh Kataria Department of Computer Science and Engineering Galgotias University Greater Noida Uttar Pradesh India Department of Computer Sciences School of Computer % System Sciences Jaipur National University Jaipur India

The improvement of large, interconnected power systems relies basically upon the stability of the power system. Because of the critical robotized needs of both home and modern requirements, the present power system keeps on developing dramatically. Soft computing approaches have accomplished very high rates, astounding adaptation to internal failure, and versatility. These variables make counterfeit brain network answers for probably the most troublesome or perplexing issues in the controls and power systems spaces appealing. For a solitary machine boundless transport bar (SMIB) system, a fuzzy based power stabilizer is to be built in the recommended paper. A reproduction software must be utilized to follow through with the whole responsibility. Within the sight of an unobtrusive system unsettling influence, recreations of the SMIB Energy system state space model with fuzzy logic regulator and regular regulator are to be run. When contrasted with traditional and fuzzy logic power system stabilizers, this clear plan approach and more modest rule base are simpler to carry out and possibly offer better execution.

关键词： Fuzzy logic Sensitivity Regulators Computational modeling Stability criteria Static VAr compensators Power system stability

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