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Heterogeneous Image Knowledge Driven Visual Perception

IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 255-257页

作者： Lan Yan Wenbo Zheng Fei-Yue Wang IEEE the College of Computer Science and Electronic Engineering Hunan UniversityChangsha 410082China the School of Computer Science and Artificial Intelligence Wuhan University of TechnologyWuhan 430070China the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

Dear Editor,This letter is concerned with visual perception closely related to heterogeneous *** the huge challenge brought by different image modalities,we propose a visual perception framework based on heterogeneous image knowledge,i.e.,the domain knowledge associated with specific vision tasks,to better address the corresponding visual perception problems.

关键词： Visual visual knowledge

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CONTINUOUS-TIME IDENTIFICATION OF DYNAMIC STATE-SPACE MODELS BY DEEP SUBSPACE ENCODING 11

CONTINUOUS-TIME IDENTIFICATION OF DYNAMIC STATE-SPACE MODELS...

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11th International Conference on Learning Representations, ICLR 2023

作者： Beintema, Gerben I. Schoukens, Maarten Tóth, Roland Department of Electrical Engineering Eindhoven University of Technology Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

Continuous-time (CT) modeling has proven to provide improved sample efficiency and interpretability in learning the dynamical behavior of physical systems compared to discrete-time (DT) models. However, even with numerous recent developments, the CT nonlinear state-space (NL-SS) model identification problem remains to be solved in full, considering common experimental aspects such as the presence of external inputs, measurement noise, latent states, and general robustness. This paper presents a novel estimation method that addresses all these aspects and that can obtain state-of-the-art results on multiple benchmarks with compact fully connected neural networks capturing the CT dynamics. The proposed estimation method called the subspace encoder approach (SUBNET) ascertains these results by efficiently approximating the complete simulation loss by evaluating short simulations on subsections of the data, by using an encoder function to estimate the initial state for each subsection and a novel state-derivative normalization to ensure stability and good numerical conditioning of the training process. We prove that the use of subsections increases cost function smoothness together with the necessary requirements for the existence of the encoder function and we show that the proposed state-derivative normalization is essential for reliable estimation of CT NL-SS models. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Cost functions

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Sector-Based Incremental Clustering and Scalable Deletion for Real-Time Big Data Streaming Application 2nd

Sector-Based Incremental Clustering and Scalable Deletion fo...

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2nd International Conference on Deep Sciences for Computing and Communications, IconDeepCom 2023

作者： Ananthi, M. Mangayarkarasi, T. Department of Computer Science and Business Systems Sri Sairam Engineering College Chennai India Department of Instrumentation and Control Engineering Sri Sairam Engineering College Chennai India

ISBN: (纸本)9783031689079

Massive streaming data is the most current technology for storing and manipulating large quantities of data. Processing the substantial amount of streaming data is still a challenging issue. The speed and throughput can be improved while extracting this huge amount of data by using a dynamic query processing technique. The sector-based incremental clustering indexing and scalable deletion approach is used in this paper to improve the system performance. Stream processing can be more effective and flexible by applying dynamic query processing technique. Incremental clustering using sector-based method and scalable deletion which removes unwanted, spurious data is proposed in this work in order to process incoming streaming data effectively. Incremental clustering is one of the clustering methods used for processing dynamic information. It is not necessary to plan the entire dataset ahead of time in this method which is suitable for live streaming data, because it processes one instance at a time. So, less space and time is used. Various queries with different types of datasets executed using sector based incremental clustering and scalable deletion methods. The proposed system achieves the highest results for more than 90% accuracy with less space and more speed when the execution time and data retrieval are empirically increased compared with conventional approaches. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Data streams

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Fuzzing:Progress,Challenges,and Perspectives

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computers, Materials & Continua 2024年第1期78卷 1-29页

作者： Zhenhua Yu Zhengqi Liu Xuya Cong Xiaobo Li Li Yin Institute of Systems Security and Control College of Computer Science and TechnologyXi'an University of Science and TechnologyXi'an710054China School of Mathematics and Information Science Baoji University of Arts and SciencesBaoji721013China Institute of Systems Engineering Macao University of Science and TechnologyTaipaMacaoChina

As one of the most effective techniques for finding software vulnerabilities,fuzzing has become a hot topic in software *** feeds potentially syntactically or semantically malformed test data to a target program to mine vulnerabilities and crash the *** recent years,considerable efforts have been dedicated by researchers and practitioners towards improving fuzzing,so there aremore and more methods and forms,whichmake it difficult to have a comprehensive understanding of the *** paper conducts a thorough survey of fuzzing,focusing on its general process,classification,common application scenarios,and some state-of-the-art techniques that have been introduced to improve its ***,this paper puts forward key research challenges and proposes possible future research directions that may provide new insights for researchers.

关键词： Fuzzing vulnerability software testing software security

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Intelligent obstacle avoidance algorithm for safe urban monitoring with autonomous mobile drones

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Journal of Electronic Science and Technology 2024年第4期22卷 19-36页

作者： Didar Yedilkhan Abzal E.Kyzyrkanov Zarina A.Kutpanova Shadi Aljawarneh Sabyrzhan K.Atanov Department of Computer Engineering Astana IT UniversityAstana010000Kazakhstan Department of Computer and Software Engineering L.N.Gumilyov Eurasian National UniversityAstana010000Kazakhstan Department of Automation and Control Systems L.N.Gumilyov Eurasian National UniversityAstana010000Kazakhstan Department of Software Engineering Jordan University of Science and TechnologyIrbid22110Jordan

The growing field of urban monitoring has increasingly recognized the potential of utilizing autonomous technologies,particularly in drone *** deployment of intelligent drone swarms offers promising solutions for enhancing the efficiency and scope of urban condition *** this context,this paper introduces an innovative algorithm designed to navigate a swarm of drones through urban landscapes for monitoring *** primary challenge addressed by the algorithm is coordinating drone movements from one location to another while circumventing obstacles,such as *** algorithm incorporates three key components to optimize the obstacle detection,navigation,and energy efficiency within a drone ***,the algorithm utilizes a method to calculate the position of a virtual leader,acting as a navigational beacon to influence the overall direction of the ***,the algorithm identifies observers within the swarm based on the current *** further refine obstacle avoidance,the third component involves the calculation of angular velocity using fuzzy *** approach considers the proximity of detected obstacles through operational rangefinders and the target’s location,allowing for a nuanced and adaptable computation of angular *** integration of fuzzy logic enables the drone swarm to adapt to diverse urban conditions dynamically,ensuring practical obstacle *** proposed algorithm demonstrates enhanced performance in the obstacle detection and navigation accuracy through comprehensive *** results suggest that the intelligent obstacle avoidance algorithm holds promise for the safe and efficient deployment of autonomous mobile drones in urban monitoring applications.

关键词： Drone swarms Fuzzy logic Intelligent solution Smart city Urban monitoring

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An Advanced Rivest Cipher 4 Algorithm to Transfer Fast and Secure Data Using Li-Fi Technology 13

An Advanced Rivest Cipher 4 Algorithm to Transfer Fast and S...

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13th IEEE International Conference on System engineering and Technology, ICSET 2023

作者： Msallam, Mohammed Majid Samet, Refik Ankara University Computer Engineering Department Ankara Turkey University of Technology Control and Systems Engineering Department Baghdad Iraq

ISBN: (纸本)9798350340891

The Light-Fidelity (Li-Fi) is a wireless communication technology that is light-based and can complete wireless fidelity (Wi-Fi) technologies for many applications. Li-Fi technology which uses light spectrum is a technology for solving the lack of radio spectrum at Wi-Fi. One of the main benefits of Li-Fi technology is the ability to communicate by light with a high level of safety. However, the signal of Li-Fi technology is the same as other signals that are susceptible to eavesdropping because no security system is infallible. The objective of this paper is to decrease the computational cost of encryption and decryption and keep the security of transformed data when transmitted via Li-Fi technology. This study proposes the advanced Rivest Cipher 4 (ARC4) algorithm that will be used in the physical layer of Li-Fi applications. The novelty will be in the pseudo-random generation algorithm (PRGA) which is part of Rivest Cipher 4 (RC4). The data will become an index of the s-array and get the value of that index in the s-array instead of using the XOR operation. The proposed ARC4 was evaluated by the National Institute of Science and Technology (NIST) test. The obtained results illustrate that ARC4 is more robust and effective than RC4 when the data is transformed by Li-Fi communication. © 2023 IEEE.

关键词： Wi-Fi

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Decentralized Game-based Auction Algorithm for Scheduling Robotic Chargers to Service EVs with Uncertain Demands

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-14页

作者： Fang, Qiuyang Zhang, Jianlei Wang, Chen Xie, Guangming Zhang, Chunyan Department of Automation College of Computer and Control Engineering Nankai University Tianjin China National Engineering Research Center of Software Engineering Peking University Beijing China State Key Laboratory for Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing China

In this study, we introduce a novel auction-based algorithm modeled as a decentralized coalition formation game, designed for the complex requirements of large-scale multi-robot task allocation under uncertain demand. This context is particularly illustrative in scenarios where robots are tasked to charge electric vehicles. The algorithm begins by partitioning a composite task sequence into distinct subsets based on spatial similarity principles. Subsequently, we employ a coalition formation game paradigm to coordinate the assembly of robots into cooperative coalitions focused on these distinct subsets. To mitigate the impact of unpredictable task demands on allocations, our approach utilizes the conditional value-at-risk to assess the risk associated with task execution, along with computing the potential revenue of the coalition with an emphasis on risk-related outcomes. Additionally, integrating consensus auctions into the coalition formation framework allows our approach to accommodate assignments for individual robot-task pairings, thus preserving the stability of individual robotic decision autonomy within the coalition structure and assignment distribution. Simulative analyses on a prototypical parking facility layout confirm that our algorithm achieves Nash equilibrium within the coalition structure in polynomial time and demonstrates significant scalability. Compared to competing algorithms, our proposal exhibits superior performance in resilience, task execution efficiency, and reduced overall task completion times. The results demonstrate that our approach is an effective strategy for solving the scheduling challenges encountered by multi-robot systems operating in complex environments. IEEE

关键词： Polynomial approximation

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AutYOLO-ATT: an attention-based YOLOv8 algorithm for early autism diagnosis through facial expression recognition

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Neural Computing and Applications 2024年第27期36卷 17199-17219页

作者： Hosney, Reham Talaat, Fatma M. El-Gendy, Eman M. Saafan, Mahmoud M. Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt Faculty of Computer Science and Engineering New Mansoura University Gamasa35712 Egypt Nile Higher Institute for Engineering and Technology Mansoura Egypt

Autism Spectrum Disorder (ASD) is a developmental condition resulting from abnormalities in brain structure and function, which can manifest as communication and social interaction difficulties. Conventional methods for diagnosing ASD may not be effective in the early stages of the disorder. Hence, early diagnosis is crucial to improving the patient's overall health and well-being. One alternative and effective method for early autism diagnosis is facial expression recognition since autistic children typically exhibit distinct facial expressions that can aid in distinguishing them from other children. This paper provides a deep convolutional neural network (DCNN)-based real-time emotion recognition system for autistic kids. The proposed system is designed to identify six facial emotions, including surprise, delight, sadness, fear, joy, and natural, and to assist medical professionals and families in recognizing facial expressions in autistic children for early diagnosis and intervention. In this study, an attention-based YOLOv8 (AutYOLO-ATT) algorithm for facial expression recognition is proposed, which enhances the YOLOv8 model's performance by integrating an attention mechanism. The proposed method (AutYOLO-ATT) outperforms all other classifiers in all metrics, achieving a precision of 93.97%, recall of 97.5%, F1-score of 92.99%, and accuracy of 97.2%. These results highlight the potential of the proposed method for real-world applications, particularly in fields where high accuracy is essential. © The Author(s) 2024.

关键词： Diagnosis

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Machine Learning-Based Calibration Approaches for Single-Beam and Multiple-Beam Distance Sensors

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IEEE Sensors Journal 2024年第1期24卷 975-983页

作者： Syu, Jia-Hao Lin, Jerry Chun-Wei Biernacki, Piotr Ziebinski, Adam National Taiwan University Department of Computer Science and Information Engineering Taipei10617 Taiwan Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Distributed Systems and Informatics Devices Gliwice44-100 Poland Silesian University of Technology Department of Distributed Systems and Informatic Devices Gliwice44-100 Poland

Sensors are the foundation to facilitate smart cities, smart grids, and smart transportation, and distance sensors are especially important for sensing the environment and gathering information. Researchers have developed various methods to calibrate distance sensors;however, they mainly focus on using external information for calibration, which is inefficient and computationally expensive. To alleviate the above problems, we propose machine learning (ML)-based calibration approaches for distance sensors. We develop two calibration mechanisms, where the naive mechanism directly predicts the true distance, while the residual mechanism predicts the residual for calibration. The conventional ML regression algorithms are then adopted to predict the true distance or calibration residual. Experimental results show that the designed residual calibration mechanism outperforms the naive mechanism by reducing the root mean square error (RMSE) by 92.2% and mean absolute error (MAE) by 93.3% in the fitting task. In prediction, the proposed 2-D-residual mechanism achieves an improvement of 78.6% and 65.7% compared to the state-of-the-art method. The experimental results show the effectiveness and generalization of the proposed ML-based calibration method, especially for the 2-D mechanism, which achieves the highest robustness in all fitting and prediction tasks. © 2001-2012 IEEE.

关键词： Calibration

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Urban Electric Vehicle Charging Station Placement Optimization with Graylag Goose Optimization Voting Classifier

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computers, Materials & Continua 2024年第7期80卷 1163-1177页

作者： Amel Ali Alhussan Doaa Sami Khafaga El-Sayed M.El-kenawy Marwa M.Eid Abdelhameed Ibrahim Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute for Engineering and TechnologyMansoura35511Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt

To reduce the negative effects that conventional modes of transportation have on the environment,researchers are working to increase the use of electric *** demand for environmentally friendly transportation may be hampered by obstacles such as a restricted range and extended rates of *** establishment of urban charging infrastructure that includes both fast and ultra-fast terminals is essential to address this ***,the powering of these terminals presents challenges because of the high energy requirements,whichmay influence the quality of *** the maximum hourly capacity of each station based on its geographic location is necessary to arrive at an accurate estimation of the resources required for charging *** is vital to do an analysis of specific regional traffic patterns,such as road networks,route details,junction density,and economic zones,rather than making arbitrary conclusions about traffic *** vehicle traffic is simulated using this data and other variables,it is possible to detect limits in the design of the current traffic engineering ***,the binary graylag goose optimization(bGGO)algorithm is utilized for the purpose of feature ***,the graylag goose optimization(GGO)algorithm is utilized as a voting classifier as a decision algorithm to allocate demand to charging stations while taking into consideration the cost variable of traffic *** on the results of the analysis of variance(ANOVA),a comprehensive summary of the components that contribute to the observed variability in the dataset is *** results of the Wilcoxon Signed Rank Test compare the actual median accuracy values of several different algorithms,such as the voting GGO algorithm,the voting grey wolf optimization algorithm(GWO),the voting whale optimization algorithm(WOA),the voting particle swarm optimization(PSO),the voting firefly algorithm(FA),and the voting genetic algori

关键词： Electric vehicle graylag goose optimization metaheuristics optimization machine learning

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