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Annual IEEE systems Conference

作者： Ehsan Adel Rastkhiz Howard Schwartz Ioannis Lambadaris Dept. of System & Computer Engineering Carleton University Ottawa Canada

ISBN: (数字)9798331508180

ISBN: (纸本)9798331508197

This paper introduces a fuzzy set-theoretic approach to environment modeling as an alternative to conventional Occupancy Grid Mapping (OGM). The proposed method generalizes the crisp definitions of map cells and sensor measurements by employing fuzzy sets. Consequently, it utilizes fuzzy inference with interpretable rules instead of binary Bayesian inference to determine occupancy states from sensory data. As a result, the fuzzy mapping technique addresses uncertainty while eliminating assumptions about data distribution or cell occupancy probabilities. A notable advantage of the proposed Fuzzy Occupancy Grid Mapping (FOGM) is its ability to evaluate the overall occupancy states of sub-regions of the environment in a single query, unlike traditional methods that require point-by-point assessments. While this advantage introduces increased computational complexity, the mapping process is designed to ensure real-time operation. Another prominent feature of the FOGM is that it creates continuous maps with smooth occupancy variations as compared to the discrete binary maps the traditional OGM generates. The performance of the proposed technique is validated through experiments involving a mobile robot localized via a motion capture system. The robot collects range data in real-time using a LiDAR sensor to be used for mapping. Results demonstrate that the fuzzy mapping approach effectively and accurately represents the environment while maintaining computational efficiency. Its capability in parallel execution on multiple processing cores further enhances real-time performance.

关键词： Fuzzy logic Fuzzy sets Uncertainty Laser radar Computational modeling Robot sensing systems Real-time systems Computational efficiency Bayes methods Mobile robots

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Lightweight Authentication of Devices in Industrial Control systems 14

Lightweight Authentication of Devices in Industrial Control ...

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14th IEEE International Conference on Software engineering and Service Science, ICSESS 2023

作者： Wang, Long Niu, Siqi Zhai, Henggao Fan, Jing Wang, Lei Engineering Research Institute of China National Computer System Beijing China University of Science and Technology Beijing Department of Computer and Communication Engineering Beijing China Coocaa Network Technology Co. Ltd. Guangdong China

ISBN: (纸本)9798350336269

A security authentication protocol based on number theory research unit (NTRU) for industrial control devices is proposed to address the security of data transmission in industrial control systems and the lack of resistance to attacks of security authentication protocols. Most of the original protocols contain complex encryption and decryption operations, which are difficult to run on industrial control devices with simple structures and cannot resist new types of network attacks. In this paper, the protocol takes advantage of the simple operation of NTRU public key encryption system and sets the authorization center to send only the public key of the device in a variant form to achieve the purpose of resisting new attacks such as quantum attacks and internal attacks, and the anti-attack capability is strengthened. © 2023 IEEE.

关键词： Authentication

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A Fuzzy Set-Theoretic Approach to Occupancy Grid Mapping 19

A Fuzzy Set-Theoretic Approach to Occupancy Grid Mapping

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19th Annual IEEE International systems Conference, SysCon 2025

作者： Rastkhiz, Ehsan Adel Schwartz, Howard Lambadaris, Ioannis Carleton University Dept. of System & Computer Engineering Ottawa Canada

ISBN: (纸本)9798331508180

关键词： Fuzzy rules

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Empowering Human-Machine Communication in Code Generation with NLP 5

Empowering Human-Machine Communication in Code Generation wi...

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5th IEEE International Conference on Recent Trends in computer Science and Technology, ICRTCST 2024

作者： Aswal, Upendra Singh Rao, Pappala Mohan Supreeth, B.R. Manikandan, G. Srinivasan, R. Tiwari, Mohit Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Andhra University College Of Engineering AU College Of Engineering Computer Science and System Engineering AP Visakhapatnam530003 India Oxford Degree and PG College Sir. M. Visveshwaraiah Layout Department of Computer Science Ullal Bangalore India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Thandalam India St. Martins Engineering College Department of Computer Science and Engineering Telangana Secunderabad India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering A-4 Rohtak Road Paschim Vihar Delhi India

ISBN: (纸本)9798350351378

The symbiotic interaction between people and computers in the realm of software development is experiencing a fundamental transition, spurred by improvements in Natural Language Processing (NLP). This study investigates how natural language processing (NLP) is changing the software development process by facilitating better human-machine collaboration throughout the coding phase. Data collection, NLP model creation, and assessment are all part of the rigorous process used in our work, which yields important results. The possibility for improving code quality and efficiency while also democratizing code production is discussed in the context of a fictional situation. Ethical concerns are also highlighted, along with the need to minimize bias in NLP-driven code production. To the benefit of developers, organizations, and society at large, this study not only adds empirical insights but also imagines a future in which software development is more inclusive, efficient, and morally responsible. From improved natural language processing models to real-time feedback loops and multimodal techniques, the future of this transforming discipline promises to further influence the landscape of software development. © 2024 IEEE.

关键词： Software design

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UNet Based onMulti-Object Segmentation and Convolution Neural Network for Object Recognition

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computers, Materials & Continua 2024年第7期80卷 1563-1580页

作者： Nouf Abdullah Almujally Bisma Riaz Chughtai Naif Al Mudawi Abdulwahab Alazeb Asaad Algarni Hamdan A.Alzahrani Jeongmin Park Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Information Technology Department College of Computing and InformaticsSaudi Electronic UniversityRiyadh13316Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Republic of Korea

The recent advancements in vision technology have had a significant impact on our ability to identify multiple objects and understand complex *** technologies,such as augmented reality-driven scene integration,robotic navigation,autonomous driving,and guided tour systems,heavily rely on this type of scene *** paper presents a novel segmentation approach based on the UNet network model,aimed at recognizing multiple objects within an *** methodology begins with the acquisition and preprocessing of the image,followed by segmentation using the fine-tuned UNet ***,we use an annotation tool to accurately label the segmented *** labeling,significant features are extracted from these segmented objects,encompassing KAZE(Accelerated Segmentation and Extraction)features,energy-based edge detection,frequency-based,and blob *** the classification stage,a convolution neural network(CNN)is *** comprehensive methodology demonstrates a robust framework for achieving accurate and efficient recognition of multiple objects in *** experimental results,which include complex object datasets like MSRC-v2 and PASCAL-VOC12,have been *** analyzing the experimental results,it was found that the PASCAL-VOC12 dataset achieved an accuracy rate of 95%,while the MSRC-v2 dataset achieved an accuracy of 89%.The evaluation performed on these diverse datasets highlights a notably impressive level of performance.

关键词： UNet segmentation blob fourier transform convolution neural network

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Skeleton-Based Volumetric Parameterizations for Lattice Structures

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computer Modeling in engineering & Sciences 2023年第4期135卷 687-709页

作者： Long Chen Shuxun Liang Nan Yan Xiangqian Yang Baotong Li School of Mechanical Engineering University of Shanghai for Science and TechnologyShanghai200093China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai200093China Key Laboratory of EducationMinistry for Modern Design&Rotor-Bearing System Xi’an Jiaotong UniversityXi’an710049China

Lattice structures with excellent physical properties have attracted great research *** this paper,a novel volume parametric modeling method based on the skeleton model is proposed for the construction of threedimensional lattice *** skeleton model is divided into three types of *** the corresponding algorithms are utilized to construct diverse types of volume parametric *** unit-cell is assembled with distinct nodes according to the geometric *** final lattice structure is created by the periodic arrangement of *** different types of volume parametric lattice structures are constructed to prove the stability and applicability of the proposed *** quality is assessed in terms of the value of the Jacobian ***,the volume parametric lattice structures are tested with the isogeometric analysis to verify the feasibility of integration of modeling and simulation.

关键词： Lattice structure volumetric parameterization skeleton model node modeling

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Data Science based Recommendation system - An Application of computer Science 14

Data Science based Recommendation System - An Application of...

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Khan, Zeba Rahman, Abdul Applied College Jazan University Department of Computer and Information System Jazan Saudi Arabia Presidency University School of Computer Science and Engineering & Information Science Rajanukunte Yelahanka Bangalore India

Recommender system (RS) has emanated as the most popular application of e-commerce websites. In the e-commerce business, collaborative filtering based RS systems suggest products to the customers and find the interesting things for the users which they may wish to purchase. The success rate of any recommendation system depends upon, information reliability as well as expression of daily life behavior in the consolidated format. The main task is to produce the best ranked list of ‘n-number’ of items for the user’s need. Due to the natural structure, Z-Numbers are more consistent to producing a recommendation list. To solve real life problem, Z-Numbers should be incorporated into decision-making models. However, Online shopping involved multicriteria group decision-making (MCGDM). Z-information persisting some difficulties with MCGDM. Therefore, to enhancing the ability of Z-numbers Complex Fuzzy Sets (CFSs), are employed. Besides this entropy, distance measure, and aggregation operator are combined to produced MCGDM based ranking of customer preference. © Grenze Scientific Society, 2023.

关键词： Recommender systems

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Real Time Flower Identification by Artificial Intelligence 4

Real Time Flower Identification by Artificial Intelligence

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Rahman, Moshiur Joy, Taushik Ahmed Akter, Samia Sattar, Abdus Daffodil International University Department of Management Information System Daffodil Smart City Dhaka Bangladesh North South University Department of Electrical and Computer Engineering Bashundhara Dhaka1229 Bangladesh Daffodil International University Department of Computer Science and Engineering Daffodil Smart City Dhaka Bangladesh

ISBN: (纸本)9798350343670

In the realm of flower-rich Bangladesh, the presence of these blossoms enriches our everyday experiences, whether encountered during leisurely strolls, along railway tracks, or within our gardens. However, the beauty of these flowers often remains unexplored due to our limited knowledge about their names and attributes. To address this, a project was initiated to close this gap and acquaint people with these unfamiliar yet frequently encountered blooms. Our endeavor involves an innovative mobile application that employs real-time camera recognition to identify flowers, powered by neural networks, particularly the Tensorflow-based image classifier on the Android platform. Machine learning's expansive applications in computer science have propelled our interest in this arena, specifically focusing on Convolutional Neural Networks (CNN) and Tensorflow for image classification. While our current application marks the inception, aspiration to further enrich and refine our system for the future. Our ultimate aim is to share the benefits of our work, enabling individuals to gain profound insights into the enchanting floral world that envelops them daily. © 2024 IEEE.

关键词： Artificial Intelligence Data Science Deep Learning Machine Learning Monitoring Smartphone Sensors

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Image Enhancement via Associated Perturbation Removal and Texture Reconstruction Learning

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IEEE/CAA Journal of Automatica Sinica 2024年第11期11卷 2253-2269页

作者： Kui Jiang Ruoxi Wang Yi Xiao Junjun Jiang Xin Xu Tao Lu IEEE the School of Computer Science and Technology Harbin Institute of Technology Zhengzhou Research Institute Harbin Institute of Technology the School of Artificial Intelligence Jianghan University the School of Geodesy and Geomatics Wuhan University the Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology the School of Computer Science and Engineering and also with the Hubei Province Key Laboratory of Intelligent Robot Wuhan Institute of Technology

Degradation under challenging conditions such as rain, haze, and low light not only diminishes content visibility, but also results in additional degradation side effects, including detail occlusion and color distortion. However, current technologies have barely explored the correlation between perturbation removal and background restoration, consequently struggling to generate high-naturalness content in challenging scenarios. In this paper, we rethink the image enhancement task from the perspective of joint optimization: Perturbation removal and texture reconstruction. To this end, we advise an efficient yet effective image enhancement model, termed the perturbation-guided texture reconstruction network(PerTeRNet). It contains two subnetworks designed for the perturbation elimination and texture reconstruction tasks, respectively. To facilitate texture recovery,we develop a novel perturbation-guided texture enhancement module(PerTEM) to connect these two tasks, where informative background features are extracted from the input with the guidance of predicted perturbation priors. To alleviate the learning burden and computational cost, we suggest performing perturbation removal in a sub-space and exploiting super-resolution to infer high-frequency background details. Our PerTeRNet has demonstrated significant superiority over typical methods in both quantitative and qualitative measures, as evidenced by extensive experimental results on popular image enhancement and joint detection tasks. The source code is available at https://***/kuijiang94/PerTeRNet.

关键词： Association learning attention mechanism image enhancement perturbation modeling

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Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 18-36页

作者： Ding Wang Ning Gao Derong Liu Jinna Li Frank L.Lewis IEEE the Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent SystemBeijing Laboratory of Smart Environmental Protectionand Beijing Institute of Artificial IntelligenceBeijing University of TechnologyBeijing 100124China the School of System Design and Intelligent Manufacturing Southern University of Science and TechnologyShenzhen 518055China the Department of Electrical and Computer Engineering University of Illinois at ChicagoChicago IL 60607 USA the School of Information and Control Engineering Liaoning Petrochemical UniversityFushun 113001China the UTA Research Institute the University of Texas at ArlingtonArlington TX 76118 USA

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, ***, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation ***, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

关键词： Adaptive dynamic programming(ADP) advanced control complex environment data-driven control event-triggered design intelligent control neural networks nonlinear systems optimal control reinforcement learning(RL)

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