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Industrial Cyber-Physical systems (ICPS)

作者： Kangyi Cai Jian Liu Edward Mwanza Mahelet G. Fikru Hongyan Ma Donald C. Wunsch Department of Civil Architectural and Environmental Engineering Missouri University of Science and Technology Rolla USA Kummer Institute Center for Artificial Intelligence and Autonomous Systems Missouri University of Science and Technology Rolla USA Department of Computer Science Missouri University of Science and Technology Rolla USA Department of Economics Missouri University of Science and Technology Rolla USA

ISBN: (数字)9798350363012

ISBN: (纸本)9798350363029

The utilization of supplementary cementitious materials (SCMs) subjected to carbonation processing represents a viable strategy to mitigate anthropogenic CO 2 emissions associated with concrete production, potentially contributing to the achievement of carbon neutrality. However, existing studies have limitations in effectively predicting the varying carbonation capacities of different SCMs, a gap that this research aims to address. Recent research efforts focused on the carbonation of waste-material-sourced SCMs are reviewed, along with a comparative discussion on diverse carbonation methods. A detailed data set encapsulating the properties of SCMs, and carbonation configurations was compiled. At the same time, six ensemble learning models were developed and evaluated, with a particular emphasis on the CatBoost model due to its exemplary performance in predicting the carbonation capacity of SCMs. This study suggests a promising direction for optimizing carbonation processes across different types of SCMs, underscoring their potential in sustainable concrete production.

关键词： Cement industry Production Predictive models Cyber-physical systems Carbon neutral Ensemble learning

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The Deep Analysis of Accuracy of Performance Level of NBC Algorithm for Diagnosing BC

The Deep Analysis of Accuracy of Performance Level of NBC Al...

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

作者： P. Valarmathi M Ramchander Sampathirao Suneetha P. Sasirekha Saad Khudhur Mohammed Ahmed Hussain Department of CSE Mookambigai College of Engineering Department of MCA Chaitanya Bharathi Institute of Technology Hyderabad Department of Computer Science & Systems Engineering Andhra University College of Engineering Andhra University Andhra Pradesh Department of Electrical and Electronics Engineering M.Kumarasamy College of Engineering Karur Collage of Pharmacy National University of Science and Technology Dhi Qar Iraq Al-Farahidi university Baghdad Iraq

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

These days, breast cancer is the foremost predominant cancer among ladies around the world and one of the driving causes of cancer-related mortality. In this ponder, an existing Observation, The study of disease transmission, and Conclusion Comes about (Diviner) dataset is utilized to assess the survival rate of breast cancer patients. Numerous of the profound and machine learning strategies accessible nowadays are so great that they can illuminate a wide course of categorization issues based on precise consider of large-scale datasets. These advances are broadly utilized in most biomedical investigate disciplines. Careful information planning, counting both investigation and visualization, offers pivotal bits of knowledge to support basic decision-making within the field. In this article, Soothsayer breast cancer information is classified utilizing machine learning based strategies. Furthermore, a two-step highlight choice strategy utilizing Foremost Component Examination and Change Edge is utilized to extricate pertinent highlights from the dataset. The dataset is classified utilizing Administered and Outfit learning approaches, counting Ada Boosting, XG Boosting, Incline Boosting, Naive Bayes varieties, and Choice Tree calculations, once the chosen highlights have been removed from the breast cancer dataset. The diverse machine learning techniques are totally overviewed with the utilize of train-test portion and k fold cross-validation. In specific, the Choice Tree calculation works exceptionally well, showing an outstandingly tall accuracy rate in both the train-test part and cross-validation procedures. Subsequently, for the Diviner Breast Cancer dataset, this inquire about illustrates the predominance of the choice Tree over the other elective approaches in Administered and Outfit learning procedures and gives a valuable knowledge into moving forward clinical choice and expectation.

关键词： Visualization Accuracy Decision making Boosting Breast cancer Bayes methods Planning

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Generalized Correspondence Matching via Flexible Hierarchical Refinement and Patch Descriptor Distillation

arXiv

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arXiv 2024年

作者： Han, Yu Long, Ziwei Zhang, Yanting Wu, Jin Fang, Zhijun Fan, Rui The School of Computer Science and Technology Donghua University Shanghai201620 China The Department of Electronics and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Hong Kong SAR Hong Kong Hong Kong Group The College of Electronics & Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China

Correspondence matching plays a crucial role in numerous robotics applications. In comparison to conventional hand-crafted methods and recent data-driven approaches, there is significant interest in plug-and-play algorithms that make full use of pre-trained backbone networks for multi-scale feature extraction and leverage hierarchical refinement strategies to generate matched correspondences. The primary focus of this paper is to address the limitations of deep feature matching (DFM), a state-of-the-art (SoTA) plug-and-play correspondence matching approach. First, we eliminate the pre-defined threshold employed in the hierarchical refinement process of DFM by leveraging a more flexible nearest neighbor search strategy, thereby preventing the exclusion of repetitive yet valid matches during the early stages. Our second technical contribution is the integration of a patch descriptor, which extends the applicability of DFM to accommodate a wide range of backbone networks pre-trained across diverse computer vision tasks, including image classification, semantic segmentation, and stereo matching. Taking into account the practical applicability of our method in real-world robotics applications, we also propose a novel patch descriptor distillation strategy to further reduce the computational complexity of correspondence matching. Extensive experiments conducted on three public datasets demonstrate the superior performance of our proposed method. Specifically, it achieves an overall performance in terms of mean matching accuracy of 0.68, 0.92, and 0.95 with respect to the tolerances of 1, 3, and 5 pixels, respectively, on the HPatches dataset, outperforming all other SoTA algorithms. Our source code, demo video, and supplement are publicly available at ***/GCM. © 2024, CC BY.

关键词： Semantics

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Performance Analysis of Energy and Secure Efficient Routing for Wireless Sensor Networks

Performance Analysis of Energy and Secure Efficient Routing ...

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Decision Aid sciences and Application (DASA), International Conference on

作者： Amjed Abbas Ahmed Zainab Mohammed Hussein Ali Hamza Najim Khattab M. Ali Alheeti Nurhizam Safie Mohd Satar Aisha Hassan Abdallah Hashim Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia Department of Computer Techniques Engineering Imam AlKadhum College (IKC) Baghdad Iraq Department of Computer Science Information System University of Technology Imam AIKadhum College (IKC) Baghdad Iraq Department of Computer Technical Engineering Imam Al-Kadhum College (IKC) Al-Diwaniyah Iraq Computer Networking Systems Department College of Computer and Information Technology University of Anbar Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia (UKM) Bangi Malaysia Department of Electrical and Computer Engineering Faculty of Engineering International Islamic University Malaysia Selangor Malaysia

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

Wireless Sensor Networks (WSNs) are essential for various applications, but their architecture makes them vulnerable to attacks. While traditional security methods like authentication and encryption offer some protection, they often fall short against specific node attacks. This paper introduces the Secure Energy Aware Routing (SEAR) strategy, which uses trust-based assessments to exclude malicious nodes while optimizing energy consumption. SEAR factors in node trust, hop time, and residual energy to ensure reliable data transmission through trustworthy nodes. MATLAB simulations on networks of varying sizes (100–500) compare SEAR with existing methods (EAD and EASER), demonstrating SEAR's superior performance. Results show significant improvements in packet delivery ratio (13-16%), network throughput (19–24 %), energy consumption (32-36 % ), packet loss (16-190/0), and end-to-end latency (21-26 % ), proving SEAR's effectiveness in enhancing WSN security, energy efficiency, and performance.

关键词： Wireless communication Wireless sensor networks Energy consumption Packet loss Routing Throughput Routing protocols Energy efficiency Encryption Security

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PatchZero: Zero-Shot Automatic Patch Correctness Assessment

arXiv

引用

arXiv 2023年

作者： Zhou, Xin Xu, Bowen Kim, Kisub Han, DongGyun Nguyen, Hung Huu Le-Cong, Thanh He, Junda Le, Bach Lo, David The School of Computing and Information Systems Singapore Management University Singapore The Department of Computer Science College of Engineering North Carolina State University United States The Department of Computer Science Royal Holloway University of London United Kingdom The School of Computing and Information Systems The University of Melbourne Melbourne Australia

Automated Program Repair (APR) techniques have shown more and more promising results in fixing real-world bugs. Despite the effectiveness, APR techniques still face an overfitting problem: a generated patch can be incorrect although it passes all tests. It is time-consuming to manually evaluate the correctness of generated patches that can pass all tests. To address this problem, many approaches have been proposed to automatically assess the correctness of patches generated by APR techniques. These approaches are mainly evaluated within the cross-validation setting. However, for patches generated by a new or unseen APR tool, users are implicitly required to manually label a significant portion of these patches (e.g., 90% in 10-fold cross-validation) in the cross-validation setting before inferring the remaining patches (e.g., 10% in 10-fold cross-validation). To mitigate the issue, in this study, we propose LLM4PatchCorrect, the patch correctness assessment by adopting a large language model for code. Specifically, for patches generated by a new or unseen APR tool, LLM4PatchCorrect does not need labeled patches of this new or unseen APR tool for training but directly queries the large language model for code to get predictions on the correctness labels without training. In this way, LLM4PatchCorrect can reduce the manual labeling effort when building a model to automatically assess the correctness of generated patches of new APR tools. To provide knowledge regarding the automatic patch correctness assessment (APCA) task to the large language model for code, LLM4PatchCorrect leverages bug descriptions, execution traces, failing test cases, test coverage, and labeled patches generated by existing APR tools, before deciding the correctness of the unlabeled patches of a new or unseen APR tool. Additionally, LLM4PatchCorrect prioritizes labeled patches from existing APR tools that exhibit semantic similarity to those generated by new APR tools, enhancing the accuracy ach

关键词： Semantics

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Intelligent Gen: An Efficient On-Demand Access Protocol in MANETs using Network Simulator - 2

Intelligent Gen: An Efficient On-Demand Access Protocol in M...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： T. Ganesan N. Murali S. Palani Murugan M. Chandrakumar Peter P. Anand Raj S. Manikandan Department of Computer Science & Engineering E.G.S. Pillay Engineering College Nagapattinam Tamil Nadu India Department of Artificial Intelligence & Data Science E.G.S. Pillay Engineering College Nagapattinam Tamil Nadu India Department of Software Engineering Periyar Maniammai Institute of Science & Technology Thanjavur Tamil Nadu India Department of Computer Science and Business Systems E.G.S. Pillay Engineering College Nagapattinam Tamil Nadu India Department of Information Technology E.G.S. Pillay Engineering College Nagapattinam Tamil Nadu India

Mobile Ad-Hoc network is a self-configuring and multi-hop collection of mobile devices for making successful communication. The wireless and mobile communication process are encountered based on bandwidth, process control and energy conception. Energy conception is considered and dynamically changed to rely on life time of batteries and power. The life time of battery get exhausted very quickly the nodes are moved and changed rapidly across the network. This paper noticed the problem of life time of power, batteries and energy consumption problem in various routing stages. The proposed Intelligent Gen Algorithm is used to get optimal solution for ensuring high robustness, flexibility and Energy consumption factor. It presents a common framework to balance various routing requirements in MANETs dynamically. The following are the main contributions includes, i. introducing Intelligent GEN Algorithms to classify the throughout bandwidth, delay, data ratio. ii. Various routing performance are evaluated and viewed by speed, packetize and simulation time. The comparison evaluated by select routing algorithm using NS-2. The simulation results are recorded and the performance is monitored by using on demand access protocl. The result clearly demonstrated the proposed Intelligent Gen algorithm used in self-organized manner by wireless channel.

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Towards Consumer-Oriented Demand Response systems 7

Towards Consumer-Oriented Demand Response Systems

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7th International Conference on Smart and Sustainable Technologies, SpliTech 2022

作者： Cortez, Vitor Rabelo, Ricardo Carvalho, Arthur Pilloni, Virginia Department of Computer Science Federal University of Piaui PI Teresina Brazil Miami University Department of Information Systems and Analytics Farmer School of Business OxfordOH United States Research Unit of Cagliari Department of Electrical and Electronic Engineering University of Cagliari Cagliari Italy

ISBN: (纸本)9789532901160

Consumer acceptance is a must-have for deploying and managing successful demand response systems. Yet, recent research trends favor automated solutions that consider end consumers as passive agents. This paper explores the notion of considering consumers as active agents that interfere with the optimal plan of a demand response system. We simulate an energy community by implementing a state-of-the-art demand response system. Active consumers are modeled to be inflexible using multi-criteria decision-making methods. Results show that the greater the number of inflexible consumers, the greater the impact on the shape of the community's aggregate demand profile. Further studies should consider the impact of this cost and inflexible consumers in their numerical analysis. © 2022 University of Split, FESB.

关键词： Smart power grids

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Navigation in Time-Varying Densities: An Operator Theoretic Approach

Navigation in Time-Varying Densities: An Operator Theoretic ...

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European Control Conference (ECC)

作者： Shankar A. Deka Umesh Vaidya Dimos V. Dimarogonas Department of Electrical Engineering Computer Science Decision and Control Systems division KTH Royal Institute of Technology Stockholm Sweden Department of Mechanical Engineering Clemson University Clemson South Carolina USA

This paper considers the problem of optimizing robot navigation with respect to a time-varying objective encoded into a navigation density function. We are interested in designing state feedback control laws that lead to an almost everywhere stabilization of the closed-loop system to an equilibrium point while navigating a region optimally and safely (that is, the transient leading to the final equilibrium point is optimal and satisfies safety constraints). Though this problem has been studied in literature within many different communities, it still remains a challenging non-convex control problem. In our approach, under certain assumptions on the time-varying navigation density, we use Koopman and Perron-Frobenius Operator theoretic tools to transform the problem into a convex one in infinite dimensional decision variables. In particular, the cost function and the safety constraints in the transformed formulation become linear in these functional variables. Finally, we present some numerical examples to illustrate our approach, as well as discuss the current limitations and future extensions of our framework to accommodate a wider range of robotics applications.

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Priority-Driven Task Scheduling and Resource Allocation in Cloud Environment

Priority-Driven Task Scheduling and Resource Allocation in C...

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Ubiquitous Networking (UNet), International Conference on

作者： Hicham Ben Alla Said Ben Alla Abdellah Ezzati Mathematics and Computer Science Department LAVETE laboratory Science and Technical Faculty Hassan 1 University Settat Morocco Industrial Systems Engineering Department LAVETE laboratory National School of Applied Sciences - Berrechid Hassan 1 University Settat Morocco

ISBN: (数字)9798350356144

ISBN: (纸本)9798350356151

Currently, efficient task scheduling and resource allocation processes are crucial for optimizing performance in cloud computing environments. In this paper, we introduce a novel approach that prioritizes tasks based on their specific requirements and allocates resources accordingly. Task priority is determined through a scoring threshold that considers different requirements such as deadline, temporal requirements, and task length. To ensure efficient matching, virtual machines (VMs) are categorized into three distinct groups using K-means clustering. Simulations conducted within the CloudSim simulator demonstrate the effectiveness of this method, leading to significant reductions in makespan and energy consumption, while simultaneously improving resource utilization compared to existing algorithms.

关键词： Cloud computing Energy consumption Processor scheduling Computational modeling Clustering algorithms Dynamic scheduling Resource management

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Securing Smart Grids: Deep Reinforcement Learning Approach for Detecting Cyber-Attacks

Securing Smart Grids: Deep Reinforcement Learning Approach f...

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International Conference on Smart Communications and Networking (SmartNets)

作者： Ahmed T. El-Toukhy Islam Elgarhy Mahmoud M. Badr Mohamed Mahmoud Mostafa M. Fouda Mohamed I. Ibrahem Fathi Amsaad Department of Electrical and Computer Engineering Tennessee Tech. University Cookeville TN USA Department of Electrical Engineering Faculty of Engineering Al-Azhar University Cairo Egypt Department of Computer Systems Faculty of Computer and Information Sciences Ain Shams University Egypt Department of Network and Computer Security: Cybersecurity SUNY Polytechnic Institute Utica NY USA Department of Electrical Engineering Faculty of Engineering at Shoubra Benha University Cairo Egypt Department of Electrical and Computer Engineering Idaho State University Pocatello ID USA Center for Advanced Energy Studies (CAES) Idaho Falls ID USA School of Computer and Cyber Sciences Augusta University Augusta GA USA Department of Computer Science and Engineering Wright State University Dayton OH USA

ISBN: (数字)9798350385328

ISBN: (纸本)9798350385335

Smart meters (SMs) are deployed in smart power grids to monitor customer power consumption and facilitate energy management. However, fraudulent customers can compromise these SMs to manipulate power readings and engage in electricity theft cyber-attacks, resulting in reduced electricity bills. While various machine learning approaches have been employed for detecting such attacks, the potential of reinforcement learning (RL) remains unexplored. To bridge this gap, we propose a deep reinforcement learning (DRL) approach that leverages RL's adapt-ability to dynamic cyber-attacks and consumption patterns. This approach integrates exploration and exploitation mechanisms, enabling optimal decision-making. In this study, we present our approach in two scenarios. Firstly, we develop comprehensive detection models using deep Q networks (DQN) and double deep Q networks (DDQN) with various deep neural network architectures. Secondly, we address the challenges of defending against newly launched cyber-attacks. Extensive experimentation provides strong evidence of the effectiveness of our DRL approach in improving the detection of electricity theft cyber-attacks, as well as its capacity to efficiently adapt and defend against newly launched cyber-attacks.

关键词： Adaptation models Power demand Electricity Power system dynamics Detectors Deep reinforcement learning Smart meters

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