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Automated Disassembly Sequence Prediction for Industry 4.0 Using Enhanced Genetic Algorithm

computers, Materials & Continua 2021年第11期69卷 2531-2548页

作者： Anil Kumar Gulivindala M.V.A.Raju Bahubalendruni R.Chandrasekar Ejaz Ahmed Mustufa Haider Abidi Abdulrahman Al-Ahmari Department of Mechanical Engineering Industrial Robotics and Manufacturing Automation LaboratoryNational Institute of TechnologyKariakal609609India Department of Computer Science Engineering National Institute of TechnologyKariakal609609India Raytheon Chair for Systems Engineering Advanced Manufacturing InstituteKing Saud UniversityRiyadh11421Saudi Arabia Industrial Engineering Department College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia

The evolution of Industry 4.0 made it essential to adopt the Internet of Things(IoT)and Cloud Computing(CC)technologies to perform activities in the new age of *** technologies enable collecting,storing,and retrieving essential information from the manufacturing *** collected at sites are shared with others where execution automatedly *** obtained information must be validated at manufacturing to avoid undesirable data losses during the de-manufacturing ***,information sharing from the assembly level at the manufacturing stage to disassembly at the product end-of-life state is a major *** current research validates the information optimally to offer a minimum set of activities to complete the disassembly *** optimal disassembly sequence plan(DSP)can possess valid information to organize the necessary actions in ***,finding an optimal DSP is complex because of its combinatorial *** genetic algorithm(GA)is a widely preferred artificial intelligence(AI)algorithm to obtain a near-optimal solution for the DSP *** converging nature at local optima is a limitation in the traditional *** study improvised the GA workability by integrating with the proposed priori crossover *** optimality function is defined to reduce disassembly effort by considering directional changes as *** enhanced GA method is tested on a real-time product to evaluate the *** obtained results reveal that diversity control depends on the operators employed in the disassembly *** proposed method’s solution can be stored in the cloud and shared through IoT devices for effective resource allocation and disassembly for maximum recovery of the *** effectiveness of the proposed enhanced GA method is determined by making a comparative assessment with traditional GA and other AI methods at different population sizes.

关键词： Automation internet of things disassembly sequence planning priori cross over operator enhanced GA disassembly predicates

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Resisting Noise in Pseudo Labels: Audible Video Event Parsing With Evidential Learning

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IEEE Transactions on Neural Networks and Learning systems 2024年第6期36卷 10874-10888页

作者： Xun Jiang Xing Xu Liqing Zhu Zhe Sun Andrzej Cichocki Heng Tao Shen Center for Future Media School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science and Technology Tongji University Shanghai China Graduate School of Medicine Faculty of Health Data Science Juntendo University Tokyo Japan Systems Research Institute Polish Academy of Sciences Warsaw Poland Nicolaus Copernicus University (UMK) Toruń Poland

Perceiving temporal events and discriminating their modality types in audible videos, which is also called audio-visual video parsing (AVVP), is becoming a research hotspot in multimodal video understanding. The AVVP task generally follows weakly supervised learning settings, since only video-level labels are provided. Most existing works usually generate modalitywise pseudo labels (PLs) first and then learn to parse audio or visual events from the audible videos. However, this paradigm inevitably results in two defects: 1) the generated PLs for each modality are not fully reliable, which may confuse models if they are adopted as supervision signals for discriminating modalities; and 2) the absence of temporal annotations increases the ambiguities in localizing foregrounds in videos, furtherly causing models prone to being disturbed by noisy labels. To tackle these problems, we propose a novel AVVP framework termed noise-resistant event parsing (NREP), which introduces evidential deep learning (EDL) to overcome the limitations of noisy pseudo supervision. Specifically, our NREP framework consists of three key components: 1) modalitywise evidential learning (MEL) that discriminates the modality-class dependency; 2) temporalwise evidential learning (TEL) that explores meaningful foregrounds; and 3) foreground-background consistency learning (FBCL) for collaborating two evidential learning branches above. Through perceiving meaningful video content and learning evidence for modality dependencies, our method suppresses the disturbance of noise in generated PLs thus achieving remarkable performance with different PL generation strategies. We evaluate our NREP method on two AVVP benchmark datasets and demonstrate it consistently to establish new state-of-the-art. Our implementation codes are available at https://***/CFM-MSG/NREP .

关键词： Noise Visualization Noise measurement Annotations Location awareness Sun Logic Deep learning Semantics computer science

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Analyzing and Mitigating Common Vulnerabilities in Smart Contracts in Web3 Ecosystem: A Comprehensive Study

Analyzing and Mitigating Common Vulnerabilities in Smart Con...

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Data, Decision and systems (ICDDS), IEEE International Conference on

作者： Rajendra Hegadi Syam Sankar Karthik Akella Koushik Reddy Pavan Kumar C Department of Data Science and Intelligent Systems Indian Institute of Information Technology Dharwad Karnataka India Department of Computer Science and Engineering Indian Institute of Information Technology Dharwad Karnataka India

As we enter a new era of decentralized applications and blockchain technology, the issue of Web3 security becomes crucial and requires immediate attention. Given the growing popularity of using smart contracts in web3, it is vital to ensure the protection of digital assets and user data online. This paper aims to examine the different security risks associated with smart contracts in web3 and propose measures to mitigate them. This research article delves into the identification, causes, and potential mitigation steps for common vulnerabilities found in smart contracts in web3. Specifically, we focus on six major vulnerabilities: overflow and underflow, access control vulnerabilities, re-entrancy attacks, price manipulation attacks, signature reply attacks and self-destruct attacks. By comprehensively discussing these vulnerabilities and offering mitigation strategies, this research aims to disseminate the security vulnerabilities in smart contracts in Web3 ecosystem and provides a path way for adoption of blockchain technology.

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Geometric structure and transversal logic of quantum Reed–Muller codes

arXiv

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arXiv 2024年

作者： Barg, Alexander Coble, Nolan J. Hangleiter, Dominik Kang, Christopher Joint Center for Quantum Information and Computer Science University of Maryland & NIST United States Institute for Systems Research Department of Electrical & Computer Engineering University of Maryland United States Department of Computer Science University of Maryland College Park United States Simons Institute for the Theory of Computing University of California at Berkeley United States Department of Computer Science University of Chicago United States

Designing efficient and noise-tolerant quantum computation protocols generally begins with an understanding of quantum error-correcting codes and their native logical operations. The simplest class of native operations are transversal gates, which are naturally fault-tolerant. In this paper, we aim to characterize the transversal gates of quantum Reed–Muller (RM) codes by exploiting the well-studied properties of their classical counterparts. We start our work by establishing a new geometric characterization of quantum RM codes via the Boolean hypercube and its associated subcube complex. More specifically, a set of stabilizer generators for a quantum RM code can be described via transversal X and Z operators acting on subcubes of particular dimensions. This characterization leads us to define subcube operators composed of single-qubit π/2k Z-rotations that act on subcubes of given dimensions. We first characterize the action of subcube operators on the code space: depending on the dimension of the subcube, these operators either (1) act as a logical identity on the code space, (2) implement non-trivial logic, or (3) rotate a state away from the code space. Second, and more remarkably, we uncover that the logic implemented by these operators corresponds to circuits of multi-controlled-Z gates that have an explicit and simple combinatorial description. Overall, this suite of results yields a comprehensive understanding of a class of natural transversal operators for quantum RM codes. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum computers

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Mitigating Insecure Outputs in Large Language Models(LLMs): A Practical Educational Module

Mitigating Insecure Outputs in Large Language Models(LLMs): ...

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IEEE Annual International computer software and Applications Conference (COMPSAC)

作者： MD Abdul Barek Md Mostafizur Rahman Shapna Akter A.B.M Kamrul Islam Riad Md Abdur Rahman Hossain Shahriar Akond Rahman Fan Wu Department of Intelligent Systems and Robotics University of West Florida USA Dept. of Cybersecurity and Information Technology University of West Florida USA Center for Cybersecurity University of West Florida USA Computer Science and Software Engineering Auburn University USA Dept. of Computer Science Tuskegee University USA

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

Large Language Models (LLMs) have extensive ability to produce promising output. Nowadays, people are increasingly relying on them due to easy accessibility, rapid and outstanding outcomes. However, the use of these results without appropriate scrutiny poses serious security risks, particularly when they are integrated with other software, APIs, or plugins. This is because the LLM outputs are highly dependent on the prompts they receive. Therefore, it is essential to carefully clean these outputs before using them in additional software environments. This paper is designed to teach students about the potential dangers of contaminated LLM output within the context of web development through prelab, hands-on, and postlab experiences. Hands-on lab provides practical guidance on how to handle LLM vulnerabilities to make applications safe with some real-world examples in Python. This approach aims to provide students with a deeper understanding of the precautions necessary to ensure software against the vulnerabilities introduced by LLM output.

关键词： Training Navigation Large language models Computational modeling software Security software development management

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Spatiotemporal Traffic Analysis: Deep Hybrid Approach for Resource and Cyberattacks Prediction

Spatiotemporal Traffic Analysis: Deep Hybrid Approach for Re...

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International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Hyeonmin Lee Dongchan Kim Donggyu Beom Gyurin Byun Van-Vi Vo Hyunseung Choo Department of AI Systems Engineering Sungkyunkwan University Suwon Republic of Korea Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Republic of Korea Convergence Research Institute Sungkyunkwan University Suwon Republic of Korea Department of Computer Science and Engineering Sungkyunkwan University Suwon Republic of Korea

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

This study proposes a hybrid deep learning approach to address the complexity and dynamic characteristics of modern network environments. The research integrates Graph Neural Networks (GNNs) and Convolutional Long and Short-Term Memory (ConvLSTM) networks to predict future network traffic and detect cyberattacks. The model was trained and evaluated on the CICIDS 2018 dataset which includes various types of normal and malicious traffic. Grid-based representations were created from the dataset. The attack type of each timestamp were visualized using distinct colors to the grid images. These images are sued as input of ConvLSTM model. Protocol and destination port are modeled as nodes in a graph, with volume of data flow represented as weighted edges. This graph-based representation is designed to capture unique traffic patterns of specific services and enhance the analysis of spatial and temporal features. Experimental results show a 13% increase in macro F1 score and a 4% increase in weighted F1 score, highlighting the effectiveness of the proposed method. This research highlights its potential to enable proactive responses through the prediction of potential cyberattack occurrences.

关键词： Solid modeling Protocols Image color analysis Image edge detection Telecommunication traffic Traffic control Graph neural networks Spatiotemporal phenomena Information management computer crime

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Virtual Reality-Based Teleoperation System for Robot Forklifts 25

Virtual Reality-Based Teleoperation System for Robot Forklif...

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25th IEEE International Conference on Autonomous Robot systems and Competitions, ICARSC 2025

作者： Couto, Manuel B. Petry, Marcelo R. Mendes, Abel Silva, Manuel F. INESC TEC - Institute for Systems and Computer Engineering Technology and Science CRIIS - Centre for Robotics in Industry and Intelligent Systems Porto4200-465 Portugal University of Porto FEUP - Faculty of Engineering Porto4200-465 Portugal Flowbotic Coimbra3030-199 Portugal ISEP - Instituto Superior de Engenharia do Porto Instituto Politécnico do Porto Porto4249-015 Portugal

ISBN: (纸本)9798331538606

The growing reliance on e-commerce and the demand for efficient intralogistics operations have increased the need for automation, while labour shortages continue to pose significant challenges. When combined with the inherent risks of forklift operation, this circumstance prompted businesses to look for robotic solutions for intralogistics tasks. However, robots are still limited when they come across situations that are outside of their programming scope and often need assistance from humans. To achieve the long-term goal of enhancing intralogistics operation, we propose the development of a virtual reality-based teleoperation system that allows remote operation of robot forklifts with minimal latency. Considering the specificities of the teleoperation process and network dynamics, we conduct detailed modelling to analyse latency factors, optimise system performance, and ensure a seamless user experience. Experimental results on a mobile robot have shown that the proposed teleoperation system achieves an average glass-to-glass latency of 368 ms, with capturing latency contributing to approximately 60% of the total delay. The results also indicate that network os-cillations significantly impact image quality and user experience, emphasising the importance of a stable network infrastructure. © 2025 IEEE.

关键词： Mobile robots

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Theory of phase reduction for systems with higher-order interactions: a route to Kuramoto models on hypergraphs

arXiv

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arXiv 2025年

作者： León, Iván Muolo, Riccardo Hata, Shigefumi Nakao, Hiroya Department of Applied Mathematics and Computer Science Universidad de Cantabria Santander39005 Spain Department of Systems and Control Engineering Tokyo Institute of Technology Tokyo152-8552 Japan Graduate School of Science and Engineering Kagoshima University Kagoshima890-0065 Japan Research Center for Autonomous Systems Materialogy Institute of Innovative Research Tokyo Institute of Technology Yokohama226-8501 Japan

Phase reduction is a powerful technique in the study of nonlinear oscillatory systems. Under certain assumptions, it allows us to describe each multidimensional oscillator by a single phase variable, giving rise to simple phase models such as the Kuramoto model. Classically, the method has been applied in the case where the interactions are only pairwise (two-body). However, increasing evidence shows that interactions in real-world systems are not pairwise but higher-order, i.e., many-body. Although synchronization in higher-order systems has received much attention, analytical results are scarce because of the highly nonlinear nature of the framework. In this paper, we fill the gap by presenting a general theory of phase reduction for the case of higher-order interactions. We show that the higher-order topology is preserved in the phase reduced model at the first order and that only odd couplings have an effect on the dynamics when certain symmetries are present. Additionally, we show the power and ductility of the phase reduction approach by applying it to a population of Stuart-Landau oscillators with an all-to-all configuration and with a ring-like hypergraph topology;in both cases, only the analysis of the phase model can provide insights and analytical results. © 2025, CC BY-NC-ND.

关键词： Topology

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A Comprehensive Approach to Identify Stroke based on Real-time Medical Data

A Comprehensive Approach to Identify Stroke based on Real-ti...

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Cognitive Robotics and Intelligent systems (ICC - ROBINS), International Conference on

作者： M. Maragatharajan Shanmugapriya S M. B. Sudhan Deeptha R. Sankar Muthu Paramasivam School of Computing Science and Engineering VIT Bhopal University Kothrikalan Madhya Pradesh India Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Srivilliputhur Tamilnadu India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai Tamilnadu Department of Information Technology SRM Institute of Science and Technology Ramapuram Chennai Tamilnadu India Distinguished Software Engineer Palo Alto Networks Inc Santa Clara California USA

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372755

Strokes or cardiovascular disorders continue to be a major global health concern. Proactive intervention and timely detection greatly reduce the morbidity and death related to strokes. The goal of this research is to build a prediction model using machine learning techniques to assess a person’s risk of stroke based on a variety of sociodemographic and health characteristics. The dataset used in this study consists of a large collection of medical records. It contains important details including age, gender, BMI, smoking habits, heart disease, hypertension, and the type of residential property. Using this information, a Decision Tree Classifier is trained to predict the likelihood of strokes, enabling early detection and customized treatment plans. A Decision Tree Classifier with a predetermined maximum depth is used in the proposed methodology’s rigorous data preprocessing, which includes handling missing values, encoding categorical features, and developing models. Both the training and testing datasets are used to evaluate the model’s performance using evaluation measures including accuracy, precision, and recall. The model’s insights provide important characteristics that influence the prediction of stroke risk, assisting medical professionals in focused risk assessment and preventive intervention. The proposed method has the potential to improve healthcare practices by facilitating prompt interventions and personalized treatment for stroke risk individuals. The proposed model has resulted in 86.78% accuracy for this dataset by using the traditional machine learning algorithms. This work highlights the value of predictive modelling in the healthcare industry by demonstrating how it may improve early detection techniques and lessen the impact of strokes on public health.

关键词： Training Machine learning algorithms Machine learning Predictive models Stroke (medical condition) Real-time systems Risk management

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Robust Domain Adaptive Object Detection with Unified Multi-Granularity Alignment

arXiv

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arXiv 2023年

作者： Zhang, Libo Zhou, Wenzhang Fan, Heng Luo, Tiejian Ling, Haibin The State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China The University of Chinese Academy of Sciences China The Department of Computer Science and Engineering University of North Texas United States The Department of Computer Science Stony Brook University United States

Domain adaptive detection aims to improve the generalization of detectors on target domain. To reduce discrepancy in feature distributions between two domains, recent approaches achieve domain adaption through feature alignment in different granularities via adversarial learning. However, they neglect the relationship between multiple granularities and different features in alignment, degrading detection. Addressing this, we introduce a unified multi-granularity alignment (MGA)-based detection framework for domain-invariant feature learning. The key is to encode the dependencies across different granularities including pixel-, instance-, and category-levels simultaneously to align two domains. Specifically, based on pixel-level features, we first develop an omni-scale gated fusion (OSGF) module to aggregate discriminative representations of instances with scale-aware convolutions, leading to robust multi-scale detection. Besides, we introduce multi-granularity discriminators to identify where, either source or target domains, different granularities of samples come from. Note that, MGA not only leverages instance discriminability in different categories but also exploits category consistency between two domains for detection. Furthermore, we present an adaptive exponential moving average (AEMA) strategy that explores model assessments for model update to improve pseudo labels and alleviate local misalignment problem, boosting detection robustness. Extensive experiments on multiple domain adaption scenarios validate the superiority of MGA over other approaches on FCOS and Faster R-CNN detectors. Code will be released at https://***/tiankongzhang/MGA. Copyright © 2023, The Authors. All rights reserved.

关键词： Object detection

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