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18th conference on Computer Science and Intelligence systems, FedCSIS 2023

作者： Czerwinski, Michal Michalak, Marcin Biczyk, Piotr Adamczyk, Blazej Iwanicki, Daniel Kostorz, Iwona Brzeczek, Maksym Janusz, Andrzej Hermansa, Marek Wawrowski, Lukasz Kozlowski, Artur Qed Software Mazowiecka 11/49 Warsaw0-052 Poland Ukasiewicz Research Network - Institute of Innovative Technologies Emag Leopolda 31 Katowice40-189 Poland Efigo Sp. Z O.o. M.Kopernika 8/6 Katowice40-064 Poland Faculty of Mathematics University of Warsaw Banacha 2 Warsaw02-097 Poland Department of Computer Networks and Systems Faculty of Automatic Control University of Technology Electronics and Computer Science Akademicka 16 Gliwice44-100 Poland

ISBN: (纸本)9788396744784

The paper discusses a data science competition centered around the development of an anomaly detection system for IoT devices. The competition utilized a unique environment that allowed for the operation and monitoring of real IoT devices, including scheduling of attacks on these devices. The environment was used to collect the data, which included both normal and attack-induced behavior of IoT devices. The paper presents the background of the competition, the top models submitted, and the competition results. The paper also includes a discussion about restrictions related to the use of synthetic attack data as input for constructing anomaly detection systems. © 2023 Polish Information Processing Society.

关键词： quality control

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MTRNet: An Efficient Hybrid network Model for Surface Defect Detection of Sheet Metal 36

MTRNet: An Efficient Hybrid Network Model for Surface Defect...

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36th International conference on Software Engineering and Knowledge Engineering, SEKE 2024

作者： Hao, Huijuan Zhu, Sijian Chen, Yu Yi, Changle Zhao, Hongge Feng, Yue Yang, Rong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)1891706594

Addressing the challenges posed by the insufficient computational power of low-spec devices to achieve the inference efficiency of prevailing deep learning models, alongside the variability in type and shape within industrial metal sheet datasets, while also catering to the demand for high-precision detection, this paper proposes a novel single-stage detection architecture termed MTRNet. In this paper, we introduce three key enhancements: Firstly, to reduce the number of parameters, we propose a novel Partial Depth Convolution (PDC) structure. By minimizing redundant computations, we aim to enhance the efficiency of spatial feature extraction. Secondly, we propose a Convolution Transformer (CTR) structure that replaces the self-attention module with a convolutional module. This modification addresses the computational inefficiency inherent in the self-attention mechanism of existing Visual Transformers (ViT). Finally, we introduce a novel Attention Convolution Transformer (ACTR) structure to enhance the extraction of global and local feature information. This architecture seamlessly integrates the strengths of both attention and convolution mechanisms, working synergistically to improve performance. Our proposed MTRNet network demonstrates superior detection performance compared to existing industrial standards, achieving a detection accuracy of 81.5% on the NEU-DET dataset. © 2024 Knowledge systems Institute Graduate School. All rights reserved.

关键词： Deep learning

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Anomaly Detection in Industrial quality control with Computer Vision and Deep Learning

Anomaly Detection in Industrial Quality Control with Compute...

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2024 International conference on Distributed systems, Computer networks and Cybersecurity, ICDSCNC 2024

作者： Mary, A. Jenita Umesh, V. Khairnar, Prerana Nilesh Sahana, R. Sangeethaa, S.N. Shanthi, H.J. Srm Institute of Science and Technology Faculty of Science and Humanities Vadapalani City Campus Department of Computer Science and Applications Chennai India Ksou Mysore Department of It Mysuru India Visvesvaraya Institute of Technology Department of Electronics and Telecommunication Engineering Chincholi Nashik India Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Avadi Department of Artificial Intelligence and Machine Learning Chennai India Bannari Amman Institute of Technology Department of Computer Science & Engineering Sathyamangalam India Hindustan Institute of Technology and Science Deemed to Be University Department of Computer Applications Padur India

ISBN: (纸本)9798350375442

This study examines the impact of the dynamic manufacturing environment on companies' efforts to improve customer satisfaction through product quality enhancement, production cost reduction, and sustainability realisation. The research focuses on the current trends and challenges companies face in adapting in response to the dynamic nature of the industrial industry. This study seeks to offer practical recommendations by analyzing these factors for companies to meet the evolving demands of their customers while ensuring long-term success and competitiveness. This study examines the impact of anomaly detection on product quality, specifically focusing on the commonly employed method of visual quality inspection. This study examines the methods of visual quality inspection for products, comparing manual and automatic approaches. This study examines the limitations of manual inspection as a monotonous task, which can result in the oversight of errors and subjective assessments. In order to keep up with the needs of smart production and the advent of Industry 4.0, manufacturing as a whole is actively seeking computerized quality inspection solutions. Innovative inspection systems can be created through the effective use of big data. This research abstract discusses the widespread use of machine learning in automated quality inspection systems to enhance performance and support data analytics methods. This study uses a convolutional neural network architecture to extract complex features and patterns from images. This enables the model to differentiate between normal and abnormal conditions effectively. This research presents a proposed model trained on a diverse dataset, including a comprehensive collection of potential defects and irregularities. This research abstract presents a proposed framework for validating effectiveness through extensive experiments on datasets from diverse industrial domains. This study presents the findings of a research project that evaluated

关键词： Smart manufacturing

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An Improved Support Vector Machine Attack Detection Algorithm for Industry controls System 3

An Improved Support Vector Machine Attack Detection Algorith...

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3rd IEEE International conference on Power, Electronics and Computer Applications, ICPECA 2023

作者： Cui, Shangqing Feng, Dongqin Zhejiang University College of Control Science and Engineering Hangzhou China

ISBN: (纸本)9781665472784

With the application of internet technologies in the industrial field, industrial control systems (ICS) have the possibility of suffering malicious network attacks. Because ICS is widely used in critical infrastructure, designing attack detection algorithms for it can effectively reduce losses. Many papers have built physical models for cyber-attacks against ICS and the corresponding detection algorithms have been proposed. However, the detection performance can be further improved. This paper establishes the simulation attack on the Tennessee Eastman (TE) process and proposes an improved support vector machine (SVM) method to detect integrity attacks. This algorithm uses contribution plots and recursive feature elimination to select features. Compared with the principal component analysis (PCA) feature extraction, the detection accuracy is improved. After adding system dynamics to the algorithm, the time to detect attacks is advanced. © 2023 IEEE.

关键词： Support vector machines

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Artificial Neural network PI-Based Compensator for DC-Link Current control in Grid-Connected Photovoltaic Current Source Inverter

Artificial Neural Network PI-Based Compensator for DC-Link C...

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2023 IEEE International conference on Artificial Intelligence and Green Energy, ICAIGE 2023

作者： Ezzeddine, Kawther Chibani, Radhia Hamouda, Mahmoud Kanaan, Hadi Y. Al-Haddad, Kamal Université de Sousse LATIS-Laboratory of Advanced Technology and Intelligent Systems Écolecole Nationale d'Ingénieurs de Sousse Sousse4023 Tunisia 1008 Tunisia Saint Joseph University Beirut Lebanon MontréalQC Canada

ISBN: (纸本)9798350325539

Conventional proportional integral (PI) controllers are the most, commonly, known and used techniques in grid-connected photovoltaic (PV) applications. Indeed, PI-based controllers are, practically, easy to design and implement. Moreover, they provide good performance, i.e., high-quality output signals, in steady-state conditions. Nevertheless, these controllers are, mainly, designed for the control of linear systems. Therefore, when implemented with non-linear systems, they suffer from slow dynamic response. Accordingly, in this paper, a non-linear and advanced PI-based controller is proposed. The proposed controller consists of an auto-tuned PI-based controller using artificial neural network (ANN). The main objective is to enhance the dynamic response of the DClink inductor current in grid-connected PV current source inverter (CSI) and to ensure that the injected grid current is equal to that generated by the DC-bus, which implies that the entire power provided by PV generator is injected into the grid. A numerical simulation model of the grid-connected PV system, i.e., CSI, PI-controller, and MPPT algorithm, is realized using Simulink and PLECS environments to validate the accuracy of the proposed solutions. The numerical results prove that the ANN auto-tuned PI-based controller leads to superior dynamic response of the DC-link inductor current and better-quality of the injected grid current compared to the conventional PI-based controller. © 2023 IEEE.

关键词： Power quality

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Enhanced Implementation of Intelligent Transportation systems (ITS) based on Machine Learning Approaches 20

Enhanced Implementation of Intelligent Transportation System...

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20th ACS/IEEE International conference on Computer systems and Applications (AICCSA)

作者： Radi, Wafaa El Badawy, Hesham M. Canadian Int Coll CIC Dept Commun & Elect Engn Cairo Egypt Natl Telecommun Inst Network Planning Dept Cairo Egypt

ISBN: (纸本)9798350319439

Intelligent Transportation systems (ITS) present a range of challenges that need to be addressed to ensure successful deployment, seamless integration, robust interconnection, and robust security. These challenges arise from the complex nature of ITS, the multitude of stakeholders involved, and the need to balance technological advancements with privacy and security concerns. Vehicle trajectory prediction is a critical task for improving traffic management and safety. It can be used to develop advanced traffic control systems, enhance autonomous vehicle navigation, and support real-world applications in intelligent transportation systems. This paper presents a detailed comparison and study on predicting vehicle trajectories using the NGSIM dataset, a publicly available dataset of high-quality vehicle trajectory data. A practical data set had been used to extract the main features of ITS, then we implemented two machine learning (ML) models for vehicle trajectory prediction by applying classification and regression tasks of both machine learning models, Random Forest (RF) and Support Vector Machine (SVM). We evaluated the models using classification accuracy, precision, recall, F1-score, mean squared error (MSE), and R-squared. The results show that both models can predict vehicle velocity and type with reasonable accuracy. However, the random forest outperformed the SVM on the regression task, while the SVM outperformed the random forest on the classification task. We also studied key factors that influence the performance of vehicle trajectory prediction models and proposed suggestions for improving their performance.

关键词： Intelligent Transportation systems (ITS) machine learning Support Vector Regression (SVR) Support Vector Classifier (SVC) Random Forest Regressor Random Forest Classifier

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performance Analysis of network Coverage Area in WSN Using Optimization Techniques 2nd

Performance Analysis of Network Coverage Area in WSN Using O...

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2nd International conference on Advances in Data-driven Computing and Intelligent systems, ADCIS 2023

作者： Prabhu, R. Nagarajan, R. Kalis, K.R.N. Kannadhasan, S. Department of Electronics and Communication Engineering Gnanamani College of Technology Tamil Nadu Namakkal India Department of Electrical and Electronics Engineering Gnanamani College of Technology Tamil Nadu Namakkal India Department of Electronics and Communication Engineering Kongunadu College of Engineering and Technology Tamil Nadu Trichy India Department of Electronics and Communication Engineering Study World College of Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9789819995233

Wireless sensor networks (WSNs) are created by deploying various sensor units in harsh environments for target monitoring, as well as by deploying various tiny sensors collectively. The sensor units monitor the data in their vicinity and send it to the sink for processing. However, due to various parameters such as coverage area, in-built sensing and processing units, and so on, the sensor nodes may consume more power when forwarding information to the sink. We concentrate on the existing issues in the coverage control of various sensor models for quantitatively and qualitatively solving the sensor network coverage area. Various optimization techniques such as spotted hyena optimization (SHO), particle swarm optimization (PSO), and moth flame optimization (MFO) are studied and discussed. The trade-off between coverage rate, total number of sensor nodes, and energy consumption was observed, and the techniques’ significance was compared. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： internet of things

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Wireless Communications for internet of Farming: An Early 5G Measurement Study

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IEEE ACCESS 2022年 10卷 105263-105277页

作者： Damsgaard, Sebastian Bro Marcano, Nestor J. Hernandez Norremark, Michael Jacobsen, Rune Hylsberg Rodriguez, Ignacio Mogensen, Preben Aalborg Univ Dept Elect Syst Wireless Commun Networks Sect DK-9220 Aalborgost Denmark Aarhus Univ Dept Elect & Comp Engn Commun Control & Automat DK-8200 Aarhus Denmark Univ Oviedo Dept Elect Engn Area Signal Theory & Commun Gijon 33203 Spain

Data-driven agriculture and internet of Farming (IoF) require reliable communication systems. Nowadays, only some of the key use cases demanded by the agricultural industry verticals get support from multiple state of the art wireless technologies such as 4G, Wi-Fi, or Low Power Wide Area network (LPWAN) technologies, combined with satellite and cloud access. However, the ones demanding very high data rates or very low latency are still not feasible. With 5G, designed for flexible support of Extreme Mobile Broadband (xMBB), Massive Machine-Type Communications (mMTC) and Ultra-reliable Machine-Type Communications (uMTC), more agricultural use cases will be possible. This paper provides a reference list of data-driven agriculture scenarios and use cases with their associated communication requirements, and whose feasibility is evaluated in a live 5G trial performed in a representative rural area scenario in the south of Denmark. The paper details a reference methodology for assessing 5G quality of Service (QoS), including multi-connectivity schemes and reports the empirical 5G performance results, which are put in perspective of the requirements for the different IoF reference scenarios. The empirical results indicate that early 5G deployments are already capable of reliably serving data-driven agriculture vertical use cases such as those related to agricultural logistics or configuration of machinery and diagnostics in 65.8-99% of the cases;but it will be necessary to wait for 5G network upgrades and coming 5G Releases in order to operate the more low latency demanding use cases.

关键词： 5G mobile communication Agriculture Farming Data models Servers quality of service Low latency communication Satellites Reliability engineering Machinery IoF data-driven agriculture 5G quality of service multi-connectivity live measurement trial

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Proceedings - 16th International conference on Signal-Image Technology and internet-Based systems, SITIS 2022

Proceedings - 16th International Conference on Signal-Image ...

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16th IEEE International conference on Signal-Image Technology and internet-Based systems, SITIS 2022

ISBN: (纸本)9781665464956

The proceedings contain 94 papers. The topics discussed include: answering an Amharic language semantic question over interlinked data;query management for a decentralized enterprise knowledge graph;towards interpretable probabilistic classification models for knowledge graphs;a corpus of encoded malware byte information as images for efficient classification;digital health data, a way to take under control the quality during the elaboration processes;machine learning for physical layer security: limitations, challenges and recommendation;implementing passive authentication with enhanced risk-based security;a shared pose regression network for pose estimation of objects from RGB images;few-shot object detection in unseen domains;improving gesture recognition with object tracking and image stabilization to communicate with industrial cobots;neural network based landing assist using remote sensing data;hierarchical DNN-based image segmentation algorithm using texton, superpixels, and layer-adaptive loss functions;and an investigation of the reconstruction capacity of stacked convolutional autoencoders for log-mel-spectrograms.

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Study and Analysis of Various Crop Prediction Techniques in IoT network: An Overview

Study and Analysis of Various Crop Prediction Techniques in ...

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2022 International conference on Automatic control and Intelligent systems

作者： Rajathi, S. Anitha Sahayadhas, Arun Melvin, Allan Vels Inst Sci Technol & Adv Studies VISTAS Sch Engn Dept CSE Chennai Tamil Nadu India Univ Malaysia Perlis Fac Elect Engn Technol Pauh Putra Campus Arau Perlis Malaysia

ISBN: (纸本)9781665495813

In the agriculture domain, the main issues determined are knowledge deficiencies regarding alterations in climate. As each crop poses its own climatic features, but the issues confronted in agriculture are managed using precise farming methods. The precision farming assists in fulfilling the demand for food, maintaining crop productivity, and increasing the yield rate. In India, the emerging needs require sustainable agriculture. The two main issues in agriculture are the selection of crops and altering climatic conditions, which are solved by examining prediction and monitoring techniques, but there is no solution for the crop suggestion. This survey examines several techniques based on crop prediction in the IoT network. This study utilizes 25 research papers focused on several methods, and review of researches based on the classical technique is devised. The assessment is done based on publication year, research technique, implementation tools, performance measures and achievement of the research methodologies towards crop prediction techniques in IoT network. At the end, the research gaps and issues of the existing techniques are devised in such a way that the motivation for developing an effective method for crop prediction techniques in IoT network is revealed.

关键词： Crop prediction internet of Things Smart agriculture Production IoT sensors

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