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Research Square 2023年

作者： Seuffert, Julian Bruno Grassi, Ana Cecilia Perez Ahmed, Hamza Seidel, Roman Hirtz, Gangolf Faculty of Electrical Engineering and Information Technology Chemnitz University of Technology Reichenhainer Str. 70 Chemnitz Saxony09126 Germany Faculty of Computer Science Chemnitz University of Technology Reichenhainer Str. 70 Chemnitz Saxony09126 Germany

Stereo vision is a key technology for 3D scene reconstruction from image pairs. Most approaches process perspective images from commodity cameras. These images, however, have a very limited field of view and only picture a small portion of the scene. In contrast, omnidirectional images, also known as fisheye images, exhibit a much larger field of view and allow a full 3D scene reconstruction with a small amount of cameras if placed carefully. However, omnidirectional images are strongly distorted which make the 3D reconstruction much more sophisticated. Nowadays, a lot of research is conducted on CNNs for omnidirectional stereo vision. Nevertheless, a significant gap between estimation accuracy and throughput can be observed in the literature. This work aims to bridge this gap by introducing a novel set of transformations, namely OmniGlasses. These are incorporated into the architecture of a fast network, i.e., AnyNet, originally designed for scene reconstruction on perspective images. Our network, Omni-AnyNet, produces accurate omnidirectional distance maps with a mean absolute error of around 13 cm at 36.4 fps and is therefore real-time capable. © 2023, CC BY.

关键词： Stereo vision

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A Deep Learning Approach For Faults Recognition of Dissolved Oxygen Sensor in Wastewater Treatment Plants

A Deep Learning Approach For Faults Recognition of Dissolved...

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Liliana Maria Ghinea Mihaela Miron Harsha Ratnaweera Department of Automatic Control and Electrical Engineering Department Faculty of Automation Computers Electrical Engineering and Electronics Galați Romania Department of Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics Galați Romania Faculty of Science and Technology Norwegian University of Life Sciences Oslo Norway

Fault diagnosis in wastewater treatment plants (WWTPs) is important to protect communities and ecosystems from toxic elements discharged into water. In this sense, fault identification of sensors plays an important role as they are the key components of the water plants control, especially because environmental legislation is very strict when referring to failures or anomalies in WWTPs. This paper analyzes the performances of two Deep Learning models, a Feedforward Neural Network (FFNN) and a 1D Convolution Neural Network (1DCNN) for identifying five operating states of the dissolved oxygen (DO) sensor: normal and faulty (bias, stuck, spike and precision degradation faults). The experiments were conducted on the Benchmark Simulator Model No 2 (BSM2) developed by the IWA Task Group. The performance of the Deep Learning (DL) classifiers was evaluated via accuracy, precision, recall, and F1-score metrics. The best overall classification accuracy was obtained by FFNN, 98.32% for training and 98.30% for testing.

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Traffic Queuing Management in the Internet of Things: An Optimized RED Algorithm Based Approach

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computers, Materials & Continua 2021年第1期66卷 359-372页

作者： Abdul Waheed Naila Habib Khan Mahdi Zareei Shahab Ul Islam Latif Jan Arif Iqbal Umar Ehab Mahmoud Mohamed Department of Information Technology Hazara UniversityMansehra21120Pakistan School of Electrical and Computer Engineering Seoul National UniversitySeoul08826Korea Department of Computer Science Institute of Management SciencesPeshawar25000Pakistan Tecnologico de Monterrey School of Engineering and SciencesZapopan45201Mexico Department of Computer Science IQRA National UniversityPeshawar25124Pakistan Department of Electrical Engineering University of Engineering TechnologyPeshawar25120Pakistan Electrical Engineering Department College of EngineeringPrince Sattam Bin Abdulaziz UniversityWadi Addwasir11991Saudi Arabia Electrical Engineering Department Faculty of EngineeringAswan UniversityAswan81542Egypt

Congestion control is one of the main obstacles in cyberspace *** in internet traffic may cause several problems;such as high packet hold-up,high packet dropping,and low packet *** the course of data transmission for various applications in the Internet of things,such problems are usually generated relative to the *** tackle such problems,this paper presents an analytical model using an optimized Random Early Detection(RED)algorithm-based approach for internet traffic *** validity of the proposed model is checked through extensive simulation-based *** analysis is observed for different functions on internet *** performance metrics are taken into consideration,namely,the possibility of packet loss,throughput,mean queue length and mean queue *** sets of experiments are observed with varying simulation *** experiments are thoroughly analyzed and the best packet dropping operation with minimum packet loss is identified using the proposed model.

关键词： Random early detection(RED) active queue management(AQM) quality of service(QOS) throughput

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Medical Image Segmentation Use Convolutional Attention Augmentation TransUNet with Skip Connection Enhancement

Medical Image Segmentation Use Convolutional Attention Augme...

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International Conference on computer Supported Cooperative Work in Design

作者： Qingzhi Zou Jing Zhao Ming Li Ling Chen Lin Yuan Ronghuan Zhang Yushuai Hu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology 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 School of Intelligence and Information Engineering Shandong University of Traditional Chinese Medicine Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

The primary objective of medical image segmentation is the isolation of pathological tissues and distinct organs from medical images, thereby assisting in medical diagnosis. The methods employed for medical image segmentation encompass convolutional neural networks and transformer-based methods. Although the self-attention mechanism in Transformers improves its capability to capture long-range dependencies, it have limitations in learning local (contextual) relationships between pixels. Some previous research has tried to solve this problem by incorporating convolutional layers into the encoder or decoder of transformers, but sometimes feature inconsistencies arise. To better extract local features from images using convolutional neural networks and to fuse low-resolution and high-resolution features from higher-level and lower features, we propose the Convolutional Attention Augmentation TransUNet (CAA-TransUNet) model. In our model, firstly, we propose a convolutional attention augmentation module that enhances both local and global features by suppressing irrelevant background information. Secondly, we have integrated attention gates into the skip connections to aggregate feature information from various stages of the encoder during the upsampling process. Finally, we employ the technique of aggregating the loss of multi-stage features to expedite convergence speed and enhance performance. The experimental results on three public datasets demonstrate that our proposed model significantly outperforms the baseline methods.

关键词： Image segmentation Pathology Computational modeling Aggregates Logic gates Feature extraction Transformers

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Customer Segmentation Using Credit Card Data Analysis

Customer Segmentation Using Credit Card Data Analysis

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International Conference on Software engineering Research, Management and Applications (SERA)

作者： Saikat Raj Santanu Roy Surajit Jana Soumyadip Roy Takaaki Goto Soumya Sen Computer Science and Engineering (Data Science) Future Institute of Engineering and Management Kolkata India A.K. Choudhury School of Information Technology University of Calcutta Kolkata India Faculty of Information Sciences and Arts Toyo University Japan

Customer segmentation is a separation of a market into multiple distinct groups of consumers who share the similar characteristics. Segmentation of market is an effective way to define and meet Customer needs and also to identify the future business plan. Unsupervised machine learning algorithms are suitable to analyze and identify the possible set of customers when the labeled data about the customers are no available. In this research work the spending of different customers who have credit cards are analyzed to segment them into different clusters and also to plan further business improvements based on the different characteristics of these identified clusters.

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Assessing the Accuracy and Ethical Implications of Automated Criminal Detection Using a Deep Learning Approach

Assessing the Accuracy and Ethical Implications of Automated...

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Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering (RMKMATE), International Conference on

作者： Jagadeesan S K. Sujigarasharma Manjula R Vetriselvi T School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore Tamil Nadu India Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of IOT School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamil Nadu India

This study examines the accuracy and ethical implications of using convolutional neural networks (CNN) for automated crime detection. A CNN model was trained on a dataset of criminal mugshots to identify potential criminal behaviour based on facial features. This study analyzed the performance of the model and achieved a high accuracy rate in identifying criminals. However, the ethical implications of automated criminal detection are also explored, including bias, privacy and human rights violations. The findings of this study highlight the need for caution and ethical considerations when implementing automated crime detection technologies. It is important to ensure that such technologies are not used to violate the rights of individuals or perpetuate societal biases.

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Analytical Survey on the Security Framework of Cyber-Physical Systems for Smart Power System Networks

Analytical Survey on the Security Framework of Cyber-Physica...

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2022 International Conference on Cyber Resilience, ICCR 2022

作者： Ali Al-Jumaili, Ahmed Hadi Muniyandi, Ravie Chandren Hasan, Mohammad Kamrul Singh, Mandeep Jit Siaw Paw, Johnny Koh Universiti Kebangsaan Malaysia Faculty of Information Science and Technology Bangi43600 Malaysia University of Fallujah Computer Centre Department Anbar00964 Iraq Universiti Kebangsaan Malaysia Faculty of Engineering and Built Environment Department of Electrical Electronic and System Engineering Selango Bangi43600 Malaysia Universiti Tenaga Nasional Department of Electronic and Communication Engineering Selango Kajang Malaysia

ISBN: (纸本)9781665461221

Cyber-Physical Power System (CPPS) is one of the most critical infrastructure systems due to deep integration between power grids and communication networks. In the power system, cascading failure is spreading more readily in CPPS, even leading to blackouts as well as there are new difficulties with the power system security simulation and faults brought by physical harm or network intrusions. The current study summarized the cross- integration of several fields such as computer and cyberspace security in terms of the robustness of Cyber-Physical Systems, viewed as Interconnected and secure network systems. Therefore, the security events that significantly influenced the power system were evaluated in this study, besides the challenges and future directions of power system security simulation technologies were investigated for posing both challenges and opportunities for simulation techniques of power system security like building a new power system to accelerate the transformation of the existing energy system to a clean, low-carbon, safe, and efficient energy system which is used to assure power system stability through fusion systems that combine the cyber-physical to integrate the battery power station, power generation and renewable energy resources through the internet with the cyber system that contains Smart energy system control and attacks. © 2022 IEEE.

关键词： Cyber Physical System

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Searching for an Accurate Robot Calibration via Improved Levenberg–Marquardt and Radial Basis Function System

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Journal of Field Robotics 2025年第0期

作者： Li, Zhibin Deng, Xun Chen, Tinghui Yang, Yuhang Chen, Linlin Yang, Xiwen Hu, Zhenzhen Hu, Lun Hu, Pengwei Li, Shuai Luo, Xin Xinjiang Technical Institute of Physics & Chemistry Chinese Academy of Sciences Urumqi China School of Software Engineering Chengdu University of Information Technology Chengdu China Dazhou Key Laboratory of Government Data Security Sichuan University of Arts and Science Dazhou China School of Software Engineering Xinjiang University Urumqi China Faculty of Information Technology and Electrical Engineering University of Oulu Oulu Finland VTT-Technical Research Centre of Finland Oulu Finland College of Computer and Information Science Southwest University Chongqing China

Robots are frequently utilized in manufacturing, aviation, and other industries, which enhance industrial production efficiency and quality. Specifically, robots perform high-precision tasks like welding, assembly and material handling, which reduce the intensity of manual labor in factories. In the logistics field, robots automatically sort and deliver goods, thereby speeding up supply chain operations. However, the prolonged operation of robots suffers from a decline in positioning accuracy, which makes them unable to satisfy task requirements. To address this challenging issue, this study designs an efficient calibration system integrating the Levenberg–Marquardt algorithm with fuzzy proportion integration differentiation controller and radial basis function neural network. The innovations of this method include: (1) integrating the fuzzy proportion integration differentiation controller into the updating rules of Levenberg–Marquardt algorithm, which further enhances the identification performance of kinematic errors;(2) adopting the radial basis function neural network to handle the robot dynamic errors, which addresses the complexity of dynamic error sources. Extensively experimental robot positioning points are gathered on an HSR JR680 robot, and then experimental validations are conducted by using the designed calibration system. The experiments indicate that the developed algorithm outperforms these existing advanced algorithms. © 2025 Wiley Periodicals LLC.

关键词： Radial basis function networks

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Expression of Concern for: Exploring the Use of Machine Learning Techniques for Enhancing Real-Time Data Access

Expression of Concern for: Exploring the Use of Machine Lear...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Sourav Rampal Shreenidhi H S Manish Shrivastava Akhilendra Pratap Singh Reena R Santosh D. Kumar Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune INDIA

ISBN: (数字)9798350319125

ISBN: (纸本)9798350319132

Actual-time statistics to date has updated, increasingly vital as the quantity of virtual information maintains updated growth. Effective choice-making depends on the capability to update access and analyze records. Device up-to-date techniques provide the capacity up-to-date enhance actual-time statistics up-to-date by using offering insights from disparate pieces of information. This paper addresses the potential benefits of using a system with date knowledge and updated, up-to-date strategies to broaden extra clever statistics up to date updated systems. It summarizes the contemporary nation of the art and highlights studies in machine learning and up-to-date imaginative and prescient real-time information up-to-date. It additionally discusses present-day challenges and shows viable instructions for future studies. The studies network up-to-date recall how gadgets are up to date know that updated strategies may be used to facilitate real-time get entry updated the maximum precious pieces of facts.

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Numerical Simulations of Static Magnetic Fields with Mu-metal Cage Shielding

Numerical Simulations of Static Magnetic Fields with Mu-meta...

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International Conference on Computational Problems of Electrical engineering (CPEE)

作者： Marek Bajtos Roman Radil Ladislav Janoušek Nhat Dang Department of Electrical Computer and Energy Engineering University of Colorado Boulder Boulder Colorado USA Department of Electromagnetic and Biomedical Engineering Faculty of Electrical Engineering and Information Technology University of Zilina Zilina Slovak Republic

This article presents numerical simulations focused on static magnetic fields, employing a Mu-metal cage for shielding against the geomagnetic field. The objective is to verify, how homogeneous and uniform magnetic field within the magnetic field applicator is created. Square Helmholtz coils are employed to ensure this homogeneity. Observations indicate a 5.3% deviation in the B-field when the coils are supplied with a DC current of 110 mA. In the absence of a DC field supply, a 12% deviation is observed. The impact of extremely low-frequency electromagnetic fields and static magnetic fields on living organisms is well-documented, emphasizing the significance of even small changes and deviations in the magnetic field. This underscores the importance of employing a Mu-metal cage when investigating the effects of these fields. Numerical simulations using specialized software demonstrate that the highly permeable Mu-metal box provides approximately 90% shielding efficiency against the geomagnetic field.

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