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Improving Surveillance Object Detection with Adaptive Omni-Attention over Both Inter-frame and Intra-frame Context 1

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16th Asian Conference on computer Vision, ACCV 2022

作者： Yu, Tingting Chen, Chen Zhou, Yichao Hu, Xiyuan School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Institute of Automation Chinese Academy of Sciences Beijing100190 China

ISBN: (数字)9783031262845

ISBN: (纸本)9783031262838

Surveillance object detection is a challenging and practical sub-branch of object detection. Factors such as lighting variations, smaller objects, and motion blur in video frames affect detection results, but on the other hand, the temporal information and stable background of a surveillance video are major advantages that does not exist in generic object detection. In this paper, we propose an adaptive omni-attention model for surveillance object detection, which effectively and efficiently integrates inter-frame contextual information to improve the detection of low-quality frames and intra-frame attention to suppress false positive detections in the background regions. In addition, the training of the proposed network can converge quickly with less epochs because during multi-frame fusion stage, the pre-trained weights of the single-frame network can be used to update simultaneously in reverse in both single-frame and multi-frame feature maps. The experimental results on the UA-DETRAC and the UAVDT datasets have demonstrated the promising performance of our proposed detector in both accuracy and speed. (Code is available at https://***/Yubzsz/Omni-Attention-VOD.) © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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Disturbance Rejection Target Tracking Control for Snake Robots in the Presence of Obstacles 3

Disturbance Rejection Target Tracking Control for Snake Robo...

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3rd IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2024

作者： Li, Ningwei Chu, Yiming Ren, Shanjun Wang, Chaoli Wang, Gang Li, Peng Institude of Machine Intelligence University of Shanghai for Science and Technology Shanghai China School of Optical Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China

ISBN: (纸本)9798331540319

In this paper, a collision-free target tracking control approach utilizing guided vector field guidance is proposed. In particular, a synthesized vector field is designed to direct the snake robot toward the target while simultaneously navigating around multiple obstacles. In addition, a saturated guidance law is designed to compute a reference angular velocity to prevent damage to the robot joints. A dynamics control law is designed based on a reduced-order structure-modified expansion state observer to compensate for external disturbances and realize the robust target tracking and tracing control of the snake robot. Simulation results show the effectiveness of the proposed guidance and control method. © 2024 IEEE.

关键词： Disturbance rejection

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Vector Logic Analysis of Big Data

Vector Logic Analysis of Big Data

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2023 IEEE East-West Design and Test Symposium, EWDTS 2023

作者： Hacimahmud, Abdullayev Vugar Khakhanova, Hanna Litvinova, Eugenia Azerbaijan State Oil and Industry University Computer Engineering Department Baku Azerbaijan Kharkov National University of Radioelectronics Design Automation Department Kharkov Ukraine

ISBN: (纸本)9798350314847

The paper is focused on identification of patterns and objects using keywords and data. The following problems are considered: searching for object identifiers based on a truth table, where keywords are formed in columns that determine the similarity and difference of objects in the flow;defining the equivalence classes of keywords based on the analysis of truth table columns, built on a digitized data flow;generating a business process model in the form of a logical vector based on a binary matrix of patterns;using a business process model to verify the actions of operators performing regulatory functionality at the enterprise. Transfer of the von Neumann architecture into memory and replacing the powerful read-write processor with logical vector transactions to reduce energy and time costs when processing big data is proposed. Smart data structures and simple algorithms based on read-write transactions for in-memory big data as address computing are being developed. © 2023 IEEE.

关键词： Vectors

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Computationally Efficient VBR-Based Sensitivity Analysis of Induction Motors

Computationally Efficient VBR-Based Sensitivity Analysis of ...

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2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Koti, Hossein Nejadi Valipoor, Zahra Demerdash, Nabeel A. O. Rockwell Automation Inc. Motion Control Team Mequon United States Marquette University Electrical and Computer Engineering Department Milwaukee United States

ISBN: (纸本)9798350316445

Sensitivity analysis is a powerful tool that can be utilized for reducing the size of the design space at hand to explore and significantly reduce the computational burden of the optimization process. This is achieved by 'smart' design variable selection. The term 'smart' indicates fixing the un-conflicting design variables that do not require exploration to their maximum or minimum values, depending on their enhancing or degrading effect on the objective functions and allowing the conflicting design variables to be explored within their allowable dynamic range. Intelligent optimization is a critical part of induction motor design process. In this paper, computationally efficient sensitivity analysis is performed to effectively reduce the number of the design variables that will be actively explored in the optimization process of the induction motors. This goal is achieved through the VBR-based TS-FEA together with Design of Experiment and Response Surface Methodology. In addition, the motor estimated losses, including the calculated core losses are fed to a thermal network model to estimate the temperature of different motor components. © 2023 IEEE.

关键词： Core Losses Induction Motor Temperature Thermal Calibration VBR-based FEA

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Output Linearization of Single-Input Single-Output Fuzzy System to Improve Accuracy and Performance

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computers, Materials & Continua 2023年第5期75卷 2413-2427页

作者： Salah-ud-din Khokhar QinKe Peng Muhammad Yasir Noor Department of Automation Science and Technology(System Engineering Institute) School of Electronics and Information EngineeringXi’an Jiaotong UniversityXi’anShaanxi710049China Department of Computer Science G.C.UniversityLahore54000PunjabPakistan Department of Physics(Electronics) G.C.UniversityLahore54000PunjabPakistan

For fuzzy systems to be implemented effectively,the fuzzy membership function(MF)is essential.A fuzzy system(FS)that implements precise input and output MFs is presented to enhance the performance and accuracy of single-input single-output(SISO)FSs and introduce the most applicable input and output MFs protocol to linearize the fuzzy system’s *** a variety of non-linear techniques,a SISO FS is *** results of FS experiments conducted in comparable conditions are then *** simulated results and the results of the experimental setup agree fairly *** findings of the suggested model demonstrate that the relative error is abated to a sufficient range(≤±10%)and that the mean absolute percentage error(MPAE)is reduced by around 66.2%.The proposed strategy to reduceMAPE using an FS improves the system’s performance and control *** using the best input and output MFs protocol,the energy and financial efficiency of every SISO FS can be improved with very little tuning of *** proposed fuzzy system performed far better than other modern days approaches available in the literature.

关键词： Mean absolute percentage error membership functions relative error fuzzy system

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Digital Educational Resources in Industrial Radiography 7

Digital Educational Resources in Industrial Radiography

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7th International Conference on Information Technologies in engineering Education, Inforino 2024

作者： Kuvshinnikov, Vladimir Kovshov, Evgeny Korchagin, Valery JSC 'Research and Development Institute of Construction Technology - Atomstroy' Research Laboratory of Digital Computer Systems and Automation Moscow Russia Dmitry Mendeleev University of Chemical Technology of Russia Disruptive Engineering School of Chemical Engineering and Machinery Moscow Russia

ISBN: (纸本)9798350371369

Nowadays, the technological level of production is an important factor for all areas of modern economic development and life quality improvement. Neither in the informational, nor in the industrial sphere it is possible to produce in-demand, modern, and therefore high-tech and complex products without the establishment of an appropriate system of quality examination at multiple stages of production. This article deals with the problem of forming a comprehensive educational environment with the use of heterogeneous educational resources in the training of specialists in the radiation type of nondestructive testing. The purpose of the research and development is to address the shortcomings of classroom, remote digital learning, and laboratory practice listed in the article by developing a solution based on virtual reality technologies. The purpose stated lies in the pursuit of intensive solutions to the existing problems. Extensive development paths are not effective in improving the efficiency of training and checking students' knowledge in the context of the rapidly growing demand for nondestructive testing specialists. The concept of a digital twin as an educational resource is considered. The structural approach to the formation of complex educational resources both at the level of organization and at the level of digital product is presented. The structure of interrelationships between data in the formation of a digital twin of a radiographic image is presented. The results of the radiographic image simulation and its quality verification are shown. © 2024 IEEE.

关键词： Nondestructive examination

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A Deep Learning Approach for Faults Recognition of Dissolved Oxygen Sensor in Wastewater Treatment Plants 28

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

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28th IEEE International Conference on Emerging Technologies and Factory automation, ETFA 2023

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

ISBN: (纸本)9798350339918

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. © 2023 IEEE.

关键词： Wastewater treatment

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Mobility and Privacy-aware Computation Offloading with Energy Harvesting in MEC-enabled Networks

Mobility and Privacy-aware Computation Offloading with Energ...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Bi, Jing Niu, Siyu Yuan, Haitao Zhai, Jiahui Zhang, Jia Zhou, MengChu College of Computer Beijing University of Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Southern Methodist University Dept. of Computer Science DallasTX75275 United States New Jersey Institute of Technology Dept. of Electrical and Computer Engineering NewarkNJ07102 United States

ISBN: (纸本)9781665410205

Many new IoT applications have emerged with the fast evolution of 5G and the Internet of Things (IoT). These applications place higher demands on network energy consumption and processing capabilities. Mobile edge computing (MEC) significantly enhances execution efficiency, while energy harvesting (EH) modules further augment the operational features of IoT devices. However, existing studies mainly concentrate on energy consumption and latency problems, often neglecting issues about user mobility and potential privacy leakage within the MEC environment. Therefore, optimizing computation offloading and resource allocation for MEC-enabled IoT networks is essential. This work proposes an innovative architecture with EH for collaborative computing between multiple mobile devices (MDs) and MEC servers. To tackle the problem, this work also proposes an advanced hybrid algorithm named Self-adaptive Bat Optimizer with Genetic operations and individual update of Grey wolf optimizer (SBG2). With SBG2, this work aims to minimize the energy consumption of MDs while providing user mobility and privacy protection. Simulation experiments show that SBG2 reduces energy consumption by 79.15%, 93.20%, and 89.58%, respectively, compared to the other three typical algorithms. © 2024 IEEE.

关键词： Differential privacy

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Evaluation of Convolutional Neural Network Model Architecture Performance

Evaluation of Convolutional Neural Network Model Architectur...

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2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Pradana, Musthofa Galih Khoirunnisa, Hilda Pinastawa, I Wayan Rangga Universitas Pembangunan Nasional Veteran Jakarta Faculty of Computer Science Jakarta Indonesia Bandung Polytechnic for Manufacturing Engineering and Mechatronics Department of Automation Bandung Indonesia

ISBN: (纸本)9798350307306

The identification of image types can be facilitated through the use of algorithms and automation in their detection. In this research, detection is performed on fashion images, which encompass various types and categorizations. The process of identification and detection can utilize the Neural Network algorithm known as Convolutional Neural Network (CNN). The Convolutional Neural Network (CNN) algorithm consists of multiple layers, and its application involves setting values for pixels as well as arranging the sequence of layers within it. In this study, two Convolutional Neural Network (CNN) algorithm models are employed for image classification in the fashion dataset. These two models differ in the order of layers within the Convolutional Neural Network (CNN) and in the values of strides applied for modification. Model 1 employs ZeroPadding2D in the first layer with a smaller stride value of 1 compared to Model 2, which places the Conv2D layer in the first layer and applies strides with 1, 2, and 3 values. This research indicates that Model 1 outperforms Model 2, achieving the highest accuracy in epoch 20 with an accuracy value of 0.9711. This is attributed to the fact that the strides value in the Conv2D layer of Model 1 is set to a smaller value compared to Model 2. © 2023 IEEE.

关键词： Classification (of information)

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3D Modeling for a Flexible Detector in VR Radiography Simulator 7

3D Modeling for a Flexible Detector in VR Radiography Simula...

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7th International Conference on Information Technologies in engineering Education, Inforino 2024

作者： Kuvshinnikov, Vladimir Kovshov, Evgeny Korchagin, Valery Institute of Construction Technology - Atomstroy Research Laboratory of Digital Computer Systems and Automation JSC 'Research and Development' Moscow Russia Dmitry Mendeleev University of Chemical Technology of Russia Disruptive Engineering School of Chemical Engineering and Machinery Moscow Russia

ISBN: (纸本)9798350371369

Digital simulators based on virtual reality expand the range of educational opportunities and improve the quality of learning in various areas of knowledge through deeper "immersion"in the learning process. Attention to the details of the modeled process plays an important role. One of the tasks of interactive simulators is to provide an opportunity to see the relationship between the user's actions and the obtained qualitative and quantitative parameters and quality attributes of the simulated process result. Considered the issue of curved shape detectors application relevance in the practice of penetrating radiation evaluation in general and in industrial radiography in particular. The prerequisites for the creation of flexible (bendable) form of digital radiographic detectors and the features of two generations of practically implemented devices are analyzed. Development of a flexible detector digital twin is considered, models and algorithms for its realization in a virtual reality environment are proposed, and the main results of computational and laboratory experiments as well as the obtained radiographic images are presented. The main task of virtual reality technologies within the qualified technical specialists training programs is the development of their professional skills and the formation of stable behavior patterns that ensure strict compliance with the rules and requirements of industrial safety, thereby ensuring the high quality of the end product. © 2024 IEEE.

关键词： Virtual reality

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