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Video Action Recognition Based on Spatio-temporal Feature Pyramid Module 13

Video Action Recognition Based on Spatio-temporal Feature Py...

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13th International symposium on computational intelligence and Design (ISCID)

作者： Gong, Suming Chen, Ying Jiangnan Univ Minist Educ Key Lab Adv Proc Control Light Ind Wuxi Jiangsu Peoples R China

ISBN: (纸本)9781728184463

Modeling the spatio-temporal information of different actions facilitates their recognition. The mainstream 2D convolutional network has low computational cost but cannot capture timing information;the mainstream 3D convolutional network can extract spatio-temporal features but has a huge amount of calculation and is difficult to deploy. In this paper, a Spatiotemporal Feature Pyramid Module(STFPM) is proposed to extract spatio-temporal feature information. STFPM captures temporal information between frames by dilated convolution and fuses feature information by weighted addition. STFPM can be flexibly inserted into the 2D backbone network in a plug-and-play manner. When equipped with STFPM, 2D ResNet-50 achieves good results on UCF101 dataset and HMDB51 dataset.

关键词： Action recognition Dilated convolution Spatiotemporal feature pyramid

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Special Session: D-MATE: A Design Methodology for Connecting Automatic Test Equipment in Industry 4.0

Special Session: D-MATE: A Design Methodology for Connecting...

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Latin American Test Workshop, LATW

作者： Francesco Biondani Francesco Tosoni Nicola Dall'Ora Enrico Fraccaroli Sara Vinco Dong Seon Cheng Franco Fummi Department of Engineering for Innovation Medicine Section of Engineering and Physics University of Verona (Italy) Department of Engineering Sciences Guglielmo Marconi University Italy Department of Control and Computer Engineering Politecnico di Torino Italy

ISBN: (数字)9781665477635

ISBN: (纸本)9781665477642

With the growing presence of semiconductor devices in healthcare, automotive, and consumer electronics, Automatic Test Equipment (ATE) systems play an increasingly vital role in ensuring quality and reliability during validation. Despite their importance, ATE systems often operate in isolation from other manufacturing processes, limiting interoperability and integration potential. Consequently, fully incorporating ATE systems within the Industry 4.0 framework remains a largely unaddressed challenge. To bridge this gap, we propose adopting Open Platform Communications Unified Architecture (OPC UA), the industry de-facto standard communication protocol for machines, with an accompanying specification tailored to ATE systems. We developed and validated our information model on an advanced ATE system, demonstrating its practical application. The results showcase the successful integration of the ATE system into a fully-fledged Industrial Computer Engineering (ICE) laboratory demonstrator. This study validates the effectiveness of our model in a real-world scenario and highlights the significance of our integration approach within the context of Industry 4.0.

关键词： Automatic test equipment Protocols Manufacturing processes computational modeling Semiconductor devices Medical services Fourth Industrial Revolution Reliability Standards Manufacturing automation

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Model of Multiagent Cooperation for Behavioral Testing

Model of Multiagent Cooperation for Behavioral Testing

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2021 ieee East-West Design and Test symposium, EWDTS 2021

作者： Martynyuk, Oleksandr Drozd, Oleksandr Stepova, Hanna Thuong, Bui Van Martynyuk, Dmitry Sugak, Lyudmila Odessa National Polytechnic University Department of Computer Intelligent Systems and Networks Odessa Ukraine Odessa Polytechnic State University Department of Computerized Control Systems Odessa Ukraine Systems and Networks Odessa National Polytechnic University Department of Compuiter Intellectual Odessa Ukraine Join Venture 'NipponAuto' Odessa Ukraine Odessa Polytechnic State University Educational Methodical Department Odessa Ukraine

ISBN: (纸本)9781665445030

Modern distributed information systems (DIS), based on the complex application of many technologies, increasingly acquire the properties of multi-agent systems (MAS). Autonomy, mobility, cooperativity, intelligence, basic for MAS, become necessary properties of DIS. At the same time, the rapidly growing complexity, distribution and criticality of the use of DIS cause no less growth in the requirements for the reliability of their functioning, provided at the systemic, behavioral level by a complex of many approaches, both for project verification and for implementation testing. Most functional verification and testing problems have NP-complexity, which requires the integration of different verification tools and the use of various types of decomposition to achieve the desired result. The proposed model of MAS cooperation for testing is based on the representation of the behavior of DIS and MAS in their composition by extended Petri nets, automata behavioral experiments of check and recognition, functional hierarchical decomposition. The features of the model are the spatio-temporal decomposition of the set of events and actions of the goal and behavior of the MAS in the resource space of the DIS deployment environment, the spatio-temporal definition of the goals and behavior of all agents on the set of constrictions of events and actions of the MAS, the corresponding definition for agents of their autonomy, mobility, cooperativity and intelligence, the definition of cooperativity as a structure of agent coordination, the definition of the MAS model, reduced for events and actions outside the cooperativeness and coordination and commutation in its composition, the construction of a behavioral check model for the reduced MAS cooperation model. The proposed model is designed to reduce the computational complexity of behavioral check and recognition of cooperative interaction of the MAS and to determine the general algebraic conditions for constructing the corresponding

关键词： Multi agent systems

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Covariance in Ordered Weighted Logarithm Aggregation Operators

Covariance in Ordered Weighted Logarithm Aggregation Operato...

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ieee symposium Series on computational intelligence (ieee SSCI)

作者： Perez-Romero, Miriam Edith Alfaro-Garcia, Victor G. Merigo, Jose M. Flores-Romero, Martha Beatriz Univ Michoacana Fac Contaduria & Ciencias Adm Morelia Michoacan Mexico Univ Chile Sch Econ & Business Dept Management Control & Informat Syst Santiago Chile Univ Technol Sydney Sch Informat Syst & Modelling Sydney NSW Australia

ISBN: (纸本)9781728125473

Covariance as a measurement of dispersion allows knowing the behavior of one variable based on another. Its characteristic mechanism makes it a basic component in decision-making processes. This paper introduces some covariance logarithmic aggregation operators including the covariance generalized weighted logarithmic aggregation (Cov-GWLA) operator, the generalized covariance ordered weighted logarithmic aggregation (Coe-GOWLA) operator, the covariance ordered weighted logarithmic aggregation (Cov-OWLA) operator, the induced generalized covariance ordered weighted logarithmic aggregation (Cov-IGOWLA) operator and the induced covariance ordered weighted logarithmic aggregation (Cov-IOWLA) operator. The introduced tools extend the available information when using logarithmic aggregation operators, thus aiding decision and policy makers in the analysis of complex phenomena.

关键词： OWA operators logarithmic aggregation operators covariance decision-making

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Elliptic Envelope Based Detection of Stealthy False Data Injection Attacks in Smart Grid control Systems

Elliptic Envelope Based Detection of Stealthy False Data Inj...

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ieee symposium Series on computational intelligence (ieee SSCI)

作者： Ashrafuzzaman, Mohammad Das, Saikat Jillepalli, Ananth A. Chakhchoukh, Yacine Sheldon, Frederick T. Univ Idaho Dept Comp Sci Moscow ID 83843 USA Univ Memphis Dept Comp Sci Memphis TN 38152 USA Univ Idaho Dept Elect & Comp Engn Moscow ID 83843 USA

ISBN: (纸本)9781728125473

State estimation is an important process in power transmission systems. Stealthy false data injection attacks (SFDIA) against slate estimation may cause electricity theft, minor disturbances or even outages. Accurate and precise detection of these attacks are very important to prevent or minimize damages. In this paper, we propose an unsupervised learning based scheme to detect SFDIA on the stale estimation. The scheme uses random forest classifier for dimensionality reduction and elliptic envelope for detecting these attacks as anomalies. We compare the performance of the elliptic envelope method with four other unsupervised methods. All five models arc trained and then tested with a dataset from a simulated ieee 14-bus system. The results demonstrate that the elliptic envelope based approach provides the best detection rate and least false alarm rate among these five unsupervised methods.

关键词： Smart grid security False data injection attack unsupervised learning elliptic envelope

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Congestion control as a Service: Towards Low Latency Mobile Uploading

Congestion Control as a Service: Towards Low Latency Mobile ...

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International Workshop on Quality of Service

作者： Heng Xu Letian Li Liang Wang Fei Song School of Electronic and Information Engineering Beijing Jiaotong University Beijing China Department of Information Engineering Chinese University of Hong Kong China

ISBN: (数字)9798350350128

ISBN: (纸本)9798350350135

Ensuring quality of service (QoS) for data transmission in dynamic network environments presents a significant challenge. To solve the conflict between complex network conditions and strict transmission quality, effective solutions employing artificial intelligence have recently been introduced. Nonetheless, the prevalent methods primarily emphasize throughput oriented designs, concentrating on data’s downlink transmission while overlooking the upload of delay sensitive data during bidirectional interactions. Resource-constrained mobile devices struggle not only with executing long-term and continuous online computing tasks but also with rapidly adjusting to the constantly changing wireless networks. Models running on mobile devices must adapt to the changing wireless links. Diverse application demands and limited hardware capabilities pose significant obstacles to deploying relevant ***, we introduce a novel congestion control scheme named Colla, which enables mobile devices to achieve on-demand configurations through edge computing services. Colla reduces the computational load on the devices by incurring manageable communication overhead. It offers flexibility to balance latency and throughput according to the application’s requirements, thereby enabling low-latency data uploads. Experimental evaluations using real-world traces across 28 wireless scenarios demonstrate that Colla achieves effective bandwidth utilization, ranging from 66% to 93% in various conditions. Moreover, while maintaining comparable goodput, Colla significantly diminishes delay, reducing the average and the 99.9th percentile delays by 40% and 64%, respectively. Notably, even under conditions of unreliable edge links, Colla outperforms its competitors.

关键词： Wireless sensor networks computational modeling Quality of service Throughput Mobile handsets Hardware Delays

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Based on feedback thrust compensation algorithm constant slip vector control 13

Based on feedback thrust compensation algorithm constant sli...

引用

13th International symposium on Linear Drives for Industry Applications, LDIA 2021

作者： Yang, Jie Chen, Chen Hu, HaiLin Long, ZhiQiang Feng, Fu Jiangxi University of Science and Technology School of Electrical Engineering and Automation JiangXi China College of Intelligence Science and Technology National University of Defense Technology HuNan China

ISBN: (纸本)9781728172101

In the suspended permanent magnet maglev train, the permanent magnet array is used as the suspension structure, and the short stator linear induction motor is used as the traction structure. The suspension system is a self-stabilizing system. As the vertical force changes, the suspension gap and the motor air gap are changing. The change in the air gap of the motor causes the output thrust to fail to meet the requirements;at the same time, the normal force of the system will also fluctuate. The normal force is the vertical disturbance of the bogie, which has a greater impact on the dynamic posture of the bogie and needs to be adjusted. control. In this paper, the secondary magnetic field orientation strategy is adopted, the end effect parameters are introduced, and the dynamic mathematical model of the linear induction motor is established. On this basis, a constant slip vector control system based on the secondary magnetic field orientation is built;The air gap between the secondary will produce dynamic changes, and a variable air gap model of the motor is built;at the same time, according to the actual operation of the train, thrust compensation is introduced. Based on the stable normal force and thrust of the original constant slip control system, the given thrust is compensated. Value so that the output thrust meets the requirements. The simulation experiment compares the proposed control strategy with the variable air gap constant slip frequency control strategy, which verifies the effectiveness of the method under typical operating conditions. © 2021 ieee

关键词： Linear motors

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Fast Full-frame Video Stabilization with Iterative Optimization

Fast Full-frame Video Stabilization with Iterative Optimizat...

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International Conference on Computer Vision (ICCV)

作者： Weiyue Zhao Xin Li Zhan Peng Xianrui Luo Xinyi Ye Hao Lu Zhiguo Cao Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Department of Computer Science University of Albany Albany NY

Video stabilization refers to the problem of transforming a shaky video into a visually pleasing one. The question of how to strike a good trade-off between visual quality and computational speed has remained one of the open challenges in video stabilization. Inspired by the analogy between wobbly frames and jigsaw puzzles, we propose an iterative optimization-based learning approach using synthetic datasets for video stabilization, which consists of two interacting submodules: motion trajectory smoothing and full-frame outpainting. First, we develop a two-level (coarse-to-fine) stabilizing algorithm based on the probabilistic flow field. The confidence map associated with the estimated optical flow is exploited to guide the search for shared regions through backpropagation. Second, we take a divide-and-conquer approach and propose a novel multi-frame fusion strategy to render full-frame stabilized views. An important new insight brought about by our iterative optimization approach is that the target video can be interpreted as the fixed point of nonlinear mapping for video stabilization. We formulate video stabilization as a problem of minimizing the amount of jerkiness in motion trajectories, which guarantees convergence with the help of fixed-point theory. Extensive experimental results are reported to demonstrate the superiority of the proposed approach in terms of computational speed and visual quality. The code will be available on GitHub.

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Machine learning methods in Smartphone-Based Activity Recognition 14

Machine learning methods in Smartphone-Based Activity Recogn...

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ieee 14th International symposium on Applied computational intelligence and Informatics (SACI)

作者： Pintye, Istvan SZTAKI Inst Comp Sci & Control Lab Parallel & Distributed Syst Budapest Hungary Obuda Univ Doctoral Sch Appl Informat & Appl Math Budapest Hungary

ISBN: (纸本)9781728173764

In this paper, we present a system for human physical Activity Recognition (AR) using smartphone with embedded sensors. This paper addresses the question whether there is a comfortable way to predict human activities based on collected data from smartphone embedded gyroscope and accelerometer. computational background of this work based on self-learning machine learning methods. In order to train the machine learning algorithms, The University of California, Irvine (UCI) dataset was used and the different models were compared. After selecting the best model further modifications were suggested in order to improve the accuracy of the model. At the end 96.88% accuracy was reached.

关键词： Human activity recognition Machine Learning Artificial Neural Net Python Sci-Kit Learn Keras

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Advancing Multi-Class Arc Welding Defect Classification: DEEPTLWELD Intelligent System Utilizing Computer Vision, Deep Learning, and Transfer Learning on Radiographic X-ray Images for Bangladesh's Manufacturing Sector

Advancing Multi-Class Arc Welding Defect Classification: DEE...

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Computing, Applications and Systems (COMPAS), ieee International Conference on

作者： Avijit Chowdhury Department of Mechanical Engineering Chittagong University of Engineering and Technology Chattogram Bangladesh

ISBN: (数字)9798331529765

ISBN: (纸本)9798331529772

Welding defects are a crucial problem in the manufacturing industry. However, the industry faces enormous losses for these defects. Conditional monitoring and quality control can reduce this loss. In Industry 4.0, artificial intelligence revolutionized every field. This research aims to improve industrial automation by creating a multi-class classification system through digital image processing, deep learning, and transfer learning—a real-time application integrated with deep learning, transfer learning, image processing, and MLOPS operations. Accurate prediction occurred by various models, such as custom CNN and pre-trained models (InceptionV3, EfficientNetB7, DenseNet201, and ResNet152). DEEPTLWELD system was developed using a bespoke dataset of 4,000 radiographic X-ray pictures with four frequent categories of defects that are essential for tackling difficulties faced by local industry challenges. This dataset is All radiographic images processed with image processing techniques (distance transform, watershed transform, edge detection). In the transfer learning part, weights were loaded manually as those weights performed better with a particular model. Out of the networks, custom CNN achieved high accuracy with 98% after fine-tuning. The deployment of the produced model in a production environment developed with a Streamlit framework is ensured by its smooth integration into a machine learning pipeline housed on Amazon EC2 servers. This intelligent system has the potential for broader applications in inclusive technology.

关键词： Radiography Deep learning Manufacturing industries computational modeling Welding Transfer learning Transforms Production Predictive models X-ray imaging

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