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Acceptance and shipping warehouse zones calculation using serverless approach 12

Acceptance and shipping warehouse zones calculation using se...

引用

12th IEEE international Scientific Technical conference on Dependable Systems, Services and Technologies (DESSERT) - Dependable and Trustworthy AI, IoE, Big Data Analytics for a Safe and Secure World

作者： Muliarevych, Oleksandr Lviv Polytech Natl Univ Dept Elect Comp Machines Lvov Ukraine

ISBN: (纸本)9798350333046

This article describes methodology of acceptance and shipping zones calculation during warehouse design procedure. This study demonstrates complexity of searching for optimal design solution because of big number of input parameters and constraints, that brings thousands of combinations to check. Developed system using prediction model and serverless computing of different parameter combinations enable decrease time for optimal result search and makes warehouse design more automated. Results showed in article demonstrates comparison of different approaches for the most complex computing part of different system parameters combination evaluation using monolith architecture, microservices with horizontal auto-scaling and serverless light-weighted functional processing. Further research will demonstrate results of picking and replenishment (forward-reserve problem-solving) zones evaluation and results of scaling serverless computing higher than 1000 executors at once.

关键词： warehouse design serverless computing acceptance and shipping zones optimal labor cost search automation pattern recognition

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Research and Implementation of Flow pattern recognition for Gas-liquid Two-phase Flows Based on GoogLeNet 2

Research and Implementation of Flow Pattern Recognition for ...

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2021 2nd international Symposium on Automation, Information and computing, ISAIC 2021

作者： Yuan, Jiaxin Kan, Zhe School of Information and Control Engineering Liaoning Petrochemical University Fushun China

In order to solve the flow pattern recognition problem of gas-liquid two-phase flow in pipelines, this paper uses high-speed photography to sample the flow patterns of transparent pipe sections and combines the GoogLeNet convolutional neural network model under migration learning to implement a flow pattern recognition method with small samples. In this paper, the GoogLeNet Inception V1 network is used, and the convolutional layer and the pooling layer weights parameters obtained from its training on the imageNet dataset are retained, and the flow pattern samples obtained on the gas-liquid two-phase flow experimental platform are used to train the network model. The recognition accuracy was 98.37% with a training set of 400 and a test set of 100 samples of each flow type. The convolutional neural network directly uses images as data input without operations such as image pre-processing and feature extraction, and its unique fine-grained feature extraction enables the recognition of images by convolutional neural networks at a nearly human level. © Published under licence by IOP Publishing Ltd.

关键词： Feature extraction

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Multi-Module Collaborative Remote Control System for Robot with Target recognition-Tracking-Capture

Multi-Module Collaborative Remote Control System for Robot w...

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IEEE international conference on Real-time computing and Robotics (RCAR)

作者： Deshan Yang Xuesong Wu Kangjia Fu Chong Sun Xiang Zhang Qi Zhang Northwestern Polytechnical University in Xi'an China National Innovation Institute of Defense Technology Beijing China National Innovation Institute of Defense Technology China

ISBN: (数字)9798350372601

ISBN: (纸本)9798350372618

This paper introduces a multi-module collaborative remote control system for real-time soft robot operation, integrating visual recognition, tracking, and capture methods. The system comprises a client-side, including visual, connection, control, and main program modules, and a server-side. The visual module captures real-time images using a camera and performs target recognition assisted with ArUco markers. The connection module manages communication with the server, transmitting real-time images and control commands. The control module comprises a scanning module, a real-time visual tracking module and a capture module. Specifically, under the scheduling of the main program module, the scanning module carries out a global scan of the environment, the visual tracking module identifies and tracks the target in real-time, and the capture module completes the capture of the target. The main program module serves as the core control unit of the entire system. The simulation environment of the robotic arm is carried out on the server side to verify the correctness of the control commands sent and received. Finally, we conduct systematic tests to evaluate the impact of two key parameters that affect the performance of this system, namely tracking precision and cycle interval based on a soft robot designed by ourselves. Experimental results verify the system’s practicability and reliability in target recognition, tracking, and capture tasks.

关键词： Visualization Target tracking Systematics Target recognition Collaboration soft robotics Real-time systems

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international conference on Intelligent Manufacturing and Energy Sustainability, ICIMES 2023

International Conference on Intelligent Manufacturing and En...

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international conference on Intelligent Manufacturing and Energy Sustainability, ICIMES 2023

ISBN: (纸本)9789819967735

The proceedings contain 53 papers. The special focus in this conference is on Intelligent Manufacturing and Energy Sustainability. The topics include: Computational Studies on Single-Wall Rotating Vaneless Diffuser of a Centrifugal Compressor Stage;conceptual Design and Structural Analysis of a Multipurpose Agricultural Robot;condition Based Assessment and Diagnostics of Transformer in Smart Grid Network Using Adaptive Neuro Fuzzy Inference System Framework;Cooling High-Powered LEDs Using an Innovative Fully Automated Heat Pipe System;coupling Code for Real-Time Simulation of a Wave Energy Converter;damage Identification on Aircraft Wing Using Convolutional Neural Network Based pattern recognition;design and Numerical Analysis of Electromagnetic Rail Gun for Defense Applications;design of Energy Efficient Reversible Full Adder and Ripple Carry Adder for Digital computing Applications;Design of Low Power 11T SRAM Cell Using CNTFET Technology;a Review of Biodegradable Oil Derived from Biomass for Internal Combustion Engine Lubrication;designing of Photovoltaic System Using Matlab;development of a Pellet Rotation System for Automated Visual Inspection;development of Conceptual Model for the Introduction Stage of Lean Intelligent Manufacturing Using Simulation Model Based on the Identification of the Appropriate Technologies of Industry 5.0;DSP Based Inbuilt Active PFC Battery Charger;effect of Rotor Tip and Stator Hub Clearance on Aero Performance in Axial Flow Compressors for Aero Gas Turbine Engines;efficiency Enhancement and Energy Optimization in Alloy Steel Drilling: An Experimental and Statistical Analysis;Efficient and Cost-Effective Renewable Energy Integration of Photovoltaic and Hydro in Rural India Using HOMER Pro: A Case Study of Chupki, Punjab.

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Understanding the Influence of Extremely High-Degree Nodes on Graph Anomaly Detection 27th

Understanding the Influence of Extremely High-Degree Nodes...

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27th international conference on pattern recognition, ICPR 2024

作者： Sun, Xun Xiao, Xi Wang, Zhaoguo Hu, Guangwu Jiang, Xuhui Zhang, Bin Li, Hao Shenzhen International Graduate School Tsinghua University Shenzhen China Ministry of Education Chengdu China Shenzhen China Shenzhen Institute of Information Technology Shenzhen China Institute of Computing Technology CAS Beijing China Peng Cheng Laboratory Shenzhen China National Key Laboratory of Advanced Communication Network Shijiazhuang China

ISBN: (纸本)9783031781827

Graph Anomaly Detection (GAD) has attracted considerable attention for its potential in detecting anomalies. However, an overlooked issue in prior research is the presence of extremely high-degree node, which can introduce noise into GAD, escalate computational costs, and intensify the problem of over-smoothing. To tackle this issue, this paper first presents a novel graph anomaly dataset, NFTGraph, characterized by a notable presence of extremely high-degree nodes. A series of experiments on this dataset sheds light on the influence of such nodes on GAD. Moreover, we introduce a novel model, the Super Node-Aware Graph Neural Network (SNGNN), designed to mitigate the noise emanating from extremely high-degree nodes. Experimental results demonstrate that SNGNN outperforms extant models, achieving an average improvement of over 2% in the Area Under the ROC Curve (AUROC), and effectively reducing noise. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Graph neural networks

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Genetic Encryption of Safeguarding IoT Image Data with DNA-Based Security Measures

Genetic Encryption of Safeguarding IoT Image Data with DNA-B...

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Sustainable computing and Smart Systems (ICSCSS), international conference on

作者： R. Yuvarani R. Mahaveerakannan Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamilnadu India

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

In order to secure Internet of Things (IoT) devices, this research has developed a revolutionary lightweight encryption method that draws inspiration from the concepts of DNA. Our approach incorporates secret code utilization, SHA-256 hashing, and harnesses the inherent randomness of DNA sequences to generate robust keys resistant to attacks and difficult to decipher. A fundamental aspect of cryptography, the avalanche effect, is crucially addressed, ensuring that even minor alterations in plaintext produce vastly distinct ciphertext, thereby thwarting pattern recognition by potential attackers. Through comprehensive experimentation, the proposed algorithm has demonstrated exceptional performance across key metrics including key size, encryption speed, resistance to deformation, and efficacy of the avalanche effect, outperforming conventional encryption methods.

关键词： Resistance Measurement PSNR DNA Green computing Encryption pattern recognition

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A Novel Ambipolar Ferroelectric Tunnel FINFET Based computing-in-Memory for Quantized Neural Networks with High Area- and Energy-Efficiency

A Novel Ambipolar Ferroelectric Tunnel FINFET Based Computin...

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China Semiconductor Technology international conference (CSTIC)

作者： Runze Han Jin Luo Shengjie Cao Qianqian Huang Ru Huang Academy for Advanced Interdisciplinary Studies Peking University Beijing China School of Integrated Circuits Peking University Beijing China Beijing Advanced Innovation Center for Integrated Circuits Beijing China

ISBN: (数字)9798331513351

ISBN: (纸本)9798331513368

In this work, for the first time, a novel ambipolar ferroelectric tunnel FET (FeTFET) based computing-in-memory (CIM) scheme is proposed and experimentally demonstrated for quantized neural networks (QNNs) with high area- and energy-efficiency. By leveraging non-monotonic transfer characteristic of ambipolar tunnel FET (TFET) for signed multiplication and utilizing the nonvolatile ferroelectric gate modulation for weight storage, the signed CIM cell with weight precision expansion can be implemented by one single FeTFET device based on 14-nm technology node platform. Based on the proposed FeTFET-based CIM design, QNNs for typical pattern recognition tasks are demonstrated with high accuracy and energy efficiency, showing its significant potential for edge AI applications.

关键词： Integrated circuits TFETs Neural networks Modulation Edge AI Logic gates In-memory computing Energy efficiency Common Information Model (computing) pattern recognition

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A Proposal of an Improved Maximum Bayes Boundary-Ness Training Method 22

A Proposal of an Improved Maximum Bayes Boundary-Ness Traini...

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5th international conference on Signal Processing and Machine Learning, SPML 2022

作者： Kishishita, Koki Katagiri, Shigeru Ohsaki, Miho Division of Information Science Nara Institute of Science and Technology Japan Graduate School of Science and Engineering Doshisha University Kyoto Japan

ISBN: (纸本)9781450396912

One ultimate objective of statistical pattern recognition is to achieve the optimal status that produces Bayes error and its corresponding Bayes boundary. To compute such parameters of pattern recognizers without requiring unrealistically long computation times and strict parameter assumptions, the Maximum Bayes Boundary-ness (MBB) training method was proposed, and its effectiveness was demonstrated. This method focuses on the fact that the posterior probabilities on the Bayes boundary are equal among the classes that comprise the boundary and aim to directly achieve it without estimating the Bayes error. In the MBB training method, a loss function is defined for each sample, its weighted average was defined as an empirical average loss, and its minimization was a training objective. However, the loss is defined slightly excessively using a convex function. In this paper, we demonstrate the high utility of a simplified MBB training that is defined by correcting or simplifying the formalization of conventional MBB training by removing the convex function in the loss. We experimentally compared five datasets and gained speed in most of them. © 2022 Association for computing Machinery.

关键词： pattern recognition

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Research on Fast Steel Surface Deficiency Detection System based on FPGA 3

Research on Fast Steel Surface Deficiency Detection System b...

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3rd international conference on Applied Mathematics, Modelling, and Intelligent computing, CAMMIC 2023

作者： Wang, Yaoyao Liu, Dengfeng School of Artificial Intelligence and Computer Jiangnan University Wuxi214122 China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi214122 China

ISBN: (数字)9781510667617

ISBN: (纸本)9781510667600

Convolutional neural network-related applications have now been developed on a variety of platforms, including CPUs, GPUs, and others, but most of them sacrificed energy consumption to achieve good performance. Therefore, in recent years, more and more hot spots have shifted to how to achieve related applications such as low energy consumption research methods. In order to make the steel surface defect detection system meet the requirements of real-time detection, this article proposes that VGG16 is used as the main network to complete the design of the FPGA's rapid detection and identification system for the FPGA. This system conducts software and hard-hard design based on the ZYNQ-7000 platform (1) The design of parallelization of convolution on the PL side The hardware acceleration is achieved by accelerating the data flow design. Each IP core accelerated with the PL end. (2) In the method of quantifying the data, in the case of almost little loss of data accuracy, the use of resources on FPGA films has been greatly reduced to achieve acceleration of algorithms. The final experimental results show that compared with the CPU, this algorithm has increased by 6 times. The power consumption ratio of the CPU platform and the FPGA platform is 12.6, and the power consumption ratio of the GPU platform and the FPGA platform is 38.2, which is more suitable for applications on the embedded platform. © 2023 SPIE.

关键词： Field programmable gate arrays (FPGA)

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Intrusion Detection System based on pattern recognition using CNN

Intrusion Detection System based on Pattern Recognition usin...

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Sustainable computing and Smart Systems (ICSCSS), international conference on

作者： k. Azarudeen S.Harish Kumar T.V. Aswin Vijay P. Thirukumaran V.S Barath Balaji Department of CSE Velammal college of engineering and technology Madurai India

The Intrusion Detection System (IDS) is a security tool that uses pattern recognition algorithms to detect unusual activity in a network. IDS-based pattern recognition techniques search through network traffic for patterns that might indicate a security issue. IDS-based pattern recognition may find attacks of various types, such as malware infections, brute-force attacks, and insider threats. IDS-based pattern recognition techniques may be trained using methods based on machine learning, which allows the system to learn from previous attacks and improve its accuracy. Even though signature-based IDS analyzes network data for known attack patterns, anomaly-based IDS looks for anomalous network behaviour. IDS uses pattern recognition-based machine learning methods to enhance detection precision while decreasing false positives. The ability to identify previously unidentified attacks and the flexibility to shifting attack patterns are just two advantages of pattern recognition-based IDS. The 2 categories of pattern recognition-based IDS are signature-based and anomaly-based systems. Security staff can get alerts and notifications, that allows them to respond quickly to reduce security hazards. IDS-based pattern recognition systems rely on training data to learn what constitutes a security threat. If the training data is biased or incomplete, this can lead to inaccurate or incomplete detection. For example, if the training data only includes examples of attacks from a specific geographic region or industry sector, the system may not be able to detect attacks that occur outside of these parameters

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