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1st International Conference on computer, Power and Communications, ICCPC 2022

作者： Prakash, M. Gnana Ponmalar, A. Deeba, S. Akilandeswari, A. Rasool, Saira Banu Mohammed Lavanya, P. Sri Sai Ram Institute of Technology Department of Information Technology Tamil Nadu Chennai India Sri Manakula Vinayagar Engineering College Department of Computer Science and Engineering Tamil Nadu Puducherry India Saveetha School of Engineering Simats Tamil Nadu Chennai India Applied College Najran University Department of Information System Saudi Arabia

ISBN: (纸本)9798350397840

Physically Challenged people who are interested in coding, in the current scenario cannot code and exhibit their knowledge in programming skills. Our project enables them to code without using a keyboard. They can code entirely by their voice and execute their code. We are converting the user's speech to text by Natural Language Processing (NLP). With the use of text obtained from NLP model we generate code snippets using our custom code generator API. The code snippets are generated in the Visual Studio Code text editor using our own VSCode extension. © 2022 IEEE.

关键词： Speech recognition

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Massive Fabrication of Carbon Nanotube Transistors by Surface Tension-Driven Inkjet-Printing Method

Massive Fabrication of Carbon Nanotube Transistors by Surfac...

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IEEE International Conference on Flexible and Printable Sensors and systems (FLEPS)

作者： Soohyun Park Minhye Shin Hongki Kang Yoonhee Lee Division of Electronics and Information System Daegu Gyeongbuk Institute of Science and Technology (DGIST) Daegu South Korea Department of Electrical Engineering and Computer Science Daegu Gyeongbuk Institute of Science and Technology (DGIST) Daegu South Korea

Carbon nanotube field-effect transistors (CNTFETs) have been ideal nanoelectronics in semiconductor technologies with exceptional electrical properties. Scaling the CNT -FET fabrication enables the expansion of the application fields. However, placing nanotubes with uniform alignment and controlled density to the electrode contacts has been the maj or challenge in massive manufacturing. Here, we introduce the in-place inkjet-printing method for fabricating CNTFETs with controlled numbers of connected CNTs at a single CNT level. A picoliter (pL) drop of CNT ink is printed to the pre-patterned electrode arrays in parallel over a four-inch silicon wafer, allowing adaptive manufacturing without additional lithographic processes. Drops form thin films on the metal electrode patterns driven by the surface tension flow, diluting the surface density of CNT on the one-micrometer channel gap and avoiding bundled coffee ring edges. Finally, single CNTs are correctly bridged to the electrodes with high statistical yield. This process enables reproducible and high-throughput single CNT deposition, integrating into the FET manufacturing process, and can be used for versatile nanoelectronics applications, such as biosensors.

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Multiple information Prompt Learning for Cloth-Changing Person Re-Identification

arXiv

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arXiv 2024年

作者： Wei, Shengxun Gao, Zan Ma, Chunjie Zhao, Yibo Guan, Weili Chen, Shengyong Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin300384 China Shandong Artificial Intelligence Institute Qilu University of Technology Shandong Academy of Sciences Jinan250014 China School of Electronics and Information Engineering Harbin Institute of Technology Shenzhen150001 China

Cloth-changing person re-identification is a subject closer to the real world, which focuses on solving the problem of person re-identification after pedestrians change clothes. The primary challenge in this field is to overcome the complex interplay between intra-class and inter-class variations and to identify features that remain unaffected by changes in appearance. Sufficient data collection for model training would significantly aid in addressing this problem. However, it is challenging to gather diverse datasets in practice. Current methods focus on implicitly learning identity information from the original image or introducing additional auxiliary models, which are largely limited by the quality of the image and the performance of the additional model. To address these issues, inspired by prompt learning, we propose a novel multiple information prompt learning (MIPL) scheme for cloth-changing person ReID, which learns identity robust features through the common prompt guidance of multiple messages. Specifically, the clothing information stripping (CIS) module is designed to decouple the clothing information from the original RGB image features to counteract the influence of clothing appearance. The bio-guided attention (BGA) module is proposed to increase the learning intensity of the model for key information. A dual-length hybrid patch (DHP) module is employed to make the features have diverse coverage to minimize the impact of feature bias. Extensive experiments demonstrate that the proposed method outperforms all state-of-the-art methods on the LTCC, Celeb-reID, Celeb-reID-light, and CSCC datasets, achieving rank-1 scores of 74.8%, 73.3%, 66.0%, and 88.1%, respectively. When compared to AIM (CVPR23), ACID (TIP23), and SCNet (MM23), MIPL achieves rank-1 improvements of 11.3%, 13.8%, and 7.9%, respectively, on the PRCC dataset. 1 © 2024, CC BY-NC-ND.

关键词： Contrastive Learning

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The Weapon Target Assignment in Adversarial Environments 1

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2nd International Conference on Cognitive Computation and systems, ICCCS 2023

作者： Gao, Rui Zheng, Shaoqiu Wang, Kai Zhang, Longlong Zhu, Zhiyuan Yuan, Xiaohu College of Electronic and Information Engineering Southwest University Chongqing400715 China Key Lab of Information System Requirement Nanjing Research Institute of Electronics Engineering Nanjing210007 China Department of Computer Science and Technology Tsinghua University Beijing100084 China

ISBN: (数字)9789819708857

ISBN: (纸本)9789819708840

In context of the current weapon target assignment (WTA) problem, consideration of target capability is often disregarded. To address this issue, we propose an optimized model for weapon target assignment in complex environments. Our model provides a comprehensive description and conducts a detailed analysis of constraints and objective functions in the context of adversarial weapon target assignment (AWTA). To efficiently tackle the assignment problem, we employ the multi-particle swarm optimization algorithm (MOPSO). During the optimization process, we incorporate learning factors and inertia weights to update particle positions, seamlessly implementing the model with a set of adjustable parameters. Experimental results demonstrate that our model effectively captures the concept of adversarial scenarios. Furthermore, by utilizing the MOPSO optimization algorithm, we achieve a superior Pareto front even for small-scale adversarial scenarios, successfully resolving the adversarial WTA problem. Our research contributes an effective optimization approach to address the weapon task assignment problem and offers valuable inspiration for future investigations into multi-objective research. In conclusion, our study reveals the potential of multi-objective optimization research in the context of weapon target assignment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Multiobjective optimization

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Parallel Optimization of Computational Fluid Dynamics Application code_saturne Based on Next Generation Sunway Supercomputer 14

Parallel Optimization of Computational Fluid Dynamics Applic...

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14th International Symposium on Parallel Architectures, Algorithms and Programming, PAAP 2023

作者： Liu, Anjun Liao, Yin Xu, Xihao Chen, YanYan Zhong, Hongjun Wu, Jianming Kong, Bo Raikov, Alexander Guo, Meng Jinan Institute of Supercomputing Technology Jinan Key Laboratory of High Performance Industrial Software Jinan China Zhejiang Yuansuan Technology Co. Ltd Jinan Supercomputing-Zhejiang Yuansuan Industrial Simulation System Joint Laboratory Hangzhou China Qilu University of Technology Shandong Academy of Sciences Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Key Laboratory of Computing Power Network and Information Security China Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Shandong Jinan China

ISBN: (纸本)9798350371024

code_saturne is an open-source general large-scale computational fluid dynamics software independently developed by Electricite de France since 1997. Due to its high computing performance, code_saturne has been widely used in the nuclear industry, heavy fuel and biomass combustion, particle tracking and other computational fluid dynamics fields. In this work, we implemented code_saturne on the new generation Sunway supercomputer (SW26010pro) at the first time. By applying the data value-taking technology of double cache, combined with Sunway shared memory technology, we have adapted the data structure and Modular programming of code_saturne conducively to the new generation Sunway supercomputer (SW26010pro) and accelerated the software hotspots the software hotspots by 1-2 times. After optimizing and porting, this software has successfully served in the numerical simulation of the lateral flow of fuel assemblies in the nuclear industry. © 2023 IEEE.

关键词： Supercomputers

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Design of Concurrent engineering systems for Global Product Development Using Artificial Intelligence 10th

Design of Concurrent Engineering Systems for Global Product ...

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10th International Conference on Innovations in computer Science and engineering, ICICSE 2022

作者： Karn, Arodh Lal Mehbodniya, Abolfazl Webber, Julian L. Jayagopal, Vellingiri Stalin David, D. Rangasamy, Rajasekar Sengan, Sudhakar Department of Financial and Actuarial Mathematics School of Mathematics and Physics Xi’an Jiaotong-Liverpool University Jiangsu Suzhou215123 China Kuwait City Kuwait Department of Software and System Engineering School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Information Technology Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Tamil Nadu Chennai600072 India Department of Computer Science and Engineering GITAM School of Technology GITAM University Karnataka Bengaluru561203 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627152 India

ISBN: (纸本)9789811974540

Competition and concurrent engineering (CE) businesses are growing. Faster product creation, quality improvement, manufacturing process adoption, and reduced client demand cost are vital for corporate success. Implement concurrent engineering to make high-quality goods. CE is a parallel, systematic method to design. The chief executive officer (CEO’s) approach impacted how many firms build products. You may use the fundamental ideas of concurrent engineering conversation and the failure mode and effects analysis (FMEA) tool. To provide a smooth and efficient CE environment, conduct the product development process from market research to detailed design. Concurrent engineering is a novel product development (NPD) approach. Concurrency and cross-functional integration separate CE from traditional product development. NPD continues through developing ideas, validation, proof-of-concept validation, Minimum Viable Product development, pre-launch, and post-launch. In other words, product discovery should continue throughout its life span. Each organization's growth operations operate simultaneously. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Product development

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Genetic Algorithm for Block Relocation in Automated Warehouse 5

Genetic Algorithm for Block Relocation in Automated Warehous...

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5th International Conference on Artificial Intelligence Technologies and Applications, ICAITA 2023

作者： Wang, Gang Lian, Mingchang Ding, Wei Chen, Songhang Chen, Hao Quanzhou Institute of Equipment Manufacturing Haixi Institutes Chinese Academy of Sciences Quanzhou362200 China College of Computer and Cyber Security Fujian Normal University Fujian Fuzhou350001 China Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China Fujian Fuzhou350108 China Fujian Provincial Key Laboratory of Intelligent Identification and Control of Complex Dynamic System Quanzhou362200 China

ISBN: (纸本)9781643684840

This paper is based on the research background of block relocation in the automated warehouse and proposes a solution utilizing AGV to relocate goods within the warehouse. Building upon the automated warehouse layout and block relocation process, we formulate a mathematical model to minimize the the number of AGV moving goods and the total travel distance of the AGV. To solve the model, we develop an enhanced genetic algorithm, which zones the block relocation process into multiple stages. Each stage integrates conventional heuristic algorithms to enhance the efficiency of the solution process. The improved genetic algorithm can effectively solve the problem, significantly enhancing the automation level of the warehouse. © 2024 The Authors.

关键词： Genetic algorithms

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A Novel Multi-layered Minutiae Extractor Based on OCT Fingerprints 16th

A Novel Multi-layered Minutiae Extractor Based on OCT Finge...

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16th Chinese Conference on Biometric Recognition, CCBR 2022

作者： Zeng, Wenfeng Zhang, Wentian Liu, Feng Tan, Xu Li, Qin College of Computer Science and Software Engineering Shenzhen University Shenzhen China SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Senzhen China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China School of Software Engineering Shenzhen Institute of Information Technology Shenzhen China

ISBN: (纸本)9783031202322

Fingerprints of Optical Coherence Tomography (OCT) imaging provide 3D volume data which have the nature property of multi-layered tissue structure. This paper, for the first time, attempts to extract minutiae for OCT-based fingerprint by making full use of the merits multi-layered structure of OCT imaging and powerful convolution al neural network (CNN). In particular, a novel multi-layered feature fusion minutiae extraction network is proposed, involving a multi-layered feature extractor to integrate the rich information of multiple subsurface fingerprints, and a two-stage object detection framework using reweighted concatenation feature to detect minutiae points. Compared with the results achieved by typical traditional and learning-based methods, our method outperforms the best one by about 2% of F1-score in minutiae extraction, and achieves the improvement of 0.09% than the best one in matching performance, which further demonstrate the effectiveness and robustness of our proposed method. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Optical tomography

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Toward Digital Transformation Adoption: A Conceptual Framework from Transformational Leadership Perspective

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Procedia computer Science 2024年 234卷 1175-1182页

作者： Marcel Ford Lumban Gaol Suhono Harso Supangkat Benny Ranti Computer Science Department BINUS Graduate Program – Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Information System Department Universitas Kristen Krida Wacana Jakarta Indonesia Smart Cities and Communities Innovation Center Faculty of Digital Communication and Hotel & Tourism Institute of Technology Bandung Bandung Indonesia Computer Science Department Faculty of Computer Science University of Indonesia Depok Indonesia

Digital transformation is about transforming processes, business models, domains, and culture. Studies show that the failure rate of digital transformation is quite high up to 90%. Studies show that the transformational leadership model has a significant impact on digital transformation adoption. This paper identifies the positive and negative attributes of transformational leadership including the components that support and are affected for successful adoption of digital transformation. Furthermore, the paper combines several findings related to the attributes and components in the form of a conceptual framework. The conceptual framework can serve as a guide for organizations for their digital transformation journey.

关键词： transformational leadership digital transformation digital adoption conceptual framework

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Dual Encoders for Diffusion-based Image Inpainting

Dual Encoders for Diffusion-based Image Inpainting

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Dezhi Zheng Kaijun Deng Jinbao Wang Linlin Shen Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Current diffusion-based inpainting models struggle to preserve unmasked regions or generate highly coherent content. Additionally, it is hard for them to generate meaningful content for 3D inpainting. To tackle these challenges, we design a plug-and-play branch that runs through the entire generation process to enhance existing models. Specifically, we utilize dual encoders - a Convolutional Neural Network (CNN) encoder and the pre-trained Variational AutoEncoder (VAE) encoder, to encode masked images. The latent code and the feature map from the dual encoders are fed to diffusion models simultaneously. In addition, we apply Zero-padded initialization to solve the problem of mode collapse caused by this branch. Experiments on BrushBench and EditBench demonstrate that models with our plug-and-play branch can improve the coherence of inpainting, and our model achieves new state-of-the-art results.

关键词： Visualization Three-dimensional displays Codes Autoencoders Coherence Signal processing Diffusion models Decoding Convolutional neural networks Speech processing

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