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2nd International Conference on Mechanical Engineering, Intelligent Manufacturing and Automation technology, MEMAT 2022

作者： Lin, Yihao Fan, Yong Wu, Shuqin Gong, Yubing Xie, Wu School of Mechanical & Electrical Engineering Guilin University of Electronic Technology Guangxi Guilin China School of Architecture and Transportation Engineering Guilin University of Electronic Technology Guangxi Guilin China School of Computer Science and Information Security Guilin University of Electronic Technology Guangxi Guilin China

ISBN: (纸本)9783800757619

The production of electronic components is becoming more and more modern and efficient. How to detect surface defects of electronic components more accurately and quickly has become a more and more important research topic. To solve this problem, this paper designs a detection method based on visual detection and image processing is proposed. The proposed method builds a target detection algorithm model based on the YOLOv5 (You Only Look Once Version 5) convolutional neural network, and combines the image target detection and recognition with the convolutional neural network to make its detection and recognition effect more accurate and efficient. This paper tested the 4 surface defects with the patch resistance SS14 to verify the reliability of the system. Through the result analysis, the loss function of the detection system is less than 0.3, and its map is above 0.96. It meets the high accuracy and high accuracy requirements of the surface defect detection of electronic components. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Surface defects

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A Real-Time Pedestrian Social Distancing Risk Alert System for COVID-19

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computer Systems Science & Engineering 2023年第10期47卷 937-954页

作者： Zhihan Liu Xiang Li Siqi Liu Wei Li Xiangxu Meng Jing Jia College of Computer Science and Technology Harbin Engineering UniversityHarbin150001China Department of Clinical Laboratory Harbin Medical University Cancer HospitalHarbinHeilongjiang150081China Faculty of Arts Design and ArchitectureSchool of Built EnvironmentThe University of New South WalesSydneyNSW 2052Australia

The COVID-19 virus is usually spread by small droplets when talking,coughing and sneezing,so maintaining physical distance between people is necessary to slow the spread of the *** World Health Organization(WHO)recommends maintaining a social distance of at least six *** this paper,we developed a real-time pedestrian social distance risk alert system for COVID-19,whichmonitors the distance between people in real-time via video streaming and provides risk alerts to the person in charge,thus avoiding the problem of too close social distance between pedestrians in public *** design a lightweight convolutional neural network architecture to detect the distance between people more *** addition,due to the limitation of camera placement,the previous algorithm based on flat view is not applicable to the social distance calculation for cameras,so we designed and developed a perspective conversion module to reduce the image in the video to a bird’s eye view,which can avoid the error caused by the elevation view and thus provide accurate risk indication to the *** selected images containing only person labels in theCOCO2017 dataset to train our *** experimental results show that our network model achieves 82.3%detection accuracy and performs significantly better than other mainstream network architectures in the three metrics of Recall,Precision and mAP,proving the effectiveness of our system and the efficiency of our technology.

关键词： Convolutional neural network pedestrian detection social distancing COVID-19

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TG-LLaVA: Text Guided LLaVA via Learnable Latent Embeddings 39

TG-LLaVA: Text Guided LLaVA via Learnable Latent Embeddings

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Yan, Dawei Li, Pengcheng Li, Yang Chen, Hao Chen, Qingguo Luo, Weihua Dong, Wei Yan, Qingsen Zhang, Haokui Shen, Chunhua School of Cybersecurity Northwestern Polytechnical University China AI Business Alibaba Group China College of Computer Science and Technology Zhejiang University China College of Information and Control Engineering Xi’an University of Architecture and Technology China School of Computer Science Northwestern Polytechnical University China

ISBN: (纸本)157735897X

Currently, inspired by the success of vision-language models (VLMs), an increasing number of researchers are focusing on improving VLMs and have achieved promising results. However, most existing methods concentrate on optimizing the connector and enhancing the language model component, while neglecting improvements to the vision encoder itself. In contrast, we propose Text Guided LLaVA (TG-LLaVA) in this paper, which optimizes VLMs by guiding the vision encoder with text, offering a new and orthogonal optimization direction. Specifically, inspired by the purpose-driven logic inherent in human behavior, we use learnable latent embeddings as a bridge to analyze textual instruction and add the analysis results to the vision encoder as guidance, refining it. Subsequently, another set of latent embeddings extracts additional detailed text-guided information from high-resolution local patches as auxiliary information. Finally, with the guidance of text, the vision encoder can extract text-related features, similar to how humans focus on the most relevant parts of an image when considering a question. This results in generating better answers. Experiments on various datasets validate the effectiveness of the proposed method. Remarkably, without the need for additional training data, our proposed method can bring more benefits to the baseline (LLaVA-1.5) compared with other concurrent methods. Furthermore, the proposed method consistently brings improvement in different settings. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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An Identification Method for Mechanical Parts Using on Deep Learning with Single Shot-multibox Detector MobileNet 2

An Identification Method for Mechanical Parts Using on Deep ...

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2nd International Conference on Mechanical Engineering, Intelligent Manufacturing and Automation technology, MEMAT 2022

作者： Liu, Junfeng Fan, Yong Wu, Shuqin Liang, Zhenghao Xie, Wu Fu, Liye School of Mechanical and Electrical Engineering Guilin University of Electronic Technology Guangxi Guilin China School of Architecture and Transportation Engineering Guilin University of Electronic Technology Guangxi Guilin China School of Computer Science and Information Security Guilin University of Electronic Technology Guangxi Guilin China

ISBN: (纸本)9783800757619

SSD (Single Shot-multibox Detector)-MobileNet is widely used in embedded systems and intelligent manufacturing systems due to its lightweight structure and acceptable precision loss. In order to identify mechanical parts, images of four parts are collected, labelled and identified based on SSD-MobileNet with deep learning model. The TensorFlow frameworks are used only for training, and the trained models are applied to implement real-time detection. The results show that the model has a good recognition effect on mechanical parts with the accuracy of 0.936 and the recall rate of 0.956 under the conditions of not large-scale quantity of parts, which is useful to meet the identification and detection of parts to achieve the application purpose of parts classifications. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Embedded systems

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Shifting to Smart Conferences: Author Perceptions of Presenting Virtually at the 2021 Associated Schools of Construction Conference 9th

Shifting to Smart Conferences: Author Perceptions of Present...

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9th International KES Conference on Smart Education and e-Learning, SEEL 2022

作者： Wentz, Blake Yates, Heather N. College of Engineering Computer Science and Construction Management California State University ChicoCA United States College of Engineering Architecture and Technology Oklahoma State University StillwaterOK United States

ISBN: (纸本)9789811931116

The COVID-19 pandemic caused significant disruption to academic institutions as well as the conferences associated with those disciplines. The Associated Schools of Construction (ASC) was among those affected and was forced to cancel their 2020 conference and shift their 2021 conference to a Smart Conference. This study collected feedback from the authors to gauge their perceptions of presenting in a virtual format to determine if the ASC should continue to integrate smart options for future events post-pandemic. The results indicated that the smart option for presenting conference proceedings papers was well received, and that almost 60% of the respondents indicated that if a smart option was available in the future, they would be more inclined to submit a paper for publication. These results indicate that an opportunity exists for the ASC to broaden its engagement with faculty members and further its mission to advance construction education by embracing Smart Conference techniques at future events. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： E-learning

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Adaptation of AI-Accelerated CFD Simulations to the IPU Platform 1

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14th International Conference on Parallel Processing and Applied Mathematics, PPAM 2022

作者： Rościszewski, Pawel Krzywaniak, Adam Iserte, Sergio Rojek, Krzysztof Gepner, Pawel Graphcore Gdańsk Poland Department of Construction and Mechanical Engineering Universitat Jaume I Castellón de la Plana Spain Barcelona Supercomputing Center Barcelona Spain Institute of Computer and Information Sciences Czȩstochowa University of Technology Czȩstochowa Poland Faculty of Mechanical and Industrial Engineering Warsaw University of Technology Warszawa Poland Department of Computer Architecture Faculty of Electronics Telecommunications and Informatics Gdańsk University of Technology Narutowicza 11/12 Gdańsk80-233 Poland

ISBN: (数字)9783031304453

ISBN: (纸本)9783031304446

Intelligence Processing Units (IPU) have proven useful for many AI applications. In this paper, we evaluate them within the emerging field of AI for simulation, where traditional numerical simulations are supported by artificial intelligence approaches. We focus specifically on a program for training machine learning models supporting a computational fluid dynamics application. We use custom TensorFlow provided by the Poplar SDK to adapt the program for the IPU-POD16 platform and investigate its ease of use and performance scalability. Training a model on data from OpenFOAM simulations allows us to get accurate simulation state predictions in test time. We show how to utilize the popdist library to overcome a performance bottleneck in feeding training data to the IPU on the host side, achieving up to 34% speedup. Due to communication overheads, using data parallelism to utilize two IPUs instead of one does not improve the throughput. However, once the intra-IPU costs have been paid, the hardware capabilities for inter-IPU communication allow for good scalability. Increasing the number of IPUs from 2 to 16 improves the throughput from 560.8 to 2805.8 samples/s. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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Modeling the failures of power-aware data centers by leveraging heat recirculation

Modeling the failures of power-aware data centers by leverag...

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作者： Feng, Hao Deng, Yuhui Yu, Liang Department of Computer Science Jinan University Guangzhou China Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China

With the explosive growth of data, hundreds of thousands of servers may be contained in a single data center. Hence, node failures are unavoidable and generally negatively effects the performance of the whole data center. Additionally, data centers with a large number of nodes will cause plenty of energy consumption. Many existing task scheduling techniques can effectively reduce the power consumption in data centers by considering heat recirculation. However, the traditional techniques do not take the situation of node failures into account. This paper proposes an airflow-based failure model for data centers by leveraging heat recirculation. In this model, the spatial distribution and time distribution of failures are considered. Furthermore, a genetic algorithm (GA) and a simulated annealing algorithm (SA) are implemented to evaluate the proposed failure model. Because the positions of node failures have a significant impact on the heat recirculation and the energy consumption of data centers, failures with different positions are analyzed and evaluated. The experimental results demonstrate that the energy consumption of data centers can be significantly reduced by using the GA and SA algorithms for task scheduling based on the proposed failure model. © 2019 John Wiley & Sons, Ltd.

关键词： Energy efficiency

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Simplified Dielectric Property Determination for Wearable Applications Using Dual CPW Lines

Simplified Dielectric Property Determination for Wearable Ap...

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World Congress on Computing and Communication Technologies (WCCCT)

作者： Jialiang Ni Kwok L. Chung Liwen Liang Jingshu Sun Wenbiao Li Xiaotian Liu Tongtong Ouyang Zhibin Chen School of Computer Science and Engineering Guangzhou Institute of Science and Technology Guangzhou China School of Intelligent Manufacturing and Electrical Engineering Guangzhou Institute of Science and Technology Guangzhou China School of Civil Engineering and Architecture Guangzhou Institute of Science and Technology Guangzhou China School of Computer Science and Engineering Huizhou University Huizhou China

ISBN: (数字)9798331512620

ISBN: (纸本)9798331512637

This paper presents an enhanced dual transmission-line method for evaluating the dielectric properties of known and unknown materials, significantly improving upon traditional techniques. The proposed method simplifies the experimental setup by eliminating the need for constructing transmission lines or additional processing on unknown materials, utilizing a straightforward simulation model created with the EM simulator CST and requiring only low-cost experimental equipment. It operates independently of prior knowledge concerning the substrate dielectric constant, streamlining data processing and saving time. Furthermore, the method demonstrates high accuracy in comparison to reference values. Applied in this study, it verified the dielectric properties of unknown cowhide leather and two commercial substrates, RO3010 and RT5880. The computed root-mean-square errors (RMSEs) were 24.1% for RT5880 and 3% for RO3010, confirming the method's effectiveness. Overall, this improved approach provides a reliable and efficient alternative for material characterization in dielectric property determination.

关键词： Dielectric constant Accuracy Transmission line matrix methods Materials reliability Data processing Transmission line measurements Propagation losses Dielectrics Coplanar waveguides Substrates

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Vulnerability Detection Based on Unified Code Property Graph 20th

Vulnerability Detection Based on Unified Code Property Graph

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Proceedings of the 20th Web Information Systems and Applications Conference, WISA 2023

作者： Li, Wei Li, Xiang Feng, Wanzheng Jin, Guanglu Liu, Zhihan Jia, Jing College of Computer Science and Technology Harbin Engineering University Harbin150001 China College of Communication Engineering Jilin University Jilin130012 China Faculty of Arts Design and Architecture School of Built Environment The University of New South Wales SydneyNSW2052 Australia

ISBN: (纸本)9789819962211

As the number of source codes grows rapidly, detecting source code vulnerabilities in current software has become an important study. Most current deep learning-based vulnerability detection technologies treat source code as a sequence, which loses the source code’s structural information, leading to many false positives in the detection results. We propose a novel source code vulnerability detection model, named UCPGVul, based on the Unified Code Property Graph (UCPG). A new graph representation, UCPG, is proposed to extract semantic features from the source code. By extracting features from UCPG, our proposed UCPGVul model can capture more vulnerability features. Experimental results on a publicly available dataset show that UCPGVul can achieve more accurate and stable detection results compared to five state-of-the-art methods. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Semantics

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Integrating Structural Information: Comparing Classification-Based and Segmentation-Based Predictors in Bioinformatics (Case Study: Protein-Peptide Region Residue-Level Interaction) Using Structural Information

Integrating Structural Information: Comparing Classification...

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International CSI computer Conference, CSICC

作者： Shima Shafiee Abdolhossein Fathi Faezeh Safari Department of Computer Engineering and Information Technology Razi University Kermanshah Iran Faculty of Eng. Architecture and Info. Tech. School of Electrical Eng. and Computer Science University of Queensland Brisbane QLD Australia

ISBN: (数字)9798331523114

ISBN: (纸本)9798331523121

Understanding the regions where proteins bind to peptides is vital for studying diseases like cancer, as it sheds light on various cellular processes and aids in drug discovery. Although researchers can experimentally investigate protein-peptide interactions at the region level by analyzing the structures of their complexes, only a limited number of these structures are known. Additionally, experiments to determine these interactions can be both costly and time-consuming. Computational methods provide an alternative and complementary approach to experimental techniques. As a result, using computational methods to predict peptide-binding regions can significantly enhance the efficiency and cost-effectiveness of experimental research. To leverage these advantages, this study introduces a new method named Structure-based Prediction of Residue-level Interaction of Peptides (SPRINT-Pep), designed to pinpoint the binding regions between proteins and peptides. This approach leverages two distinct predictive models: a classification-based model combining DeepResNet with a Support Vector Machine (SVM) and a segmentation-based model called DeepU-Net. Our predictions outperform other state-of-the-art methods in terms of F-measures (by at least 23%), precision (by at least 3.7%), and the balance between sensitivity and specificity (by about 7.7%). These findings demonstrate the proficiency of the proposed method.

关键词： Proteins Support vector machines Deep learning Peptides Predictive models Sensitivity and specificity Prediction algorithms Robustness Drug discovery Testing

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