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IEEE Transactions on Artificial Intelligence 2023年第2期4卷 268-281页

作者： Tian, Ye Pan, Jingwen Yang, Shangshang Zhang, Xingyi He, Shuping Jin, Yaochu Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Hefei230601 China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei230088 China Anhui University School of Computer Science and Technology Hefei230601 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Hefei230601 China Anhui University Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and Automation Hefei230601 China Bielefeld University Faculty of Technology Bielefeld33619 Germany

The sparse adversarial attack has attracted increasing attention due to the merit of a low attack cost via changing a small number of pixels. However, the generated adversarial examples are easily detected in vision since the perturbation to each pixel is relatively large. To achieve imperceptible and sparse adversarial attacks, this article formulates a bi-objective constrained optimization problem, simultaneously minimizing the 0 and 2 distances to the original image, and proposes a dual-population-based constrained evolutionary algorithm to solve it. The proposed method solves the optimization problem by evolving two populations, where one population is responsible for finding feasible solutions (i.e., successful attacks) and the other one is to minimize both the 0 and 2 distances. Moreover, a population initialization strategy and two genetic operators are customized to accelerate the convergence speed. Experimental results indicate that the proposed method can achieve high success rates with low attack costs, and strikes a better balance between the 0 and 2 distances than state-of-the-art methods. © 2020 IEEE.

关键词： Evolutionary algorithms

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Study on the Effectiveness of New Development Methods in Software Engineering Based on Experiments with AB Testing 24

Study on the Effectiveness of New Development Methods in Sof...

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9th International Conference on Cyber Security and Information Engineering, ICCSIE 2024

作者： Chen, Lei Zhang, Difeng Li, Simu School of Computer Science and Engineering Wuhan Institute of Technology China Hubei Key Laboratory of Intelligent Robot Wuhan Institution of Technology China School of Electrical and Information Engineering Wuhan Institute of Technology China Economic and Business Department College of Post and Telecommunication of Wuhan Institute of Technology China

ISBN: (纸本)9798400718137

This study aims to evaluate the effectiveness of an emerging software engineering development method through AB testing experiments. As a commonly used online experimental method, AB testing is used to compare the performance of two or more solutions. It has been widely used in the software development process to optimize user experience and function implementation. This study proposes a new development framework that combines AB testing and software testing, which quantifies the impact of different development strategies on software quality by constructing a correlation matrix. In order to verify the effectiveness of this method, this paper designed a series of experiments and implemented AB testing in multiple software projects. Experimental results show that by accurately adjusting the variable combinations in AB testing, software stability and user satisfaction can be significantly improved. In addition, this paper also develops a set of computer algorithm models to simulate different development scenarios and predict the potential benefits of new development methods through simulation technology. Through the analysis of simulation results, this article found that projects using new development methods have improved in key performance indicators, especially in shortening development cycles and reducing defect rates. The development method that combines AB testing and software testing not only improves the quality of software products, but also enhances the flexibility and response speed of the development process. © 2024 Copyright held by the owner/author(s).

关键词： Software quality

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Modeling and Control of Differential-Drive Chassis for a Homecare Assistive robot 32

Modeling and Control of Differential-Drive Chassis for a Hom...

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32nd IEEE International Symposium on Industrial Electronics, ISIE 2023

作者： He, Ping Lv, Honghao Wu, Haiteng Yang, Geng Polytechnic Institute State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang University Hangzhou China Zhejiang University State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Enginerring Hangzhou China Hangzhou Shenhao Technology Co. LTD. Zhejiang Key Laboratory of Intelligent Operation and Maintenance Robot Hangzhou China Zhejiang University Zhejiang Key Laboratory of Intelligent Operation and Maintenance Robot State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Enginerring Hangzhou China

ISBN: (纸本)9798350399714

With the increasing of the aging population, the demand for homecare services is escalating and the homecare assistive robots are becoming increasingly popular. In this paper, a differential mobile chassis design for a homecare assistive robot is presented. The mobile chassis is made of aluminum sheet and profile, and adopts a two-wheel differential model as the kinematic model. The STM32 single chip microcomputer is used as the cerebellum to control chassis movement, and the NVIDIA Jetson Nano Developer Kit is employed as the brain for perception and decision-making. The software architecture is based on the back-fore ground system and the ROS. A prototype is developed and experiments on simultaneous localization and mapping (SLAM) as well as autonomous navigation experiments are conducted to verify the practicality of the chassis design. © 2023 IEEE.

关键词： Chassis

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PINN-based neural network for photoelastic stress recovery

PINN-based neural network for photoelastic stress recovery

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2023 Advanced Fiber Laser Conference, AFL 2023

作者： Lin, Peng Tao, Bo Wang, Yan Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan University of Science and Technology Wuhan China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan China Precision Manufacturing Institute Wuhan University of Science and Technology Wuhan China Research Center for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9781510677661

Optical measurement techniques have emerged as a promising alternative to traditional invasive methods for stress recovery. Recent developments in artificial intelligence have enabled researchers to harness Physics-Informed Neural Networks (PINNs) for solving complex physics-based problems, including stress field recovery from photoelastic fringes. This paper explores the potential of PINNs for non-invasive stress analysis, presenting a novel methodology that integrates photoelastic fringes and physical stress distribution constraints within a deep learning framework. Our results demonstrate the model's robustness in handling diverse stress patterns and its ability to accurately recover stress distributions even when trained on significantly reduced datasets. A comparative analysis shows our PINN-based model's performance surpassing or closely matching existing methods like PhotoelastNet, underscoring its competitive advantage in stress recovery. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Stress concentration

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Object Detection with Depth Information in Road Scenes 8th

Object Detection with Depth Information in Road Scenes

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8th International Conference on Cognitive Systems and Information Processing, ICCSIP 2023

作者： Liu, Ruowang Chen, Xinbo Tao, Bo Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan University of Science and Technology Wuhan China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan China Precision Manufacturing Institute Wuhan University of Science and Technology Wuhan China Research Center for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9789819980208

In recent years, depth estimation has witnessed significant advancements because of the development of deep learning. It's important to note that depth estimation tasks focus solely on predicting the depth of each pixel in an image and do not include object detection or object recognition. Depth estimation is the use of pixel transformations in the image to obtain distance information from each point in the scene to the camera to generate a depth map. Object detection is the process of classifying and localizing an image, given a picture, so as to identify the objects in the picture and determine their location. To overcome this limitation and integrate object detection into the depth estimation process, this paper proposes a novel self-supervised monocular depth estimation algorithm that leverages an attention mechanism. By combining object detection and depth estimation, a real-time multi-task model is designed to enable simultaneous detection and depth estimation of objects. The framework comprises four essential components: an object detection sub-network, a depth estimation sub-network, a lateral sharing unit, and an attention loss. These components work collaboratively to enhance distance estimation accuracy for objects and improve the object detection performance. Throughout experiments, it is evident that the proposed approach can effectively estimate distances to objects and enhances the accuracy of object detection. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Object detection

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Research on defect detection method for thin-walled parts based on point cloud data processing

Research on defect detection method for thin-walled parts ba...

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IEEE International Conference on Mechatronics and Automation

作者： Yongxiang Jiang Baihui Li Hongchang Sun Guifen Sun Sanpeng Deng Yingwei Qiao Institute of Robot and Intelligent Equipment Tianjin Intelligent Robot Technology and Application Enterprise Key Laboratory School of Mechanical Engineering Tianjin University of Technology and Education Tianjin City China

ISBN: (数字)9798350388077

ISBN: (纸本)9798350388084

External impacts for Thin walled components can cause surface depressions and result in serious consequences. In order to detect these depressions , this article performs point cloud filtering and simplification prepossessing on the obtained defect point cloud, and uses the defective point cloud to fit a standard point cloud model; We used a point cloud feature description algorithm based on Fast Point Feature Histogram (FPFH) and the Nearest Point Iteration (ICP) algorithm to perform coarse and fine registration on two point clouds. Finally, we calculated the Euclidean distanceto defect the registered point clouds.

关键词： Point cloud compression Performance evaluation Three-dimensional displays Feature extraction Depression Size measurement Surface fitting

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Updating Large Language Models' Memories with Time Constraints

Updating Large Language Models' Memories with Time Constrain...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Wu, Xin Bu, Yuqi Cai, Yi Wang, Tao School of Software Engineering South China University of Technology China Key Laboratory of Big Data and Intelligent Robot South China University of Technology Ministry of Education China Department of Biostatistics and Health Informatics King's College London United Kingdom

ISBN: (纸本)9798891761681

By incorporating the latest external knowledge, large language models (LLMs) can modify their internal memory. However, in practical applications, LLMs may encounter outdated information, necessitating the filtering of such data and updating of knowledge beyond internal memory. This paper explores whether LLMs can selectively update their memories based on the time constraints between internal memory and external knowledge. We evaluate existing LLMs using three types of data that exhibit different time constraints. Our experimental results reveal the challenges most LLMs face with time-constrained knowledge and highlight the differences in how various LLMs handle such information. Additionally, to address the difficulties LLMs encounter in understanding time constraints, we propose a two-stage decoupling framework that separates the identification and computation of time constraint into a symbolic system. Experimental results demonstrate that the proposed framework yields an improvement of over 60% in ChatGPT's performance, and achieves a 12-24% enhancement in state-of-the-art LLM GPT-4. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Keeping Workers Safe in Electric Working: A robot System for High-Voltage Live Operation

Keeping Workers Safe in Electric Working: A Robot System for...

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2023 IEEE International Conference on Industrial technology, ICIT 2023

作者： Wang, Longqiang Wang, Ruohan Wu, Haiteng Yang, Geng School of Mechanical Engineering Zhejiang University State Key Laboratory of Fluid Power & Mechanical Systems Hangzhou China Hangzhou Shenhao Technology Co. LTD. Zhejiang Key Laboratory of Intelligent Operation and Maintenance Robot Hangzhou China

ISBN: (纸本)9798350336504

In conventional high-voltage live work, operators are required to work near high-voltage cables, which poses risks such as falling and getting an electric shock. To promise the worker's safety, regional breakout of power is one of the common methods that the power sector would deploy. However, with the development of society, a lot of trouble will be brought about due to the economic loss and social impact caused by regional power outages, which pose higher requirements for the power sector. Hence, it is demanding to carry out a safe and effective method for high-voltage live work without power failure. In such a context, we have developed a dual-arm collaborative robotic system for high-voltage live work. The system includes a human-computer interaction interface and a pair of Omega handles, which allow workers to operate remotely at the control center. In laboratory experiments, the system can accurately complete stripping, trimming, and wiring operations. Also, some actions are pre-programmed inside the control system to realize simple operations automatically. This dual-arm robot collaboration system with a teleoperation function can greatly reduce the labor intensity of workers and ensure their safety. © 2023 IEEE.

关键词： Remote control

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Reinforcement Learning Framework for Delivery Capacity Scheduling with Vehicle Constraints 20

Reinforcement Learning Framework for Delivery Capacity Sched...

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20th IEEE International Conference on e-Business Engineering, ICEBE 2024

作者： Ma, Zhongneng Qin, Zhuorui Shi, Shuxia Wu, Qingyao South China University of Technology Guangzhou China School of Software Engineering South China University of Technology China Key Laboratory of Big Data and Intelligent Robot Ministry of Education China Guangzhou Henll Electronics Equipment Co. Ltd Guangzhou China Peng Cheng Laboratory China

ISBN: (纸本)9798350365856

Improving the risk prevention capability of grid operations is a crucial requirement for smart resilient grids, where efficient dispatch and scheduling of delivery capacity play a pivotal role in enhancing grid resilience. Therefore, it is necessary to develop targeted intelligent dispatch algorithms based on operational data. To address this issue, this paper proposes a framework with inherent constraints and utilizes reinforcement learning using recursive neural networks. A constraint module is designed to identify available vehicle combinations based on de-livery capacity, allocate goods considering vehicle number, load, and size constraints, and generate delivery routes sequentially. Additionally, a reinforcement learning model is introduced, using negative trip length as a reward signal, optimizing parameters of the recursive neural network through policy gradient methods to automatically improve strategies for selecting optimal deliv-ery routes with improved vehicle combinations. To validate the effectiveness of this algorithm, extensive experiments were conducted using both real-world application validation data and the TSPLIB public dataset. Within a limited computational budget, the proposed algorithm outperforms several mature methods in terms of effectiveness and real-time performance, achieving near-optimal results in Euclidean graphs with nearly 100 nodes. These results demonstrate that this approach effectively addresses the intelligent scheduling of delivery capacity in digital twin systems for power storage © 2024 IEEE.

关键词： Budget control

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Chinese Text Paraphrase Recognition Based on OpenPrompt Introducing Hybrid Prompts

Chinese Text Paraphrase Recognition Based on OpenPrompt Intr...

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2022 International Conference on Cloud Computing, Performance Computing, and Deep Learning, CCPCDL 2022

作者： Mao, Chengbo Lu, Menglin Zhao, Tongzhou School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology China

ISBN: (纸本)9781510655867

The fine-tune paradigm adopted by traditional paraphrase recognition tasks cannot fully exploit the knowledge of pretrained language models (PLMs). At this stage, the Prompt paradigm reconstructs downstream tasks by constructing templates to make it more suitable for the training form of PLM. However, there are many processes, the code base is not supervised, and a single discrete template limits the model prediction ability. In response to this problem, this paper proposes a method of introducing hybrid prompts based on OpenPrompt. OpenPrompt makes the Prompt process have a unified framework, and hybrid prompts solve the problem that discrete templates cannot fully mine PLM knowledge. This paper first constructs a hybrid template with [mask] slots, and then transforms the original input through the template to obtain xprompt, and then xprompt is input to the bert-base-chinese model of the multi-layer bidirectional transformer based on the attention mechanism for training. When the model trains the optimal prompt, calculate the label with the highest probability of filling in the label set, and finally map the label to the prediction result. The experimental results show that compared with the fine-tune paradigm, the F1 value of the prompt paradigm exceeds 4.69% under the same PLM and hyperparameters. Compared with not using soft prompt, the average accuracy and average F1 value obtained with soft hints are 2.294% and 2.31% higher, respectively. Meanwhile, when the number of soft hints is 6, the accuracy and F1 value reach the highest. © 2022 SPIE.

关键词： Character recognition

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