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International Conference on intelligent Transportation

作者： Andrew Holliday Gregory Dudek Mobile Robotics Laboratory Center for Intelligent Machines School of Computer Science McGill University Montreal Canada

This paper considers the use of deep learning models to enhance optimization algorithms for transit network design. Transit network design is the problem of determining routes for transit vehicles that minimize travel time and operating costs, while achieving full service coverage. State-of-the-art meta-heuristic search algorithms give good results on this problem, but can be very time-consuming. In contrast, neural networks can learn sub-optimal but fast-to-compute heuristics based on large amounts of data. Combining these approaches, we develop a fast graph neural network model for transit planning, and use it to initialize state-of-the-art search algorithms. We show that this combination can improve the results of these algorithms on a variety of metrics by up to 17%, without increasing their run time; or they can match the quality of the original algorithms while reducing the computing time by up to a factor of 50.

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Ancient Character Recognition: A Novel Image Dataset of Shui Manuscript Characters and Classification Model

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Chinese Journal of Electronics 2023年第1期32卷 64-75页

作者： TANG Minli XIE Shaomin LIU Xiangrong Department of Computer Science and Technology School of Informatics Xiamen University School of Big Data Engineering KaiLi University Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism Xiamen University

Shui manuscripts are part of the national intangible cultural heritage of China. Owing to the particularity of text reading, the level of informatization and intelligence in the protection of Shui manuscript culture is not adequate. To address this issue, this study created Shuishu＿C, the largest image dataset of Shui manuscript characters that has been reported. Furthermore, after extensive experimental validation, we proposed ShuiNet-A,a lightweight artificial neural network model based on the attention mechanism, which combines channel and spatial dimensions to extract key features and finally recognize Shui manuscript characters. The effectiveness and stability of ShuiNet-A were verified through multiple sets of experiments. Our results showed that, on the Shui manuscript dataset with 113 categories, the accuracy of ShuiN et-A was 99.8%, which is 1.5% higher than those of similar studies. The proposed model could contribute to the classification accuracy and protection of ancient Shui manuscript characters.

关键词： Training Speech recognition Artificial neural networks Feature extraction Stability analysis Character recognition Machine translation

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Wearable Detection Application of Protective Equipment for Live Working Based on YOLOv9 8

Wearable Detection Application of Protective Equipment for L...

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8th International Conference on Smart Grid and Smart Cities, ICSGSC 2024

作者： Tang, Pan Xiao, Bin Su, Ziming Gao, Fangqun China Electric Power Research Institute WuHan Branch State Key Laboratory Of Power Grid Environmental Protection WuHan China Wuhan Institute of Technology Hubei Key Laboratory of Intelligent Robot Computer and Science Department WuHan China

ISBN: (纸本)9798350377408

In order to solve the problems of relying on manual supervision and low degree of automation in the wearing of safety protective equipment for non-outage workers in the power sector, this paper proposes an automatic detection model based on improved YOLOv9. Firstly, a self-calibrating convolution is designed, which can explicitly expand the field of view of each convolution layer, thus enriching the output features and enhancing the feature extraction ability of the model for targets in complex environments. Secondly, the model down-sampling module is improved. In order to reduce the loss of information caused by the down-sampling stage, a down-sampling module based on wavelet transform is designed to maximize the integrity of information. Finally, through experiments on the self-built live working safety wearable data set, the average accuracy mean is used as the model evaluation index, and compared with the existing target detection algorithm. The experimental results show that the im-proved algorithm proposed in this paper has a significant improvement in detection accuracy and real-time performance. © 2024 IEEE.

关键词： Wavelet transforms

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Conditional selection with CNN augmented transformer for multimodal affective analysis

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 917-931页

作者： Jianwen Wang Shiping Wang Shunxin Xiao Renjie Lin Mianxiong Dong Wenzhong Guo College of Computer and Data Science Fuzhou UniversityFuzhouChina College of Computer and Cyber Security Fujian Normal UniversityFuzhouChina Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhouChina Digital Fujian Institute of Big Data Security Technology Fujian Normal UniversityFuzhouChina Department of Sciences and Informatics Muroran Institute of TechnologyMuroranJapan

Attention mechanism has been a successful method for multimodal affective analysis in recent years. Despite the advances, several significant challenges remain in fusing language and its nonverbal context information. One is to generate sparse attention coefficients associated with acoustic and visual modalities, which helps locate critical emotional se-mantics. The other is fusing complementary cross‐modal representation to construct optimal salient feature combinations of multiple modalities. A Conditional Transformer Fusion Network is proposed to handle these problems. Firstly, the authors equip the transformer module with CNN layers to enhance the detection of subtle signal patterns in nonverbal sequences. Secondly, sentiment words are utilised as context conditions to guide the computation of cross‐modal attention. As a result, the located nonverbal fea-tures are not only salient but also complementary to sentiment words directly. Experi-mental results show that the authors’ method achieves state‐of‐the‐art performance on several multimodal affective analysis datasets.

关键词： affective computing data fusion information fusion multimodal approaches

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Multiple Random Feature Superpixel-Based Fuzzy Clustering for Image Segmentation 9

Multiple Random Feature Superpixel-Based Fuzzy Clustering fo...

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9th International Conference on Information, Cybernetics, and Computational Social Systems, ICCSS 2023

作者： Zhang, Chuanbin Chen, Long Tian, Yifei Shi, Zhaoyin Wang, Yingxu University of Macau Department of Computer and Information Science China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Shandong Provincial Key Laboratory of Network-Based Intelligent Computing University of Jinan Jinan China

ISBN: (纸本)9798350342239

Random feature maps attempt to approximate the kernel method with low computational complexity, and they are efficient and effective algorithms for dealing with the non-linear structure of data. Nevertheless, the existing random feature-based methods are sensitive to hyper-parameters, and their performances are affected by the randomness in feature maps. To improve the robustness of random feature maps and further speed up kernel-based image segmentation algorithms, a multiple random feature superpixel-based fuzzy clustering is proposed in this paper. First, we generate a group of random features from the original image via a novel multiple random feature scheme. Then, we use the superpixel segmentation to reduce the scale of multiple random features. The final segmentation results are obtained by performing the multi-view fuzzy clustering on the multiple random feature superpixels. Experiments on two shared benchmark data sets verify the superiority of our approach. © 2023 IEEE.

关键词： Image segmentation

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Fault Diagnostic Opportunities for Robot Servo Motors Using the Physics of Failure Analysis 14

Fault Diagnostic Opportunities for Robot Servo Motors Using ...

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14th International Conference on Reliability, Maintainability and Safety, ICRMS 2023

作者： Zhang, Bo Liang, Shan Wang, Kai Key Laboratory of Networked Control Systems Shenyang Institute of Automation Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China College of Computer Science and Technology University of Chemical Technology Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9798350329988

Joint robots are widely used in various industries, such as aviation, aerospace, and automotive manufacturing. The performance degradation of servo motors can significantly affect the overall performance of the robots because of the core component of joint robots, and therefore, it is critical to detect and address servo motor failures promptly. In this study, we analyze some common causes and mechanisms of performance degradation in robot servo motors using the physics of failure analysis. We also discuss strategies for detecting and diagnosing faults using these mechanisms before the servo motors fail completely. © 2023 IEEE.

关键词： Servomotors

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The optimization of deep learning classification through the comparison between different algorithm by using LABR

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Procedia computer science 2024年 246卷 3575-3584页

作者： Rasha kadhem Souheyl Mallat Mounir Zrigui Department of Computer Science Faculty of Sciences of Monastir University of Monastir Department of Computer Science University of ALQadisiyah Iraq Research Laboratory in Algebra Numbers theory and Intelligent Systems Monastir Tunisia

The deep learning model depends on the specific requirements of our recommender system and the characteristics of the Arabic textual data we are working with it Consider experimenting with multiple models to determine which one performs best for our particular use case, in our case we used Large Arabic Book Review (LABR) is a large sentiment analysis dataset for the Arabic language. It consists of over 63,000 book reviews and Other Datasets such as Amazon Products reviews that we can translate to Arabic and then use for the task, each dataset are tested by three models of deep learning (CNN, RNN, LSTM)and classified by three method (rating, polarity, balance polarity)classification for three type of feature extraction (Bow, TF-IDF, word embedding) after do train 70% and test 30% we get based on the results and the task at hand, if CNN (Convolutional Neural Network) has proven in both training and testing phases to be the most effective among LSTM (Long Short-Term Memory) and RNN (Recurrent Neural Network).

关键词： Sentiment analysis LABR LSTM CNN model RNN model

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DETECTING MACHINE-GENERATED TEXTS BY MULTI-POPULATION AWARE OPTIMIZATION FOR MAXIMUM MEAN DISCREPANCY 12

DETECTING MACHINE-GENERATED TEXTS BY MULTI-POPULATION AWARE ...

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12th International Conference on Learning Representations, ICLR 2024

作者： Zhang, Shuhai Song, Yiliao Yang, Jiahao Li, Yuanqing Han, Bo Tan, Mingkui South China University of Technology China Pazhou Laboratory China The University of Adelaide Australia Key Laboratory of Big Data and Intelligent Robot Ministry of Education China Department of Computer Science Hong Kong Baptist University Hong Kong

Large language models (LLMs) such as ChatGPT have exhibited remarkable performance in generating human-like texts. However, machine-generated texts (MGTs) may carry critical risks, such as plagiarism issues, misleading information, or hallucination issues. Therefore, it is very urgent and important to detect MGTs in many situations. Unfortunately, it is challenging to distinguish MGTs and human-written texts because the distributional discrepancy between them is often very subtle due to the remarkable performance of LLMs. In this paper, we seek to exploit maximum mean discrepancy (MMD) to address this issue in the sense that MMD can well identify distributional discrepancies. However, directly training a detector with MMD using diverse MGTs will incur a significantly increased variance of MMD since MGTs may contain multiple text populations due to various LLMs. This will severely impair MMD's ability to measure the difference between two samples. To tackle this, we propose a novel multi-population aware optimization method for MMD called MMD-MP, which can avoid variance increases and thus improve the stability to measure the distributional discrepancy. Relying on MMD-MP, we develop two methods for paragraph-based and sentence-based detection, respectively. Extensive experiments on various LLMs, e.g., GPT2 and ChatGPT, show superior detection performance of our MMD-MP. The source code is available at https://***/ZSHsh98/MMD-MP. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

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Real-Time Imitation of Human Head Motions, Blinks and Emotions by Nao Robot: A Closed-Loop Approach 11

Real-Time Imitation of Human Head Motions, Blinks and Emotio...

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11th RSI International Conference on robotics and Mechatronics, ICRoM 2023

作者： Rayati, Keyhan Feizi, Amirhossein Beigy, Alireza Shahverdi, Pourya Masouleh, Mehdi Tale Kalhor, Ahmad Louie, Wing-Yue Geoffrey University of Tehran Human and Robot Interaction Lab Electrical and Computer Engineering Tehran Iran Shahrood University of Technology Department of Electrical Engineering Shahrood Iran Oakland University Intelligent Robotics Laboratory Michigan United States

ISBN: (纸本)9798350308105

This paper introduces a novel approach for enabling real-time imitation of human head motion by a Nao robot, with a primary focus on elevating human-robot interactions. By using the robust capabilities of the MediaPipe as a computer vision library and the DeepFace as an emotion recognition library, this research endeavors to capture the subtleties of human head motion, including blink actions and emotional expressions, and seamlessly incorporate these indicators into the robot's responses. The result is a comprehensive framework which facilitates precise head imitation within human-robot interactions, utilizing a closed-loop approach that involves gathering real-time feedback from the robot's imitation performance. This feedback loop ensures a high degree of accuracy in modeling head motion, as evidenced by an impressive R2 score of 96.3 for pitch and 98.9 for yaw. Notably, the proposed approach holds promise in improving communication for children with autism, offering them a valuable tool for more effective interaction. In essence, proposed work explores the integration of real-time head imitation and real-time emotion recognition to enhance human-robot interactions, with potential benefits for individuals with unique communication needs. © 2023 IEEE.

关键词： Emotion Recognition

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Nested Deep Feature Attention Module for Underwater Image Enhancement 3

Nested Deep Feature Attention Module for Underwater Image En...

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3rd International conference on computer, Big Data and Artificial Intelligence, ICCBDAI 2022

作者： Xiao, Yeqing Wang, Qiang Li, Yupeng Tang, Yandong School of Computer Science and Engineering Shenyang Jianzhu University Shenyang110168 China Key Laboratory of Manufacturing Industrial Integrated Shenyang University Shenyang110044 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China

The captured underwater images always suffer degradations because of absorption and light scattering in water. Thus, underwater image enhancement becomes indispensable as a precondition to carry out underwater tasks. Thus, we put forward an end-to-end structure to solve the problem of underwater image degradation. Our network uses continuously stacked deep-layer feature extraction modules to exploit more significant features. For purpose of enhancing the image contrast, we recover high-frequency information by hiring the nested residual attention groups in feature extraction modules, which can adaptively extract more deeper features. Moreover, channel attention module is hired in residual groups to emphasize important information. Comparing with other state-of-the-art approaches, our network performs best on quantitative and qualitative evaluations at datasets EUVP and UIEBD. © Published under licence by IOP Publishing Ltd.

关键词： Feature extraction

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