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Chinese Control and Decision Conference, CCDC

作者： Lunhui Xu Runnan Xu Mengyao Zhu School of Computer Guangdong University of Science and Technology Dongguan School of Electrical Engineering and Automation Jiangxi University of Science and Technology Ganzhou Department of Electronics Software Engineering Institute of Guangzhou Guangzhou

In order to solve the contradiction between over-luminance and under-luminance in the highway tunnel lighting system, an entrance section luminance curve optimization design method is proposed to the purpose of traffic safety and energy consumption reduction. Firstly, under the constraints of the Rules and Guidelines, the objective function of luminance curves optimization considering over-luminance and under-luminance is constructed, and the four-step optimization model and design parameters of the entrance section luminance curve are given. Then the luminance curve optimization model is evaluated and analyzed in terms of comfort, safety and energy saving ***, combined with the actual tunnel, it is verified that compared with the Rule method, the proposed design method reduces the maximum luminance ratio of adjacent sections by 26.5%, and the lighting step curve in the second half of the entrance section is reduced by 95.7% compared with the adaptive *** power consumption was reduced by 6.1% and 6.3% for HPS and LED lights, respectively, compared to the design method considering only traffic safety.

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Research on Campus Physical Measurement System Based on CNN-LSTM

Research on Campus Physical Measurement System Based on CNN-...

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International Symposium on Distributed Computing and Applications to Business, engineering and Science (DCABES)

作者： Mengzhong Wei Yuting Fu Chao Li Yuanyuan Li Wanli Feng Quanyin Zhu Huaiyin Institute of Technology Computer&Software Engineering Huai'an China

ISBN: (纸本)9781665428903

In response to the 14th Five-Year Plan and the 2035 long-term goal outline, the goals of building smart communities and smart industries are clearly defined. Starting from the campus, there are many physical test projects in colleges and universities. Problem, so the campus physical test is transformable and innovative. This article first proposes the idea of building a smart body measurement system; then, analyzes the CNN-LSTM (Convolutional Neural Network-Long short term memor)algorithm that is mainly used in the construction of the system; secondly, it analyzes the accuracy of the recognition of specific action processes and the debugging process of the actual parameters. Carry out optimization and improvement; finally, make a report on the enforceability of the campus physical test system.

关键词： Industries Smart cities Buildings Debugging Convolutional neural networks Distributed computing Optimization

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SG-Transunet: A segmentation-guided Transformer U-Net model for KRAS gene mutation status identification in colorectal cancer

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computers in Biology and Medicine 2024年 173卷 108293-108293页

作者： Ma, Yulan Guo, Yuzhu Cui, Weigang Liu, Jingyu Li, Yang Wang, Yingsen Qiang, Yan Department of Automation Science and Electrical Engineering Beihang University Beijing100191 China School of Engineering Medicine Beihang University Beijing100191 China School of Medical Technology Beijing Institute of Technology Beijing100081 China School of Software North University of China Taiyuan China College of Computer Science and Technology Taiyuan University of Technology Taiyuan China

Accurately identifying the Kirsten rat sarcoma virus (KRAS) gene mutation status in colorectal cancer (CRC) patients can assist doctors in deciding whether to use specific targeted drugs for treatment. Although deep learning methods are popular, they are often affected by redundant features from non-lesion areas. Moreover, existing methods commonly extract spatial features from imaging data, which neglect important frequency domain features and may degrade the performance of KRAS gene mutation status identification. To address this deficiency, we propose a segmentation-guided Transformer U-Net (SG-Transunet) model for KRAS gene mutation status identification in CRC. Integrating the strength of convolutional neural networks (CNNs) and Transformers, SG-Transunet offers a unique approach for both lesion segmentation and KRAS mutation status identification. Specifically, for precise lesion localization, we employ an encoder–decoder to obtain segmentation results and guide the KRAS gene mutation status identification task. Subsequently, a frequency domain supplement block is designed to capture frequency domain features, integrating it with high-level spatial features extracted in the encoding path to derive advanced spatial-frequency domain features. Furthermore, we introduce a pre-trained Xception block to mitigate the risk of overfitting associated with small-scale datasets. Following this, an aggregate attention module is devised to consolidate spatial-frequency domain features with global information extracted by the Transformer at shallow and deep levels, thereby enhancing feature discriminability. Finally, we propose a mutual-constrained loss function that simultaneously constrains the segmentation mask acquisition and gene status identification process. Experimental results demonstrate the superior performance of SG-Transunet over state-of-the-art methods in discriminating KRAS gene mutation status. © 2024

关键词： Aggregates

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Sentiment Analysis for Product Reviews Based on Deep Learning 2

Sentiment Analysis for Product Reviews Based on Deep Learnin...

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2020 2nd International Conference on Artificial Intelligence Technologies and Application, ICAITA 2020

作者： Xiao, Shaozhang Wang, Hexiang Ling, Zhi Wang, Lanfang Tang, Zhaoxia Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huai'an Jiangsu223003 China

With the popularity of the Internet and e-commerce, the sentiment analysis of text can help users to quickly and accurately obtain effective information they are interested in from massive product reviews to purchase satisfactory products. In this paper, a sentiment analysis system for product reviews was designed based on deep learning, and the digital and electronic products on Jingdong Mall with at least 100,000 reviews were crawled as the training data set. After data pre-processing operations such as word segmentation and removal of stop words for product reviews to remove useless features, the feature vectors were constructed based on the bag of words model and word2vec method, and then three classification algorithms, namely LSTM, Naive Bayes and logistic regression were used to model reviews. The LSTM algorithm is significantly superior to Naive Bayes and logistic regression algorithm in the training stage, and provides a reliable reference for the analysis of product reviews. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Sentiment analysis

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UncTrack: Reliable Visual Object Tracking With Uncertainty-Aware Prototype Memory Network

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IEEE Transactions on Image Processing 2025年 34卷 3533-3546页

作者： Siyuan Yao Yang Guo Yanyang Yan Wenqi Ren Xiaochun Cao School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Cyber Science and Technology Sun Yat-sen University Shenzhen Campus Shenzhen China

Transformer-based trackers have achieved promising success and become the dominant tracking paradigm because of their accuracy and efficiency. Despite the substantial progress, most of the existing approaches handle object tracking as a deterministic coordinate regression problem, while the target localization uncertainty has been largely overlooked, which hampers trackers’ ability to maintain reliable target state prediction in challenging scenarios. To address this issue, we propose UncTrack, a novel uncertainty-aware transformer-based tracker that predicts the target localization uncertainty and incorporates this uncertainty information for accurate target state inference. Specifically, UncTrack uses a transformer encoder to perform feature interactions between the template and search images. The output features are passed into an uncertainty-aware localization decoder (ULD) to coarsely predict the corner-based localization and the corresponding localization uncertainty. Then, the localization uncertainty is sent into a prototype memory network (PMN) to excavate valuable historical information to identify whether the target state prediction is reliable. To enhance the template representation, the samples with high confidence are fed back into the prototype memory bank for memory updating, which makes the tracker more robust to challenging appearance variations. Extensive experiments demonstrate that our method outperforms the state-of-the-art methods. Our code is available at https://***/ManOfStory/UncTrack

关键词： Uncertainty Location awareness Target tracking Transformers Reliability Prototypes Object tracking Visualization Accuracy Training

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A Real-time Cutting Model Based on Finite Element and Order Reduction

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computer Systems Science & engineering 2022年第10期43卷 1-15页

作者： Xiaorui Zhang Wenzheng Zhang Wei Sun Hailun Wu Aiguo Song Sunil Kumar Jha Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing210044China State Key Laboratory of Bioelectronics Jiangsu Key Lab of Remote Measurement and ControlSchool of Instrument Science and EngineeringSoutheast UniversityNanjing210096China IT Fundamentals and Education Technologies Applications University of Information Technology and Management in RzeszowRzeszow Voivodeship100031Poland

Telemedicine plays an important role in Corona Virus Disease 2019(COVID-19).The virtual surgery simulation system,as a key component in telemedicine,requires to compute in ***,this paper proposes a realtime cutting model based on finite element and order reduction method,which improves the computational speed and ensure the real-time *** proposed model uses the finite element model to construct a deformation model of the virtual ***,a model order reduction method combining proper orthogonal decomposition and Galerkin projection is employed to reduce the amount of deformation *** addition,the cutting path is formed according to the collision intersection position of the surgical instrument and the lesion area of the virtual ***,the Bezier curve is adopted to draw the incision outline after the virtual lung has been ***,the simulation system is set up on the PHANTOM OMNI force haptic feedback device to realize the cutting simulation of the virtual *** results show that the proposed model can enhance the real-time performance of telemedicine,reduce the complexity of the cutting simulation and make the incision smoother and more natural.

关键词： Virtual surgery cutting model finite element model model order reduction Bezier curve

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CardiacMamba: A Multimodal RGB-RF Fusion Framework with State Space Models for Remote Physiological Measurement

arXiv

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arXiv 2025年

作者： Wu, Zheng Xie, Yiping Zhao, Bo He, Jiguang Luo, Fei Deng, Ning Yu, Zitong School of Computing and Information Technology Great Bay University Dongguan523000 China Dongguan Key Laboratory for Intelligence and Information Technology Dongguan523000 China Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University Shenzhen518060 China

Heart rate (HR) estimation via remote photoplethysmography (rPPG) offers a non-invasive solution for health monitoring. However, traditional single-modality approaches (RGB or Radio Frequency (RF)) face challenges in balancing robustness and accuracy due to lighting variations, motion artifacts, and skin tone bias. In this paper, we propose CardiacMamba, a multimodal RGB-RF fusion framework that leverages the complementary strengths of both modalities. It introduces the Temporal Difference Mamba Module (TDMM) to capture dynamic changes in RF signals using timing differences between frames, enhancing the extraction of local and global features. Additionally, CardiacMamba employs a Bidirectional SSM for cross-modal alignment and a Channel-wise Fast Fourier Transform (CFFT) to effectively capture and refine the frequency domain characteristics of RGB and RF signals, ultimately improving heart rate estimation accuracy and periodicity detection. Extensive experiments on the EquiPleth dataset demonstrate state-of-the-art performance, achieving marked improvements in accuracy and robustness. CardiacMamba significantly mitigates skin tone bias, reducing performance disparities across demographic groups, and maintains resilience under missing-modality scenarios. By addressing critical challenges in fairness, adaptability, and precision, the framework advances rPPG technology toward reliable real-world deployment in healthcare. The codes are available at: https://***/WuZheng42/CardiacMamba. Copyright © 2025, The Authors. All rights reserved.

关键词： Fast Fourier transforms

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Generalizing 6-DoF Grasp Detection via Domain Prior Knowledge

arXiv

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arXiv 2024年

作者： Ma, Haoxiang Shi, Modi Gao, Boyang Huang, Di State Key Laboratory of Software Development Environment Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China School of Computer Science and Technology Harbin Institute of Technology Harbin China Geometry Robotics China

We focus on the generalization ability of the 6-DoF grasp detection method in this paper. While learning-based grasp detection methods can predict grasp poses for unseen objects using the grasp distribution learned from the training set, they often exhibit a significant performance drop when encountering objects with diverse shapes and structures. To enhance the grasp detection methods’ generalization ability, we incorporate domain prior knowledge of robotic grasping, enabling better adaptation to objects with significant shape and structure differences. More specifically, we employ the physical constraint regularization during the training phase to guide the model towards predicting grasps that comply with the physical rule on grasping. For the unstable grasp poses predicted on novel objects, we design a contact-score joint optimization using the projection contact map to refine these poses in cluttered scenarios. Extensive experiments conducted on the GraspNet-1billion benchmark demonstrate a substantial performance gain on the novel object set and the real-world grasping experiments also demonstrate the effectiveness of our generalizing 6-DoF grasp detection method. Code is available at https://***/mahaoxiang822/ Generalizing-Grasp. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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Defining and Detecting the Defects of the Large Language Model-based Autonomous Agents

arXiv

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arXiv 2024年

作者： Ning, Kaiwen Chen, Jiachi Zhang, Jingwen Li, Wei Wang, Zexu Feng, Yuming Zhang, Weizhe Zheng, Zibin The School of Software Engineering Sun Yat-Sen University Peng Cheng Laboratory China The School of Software Engineering Sun Yat-Sen University China The School of Computer Science and Technology Harbin Institute of Technology China Peng Cheng Laboratory China The Department of New Networks Peng Cheng Laboratory China

Artificial intelligence (AI) agents are systems capable of perceiving their environment, autonomously planning and executing tasks. Recent advancements in Large Language Models (LLMs) have introduced a transformative paradigm for AI agents, enabling them to interact with external resources and tools through prompt techniques. This advancement has significantly extended the capabilities of LLMs, positioning LLM-based AI Agents as an important research area. In such agents, the workflow integrates developer-written code, which manages framework construction and logic control, with LLM-generated natural language that enhances dynamic decision-making and interaction. However, discrepancies between developer-implemented logic and the dynamically generated content of LLMs in terms of behavior and expected outcomes can lead to defects, such as tool invocation failures and task execution errors. These issues introduce specific risks, leading to various defects in LLM-based AI Agents, including service interruptions and incorrect output. Despite the importance of these issues, there is a lack of systematic work that focuses on analyzing LLM-based AI Agents to uncover defects in their code. To address this gap, we present the first study focused on identifying and detecting defects in LLM Agents. We collected and analyzed 6, 854 relevant posts from StackOverflow to define and classify eight types of agent defects. For each defect type, we provided detailed descriptions and illustrated with an example. Then, we designed a static analysis tool, named Agentable, to detect the defined defects. Agentable leverages Code Property Graphs (CPGs) and LLMs to analyze Agent workflows by efficiently identifying specific code patterns and analyzing natural language descriptions. To evaluate Agentable, we constructed two datasets: AgentSet, which consists of 84 real-world Agent projects, and AgentTest, which contains 78 Agent projects specifically designed to include various types of defect

关键词： Autonomous agents

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Efficient Routing Protection Algorithm Based on Optimized Network Topology

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computers, Materials & Continua 2022年第9期72卷 4525-4540页

作者： Haijun Geng Zikun Jin Jiangyuan Yao Han Zhang Zhiguo Hu Bo Yang Yingije Guo Wei Wang Qidong Zhang Guoao Duan School of Computer and Information Technology Shanxi UniversityShanxi030006China School of Automation and Software Engineering Shanxi UniversityShanxi030006China School of Computer Science and Technology Hainan UniversityHaikou570228China Department of Computer Science&Technology Tsinghua UniversityBeijing100084China Department of Urban&Regional Planning San JoséState University San JoséCA95192USA Key Laboratory of Embedded System and Service Computing(Tongji University) Ministry of EducationShanghai201804China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengdu610054China Beijinng University of Technology Beijing-Dublin International CollegeBeijing University of TechnologyBeijing100124China

Network failures are unavoidable and occur *** the network fails,intra-domain routing protocols deploying on the Internet need to undergo a long convergence *** this period,a large number of messages are discarded,which results in a decline in the user experience and severely affects the quality of service of Internet Service Providers(ISP).Therefore,improving the availability of intra-domain routing is a trending research question to be *** usually employs routing protection algorithms to improve intra-domain routing ***,existing routing protection schemes compute as many backup paths as possible to reduce message loss due to network failures,which increases the cost of the network and impedes the methods deployed in *** address the issues,this study proposes an efficient routing protection algorithm based on optimized network topology(ERPBONT).ERPBONT adopts the optimized network topology to calculate a backup path with the minimum path coincidence degree with the shortest path for all source ***,the backup path with the minimum path coincidence with the shortest path is described as an integer programming *** the simulated annealing algorithm ERPBONT is used to find the optimal ***,the algorithm is tested on the simulated topology and the real *** experimental results show that ERPBONT effectively reduces the path coincidence between the shortest path and the backup path,and significantly improves the routing availability.

关键词： Routing protection algorithm network availability network failure shortest path backup path

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