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14th IEEE International Conference on Cyber technology in Automation, Control, and Intelligent Systems, CYBER 2024

作者： Xu, Zihang Yang, Xinyu Ruan, Shaocheng Dong, Min Sun, Xianlun Qiu, Wenjie Bi, Sheng School of Computer Science and Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9798331506056

With the global population aging, the need to ensure the health and safety of elderly individuals living alone has become a pressing societal issue. This paper introduces an activity sensing system utilizing millimeter-wave radar and depth camera technology to monitor the movements and behaviors of elderly people residing independently in home environments. The system's architecture features precise indoor positioning and trajectory tracking through the integration of data from both the millimeter wave radar and the depth camera, enhanced by cross-modal supervision. Employing a neural network, the system learns the relationships between the radar and camera data, which significantly improves positioning accuracy. Additionally, a lightweight convolutional neural network is integrated to detect hazardous situations, such as falls, facilitating swift emergency responses. Extensive experiments validate the system's performance, demonstrating successful tracking of movement trajectories and accurate recognition of activities including walking, standing, falling, and sitting. The system also includes a user-friendly front-end platform that facilitates real-time observation of the elderly's location and activities, fostering remote monitoring capabilities. The proposed activity sensing system offers a privacy-preserving and environmentally robust solution tailored to address the growing challenges of elderly care and independent living scenarios. © 2024 IEEE.

关键词： Convolutional neural networks

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Extrinsic Calibration Method for LiDAR and Millimeter-Wave Radar Systems

Extrinsic Calibration Method for LiDAR and Millimeter-Wave R...

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2024 International Conference on Advanced Control Systems and Automation Technologies, ACSAT 2024

作者： Yang, Yanping Ren, Mingwu Nanjing University of Science & Technology School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9798331541668

Millimeter-wave radar, with its high frequency and short wavelength, is increasingly becoming a critical sensor in autonomous driving systems, where precise external calibration with lidar is essential for efficient sensor fusion. However, existing calibration algorithms for these two sensors are still limited. To address this, we propose an offline external calibration method based on a novel 3D calibration target that combines absorbing materials and corner reflectors. Using a point cloud segmentation network, we extract lidar target point clouds and utilize RCS information with clustering to accurately locate radar point clouds, achieving precise data extraction for different sensors. Additionally, we introduce a model coordinate system as a bridge to resolve the spatial relationship between lidar and radar, and enhance the ICP alignment algorithm by calculating an initial alignment matrix with minimal error straight-line alignment, allowing for coarse-to-fine optimization. This approach significantly improves ICP precision and efficiency, with experimental results in various scenarios validating the calibration accuracy and reliability of this method. © 2024 IEEE.

关键词： Calibration

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Grape Leaf Black Rot Detection Based on Lightweight Network 6

Grape Leaf Black Rot Detection Based on Lightweight Network

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6th IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2024

作者： Zhu, Jiajun Liu, Jingyi Zhang, Haifei Xiaojing, Jiang Nantong Institute of Technology School of Computer and Information Engineering Nantong226600 China

ISBN: (纸本)9798350316520

In order to improve the detection of grape leaf black rot and facilitate deployment on mobile devices, this paper introduced a lightweight network detection model called SM-YOLO v4 based on YOLO v4 architecture. The model adopts MobileNet V1 as the backbone feature extraction network, effectively reducing the number of model parameters. Meanwhile, deep separable convolutional blocks are introduced in the feature fusion part of the model to reduce computational complexity. Furthermore, an SPP (Spatial Pyramid Pooling) module is added to enhance the feature extraction process, enabling the fusion of local and global features. The input data is preprocessed using super-resolution image enhancement techniques to enhance the pixel values and improve the model's recognition accuracy. Experimental results show that the SM-YOLOv4 detection model achieves an evaluation metric (P) of 94.85% and a FPS (Frames Per Second) of 1.25s, with a reduction of 80% in model parameters compared to the original *** experiments demonstrate that the improved model can accurately detect grape leaf black rot with strong generalization, real-time capability, and lightweight characteristics, providing valuable insights for deployment on mobile devices. © 2024 IEEE.

关键词： Feature extraction

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Arbitrary Time Information Modeling via Polynomial Approximation for Temporal Knowledge Graph Embedding 30

Arbitrary Time Information Modeling via Polynomial Approxima...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Fang, Zhiyu Qin, Jingyan Zhu, Xiaobin Yang, Chun Yin, Xu-Cheng School of Computer & Communication Engineering University of Science and Technology Beijing Beijing China

ISBN: (纸本)9782493814104

Distinguished from traditional knowledge graphs (KGs), temporal knowledge graphs (TKGs) must explore and reason over temporally evolving facts adequately. However, existing TKG approaches still face two main challenges, i.e., the limited capability to model arbitrary timestamps continuously and the lack of rich inference patterns under temporal constraints. In this paper, we propose an innovative TKGE method (PTBox) via polynomial decomposition-based temporal representation and box embedding-based entity representation to tackle the above-mentioned problems. Specifically, we decompose time information by polynomials and then enhance the model's capability to represent arbitrary timestamps flexibly by incorporating the learnable temporal basis tensor. In addition, we model every entity as a hyperrectangle box and define each relation as a transformation on the head and tail entity boxes. The entity boxes can capture complex geometric structures and learn robust representations, improving the model's inductive capability for rich inference patterns. Theoretically, our PTBox can encode arbitrary time information or even unseen timestamps while capturing rich inference patterns and higher-arity relations of the knowledge base. Extensive experiments on real-world datasets demonstrate the effectiveness of our method. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Polynomial approximation

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Choosing the Right Cloud Provider for Limitless Growth and Maximize ROI

Choosing the Right Cloud Provider for Limitless Growth and M...

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2024 International Conference on Artificial Intelligence and Emerging technology, Global AI Summit 2024

作者： Kumar, Naween Saini, Vaibhav School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9798350379716

The present article explores the most critical criteria organizations must consider in choosing a proper Cloud Service Provider (CSP) to enable growth strategies and optimize Return on Investment (ROI). It assists organizations in decision-making by comparing the critical aspects of cloud computing, including the major services offered, cost structures, and ROI measurements among the top platforms. The first category of comparative analysis is conducted based on cloud service categories and different Service Providers, economic and technical factors to enable businesses to optimize cost efficiency and performance. The second comparative category is evaluated based on scalability, performance, security, compatibility, monthly estimated costs, Potential savings vs. On-premises., and ROI factors. Such analysis allows for an informed decision that maximizes operational efficiency and cost-effectiveness. By combining and evaluating this analysis, organizations can decide which cloud platform to adopt for the best performance and future growth. The final objective is to provide people with the tools needed to harness the power of cloud computing for better efficiency and effectiveness of business technologies to gain a better competitive advantage. © 2024 IEEE.

关键词： Cloud platforms

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The Application of an Improved UCB Algorithm Based on Deep Reinforcement Learning in the Game of Surakarta 6

The Application of an Improved UCB Algorithm Based on Deep R...

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6th International Conference on Frontier Technologies of Information and computer, ICFTIC 2024

作者： Chen, Hongxiang Deng, Tianqi Liu, Ziyuan Wang, Jiajie Liu, Mingshuo Zhao, Guodong School of computer science and technology Harbin Engineering University Harbin150001 China

ISBN: (纸本)9798331541750

Monte Carlo Tree Search (MCTS) is a widely used search algorithm in game theory. Its core idea is to estimate the value of each decision node by simulating random games. The Upper Confidence Bound (UCB) algorithm, as a key node selection strategy within MCTS, balances the exploration-exploitation trade-off. To further enhance the performance of MCTS, this paper proposes an improved approach that utilizes the prior probability P generated by a policy neural network to guide node selection and effectively prune the search tree, thereby improving search efficiency. For the complex game of Surakarta, the paper introduces a control parameter A to adjust the exploration aspect. Additionally, by incorporating a Deep Q Network (DQN), both P and A are optimized through policy adjustment, with the agent continuously interacting with the environment to fine-tune the weight coefficient w and maximize the balanced guidance resulting from the weighted combination. The improved MCTS significantly enhances the decision-making efficiency and game performance of Surakarta, especially in scenarios with numerous pieces. This approach provides a novel perspective for optimizing MCTS. © 2024 IEEE.

关键词： Deep reinforcement learning

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A Fast Spectral Clustering Algorithm Based on Granular-Ball and Similarity Graph Approximation 3

A Fast Spectral Clustering Algorithm Based on Granular-Ball ...

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3rd International Conference on Cloud Computing, Big Data Application and Software engineering, CBASE 2024

作者： Ye, Xueqi Chen, Linshu Hu, Anxing Liang, Wei School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China

ISBN: (纸本)9798331506582

Spectral clustering is widely used due to its effectiveness in handling nonlinear data, but its performance is limited by high computational complexity when processing large-scale datasets. To address this issue, this paper proposes a fast spectral clustering algorithm based on granular-balls and similarity graph approximation (GBASC). Through granular-ball computation, fine-grained data is transformed into coarse-grained granular-balls, reducing the scale of the similarity graph. The paper proposes a fast approximation algorithm for granular-ball similarity graphs based on kernel density estimation, achieving a sparse approximation of the fully connected granular-ball similarity graph. This method significantly reduces the time and space complexity of constructing the granular-ball similarity graph, and by generating sparse matrices, it improves the efficiency of Laplacian matrix eigenvalue decomposition. Experimental results demonstrate that GBASC achieves high clustering performance and computational efficiency on large-scale datasets. © 2024 IEEE.

关键词： Eigenvalues and eigenfunctions

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Instance-Level Neural Feature Selection Based on Disentanglement Enhancement 15

Instance-Level Neural Feature Selection Based on Disentangle...

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15th IEEE International Conference on Knowledge Graph, ICKG 2024

作者： Liu, Zihao Pan, Jun Wang, Hao Yu, Kui Hefei University of Technology School of Computer Science and Information Engineering Hefei China

ISBN: (纸本)9798331508821

Feature selection aims to select the optimal subset from the original features to reduce data dimensionality, eliminate irrelevant features, and improve the interpretability and performance of the model. In recent years, feature selection methods using neural networks have received much attention, but they often lead to suboptimal results due to feature entanglement problems. To this end, we propose a novel instance-level neural feature selection method based on disentanglement enhancement, including a feature selection network, a label prediction network, and a disentanglement bootstrap network. The feature selection network divides the features into strongly and weakly relevant ones, the label prediction network uses the strongly relevant features for supervised learning, and the disentanglement bootstrap network promotes feature disentanglement by minimizing the mutual information between strong and weak features. The designed gradient-based solution algorithm effectively solves the joint optimization problem. Experimental results validate the effectiveness of the method on tabular and image datasets. © 2024 IEEE.

关键词： Feature Selection

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EIDA: An Effective Image Deraining Approach Based on Upscaling Method 2

EIDA: An Effective Image Deraining Approach Based on Upscali...

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2nd IEEE International Conference on Interdisciplinary Approaches in technology and Management for Social Innovation, IATMSI 2024

作者： Jain, Arti Kaliyar, Rohit Kumar Sharma, Shallu Bennett University School of Computer Science Engineering and Technology Greater Noida India

ISBN: (纸本)9798350360523

The process of removing wet pixels from a picture is known as image deraining. As the image is being captured from different angles, the wet pixels add character with it. Many filtering techniques are employed in the literature for picture de-raining. Nevertheless, precipitation caused the image's resolution to decrease. Therefore, In the proposed work, deraining is unified with an upscaling technique to get a rain-free informative, and magnificent image. In the proposed approach, the first deraining is performed with the restormer to remove rainy pixels from the image. The Restormer has been extensively used and preferred as it captures long-range pixel interactions without generating unrealistic pixels. The derained image is then unsampled to have good visual perception quality. The results indicate that the suggested approach outperforms the basic deraining strategies in terms of both PSNR and SSIM. © 2024 IEEE.

关键词： Pixels

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Dynamic strategy to use optimum memory space in real-time video surveillance

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Journal of Ambient Intelligence and Humanized Computing 2023年第3期14卷 2771-2784页

作者： Biswas, Tamal Bhattacharya, Diptendu Mandal, Gouranga Department of Computer Science and Engineering National Institute of Technology Agartala Tripura Agartala India School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India

In present days real-time video surveillance is a very essential aspect of establishing safety, observing traffic, and detecting violence and crimes. Intelligent video surveillance (IVS) is one of the most acknowledged frameworks in a security application. But, video surveillance systems consume huge memory space to store the recorded video, especially in the case of high-resolution recording. However, if we use low-resolution video recording then the video quality will be reduced and the objects/violence cannot be identified clearly. To overcome the stated limitation, this article introduced a smart video surveillance system with a dynamic strategy to use optimum memory space with the best surveillance objective. The proposed system can dynamically record video with high-resolution during suspicious movement of objects/violence as well as low-resolution video at other times. The system is based on the detection of suspicious movement of objects in consecutive frames, saving of suspicious frames with high resolution, and neglecting of less important frames. Finally, the Contrast Limited Adaptive Histogram Equalization (CLAHE) based on Color Channel is applied to all the suspicious frames as a post-processing step for better contract adjustment and to provide a clear view of suspicious objects in frames. The quality of enhanced frames are assessed by two no-reference methods, i.e. NIQMC and BIQME. Multiple visual quality parameters like sharpness, contrast, colorfulness, brightness, and naturalness of frames, etc. are used for assessment Several real-time experiments reveal that the proposed system can detect suspicious objects with 97.65% accuracy. Besides, it consumes 75% less memory space in real-time surveillance. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Video recording

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