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4th Asia-Pacific Artificial Intelligence and Big Data Forum, AIBDF 2024

作者： Cai, Yiting Ma, Cunliang Xiao, Wanhai Jiangxi Provincial Key Laboratory of Multidimensional Intelligent Perception and Control Jiangxi University of Science and Technology Jiangxi Ganzhou China

ISBN: (纸本)9798400710865

Detecting gravitational waves is essential for the success of the LIGO and Virgo experiments, requiring thorough comprehension of how these instruments are affected by environmental and instrumental noise. In particular, the data recorded by the LIGO/Virgo detectors frequently include transient noise events, referred to as"glitches". These glitches can interfere with real gravitational wave signals and, in some cases, their characteristics may resemble those of actual gravitational wave signals, leading to misidentification. Identifying and differentiating these glitches plays a vital role in improving the precision and dependability of gravitational wave observations. LIGO’s initial observation of gravitational waves marked a transformative achievement in expanding our comprehension of the universe. However, because of the detectors’ exceptional sensitivity, they also pick up many other noise sources that can interfere with the observation of gravitational waves. To address this challenge, numerous classification methods have been proposed;however, as model complexity increases, so does the risk of overfitting, requiring more computational resources and resulting in significantly longer training and inference times. The Kolmogorov-Arnold Network (KAN), with its unique high-dimensional mapping and powerful feature extraction capabilities, demonstrates greater robustness and accuracy than traditional methods, thus improving classification performance. In this paper, we explore multi-view classification of gravitational wave glitches using KAN networks, where the linear and convolutional layers of conventional neural networks are replaced with KAN-based structures, and residual connections are incorporated within the KAN modules. © 2024 Copyright held by the owner/author(s).

关键词： Gravitational effects

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Temporal Dynamic Frequency Autoencoder: Enhancing the Performance of Unsupervised Dimensionality Reduction for Gravitational Wave Glitch Clustering 24

Temporal Dynamic Frequency Autoencoder: Enhancing the Perfor...

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4th Asia-Pacific Artificial Intelligence and Big Data Forum, AIBDF 2024

作者： Xiao, Wanhai Ma, Cunliang Cai, Yiting Jiangxi Provincial Key Laboratory of Multidimensional Intelligent Perception and Control Jiangxi University of Science and Technology Jiangxi Ganzhou China

ISBN: (纸本)9798400710865

With the ongoing advancements in gravitational wave detection technology, the results are becoming increasingly precise. However, it is precisely this high precision that has led to the detection of noise signals that should not have been recorded, which are then mixed with real gravitational wave signals. As a result, efficient and accurate algorithms are needed to distinguish between these signals and noise. Past research has mostly relied on fully supervised or semi-supervised learning methods to classify and cluster these glitches, thereby improving the accuracy and reliability of glitch detection. However, neural networks typically require a large amount of training data to perform well, and in future work, it is impractical to manually label a vast number of experimental samples. Additionally, as time progresses, new unlabeled glitches may emerge. To address this issue, we adopted a Temporal Dynamic Frequency Autoencoder (TDFAE) based on a Kolmogorov-Arnold Network (KAN) that integrates Vision Transformers (ViT) and a four-branch encoder. To enhance feature extraction accuracy, we utilized a multi-branch fusion approach with the CLS (Class) token. This innovative unsupervised learning technique focuses on dimensionality reduction and clustering of gravitational wave glitches. Our approach, tested on the GravitySpy O3 dataset, outperforms current semi-supervised learning methods in clustering tasks. Additionally, our work on unsupervised clustering methods for traditional LIGO datasets offers significant potential, representing a key advancement in gravitational wave research. © 2024 Copyright held by the owner/author(s).

关键词： Dimensionality reduction

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Edge Gradient Exploration Attack based on Graph Neural Networks 24

Edge Gradient Exploration Attack based on Graph Neural Netwo...

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4th Asia-Pacific Artificial Intelligence and Big Data Forum, AIBDF 2024

作者： Yang, Shuxin Ge, Ran Jiangxi Province Key Laboratory of Multidimensional Intelligent Perception and Control Jiangxi University of Science and Technology Jiangxi Ganzhou China

ISBN: (纸本)9798400710865

Graph Neural Networks (GNNs) have achieved remarkable success in various graph-structured tasks due to their powerful graph representation learning capabilities. By aggregating and transforming information from the neighboring nodes, GNNs explore the structure of the graph and the features of the nodes. However, recent theoretical and empirical analyses have revealed that GNNs are particularly vulnerable to adversarial attacks. Among these, gradient-based attacks have been widely studied for their inherent efficiency and effectiveness. Nevertheless, due to the discrete nature of graph data, gradient-based attacks often result in suboptimal outcomes. Furthermore, the portability of some previously proposed attack algorithms requires further improvement. To address these two issues, we propose a novel Multi-Network Exploratory Attack (MNEA) aimed at enhancing gradient-based graph perturbations and improving their transferability. MNEA consists of two phases: the generation phase and the reconstruction phase. In the generation phase, multiple networks are utilized to generate a variety of candidate edges, preventing the generated perturbed edges from being too targeted toward a single model, which could lead to decreased transferability. The reconstruction phase introduces multiple edges for recombination, with the objective of avoiding misleading information potentially provided by the maximum gradient. Experimental results that are conducted on three publicly available benchmark datasets demonstrate that MNEA significantly outperforms baseline methods in terms of performance. © 2024 Copyright held by the owner/author(s).

关键词： Generative adversarial networks

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Monocular Depth Estimation Algorithm Guided by Global Structure and Local Details 24

Monocular Depth Estimation Algorithm Guided by Global Struct...

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4th Asia-Pacific Artificial Intelligence and Big Data Forum, AIBDF 2024

作者： Wang, Wenhao Ma, Cunliang Jiangxi Provincial Key Laboratory of Multidimensional Intelligent Perception and Control Jiangxi University of Science and Technology Jiangxi Ganzhou China

ISBN: (纸本)9798400710865

In the current encoder-decoder architecture for monocular depth estimation, there are issues with unclear structural details of depth scene objects during the decoder’s upsampling process, as well as insufficient capability to extract contextual information from the scene. To address these problems, this paper proposes a monocular depth estimation algorithm guided by global structure and local details. The algorithm utilizes a pre-trained Swin-Transformer as the encoder to downsample the monocular image and extract multi-scale features from different levels of encoder representations. During the decoder phase, high-level features are fused with low-level features to create feature guidance maps enriched with structural semantic and positional information. These maps are then refined layer by layer with local detail feature maps output from a context aggregation module. This process ensures the model retains more global and local information during the multi-scale feature fusion. Finally, global structure guidance maps and local detail guidance maps from all layers are combined to predict the final depth map. Extensive experimental results on both indoor and outdoor datasets demonstrate the superiority of the proposed model. Compared to the AdaBins algorithm, our approach reduces RMSE by 10.46% and 7.41% on the KITTI and NYU Depth v2 datasets, respectively. © 2024 Copyright held by the owner/author(s).

关键词： Decoding

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Solar evaporation for simultaneous oil-water separation and electricity generation with Janus wood-based absorbers

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Frontiers of Environmental Science & Engineering 2024年第2期18卷 13-23页

作者： Yue Yang Ze Fu Qi Zhang Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse School of Environmental and Biological EngineeringNanjing University of Science&TechnologyNanjing 210094China Key Laboratory of Industrial Ecology and Environmental Engineering(Ministry of Education) School of Environmental Science and TechnologyDalian University of TechnologyDalian 116024China National Laboratory of Solid State Microstructures Collaborative Innovation Center of Advanced MicrostructuresCollege of Engineering and Applied SciencesKey Laboratory of Intelligent Optical Sensing and Manipulation(Ministry of Education)Jiangsu Key Laboratory of Artificial Functional MaterialsNanjing UniversityNanjing 210093China

Oily wastewater from ocean oil spills endangers marine ecosystems and human health. Therefore, developing an effective and sustainable solution for separating oil-water mixtures is urgent. Interfacial solar photothermal evaporation is a promising approach for the complete separation of two-phase mixtures using only solar energy. Herein, we report a carbonized wood-based absorber with Janus structure of comprising a hydrophobic top-layer and an oleophobic bottom-layer for simultaneous solar-driven oil-water separation and electricity generation. Under sunlight irradiation, the rapid evaporation of seawater will induce a separation of oil-water mixtures, and cause a high salt concentration region underlying the interface, while the bottom “bulk water” maintains in a low salt concentration, thus forming a salinity gradient. Electricity can be generated by salinity gradient power. Therefore, oil-water separation efficiency of > 99% and derived extra electricity power of ~0.1 W/m2 is achieved under solar radiation, demonstrating the feasibility of oil-water separation and electricity production synchronously directly using solar energy. This work provides a green and cost-effective path for the separation of oil-water mixtures.

关键词： Oily wastewater Carbonized wood Salinity gradient Electricity generation Solar irradiation

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Non-Line-of-Sight Identification and Localization Using UWB Based on Deep Learning 24

Non-Line-of-Sight Identification and Localization Using UWB ...

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4th Asia-Pacific Artificial Intelligence and Big Data Forum, AIBDF 2024

作者： Huang, Kangni Yu, Meng Song, Penghui Long, Keliu Jiangxi Provincial Key Laboratory of Multidimensional Intelligent Perception and Control Jiangxi University of Science and Technology Jiangxi Ganzhou China

ISBN: (纸本)9798400710865

Due to its characteristics of high precision, high reliability, and low power consumption, UWB technology is widely used in indoor positioning. However, the performance of UWB positioning systems can be affected by issues such as multipath effects and signal attenuation in both non-line-of-sight (NLOS) and line-of-sight (LOS) environments. This paper proposes an algorithm for NLOS and LOS state identification and localization using UWB, based on deep learning. First, a recognition model suitable for NLOS and LOS environments is established by analyzing UWB channel models and signal characteristics. Then, a method for identifying NLOS and LOS states is designed using deep learning techniques, with classification accuracy metrics introduced to evaluate model performance. To validate the proposed positioning algorithm, a UWB node positioning system is designed and tested in real environments. Experiments were conducted in two different environments-a basement and a lounge-to verify the positioning performance. Subsequently, the deep learning-based NLOS error identification and mitigation algorithm proposed in this paper was tested. The experiments compared the effects of different deep learning algorithms on NLOS error identification and mitigation. Results show that the positioning algorithm using the Convolutional Neural Network Long Short-Term Memory (CNN-LSTM) model performs best in identifying and mitigating NLOS errors, achieving the highest positioning accuracy. The study results demonstrate that the proposed algorithm not only has theoretical value but also shows great potential for practical applications. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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Distributed Group Coordination of Random Communication Constrained Cyber-Physical Systems Using Cloud Edge Computing

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2024年 2卷 196-205页

作者： Ren, Hongru Long, Yinren Ma, Hui Li, Hongyi Guangdong University of Technology School of Automation Guangdong-Hong Kong Joint Laboratory for Intelligent Decision and Cooperative Control Guangdong Provincial Key Laboratory for Intelligent Decision and Cooperative Control Guangzhou510006 China Guangdong University of Technology School of Mathematics and Statistics Guangzhou510006 China Southwest University College of Electronic and Information Engineering Chongqing Key Laboratory of Generic Technology and System of Service Robots Chongqing400715 China

This paper studied the distributed group coordinated control problem of cyber-physical systems (CPSs) with multi-agent architecture. We build the distributed networked multi-group agent systems (NMGASs) with nonlinear and unknown dynamics via cloud edge computing. The common and challenging situations of random communication constraints in CPSs are considered, including network-induced delay, packet dropout, and packet disorder, which are treated as round-trip time (RTT) delay. To actively compensate for RTT delay and achieve coordination among all agents, a data-driven cloud edge predicted control strategy is designed. This strategy only needs to obtain the I/O measurement data of the systems, and can automatically carry out adaptive learning, which has more extensive application scenarios compared to model-based control methods. Theoretical analysis yields the conditions of simultaneous stability and consensus of the closed-loop systems with the proposed strategy. Finally, the practical examples are provided to illustrate the effectiveness of the proposed strategy. © 2023 IEEE.

关键词： Edge computing

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Targets capture by distributed active swarms via bio-inspired reinforcement learning

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Science China(Physics,Mechanics & Astronomy) 2025年第1期68卷 206-217页

作者： Kun Xu Yue Li Jun Sun Shuyuan Du Xinpeng Di Yuguang Yang Bo Li Institute of Biomechanics and Medical Engineering Applied Mechanics LaboratoryDepartment of Engineering MechanicsTsinghua UniversityBeijing 100084China Institute of Nuclear and New Energy Technology Tsinghua UniversityBeijing 100084China Shanghai Aerospace Control Technology Institute Shanghai 201109China Shanghai Key Laboratory of Aerospace Intelligent Control Technology Shanghai 201109China College of Automation Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016China

Natural swarms arranged from cells to herds are usually decentralized but display intriguing collective intelligence in coordinating individuals across large scales to efficiently achieve their common *** from nature may provide new strategies for controlling collective dynamics of synthetic swarms to accomplish specific ***,we present a bioinspired computational framework that steers distributed active swarms to collectively capture and merge targets via reinforcement *** exploit collective milling structures of natural herds to cage the targets,and adopt a switching control policy inspired by sperms’chiral dynamics to optimize the trajectories of individuals,through which the active swarms can selforganize to enclose single or multiple distant targets in a dynamical,adaptive and scalable *** exists a critical swarm size,beyond which the excessive competition between agents would generate large mechanical forces,leading to capture instability but enabling the transition from short-distance to long-distance merging capture of multiple *** work provides physical insights into distributed active swarms and could offer a multilevel,decentralized strategy toward controlling swarm robotics in wide applications such as bio-medical devices,machine immunity,and target clearance.

关键词： swarm intelligence robotics multi-tasks artificial intelligence bionics

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SSD object detection algorithm based on knowledge map

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International Journal of Wireless and Mobile Computing 2024年第2期27卷 152-160页

作者： Huang, Li Wang, Xiaofeng Lu, Jianhua Hu, Wei Zhang, Changrong College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan China China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Hubei Wuhan China Key Laboratory of Metallurgical Equipment and Control Technology of the Ministry of Education Wuhan University of Science and Technology Hubei Wuhan China Precision Manufacturing Research Institute Research Centre for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Hubei Wuhan China

With the pervasive integration of artificial intelligence into all aspects of human life, talent emerges as a primary resource. Upon analysing the current state of talent training in higher education institutions, issues such as dispersed knowledge points, overlapping content, a singular practice approach and ineffective evaluation have been identified. In response to these challenges, this paper proposes a multidisciplinary and comprehensive practical teaching methodology grounded in the knowledge graph framework. It delves into diverse paths for practical teaching and assessment, including aspects like teaching objectives, problem decomposition, resource integration, implementation methods and performance evaluation. The practical application of the SSD algorithm in researching service robot indoor object detection serves as an illustrative example. Employing the holistic practical teaching approach facilitated by the knowledge graph, this model guides students in acquiring object detection expertise, thereby enhancing their comprehensive development. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Object detection

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Synthesis of thiophene-isoindigo receptor small molecules and its application in photodetectors 3

Synthesis of thiophene-isoindigo receptor small molecules an...

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2024 3rd International Conference on Applied Mechanics and Advanced Materials, ICAMAM 2024

作者： Guan, Sheng Guo, Pengzhi Lanzhou Jiaotong University Key Laboratory of Optoelectronic Technology and Intelligent Control Ministry of Education Lanzhou730070 China

Organic photodetectors (OPDs) have attracted wide attention from researchers on account of their characteristics such as easy to manufacture, low cost, lightweight, and flexible detectors. Many efforts have been made to improve the performance of OPDs. Scientists have made many efforts to improve the performance of organic photodetectors. Among them, narrow-band-gap organic photoelectric functional materials with wide spectral response exhibit application prospects in photoelectric detection. In this paper, a series of narrow-band-gap thienoisoindigo receptor small molecules were synthesized by using thienoisoindigo, and electron deficient terminal octyl cyanoacetate, 3-ethylrhodanine, and 2-(3-ethyl-4-oxothiazolidine-2-ylidene) malonitrile as receptor units, and 3-dodecylthiophene as donor unit, The photodetector with wide spectral response and high detection rate is fabricated using a Bulk Heterojunction (BHJ) structure. © Published under licence by IOP Publishing Ltd.

关键词： Photodetectors

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