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Explainable-AI-based two-stage solution for WSN object localization using zero-touch mobile transceivers

Science China(Information Sciences) 2024年第7期67卷 27-45页

作者： Kai FANG Junxin CHEN Han ZHU Thippa Reddy GADEKALLU Xiaoping WU Wei WANG School of Mathematics and Computer Science Zhejiang A&F University School of Software Dalian University of Technology Faculty of Applied Sciences Macao Polytechnic University department of Electrical and Computer engineering Lebanese American University School of Information engineering Huzhou University Guangdong-Hong Kong-Macao Joint Laboratory for Emotional intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BIT University

artificial intelligence technology is widely used in the field of wireless sensor networks(WSN).Due to its inexplicability, the interference factors in the process of WSN object localization cannot be effectively eliminated. In this paper, an explainable-AI-based two-stage solution is proposed for WSN object localization. In this solution, mobile transceivers are used to enlarge the positioning range and eliminate the blind area for object localization. The motion parameters of transceivers are considered to be unavailable,and the localization problem is highly nonlinear with respect to the unknown parameters. To address this,an explainable AI model is proposed to solve the localization problem. Since the relationship among the variables is difficult to fully include in the first-stage traditional model, we develop a two-stage explainable AI solution for this localization problem. The two-stage solution is actually a comprehensive consideration of the relationship between variables. The solution can continue to use the constraints unused in the firststage during the second-stage, thereby improving the performance of the solution. Therefore, the two-stage solution has stronger robustness compared to the closed-form solution. Experimental results show that the performance of both the two-stage solution and the traditional solution will be affected by numerical changes in unknown parameters. However, the two-stage solution performs better than the traditional solution, especially with a small number of mobile transceivers and sensors or in the presence of high noise. Furthermore,we have also verified the feasibility of the proposed explainable-AI-based two-stage solution.

关键词： explainable AI object localization semidefinite relaxation mobile transceiver two-stage solution closed-form solution

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UAV image target localization method based on outlier filter and frame buffer

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Chinese Journal of Aeronautics 2024年第7期37卷 375-390页

作者： Yang WANG Hongguang LI Xinjun LI Zhipeng WANG Baochang ZHANG Institute of Unmanned System Beihang UniversityBeijing 100191China School of Electronics and Information engineering Beihang UniversityBeijing 100191China Institute of artificial intelligence Beihang UniversityBeijing 100191China

With rapid development of UAV technology,research on UAV image analysis has gained *** the existing techniques of UAV target localization often rely on additional equipment,a method of UAV target localization based on depth estimation has been ***,the unique perspective of UAVs poses challenges such as the significant field of view variations and the presence of dynamic objects in the *** a result,the existing methods of depth estimation and scale recovery cannot be directly applied to UAV ***,there is a scarcity of depth estimation datasets tailored for UAV perspectives,which makes supervised algorithms *** address these issues,an outlier filter is introduced to enhance the applicability of depth estimation networks to target localization.A frame buffer method is proposed to achieve more accurate scale recovery,so as to handle complex scene textures in UAV *** proposed method demonstrates a 14.29%improvement over the *** with the average recovery results from UAV perspectives,the difference is only 0.88%,approaching the performance of scale recovery using ground truth ***,to overcome the limited availability of traditional UAV depth datasets,a method for generating depth labels from video sequences is *** to state-of-the-art methods,the proposed approach achieves higher accuracy in depth estimation and stands for the first attempt at target localization using image *** algorithm and dataset are available at https://***/uav-tan/uav-object-localization.

关键词： Object localization Deep learning Depth estimate Scale recovery Unmanned Aerial Vehicle(UAV)

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DeepGCN based on variable multi‐graph and multimodal data for ASD diagnosis

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CAAI Transactions on intelligence Technology 2024年第4期9卷 879-893页

作者： Shuaiqi Liu Siqi Wang Chaolei Sun Bing Li Shuihua Wang Fei Li College of Electronic and Information engineering Hebei UniversityBaodingChina machine Vision Technology Innovation Center of Hebei Province BaodingChina The State Key Laboratory of Multimodal artificial intelligence Systems Institute of AutomationChinese Academy of SciencesBeijingChina School of Computer Science and Technology Henan Polytechnic UniversityJiaozuoChina

Diagnosing individuals with autism spectrum disorder(ASD)accurately faces great chal-lenges in clinical practice,primarily due to the data's high heterogeneity and limited sample *** tackle this issue,the authors constructed a deep graph convolutional network(GCN)based on variable multi‐graph and multimodal data(VMM‐DGCN)for ASD ***,the functional connectivity matrix was constructed to extract primary ***,the authors constructed a variable multi‐graph construction strategy to capture the multi‐scale feature representations of each subject by utilising convolutional filters with varying kernel ***,the authors brought the non‐imaging in-formation into the feature representation at each scale and constructed multiple population graphs based on multimodal data by fully considering the correlation between *** extracting the deeper features of population graphs using the deep GCN(DeepGCN),the authors fused the node features of multiple subgraphs to perform node classification tasks for typical control and ASD *** proposed algorithm was evaluated on the Autism Brain Imaging Data Exchange I(ABIDE I)dataset,achieving an accuracy of 91.62%and an area under the curve value of 95.74%.These results demon-strated its outstanding performance compared to other ASD diagnostic algorithms.

关键词： machine learning medical image processing medical signal processing

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Autonomous multi-drone racing method based on deep reinforcement learning

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Science China(Information Sciences) 2024年第8期67卷 35-48页

作者： Yu KANG Jian DI Ming LI Yunbo ZHAO Yuhui WANG department of Automation University of Science and Technology of China Institute of Advanced Technology University of Science and Technology of China Institute of artificial intelligence Hefei Comprehensive National Science Center College of Automation engineering Nanjing University of Aeronautics and Astronautics

Racing drones have attracted increasing attention due to their remarkable high speed and excellent maneuverability. However, autonomous multi-drone racing is quite difficult since it requires quick and agile flight in intricate surroundings and rich drone interaction. To address these issues, we propose a novel autonomous multi-drone racing method based on deep reinforcement learning. A new set of reward functions is proposed to make racing drones learn the racing skills of human experts. Unlike previous methods that required global information about tracks and track boundary constraints, the proposed method requires only limited localized track information within the range of its own onboard sensors. Further, the dynamic response characteristics of racing drones are incorporated into the training environment, so that the proposed method is more in line with the requirements of real drone racing scenarios. In addition, our method has a low computational cost and can meet the requirements of real-time racing. Finally, the effectiveness and superiority of the proposed method are verified by extensive comparison with the state-of-the-art methods in a series of simulations and real-world experiments.

关键词： autonomous multi-drone racing deep reinforcement learning Markov game racing drone sim-to-real

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Programmable directional color dynamics using plasmonics

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Microsystems & Nanoengineering 2024年第1期10卷 325-334页

作者： Gyurin Kim Doeun Kim Soeun Ko Jang-Hwan Han Juhwan Kim Joo Hwan Ko Young Min Song Hyeon-Ho Jeong School of Electrical engineering and Computer Science Gwangju Institute of Science and TechnologyGwangju 61005Republic of Korea department of Semiconductor engineering Gwangju Institute of Science and TechnologyGwangju 61005Republic of Korea artificial intelligence(AI)Graduate School Gwangju Institute of Science and TechnologyGwangju 61005Republic of Korea

Adaptive multicolor filters have emerged as key components for ensuring color accuracy and resolution in outdoor visual ***,the current state of this technology is still in its infancy and largely reliant on liquid crystal devices that require high voltage and bulky structural ***,we present a multicolor nanofilter consisting of multilayered‘active’plasmonic nanocomposites,wherein metallic nanoparticles are embedded within a conductive polymer *** nanocomposites are fabricated with a total thickness below 100 nm using a‘lithography-free’method at the wafer level,and they inherently exhibit three prominent optical modes,accompanying scattering phenomena that produce distinct dichroic reflection and transmission ***,a pivotal achievement is that all these colors are electrically manipulated with an applied external voltage of less than 1 V with 3.5 s of switching speed,encompassing the entire visible ***,this electrically programmable multicolor function enables the effective and dynamic modulation of the color temperature of white light across the warm-to-cool spectrum(3250 K-6250 K).This transformative capability is exceptionally valuable for enhancing the performance of outdoor optical devices that are independent of factors such as the sun’s elevation and prevailing weather conditions.

关键词： directional prevailing exceptional

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Intelligent 3D garment system of the human body based on deep spiking neural network

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虚拟现实与智能硬件（中英文） 2024年第1期6卷 43-55页

作者： Minghua JIANG Zhangyuan TIAN Chenyu YU Yankang SHI Li LIU Tao PENG Xinrong HU Feng YU School of Computer and artificial intelligence Wuhan Textile UniversityWuhan 430200China engineering Research Center of Hubei Province for Clothing Information Wuhan 430200China

Background Intelligent garments,a burgeoning class of wearable devices,have extensive applications in domains such as sports training and medical ***,existing research in the smart wearables domain predominantly emphasizes sensor functionality and quantity,often skipping crucial aspects related to user experience and *** To address this gap,this study introduces a novel real-time 3D interactive system based on intelligent *** system utilizes lightweight sensor modules to collect human motion data and introduces a dual-stream fusion network based on pulsed neural units to classify and recognize human movements,thereby achieving real-time interaction between users and ***,the system incorporates 3D human visualization functionality,which visualizes sensor data and recognizes human actions as 3D models in real time,providing accurate and comprehensive visual feedback to help users better understand and analyze the details and features of human *** system has significant potential for applications in motion detection,medical monitoring,virtual reality,and other *** accurate classification of human actions contributes to the development of personalized training plans and injury prevention *** This study has substantial implications in the domains of intelligent garments,human motion monitoring,and digital twin *** advancement of this system is expected to propel the progress of wearable technology and foster a deeper comprehension of human motion.

关键词： Intelligent garment system Internet of things Human action recognition Deep learning 3D visualization

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Orbital angular momentum-mediated machine learning for high-accuracy mode-feature encoding

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Light(Science & Applications) 2024年第3期13卷 466-477页

作者： Xinyuan Fang Xiaonan Hu Baoli Li Hang Su Ke Cheng Haitao Luan Min Gu Institute of Photonic Chips University of Shanghai for Science and TechnologyShanghai 200093China Centre for artificial-intelligence Nanophotonics School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China

machine learning with optical neural networks has featured unique advantages of the information processing including high speed,ultrawide bandwidths and low energy consumption because the optical dimensions(time,space,wavelength,and polarization)could be utilized to increase the degree of ***,due to the lack of the capability to extract the information features in the orbital angular momentum(OAM)domain,the theoretically unlimited OAM states have never been exploited to represent the signal of the input/output nodes in the neural network ***,we demonstrate OAM-mediated machine learning with an all-optical convolutional neural network(CNN)based on Laguerre-Gaussian(LG)beam modes with diverse diffraction *** proposed CNN architecture is composed of a trainable OAM mode-dispersion impulse as a convolutional kernel for feature extraction,and deep-learning diffractive layers as a *** resultant OAM mode-dispersion selectivity can be applied in information mode-feature encoding,leading to an accuracy as high as 97.2%for MNIST database through detecting the energy weighting coefficients of the encoded OAM modes,as well as a resistance to eavesdropping in point-to-point free-space ***,through extending the target encoded modes into multiplexed OAM states,we realize all-optical dimension reduction for anomaly detection with an accuracy of 85%.Our work provides a deep insight to the mechanism of machine learning with spatial modes basis,which can be further utilized to improve the performances of various machine-vision tasks by constructing the unsupervised learning-based auto-encoder.

关键词： polarization mode momentum

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Ultrahigh performance passive radiative cooling by hybrid polar dielectric metasurface thermal emitters

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Opto-Electronic Advances 2024年第4期7卷 17-25页

作者： Yinan Zhang Yinggang Chen Tong Wang Qian Zhu Min Gu Institute of Photonic Chips University of Shanghai for Science and TechnologyShanghai 200093China Centre for artificial-intelligence Nanophotonics School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China

Real-world passive radiative cooling requires highly emissive,selective,and omnidirectional thermal emitters to maintain the radiative cooler at a certain temperature below the ambient temperature while maximizing the net cooling *** various selective thermal emitters have been demonstrated,it is still challenging to achieve these conditions sim-ultaneously because of the extreme difficulty in controlling thermal emission of photonic structures in *** we demonstrated hybrid polar dielectric metasurface thermal emitters with machine learning inverse design,en-abling a high emissivity of~0.92 within the atmospheric transparency window 8-13μm,a large spectral selectivity of~1.8 and a wide emission angle up to 80 degrees,*** selective and omnidirectional thermal emitter has led to a new record of temperature reduction as large as~15.4°C under strong solar irradiation of~800 W/m2,signific-antly surpassing the state-of-the-art *** designed structures also show great potential in tackling the urban heat island effect,with modelling results suggesting a large energy saving and deployment area *** research will make significant impact on passive radiative cooling,thermal energy photonics and tackling global climate change.

关键词： radiative cooling dielectric metasurfaces machine learning thermal emitters

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Proposing a machine learning approach to analyze and predict basic high-temperature properties of iron ore fines and its factors

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Journal of Iron and Steel Research(International) 2024年第5期31卷 1082-1094页

作者： Qing-ke Sun Yao-zu Wang Jian-liang Zhang Zheng-jian Liu Le-le Niu Chang-dong Shan Yun-fei Ma Institute of artificial intelligence University of Science and Technology BeijingBeijing100083China School of intelligence Science and Technology University of Science and Technology BeijingBeijing100083China School of Metallurgical and Ecological engineering University of Science and Technology BeijingBeijing100083China

The basic high-temperature properties of iron ore play a crucial role in optimizing sintering and ore blending,but the testing process for these properties is complex and has significant lag time,which cannot meet the actual needs of ore blending.A prediction model for the basic high-temperature properties of iron ore fines was thus proposed based on a combination of machine learning algorithms and genetic ***,the prediction accuracy of different machine learning models for the basic high-temperature properties of iron ore fines was ***,a random forest model optimized by genetic algorithms was built,further improving the prediction accuracy of the *** test results show that the random forest model optimized by genetic algorithms has the highest prediction accuracy for the lowest assimilation temperature and liquid phase fluidity of iron ore,with a determination coefficient of 0.903 for the lowest assimilation temperature and 0.927 for the liquid phase fluidity after *** trained model meets the fluctuation requirements of on-site testing and has been successfully applied to actual production on site.

关键词： Iron ore Basic high-temperature property machine learning Random forest Genetic algorithm

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Reinforcement learning based adaptive control for uncertain mechanical systems with asymptotic tracking

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Defence Technology（防务技术） 2024年第4期34卷 19-28页

作者： Xiang-long Liang Zhi-kai Yao Yao-wen Ge Jian-yong Yao School of Mechanical engineering Nanjing University of Science and TechnologyNanjing210094China College of Automation&College of artificial intelligence Nanjing University of Post and TelecommunicationNanjing210023China

This paper mainly focuses on the development of a learning-based controller for a class of uncertain mechanical systems modeled by the Euler-Lagrange *** considered system can depict the behavior of a large class of engineering systems,such as vehicular systems,robot manipulators and *** these systems are often characterized by highly nonlinear characteristics,heavy modeling uncertainties and unknown perturbations,therefore,accurate-model-based nonlinear control approaches become *** by the challenge,a reinforcement learning(RL)adaptive control methodology based on the actor-critic framework is investigated to compensate the uncertain mechanical *** approximation inaccuracies caused by RL and the exogenous unknown disturbances are circumvented via a continuous robust integral of the sign of the error(RISE)control *** from a classical RISE control law,a tanh(·)function is utilized instead of a sign(·)function to acquire a more smooth control *** developed controller requires very little prior knowledge of the dynamic model,is robust to unknown dynamics and exogenous disturbances,and can achieve asymptotic output ***,co-simulations through ADAMS and MATLAB/Simulink on a three degrees-of-freedom(3-DOF)manipulator and experiments on a real-time electromechanical servo system are performed to verify the performance of the proposed approach.

关键词： Adaptive control Reinforcement learning Uncertain mechanical systems Asymptotic tracking

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