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arXiv 2021年

作者： Li, Na Zhao, Xinbo National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University China

Human gait is one of important biometric characteristics for human identification at a distance. In practice, occlusion usually occurs and seriously affects accuracy of gait recognition. However, there is no available database to support in-depth research of this problem, and state-of-arts gait recognition methods have not paid enough attention to it, thus this paper focuses on gait recognition under occlusion. We collect a new gait recognition database called OG RGB+D database, which breaks through the limitation of other gait databases and includes multimodal gait data of various occlusions (self-occlusion, active occlusion, and passive occlusion) by our multiple synchronous Azure Kinect DK sensors data acquisition system (multi-Kinect SDAS) that can be also applied in security situations. Because Azure Kinect DK can simultaneously collect multimodal data to support different types of gait recognition algorithms, especially enables us to effectively obtain camera-centric multi-person 3D poses, and multi-view is better to deal with occlusion than single-view. In particular, the OG RGB+D database provides accurate silhouettes and the optimized human 3D joints data (OJ) by fusing data collected by multi-Kinects which are more accurate in human pose representation under occlusion. We also use the OJ data to train an advanced 3D multi-person pose estimation model to improve its accuracy of pose estimation under occlusion for universality. Besides, as human pose is less sensitive to occlusion than human appearance, we propose a novel gait recognition method SkeletonGait based on human dual skeleton model using a framework of siamese spatio-temporal graph convolutional networks (siamese ST-GCN). The evaluation results demonstrate that SkeletonGait has competitive performance compared with state-of-art gait recognition methods on OG RGB+D database and popular CAISA-B database. This database will be available at https://***/cvNXE/*** Codes 68T

关键词： Gait analysis

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Using Double Well Function as a Benchmark Function for Optimization Algorithm

Using Double Well Function as a Benchmark Function for Optim...

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Congress on Evolutionary Computation

作者： Peng Wang Guosong Yang School of Computer Science and Engineering Southwest Minzu University Chengdu China Chengdu Institute of Computer Application Chinese Academy of Sciences Chengdu China University of Chinese Academy of Sciences Beijing China

The double well function (DWF) is an important theoretical model originating from quantum physics and has been used to describe the energy constraint problem in quantum mechanics and structural chemistry. Although its form may vary, the DWF has two different local minima in the one-dimensional case, and the number of local minima increases as its dimension grows. As a multi-stable function, the DWF is assumed to be a potential candidate for testing the performance of the heuristic optimization algorithm, which aims to seek the global minimum. To verify this idea, a typical DWF is employed in this paper, and a mathematical analysis is presented herein, and its properties as a benchmark function are discussed in different cases. In addition, we conducted a set of experiments utilizing a few optimization algorithms, such as the multi-scale quantum harmonic oscillator algorithm and covariance matrix adaptation evolution strategy; thus the analysis has been illustrated by the results of the numerical experiment. Moreover, to guide the design of an ideal DWF used as a benchmark function in experiment, different values of the decisive parameters were tested corresponding to our analysis, and some useful rules were given based on the discussion of the results.

关键词： Chemistry Heuristic algorithms Mathematical analysis Quantum mechanics Evolutionary computation Benchmark testing Harmonic analysis

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Bi-level Doubly Variational Learning for Energy-based Latent Variable Models

arXiv

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arXiv 2022年

作者： Kan, Ge Lü, Jinhu Wang, Tian Zhang, Baochang Zhu, Aichun Huang, Lei Guo, Guodong Snoussi, Hichem School of Automation Science and Electrical Engineering Institue of Artificial Intelligence Beihang University Beijing China School of Computer Science and Technology Nanjing Tech University Nanjing China Institute of Deep Learning Baidu Research National Engineering Laboratory for Deep Learning Technology and Application Beijing China University of Technology of Troyes Troyes France

Energy-based latent variable models (EBLVMs) are more expressive than conventional energy-based models. However, its potential on visual tasks are limited by its training process based on maximum likelihood estimate that requires sampling from two intractable distributions. In this paper, we propose Bi-level doubly variational learning (BiDVL), which is based on a new bi-level optimization framework and two tractable variational distributions to facilitate learning EBLVMs. Particularly, we lead a decoupled EBLVM consisting of a marginal energy-based distribution and a structural posterior to handle the difficulties when learning deep EBLVMs on images. By choosing a symmetric KL divergence in the lower level of our framework, a compact BiDVL for visual tasks can be obtained. Our model achieves impressive image generation performance over related works. It also demonstrates the significant capacity of testing image reconstruction and out-of-distribution detection. Copyright © 2022, The Authors. All rights reserved.

关键词： Vision

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Robotic Restroom Hygiene Solutions with IoT and Recurrent Neural Networks for Clean Facilities

Robotic Restroom Hygiene Solutions with IoT and Recurrent Ne...

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International Conference on Inventive Computation Technologies (ICICT)

作者： A. Kathija Nasreen M. Shenbagapriya Senthil Kumar Seeni Parthiban Veda B. Meenakshi S. Murugan Department of Computer Science and Engineering RMK College of Engineering and Technology Chennai Tamil Nadu India Department of Biomedical Engineering Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India Department of Mobile Application Development Cognizant Technology Solutions Buffalo Grove Illinois United States of America Centre For Yoga College of Engineering and Technology SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Sri Sairam Engineering College Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

Public restrooms needs to be frequently cleaned to maintain public health and hygiene. Traditional bathroom cleaning techniques may not solve current cleanliness issues. This research proposes an advanced robotics, Internet of Things (IoT), and Recurrent Neural Networks (RNNs) solution to these problems. The proposed system uses autonomous robots with IoT sensors and cameras. Robots identify cleaning and maintenance needs in restrooms. A central system receives data from IoT sensors on cleanliness indicators, including toilet paper, soap, and foot movement. Recurrent Neural Networks (RNN) processes this data to predict and prioritize cleaning requirements. The RNN monitors toilet conditions in real time and reacts to changing use and cleaning needs. This dynamic technique optimizes cleaning resource allocation and maintains facility cleanliness. The device also warns cleaning personnel when certain areas need quick attention. This novel technique makes restroom care more cost-effective, responsive, and environmentally friendly by combining robots, IoT, and RNNs. This study advances smart facility management, which uses technology to improve public space cleanliness, user experience, and resource usage.

关键词： Recurrent neural networks Cleaning Sensor systems Real-time systems Sensors Resource management Internet of Things

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Nonequilibrium transport and the fluctuation theorem in the thermodynamic behaviors of nonlinear photonic systems

arXiv

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arXiv 2024年

作者： Liu, Yang Lu, Jincheng Xiong, Zhongfei Wu, Fan O. Christodoulides, Demetrios Chen, Yuntian Jiang, Jian-Hua School of Physical Science and Technology Collaborative Innovation Center of Suzhou Nano Science and Technology Soochow University 1 Shizi Street Suzhou215006 China Jiangsu Key Laboratory of Micro and Nano Heat Fluid Flow Technology and Energy Application School of Physical Science and Technology Suzhou University of Science and Technology Suzhou215009 China School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan430074 China CREOL College of Optics and Photonics University of Central Florida OrlandoFL32816-2700 United States Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90089 United States School of Biomedical Engineering Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China School of Physics University of Science and Technology of China Hefei230026 China

Nonlinear multimode optical systems have attracted substantial attention due to their rich physical properties. Complex interplay between the nonlinear effects and mode couplings makes it difficult to understand the collective dynamics of photons. Recent studies show that such collective phenomena can be effectively described by a Rayleigh–Jeans thermodynamics theory which is a powerful tool for the study of nonlinear multimode photonic systems. These systems, in turn, offer a compelling platform for investigating fundamental issues in statistical physics, attributed to their tunability and the ability to access negative temperature regimes. However, to date, a theory for the nonequilibrium transport and fluctuations is yet to be established. Here, we employ the full counting statistics theory to study the nonequilibrium transport of particle and energy in nonlinear multimode photonic systems in both positive and negative temperature regimes. Furthermore, we discover that in situations involving two reservoirs of opposite temperatures and chemical potentials, an intriguing phenomenon known as the loop current effect can arise, wherein the current in the positive energy sector runs counter to that in the negative energy sector. In addition, we numerically confirm that the fluctuation theorem remains applicable in optical thermodynamics systems across all regimes, from positive temperature to negative ones. Our findings closely align with numerical simulations based on first-principles nonlinear wave equations. Our work seeks to deepen the understanding of irreversible non-equilibrium processes and statistical fluctuations in nonlinear many-body photonic systems which will enhance our grasp of collective phenomena of photons and foster a fruitful intersection between optics and statistical physics. © 2024, CC BY-NC-ND.

关键词： Temperature

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Training of Spiking Neural Network joint Curriculum Learning Strategy

arXiv

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arXiv 2023年

作者： Tang, Lingling Chu, Jielei Gong, Zhiguo Li, Tianrui The School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China The State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau China

Starting with small and simple concepts, and gradually introducing complex and difficult concepts is the natural process of human learning. Spiking Neural Networks (SNNs) aim to mimic the way humans process information, but current SNNs models treat all samples equally, which does not align with the principles of human learning and overlooks the biological plausibility of SNNs. To address this, we propose a CL-SNN model that introduces Curriculum Learning(CL) into SNNs, making SNNs learn more like humans and providing higher biological interpretability. CL is a training strategy that advocates presenting easier data to models before gradually introducing more challenging data, mimicking the human learning process. We use a confidence-aware loss to measure and process the samples with different difficulty levels. By learning the confidence of different samples, the model reduces the contribution of difficult samples to parameter optimization automatically. We conducted experiments on static image datasets MNIST, Fashion-MNIST, CIFAR10, and neuromorphic datasets N-MNIST, CIFAR10-DVS, DVS-Gesture. The results are promising. To our best knowledge, this is the first proposal to enhance the biologically plausibility of SNNs by introducing CL. Copyright © 2023, The Authors. All rights reserved.

关键词： Curricula

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Simulated Bifurcation with High-dimensional Expansion for Traffic Signal Optimization on Real-world Networks

arXiv

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arXiv 2025年

作者： Zhao, Shengda Liu, Zhekun Yu, Jiaxin Ju, Bocheng Wang, Liang Zhang, Xiaodong Zhang, Xinghua School of Physical Sciences and Engineering Beijing Jiaotong University Haidian District Beijing100044 China Beijing Key Laboratory of Novel Materials Genetic Engineering and Application for Rail Transit Beijing Jiaotong University Haidian District Beijing100044 China School of Computer Science and Technology Beijing Jiaotong University Haidian District Beijing100044 China National School of Elite Engineers Beijing Jiaotong University Haidian District Beijing100044 China Beijing Municipal Institute of City Planning & Design Xicheng District Beijing100045 China

In the context of accelerating urbanization and increasingly severe traffic congestion, optimizing traffic signal systems to enhance road throughput and reduce congestion has become a critical issue that requires attention. This study proposes a short-term traffic situation prediction model based on real-world road topologies and a typical four-way, eight-phase traffic signal control scheme. The model takes into account the traffic flow disparities across different directions and the frequency of signal phase changes, integrating these factors into an optimization objective function aimed at achieving global optimization of urban traffic. The structure of this objective function is analogous to the spin-glass systems in statistical physics. For this model, a Simulated Bifurcation optimization algorithm is introduced, with traditional simulated annealing optimization methods used as a benchmark. The results demonstrate that the Simulated Bifurcation algorithm outperforms simulated annealing in terms of both efficiency and effectiveness. Using real traffic flow and road network data from Beijing, we initialized the model and conducted numerical optimization experiments. The experimental results indicate that the Simulated Bifurcation algorithm significantly outperforms simulated annealing in terms of computational efficiency, effectively addressing combinatorial optimization problems in systems with multiple spin interactions, and reducing the time complexity of the problem to O(N1.35). This successful solution addresses the NP-hard problem of global traffic signal optimization. More importantly, the signal phase patterns generated by the Simulated Bifurcation algorithm are fully compatible with the operational requirements of actual traffic signal systems, showcasing its enormous potential and applicability in optimizing signal control in real-world traffic networks, especially for large, complex urban traffic systems. This work provides strong theoretical support an

关键词： Combinatorial optimization

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AdaPrompt-IR: Adaptive learning to perceive degradation semantic and prompting for all-in-one image restoration

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Pattern Recognition 2026年 169卷

作者： Wei Sun Qianzhou Wang Yaqi Wang Zhiqiang Hou Qingsen Yan Yanning Zhang School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology China School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China

Image restoration is a critical task aimed at recovering high-quality, clean images from their degraded counterparts. While deep learning-based methods have made significant strides in this area, they still struggle with generalizing across a wide range of degradation types and levels. This limitation restricts their practical applicability, as it often requires training separate models for each degradation type and knowing the specific degradation in advance to apply the appropriate model. In this work, we introduce AdaPrompt-IR, an adaptive all-in-one image restoration network that combines degradation semantic mining and prompt learning. Our approach leverages a degradation representation codebook to effectively decompose and capture various degradation types. Building upon this foundation, we propose a degradation activation module, integrated with a dual-branch architecture, which explicitly facilitates the restoration of missing degradation semantics. Additionally, we incorporate degradation-specific information as prompts to implicitly guide the restoration process. Extensive experiments demonstrate that our method achieves state-of-the-art performance across multiple image restoration tasks, including image denoising, dehazing, deraining, motion deblurring, and low-light image enhancement. Our code is available at https://***/hagerwang/AdaPromote-IR .

关键词：

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Examining the Fourier Spectrum of Speech Signal from a Time-Frequency Perspective for Automatic Depression Level Prediction

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IEEE Transactions on Affective Computing 2025年

作者： Niu, Mingyue Tao, Jianhua He, Yongjun Zhang, Shiqing Li, Ming YanShan University School of Information Science and Engineering Qinhuangdao066004 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province China Tsinghua University Department of Automation Bejing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Taizhou University Institute of Intelligent Information Processing Taizhou318000 China Zhejiang Normal University Zhejiang Key Laboratory of Intelligent Education Technology and Application Jinhua321004 China Zhejiang Institute of Optoelectronics Jinhua321004 China

Currently, many studies use Fourier amplitude spectra of speech signals to predict depression levels. However, those works often treat Fourier amplitude spectra as images or sequences to capture depression cues using convolutional neural networks or multilayer perceptrons. Therefore, they ignore the complex element composition and time-frequency attributes of Fourier spectra, which is not conducive to capturing the differences among individuals with different depression levels. For this reason, we construct a Time-Frequency Self-Embedding (TFSE) module, which not only stores the correlation relationship among real (imaginary) parts of Fourier spectra of different subjects from the time-frequency perspective, but also maintain the physical properties of data through the weight embedding process. Besides, Global Average Pooling (GAP) or linear layers are difficult to balance both temporal and frequency dimensions in the vectorization process. Therefore, we construct a Time-Frequency Tensor Vectorization (TFTV) module, which summarizes each channel along time and frequency dimensions, and then generates the vectorization result by integrating various channels. In this way, we combine TFSE and TFTV modules to form our SpectrumFormer model for predicting depression levels. Evaluation indicators on AVEC 2013 and AVEC 2014 depression databases imply the progressiveness of our model. © 2010-2012 IEEE.

关键词： Depression level prediction Real and imaginary parts Tensor vectorization Time-frequency attributes

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Bionic Soft Fingers with Hybrid Variable Stiffness Mechanisms for Multimode Grasping

Bionic Soft Fingers with Hybrid Variable Stiffness Mechanism...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Xiangbo Wang Tianran Zhang Hongze Yu Zhenwei Wen Lide Fang Huaping Liu Fuchun Sun Lixue Tang Bin Fang College of Quality and Technical Supervision Hebei University Baoding China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China School of Mechanical Engineering & Automation Beihang University Beijing China International School Beijing University of Posts and Telecommunications Beijing China Department of Computer Science and Technology Beijing National Research Center for Information Science and Technology Institute for Artificial Intelligence State Key Lab of Intelligent Technology and Systems Tsinghua University Beijing China School of Biomedical Engineering Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application Capital Medical University Beijing China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

This paper presents a novel Bionic Soft Finger (BSF) that aims to overcome the limitations of conventional rigid manipulators in terms of adaptability and safety, as well as the challenges faced by soft hands regarding carrying capacity and stability. The BSF design uses a hybrid variable stiffness mechanism combining memory alloy actuators with particle jamming to achieve the desired bending angle and actuator stiffness. Our innovative approach utilizes a bionic finger design that incorporates a memory alloy skeleton and a water-cooled recirculation system, leading to a substantial reduction in the time required for each operation. Through the integration of particle jamming, we have enhanced the overall stiffness and performance of the manipulator, enabling load capacities of up to 3N per finger and more than twice the stiffness of a normal condition. Additionally, our design enables multimode grasping and incorporates a liquid metal strain sensor (METT) for real-time monitoring of finger bending angles. Comparative analyses demonstrate that our design exhibits superior stiffness and enables five-mode grasping in comparison to pneumatic actuators. We believe that bionic soft fingers present a promising solution for enhancing adaptability, safety, and performance in human-robot interaction applications.

关键词： Metals Grasping Bending Manipulators Stability analysis Skeleton Real-time systems

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