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Artificial Intelligence technology (ACAIT), Asian Conference on

作者： Yuerong Tong Lina Yu Weijun Li Jingyi Liu Luyang Hou Linjun Sun Min Wu AnnLab Institute of Semiconductors Chinese Academy of Sciences Beijing China School of Materials Science and Optoelectronic Technology & School of Integrated Circuits University of Chinese Academy of Sciences Beijing China Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology Beijing China Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350359145

ISBN: (纸本)9798350359152

Implicit polynomial can efficiently represent the object contour for the curve fitting, and fractional implicit polynomial (FIP) is capable of describing complex objects at lower degree. However, both of IP and FIP based curve fitting methods suffer from the problem of insufficient fitting accuracy because of spurious zero sets. In this paper, we propose a particle swarm optimization (PSO)-assisted monomial combination optimization framework to fit curves based on FIP. In the proposed PSO-FIP framework, we adopt a monomial combination optimization strategy to remove redundant monomials and reduce spurious zero sets accordingly. And then, we solve the combinatorial explosion problem by using PSO in the monomial combination optimization. We further propose a geometric distance metric to serve as the fitness function in PSO which overcomes the trivial solution problem of algebraic distance. We conduct extensive experiments to validate the PSO-FIP framework and the results demonstrate that our approach can achieve higher accuracy with fewer coefficients in curve fitting.

关键词： Measurement Fitting Noise Interference Polynomials Surface fitting IP networks

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A Fault Detection Method for Tree Contact Single-Phase-To-Ground Fault

SSRN

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SSRN 2023年

作者： Wu, Juping Xv, Huikai Jin, Min Gong, Guoliang Chen, Gang Mao, Wenyu Lu, Huaxiang Chen, Tianxiang Institute of Semiconductors CAS Beijing100083 China University of Chinese Academy of Sciences Beijing China Semiconductor Neural Network Intelligent Perception and Computing Technology Beijing Key Laboratory Beijing China Collage of Microelectronics University of Chinese Academy of Sciences Beijing China Materials and Optoelectronics Research Center University of Chinese Academy of Sciences Beijing China Tianjin University of Science and Technology Tianjin300457 China College of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu610059 China

High impedance fault(HIF), especially tree contact single-phase-to-ground fault(TSF), often occurs in resonant grounding systems and are difficult to detect. Therefore, there is an urgent need for an effective TSF detection technology. In this paper, by analyzing the equivalent circuits of different impedance faults in resonant grounding system, and analyzing the transient zero-sequence components of underdamped state and overdamped state under different impedance, the conclusion is drawn: When a TSF occurs, the transient zero-sequence voltage will have a unique " Beat frequency " characteristic, and the problem of covering this phenomenon due to the imbalance of distribution network is effectively solved by fast independent component analysis(FastICA). Based on this conclusion, A fault detection method for TSF based on FastICA is proposed. Finally, the study conducted real experiments using two different impedance trees, bamboo and pine, to verify the proposed detection method. The results of the experiment found clear support for that the detection method is accurate and effective. © 2023, The Authors. All rights reserved.

关键词： Fault detection

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Artificial neural network-Based Approach to Modeling Energy Bands of GaN-Based Heterojunction Materials

Artificial Neural Network-Based Approach to Modeling Energy ...

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High Performance Big Data and intelligent Systems (HPBD&IS)

作者： Meilan Hao Shu Wei Lina Yu Weijun Li Min Wu Jingyi Liu Wenqiang Li Yanjie Li AnnLab Institute of Semiconductors Chinese Academy of Sciences Beijing China School of Information and Electrical Engineering Hebei University of Engineering Handan China Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology Beijing China School of Integrated Circuits University of Chinese Academy of Sciences Beijing China

This work reports a preliminary investigation of energy bands of AlxGal-xN/GaN heterojunction based on the use of artificial neural networks (ANN). Numerical energy bands simulations were used to generate training and testing dataset for ANN model. The input parameters of the ANN are the Al content, the thicknesses of AlxGal-xN barrier layer and position description of two-Layer Materials, respectively. The outputs of the ANN are the conduction band energy profile of the AlxGal_xN/GaN heterojunction and the channel Two-dimensional electron gas (2DEG) concentration distributions. The results show that trained ANN model can effectively predict the energy bands structure. The model achieved a mean squared error of 7.94×10 6 on the testing dataset, with an average relative L2 error of 4.44×10 3 for electron concentration prediction and 2.57×10 3 for conduction band energy prediction.

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Producing Monomial Sets with Lower Calculation Complexity for Polynomial Fitting

Producing Monomial Sets with Lower Calculation Complexity fo...

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High Performance Big Data and intelligent Systems (HPBD&IS)

作者： Jingyi Liu Lina Yu Min Wu Yuerong Tong Jian Xu Zhiwei Li Xuan Hu Weijun Li Chinese Academy of Sciences Institute of Semiconductors Beijing China Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology Beijing China School of Microelectronics University of Chinese Academy of Sciences Beijing China Zhongnan University of Economics and Law Wuhan China Shenzhen DAPU Microelectronics Co. Ltd. Shenzhen China

ISBN: (纸本)9781665413282

In the physic world, exploring and discovering the mechanism behind the various phenomenon is crucial for us to know the world better. However, it is hard to discover the principle in case of enormous data and the mechanism may be too hard for a human to figure out. Data science gives us a way of knowing the world and finding the mechanism hidden in the data. Automatic tool like polynomial fitting is a useful method to fit the data well. When the variable number and degree are relatively low, the computation amount of polynomial is small. However, the number of monomials grows exponentially with the increasing variable number and degree. Problems in the real world are always in a high-dimension, and the problem may be complex that needs to use a high degree to fit data well. Plus, with the huge data, the computation complexity is high. Therefore, we think it is necessary to find a way to reduce the computation amount of fitted polynomial. In this paper, we propose to use the PSO algorithm to find the monomial sets that have lower computation amounts. Experiments show the effectiveness of our method.

关键词： Fitting Data science Computational complexity Physics

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A neural-Guided Dynamic Symbolic network for Exploring Mathematical Expressions from Data

arXiv

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arXiv 2023年

作者： Li, Wenqiang Li, Weijun Yu, Lina Wu, Min Sun, Linjun Liu, Jingyi Li, Yanjie Wei, Shu Deng, Yusong Hao, Meilan AnnLab Institute of Semiconductors Chinese Academy of Sciences Beijing China School of Electronic Electrical and Communication Engineering School of Integrated Circuits University of Chinese Academy of Sciences Beijing China Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology Beijing China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing China

Symbolic regression (SR) is a powerful technique for discovering the underlying mathematical expressions from observed data. Inspired by the success of deep learning, recent deep generative SR methods have shown promising results. However, these methods face difficulties in processing high-dimensional problems and learning constants due to the large search space, and they don’t scale well to unseen problems. In this work, we propose DYSYMNET, a novel neural-guided Dynamic Symbolic network for SR. Instead of searching for expressions within a large search space, we explore symbolic networks with various structures, guided by reinforcement learning, and optimize them to identify expressions that better-fitting the data. Based on extensive numerical experiments on low-dimensional public standard benchmarks and the well-known SRBench with more variables, DYSYMNET shows clear superiority over several representative baseline models. Open source code is available at https://***/AILWQ/DySymNet. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Camo: Capturing the Modularity by End-to-End Models for Symbolic Regression

SSRN

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SSRN 2024年

作者： Liu, Jingyi Wu, Min Yu, Lina Li, Weijun Li, Wenqiang Li, Yanjie Hao, Meilan Deng, Yusong Wei, Shu Annlab Institute of Semiconductors Chinese Academy of Sciences Beijing100083 China Center of Materials Science and Optoelectronics Engineering School of Integrated Circuits University of Chinese Academy of Sciences Beijing100049 China Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology Beijing100083 China School of Information and Electrical Engineering Hebei University of Engineering Handan056038 China

Modularity serves as an omnipresent paradigm across the spectrum of natural phenomena, societal constructs, and human pursuits, spanning from biological systems to corporate hierarchies and further. Within the realm of Symbolic Regression, which seeks to deduce explicit formulas from empirical data, modularity is seen as a strategic asset for capturing essential substructures to augment fitting accuracy. Symbolic Regression inherently addresses a problem of compositional optimization;hence, preserving beneficial sub-structures enhances the efficiency of future explorations. In this study, we introduce a methodology to integrate modularity into the search mechanism, designating the term "module" to reflect invaluable sub-structures. We select an end-to-end model to assimilate the module into the search process, chosen for its scalability and ability to generalize. Modules function as higher-order knowledge and serve as primary operators, thereby enriching the search directory for Symbolic Regression. Our innovative algorithm fosters autonomous learning or evolution of modules within the learning framework. Furthermore, we introduce a hierarchical module extraction strategy from the expression tree and a module refinement system aimed at discarding redundant elements while assimilating new advantageous ones. We carried out ablation studies, the highest average R2 across several datasets was achieved by the model with module extraction and update mechanism. Furthermore, our method reached comparable R2 against state-of-the-art with half of the expression complexity. © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Larger Receptive Field and Context Information for Pose Estimation: Larger Gaussian Kernel

Larger Receptive Field and Context Information for Pose Esti...

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High Performance Big Data and intelligent Systems (HPBD&IS)

作者： Junxiao Ma Kai Yang Zunwang Ke Gang Wang Yugui Zhang Fengcai Cao Qi Zhou Yang Fei School of Information Beijing Forestry University Beijing China AnnLab Institute of Semiconductors Chinese Academy of Sciences Beijing China CGNPC Uranium Industry Development Co. Ltd. Beijing China School of Software Xinjiang University Urumqi China School of Computing and Data Engineering NingboTech University Ningbo China Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology Beijing China School of Integrated Circuits University of Chinese Academy of Sciences Beijing China Zhongke Shangyi Health Technology (Beijing) Co. Ltd. Beijing China School of Semiconductor Science and Technology South China Normal University GuangZhou China

The field of pose estimation has a wide range of application prospects in various industries in the current era. With the continuous development of deep learning techniques, the effects in the field of human pose estimation are constantly being optimized. However, due to the influence of factors such as occlusion and human posture variability, it is easy to lead to inaccurate identification of keypoints and even keypoints' missing and matching errors, there is still a lot of optimization space for the effect of pose estimation. In this paper, we propose an improvement to generate heatmaps in the training process: increase the radius of the Gaussian kernel and adjust the smoothness of Gaussian distribution to get a larger receptive field, encourage the model to use more context information in prediction, and control other parameters unchanged on the premise of using heatmap regression. Improve keypoint detection performance by optimizing this item. In the experiment, for the COCO-Keypoint data set, we selected different methods published in recent years, and after our improvement, we obtained better accuracy and performance than the original method, which proves the validity of our method.

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Deep Learning Strategies for Addressing Anomalous Exposure in Image Processing: The FARDBUNet Approach

Deep Learning Strategies for Addressing Anomalous Exposure i...

引用

High Performance Big Data and intelligent Systems (HPBD&IS)

作者： Qi Zhou Kai Yang Zunwang Ke Gang Wang Yugui Zhang Yizhen Jia Fengcai Cao Junxiao Ma Changlin Liu Kaijie Zhang Min Wu School of Semiconductor Science and Technology South China Normal University GuangZhou China AnnLab Institute of Semiconductors Chinese Academy of Sciences Beijing China Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology Beijing China CGNPC Uranium Industry Development Co. Ltd. Beijing China School of Software Xinjiang University Urumqi China School of Computing and Data Engineering NingboTech University Ningbo China School of Integrated Circuits University of Chinese Academy of Sciences Beijing China Zhongke Shangyi Health Technology (Beijing) Co. Ltd. Beijing China School of Information Beijing Forestry University Beijing China

In real-world scenarios, capturing scenes with excessive dynamic range often leads to the partial loss of highlight or dark area information due to irradiance variations and limitations in the capture capabilities of imaging devices caused by their size and material constraints. This phenomenon gives rise to anomalous exposure problems and triggers the loss of crucial initial details, thereby becoming a significant obstacle to achieving high image quality. Despite numerous researchers' integration of exposure compensation techniques into the imaging process, the anomalous exposure problem remains unsolved, hindering progress in image processing. Consequently, considerable research is valuable in implementing exposure calibration techniques on visual perception and multimedia devices. This dissertation proposes FARDBUNet, a novel end-to-end exposure calibration model designed to address the anomalous exposure problem. A key innovation of FARDBUNet is the incorporation of a fused attention mechanism, enabling the model to prioritize regions needing enhanced correction while accounting for the global exposure characteristics of the image. Additionally, to tackle the challenge of inadequate information availability in the localized areas due to exposure issues, we introduce an atrous residual dense block that efficiently captures local information by expanding the receptive field. Comprehensive tests validate the effectiveness of our suggested model, which performs on par with leading techniques on expansive exposure datasets.

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A survey on few-shot class-incremental learning

arXiv

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arXiv 2023年

作者： Tian, Songsong Li, Lusi Li, Weijun Ran, Hang Ning, Xin Tiwari, Prayag Institute of Semiconductors Chinese Academy of Sciences Beijing100083 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China School of Integrated Circuits University of Chinese Academy of Sciences Beijing100083 China Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology Beijing100083 China Department of Computer Science Old Dominion University NorfolkVA23529 United States School of Information Technology Halmstad University Halmstad30118 Sweden

Large deep learning models are impressive, but they struggle when real-time data is not available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for deep neural networks to learn new tasks from just a few labeled samples without forgetting the previously learned ones. This setup can easily leads to catastrophic forgetting and overfitting problems, severely affecting model performance. Studying FSCIL helps overcome deep learning model limitations on data volume and acquisition time, while improving practicality and adaptability of machine learning models. This paper provides a comprehensive survey on FSCIL. Unlike previous surveys, we aim to synthesize few-shot learning and incremental learning, focusing on introducing FSCIL from two perspectives, while reviewing over 30 theoretical research studies and more than 20 applied research studies. From the theoretical perspective, we provide a novel categorization approach that divides the field into five subcategories, including traditional machine learning methods, meta learning-based methods, feature and feature space-based methods, replay-based methods, and dynamic network structure-based methods. We also evaluate the performance of recent theoretical research on benchmark datasets of FSCIL. From the application perspective, FSCIL has achieved impressive achievements in various fields of computer vision such as image classification, object detection, and image segmentation, as well as in natural language processing and graph. We summarize the important applications. Finally, we point out potential future research directions, including applications, problem setups, and theory development. Overall, this paper offers a comprehensive analysis of the latest advances in FSCIL from a methodological, performance, and application perspective. Copyright © 2023, The Authors. All rights reserved.

关键词： Image segmentation

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Sdgan: Improve Speech Enhancement Quality by Information Filter

引用

Journal of Physics: Conference Series 2021年第1期1871卷

作者： Xiaozhou Guo Yi Liu Wenyu Mao Jixing Li Wenchang Li Guoliang Gong Huaxiang Lu Institute of Semiconductors Chinese Academy of Sciences Beijing 100083 China University of Chinese Academy of Sciences Beijing 100089 China CAS Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Beijing 200031 China Semiconductor Neural Network Intelligent Perception and Computing Technology Beijing Key Lab Beijing 100083 China

The speech denoising model based on adversarial generative network has achieved better results than the traditional machine learning model. In this paper, for the short cut connection in the generator, we discuss its influence on the information transfer between encoder and decoder, and propose SDGAN at target. SDGAN sets linear and convolution filters in the short cut connection which adaptively learn the optimal information processing. The information filter still enables the generator to solve the gradient vanishing problem, and it can also avoid information redundancy and improve expression ability. In addition, SDGAN replaces the L1 regularization term in loss function with the L2 regularization term, which not only makes the output speech of the generator closer to the clean speech, but also avoids sparsity. In the experiments, SDGAN significantly performs better than other traditional GAN in five performance metrics (such as PESQ), and the effect of convolution filter is better than that of linear filter.

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