检索结果-内蒙古大学图书馆

SSRN 2022年

作者： Han, Dan Chen, Yi Li, Donghui Dong, Hailiang Xu, Bingshe Sang, Shengbo He, Xiuli Shanxi Key Laboratory of Micro Nano Sensors & Artificial Intelligence Perception College of Information and Computer Taiyuan University of Technology Taiyuan030024 China Key Lab of Advanced Transducers and Intelligent Control System Ministry of Education Taiyuan030024 China Key Laboratory of Interface Science and Engineering in Advanced Materials Taiyuan University of Technology Ministry of Education Taiyuan030024 China State Key Laboratory of Transducer Technology Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China Shanxi Research Institute of 6D Artificiat Intelligence Biomedical Science Taiyuan030024 China

Gallium nitride (GaN) has some challenges for gas sensing, such as high detection lower limit (LOD) or controlling the epitaxy of nanostructures. To address these issues, we exclusively designed a device to enhance the gas interaction capabilities of GaN quantum dots (QDs) film fabricated by metal-organic chemical vapor deposition (MOCVD). The GaN QDs film gas sensor exhibited a low LOD (5 ppb) to NO2 at room temperature (RT), which has much superior gas performance than other GaN-based sensors. The results manifest that many ultra-small QDs around 2 nm in diameter are stacked to form QDs of larger size, and confirm the existence of vacancies. The sensor displays a ultra-high electron mobility of 1237.37 cm2 V−1s−1, demonstrate that great shallow donor impurities in GaN QDs film that ionize easily at RT, which is greatly beneficial for gas sensing. The sensing mechanisms were analyzed based on its abundant active sites, ultrahigh carrier mobility, high surface energy and chemical activity, grain boundary barrier and good electrical conductivity. Moreover, the sensor processes high selectivity, repeatability, stability and reusability based on MOCVD technology which can realize precise control of structure, morphology and surface energy level, which is expected to achieve rapid industrialization. © 2022, The Authors. All rights reserved.

关键词： Gallium nitride

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Data-Driven Modeling and Random Search Optimization Method for the Polysilicon Cvd Reactor

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SSRN 2022年

作者： Xi, Bangwen Xiong, Gang Shen, Zhen Tamir, Tariku Sinshaw He, Chang Song, Yonggang Liu, Xiong State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing100190 China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan523808 China Intelligent Manufacturing Center Qingdao Academy of Intelligent Industries Qingdao266109 China Xinte Energy Co. Ltd Urumqi830000 China

Polysilicon is mainly obtained by chemical vapor deposition(CVD) reaction, in which hydrogen gas and trichlorosilane(TCS) are fed into the CVD reactor to produce polysilicon. However, the feeding parameters are mainly adjusted in a step-by-step manner according to the actual deposition results, which is inefficient to optimize and prone to resource wastage. In this paper, based on the existing mechanistic model of the CVD reactor, we combine the history data with the specific parameters to construct a simulator. A random search algorithm is used to determine the optimal feeding parameters. With the simulator, the polysilicon productivity can be increased by 704 kg, and the power consumption reduced by 3.8 kWh/kg. © 2022, The Authors. All rights reserved.

关键词： Simulators

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Fast Depth Intra Coding Based on Layer-Classification and CNN for 3D-HEVC

Fast Depth Intra Coding Based on Layer-Classification and CN...

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Data Compression Conference (DCC)

作者： Chang Liu Kebin Jia Pengyu Liu Zhonghua Sun Faculty of Information Technology Bejing University of Technology Beijing China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China

ISBN: (数字)9781728164571

ISBN: (纸本)9781728164588

View synthesis optimization (VSO) introduces heavy computational complexity caused by the VSO-based iterative search of all possible quad-tree partitions. To reduce the complexity, this paper proposes a convolutional neural network (CNN) scheme based on layer-classification for fast depth intra coding. First, a layer-classification model based on texture smoothness is proposed to determine the smoothest depth map. Then, a CNN network incorporating SENet (CNN-SENet) structure is designed and trained. Finally, the layer-classification model and the CNN-SENet network are combined to predict the coding unit (CU) partition of all coding units (CUs) for depth map at a specific view.

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CASIA-Face-Africa: A large-scale African face image database

arXiv

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arXiv 2021年

作者： Muhammad, Jawad Wang, Yunlong Wang, Caiyong Zhang, Kunbo Sun, Zhenan The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing100044 China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing100044 China

Face recognition is a popular and well-studied area with wide applications in our society. However, racial bias had been proven to be inherent in most State Of The Art (SOTA) face recognition systems. Many investigative studies on face recognition algorithms have reported higher false positive rates of African subjects cohorts than the other cohorts. Lack of large-scale African face image databases in public domain is one of the main restrictions in studying the racial bias problem of face recognition. To this end, we collect a face image database namely CASIA-Face-Africa which contains 38,546 images of 1,183 African subjects. Multi-spectral cameras are utilized to capture the face images under various illumination settings. Demographic attributes and facial expressions of the subjects are also carefully recorded. For landmark detection, each face image in the database is manually labeled with 68 facial keypoints. A group of evaluation protocols are constructed according to different applications, tasks, partitions and scenarios. The performances of SOTA face recognition algorithms without retraining are reported as baselines. The proposed database along with its face landmark annotations, evaluation protocols and preliminary results form a good benchmark to study the essential aspects of face biometrics for African subjects, especially face image preprocessing, face feature analysis and matching, facial expression recognition, sex/age estimation, ethnic classification, face image generation, etc. The database can be downloaded from our website* Copyright © 2021, The Authors. All rights reserved.

关键词： Face recognition

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Learning to Branch in Combinatorial Optimization with Graph Pointer Networks

arXiv

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arXiv 2023年

作者： Wang, Rui Zhou, Zhiming Zhang, Tao Wang, Ling Xu, Xin Liao, Xiangke Li, Kaiwen The College of Systems Engineering National University of Defense Technology Changsha410073 China The Hunan Key Laboratory of Multi-Energy System Intelligent Interconnection Technology HKLMSI2T Changsha410073 China Institute of Automation Chinese Academy of Sciences Beijing100190 China The College of Intelligence Science and Technology National University of Defense Technology Changsha410073 China Department of Automation Tsinghua University Beijing100084 China The College of Computer Science and Technology National University of Defense Technology Changsha410073 China

Branch-and-bound is a typical way to solve combinatorial optimization problems. This paper proposes a graph pointer network model for learning the variable selection policy in the branch-and-bound. We extract the graph features, global features and historical features to represent the solver state. The proposed model, which combines the graph neural network and the pointer mechanism, can effectively map from the solver state to the branching variable decisions. The model is trained to imitate the classic strong branching expert rule by a designed top-k Kullback-Leibler divergence loss function. Experiments on a series of benchmark problems demonstrate that the proposed approach significantly outperforms the widely used expert-designed branching rules. Our approach also outperforms the state-of-the-art machine-learning-based branch-and-bound methods in terms of solving speed and search tree size on all the test instances. In addition, the model can generalize to unseen instances and scale to larger instances. Copyright © 2023, The Authors. All rights reserved.

关键词： Combinatorial optimization

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Conditional GANs with Auxiliary Discriminative Classifier

arXiv

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arXiv 2021年

作者： Hou, Liang Cao, Qi Shen, Huawei Pan, Siyuan Li, Xiaoshuang Cheng, Xueqi Data Intelligence System Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Shanghai Jiao Tong University Shanghai China CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China

Conditional generative models aim to learn the underlying joint distribution of data and labels to achieve conditional data generation. Among them, the auxiliary classifier generative adversarial network (AC-GAN) has been widely used, but suffers from the problem of low intra-class diversity of the generated samples. The fundamental reason pointed out in this paper is that the classifier of AC-GAN is generator-agnostic, which therefore cannot provide informative guidance for the generator to approach the joint distribution, resulting in a minimization of the conditional entropy that decreases the intra-class diversity. Motivated by this understanding, we propose a novel conditional GAN with an auxiliary discriminative classifier (ADC-GAN) to resolve the above problem. Specifically, the proposed auxiliary discriminative classifier becomes generator-aware by recognizing the class-labels of the real data and the generated data discriminatively. Our theoretical analysis reveals that the generator can faithfully learn the joint distribution even without the original discriminator, making the proposed ADC-GAN robust to the value of the coefficient hyperparameter and the selection of the GAN loss, and stable during training. Extensive experimental results on synthetic and real-world datasets demonstrate the superiority of ADC-GAN in conditional generative modeling compared to state-of-the-art classifier-based and projection-based conditional GANs. Copyright © 2021, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Group-wise Inhibition based Feature Regularization for Robust Classification

Group-wise Inhibition based Feature Regularization for Robus...

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International Conference on Computer Vision (ICCV)

作者： Haozhe Liu Haoqian Wu Weicheng Xie Feng Liu Linlin Shen Computer Vision Institute College of Computer Science and Software Engineering SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society National Engineering Laboratory for Big Data System Computing Technology Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China

The convolutional neural network (CNN) is vulnerable to degraded images with even very small variations (e.g. corrupted and adversarial samples). One of the possible reasons is that CNN pays more attention to the most discriminative regions, but ignores the auxiliary features when learning, leading to the lack of feature diversity for final judgment. In our method, we propose to dynamically suppress significant activation values of CNN by group-wise inhibition, but not fixedly or randomly handle them when training. The feature maps with different activation distribution are then processed separately to take the feature independence into account. CNN is finally guided to learn richer discriminative features hierarchically for robust classification according to the proposed regularization. Our method is comprehensively evaluated under multiple settings, including classification against corruptions, adversarial attacks and low data regime. Extensive experimental results show that the proposed method can achieve significant improvements in terms of both robustness and generalization performances, when compared with the state-of-the-art methods. Code is available at https://***/LinusWu/TENET_Training.

关键词： Training Computer vision Codes Heuristic algorithms Robustness Classification algorithms Convolutional neural networks

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Cloud Recognition Based on Lightweight Neural Network

Cloud Recognition Based on Lightweight Neural Network

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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Liang Zhang Kebin Jia Pengyu Liu Chunyao Fang Faculty of Information Technology Beijing University of Technology Beijing China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing

ISBN: (数字)9789881476883

ISBN: (纸本)9781728181301

Cloud type recognition is a basic task in the field of ground-based cloud meteorological observation. It is of great significance to identify cloud type accurately for improving the accuracy of weather forecast. In this paper, we propose a new lightweight convolutional neural network model, called LCCNet, to achieve accurate cloud recognition of ground-based cloud images. We build a standard ground-based cloud data set contains 11 categories, called HBMCD, which is recognized by professional meteorological stations and conforms to the standards of the World Meteorological organization. Compared with other existing ground-based cloud data sets, HBMCD have larger data volume, complete cloud categories and uniform quality, and are more professional and comprehensive. A number of comparative experiments demonstrates that the proposed LCCNet model has stronger characterization ability and higher classification accuracy, which is up to 97.35%. At the same time, its parameter amount and operation complexity are lower than the existing network models, which makes it possible for equipment integration and practical application.

关键词： Clouds Feature extraction Image recognition Shape Data mining Sun Satellites

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Constructing Sample-to-Class Graph for Few-Shot Class-Incremental Learning

SSRN

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SSRN 2024年

作者： Hu, Fuyuan Zhang, Jian Lyu, Fan Zhao, Linglan Liu, Chengyan Zhang, Tao Yu, Baoqing School of Electronics and Information Engineering Suzhou University of Science and Technology No.99 Xuefu Road Jiangsu Suzhou215000 China Jiangsu Industrial Intelligent Low Carbon Technology Engineering Center Jiangsu Suzhou215009 China Suzhou Key Laboratory of Intelligent Low Carbon Technology Application Jiangsu Suzhou215009 China New Laboratory of Pattemn Recognition Institute of Automation Chinese Academy of Sciences College of Intelligence and Computing No.95 Zhongguancun East Road Beijing100190 China Tencent Youtu Lab Shanghai201600 China Seventh People’s Hospital Shanghai University of Traditional Chinese Medicine No.358 Datong Road Pudong New District Shanghai200137 China

Few-shot class-incremental learning (FSCIL) aims to build machine learning models that can continually learn novel classes from a few training samples, without forgetting knowledge of old classes. The challenges of FSCIL lie in the limited data of novel classes, which not only lead to significant overfitting issues but also exacerbate the notorious catastrophic forgetting problems. As proved in early studies, building sample relationships is beneficial for learning from few-shot classes. In this paper, we advance the idea to the incremental scenario and propose a Sample-to-Class (S2C) graph learning method for FSCIL. Concretely, we propose a Sample-level Graph Network (SGN) that focuses on analyzing sample relationships within a single session. This network helps aggregate similar samples, ultimately leading to the extraction of more refined class-level features. Then, we present a Class-level Graph Network (CGN) that establishes connections across class-level features of both novel and old classes. This network plays a crucial role in linking the knowledge between different sessions and helps improve overall learning performance in the FSCIL scenario. Moreover, we design a multi-stage strategy for training the S2C model, which mitigates the training challenges posed by limited data in the incremental learning process. The multi-stage training strategy is designed to build an S2C graph from base to incremental sessions and improve the capacity via an extra pseudo-incremental stage. Extensive experiments on three popular benchmark datasets show that our method outperforms the baselines and sets new state-of-the-art results in FSCIL. Our codes will be available upon acceptance. © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Let's Play Music: Audio-Driven Performance Video Generation

Let's Play Music: Audio-Driven Performance Video Generation

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International Conference on Pattern Recognition

作者： Hao Zhu Yi Li Feixia Zhu Aihua Zheng Ran He Center for Research on Intelligent Perception and Computing (CRIPAC) National Laboratory of Pattern Recognition (NLPR) CASIA Beijing China Center for Excellence in Brain Science and Intelligence Technology CAS Beijing China Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University

We propose a new task named Audio-driven Performance Video Generation (APVG), which aims to synthesize the video of a person playing a certain instrument guided by a given music audio clip. It is a challenging task to generate the high-dimensional temporal consistent videos from low-dimensional audio modality. In this paper, we propose a multi-staged framework to generate realistic and synchronized performance video from given music. Firstly, we provide both global appearance and local spatial information by generating the coarse videos and keypoints of body and hands from a given music respectively. Then, we propose to transform the generated keypoints to heatmap via a differentiable space transformer, since the heatmap provides more spatial information but is harder to generate directly from audio. Finally, we propose a Structured Temporal UNet (STU) to extract both intra-frame structured information and interframe temporal consistency. They are obtained via graph-based structure module, and CNN-GRU based high-level temporal module respectively for final video generation. Comprehensive experiments validate the effectiveness of our proposed framework.

关键词： Instruments Transforms Synchronization Data mining Task analysis Space heating

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