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Asymptotic formula for the M2-ranks of overpartitions

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arXiv 2022年

作者： Zhang, Helen W.J. Zhong, Ying School of Mathematics Hunan University Changsha410082 China Hunan Provincial Key Laboratory Intelligent Information Processing and Applied Mathematics Changsha410082 China

Let N 2(a, c, n) be the number of overpartitions of n whose the M2-rank is congruent to a modulo c. In this paper, we obtain the asymptotic formula of N 2(a, c, n) utilizing the Ingham Tauberian Theorem. As applications, we derive inequalities concerning with N 2(a, c, n) including its strict concavity and *** Codes 05A17, 11P72, 11P82 © 2022, CC BY.

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Gated SwitchGAN for multi-domain facial image translation

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arXiv 2021年

作者： Zhang, Xiaokang Zhu, Yuanlue Chen, Wenting Liu, Wenshuang Shen, Linlin Computer Vision Institute School of Computer Science & Software Engineering Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China

Recent studies on multi-domain facial image translation have achieved impressive results. The existing methods generally provide a discriminator with an auxiliary classifier to impose domain translation. However, these methods neglect important information regarding domain distribution matching. To solve this problem, we propose a switch generative adversarial network (SwitchGAN) with a more adaptive discriminator structure and a matched generator to perform delicate image translation among multiple domains. A feature-switching operation is proposed to achieve feature selection and fusion in our conditional modules. We demonstrate the effectiveness of our model. Furthermore, we also introduce a new capability of our generator that represents attribute intensity control and extracts content information without tailored training. Experiments on the Morph, RaFD and CelebA databases visually and quantitatively show that our extended SwitchGAN (i.e., Gated SwitchGAN) can achieve better translation results than StarGAN, AttGAN and STGAN. The attribute classification accuracy achieved using the trained ResNet-18 model and the FID score obtained using the ImageNet pretrained Inception-v3 model also quantitatively demonstrate the superior performance of our models. Copyright © 2021, The Authors. All rights reserved.

关键词： Generative adversarial networks

State-of-Charge Balancing Control for Battery Energy Storage Systems of Near-Space Vehicles with an Electric-Thermal-Aging Coupling Model

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State-of-Charge Balancing Control for Battery Energy Storage...

Chinese Control Conference (CCC)

作者： Zhiqiang Zuo Yunhao Du Yijing Wang Zhicheng Zhang Peng Li Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System School of Electrical and Information Engineering Tianjin University Tianjin P. R. China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China

State-of-charge (SoC) balancing is crucial for improving the efficiency and lifetime of the battery energy storage system in near-space vehicles. In this paper, the SoC balancing control problem is investigated by a coupling battery model with electric-thermal-aging dynamics. Firstly, a system identification experiment is carried out to obtain the internal parameters of the battery. Secondly, a weight optimization index is designed by integrating the balancing speed and battery state-of-health (SoH). Then, a receding horizon control algorithm is proposed to realize multi-unit battery SoC balancing with partial swarm optimization (PSO). Finally, the effectiveness of the proposed strategy is verified by simulation results.

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RELIABLY DETECTING HUMANS WITH RGB-D CAMERA WITH PHYSICAL BLOB DETECTOR FOLLOWED BY LEARNING-BASED FILTERING

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RELIABLY DETECTING HUMANS WITH RGB-D CAMERA WITH PHYSICAL BL...

IEEE International Conference on Acoustics, Speech and Signal processing

作者： Guyue Zhang Jun Liu Luchao Tian Yan Qiu Chen School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University China School of Electrical and Electronic Engineering Nanyang Technological University Singapore

ISBN: (纸本)9781479999897

This paper proposes a two-staged approach to real-time human detection in cluttered environments using RGB-D camera. The first stage is a novel physical blob (P-Blob) detection that can quickly find plausible human heads. The second stage uses a combination of human upper-body features to filter out false positives. Experiment results on three publicly available datasets show that the proposed method can reliably detect people in RGB-D video in real time.

关键词： Human detection RGB-D camera Physical blob detector Cameras Filtration detector Persons

SAIS: Single-stage anchor-free instance segmentation

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arXiv 2019年

作者： Xiang, Canqun Tian, Shishun Zou, Wenbin Xu, Chen Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen University Shenzhen518060 China College of Mathematics and Statistics Shenzhen University Shenzhen518060 China

In this paper, we propose a simple yet efficient instance segmentation approach based on the single-stage anchor-free detector, termed SAIS. In our approach, the instance segmentation task consists of two parallel subtasks which respectively predict the mask coefficients and the mask prototypes. Then, instance masks are generated by linearly combining the prototypes with the mask coefficients. To enhance the quality of instance mask, the information from regression and classification is fused to predict the mask coefficients. In addition, center-aware target is designed to preserve the center coordination of each instance, which achieves a stable improvement in instance segmentation. The experiment on MS COCO shows that SAIS achieves the performance of the exiting state-of-the-art single-stage methods with a much less memory footprint. Copyright © 2019, The Authors. All rights reserved.

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Patch-wise label propagation for MR brain segmentation based on multi-atlas images

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Patch-wise label propagation for MR brain segmentation based...

作者： Wang, Yan Zu, Chen Ma, Zongqing Luo, Yong He, Kun Wu, Xi Zhou, Jiliu College of Computer Science Sichuan University Chengdu China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Department of Head and Neck and Mammary Oncology West China Hospital Sichuan University Chengdu China College of Computer Science Chengdu University of Information Technology Chengdu China

In many neuroscience and clinical studies, accurate and automatic segmentation of subcortical structures is an important and difficult task. Multi-atlas-based segmentation method has been focus of considerable research due to its promising performance. In general, this technique first employs deformable image registration to construct the correspondences between pre-labeled atlas images and the target image. Then, using the acquired deformation field, labels in the atlas are propagated to the target image space. Obviously, anatomical differences between the target image and atlas images possibly affect the image registration accuracy, thus influencing the final segmentation performance. Another limitation is that the label propagation in most conventional multi-atlas based methods is implemented under a voxel-wise strategy, which cannot adequately utilize the local label information to determine the final label of the target sample. In this paper, we propose a patch-wise label propagation method based on multiple atlases for MR brain segmentation. First, each image patch is characterized by patch intensities and abundant texture features, to increase the accuracy of the patch-based similarity measurement. To determine the weights of the training patches for representing the test sample, a patch-based sparse coding procedure is employed. In the label propagation stage, to alleviate possible misalignment from the registration stage, we perform a patch-wise label propagation strategy in a nonlocal manner to predict the final label for each target sample. To evaluate the proposed segmentation method, we comprehensively implement our proposed method by conducting hippocampus segmentation on the ADNI data set. Experimental results demonstrate the effectiveness of the proposed method and show that the proposed method outperforms two conventional multi-atlas-based methods. © 2017, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Image registration

Real-time information Service System for the Meteorological Screen Base on CDMA

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Real-time Information Service System for the Meteorological ...

2016 International Conference on Wireless Communication and Network Engineering (WCNE2016)

作者： Tai-sheng ZENG Ji-chang WEN College of Mathematics and Computer Science Quanzhu Normal University Fujian Provincial Key Laboratory of Data Intensive Computing Key Laboratory of Intelligent Computing and Information Processing Fujian Province University College of Engineering Quanzhou Meteorological Bureau

Real-time information Service System for the Meteorological Screen Base on CDMA is designed, a complete set of information gathering, processing, aggregating and publishing system for weather prediction products, disaster warning information, Typhoon real-time position information, weather information every ten minutes. Real-time weather information to the grouping of the LED display live information and notice is automatically released. The system contains user classification and display management module, disaster warning information into the module, all kinds Notice entry module, real-time weather information collected the summary module as well as real-time weather information automatically release the module.

关键词： LED CDMA information service system

Scheduling optimization of silicon single crystal production process based on adaptive improved particle swarm optimization

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Scheduling optimization of silicon single crystal production...

Chinese Automation Congress (CAC)

作者： Lu Kang Ding Liu Yali Wu Guozheng Ping School of Automation and Information Engineering Xi’an University of Technology National and Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an China School of Automation and Information Engineering Xi’an University of Technology National and Local Joint Engineering Research Center of Crystal Growth Xi’an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an China School of Automation and Information Engineering Xi’an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an China

ISBN: (纸本)9781665426480

Aiming at the potential safety hazards of silicon single crystal production enterprises in the process of multi-furnace silicon single crystal production, and considering the maximum power load requirements in actual enterprises, a silicon single crystal production process scheduling model with the goal of minimizing the maximum completion time was established. Based on this model, an adaptive improved particle swarm optimization algorithm (Adaptive GPSO) is proposed to solve it. The algorithm retains the individual optimal value and global optimal value of the particle swarm algorithm, and introduces the crossover operation and mutation operation in the genetic algorithm to improve the new individual generation mechanism. At the same time, it performs dynamic iterative calculations on the mutation probability and crossover probability to avoid the algorithm fall into the local optimum, and improve the diversity and convergence of the algorithm. By solving different scales scheduling problems of silicon single crystal production process, the experiments on real-world data shows that the feasibility and effectiveness of the proposed algorithm.

关键词： Adaptation models Heuristic algorithms Crystals Production Probability Silicon Scheduling

Multi-scale context aggregation network with attention-guided for Crowd counting

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arXiv 2021年

作者： Wang, Xin Lv, Rongrong Zhao, Yang Yang, Tangwen Ruan, Qiuqi Institute of Information Science School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Guangdong Key Laboratory of Intelligent Information processing Shenzhen University ShenzhenGuangdong518060 China

Crowd counting aims to predict the number of people and generate the density map in the image. There are many challenges, including varying head scales, the diversity of crowd distribution across images and cluttered backgrounds. In this paper, we propose a multi-scale context aggregation network (MSCANet) based on single column encoder-decoder architecture for crowd counting, which consists of an encoder based on dense context-aware module (DCAM) and a hierarchical attention-guided decoder. To handle the issue of scale variation, we construct the DCAM to aggregate multi-scale contextual information through densely connecting the dilated convolution with varying receptive fields. The proposed DCAM can capture rich contextual information of crowd areas due to its long-range receptive fields and dense scale sampling. Moreover, to suppress the background noise and generate a high-quality density map, we adopt a hierarchical attention-guided mechanism in the decoder. This helps to integrate more useful spatial information from shallow feature maps of the encoder by introducing multiple supervision based on semantic attention module (SAM). Extensive experiments demonstrate that the proposed approach achieves better performance than other similar state-of-the-art methods on three challenging benchmark datasets for crowd counting. The code is available at https://***/KingMV/MSCANet. © 2021, CC BY.

关键词： Semantics