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Generative adversarial network in wavelet domain for single image super-resolution

Journal of Computers (Taiwan) 2021年第3期32卷 249-262页

作者： Zhang, Fan Wang, Xinwei Cao, Lin Du, Kangning Guo, Yanan Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument Beijing Information Science and Technology University Beijing100101 China School of Information and Communication Engineering Beijing Information Science and Technology University Beijing100101 China

Nowadays, generative adversarial network for single image super-resolution has achieved superior performance. However, there are few studies on large scaling factor, and the reconstruction performance is relatively poor. Therefore, this paper tackles the above problem by proposing a generative adversarial network in wavelet domain to reconstruct high-resolution image. Specifically, the generator takes low-resolution image as input, and generates wavelet packet decomposition coefficients corresponding to the reconstructed high-resolution image. Then, the discriminator constructs adversarial loss to constrain the generation process of the generator in the wavelet domain. Finally, the high-resolution image is reconstructed based on the inverse wavelet packet transform. The experimental results on CelebA dataset show that our proposed method can achieve better performance than that of comparison methods. The minimum improvements of PSNR and SSIM are 1.783dB and 0.013 on scaling factor ×4, and 0.685dB and 0.016 on scaling factor ×8 when compared with the comparison methods. © 2021 Computer Society of the Republic of China. All rights reserved.

关键词： Generative adversarial networks

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Distributed Multi-Sensor Multi-View Fusion of PHD Filter for Maneuvering Targets

Distributed Multi-Sensor Multi-View Fusion of PHD Filter for...

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IEEE International Conference on Radar

作者： Cong Peng Lei Chai Wei Yi Linxiu Chen School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (纸本)9781665468893

This paper addresses the problem of maneuvering multi-target tracking by a network of sensors having different and limited fields of view (FoV s). Each local sensor runs the Gaussian Mixture Probability Hypothetical Density (GMPHD) filter. Due to the target maneuver, based on a single motion model, severe tracking performance degradation can be observed. We propose to use multi-model (MM) method to realize the adaptation to motion characteristic so as to overcome maneuverability of targets. Then considering FoV s of sensors in the network are different and limited, the standard weighted arithmetic average (WAA) fusion is no longer applicable and leads to an underestimation of the target number. Therefore, we use state-dependent WAA (SD-WAA) fusion rule, which performs the WAA in a more robust way by calculating a set of state-related fusion weights. Numerical experiment is designed to demonstrate the efficacy of the proposed method.

关键词： Degradation Adaptation models Target tracking Fuses Radar Sensor fusion Sensor phenomena and characterization

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Optimization Algorithm of RGB-D SLAM Visual Odometry based on Triangulation

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International Journal of Performability engineering 2020年第3期16卷 438-445页

作者： Dong, Jingwei Jiang, Yiming Han, Zhiyu School of Measure Control Technology and Communication Engineering Harbin University of Science and Technology Harbin150080 China

In this paper, an improved RGB-D SLAM scheme is proposed to solve the problems of great tracking error, long time-consuming in front-end, and high pressure of algorithm in back-end. The realization scheme is mainly aimed at improving the visual odometry. First, the ORB extraction algorithm is used to extract the feature points of the current frame and calculate the descriptors. Then, the current frame is matched with the local map by descriptors, and the matched feature points are added to the local map. Finally, the camera pose is calculated by the PnP algorithm. In order to ensure the tracking accuracy, a triangulation algorithm is proposed to optimize the depth value of map points in the local map. A series of fr1 datasets in the tum database are tested. The experimental results show that the real-time performance is guaranteed, and the RMSE (root mean square error) in the tracking process is reduced by 9% on average. Moreover, the image indexes of the point cloud image generated in the tracking process are also significantly improved. © 2020 Totem Publisher, Inc. All rights reserved.

关键词： Triangulation

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The Optimal Wavelet Basis for Electroencephalogram Denoising 20

The Optimal Wavelet Basis for Electroencephalogram Denoising

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2020 International Symposium on Artificial Intelligence in Medical Sciences, ISAIMS 2020

作者： Cheng, Liwei Li, Duanling Yu, Gongjing Zhang, Zhonghai Yu, Shuyue School of Automation Beijing University of Posts and Telecommunications Beijing China College of Mechanical and Electrical Engineering Shaanxi University of Science and Technology Xi'an China Beijing Aerospace Measurement and Control Technology Co. Ltd. China

ISBN: (纸本)9781450388603

To solve the problem of optimal wavelet basis selection in motor imagery electroencephalogram (MI-EEG) denoising by wavelet transform, based on the analysis of wavelet basis parameters and characteristics, combined with the characteristics of MI-EEG, we summarized the characteristics of wavelet basis suitable for MI-EEG denoising. Signal to noise ratio (SNR) and root mean squared error (RMSE) are introduced as evaluation criteria of signal denoising effect, it is concluded that the bior and rbio wavelet basis functions are better at denoising MI-EEG among the 7 types of wavelet clusters. Among them, the rbio2.2 wavelet basis is the most suitable for MI-EEG denoising. The comparison of simulation results verifies the correctness of the conclusions. © 2020 ACM.

关键词： Signal to noise ratio

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Life Prediction of Electromagnetic Relay Based on Bayesian Method

Life Prediction of Electromagnetic Relay Based on Bayesian M...

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2018 IEEE International Conference of Safety Produce Informatization, IICSPI 2018

作者： Jiang, Wenman Zhou, Zhen Qi, Jia Ma, Dezhong School of Measurement and Communication Engineering Harbin University of Science and Technology Harbin150080 China

ISBN: (纸本)9781538655139

So far, there is a question about the existing study of electromagnetic relay's life prediction: they mainly focused on the research of batch products as a whole, which cannot reflect individual differences. This paper proposes one kind of way about the life prediction of electromagnetic relay based on Bayesian method. Firstly, a prior distribution of the model parameters is determined on the basis of the determined degradation model. Then, the degradation data (contact resistance) based on the prior distribution is fused, and the parameters of the degenerate trajectory model are updated to obtain the reliability function. Finally, we can predict the lifetime of a single electromagnetic relay. The results show that the lifetime prediction of a single electromagnetic relay is realized by the Bayesian method, in which the No.10 electromagnetic relay predicts the life of the electromagnetic relay to be 1.28 × 105 times and the relative error is 0.06 for the test lifetime value is 1.21 × 105 times under the condition of a reliability of 90%. The feasibility of predicting the lifetime of a single electromagnetic relay using fusion degradation data is verified. © 2018 IEEE.

关键词： Reliability

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Emerging contaminants:A One Health perspective

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The Innovation 2024年第4期5卷 140-170,139页

作者： Fang Wang Leilei Xiang Kelvin Sze-Yin Leung Martin Elsner Ying Zhang Yuming Guo Bo Pan Hongwen Sun Taicheng An Guangguo Ying Bryan WBrooks Deyi Hou Damian EHelbling Jianqiang Sun Hao Qiu Timothy MVogel Wei Zhang Yanzheng Gao Myrna JSimpson Yi Luo Scott XChang Guanyong Su Bryan MWong Tzung-May Fu Dong Zhu Karl JJobst Chengjun Ge Frederic Coulon Jean Damascene Harindintwali Xiankui Zeng Haijun Wang Yuhao Fu Zhong Wei Rainer Lohmann Changer Chen Yang Song Concepcion Sanchez-Cid Yu Wang Ali El-Naggar Yiming Yao Yanran Huang Japhet Cheuk-Fung Law Chenggang Gu Huizhong Shen Yanpeng Gao Chao Qin Hao Li Tong Zhang Natàlia Corcoll Min Liu Daniel SAlessi Hui Li Kristian KBrandt Yolanda Pico Cheng Gu Jianhua Guo Jianqiang Su Philippe Corvini Mao Ye Teresa Rocha-Santos Huan He Yi Yang Meiping Tong Weina Zhang Fidèle Suanon Ferdi Brahushi Zhenyu Wang Syed AHashsham Marko Virta Qingbin Yuan Gaofei Jiang Louis A.Tremblay Qingwei Bu Jichun Wu Willie Peijnenburg Edward Topp Xinde Cao Xin Jiang Minghui Zheng Taolin Zhang Yongming Luo Lizhong Zhu Xiangdong Li DamiàBarceló Jianmin Chen Baoshan Xing Wulf Amelung Zongwei Cai Ravi Naidu Qirong Shen Janusz Pawliszyn Yong-guan Zhu Andreas Schaeffer Matthias C.Rillig Fengchang Wu Gang Yu James M.Tiedje State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil ScienceChinese Academy of SciencesNanjing 210008China University of Chinese Academy of Sciences Beijing 100049China Department of Chemistry Hong Kong Baptist UniversityHong KongChina HKBU Institute of Research and Continuing Education Shenzhen Virtual University ParkShenzhenChina Technical University of Munich TUM School of Natural SciencesInstitute of Hydrochemistry85748 GarchingGermany School of Resources&Environment Northeast Agricultural UniversityHarbin 150030China Climate Air Quality Research UnitSchool of Public Health and Preventive MedicineMonash UniversityMelbourneVICAustralia Faculty of Environmental Science&Engineering Kunming University of Science&TechnologyKunming 650500China Ministry of Education Key Laboratory of Pollution Processes and Environmental Criteria College of Environmental Science and EngineeringNankai UniversityTianjin 300350China Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control School of Environmental Science and EngineeringInstitute of Environmental Health and Pollution ControlGuangdong University of TechnologyGuangzhou 510006China Ministry of Education Key Laboratory of Environmental Theoretical Chemistry South China Normal UniversityGuangzhouGuangdong 510006China Department of Environmental Science Baylor UniversityWacoTXUSA Center for Reservoir and Aquatic Systems Research(CRASR) Baylor UniversityWacoTXUSA School of Environment Tsinghua UniversityBeijing 100084China School of Civil and Environmental Engineering Cornell UniversityIthacaNY 14853USA Key Laboratory of Microbial Technology for Industrial Pollution Control of Zhejiang Province Zhejiang University of TechnologyHangzhou 310014China School of Environmental Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China Laboratoire d’Ecologie Microbienne Universite Claude Bernard Lyon 1UMR CNRS 5557UMR INRAE 1418VetAgro Sup69622 VilleurbanneFrance Department of Pla

Environmental pollution is escalating due to rapid global development that often prioritizes human needs over planetary *** global efforts to mitigate legacy pollutants,the continuous introduction of new substances remains a major threat to both people and the *** response,global initiatives are focusing on risk assessment and regulation of emerging contaminants,as demonstrated by the ongoing efforts to establish the UN’s Intergovernmental Science-Policy Panel on Chemicals,Waste,and Pollution *** review identifies the sources and impacts of emerging contaminants on planetary health,emphasizing the importance of adopting a One Health *** for monitoring and addressing these pollutants are discussed,underscoring the need for robust and socially equitable environmental policies at both regional and international *** actions are needed to transition toward sustainable pollution management practices to safeguard our planet for future generations.

关键词： pollution planet contaminants

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A Solution Scheme Based on Quantum Enhancement for Multi Classification Problems

SSRN

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SSRN 2022年

作者： Zhang, Yijun Mu, Xiaodong Zhang, Peng Zhao, Dao Institute of Information and Communication National University of Defense Technology Wuhan430079 China Graduate School Rocket Force University of Engineering Xi’an710025 China State Key Laboratory of Astronautics Dynamics Xi’an satellite control center Xi’an710043 China

With the increasing data size of multi classification problems, the running efficiency of classical algorithms is seriously affected. In the paper, we propose a solution scheme based on quantum enhancement binary tree support vector machine for multi classification problems. On the basis of Euclidean distance binary tree support vector machine, the scheme quantizes the sample data. Aiming at the two parts of Euclidean distance and kernel function which consume a lot of computing resources in the classical algorithm, a general quantum circuit for the inner product of quantum states is constructed. By combining the parallel computing ability of the quantum with the classical algorithm, the exponential acceleration of these two parts is realized. Aiming at the linear equation of the classical algorithm which consumes a lot of computing resources. A quantum circuit based on quantum singular value estimation is proposed. Using the acceleration advantage of the quantum computing in matrix computing, the quantum enhancement of this part is realized. The experimental results show the proposed scheme has at least polynomial reduction in the time complexity and spatial complexity compared with classical methods for solving multi classification problems. © 2022, The Authors. All rights reserved.

关键词： Support vector machines

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Optimal Minimum Energy Path Planning of Mobile Robot Based on Improved WOA-GA with Hybrid Strategy for Community-Centric Application

Optimal Minimum Energy Path Planning of Mobile Robot Based o...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Jiang Du Zhou Da-Lin Li Gong-Fa Lu Bao-Li Yun Jun-Tong Liu Ying Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Wuhan China School of Computing University of Portsmouth Portsmouth UK Hubei key laboratory of hydroelectric machinery design & maintenance China Three Gorges University Yichang China Key Laboratory of Mechanical Transmission and Manufacturing Engineering of Hubei Province Wuhan University of Science and Technology Wuhan China Precision Manufacturing Research Institute Wuhan University of Science and Technology Wuhan China Institute of Semiconductors Chinese Academy of Sciences Beijing China Research Center for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan China

The path planning of mobile robots is investigated to minimize energy loss in intelligent manufacturing. To achieve optimal energy consumption planning, this paper proposes a hybrid strategy that enhances the whale optimization algorithm (WOA) and the genetic algorithm (GA). The WOA-GA algorithm combines their respective advantages and effectively mitigates premature convergence exhibited by the standard traditional algorithm. To enhance the global and local search capabilities of the standard WOA, we employ adaptive weighting and a Cauchy mutation strategy. Additionally, we introduce an adaptive crossover mutation probability and an elite strategy to mitigate the detrimental effects of the genetic algorithm's evolutionary operation in the late iterations, thereby improving its performance. This paper compares the performance of the standard GA, the standard WOA-GA algorithm, and the enhanced WOA-GA algorithm. The experimental results demonstrate that the enhanced WOA-GA algorithm can achieve optimal energy-efficient path planning using the hybrid strategy. The average energy consumption of the enhanced WOA-GA algorithm can be reduced by 30%, while the maximum energy consumption can be reduced by 47%. The results confirm the effectiveness and feasibility of the proposed method in enhancing the level of green manufacturing.

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Accelerating Diffusion Models Via Pre-Segmentation Diffusion Sampling for Medical Image Segmentation

Accelerating Diffusion Models Via Pre-Segmentation Diffusion...

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IEEE International Symposium on Biomedical Imaging

作者： Xutao Guo Yanwu Yang Chenfei Ye Shang Lu Bo Peng Hua Huang Yang Xiang Ting Ma Electronic & Informatin Engineering School Harbin Institute of Technology (Shenzhen) Shenzhen China Peng Cheng Laboratory Shenzhen China International Research Institute for Artifcial Intelligence Harbin Institute of Technology (Shenzhen) Shenzhen China Institute of Pathogen Biology Shenzhen Center for Disease Control and Prevention Shenzhen China Department of Radiology The Third People’s Hospital of Shenzhen Shenzhen China Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology Harbin Institute of Technology (Shenzhen) Shenzhen China

Based on the Denoising Diffusion Probabilistic Model (DDPM), medical image segmentation can be described as a conditional image generation task, which allows to compute pixel-wise uncertainty maps of the segmentation and allows an implicit ensemble of segmentations to boost the segmentation performance. However, DDPM requires many iterative denoising steps to generate segmentations from Gaussian noise, resulting in extremely inefficient inference. To mitigate the issue, we propose a principled acceleration strategy, called pre-segmentation diffusion sampling DDPM (PD-DDPM), which is specially used for medical image segmentation. The key idea is to obtain pre-segmentation results based on a separately trained segmentation network, and construct noise predictions (non-Gaussian distribution) according to the forward diffusion rule. We can then start with noisy predictions and use fewer reverse steps to generate segmentation results. Experiments show that PD-DDPM yields better segmentation results over representative baseline methods even if the number of reverse steps is significantly reduced. Moreover, PD-DDPM is orthogonal to existing advanced segmentation models, which can be combined to further improve the segmentation performance.

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Research on Nonlinear Frequency Compression Method of Hearing Aid with Adaptive Compression Ratio

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Computer Modeling in engineering & Sciences 2019年第11期121卷 551-567页

作者： Xia Wang Hongming Shen Huawei Tao Ruiyu Liang Xing Deng Haijian Shao Li Zhao Cairong Zou School of Information Science and Technology Nantong UniversityNantong226019China School of Information Science and Engineering Henan University of TechnologyZhengzhou450001China School of Communication Engineering Nanjing Institute of TechnologyNanjing211167China School of Automation Key Laboratory of Measurement and Control for CSEMinistry of EducationSoutheast UniversityNanjing210096China School of Computer Science Jiangsu University of Science and TechnologyZhenjiang212003China School of Information Science and Engineering Southeast UniversityNanjing210096China

To make full use of the residual audible frequency band of hearing-loss patients and improve the intelligibility of speech,an adaptive nonlinear frequency compression(NFC)algorithm is proposed,which amplifies signals below the cutoff frequency while compresses signals above the cutoff ***,high-frequency signals are decomposed to critical band signals according to the BARK ***,the global compression ratio is determined according to the patient's cutoff frequency and maximum audible ***,the sub-band compression ratio is adaptively determined based on the global compression ratio and normalized average energy of subband ***,the high frequency signals are transposed to low frequency bands by compression mapping,and the phases are adjusted to the same as the original low frequency *** results of speech intelligibility with nine subjects demonstrate that compared to conventional amplitude amplification and nonlinear frequency compression algorithms the proposed algorithm significantly improves the intelligibility of initials and sentences,while not affects the intelligibility of finals and tones significantly.

关键词： Frequency compression sensorineural hearing loss hearing aids

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