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	Infrared small target segmentation networks: A survey 期刊论文 OAI收割 PATTERN RECOGNITION, 2023, 卷号: 143 作者: Kou, Renke; Wang, Chunping; Peng, Zhenming; Zhao, Zhihe; Chen, Yaohong \| 收藏 \| 浏览/下载：31/0 \| 提交时间：2023/08/23 Infrared small target Characteristic analysis Segmentation network Deep learning Collaborative technology Data-driven False alarm Missed detection
	A Data-Characteristic-Aware Latent Factor Model for Web Services QoS Prediction 期刊论文 OAI收割 IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2022, 卷号: 34, 期号: 6, 页码: 2525-2538 作者: Wu, Di; Luo, Xin; Shang, Mingsheng; He, Yi; Wang, Guoyin \| 收藏 \| 浏览/下载：38/0 \| 提交时间：2022/08/22 Web Service quality-of-service QoS latent factor analysis density peak data-characteristic-aware missing data big data topological neighborhood noise data service selection data science
	Multiscale mechanical properties of shales: grid nanoindentation and statistical analytics 期刊论文 OAI收割 ACTA GEOTECHNICA, 2021, 期号: -, 页码: 16 作者: Du, Jianting; Luo, Shengmin; Hu, Liming; Guo, Brandon; Guo, Dongdong \| 收藏 \| 浏览/下载：87/0 \| 提交时间：2021/09/01 Characteristic length Data analytics Elastic properties Grid nanoindentation Shale Statistical analysis
	A decomposition-ensemble approach for tourism forecasting 期刊论文 OAI收割 ANNALS OF TOURISM RESEARCH, 2020, 卷号: 81, 页码: 16 作者: Xie, Gang; Qian, Yatong; Wang, Shouyang \| 收藏 \| 浏览/下载：17/0 \| 提交时间：2020/06/30 Tourism demand Complete ensemble empirical mode decomposition with adaptive noise Data characteristic analysis Time series forecasting
	Forecasting container throughput based on wavelet transforms within a decomposition-ensemble methodology: a case study of China 期刊论文 OAI收割 MARITIME POLICY & MANAGEMENT, 2019, 卷号: 46, 期号: 2, 页码: 178-200 作者: Xie, Gang; Qian, Yatong; Yang, Hewei \| 收藏 \| 浏览/下载：49/0 \| 提交时间：2019/03/05 Container throughput wavelet transform data characteristic analysis time series forecasting decomposition-ensemble methodology
	Data characteristic analysis and model selection for container throughput forecasting within a decomposition-ensemble methodology 期刊论文 OAI收割 TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW, 2017, 卷号: 108, 页码: 160-178 作者: Xie, Gang; Zhang, Ning; Wang, Shouyang \| 收藏 \| 浏览/下载：26/0 \| 提交时间：2018/07/25 Container throughput Data characteristic analysis Model selection Time series forecasting Decomposition-ensemble methodology
	A novel mode-characteristic-based decomposition ensemble model for nuclear energy consumption forecasting 期刊论文 OAI收割 ANNALS OF OPERATIONS RESEARCH, 2015, 卷号: 234, 期号: 1, 页码: 111-132 作者: Tang, Ling; Wang, Shuai; He, Kaijian; Wang, Shouyang \| 收藏 \| 浏览/下载：24/0 \| 提交时间：2018/07/30 Decomposition ensemble model Data-characteristic-based modeling Nuclear energy consumption forecasting Time series analysis Intelligent knowledge management
	Resource allocation based on dea and modified shapley value 期刊论文 iSwitch采集 Applied mathematics and computation, 2015, 卷号: 263, 页码: 280-286 作者: Yang, Zhihua; Zhang, Qianwei 收藏 \| 浏览/下载：31/0 \| 提交时间：2019/05/10 Resource allocation Dea (data envelopment analysis) Modified shapley value Characteristic function
	The new approach for infrared target tracking based on the particle filter algorithm (EI CONFERENCE) 会议论文 OAI收割 International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Infrared Imaging and Applications, May 24, 2011 - May 24, 2011, Beijing, China 作者: Sun H.; Han H.-X.; Sun H. 收藏 \| 浏览/下载：59/0 \| 提交时间：2013/03/25 Target tracking on the complex background in the infrared image sequence is hot research field. It provides the important basis in some fields such as video monitoring precision and video compression human-computer interaction. As a typical algorithms in the target tracking framework based on filtering and data connection the particle filter with non-parameter estimation characteristic have ability to deal with nonlinear and non-Gaussian problems so it were widely used. There are various forms of density in the particle filter algorithm to make it valid when target occlusion occurred or recover tracking back from failure in track procedure but in order to capture the change of the state space it need a certain amount of particles to ensure samples is enough and this number will increase in accompany with dimension and increase exponentially this led to the increased amount of calculation is presented. In this paper particle filter algorithm and the Mean shift will be combined. Aiming at deficiencies of the classic mean shift Tracking algorithm easily trapped into local minima and Unable to get global optimal under the complex background. From these two perspectives that "adaptive multiple information fusion" and "with particle filter framework combining" we expand the classic Mean Shift tracking framework.Based on the previous perspective we proposed an improved Mean Shift infrared target tracking algorithm based on multiple information fusion. In the analysis of the infrared characteristics of target basis Algorithm firstly extracted target gray and edge character and Proposed to guide the above two characteristics by the moving of the target information thus we can get new sports guide grayscale characteristics and motion guide border feature. Then proposes a new adaptive fusion mechanism used these two new information adaptive to integrate into the Mean Shift tracking framework. Finally we designed a kind of automatic target model updating strategy to further improve tracking performance. Experimental results show that this algorithm can compensate shortcoming of the particle filter has too much computation and can effectively overcome the fault that mean shift is easy to fall into local extreme value instead of global maximum value.Last because of the gray and fusion target motion information this approach also inhibit interference from the background ultimately improve the stability and the real-time of the target track. 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).