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| 1 | Fine particulate matter(PM_(2.5)/PM_(1.0)) in Beijing,China: Variations and chemical compositions as well as sources显示文摘Particulate matter(i.e.,PM_(1.0) and PM_(2.5)),considered as the key atmospheric pollutants,exerts negative effects on visibility,global climate,and human health by associated chemical compositions.However,our understanding of PM and its chemical compositions in Beijing under the current atmospheric environment is still not complete after witnessing marked alleviation during 2013–2017.Continuous measurements can be crucial for further air quality improvement by better characterizing PM pollution and chemical compositions in Beijing.Here,we conducted simultaneous measurements on PM in Beijing during 2018–2019.Results indicate that annualmean PM_(1.0) and PM_(2.5) concentrations were 35.49±18.61μg/m^(3) and 66.58±60.17μg/m^(3),showing a positive response to emission controls.The contribution of sulfate,nitrate,and ammonium(SNA)played an enhanced role with elevated PM loading and acted as the main contributors to pollution episodes.Discrepancies observed among chemical species between PM_(1.0) and PM_(2.5) in spring suggest that sand particles trend to accumulate in the range of 1–2.5μm.Pollution episodes occurred accompanied with southerly clusters and high formation of SNA by heterogeneous reactions in summer and winter,respectively.Results from positive matrix factorization(PMF)combined with potential source contribution function(PSCF)models showed that potential areas were seasonal dependent,secondary and vehicular sources became much more important compared with previous studies in Beijing.Our study presented a continuous investigation on PM and sources origins in Beijing,which provides a better understanding for further emission control as well as a reference for other cities in developing countries. | Lining Luo Xiaoxuan Bai Shuhan Liu Bobo Wu Wei Liu Yunqian Lv Zhihui Guo Shumin Lin Shuang Zhao Yan Hao Jiming Hao Kai Zhang Aihua Zheng Hezhong Tian | 2022 | Journal of Environmental Sciences2022，34，11： | 3 |
| 2 | Visualized analysis of mixed numeric and categorical data via extended self-organizing map显示文摘 | Hsu C C Lin Shuhan | 2012 | IEEE Trans on Neural Networks and Learning Systems2012，23，1： | 1 |
| 3 | Novel tracking method for the drinking behavior trajectory of pigs显示文摘Identifying and tracking the drinking behavior of pigs is of great significance for welfare feeding and piggery management. Research on pigs’ drinking behavior not only needs to indicate whether the snout is in contact with the water fountain, but it also needs to establish whether the pig is drinking water and for how long. To solve target loss and identification errors, a novel method for tracking the drinking behavior of pigs based on L-K Pyramid Optical Flow (L-K OPT), Kernelized Correlation Filters (KCF), and DeepLabCut (DLC) was proposed. First, the feature model of the drinking behavior of a sow was established by L-K OPT. In addition, the water flow vector was used to determine whether the animal drank water and to demonstrate the details of the movements. Then, on the basis of the improved KCF, the relocation model of the sow’s snout was established to resolve the problem of tracking loss in the snout. Finally, the tracking model of piglets’ drinking behavior was established by DLC to build the mapping association between the pig’s snout and the drinking fountain. By using 200 episodes of drinking water videos (30-60 s each) to verify the method proposed in this study, the results are explained that 1) according to the two important drinking water indexes, the Down (−135°, −45°) direction feature and the V2 (>10 pixels) speed feature, the drinking time could be accurate to the frame level, with an error within 30 frames;2) The overlapping precision (OP) was 95%, the center location error (CLE) was 3 pixels, and the speed was 300 fps, which were all superior to other traditional algorithms;3) The optimal learning rate was 0.005, and the loss value was 0.0 002. The method proposed in this study realized accurate and automatic monitoring of the drinking behavior of pigs, which could provide reference for other animal behavior monitoring. | Chengqi Liu Haijian Ye Longhe Wang Shuhan Lu Lin Li | 2023 | International Journal of Agricultural and Biological Engineering2023，16，6： | 0 |
| 4 | Skeleton extraction and pose estimation of piglets using ZS-DLC-PAF显示文摘The accurate identification of various postures in the daily life of piglets that are directly reflected by their skeleton morphology is necessary to study the behavioral characteristics of pigs.Accordingly,this study proposed a novel approach for the skeleton extraction and pose estimation of piglets.First,an improved Zhang-Suen(ZS)thinning algorithm based on morphology was used to establish the chain code mechanism of the burr and the redundant information deletion templates to achieve a single-pixel width extraction of pig skeletons.Then,body nodes were extracted on the basis of the improved DeepLabCut(DLC)algorithm,and a part affinity field(PAF)was added to realize the connection of body nodes,and consequently,construct a database of pig behavior and postures.Finally,a support vector machine was used for pose matching to recognize the main behavior of piglets.In this study,14000 images of piglets with different types of behavior were used in posture recognition experiments.Results showed that the improved algorithm based on ZS-DLC-PAF achieved the best thinning rate compared with those of distance transformation,medial axis transformation,morphology refinement,and the traditional ZS algorithm.The node tracking accuracy reached 85.08%,and the pressure test could accurately detect up to 35 nodes of 5 pigs.The average accuracy of posture matching was 89.60%.This study not only realized the single-pixel extraction of piglets’skeletons but also the connection among the different behavior body nodes of individual sows and multiple piglets.Furthermore,this study established a database of pig posture behavior,which provides a reference for studying animal behavior identification and classification and anomaly detection. | Chengqi Liu Haijian Ye Shuhan Lu Zhan Tang Zhao Bai Lei Diao Longhe Wang Lin Li | 2023 | International Journal of Agricultural and Biological Engineering2023，16，3： | 0 |
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