25 results on '"Jin, Zhongming"'
Search Results
2. Behavior classification and spatiotemporal analysis of grazing sheep using deep learning
- Author
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Jin, Zhongming, Shu, Hang, Hu, Tianci, Jiang, Chengxiang, Yan, Ruirui, Qi, Jingwei, Wang, Wensheng, and Guo, Leifeng
- Published
- 2024
- Full Text
- View/download PDF
3. Predicting physiological responses of dairy cows using comprehensive variables
- Author
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Shu, Hang, Li, Yongfeng, Bindelle, Jérôme, Jin, Zhongming, Fang, Tingting, Xing, Mingjie, Guo, Leifeng, and Wang, Wensheng
- Published
- 2023
- Full Text
- View/download PDF
4. Analysis and Comparison of New-Born Calf Standing and Lying Time Based on Deep Learning.
- Author
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Zhang, Wenju, Wang, Yaowu, Guo, Leifeng, Falzon, Greg, Kwan, Paul, Jin, Zhongming, Li, Yongfeng, and Wang, Wensheng
- Subjects
DEEP learning ,MILK quality ,CALVES ,COMPUTER vision ,FARM management ,INDUSTRIAL costs ,MEAT quality - Abstract
Simple Summary: In the process of calf rearing, it is inevitable to encounter issues of illness and death among calves. Often, due to the inability to detect sicknesses such as diarrhoea in a timely fashion, these sicknesses lead to the calves' demise. This research starts from the practical application needs, and proposes the development of a monitoring system using deep learning technology to monitor the daily standing and lying behaviour of calves to predict their condition and adaptation to the environment. By analysing the standing and lying time of calves, the system can provide early warnings about calves' condition and health status. This research helps to promptly grasp calves' condition and growth status, thereby improving their welfare and management, enhancing the health condition of reared calves, ensuring the quality and safety of meat and milk, and reducing production costs. This research method also offers a new idea for the construction of smart ranches, as the construction of precision and smart ranches is not only a demand of consumers but also an inevitable direction for the development of the breeding industry. Standing and lying are the fundamental behaviours of quadrupedal animals, and the ratio of their durations is a significant indicator of calf health. In this study, we proposed a computer vision method for non-invasively monitoring of calves' behaviours. Cameras were deployed at four viewpoints to monitor six calves on six consecutive days. YOLOv8n was trained to detect standing and lying calves. Daily behavioural budget was then summarised and analysed based on automatic inference on untrained data. The results show a mean average precision of 0.995 and an average inference speed of 333 frames per second. The maximum error in the estimated daily standing and lying time for a total of 8 calf-days is less than 14 min. Calves with diarrhoea had about 2 h more daily lying time (p < 0.002), 2.65 more daily lying bouts (p < 0.049), and 4.3 min less daily lying bout duration (p = 0.5) compared to healthy calves. The proposed method can help in understanding calves' health status based on automatically measured standing and lying time, thereby improving their welfare and management on the farm. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
5. Decouple co-adaptation: Classifier randomization for person re-identification
- Author
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Wei, Long, Wei, Zhenyong, Jin, Zhongming, Wei, Qianxiao, Huang, Jianqiang, Hua, Xian-Sheng, Cai, Deng, and He, Xiaofei
- Published
- 2020
- Full Text
- View/download PDF
6. Large scale multi-class classification with truncated nuclear norm regularization
- Author
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Hu, Yao, Jin, Zhongming, Shi, Yi, Zhang, Debing, Cai, Deng, and He, Xiaofei
- Published
- 2015
- Full Text
- View/download PDF
7. Behavior Classification and Analysis of Grazing Sheep on Pasture with Different Sward Surface Heights Using Machine Learning.
- Author
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Jin, Zhongming, Guo, Leifeng, Shu, Hang, Qi, Jingwei, Li, Yongfeng, Xu, Beibei, Zhang, Wenju, Wang, Kaiwen, and Wang, Wensheng
- Subjects
- *
RANGE management , *BEHAVIORAL assessment , *GRAZING , *SHEEP , *AUTOMATIC classification , *MACHINE learning , *HUMAN activity recognition - Abstract
Simple Summary: The monitoring and analysis of sheep behavior can reflect their welfare and health, which is beneficial for grazing management. For automatic classification and the continuous monitoring of grazing sheep behavior, wearable devices based on inertial measurement unit (IMU) sensors are important. The accuracy of different machine learning algorithms was compared, and the best one was used for the continuous monitoring and behavior classification of three grazing sheep on pasture with three different sward surface heights. The results showed that the algorithm automatically monitored the behavior of grazing sheep individuals and quantified the time of each behavior. Behavior classification and recognition of sheep are useful for monitoring their health and productivity. The automatic behavior classification of sheep by using wearable devices based on IMU sensors is becoming more prevalent, but there is little consensus on data processing and classification methods. Most classification accuracy tests are conducted on extracted behavior segments, with only a few trained models applied to continuous behavior segments classification. The aim of this study was to evaluate the performance of multiple combinations of algorithms (extreme learning machine (ELM), AdaBoost, stacking), time windows (3, 5 and 11 s) and sensor data (three-axis accelerometer (T-acc), three-axis gyroscope (T-gyr), and T-acc and T-gyr) for grazing sheep behavior classification on continuous behavior segments. The optimal combination was a stacking model at the 3 s time window using T-acc and T-gyr data, which had an accuracy of 87.8% and a Kappa value of 0.836. It was applied to the behavior classification of three grazing sheep continuously for a total of 67.5 h on pasture with three different sward surface heights (SSH). The results revealed that the three sheep had the longest walking, grazing and resting times on the short, medium and tall SHH, respectively. These findings can be used to support grazing sheep management and the evaluation of production performance. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
8. Progressive Transfer Learning.
- Author
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Yu, Zhengxu, Shen, Dong, Jin, Zhongming, Huang, Jianqiang, Cai, Deng, and Hua, Xian-Sheng
- Subjects
IMAGE recognition (Computer vision) ,FEATURE extraction ,OBJECT tracking (Computer vision) ,COMPUTER architecture - Abstract
Model fine-tuning is a widely used transfer learning approach in person Re-identification (ReID) applications, which fine-tuning a pre-trained feature extraction model into the target scenario instead of training a model from scratch. It is challenging due to the significant variations inside the target scenario, e.g., different camera viewpoint, illumination changes, and occlusion. These variations result in a gap between each mini-batch’s distribution and the whole dataset’s distribution when using mini-batch training. In this paper, we study model fine-tuning from the perspective of the aggregation and utilization of the dataset’s global information when using mini-batch training. Specifically, we introduce a novel network structure called Batch-related Convolutional Cell (BConv-Cell), which progressively collects the dataset’s global information into a latent state and uses it to rectify the extracted feature. Based on BConv-Cells, we further proposed the Progressive Transfer Learning (PTL) method to facilitate the model fine-tuning process by jointly optimizing BConv-Cells and the pre-trained ReID model. Empirical experiments show that our proposal can greatly improve the ReID model’s performance on MSMT17, Market-1501, CUHK03, and DukeMTMC-reID datasets. Moreover, we extend our proposal to the general image classification task. The experiments in several image classification benchmark datasets demonstrate that our proposal can significantly improve baseline models’ performance. The code has been released at https://github.com/ZJULearning/PTL [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
9. Classification and Analysis of Multiple Cattle Unitary Behaviors and Movements Based on Machine Learning Methods.
- Author
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Li, Yongfeng, Shu, Hang, Bindelle, Jérôme, Xu, Beibei, Zhang, Wenju, Jin, Zhongming, Guo, Leifeng, and Wang, Wensheng
- Subjects
MACHINE learning ,DAIRY cattle behavior ,ANIMAL behavior ,ANIMAL welfare ,MOTION detectors ,BOOSTING algorithms ,DAIRY cattle ,WINDOWS - Abstract
Simple Summary: Traditionally, farmers are unable to pay enough attention to individual livestock. An increasing number of sensors are being used to monitor animal behavior, early disease detection, and evaluation of animal welfare. In this study, we used machine learning algorithms to identify multiple unitary behaviors and movements of dairy cattle recorded by motion sensors. We also investigated the effect of time window on the performance of unitary behaviors classification and discussed the necessity of movement analysis. This study shows a feasible way to explore more detailed movements based on the result of unitary behaviors classification. Low-cost sensors provide remote monitoring of animal behaviors to help producers comprehensively and accurately identify the health status of individual livestock in real-time. The behavior of livestock on farms is the primary representation of animal welfare, health conditions, and social interactions to determine whether they are healthy or not. The objective of this study was to propose a framework based on inertial measurement unit (IMU) data from 10 dairy cows to classify unitary behaviors such as feeding, standing, lying, ruminating-standing, ruminating-lying, and walking, and identify movements during unitary behaviors. Classification performance was investigated for three machine learning algorithms (K-nearest neighbors (KNN), random forest (RF), and extreme boosting algorithm (XGBoost)) in four time windows (5, 10, 30, and 60 s). Furthermore, feed tossing, rolling biting, and chewing in the correctly classified feeding segments were analyzed by the magnitude of the acceleration. The results revealed that the XGBoost had the highest performance in the 60 s time window with an average F1 score of 94% for the six unitary behavior classes. The F1 score of movements is 78% (feed tossing), 87% (rolling biting), and 87% (chewing). This framework offers a possibility to explore more detailed movements based on the unitary behavior classification. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
10. A Fast Sampling Gradient Tree Boosting Framework
- Author
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Zhou, Daniel Chao, Jin, Zhongming, and Zhang, Tong
- Subjects
FOS: Computer and information sciences ,Computer Science - Machine Learning ,Statistics - Machine Learning ,MathematicsofComputing_NUMERICALANALYSIS ,Machine Learning (stat.ML) ,Machine Learning (cs.LG) - Abstract
As an adaptive, interpretable, robust, and accurate meta-algorithm for arbitrary differentiable loss functions, gradient tree boosting is one of the most popular machine learning techniques, though the computational expensiveness severely limits its usage. Stochastic gradient boosting could be adopted to accelerates gradient boosting by uniformly sampling training instances, but its estimator could introduce a high variance. This situation arises motivation for us to optimize gradient tree boosting. We combine gradient tree boosting with importance sampling, which achieves better performance by reducing the stochastic variance. Furthermore, we use a regularizer to improve the diagonal approximation in the Newton step of gradient boosting. The theoretical analysis supports that our strategies achieve a linear convergence rate on logistic loss. Empirical results show that our algorithm achieves a 2.5x--18x acceleration on two different gradient boosting algorithms (LogitBoost and LambdaMART) without appreciable performance loss.
- Published
- 2019
11. Effect of bearing geometry and structure support on transient elastohydrodynamic lubrication of metal-on-metal hip implants
- Author
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Liu, Feng, Jin, Zhongming, Roberts, Paul, and Grigoris, Peter
- Published
- 2007
- Full Text
- View/download PDF
12. In Vitro Investigation of Friction under Edge-Loading Conditions for Ceramic-on-Ceramic Total Hip Prosthesis
- Author
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Sariali, Elhadi, Stewart, Todd, Jin, Zhongming, and Fisher, John
- Published
- 2010
- Full Text
- View/download PDF
13. Deep Active Learning for Video-based Person Re-identification
- Author
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Wang, Menglin, Lai, Baisheng, Jin, Zhongming, Gong, Xiaojin, Huang, Jianqiang, and Hua, Xiansheng
- Subjects
FOS: Computer and information sciences ,Computer Vision and Pattern Recognition (cs.CV) ,Computer Science - Computer Vision and Pattern Recognition - Abstract
It is prohibitively expensive to annotate a large-scale video-based person re-identification (re-ID) dataset, which makes fully supervised methods inapplicable to real-world deployment. How to maximally reduce the annotation cost while retaining the re-ID performance becomes an interesting problem. In this paper, we address this problem by integrating an active learning scheme into a deep learning framework. Noticing that the truly matched tracklet-pairs, also denoted as true positives (TP), are the most informative samples for our re-ID model, we propose a sampling criterion to choose the most TP-likely tracklet-pairs for annotation. A view-aware sampling strategy considering view-specific biases is designed to facilitate candidate selection, followed by an adaptive resampling step to leave out the selected candidates that are unnecessary to annotate. Our method learns the re-ID model and updates the annotation set iteratively. The re-ID model is supervised by the tracklets' pesudo labels that are initialized by treating each tracklet as a distinct class. With the gained annotations of the actively selected candidates, the tracklets' pesudo labels are updated by label merging and further used to re-train our re-ID model. While being simple, the proposed method demonstrates its effectiveness on three video-based person re-ID datasets. Experimental results show that less than 3\% pairwise annotations are needed for our method to reach comparable performance with the fully-supervised setting.
- Published
- 2018
14. Dynamic Spatio-temporal Graph-based CNNs for Traffic Prediction
- Author
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Chen, Ken, Chen, Fei, Lai, Baisheng, Jin, Zhongming, Liu, Yong, Li, Kai, Wei, Long, Wang, Pengfei, Tang, Yandong, Huang, Jianqiang, and Hua, Xian-Sheng
- Subjects
FOS: Computer and information sciences ,Computer Vision and Pattern Recognition (cs.CV) ,Computer Science - Computer Vision and Pattern Recognition - Abstract
Forecasting future traffic flows from previous ones is a challenging problem because of their complex and dynamic nature of spatio-temporal structures. Most existing graph-based CNNs attempt to capture the static relations while largely neglecting the dynamics underlying sequential data. In this paper, we present dynamic spatio-temporal graph-based CNNs (DST-GCNNs) by learning expressive features to represent spatio-temporal structures and predict future traffic flows from surveillance video data. In particular, DST-GCNN is a two stream network. In the flow prediction stream, we present a novel graph-based spatio-temporal convolutional layer to extract features from a graph representation of traffic flows. Then several such layers are stacked together to predict future flows over time. Meanwhile, the relations between traffic flows in the graph are often time variant as the traffic condition changes over time. To capture the graph dynamics, we use the graph prediction stream to predict the dynamic graph structures, and the predicted structures are fed into the flow prediction stream. Experiments on real datasets demonstrate that the proposed model achieves competitive performances compared with the other state-of-the-art methods.
- Published
- 2018
15. Effects of a high-gradient magnetic field on the migratory behavior of primary crystal silicon in hypereutectic Al–Si alloy
- Author
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Fangwei Jin, Zhongming Ren, Weili Ren, Kang Deng, Yunbo Zhong and Jianbo Yu
- Subjects
high magnetic field ,solidification ,gradient magnetic field ,Al-Si alloy ,magnetization force ,migration ,Materials of engineering and construction. Mechanics of materials ,TA401-492 ,Biotechnology ,TP248.13-248.65 - Abstract
The migration of primary Si grains during the solidification of Al–18 wt%Si alloy under a high-gradient magnetic field has been investigated experimentally. It was found that under a gradient magnetic field, the primary Si grains migrated toward one end of the specimen, forming a Si-rich layer, and the thickness of the Si-rich layer increased with increasing magnetic flux density. No movement of Si grains was apparent under a magnetic field below 2.3 T. For magnetic fields above 6.6 T, however, the thickness of the Si-rich layer was almost constant. It was shown that the static field also played a role in impeding the movement of the grains. The primary Si grains were refined in the Si layer, even though the primary silicon grains were very dense. The effect of the magnetic flux density on the migratory behavior is discussed.
- Published
- 2008
16. SIF: Self-Inspirited Feature Learning for Person Re-Identification.
- Author
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Wei, Long, Wei, Zhenyong, Jin, Zhongming, Yu, Zhengxu, Huang, Jianqiang, Cai, Deng, He, Xiaofei, and Hua, Xian-Sheng
- Subjects
CONVOLUTIONAL neural networks - Abstract
The re-identification (ReID) task has received increasing studies in recent years and its performance has gained significant improvement. The progress mainly comes from searching for new network structures to learn person representations. However, limited efforts have been made to explore the potential performance of existing ReID networks directly by better training scheme, which leaves a large space for ReID research. In this paper, we propose a Self-Inspirited Feature Learning (SIF) method to enhance the performance of given ReID networks from the viewpoint of optimization. We design a simple adversarial learning scheme to encourage a network to learn more discriminative person representation. In our method, an auxiliary branch is added into the network only in the training stage, while the structure of the original network stays unchanged during the testing stage. In summary, SIF has three aspects of advantages: 1) it is designed under general setting; 2) it is compatible with many existing feature learning networks on the ReID task; 3) it is easy to implement and has steady performance. We evaluate the performance of SIF on three public ReID datasets: Market1501, DuckMTMC-reID, and CUHK03(both labeled and detected). The results demonstrate significant improvement in performance brought by SIF. We also apply SIF to obtain state-of-the-art results on all the three datasets. Specifically, mAP / Rank-1 accuracy are: 87.6%/95.2% (without re-rank) on Market1501, 79.4%/89.8% on DuckMTMC-reID, 77.0%/79.5% on CUHK03 (labeled) and 73.9%/76.6% on CUHK03 (detected), respectively. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
17. Determination of apatinib and its three active metabolites by UPLC–MS/MS in a Phase IV clinical trial in NSCLC patients.
- Author
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Guan, Shaoxing, Shi, Wei, Zhao, Zerui, Wang, Fei, Jiang, Fulin, Zhang, Caibin, Jin, Zhongming, Guan, Yanping, Liu, Dihan, Zhong, GuoPing, Huang, Min, Long, Hao, and Wang, Xueding
- Published
- 2019
- Full Text
- View/download PDF
18. Sharp Attention Network via Adaptive Sampling for Person Re-Identification.
- Author
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Shen, Chen, Qi, Guo-Jun, Jiang, Rongxin, Jin, Zhongming, Yong, Hongwei, Chen, Yaowu, and Hua, Xian-Sheng
- Subjects
MACHINE learning ,TASK analysis - Abstract
In this paper, we present novel sharp attention networks by adaptively sampling feature maps from convolutional neural networks for person re-identification (re-ID) problems. Due to the introduction of sampling-based attention models, the proposed approach can adaptively generate sharper attention-aware feature masks. This greatly differs from the gating-based attention mechanism that relies on soft gating functions to select the relevant features for person re-ID. In contrast, the proposed sampling-based attention mechanism allows us to effectively trim irrelevant features by enforcing the resultant feature masks to focus on the most discriminative features. It can produce sharper attentions that is more assertive in localizing subtle features relevant to re-identifying people across cameras. For this purpose, a differentiable Gumbel-Softmax sampler is employed to approximate the Bernoulli sampling to train the sharp attention networks. Extensive experimental evaluations demonstrate the superiority of this new sharp attention model for person re-ID over other related existing, published state-of-the-art works on three challenging benchmarks, including CUHK03, Market-1501, and DukeMTMC-reID. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
19. Sparse Learning with Stochastic Composite Optimization.
- Author
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Zhang, Weizhong, Zhang, Lijun, Jin, Zhongming, Jin, Rong, Cai, Deng, Li, Xuelong, Liang, Ronghua, and He, Xiaofei
- Subjects
EDUCATION ,MATHEMATICAL programming ,ALGORITHMS ,MATHEMATICAL optimization ,FIBERS - Abstract
In this paper, we study Stochastic Composite Optimization (SCO) for sparse learning that aims to learn a sparse solution from a composite function. Most of the recent SCO algorithms have already reached the optimal expected convergence rate \mathcal O(1/\lambda T)
with $\delta$- Published
- 2017
- Full Text
- View/download PDF
20. Fast and Accurate Hashing Via Iterative Nearest Neighbors Expansion.
- Author
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Jin, Zhongming, Zhang, Debing, Hu, Yao, Lin, Shiding, Cai, Deng, and He, Xiaofei
- Abstract
Recently, the hashing techniques have been widely applied to approximate the nearest neighbor search problem in many real applications. The basic idea of these approaches is to generate binary codes for data points which can preserve the similarity between any two of them. Given a query, instead of performing a linear scan of the entire data base, the hashing method can perform a linear scan of the points whose hamming distance to the query is not greater than rh , where rh is a constant. However, in order to find the true nearest neighbors, both the locating time and the linear scan time are proportional to O(\sumi=0^{rh}{c\choose i}) ( $c$ is the code length), which increase exponentially as rh increases. To address this limitation, we propose a novel algorithm named iterative expanding hashing in this paper, which builds an auxiliary index based on an offline constructed nearest neighbor table to avoid large rh . This auxiliary index can be easily combined with all the traditional hashing methods. Extensive experimental results over various real large-scale datasets demonstrate the superiority of the proposed approach. [ABSTRACT FROM PUBLISHER]
- Published
- 2014
- Full Text
- View/download PDF
21. Density Sensitive Hashing.
- Author
-
Jin, Zhongming, Li, Cheng, Lin, Yue, and Cai, Deng
- Abstract
Nearest neighbor search is a fundamental problem in various research fields like machine learning, data mining and pattern recognition. Recently, hashing-based approaches, for example, locality sensitive hashing (LSH), are proved to be effective for scalable high dimensional nearest neighbor search. Many hashing algorithms found their theoretic root in random projection. Since these algorithms generate the hash tables (projections) randomly, a large number of hash tables (i.e., long codewords) are required in order to achieve both high precision and recall. To address this limitation, we propose a novel hashing algorithm called density sensitive hashing (DSH) in this paper. DSH can be regarded as an extension of LSH. By exploring the geometric structure of the data, DSH avoids the purely random projections selection and uses those projective functions which best agree with the distribution of the data. Extensive experimental results on real-world data sets have shown that the proposed method achieves better performance compared to the state-of-the-art hashing approaches. [ABSTRACT FROM PUBLISHER]
- Published
- 2014
- Full Text
- View/download PDF
22. On the Educational Innovation in the Evolution of Xue and Shu.
- Author
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Jin Zhongming
- Abstract
Given that xue (study) and Shu (skill) originated from experience and imitation, in ancient China scholars aimed to find out logical cases, while businessmen to make commercial successes. That predicted that scholarship (knowledge/theory) would be separated from practice (application/empiricism) in China's social strata. Furthermore, this historical phenomenon in China found expression in the course of Western history. In other words, scholarship and practice in China would evolve from integration through separation to re-integration. The modern tendency of China's higher education is characterized by the separation of academic school from technological school and the integration of arts and science. The logical origin (academic cognition) and basis for judgment (academic value) in traditional Chinese education focused on practical function. The social intermedium in Western countries for cultural innovation and scientific development was known as salons and cafes, which evolved into the new platforms for modern academic groups and periodicals. The integration of cultural resources for educational innovation, the strengthening of the psychological motivation of academic development, and the cultivation of the physical foundation and intellectual atmosphere of an "invisible college" can serve to make the academic triple function (cognitive innovation, cultural entertainment, and economic utility) become a human need for life, which is the mission of school education in the new era. [ABSTRACT FROM AUTHOR]
- Published
- 2008
- Full Text
- View/download PDF
23. Analysis and Comparison of New-Born Calf Standing and Lying Time Based on Deep Learning.
- Author
-
Zhang W, Wang Y, Guo L, Falzon G, Kwan P, Jin Z, Li Y, and Wang W
- Abstract
Standing and lying are the fundamental behaviours of quadrupedal animals, and the ratio of their durations is a significant indicator of calf health. In this study, we proposed a computer vision method for non-invasively monitoring of calves' behaviours. Cameras were deployed at four viewpoints to monitor six calves on six consecutive days. YOLOv8n was trained to detect standing and lying calves. Daily behavioural budget was then summarised and analysed based on automatic inference on untrained data. The results show a mean average precision of 0.995 and an average inference speed of 333 frames per second. The maximum error in the estimated daily standing and lying time for a total of 8 calf-days is less than 14 min. Calves with diarrhoea had about 2 h more daily lying time ( p < 0.002), 2.65 more daily lying bouts ( p < 0.049), and 4.3 min less daily lying bout duration ( p = 0.5) compared to healthy calves. The proposed method can help in understanding calves' health status based on automatically measured standing and lying time, thereby improving their welfare and management on the farm.
- Published
- 2024
- Full Text
- View/download PDF
24. Classification and Analysis of Multiple Cattle Unitary Behaviors and Movements Based on Machine Learning Methods.
- Author
-
Li Y, Shu H, Bindelle J, Xu B, Zhang W, Jin Z, Guo L, and Wang W
- Abstract
The behavior of livestock on farms is the primary representation of animal welfare, health conditions, and social interactions to determine whether they are healthy or not. The objective of this study was to propose a framework based on inertial measurement unit (IMU) data from 10 dairy cows to classify unitary behaviors such as feeding, standing, lying, ruminating-standing, ruminating-lying, and walking, and identify movements during unitary behaviors. Classification performance was investigated for three machine learning algorithms (K-nearest neighbors (KNN), random forest (RF), and extreme boosting algorithm (XGBoost)) in four time windows (5, 10, 30, and 60 s). Furthermore, feed tossing, rolling biting, and chewing in the correctly classified feeding segments were analyzed by the magnitude of the acceleration. The results revealed that the XGBoost had the highest performance in the 60 s time window with an average F1 score of 94% for the six unitary behavior classes. The F1 score of movements is 78% (feed tossing), 87% (rolling biting), and 87% (chewing). This framework offers a possibility to explore more detailed movements based on the unitary behavior classification.
- Published
- 2022
- Full Text
- View/download PDF
25. Quantitative bioanalytical LC-MS/MS assay for S130 in rat plasma-application to a pharmacokinetic study.
- Author
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Guan Y, Fu Y, Liu Y, Wang S, Zhao M, Jin Z, Jiang F, Hong L, Huang M, Li M, and Zhong G
- Subjects
- Animals, Chromatography, Liquid standards, Cysteine Proteinase Inhibitors pharmacokinetics, Female, Humans, Mice, Rats, Reference Standards, Tandem Mass Spectrometry standards, Chromatography, Liquid methods, Colorectal Neoplasms enzymology, Cysteine Proteinase Inhibitors blood, Tandem Mass Spectrometry methods
- Abstract
Aim: An innovative Atg4B inhibitor, S130, exhibited a negative influence on colorectal cancer cells in vitro and in vivo . To assist reliable toxicodynamic and pharmacokinetic evaluation, an LC-MS/MS assay of S130 in rat plasma must be necessary. Results: An LC-MS/MS assay for determination of S130 in rat plasma has been first developed and fully verified whose values met the admissible limits as per the US FDA guidelines. Chromatographic separation was achieved by using an isocratic elution after 3 min. MS was conducted under the ESI
+ mode fitted with selected reaction monitoring. The calibration curve proved acceptable linearity over 0.50-800 ng/ml. Conclusion: The developed LC-MS/MS assay of S130 in rat plasma is easily applicable in pharmacokinetics study and the further toxicological evaluation.- Published
- 2019
- Full Text
- View/download PDF
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