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Prediction of Mastitis in Dairy Cows Based on Milk Yield, Rumination Time, and Milk Electrical Conductivity Using Machine Learning Algorithms

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SSRN

SSRN 2023年

作者： Zhou, XiaoJing Wang, Hao Xu, Chuang Zhao, Zixuan Xu, Wei Deng, Zhaoju Department of Information and Computing Science Heilongjiang Bayi Agricultural University No. 5 Xinyang Road Heilongjiang Province Daqing163319 China Heilongjiang Provincial Key Laboratory of Prevention and Control of Bovine Diseases College of Animal Science and Veterinary Medicine Heilongjiang Bayi Agricultural University No. 5 Xinyang Road Heilongjiang Province Daqing163319 China Animal Husbandry and Veterinary Branch of Heilongjiang Academy of Agricultural Science Heilongjiang Province Qiqihar161005 China Department of Biosystems Division of Animal and Human Health Engineering KU Oude Markt 13 Leuven3000 Belgium College of Veterinary Medicine China Agricultural University Haidian District Tsinghua East road No.17 Beijing10010 China

In commercial dairy farms, mastitis is associated with potentially increased use of antimicrobials and associated resistance, thereby affecting the welfare of dairy cows and increasing the rate of culling and/or death. Therefore, accurate and efficient mastitis prediction is valuable for timely intervention. Machine learning algorithms have been successfully used to solve many biological problems such as predicting or detecting subclinical mastitis using high-dimensional and noisy data obtained from sensors used on the farm. Therefore, this study aimed to develop sensor-based prediction models for bovine mastitis using variables related to daily activity and rumination time generated from collars, coupled with daily composite milk yield, and milk electrical conductivity from rotary milking systems from five commercial farms in Northeast China. We employed nine machine learning algorithms to analyze original data of 447 diseased and 2146 healthy cows obtained over 2.5 years and the corresponding Z-standardized dataset of four specific stages of lactation to train and test the prediction models for recognizing and predicting naturally occurring mastitis in these dairy cows. The sensitivity, specificity, accuracy, precision, AUC, and Matthews’s correlation coefficient of these models were calculated to evaluate the model performance. For Z-standardized data of cows in the 0–28 DIM and 29–100 DIM subgroups, the multi-layer artificial neural net (MNET) algorithm showed the best performance, with the sensitivity, specificity, and accuracy of 91.89%, 93.69%, and 93.24% for data of cows in 0–28 DIM, and 91.84%, 96.73, and 95.44% for that in 29–100 DIM, respectively. For data of the subgroups 101–200 DIM and 201–305 DIM, random forest performed best on both the non-standardized and the Z-standardized datasets. Results of the Z-standardized dataset for all four subgroups were better than those of the non-standardized data. We also calculated the peak milk yield (PMY) and days

关键词： Forecasting

Efficient Pyramid Channel Attention Network for Pathological Myopia Recognition

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arXiv 2023年

作者： Zhang, Xiaoqing Zhao, Jilu Li, Yan Wu, Hao Zhou, Xiangtian Liu, Jiang Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Center for High Performance Computing Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China National Clinical Research Center for Ocular Diseases Eye Hospital Wenzhou Medical University Wenzhou325027 China State Key Laboratory of Ophthalmology Optometry and Visual Science Eye Hospital Wenzhou Medical University Wenzhou325027 China Research Unit of Myopia Basic Research and Clinical Prevention and Control Chinese Academy of Medical Sciences Wenzhou325027 China Singapore Eye Research Institute 169856 Singapore

Pathological myopia (PM) is the leading ocular disease for impaired vision worldwide. Clinically, the characteristic of pathology distribution in PM is global-local on the fundus image, which plays a significant role in assisting clinicians in diagnosing PM. However, most existing deep neural networks focused on designing complex architectures but rarely explored the pathology distribution prior of PM. To tackle this issue, we propose an efficient pyramid channel attention (EPCA) module, which fully leverages the potential of the clinical pathology prior of PM with pyramid pooling and multi-scale context fusion. Then, we construct EPCA-Net for automatic PM recognition based on fundus images by stacking a sequence of EPCA modules. Moreover, motivated by the recent pretraining-and-finetuning paradigm, we attempt to adapt pre-trained natural image models for PM recognition by freezing them and treating the EPCA and other attention modules as adapters. In addition, we construct a PM recognition benchmark termed PM-fundus by collecting fundus images of PM from publicly available datasets. The comprehensive experiments demonstrate the superiority of EPCA-Net over state-of-the-art methods in the PM recognition task. For example, EPCA-Net achieves 97.56% accuracy and outperforms ViT by 2.85% accuracy on the PM-fundus dataset. The results also show that our method based on the pretraining-and-finetuning paradigm achieves competitive performance through comparisons to part of previous methods based on traditional fine-tuning paradigm with fewer tunable parameters, which has the potential to leverage more natural image foundation models to address the PM recognition task in limited medical data regime. Copyright © 2023, The Authors. All rights reserved.

关键词： Pathology

Autonomous docking and energy sharing between two types of robotic agents

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IFAC-PapersOnLine 2018年第29期51卷 406-411页

作者： Babi, Anja Mandi, Filip Vasiljevi, Goran Mikovi, Nikola Faculty of Electrical Engineering and Computing LABUST Laboratory for Underwater Systems and Technologies University of Zagreb Unska 3 Zagreb Croatia Faculty of Electrical Engineering and Computing LARICS Laboratory for Robotics and Intelligent Control Systems University of Zagreb Unska 3 Zagreb Croatia

In order to achieve long-term autonomy after deployment, a method of energy sharing within a heterogeneous swarm of marine robots was devised and implemented. This paper describes hardware and software solutions as well as guidance and control algorithms developed with the goal of allowing powerful energy-rich overactuated surface platforms to approach, dock, and wirelessly recharge less mobile sensor-rich agents upon request using visual servoing. The basic image processing algorithm uses data acquired by the Microsoft Kinect sensor alternating between RGB and infra-red camera modes, depending on lighting conditions. The full search and docking procedure developed for long-term operation is described. The results of a series of experiments done both in an indoor testbed and in realistic outdoor conditions are discussed and some goals for future work are given. © 2018

关键词： Autonomous agents

Transverse-momentum-dependent wave functions of the pion from lattice QCD

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Physical Review D 2024年第9期109卷 L091503-L091503页

作者： Min-Huan Chu Jin-Chen He Jun Hua Jian Liang Xiangdong Ji Andreas Schäfer Hai-Tao Shu Yushan Su Ji-Hao Wang Wei Wang Yi-Bo Yang Jun Zeng Jian-Hui Zhang Qi-An Zhang INPAC Shanghai Key Laboratory for Particle Physics and Cosmology Key Laboratory for Particle Astrophysics and Cosmology (MOE) School of Physics and Astronomy Shanghai Jiao Tong University Shanghai 200240 China Yang Yuanqing Scientific Computering Center Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai 200240 China Department of Physics University of Maryland College Park Maryland 20742 USA Key Laboratory of Atomic and Subatomic Structure and Quantum Control (MOE) Guangdong Basic Research Center of Excellence for Structure and Fundamental Interactions of Matter Institute of Quantum Matter South China Normal University Guangzhou 510006 China Guangdong-Hong Kong Joint Laboratory of Quantum Matter Guangdong Provincial Key Laboratory of Nuclear Science Southern Nuclear Science Computing Center South China Normal University Guangzhou 510006 China Institut für Theoretische Physik Universität Regensburg D-93040 Regensburg Germany CAS Key Laboratory of Theoretical Physics Institute of Theoretical Physics Chinese Academy of Sciences Beijing 100190 China School of Fundamental Physics and Mathematical Sciences Hangzhou Institute for Advanced Study UCAS Hangzhou 310024 China Southern Center for Nuclear-Science Theory (SCNT) Institute of Modern Physics Chinese Academy of Sciences Huizhou 516000 Guangdong Province China International Centre for Theoretical Physics Asia-Pacific Beijing/Hangzhou China School of Physical Sciences University of Chinese Academy of Sciences Beijing 100049 China School of Science and Engineering The Chinese University of Hong Kong Shenzhen 518172 China Center of Advanced Quantum Studies Department of Physics Beijing Normal University Beijing 100875 China School of Physics Beihang University Beijing 102206 China

We present a lattice QCD calculation of the transverse-momentum-dependent wave function (TMDWF) of the pion using large-momentum effective theory. Numerical simulations are based on one ensemble with 2+1+1 flavors of highly improved staggered quarks with lattice spacing a=0.121 fm from the MILC collaboration, and one with 2+1 flavor clover fermions and tree-level Symanzik gauge action generated by the CLS collaboration with a=0.098 fm. As a key ingredient, the soft function is first obtained by incorporating the one-loop perturbative contributions and a proper normalization. Based on this and the equal-time quasi-TMDWF simulated on the lattice, we extract the TMDWF. The results for both lattice ensembles are compatible and a comparison with a phenomenological parametrization is made. Our studies provide a first attempt of ab initio calculation of TMDWFs which will eventually lead to crucial theory inputs for making predictions for exclusive processes under QCD factorization.

关键词： Lattice QCD Transverse momentum dependent distribution Pions

Human intention recognition for human aware planning in integrated warehouse systems

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arXiv 2020年

作者： Petković, Tomislav Hvězda, Jakub Rybecký, Tomáš Marković, Ivan Kulich, Miroslav Přeučil, Libor Petrović, Ivan University of Zagreb Faculty of Electrical Engineering and Computing Department of Control and Computer Engineering Laboratory for Autonomous Systems and Mobile Robotics Unska 3 ZagrebHR-10000 Croatia Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Prague160 00 Czech Republic

With the substantial growth of logistics businesses the need for larger and more automated warehouses increases, thus giving rise to fully robotized shop-floors with mobile robots in charge of transporting and distributing goods. However, even in fully automatized warehouse systems the need for human intervention frequently arises, whether because of maintenance or because of fulfilling specific orders, thus bringing mobile robots and humans ever closer in an integrated warehouse environment. In order to ensure smooth and efficient operation of such a warehouse, paths of both robots and humans need to be carefully planned;however, due to the possibility of humans deviating from the assigned path, this becomes an even more challenging task. Given that, the supervising system should be able to recognize human intentions and its alternative paths in real-time. In this paper, we propose a framework for human deviation detection and intention recognition which outputs the most probable paths of the humans workers and the planner that acts accordingly by replanning for robots to move out of the human's path. Experimental results demonstrate that the proposed framework increases total number of deliveries, especially human deliveries, and reduces human-robot encounters. Copyright © 2020, The Authors. All rights reserved.

关键词： Mobile robots

End-to-End AI-Based Point-of-Care Diagnosis System for Classifying Respiratory Illnesses and Early Detection of COVID-19

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arXiv 2020年

作者： Belkacem, Abdelkader Nasreddine Ouhbi, Sofia Lakas, Abderrahmane Benkhelifa, Elhadj Chen, Chao Department of Computer and Network Engineering CIT UAEU UAE Department of Computer Science and Software Engineering CIT UAEU UAE Cloud Computing and Applications Reseach Lab Staffordshire University United Kingdom Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology China

Respiratory symptoms can be a caused by different underlying conditions, and are often caused by viral infections, such as Influenza-like illnesses or other emerging viruses like the Coronavirus. These respiratory viruses, often, have common symptoms, including coughing, high temperature, congested nose, and difficulty breathing. However, early diagnosis of the type of the virus, can be crucial, especially in cases such as the recent COVID-19 pandemic. One of the factors that contributed to the spread of the pandemic, was the late diagnosis or confusing it with regular flu-like symptoms. Science has proved that one of the possible differentiators of the underlying causes of these different respiratory diseases is coughing, which comes in different types and forms. Therefore, a reliable lab-free tool for early and more accurate diagnosis that can differentiate between different respiratory diseases is very much needed. This paper proposes an end-to-end portable system that can record data from patients with symptom, including coughs (voluntary or involuntary) and translate them into health data for diagnosis, and with the aid of machine learning, classify them into different respiratory illnesses, including COVID-19. With the ongoing efforts to stop the spread of the COVID-19 disease everywhere today, and against similar diseases in the future, our proposed low cost and user-friendly solution can play an important part in the early diagnosis. Copyright © 2020, The Authors. All rights reserved.

关键词： Diagnosis

ANN Based Sensor and Actuator Fault Detection in Nuclear Reactors

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ANN Based Sensor and Actuator Fault Detection in Nuclear Rea...

International Conference on control, Mechatronics and Automation (ICCMA)

作者： Shohan Banerjee Jiamei Deng Chris Gorse Vineet Vajpayee Victor Becerra S. R. Shimjith School of Built Environment Engineering and Computing Leeds Beckett University Leeds United Kingdom School of Energy and Electronic Engineering University of Portsmouth Portsmouth United Kingdom Reactor Control System Design Section Bhabha Atomic Research Centre and Homi Bhabha National Institute Mumbai India

ISBN: (数字)9781728192109

ISBN: (纸本)9781728192116

In the nuclear power plants (NPPs), fault detection and diagnosis (FDD) methods are very important to improve the safety and reliability of plants. Researchers have established various FDD methods such as model-based methods, data-driven methods, and signal-based methods. In practical applications, model-based methods are very difficult to achieve. Thus, various data-driven methods and signal- based methods have been applied for monitoring key subsystems in NPPs. In this paper, a brief overview of the Artificial Neural Network (ANN) based FDD method is presented. Simulated data have been generated to train the ANNs as per requirement and to compare with the plant signal during a fault. A technique has been proposed analyzing two sensors data (power sensor and coolant sensor) to determine the sensor and actuator fault in a closed-loop in presence of robust (Proportional-Integral-Derivative) PID controller. Results are produced with credible MATLAB simulation.

关键词： Artificial neural networks Coolants Fault detection Inductors Temperature sensors Actuators Training

Collaborative Scheduling of computing Tasks for Edge computing

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Collaborative Scheduling of Computing Tasks for Edge Computi...

Chinese Automation Congress (CAC)

作者： Yue Liu Gang Xiong Fenghua Zhu Shichao Chen Liguo Zhang Xudong Liu School of Artificial Intelligence and Automation Beijing University of Technology The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation and the Cloud Computing Center Chinese Academy of Sciences Beijng China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation and Qingdao Academy of Intelligent Industries Chinese Academy of Sciences Beijng China Faculty of Information and Technology Beijing University of Technology Beijing China

ISBN: (纸本)9781665426480

In the face of the rapid development of the IoT, the traditional cloud computing, while providing services for mobile intelligent devices, also brings problems such as network bandwidth occupation and communication delay. Edge computing can solve these problems to some extent. But too many offloaded tasks may result in an edge server becoming overloaded and execution delay of tasks increasing, the computing resources of idle edge devices are also not fully utilized. In order to make full use of the computing resources of idle edge devices in the local network and reduce the execution delay for computing tasks, this paper takes the average delay time for all end devices as the optimization objective and proposes an end-edge cooperative scheduling (EECS) model to schedule tasks for edge computing. The scheduling algorithm used in EECS model is improved based on MCT algorithm. We use the EdgeCloudSim to verily the EECS model and scheduling algorithm. The simulation results show that the average completion time of computing tasks in EECS model, compared with only single edge server (SEE) task computing model, can be reduced by up to three times. In addition, the system load can be balanced to a certain extent in local network.

关键词： Analytical models Scheduling algorithms Computational modeling Collaboration Scheduling Delays Servers

Artificial Intelligence without Restriction Surpassing Human Intelligence with Probability One: Theoretical Insight into Secrets of the Brain with AI Twins of the Brain

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arXiv 2024年

作者： Huang, Guang-Bin Westover, M. Brandon Tan, Eng-King Wang, Haibo Cui, Dongshun Ma, Wei-Ying Wang, Tiantong He, Qi Wei, Haikun Wang, Ning Tian, Qiyuan Lam, Kwok-Yan Yao, Xin Wong, Tien Yin School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China Beth Israel Deaconess Medical Center Harvard Medical School Boston United States Department of Neurology National Neuroscience Institute Singapore Research Centre of Big Data and Artificial Intelligence for Medicine First Affiliated Hospital of Sun Yat-Sen University Guangzhou China Duke-NUS Medical School National University of Singapore Singapore Mind PointEye Singapore Institute for AI Industry Research Tsinghua University Beijing China College of Computing and Data Science Nanyang Technological University Singapore School of Biomedical Engineering Tsinghua University Beijing China Singapore AI Safety Institute Nanyang Technological University Singapore School of Data Science Lingnan University Hong Kong School of Computer Science University of Birmingham United Kingdom Singapore Eye Research Institute Singapore National Eye Centre Singapore Tsinghua Medicine Tsinghua University Beijing China

Artificial Intelligence (AI) has apparently become one of the most important techniques discovered by humans in history while the human brain is widely recognized as one of the most complex systems in the universe. One fundamental critical question which would affect human sustainability remains open: Will artificial intelligence (AI) evolve to surpass human intelligence in the future? This paper shows that in theory new AI twins with fresh cellular level of AI techniques for neuroscience could approximate the brain and its functioning systems (e.g. perception and cognition functions) with any expected small error and AI without restrictions could surpass human intelligence with probability one in the end. This paper indirectly proves the validity of the conjecture made by Frank Rosenblatt 70 years ago about the potential capabilities of AI, especially in the realm of artificial neural networks. This paper also gives the answer to the two widely discussed fundamental questions: 1) whether AI could have potentials of discovering new principles in nature;2) whether error backpropagation (BP) algorithm commonly and efficiently used in tuning parameters in AI applications is also adopted in the brain. Intelligence is just one of fortuitous but sophisticated creations of the nature which has not been fully discovered. Like mathematics and physics, with no restrictions artificial intelligence would lead to a new subject with its self-contained systems and principles. We anticipate that this paper opens new doors for 1) AI twins and other AI techniques to be used in cellular level of efficient neuroscience dynamic analysis, functioning analysis of the brain and brain illness solutions;2) new worldwide collaborative scheme for interdisciplinary teams concurrently working on and modelling different types of neurons and synapses and different level of functioning subsystems of the brain with AI techniques;3) development of low energy of AI techniques with the aid of fundament

关键词： Neurology