Presentations -
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Dam Water Overflow Estimation using Time Series International conference
Mie Mie Khin, Mie Mie Tin, Thi Thi Zin, Pyke Tin
2020 IEEE 9th Global Conference on Consumer Electronics (GCCE) (Kobe, Japan) IEEE
Event date： 2020.10.13 - 2020.10.16
Language：English Presentation type：Oral presentation (general)
Venue：Kobe, Japan
This paper will implement the water level estimation of the dam from Myanmar. We use the time series stochastic model to calculate the water level estimation varying on in flow and consumption of dam. This approach is applying probability of Markovian Time Series. This paper based on rainfall and the other factors of dam water storage such as inflow and outflow of dam. This result estimate actual monthly water spread area and shows error is small. This research result also highlight that Markovian Time Series model is one of the best estimate processes for water level estimation.
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Predicting Calving Time of Dairy Cows by Exponential Smoothing Models International conference
Tunn Cho Lwin, Thi Thi Zin, Pyke Tin
2020 IEEE 9th Global Conference on Consumer Electronics (GCCE) (Kobe, Japan) IEEE
Event date： 2020.10.13 - 2020.10.16
Language：English Presentation type：Oral presentation (general)
Venue：Kobe, Japan
In dairy farming, calving time prediction is crucial because the calf loss rate during calving time is rising for many reasons. In this paper, we propose a time series model with exponential smoothing technique to predict the time of calving event occurs. By investigating the changes in behavior patterns a few days before the calving events, the proposed method can predict accurately the time of occurrence of calving events. To be specific, we model the number of changes in behaviors of standing and lying as a time series in hourly basis. We then employ the exponential smoothing techniques and survival probability with exponential distribution to make the prediction process. To confirm the proposed method, some experimental works are performed by using video records of 25 cows in calving pen from a large farm at Oita prefecture, Japan.
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Detection of Estrus in Cattle by using Image Technology and Machine Learning Methods International conference
Su Myat Noe, Thi Thi Zin, Pyke Tin, Hiromitsu Hama
2020 IEEE 9th Global Conference on Consumer Electronics (GCCE) (Kobe, Japan) IEEE
Event date： 2020.10.13 - 2020.10.16
Language：English Presentation type：Oral presentation (general)
Venue：Kobe, Japan
Detection of estrus in cattle in early phase is especially vital in the era of precision farming. This paper focuses on the detection of estrus in cattle by using image processing techniques and machine learning methods. In doing so, we first utilize an image analysis to investigate some behaviors of cattle in estrus, which is standing when mounted by the other cattle. We then extract some statistical measures based on polyline shape features of detected cattle images and utilize these measures as an input to machine learning algorithms. Specifically, in this paper, we employ the three supervised machine learning methods, which is Support Vector Machine (SVM), Logistic Regression (LR), and Multiple Linear Regression (MLR) classifiers. Some experimental works are performed by using real-life video sequences. The results show promising and capable to detect the behavior of estrus both cost-effectively (only image) and specifically with the detection rate of SVM is 97%, LR is 94%, and MLR is 94%, respectively.
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Image Processing and Statistical Analysis Approach to Predict Calving Time in Dairy Cows International conference
Swe Zar Maw, Thi Thi Zin, Pyke Tin
2020 IEEE 9th Global Conference on Consumer Electronics (GCCE) (Kobe, Japan) IEEE
Event date： 2020.10.13 - 2020.10.16
Language：English Presentation type：Oral presentation (general)
Venue：Kobe, Japan
An accurate prediction of calving time in dairy cows is one of the most important factors to make an optimal reproduction process in dairy farming. This paper proposes an image processing and statistical analysis approach to predict calving time in dairy cows. Specifically, we extract the behavior changes patterns of the expected cows by using simple effective motion history images (MHI) a few days before the occurrence of calving event from the video sequences taken in the maternity bans. We then classify extracted features with support vector machine (SVM) and analyze the behavior changes by using statistical method, Hidden Markov model (HMM) for prediction process. To confirm the validity of proposed method, we perform some experiments by installing 360-degree view cameras at the top of calving bans. At the first stage, we analyzed the behaviors of 25 dairy cows for 72 hours before giving birth. As a result, we find that the proposed method is promising.
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Feature Detection and Classification of Cow Motion for Predicting Calving time International conference
Thi Thi Zin, Saw Zay Maung Maung, Pyke Tin, Yoichiro HORII
2020 IEEE 9th Global Conference on Consumer Electronics (GCCE) (Kobe, Japan) IEEE
Event date： 2020.10.13 - 2020.10.16
Language：English Presentation type：Oral presentation (general)
Venue：Kobe, Japan
The monitoring and automatic detecting of cow behaviors is a key factor for predicting cow calving times. This paper describes the analysis of cow motion patterns by using 360 camera in order to identify various views of cow states. Firstly, Principle Component Analysis (PCA) is applied to solve the rotation variant problem in different postures of cow body and then the dominant features (shape distances) are extracted for cow motion classification such as Standing, Lying, and Transition States (Standing-to-Lying and Lying-to-Standing). During the movement of cow motions, the increasing and decreasing trends of shape (Beta features) from cow body are used to classify transition activities of cows. We prepared the datasets by grouping similar motion sequences and tested against with the proposed features. According to experimental results, the proposed system can give the high accuracy with low computational cost in case of detecting and classifying cow motions.
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A Mobile Application for Offline Handwritten Character Recognition International conference
Thi Thi Zin, Moe Zet Pwint, Shin Thant
2020 IEEE 9th Global Conference on Consumer Electronics (GCCE) (Kobe, Japan) IEEE
Event date： 2020.10.13 - 2020.10.16
Language：English Presentation type：Oral presentation (general)
Venue：Kobe, Japan
The handwritten character recognition is a computerized system that is able to identify and recognize characters and words written by user. In this paper, we proposed offline handwritten character recognition using deep learning architecture. The Android application of the proposed system is also created by using OpenCV and TensorFlow Lite. The proposed system is aimed to use as a teaching aid in helping kindergarten to primary school level students, especially for practicing their writing and learning. The local handwritten dataset, which includes digit, English alphabet and mathematical symbols that are collected from students, is used for training and testing operations. According to the experimental results, the proposed system is very promising and it will be a useful application for educational environment.
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Elderly Monitoring and Action Recognition System Using Stereo Depth Camera International conference
Thi Thi Zin, Ye Htet, Yuya Akagi, Hiroki Tamura, Kazuhiro Kondo, Sanae Araki
2020 IEEE 9th Global Conference on Consumer Electronics (GCCE) (Kobe, Japan) IEEE
Event date： 2020.10.13 - 2020.10.16
Language：English Presentation type：Oral presentation (general)
Venue：Kobe, Japan
The proposed system used stereo type depth camera by examining the human action recognition and also sleep monitoring in the elderly care center. Different regions of interest (ROI) are extracted using the U-Disparity and V-Disparity maps. The main information used for recognition is 3D human centroid height relative to the floor and percentage of movement from frame differencing for sleep monitoring. The results from the experiments of the proposed method show that this system can detect the person location, sitting or lying and also sleep behaviors effectively.
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画像処理技術を用いた牛のモニタリングシステム Invited
Thi Thi Zin, 小林 郁雄, 椎屋 和久, PYKE TIN, 堀井 洋一郎, 濱 裕光
電気・情報関係学会九州支部連合大会 （第７３回連合大会） (オンライン開催 （大会本部：九州産業大学）) 電気・情報関係学会九州支部連合大会委員会
Event date： 2020.9.26 - 2020.9.27
Language：Japanese Presentation type：Oral presentation (general)
Venue：オンライン開催 （大会本部：九州産業大学）
高齢化、大規模化する現代の畜産で、24時間 365日にわたり家畜の健康管理を適切に行い、異常や変化に注意し続けながら経営を継続することは容易ではない。本研究開発では、家畜生産性の向上と地域活性化の実現を目的とする牛のモニタリングシステム構築に必要な要素技術の開発を行う。ここでは、牛の体脂肪率の指標となる BCS（Body Condition Score)を、画像解析技術を用いて自動的に評価し、その経時変化から健康状態をモニタリングできるシステムに関する開発について発表する。
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Interdisciplinary Approach to Smart Dairy Farming Invited International conference
Thi Thi Zin
The 3rd University Conference on Science, Engineering and Research, 2020 (3rd UCSER, 2020) (Technological University ( Kyaukse), Myanmar) Ministry Of Education Deportment Of Higher Education, Technological University ( Kyaukse)
Event date： 2020.8.27
Language：English Presentation type：Oral presentation (invited, special)
Venue：Technological University ( Kyaukse), Myanmar
Smart dairy farming emerged from the concept of Precision Agriculture, in which IoT technologies and artificial intelligence analysis are put to efficient use. Using these technologies to provide individual care for cows is fundamental to the future of dairy farming. Most dairy farms around the globe adhere to international ISO standards in identifying individual cows. On the other hand, 5G communications are being developed widely and nicely making the dairy farming smarter and faster in wealth and health. Thus, the dairy farming in agriculture should be explored from the perspectives of engineering disciplines. In this talk, we shall focus on how image processing techniques can be utilized to develop a tracking system for individual cows using an ear tag visual analysis.
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Time To Dairy Cow Calving Event Prediction by Using Time Series Analysis International conference
Thi Thi Zin, Kosuke Sumi, Pyke Tin
The 9th International Conference on Intelligent Computing and Applications (ICICA 2020) (Brisbane, Australia) Central Queensland University, Dalian University of Technology (DUT),National Tsing Hua University, Swinburne University of Technology, University of Technology Sydney, University of Wollongong, Australia
Event date： 2020.6.23 - 2020.6.25
Language：English Presentation type：Oral presentation (general)
Venue：Brisbane, Australia
In these days the precision dairy farming which is utilization of the Information and Communication Technologies (ICT) has become one of front line research topics in dairy science as well as in data science leading to Agriculture 4.0. An increase in on-farm mortality due to the occurrence calving difficulties with late assistance can cause possible problems not only for animal welfare but also economic losses to the farmers. In this aspect, calving is an extremely important event in the life of a dairy cow. On the other hand, the time around calving is also a critical period since clinical disorders, and calving problems can occur. Calving difficulties are also becoming increasingly common with many dairy cows requiring assistance at the time of calving. To maximize welfare and minimize losses due to calving difficulties, all animals need to be individually monitored to identify any calving difficulties or health problems as early as possible. In addition, it is important to know the exact time of calving event occur so that timely assistance can be made. In this paper, we propose a continuous video monitoring system for time-to calving event investigation based on time series analysis to achieve an accurate calving time prediction. In doing so we have employed three time series models of autoregressive, moving average smoothing. At the same time, we have confirmed the validity of the proposed method by using the real life data experimented on the University Dairy Farm and one of large dairy farms in Japan. The experimental results show that the proposed time series method is promising and can lead to a new prospect in modern precision dairy farming.
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Framework of Cow Calving Monitoring System Using Video Images International conference
Kosuke Sumi, Thi Thi Zin, Ikuo Kobayashi, Yoichiro Horii
The 9th International Conference on Intelligent Computing and Applications (ICICA 2020) (Brisbane, Australia) Central Queensland University, Dalian University of Technology (DUT),National Tsing Hua University, Swinburne University of Technology, University of Technology Sydney, University of Wollongong, Australia
Event date： 2020.6.23 - 2020.6.25
Language：English Presentation type：Oral presentation (general)
Venue：Brisbane, Australia
In modern dairy farms, calving is a very critical point in the life cycle of productive cows and has played a major role in making farm profits and welfare of cows. In this time, a tremendous number of researchers have been studied the problem of calving mostly to predict the time about to calve and to investigate calving process by using wearable sensors. Like human beings, cows also have environmental pressures by wearing sensors on their bodies sometimes may cause calving difficulties. Thus in this paper, an automatic video based cow monitoring system is proposed to reduce losses of dairy farms caused from calving problems. Specifically, this paper investigates some behaviors of cows to predict time for calving process including cow movements, tail up, stretching the legs, repeating standing and sitting. In doing so, we focus on increasing movement and tail up. Here, the inter-frame difference is used for analyzing the movement and count in every frame. In addition, by extracting the head and tail position the activity of tail up or not will be recognized so that time for calving can be estimated. Finally, the proposed method for calving is confirmed by using self-collected video sequences.
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Body Condition Score Assessment of Depth Image using Artificial Neural Network International conference
Thi Thi Zin, Pann Thinzar Seint, Pyke Tin, Yoichiro Horii
The 9th International Conference on Intelligent Computing and Applications (ICICA 2020) (Brisbane, Australia) Central Queensland University, Dalian University of Technology (DUT),National Tsing Hua University, Swinburne University of Technology, University of Technology Sydney, University of Wollongong, Australia
Event date： 2020.6.23 - 2020.6.25
Language：English Presentation type：Oral presentation (general)
Venue：Brisbane, Australia
Body Condition Score (BCS) is a visual sign for managing feeding program. It can be described by numeric numbers to estimate available fat reserves and it also allows producers to make better management decisions. Ignoring BCS until it becomes too thin or fat may result in production and animal losses economically. In this paper, we proposed the BCS assessment tool by using depth information from 3D camera. The experimental data were collected in Oita prefecture and BCS scores were taken under the guidance of experts. The information of depth images are used as feature vectors to the input of Artificial Neural Network (ANN) classifier. The proposed system has achieved the good results for classifying two groups of BCS (3.5 and 3.75) with the overall accuracy of 86%.
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Cow Identification System using Ear Tag Recognition International conference
Thi Thi Zin, S. Misawa, Moe Zet Pwint, Shin Thant, Pann Thinzar Seint, K. Sumi, K. Yoshida
The 2020 IEEE 2nd Global Conference on Life Sciences and Technologies (LifeTech 2019) (Mielparque Kyoto (Kyoto, Japan)) IEEE Life Sciences Technical Community
Event date： 2020.3.10 - 2020.3.12
Language：English Presentation type：Oral presentation (general)
Venue：Mielparque Kyoto (Kyoto, Japan)
In precision dairy farming, the valid record of individual cow identification is an important factor in large herds management. In this paper, we propose a cow's ear tag recognition system that can be used in dairy cow management. Firstly, cow's head detection is performed by using You Only Look Once (YOLO) object detector followed by ear tag recognition. The ear tag extraction and recognition processes are carried out by image processing techniques and Convolutional Neural Network (CNN) classifier on detected cow's head images. The experiments are conducted by using videos from dairy farm at Hokkaido prefecture, Japan. The proposed system achieved the reliable results which will support to give the informative status in smart farming.
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Motion Detection Method for Reducing Foreground Aperture Problem in Background Modelling International conference
Thi Thi Zin, Pyke Tin, Cho Nilar Phyo
The 2020 IEEE 2nd Global Conference on Life Sciences and Technologies (LifeTech 2019) (Mielparque Kyoto (Kyoto, Japan)) IEEE Life Sciences Technical Community
Event date： 2020.3.10 - 2020.3.12
Language：English Presentation type：Oral presentation (general)
Venue：Mielparque Kyoto (Kyoto, Japan)
Motion detection plays as an important role in the implementation of video surveillance system and video analysis applications. The detection of moving foreground objects from the complex background scene is the first step in most of the computer vision based surveillance applications. In this paper, we present a new motion detection method using background modelling technique and moving average feature. For establishing the robust background model, we create the Gamma Mixture Model based on the Gamma distribution function. For handling the foreground aperture problem, we use the feature called moving average which can well recognize the alive silent objects. As post-processing, we handle the shadow removing process by generating the dynamic shadow threshold. The experimental results show that the proposed motion detection method can detect the accurate foreground object even though the foreground object appears as silent background objects in real-time environment.
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Handwritten Characters Segmentation using Projection Approach International conference
Thi Thi Zin, Shin Thant, Ye Htet, Pyke Tin
The 2020 IEEE 2nd Global Conference on Life Sciences and Technologies (LifeTech 2019) (Mielparque Kyoto (Kyoto, Japan)) IEEE Life Sciences Technical Community
Event date： 2020.3.10 - 2020.3.12
Language：English Presentation type：Poster presentation
Venue：Mielparque Kyoto (Kyoto, Japan)
In the area of optical character recognition, handwritten character segmentation is still an ongoing process. Having good segmentation result can provide the better recognition accuracy. In the proposed system, segmentation is carried out mainly on labelling and projection concepts. The input word is firstly labelled. Then, the modified word is segmented with projection approach. The experiments are performed on local dataset with 1600 words approximately and the system gets segmentation accuracy around 85.75 percentage.
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The Body Condition Score Indicators for Dairy Cows Using 3D Camera International conference
Thi Thi Zin, Pann Thinzar Seint, Pyke Tin, Y. Horii
International Workshop on Frontiers of Computer Vision (IW-FCV) (Ibusuki, Kagoshima, Japan) Institute of Electrical Engineers of Japan
Event date： 2020.2.20 - 2020.2.22
Language：English Presentation type：Oral presentation (general)
Venue：Ibusuki, Kagoshima, Japan
Body Condition Score (BCS) evaluates energy reserves of cows when making nutritional management. This information is also useful in fertili-ty rate and milk production. Keeping cows in appropriate condition throughout the production cycle can improve reproductive efficiency and positive impact for economics. In this paper, we propose the effective indicators for automated BCS measurement system and the experimental data are collected at the milk-ing station. We compute the variation of angular area from automatic anatom-ical point extraction and learning parameters on Region of Interest (ROI) from 3D information. The statistical analysis of BCS trend on year-round data are presented to give the detailed illustrations for dairy farmers. According to the recorded BCS trends, the proposed method can validate that the lower BCS were obtained in early stage of lactation period rather than other seasons.
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Background Modelling Using Temporal Average Filter and Running Gaussian Average International conference
Thi Thi Zin, Cho Cho Mar, K. Sumi
International Workshop on Frontiers of Computer Vision (IW-FCV) (Ibusuki, Kagoshima, Japan) Institute of Electrical Engineers of Japan
Event date： 2020.2.20 - 2020.2.22
Language：English Presentation type：Poster presentation
Venue：Ibusuki, Kagoshima, Japan
Object detection is very important and fundamental stage for further post processing stages such as individual identification, action recognition, track-ing and behavior analysis. Traditional object detection methods start from back-ground modelling stage. Background modelling is a complex and challenging task which highly depends on the speed of moving objects (foreground objects) and stability of static regions (background region). In this paper, we proposed pixel level background modelling method. In this background modelling task, the two main components of background modelling are proposed; (1) background model initialization by selecting the dynamic frames from video sequence and building the model with Temporal Average filter and (2) updating the back-ground model by using Running Gaussian Average method. These methods are applied to RGB images.
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Some Aspects of Mathematical Modeling Techniques in Dairy Science International conference
Thi Thi Zin, Pyke Tin, H. Hama
International Workshop on Frontiers of Computer Vision (IW-FCV) (Ibusuki, Kagoshima, Japan) Institute of Electrical Engineers of Japan
Event date： 2020.2.20 - 2020.2.22
Language：English Presentation type：Poster presentation
Venue：Ibusuki, Kagoshima, Japan
Today world is floating in the ocean of data science which is a multi-disciplinary approach to every aspect of our better life. Agriculture 4.0 is coming in and Industry 4.0 is moving forward so that second Information and Commu-nication revolutionary becomes to play a key role in this era. On the other hands, consumers are demanding more quality dairy products than before. Dairy farmers are eager to use the information and communication technologies that push the precision dairy farming to frontier innovations. Due to a shortage of labor in dairy farms and at the same time the farm sizes are bigger, the utilization of ICT along with artificial intelligence and the internet of things are becoming appropriate technologies. Whatever it may be, fundamentally we need to do some preliminary research prior to large scale and wide applications to the real world. In almost analytical and empirical research, we are trying in the ocean of data science. These data could not be used efficiently unless we do further processing and pro-jections. The raw data need to be processed to obtain useful information and knowledge, which could be used for important on-farms decisions. This kind of process is called mathematical modeling which could support to decision-makers to produce correct and valid decisions. In this paper, we will present and analyze some mathematical models in the frame works of dairy science. Moreover, we shall illustrate how these models could be used in practical ways.
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Feature Analysis for Non Formal Education Project in Myanmar International conference
Mie Mie Khin, Mie Mie Tin, Thi Thi Zin, Pyke Tin
2019 IEEE 8th Global Conference on Consumer Electronics (GCCE 2018) (Senri Life Science Center, Osaka, Japan) IEEE Consumer Electronics Society
Event date： 2019.10.15 - 2019.10.18
Language：English Presentation type：Oral presentation (general)
Venue：Senri Life Science Center, Osaka, Japan
In a digitally mediated world, Literacy is vital to live. IT based Digital technology is the creation of quality education. It is the main pillar to develop a sustainable socioeconomic development in the country. Myanmar also starts to develop E-government system for every sector. This paper describes feature selection for non-formal education system using literacy project. With this approach, we consider that feature selection of linear regression the analysis is more suitable than support vector method. We are also continuing analysis of this e-education system for rural area in Mandalay division of Myanmar. This paper analysis based on big data concept.
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Consumer Technology Perspective of a Trinomial Random Walk Model International conference
Thi Thi Zin, Pyke Tin, H. Hama
2019 IEEE 8th Global Conference on Consumer Electronics (GCCE 2018) (Senri Life Science Center, Osaka, Japan) IEEE Consumer Electronics Society
Event date： 2019.10.15 - 2019.10.18
Language：English Presentation type：Oral presentation (general)
Venue：Senri Life Science Center, Osaka, Japan
Recently, the consumer technology association has widened the horizons of the consumer electronic society to include a variety of technologies such as the Internet of Things, Artificial Intelligence, vertical farming and etc. Moreover, among many others, the consumer technology highlights the importance of organic dairy cows and they are well beings so that timely based energy balance and body condition scores of individual cows are needed to be thoroughly studied. In this paper, we shall introduce a Trinomial Random Walk Model as an illustration of consumer technology for analyzing changes in dairy cow body conditions from time to time. In doing so, we will use the 5-point scale system with increment 0.25. Some experimental simulation results are presented by varying the parameters involved.