Presentations -
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Markov Chain Monte Carlo Method for the Modeling of Posture Changes Prior to Calving International conference
Thi Thi Zin, Pyke Tin, Pann Thinzar Seint, Yoichiro Horii
The 2021 IEEE 3rd Global Conference on Life Sciences and Technologies (LifeTech 2021) (Nara Royal Hotel (Nara, Japan)) IEEE Life Sciences Technical Community
Event date： 2021.3.9 - 2021.3.11
Language：English Presentation type：Oral presentation (general)
Venue：Nara Royal Hotel (Nara, Japan)
An accurate and careful analysis of posture changes for a dairy cow prior to calving plays an important role in making calving time prediction. The patterns of activities such as frequent changes in postures of a pregnant cows during the time closer to calving are utilized as indicators to predict the time of calving. In this paper, we introduce Markov Chain Monte Carlo (MCMC) method to generate the patterns of four states activities such lying, transitions from lying to standing, standing itself and transitions from standing to lying based on the monitored cow activity changes data three days prior to calving. The validity of the generated cow activities in posture changes data is compared with the actual collected data in terms of Euclidean and Cosine distance measures. The experimental results show that the method in this paper can be used as a generalized method to generate synthetic data series of dairy cow activities prior to calving.
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Image Technology Based Detection of Infected Shrimp in Adverse Environments International conference
Thi Thi Zin, Takehiro Morimoto, Naraid Suanyuk, Toshiaki Itami, Chutima Tantikitti
The 1st International Conference on Sustainable Agriculture and Aquaculture: For Well Being and Food Security (Prince of Songkla University) www.psu.ac.th, www.kku.ac.th, www.ku.ac.th, www.cmu.ac.th,
Event date： 2021.1.11 - 2021.1.12
Language：English Presentation type：Oral presentation (general)
Venue：Prince of Songkla University
In recent years, the cultivation of white leg shrimp (Litopenaeus vannamei) has become popular in countries around Japan, especially in Southeast Asia, and at the same time, various diseases have occurred in the farms [1]. In the early stages of infection, shrimp show three abnormal behaviors: (1) they appear in the shallow waters of the farm, (2) they do not move and do not eat even when fed, and (3) they suddenly start moving. Early detection is important step to control this disease because there are no preventive measures. In addition, we are currently visually confirming shrimp that show characteristic of the disease. However, these lead to a burden on the farmers and delay in discovery [2]. Therefore, we propose an image technology based monitoring system for detecting shrimp showing the characteristics of diseases.
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Smart Irrigation: An Intelligent System for Growing Strawberry Plants in Different Seasons of the Year International conference
Ye Htet, Htin Kyaw Oo and Thi Thi Zin
4th International Symposium on Information and Knowledge Management (ISIKM2020) (Online Conference) ICIC International
Event date： 2020.12.12 - 2020.12.13
Language：English Presentation type：Oral presentation (general)
Venue：Online Conference
Agriculture is an important source of livelihood and varying the way of cultivating plants could provide more productivity and sustainability of foods than before and thus smart irrigation would be one of the best solutions. Therefore, the proposed system mainly focused on the strawberry plants to grow within small-scale farm using intelligent systems to bear and produce fruits in all seasons of the country. A challenging problem which arises for this objective is the precise temperature, water and fertilizer management for plants. So, this system emphasized on automatic environmental adjustment system integrated with sensors to control temperature and also drip irrigation system for efficient water and fertilizers usage. Moreover, leaf analysis using computer vision which is controlled by Raspberry Pi is implemented for detection of the nutrient deficiency symptoms of plants. As for the communication unit to inform the users via sensors and image processing, Internet of Things is adopted.
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Consumer Behavior Analyzer in Internet of Things (IoT) Environments International conference
Swe Nwe Nwe Htun, Thi Thi Zin and Pyke Tin
4th International Symposium on Information and Knowledge Management (ISIKM2020) (Online Conference) ICIC International
Event date： 2020.12.12 - 2020.12.13
Language：English Presentation type：Oral presentation (general)
Venue：Online Conference
This paper proposes an analyzer of consumer behavior in Internet of Things (IoT) environments. This analyzer is most useful in predicting the intentions of users during searches, and especially during image searches. Since most technologies are connected on the internet, search results can be characterized using image-similarity measures. In this paper, information on image similarities is extracted using a Convolutional Neural Network (CNN) in IoT environments. In this proposed consumer behavior analyzer, the similarity measures characterizing the relationships between images are transformed into Markov Chain transition probabilities, and their stationary probabilities are then analyzed to describe the priority order for search results conforming with consumer intentions. In order to confirm the validity of the proposed method, the Yelp public dataset was used. The outcomes using this analyzer are promising, and this analyzer might be instrumental in making further improvements in practical applications of consumer technologies.
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Systematic Inclusion Study on Some Rare Gemstones of the Mogok Area, Mandalay Region, Myanmar International conference
Htin Lynn Aung, Thaire Phyu Win and Thi Thi Zin
4th International Symposium on Information and Knowledge Management (ISIKM2020) (Online Conference) ICIC International
Event date： 2020.12.12 - 2020.12.13
Language：English Presentation type：Oral presentation (general)
Venue：Online Conference
In this paper we shall explore and examine an inclusion aspect of some rare gemstones of Mogok area which is known as the Ruby Land of Myanmar where 90% of world rubies come from. The materials presented in this paper are the products of our research team investigated some rock sequences in the Mogok area situated about 280 miles north of the capital Naypyidaw, has unearthed some of the rarest and most luxurious rubies including the legendary 82-carat Nga Mauk ruby discovered centuries ago.. The rock sequence of the study area consists of medium to high grade metamorphic rocks, marble, gneiss and intrusive igneous rocks, mainly Kabaing granite, leucogranite and syenite. It is famous for presence of ruby and sapphire. Exceptionally some rare gemstones are also discovered. The present work is mainly intended to describe systematically the inclusions of some rare gemstones from the Mogok area. Liquid feather inclusions present in jeremejevite. Two-phase inclusions occur in morganite and petalite. In petalite, tube-like inclusions also present. Opaque inclusion and solid inclusion occur in rutile and treacle granular inclusion and finger print inclusion observe in sinhalite.
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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.