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Engineering educational research section Information and Communication Technology Program |
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THI THI ZIN
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Degree 【 display / non-display 】
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Doctor of Engineering ( 2007.3 Osaka City University )
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Master of Engineering ( 2004.3 Osaka City University )
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Master of Information Science ( 1999.5 University of Computer Studies, Yangon (UCSY) )
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B.Sc (Hons) (Mathematics) ( 1995.5 University of Yangon (UY) )
Research Interests 【 display / non-display 】
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Image Processing and Its Application
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工場での作業の見える化
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高度な画像処理技術やAI技術を活用した 研究開発
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24-hour monitoring system for the elderly to support independent living
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ICT Farm Monitoring System
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Perceptual information processing
Research Areas 【 display / non-display 】
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Informatics / Perceptual information processing / Image Processing
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Informatics / Database
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Life Science / Animal production science
Papers 【 display / non-display 】
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AI-enhanced real-time cattle identification system through tracking across various environments Reviewed
Su Larb Mon, T. Onizuka, Pyke Tin, M. Aikawa, I. Kobayashi, Thi Thi Zin
Scientific Reports 14 ( 1 ) 17779 2024.12
Authorship：Lead author, Last author Language：English Publishing type：Research paper (scientific journal) Publisher：Scientific Reports
Video-based monitoring is essential nowadays in cattle farm management systems for automated evaluation of cow health, encompassing body condition scores, lameness detection, calving events, and other factors. In order to efficiently monitor the well-being of each individual animal, it is vital to automatically identify them in real time. Although there are various techniques available for cattle identification, a significant number of them depend on radio frequency or visible ear tags, which are prone to being lost or damaged. This can result in financial difficulties for farmers. Therefore, this paper presents a novel method for tracking and identifying the cattle with an RGB image-based camera. As a first step, to detect the cattle in the video, we employ the YOLOv8 (You Only Look Once) model. The sample data contains the raw video that was recorded with the cameras that were installed at above from the designated lane used by cattle after the milk production process and above from the rotating milking parlor. As a second step, the detected cattle are continuously tracked and assigned unique local IDs. The tracked images of each individual cattle are then stored in individual folders according to their respective IDs, facilitating the identification process. The images of each folder will be the features which are extracted using a feature extractor called VGG (Visual Geometry Group). After feature extraction task, as a final step, the SVM (Support Vector Machine) identifier for cattle identification will be used to get the identified ID of the cattle. The final ID of a cattle is determined based on the maximum identified output ID from the tracked images of that particular animal. The outcomes of this paper will act as proof of the concept for the use of combining VGG features with SVM is an effective and promising approach for an automatic cattle identification system
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Development of a real-time cattle lameness detection system using a single side-view camera. Reviewed
Bo Bo Myint, T. Onizuka, Pyke Tin, M. Aikawa, I. Kobayashi, Thi Thi Zin
Scientific reports 14 ( 1 ) 13734 2024.6
Authorship：Last author, Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：Scientific Reports
Recent advancements in machine learning and deep learning have revolutionized various computer vision applications, including object detection, tracking, and classification. This research investigates the application of deep learning for cattle lameness detection in dairy farming. Our study employs image processing techniques and deep learning methods for cattle detection, tracking, and lameness classification. We utilize two powerful object detection algorithms: Mask-RCNN from Detectron2 and the popular YOLOv8. Their performance is compared to identify the most effective approach for this application. Bounding boxes are drawn around detected cattle to assign unique local IDs, enabling individual tracking and isolation throughout the video sequence. Additionally, mask regions generated by the chosen detection algorithm provide valuable data for feature extraction, which is crucial for subsequent lameness classification. The extracted cattle mask region values serve as the basis for feature extraction, capturing relevant information indicative of lameness. These features, combined with the local IDs assigned during tracking, are used to compute a lameness score for each cattle. We explore the efficacy of various established machine learning algorithms, such as Support Vector Machines (SVM), AdaBoost and so on, in analyzing the extracted lameness features. Evaluation of the proposed system was conducted across three key domains: detection, tracking, and lameness classification. Notably, the detection module employing Detectron2 achieved an impressive accuracy of 98.98%. Similarly, the tracking module attained a high accuracy of 99.50%. In lameness classification, AdaBoost emerged as the most effective algorithm, yielding the highest overall average accuracy (77.9%). Other established machine learning algorithms, including Decision Trees (DT), Support Vector Machines (SVM), and Random Forests, also demonstrated promising performance (DT: 75.32%, SVM: 75.20%, Random Forest: 74.9%). The presented approach demonstrates the successful implementation for cattle lameness detection. The proposed system has the potential to revolutionize dairy farm management by enabling early lameness detection and facilitating effective monitoring of cattle health. Our findings contribute valuable insights into the application of advanced computer vision methods for livestock health management.
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Enhancing Fetal Heart Rate Monitoring Through Digital Twin Technology Reviewed
Tunn Cho Lwin, Thi Thi Zin, Pyke Tin, T. Ikenoue, E. Kino
2024 IEEE Gaming, Entertainment, and Media Conference, GEM 2024 2024.6
Authorship：Corresponding author Language：English Publishing type：Research paper (international conference proceedings) Publisher：2024 IEEE Gaming, Entertainment, and Media Conference, GEM 2024
In the era of digital twin technology (DTT), which involves creating virtual replicas of physical systems, healthcare applications have seen a surge in innovation. Fetal heart rate monitoring, a rapidly advancing field within healthcare, is benefiting greatly from the effective implementation of DTTs. Digital twin, supported by Artificial Intelligence (AI), Virtual Reality (VR), and Extended Virtuality (EV), have already demonstrated significant impact in entertainment, gaming, and media sectors. This paper explores and analyzes fetal heart rate monitoring systems during labor using digital twin technologies. During labor, there is an elevated risk of developing non-communicable diseases (NCDs) such as diabetes, cardiovascular diseases, cancer, and chronic respiratory diseases. The developmental origins of health and disease hypothesis posits that environmental conditions during fetal and early postnatal development have enduring effects on growth, structure, and metabolism, ultimately influencing long-term health and well-being. Understanding and effectively monitoring fetal heart rate dynamics during labor using digital twin technologies can provide valuable insights into maternal and fetal health. The findings underscore the value of entropy analysis in FHRV assessment, presenting a pioneering predictor of fetal health via ECG data. Moreover, it reveals the current landscape of fetal heart rate monitoring, discusses the integration of digital twins, and proposes future directions for optimizing healthcare outcomes in the digital era.
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A Stochastics Branching Process Model for Analyzing Rumor Spreading in Social Media Networks Reviewed
Thi Thi Zin, Pyke Tin, H. Hama
2024 IEEE Gaming, Entertainment, and Media Conference, GEM 2024 2024.6
Authorship：Lead author, Corresponding author Language：English Publishing type：Research paper (international conference proceedings) Publisher：2024 IEEE Gaming, Entertainment, and Media Conference, GEM 2024
This paper introduces a stochastics branching process model as an analytical tool to study the dynamics of rumor spreading in social media networks. Employing a specialized first-order conditional probability generating function method, we derive a second-order linear regressive equation to characterize the intricate dynamics of rumor propagation. The validation of our model's accuracy is established through a comparative analysis between numerical computations and Monte Carlo simulations. Additionally, we present a derived threshold condition for the spread control factor, also known as the reproduction rate. Numerical simulation results demonstrate that the interactions and responses within social platforms contribute to the rapid onset of rumors while simultaneously diminishing the maximum density of spreaders and the overall scale of the rumors. To provide further insights, we explore analogies between rumor-spreading models and epidemic models. In conclusion, our proposed stochastics branching process model not only enhances our understanding of rumor spreading in social media networks but also offers a valuable framework for investigating the interplay between various factors influencing the dynamics of information diffusion.
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A Markovian Game Theoretic Framework for Analysing a Queueing System with Multiple Servers Reviewed
Pyke Tin, Thi Thi Zin
2024 IEEE Gaming, Entertainment, and Media Conference, GEM 2024 2024.6
Authorship：Last author, Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：2024 IEEE Gaming, Entertainment, and Media Conference, GEM 2024
This paper introduces a Markov game theoretic framework designed to analyze a Markovian queueing system equipped with multiple servers. Our focus lies in modelling a message transmission system, where messages traverse various transmission options, each associated with a cost and governed by a decision process. The primary objective is to investigate the impact of cooperation and communication, or their absence, among servers. The inherent uncertainty regarding the characteristics of the available transmission alternatives is mathematically captured through a Markovian game formulation. Within this framework, we quantify the inefficiency resulting from the self-interested management of individual servers and the associated loss attributed to the decision-making process. Our analysis encompasses diverse scenarios of signaling exchange among servers, providing valuable insights into the system's behavior under varying conditions.
Books 【 display / non-display 】
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Big Data Analysis and Deep Learning Applications: Proceedings of the First International Conference on Big Data Analysis and Deep Learning (Advances in Intelligent Systems and Computing Book 744)
Thi Thi Zin (Editor), Jerry Chun-Wei Lin (Editor) （ Role： Joint editor）
Springer 2018.6
Total pages：Springer Language：English
Other Link： https://www.amazon.com/Data-Analysis-Deep-Learning-Applications-ebook/dp/B07DL46RJX
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Systems and Computing: A New Look into Web Page Ranking Systems
Thi Thi Zin, Pyke Tin, H. Hama, T. Toriu（ Role： Joint author）
Springer International Publishing 2014.10
Language：English Book type：Scholarly book
MISC 【 display / non-display 】
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A Study on the Possibility of Distinguishing between Parkinson's disease and Essential Tremor using Motor Symptoms Observed by an RGB camera
Proceedings of the 34th Annual Conference of Biomedical Fuzzy Systems Association (BMFSA2022) 2022.12
Authorship：Corresponding author Language：Japanese Publishing type：Research paper, summary (national, other academic conference) Publisher：Biomedical Fuzzy Systems Association
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Artificial Intelligence Topping on Spectral Analysis for Lameness Detection in Dairy Cattle
Thi Thi Zin, Ye Htet, San Chain Tun and Pyke Tin
第 35 回バイオメディカル・ファジィ・システム学会年次大会 講演論文集 (BMFSA2022) 2022.12
Authorship：Lead author, Corresponding author Language：English Publishing type：Research paper, summary (national, other academic conference) Publisher：バイオメディカル・ファジィ・システム学会
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Tracking A Group of Black Cows Using SORT based Tracking Algorithm
Cho Cho Aye, Thi Thi Zin, M. Aikawa, I. Kobayashi
第 35 回バイオメディカル・ファジィ・システム学会年次大会 講演論文集 (BMFSA2022) 2022.12
Authorship：Corresponding author Language：English Publishing type：Research paper, summary (national, other academic conference) Publisher：バイオメディカル・ファジィ・システム学会
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Introduction to IEEE LifeTech 2022 Overview Invited International coauthorship
Thi Thi Zin and Ryota Nishimura
IEEE LifeTech2022 Abstract Book 2022.3
Authorship：Lead author, Corresponding author Language：English Publishing type：Research paper, summary (international conference) Publisher：IEEE CT Soc
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高度な画像処理技術やAI技術を活用した研究開発 Invited
Thi Thi Zin
ICT研究開発支援セミナーin九州 2022.2
Authorship：Lead author, Corresponding author Language：Japanese Publishing type：Lecture material (seminar, tutorial, course, lecture, etc.) Publisher：戦略的情報通信研究開発推進事業（SCOPE）
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、異常や変化に留意し続けながら経営を継続することは容易でない。本研究開発では、家畜生産性の改善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。
Presentations 【 display / non-display 】
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Automatic Cattle Detection and Tracking for Lameness Classification Using a Single Side-View Camera International conference
Bo Bo Myint, T. Onizuka, Pyke Tin, M. Aikawa, I. Kobayashi, Thi Thi Zin
Second International Symposium on Data-Driven Intelligent Optimization for Decision Making (DIODM2024) 2024.3.26
Event date： 2024.3.24 - 2024.3.27
Language：English Presentation type：Oral presentation (general)
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画像処理技術を用いた歩行状態の数値化に関する研究
丹野 龍晟、ティティズイン
動的画像処理実利用化 ワークショップ2024 (DIA 2024) 2024.3.4
Event date： 2024.3.4 - 2024.3.5
Language：Japanese Presentation type：Poster presentation
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Transition-Aware Elderly Action Recognition: Unveiling Insights with CNN-RNN Integration, International conference
Ye Htet, Thi Thi Zin, H. Tamura, K. Kondo, E. Chosa
2024 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP'24) 2024.2.29
Event date： 2024.2.27 - 2024.3.1
Language：English Presentation type：Oral presentation (general)
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Kalman Velocity-based Multi-Stage Classification Approach for Recognizing Black Cow Actions International conference
Cho Cho Aye, Thi Thi Zin, M. Aikawa, I. Kobayashi
2024 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP'24) 2024.2.29
Event date： 2024.2.27 - 2024.3.1
Language：English Presentation type：Oral presentation (general)
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A Novel Stochastic Model for Analysing Heart Rate Variability in the Heart-Brain Signal Communication System International conference
Thi Thi Zin, Tunn Cho Lwin, Pyke Tin
2024 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP'24) 2024.2.29
Event date： 2024.2.27 - 2024.3.1
Language：English Presentation type：Oral presentation (general)
Awards 【 display / non-display 】
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Student paper Award
2024.3 2024 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing Enhancing Precision Agriculture: Innovative Tracking Solutions for Black Cattle Monitoring
Su Myat Noe, Thi Thi Zin, Pyke Tin, and Ikuo Kobayashi
Award type：Award from international society, conference, symposium, etc.
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Student paper Award
2024.3 2024 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing Kalman Velocity-based Multi-Stage Classification Approach for Recognizing Black Cow Actions
Cho Cho Aye, Thi Thi Zin, M. Aikawa, I. Kobayashi
Award type：Award from international society, conference, symposium, etc.
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Best Paper Award
2023.11 The 9th International Conference on Science and Technology (ICST UGM 2023) An Innovative Framework for Cattle Activity Monitoring: Combining AI-Based Markov Chain Model with IoT Devices
Y. Hashimoto, Thi Thi Zin, Pyke Tin, I. Kobayashi and H. Hama
Award type：Award from international society, conference, symposium, etc.
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IEEE GCCE 2022 Excellent Student Paper Awards (Outstanding Prize)
2022.10 2022 IEEE 11th Global Conference on Consumer Electronics (GCCE2022) Video-Based Automatic Cattle Identification System
Su Larb Mon, Thi Thi Zin, Pyke Tin, I. Kobayashi
Award type：Award from international society, conference, symposium, etc. Country：Japan
In this paper, we propose a method to identify the cattle by using video sequences. In order to do so, we first collect 360-degree top-view video sequences to form dataset. The proposed system is composed of two parts: cattle detection and cattle identification. In the detection process, we utilize YOLOv5(You Only Look Once) model to detect the cattle region in the lane. In this stage, cattle’s location and region information are extracted and the cropped images of detected cattle regions are saved for the next stage. We then apply Convolutional Neural Network model (VGG16) to extract the features which will be used to identify individual cattle. For the classification, the proposed system used two supervised machine learning methods, Random Forest and SVM (Support Vector Machine). The accuracy of Random Forest is 98.5% and the accuracy of SVM is 99.6%. After comparing the accuracy rate of two methods, SVM get the better accuracy result. The proposed system achieved the accuracy of over 90% for both cattle detection and identification.
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Best Presentation Award
2022.9 The 16th International Conference on Innovative Computing, Information and Control (ICICIC2022) Comparative Study on Color Spaces, Distance Measures and Pretrained Deep Neural Networks for Cow Recognition
Cho Cho Mar, Thi Thi Zin, Pyke Tin, I. Kobayashi, K. Honkawa, Y. Horii
Award type：Award from international society, conference, symposium, etc. Country：Japan
Grant-in-Aid for Scientific Research 【 display / non-display 】
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画像処理技術と非接触センサを用いた牛の発情検知及び分娩監視システムの開発
2017.04 - 2021.03
科学研究費補助金 基盤研究(C)
Authorship：Principal investigator
畜産は全国農業総生産額の3 割以上を占める重要な産業であるが、不適切な家畜管理による生産性の低下が大きな問題となっている。その主たる原因は飼養形態の変化による1 頭あたり観察時間の短縮であり、飼養頭数の多頭化・農家の高齢化が進む畜産現場において、365 日24 時間にわたり家畜の異常や変化を観察し続けることは困難である。
申請者らは、主に非接触・非侵襲センサ情報のアルゴリズム解析技術に着目し、距離画像とビデオ画像を用いて牛の発情を検知できる独自アルゴリズムの開発に取り組んできた。本研究では、これらの技術を応用することで、牛の発情や分娩監視時の異常を自動検知できる省力的な24 時間
家畜管理システムを開発する。 -
牛の分娩監視システムに関する研究
2018.04 - 2020.03
科学研究費補助金 特別研究員奨励費
須見 公祐、Thi Thi Zin（受入研究者）
Authorship：Coinvestigator(s)
精度や耐久性が不十分な割に高価なウェラブル型センサの装着や、肉体的・精神的に大きな負担を強いられる目視によるカメラ映像のモニタリング等は、大規模化する畜産現場において現実的なコストで利用できるものが極めて少ない。そこで本研究では、監視カメラから得られる映像を用いて非接触型の分娩管理システムを開発することで、農家そして牛、両方の負担を減らすことを目的とする。
本来、牛は牛群と呼ばれるグループで行動を行う。そして、分娩が間近になると分娩室という分娩専用の牛舎に移される。分娩室には2 頭以上を同時に入れるケースも多く、どの牛で分娩が始まったかを識別する必要があることから、個体識別と追跡処理が必要となる。次に、分娩行動の段階を追って検知を行う。抽出する特徴としては、尻尾が上がっているかどうか、牛が立っているか座っているか、落ち着きがなくなり移動量が増加するか、子牛を出産したかどうか、親牛が子牛を舐めているかどうかなど、それぞれの過程で自動的に異常を見つけ通報を行うアルゴリズムの開発を進める。分娩行動が起きたかどうかの判断は、これらのデータから各特徴の重要度（重み）を学習させることによって行う。そして、最終目標として難産など異常行動の検知を行うために事例を蓄積しながら知識ベースを充実させ、異常事態の検知を行い、分娩の各段階を監視して異常事態の検知ならびに通報が可能なシステムの開発を目指す。 -
Development of forensic imaging modality for person identification using integration method of feature correspondences between heterogeneous images
2015.04 - 2018.03
Grant-in-Aid for Scientific Research Grant-in-Aid for challenging Exploratory Research
Authorship：Coinvestigator(s)
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Study of interactive teaching systems using an image processing techniques
2015.04 - 2018.03
Grant-in-Aid for Scientific Research Grant-in-Aid for Scientific Research(C)
Authorship：Coinvestigator(s)
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Development of automatic estrus detection systems of cattle using complementary the plane distance image and video footage
2015.04 - 2017.03
Grant-in-Aid for Scientific Research Grant-in-Aid for challenging Exploratory Research
Authorship：Principal investigator
Available Technology 【 display / non-display 】
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ICTを活用した牛のモニタリングシステムの開発に関する研究
安全・安心のための24時間自動見守り・監視システムの開発に関する研究
工場の作業効率化のための作業グループ検出に関する研究Home Page： 研究者データベース
Related fields where technical consultation is available：ビッグデータからの新しい知見の獲得・発見を体系的に行える数理的道具の開発
牛のモニタリング情報分析システム
高度な画像処理技術とAI活用による身体・精神機能低下患者の行動認識に関する研究
ビデオ画像を利用した新生児運動モニタリングシステムの開発に関する研究
画像処理技術を用いた疾病の特徴を示すエビを検出するシステムMessage：『画像処理を用いて様々な問題を解決すること』を目的として、農学や医学の分野に係わる学際領域の研究を幅広く行っています。多種多様な課題に対して各分野の専門家と協力して、画像処理分野からの貢献を目指して、研究・開発を行っています。