Papers - THI THI ZIN
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Petrochemical characteristics of the granitoid rocks of Northern Myanmar Reviewed
Htin Lynn Aung, Thaire Phyu Win, Thi Thi Zin
LifeTech 2021 - 2021 IEEE 3rd Global Conference on Life Sciences and Technologies 229 - 230 2021.3
Authorship：Last author, Corresponding author Language：English Publishing type：Research paper (international conference proceedings) Publisher：LifeTech 2021 - 2021 IEEE 3rd Global Conference on Life Sciences and Technologies
The research area is located on the Mogaung - Kamaing-Hpakant road in Hpakant Township, Kachin State, northern Myanmar. The dominant lithologic units comprise igneous and metamorphic rocks. The present work is mainly intended to establish the petrogenesis of the igneous rocks based on the petrochemical analysis results. The igneous rocks are mainly microgranite and serpentinite. Major element analysis of some rocks was determined by XRF spectrometer and interpreted the genesis of these rock units. On the basis of the petrochemical characteristics, the microgranite of the study area is I-type peraluminous granitoid formed by partial melting of mantle and / or lower crust in the extensional tectonics.
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Markov chain monte carlo method for the modeling of posture changes prior to calving Reviewed
Thi Thi Zin, Pyke Tin, Pann Thinzar Seint, Yoichiro Horii
LifeTech 2021 - 2021 IEEE 3rd Global Conference on Life Sciences and Technologies 291 - 292 2021.3
Authorship：Lead author, Corresponding author Language：English Publishing type：Research paper (international conference proceedings) Publisher：LifeTech 2021 - 2021 IEEE 3rd Global Conference on Life Sciences and Technologies
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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Activity-integrated hidden markov model to predict calving time Reviewed International journal
K. Sumi, Swe Zar Maw, Thi Thi Zin, Pyke Tin, I. Kobayashi, Y. Horii
Animals 11 ( 2 ) 1 - 12 2021.2
Authorship：Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：Animals
Accurately predicting when calving will occur can provide great value in managing a dairy farm since it provides personnel with the ability to determine whether assistance is necessary. Not providing such assistance when necessary could prolong the calving process, negatively affecting the health of both mother cow and calf. Such prolongation could lead to multiple illnesses. Calving is one of the most critical situations for cows during the production cycle. A precise video-monitoring system for cows can provide early detection of difficulties or health problems, and facilitates timely and appropriate human intervention. In this paper, we propose an integrated approach for predicting when calving will occur by combining behavioral activities extracted from recorded video sequences with a Hidden Markov Model. Specifically, two sub-systems comprise our proposed system: (i) Behaviors extraction such as lying, standing, number of changing positions between lying down and standing up, and other significant activities, such as holding up the tail, and turning the head to the side; and, (ii) using an integrated Hidden Markov Model to predict when calving will occur. The experiments using our proposed system were conducted at a large dairy farm in Oita Prefecture in Japan. Experimental results show that the proposed method has promise in practical applications. In particular, we found that the high frequency of posture changes has played a central role in accurately predicting the time of calving.
DOI： 10.3390/ani11020385
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Consumer behavior analyzer in internet of things (Iot) environments Reviewed
Swe Nwe Nwe Htun, Thi Thi Zin, Pyke Tin
International Journal of Innovative Computing, Information and Control 17 ( 1 ) 345 - 353 2021.2
Authorship：Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：International Journal of Innovative Computing, Information and Control
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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Image Technology Based Detection of Infected Shrimp in Adverse Environments Reviewed
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: Book of Abstracts 115 - 115 2021.1
Language：English Publishing type：Research paper (bulletin of university, research institution)
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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A study on detecting violence using image processing technology Reviewed
S. Misawa, Thi Thi Zin
ICIC Express Letters, Part B: Applications 12 ( 1 ) 59 - 66 2021.1
Authorship：Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：ICIC Express Letters, Part B: Applications
In recent years, many security cameras have been installed for crime prevention in downtown areas and public facilities. These cameras have greatly contributed to crime prevention and criminal identification. However, the large number of installed cameras is problematic due to difficulties in manually monitoring and detecting violence and crime in real time, as well as in finding specific video footage recording the inci-dents. This paper describes the use of the background difference method in extracting human regions from data obtained using security cameras. In addition, the paper describes a method of detecting violence using features such as speed and moving distance after contact. Using video footage from seven data sets, these methods have been experimentally evaluated, confirming a high detection rate for incidents involving two people side by side.
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Intelligent monitoring for elder care using vision-based technology Reviewed
Pann Thinzar Seint, Thi Thi Zin, Pyke Tin
International Journal of Innovative Computing, Information and Control 17 ( 3 ) 905 - 918 2021
Language：English Publishing type：Research paper (scientific journal) Publisher：International Journal of Innovative Computing, Information and Control
Nowadays, smart home care systems are being developed in response to various demands, though challenges remain in realizing various required functionalities. Among many considerations used in developing the proposed system, this paper focuses on ways of recording the consumption of medicine and food by elderly people living alone, as well as ways of communicating information to caregivers. Primarily, we used color coding for objects to facilitate their identification and use. Firstly, we propose useful features, not only between the skin surfaces of hands and mouth, but also the contact between body parts and the objects involved. An Eigen value detector is used to overcome the skin occlusion problem. And then, action detection is performed (such as for picking up or grasping medicine, taking medicine, eating, drinking water, and using a towel) by using a combination of the proposed feature and conditional rule-based learning. Secondly, the proposed system uses context awareness for assessing the subject’s actions using statistical analysis. Finally, the entire system is implemented through the user interface of the application platform. Using this system, caregivers can easily see a record of daily activities, provided with contextual information useful in improving the quality of care. Our proposed system is easy to learn and can provide an economical labor-saving solution for caregivers.
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A Simple Random Walk Model for Dairy Cow Calving Time Prediction Reviewed
Thi Thi Zin, Pyke Tin, Pann Thinzar Seint, K. Sumi, I. Kobayashi, Y. Horii
2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021 756 - 757 2021
Authorship：Lead author, Corresponding author Language：English Publishing type：Research paper (international conference proceedings) Publisher：2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021
In this paper, we propose a simple random walk model to predict time of calving event occurs for a pregnant dairy cow. Dairy farmers and experts have well recognized that an accurate calving time prediction is quite important in modern smart dairy farming. To meet these demands, we consider the number of posture changes of a pregnant cow during a few days before the expected dates as a random walk to predict the time at which the calving event occurs. For validation, we show some experimental results by using real life data collected from a large dairy farm in Japan.
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T. Hayashida, Thi Thi Zin, K. Sakai, H. Mochizuki
2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021 760 - 761 2021
Authorship：Corresponding author Language：English Publishing type：Research paper (international conference proceedings) Publisher：2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021
Discrepancy of exam findings at the same patient make it difficult to ascertain chronological change in the disease and the efficacy of the medicine. Quantitative evaluation of severity is important for improving the discrepancy. In this study, we examine the efficacy of quantitative evaluation of tremor when using single camera. Recording the hand movements of tremor with single camera, and the displacement, velocity, and acceleration signals are acquired using the hand shift between two adjacent video frames. Quantitative evaluation of tremor is performed based on features obtained from each signal. According to the validation results, our method using single camera is possible to classify with an accuracy of up to 82.6%.
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Cattle Region Extraction using Image Processing Technology Reviewed
Y. Motomura, Thi Thi Zin, Y. Horii
2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021 762 - 763 2021
Authorship：Corresponding author Language：English Publishing type：Research paper (international conference proceedings) Publisher：2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021
In recent years, the number of dairy and beef cattle farms has been decreasing, while the number of cattle and the number of cattle per farm have been increasing, so systems for automatically monitoring cattle have been actively introduced. However, most of them are contact type, which causes physical or mental stress to the cows and is costly when the equipment is damaged. Therefore, in this research, we proposed a method for extracting the approximate shape of cattle using a non-contact 360-degree camera to reduce the burden on livestock farmers and cattle, and confirmed its effectiveness through experiments.
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Imaging tremor quantification for neurological disease diagnosis Reviewed International journal
Y. Mitsui, Thi Thi Zin, N. Ishii, H. Mochizuki
Sensors (Switzerland) 20 ( 22 ) 1 - 14 2020.11
Authorship：Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：Sensors (Switzerland)
In this paper, we introduce a simple method based on image analysis and deep learning that can be used in the objective assessment and measurement of tremors. A tremor is a neurological disorder that causes involuntary and rhythmic movements in a human body part or parts. There are many types of tremors, depending on their amplitude and frequency type. Appropriate treatment is only possible when there is an accurate diagnosis. Thus, a need exists for a technique to analyze tremors. In this paper, we propose a hybrid approach using imaging technology and machine learning techniques for quantification and extraction of the parameters associated with tremors. These extracted parameters are used to classify the tremor for subsequent identification of the disease. In particular, we focus on essential tremor and cerebellar disorders by monitoring the finger–nose–finger test. First of all, test results obtained from both patients and healthy individuals are analyzed using image processing techniques. Next, data were grouped in order to determine classes of typical responses. A machine learning method using a support vector machine is used to perform an unsupervised clustering. Experimental results showed the highest internal evaluation for distribution into three clusters, which could be used to differentiate the responses of healthy subjects, patients with essential tremor and patients with cerebellar disorders.
DOI： 10.3390/s20226684
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Gemological analysis of some rare gemstones from mogok area, mandalay region Reviewed International coauthorship
Htin Lynn Aung, Thi Thi Zin
ICIC Express Letters, Part B: Applications 11 ( 11 ) 1077 - 1086 2020.11
Authorship：Last author Language：English Publishing type：Research paper (scientific journal) Publisher：ICIC Express Letters, Part B: Applications
Mogok has long been noted as a supplier of various gemstones over the past decades. The principal gemstones are ruby, sapphire and spinel. Nowadays, fabulous rare gemstones from Mogok are being sold in foreign markets. This area is mainly composed of igneous and metamorphic rocks. Exceptionally rare gemstones are also discovered and they are johachidolite, poudretteite, thorite, etc. The fantastic occurrences of rare gemstones provoke attraction and well attention to mineralogists and gemmologists. Most of the rare gemstones in the present research work are studied from gems dealers from Mogok. Other rare samples are recorded and studied in the favor of the gems collectors. The data on primary occurrence of these rare gemstones are still uncertain and further investigation should be required. In the Mogok area, these rare minerals are recovered from alluvial, eluvial, residual deposits along the riverside, hill slope, flat plains and low-lying area. Economically, rare gemstones are highly important for both local and foreign gem markets. Some gemstones are important economically as well as technologically for its composition, such as thorite and beryl, which are used in space and aeronautical purposes. Most of the rare gemstones are valuable for its rarity and collected as museum pieces and collector’s stones. Thus, they are invaluable.
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Predicting calving time of dairy cows by exponential smoothing models Reviewed International coauthorship
Tunn Cho Lwin, Thi Thi Zin, Pyke Tin
2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020 322 - 323 2020.10
Authorship：Corresponding author Language：English Publishing type：Research paper (international conference proceedings) Publisher：2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020
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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Image Processing and Statistical Analysis Approach to Predict Calving Time in Dairy Cows Reviewed
Swe Zar Maw, Thi Thi Zin, Pyke Tin
2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020 318 - 319 2020.10
Authorship：Corresponding author Language：English Publishing type：Research paper (international conference proceedings) Publisher：2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020
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 Reviewed
Thi Thi Zin, Saw Zay Maung Maung, Pyke Tin, Y. Horii
2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020 305 - 306 2020.10
Authorship：Lead author, Corresponding author Language：English Publishing type：Research paper (international conference proceedings) Publisher：2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020
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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Elderly monitoring and action recognition system using stereo depth camera Reviewed
Thi Thi Zin, Ye Htet, Y. Akagi, H. Tamura, K. Kondo, S. Araki
2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020 316 - 317 2020.10
Authorship：Lead author, Corresponding author Language：English Publishing type：Research paper (international conference proceedings) Publisher：2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020
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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Detection of Estrus in Cattle by using Image Technology and Machine Learning Methods Reviewed
Su Myat Noe,Thi Thi Zin, Pyke Tin, H. Hama
2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020 320 - 321 2020.10
Authorship：Corresponding author Language：English Publishing type：Research paper (international conference proceedings) Publisher：2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020
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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Dam Water Overflow Estimation using Time Series Reviewed International coauthorship International journal
Mie Mie Khin, Mie Mie Tin, Thi Thi Zin, Pyke Tin
2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020 285 - 286 2020.10
Language：English Publishing type：Research paper (international conference proceedings) Publisher：2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020
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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A mobile application for offline handwritten character recognition Reviewed
Thi Thi Zin, Moe Zet Pwint, Shin Thant
2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020 10 - 11 2020.10
Authorship：Lead author, Corresponding author Language：English Publishing type：Research paper (international conference proceedings) Publisher：2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020
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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A stochastic model for dairy cow body condition scores changes between two successive calving events Reviewed
Thi Thi Zin, Pyke Tin, H. Hama
ICIC Express Letters 14 ( 10 ) 1009 - 1015 2020.10
Authorship：Lead author, Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：ICIC Express Letters
In this paper, we shall propose a stochastic model to investigate and analyze the patterns of dairy cow body condition scores between two successive calving events. Also, a robust Markov chain was introduced and used for stochastic evaluation of body condition score fluctuations from time to time. The study confirmed greater beneficial with increased healthy performance, but a great variation among farms needs to be taken into account. The stochastic model can fully describe the pattern and quantify its characteristics composed of the sum of random variables derived from milk yields, feeding intakes and transition periods in the body energy reserves changes. For this purpose, mathematical modeling techniques can be used to develop decision making systems, in order to achieve optimality of dairy farm management systems. In this aspect, the body condition score plays a key role to make the system successfully carried out. That is to achieve maintaining target score in corresponding periods such as a few weeks after calving, early lactation, mid lactation and dry periods. This concept leads to looking into the dairy cow energy reserves problem of within-the-two successive calving events since the body condition score fluctuation is critical especially at the time of calving, with improvements in production. However, a little has known the statistical and probabilistic tools for relating the body condition score pattern change and milk production, feeding management and animal health during the inter-calving periods. Therefore, we shall formulate the problem of energy reserves in dairy cow body, as a stochastic model of special in which inputs (feed intakes), outputs (mike produced) and the body condition score (energy research storage) are used as random variables. Utilizing a generalized gamma distribution and the univariate normal distribution functions for the marginal and joint distributions of the inputs and outputs in the model, the expected change patterns in body condition scores with respect to time are derived and analyzed. In order to confirm the validity of the proposed method, some simulation results are obtained by using the estimated parameters for inputs and outputs derived from real life dataset. These results show that the proposed approach is well suited to analyze the behaviors of dairy cows associations with body condition scores changing patterns.