Anuwongpinit, Thanavit

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Anuwongpinit, Thanavit
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Anuwongpinit, T.
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thanavit.an@kmitl.ac.th

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    Medical Drone Managing System for Automated External Defibrillator Delivery Service
    (2022-04-01)
    Purahong, Boonchana
    ;
    Anuwongpinit, Thanavit
    ;
    Juhong, Aniwat
    ;
    Kanjanasurat, Isoon
    ;
    Pintaviooj, Chuchart
    One of the common causes of a heart attack is fibrillation, a condition that causes an irregular and often abnormally fast heart rate. There is scientific evidence that the survival rate of sudden cardiac arrest patients who are rescued with cardiopulmonary resuscitation (CPR) and with the use of an automated external defibrillator (AED) is significantly increased. Despite the recommendation that automated external defibrillators should be installed in the workplace, along with a proper management system and training for employees on how to use the device, less than 70% of non-residential areas have an AED installed. The situation is even worse in residential areas, with less than 30% having an AED installed. This research concerns the development of a medical drone managing system that can deliver an AED in case of emergency. An application was developed that can be installed on the mobile phone and/or tablet of the patient or the accompanying person. In the event of a heart attack, the patient or the accompanying person can call a medical drone by sending coordinates to the drone station and a notification to medical staff. The drone station administrator can respond by sending the drone, which automatically lands at the patient’s location. After being tested in a simulation situation, the operational field test yielded satisfactory results. The medical drone can land within 1.5 meters of the destination. The designed AED drone can be used not only to deliver AEDs, but also first aid kits and prescribed drugs suitable for medical care. Such a system is especially useful in the current context of the COVID‐19 pandemic.
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    Engineering Education Roadmap of the Future Trend of Basic Metaverse based on VR with cooperation between the government and the private sector
    (2022-01-01)
    Purahong, Boonchana
    ;
    Anuwongpinit, Thanavit
    ;
    Kanjanasurat, Isoon
    ;
    Chansuthirangkool, Manit
    ;
    Singto, Kamphon
    This article presents an educational roadmap of future trends of Metaverse in VR-based by collaborating between the School of Engineering, King Mongkut's Institute of Technology Ladkrabang (KMITL) with iMAKE company to make a part-time learning plan. The objectives were to measure the achievement and evaluate satisfaction with the development of part-time learning skills in technology on the topic 'Basic Metaverse based on VR'. The sample group was students in a double-degree bachelor's degree program (Dual Degree) between the School of Engineering and the Faculty of Science, KMITL: Bachelor of Engineering (IoT System and Information Engineering) and Bachelor of Science (Industrial Physics) for 16 students by selecting a specific sample group, the engineering education program has a systematic process. The results showed that the achievement of part-time learning skills development in technology on the topic 'Basic Metaverse based on VR' higher than the set criteria 74 %, the overall satisfaction is at a very good level, the mean satisfaction was 4.636 and the sample standard deviation was 0.39.
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    Voice over IP Integration Platform Performance Using EC2 AWS Cloud Service
    (2022-01-01)
    Sathaporn, Posathip
    ;
    Krungseanmuang, Woranidtha
    ;
    Chaowalittawin, Vasutorn
    ;
    Anuwongpinit, Thanavit
    ;
    Purahong, Boonchana
    This article describes a method for integrating a mobile application for controlling and transmitting voice data levels in various departments within an organization with Amazon Elastic Compute Cloud (AWS EC2) to reduce hardware location costs and create more convenient in-house management at a single point. To begin, the paper introduces the project objective with a business scenario from an organization. Second, the SIP server implementation method is provided by Asterisk on AWS EC2 Ubuntu operating system and connection with a Mobile application that is used by flutter framework. Finally, the project experiments and discussions will be presented, and the obtained results show that the call setup time for the iOS/Android platforms to PC performed the best, taking less than one second, and was the fastest when compared to other testing metrics. However, there are many more metrics that should be considered, which are presented in the research's results section. With high performance and stability, this article was able to broadcast voice data via mobile applications.
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    Image Enhancement and 27 Pretrained Convolutional Neural Network Models for Diabetic Retinopathy Grading
    (2023-01-01)
    Kanjanasurat, Isoon
    ;
    Anuwongpinit, Thanavit
    ;
    Purahong, Boonchana
    Diabetic retinopathy (DR) affects the retina's blood vessels and causes vision loss. Fundus images are used to diagnose DR, which is a lengthy process because experienced clinicians must accurately diagnose the disease and identify microlesions early to prevent blindness. Computer vision can be used for retinal image classification. The APTOS dataset contains 5990 normal, moderate, mild, proliferate, and severe retinal images. In this study, we proposed a convolutional neural network (CNN) ensemble for DR fundus grading. Each image channel was enhanced by contrast-limited adaptive histogram equalization (CLAHE) and gamma correction and then fed to 27 pretrained CNN models for one-time training to examine the DR grading. The results showed that MobileNet's green channel with the CLAHE technique is sufficiently fast and accurate for disease classification. The grading retinal images had an accuracy of 96.95%, a precision of 96.17%, a sensitivity of 97.80%, an F1 score of 96.98%, and a specificity of 97.75%. In addition, the proposed method improves the speed and robustness of retinal DR grading.