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Dissolved gas analysis (DGA) is a commonly used method that allows pending or occurring faults within transformers to be determined. DGA has been widely used for many years. There are various DGA techniques, including the evaluation of individual and total dissolved combustible gas (TDCG) concentrations, the Doernenburg method, the Rogers method, and Duval's triangle. To facilitate the process of monitoring the condition of transformers and reduce potential human error, we discuss how to create a user-friendly system to monitor and evaluate the condition of high-voltage transformers. The system receives key gas values, which are extracted from oil samples inside transformers obtained by gas chromatography (GC), and is capable of automatically generating downloadable reports of specified transformers and gives insight on any faults found. It also visualizes the changes in extracted key gas values of transformers over time. To develop this system, the front end of the application was made with HTML and CSS, and the back end was made with JavaScript with MySQL as a database combined with Microsoft SharePoint. The automatic downloadable report generator was made with Power Automate. This system assists the monitoring of transformer conditions.

In this paper, we present an end-to-end monitoring system, which is used for patients who have foot or ankle impairments. This system has been created to help orthopedic doctors optimize treatment for patients recovering from foot and ankle injuries. The system consists of three main parts: a wearable controlled ankle motion (CAM) boot equipped with inertial and load sensors, a web application that provides visual feedback obtained from sensors, and the implementation of machine learning and deep learning to analyze walking activity and gait. Sensors used on the CAM boot include an accelerometer, a gyroscope, and load cells. Values from sensors attached to the CAM boot are sent wirelessly to the database. The web application takes sensor values from the database and returns visual feedback on the patient's walking patterns in the form of different graphs. The graphs can be used to analyze and determine abnormalities in the patient's gait and serve as a visual aid for patients during rehabilitation. Sensor values obtained from the database are used to train machine learning and deep learning models to recognize and differentiate between seven activities performed by the patient. We study and compare three dimensionality reduction methods and six classifiers. As a result, we find that the joint incorporation of the dimensionality reduction method of sparse principal component analysis (PCA) and the classifier random forest (RF) gives the best result with an accuracy of 99.5%.

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