This paper present the performance of Fuzzy logic controller in maintain level of water in water tank system. The mathematical modelling was developed to get the initial idea of the system performance. Later, the prototype of water tank system were constructed and tested to get the real time results. The Takagi-Sugeno “on” and “off” interference technique method was implemented due to the control limitation of the pump motor that being used in the experimental setup. The fuzzy logic controller was realized by embedded the algorithm in microcontroller of the water tank system. The experimental results show acceptable level of water within the range of 18cm to 20.5cm and settling time 59 seconds with 20 cm set point.

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Monitoring complex engineering systems is an important countermeasure in managing the risk of faulty events. Observing the response of each process flow will avoid further damages in the production cycle. Both fault-tolerant approach that bears with faulty events and scheduled maintenance that helps to reduce tool wearing are deeply involved in condition-based monitoring methods implemented in factories. Thus, identification of faulty equipment is need to avoid major breakdown in the production system. A classification framework shows good performance in classifying faulty events, but a labelled dataset is usually financially consuming. Machine learning (ML) techniques have become a prospective tool in the unsupervised fault detection (UFD) approach to prevent total failures in complex engineering system. However, the efficiency of UFD applications, on the other hand, is determined by the selected ML method. This paper presents a systematic literature review of ML methods applied for UFD, highlighting the methods explored in this field and the success of today's state-of-the-art machine learning techniques. This review focuses on the Scopus scientific database and provides a useful information on ML techniques, challenges and opportunities, and new research works in the UFD field.

Artificial intelligence of things (AIoT) has become a potential tool for use in a wide range of fields, and its use is expanding in interdisciplinary sciences. On the other hand, in a clinical scenario, human blood-clotting disease (Royal disease) detection has been considered an urgent issue that has to be solved. This study uses AIoT with deep long short-term memory networks for biosensing application and analyzes the potent clinical target, human blood clotting factor IX, by its aptamer/antibody as the probe on the microscaled fingers and gaps of the interdigitated electrode. The earlier results by the current-volt measurements have shown the changes in the surface modification. The limit of detection (LOD) was noticed as 1 pM with the antibody as the probe, whereas the aptamer behaved better with the LOD at 100 fM. The time-series predictions from the AIoT application supported the obtained results with the laboratory analyses using both probes. This application clearly supports the results obtained from the interdigitated electrode sensor as aptamer to be the better option for analyzing the blood clotting defects. The current study supports a great implementation of AIoT in sensing application and can be followed for other clinical biomarkers.

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