Environmental radiation monitoring based gamma spectroscopy require a multichannel analyzer (MCA) which functions to record and analyze nuclear radiation spectrum data. There are currently many studies related to MCA based on field programmable gate array (FPGA). But, the scope of these published research is complex and fragmented. This systematic literature review (SLR) main objective to analyze research trends and emerging themes research of MCA based on FPGA for gamma spectroscopy that was developed during last 10 years. This review was conducted by selecting papers from leading journals in scientific databases namely ScienceDirect, IEEE Xplore and Scopus. For papers selection process, specific keyword strings used for each database and paper selecting using inclusion and exclusion criterion. Selection of papers resulted in 38 selected papers. During 10 last 10 years, This reseach become hot of topic research especially in Asia. This SLR proves that performance of MCA based FPGA is reliable and compatible with various of detector types. This MCA has been used for spectroscopy applications such as monitoring radiation, diagnostic medicine, industrial imaging and security screening. The implications of this study can used as reference for future research of development MCA based FPGA for enviromental radiation monitoring based gamma spectroscopy.
Thermal Mixing Process, which is important in various industries, is a multiple-input multiple-output process (MIMO). It works by regulating hot-water and cold-water flows to control the temperature and level of the mixture. The Adaptive Fuzzy PID Control (AFPIDC) algorithm is a combination of two types of controller, has a simple PID basis with added Fuzzy aspects to speed up control. The AFPIDC algorithm is applied to the simulation of water thermal mixing process and is done with MATLAB/SIMULINK program. The fuzzy algorithm uses two inputs (errors & changes of errors) and has three outputs (changes of PID constants). The system is tested by simulating setpoint changes and adding disturbance. The testing result shows that AFPIDC controllers performs better than PID in controlling temperature and level. In temperature control, the PID settling time is 830 seconds, AFPIDC is 328 seconds and the PID overshoot is 6,3% and AFPIDC is 0%. In level control, the settling time of PID is 3221 seconds, while AFPIDC is 235 seconds, with PID overshoot is 10,5%, while AFPIDC 0%. From testing the system with leakage disturbance, the temperature controller needs time to regain stability at PID 780 seconds, AFPIDC 250 seconds. Meanwhile the level controlling stabilizes at PID 4510 seconds, and AFPIDC at 225 seconds.
Flow rate is a fundamental physical quantity in the fluid transportation system from one place to another. To achieve this, a reliable controller that is able to produce a constant flowrate in industry is needed. The most used flow controllers in industries are PID-based controllers that are implemented using PLCs. However, there are still shortcomings, they can perform poorly in some applications, for example in the highly nonlinear system which cannot be overcome by conventional PID controllers. There are some other limitations of PID controller, such as PID has the overshoot and undershoots in the output of controlled system and PID gives late response in this study, a neural network-based flow controller is proposed to deal with that problems. The controller will be operated in a miniature plant which consists of a water tank, water pump, a control valve, and a flow transmitter. Due to PLC limitation that cannot be programmed with common programming languages such as MATLAB, a personal computer (PC) is used to run the proposed neural network controller. The PC communicates with the PLC using OPC (OLE for Process Control) server, while the PLC reads the flow transmitter and also controls the control valve directly based on the result output of the neural network controller. In order to evaluate the performance of the proposed controller, several experiments have been conducted. The performance of the proposed controller has been compared with the conventional PID controller. It shows that neural network-based controller outperformed the conventional PID controller, in terms of maximum overshoot and steady-state error, where the neural network controller has maximum overshoot = 5.36% and steady-state error = 0.85%, while the PID controller has 11.3% for overshoot and 1.10 % for steady-state error.
Electroencephalography (EEG) is an electrical signal data that can describe brain activity in which the signal contains important information that can be used to detect several diseases. One of the diseases that can be detected by EEG signals is stroke. The most common type of stroke is the acute ischemic stroke (AIS) due to blockage of blood supply to the brain which can generate the tissue damage in the brain EEG signal recording uses several electrodes where the more electrodes used in the recording, the greater the number of EEG features produced (high dimensional data). This can make it difficult for models of machine learning to have optimal performance on high-dimensional data. In this study, for optimizing the performance of the machine learning model by selecting features with the Least Absolute Shrinkage and Selection Operator (Lasso) method, where this method can select the relevant features by shrinking some coefficients to zero. The type of classification used in this study is random forest with features used for classification are Brain Symmetry Index (BSI), Delta-Alpha Ratio (DAR), Delta-Theta-Alpha-Beta Ratio (DTABR). The results showed that the Lasso method can optimize the performance of learning machines with an accuracy value of 75% with 24 features out of 45 features.
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