Prasitmeeboon, Pitcha

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Prasitmeeboon, Pitcha
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pitcha.pr@kmitl.ac.th

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    Item type:Publication,
    Real-time seafood quality monitoring system using interdigital sensor
    (2020-01-01)
    Kaewpoonsuk, Anucha
    ;
    Luangpol, Amata
    ;
    Prasitmeeboon, Pitcha
    ;
    Rerkratn, Apinai
    Many studies showed that seafood consumption contains many health benefits. However, the spoilage of such products causes the vast economic loss each year. An effective monitoring and inspection system for quality is required during the storage of these products. Most existing methods for such inspection require laboratory tests. In this study, the interdigital sensor was developed and applied to inspect the spoilage of seafood. The sensor was designed to monitor the change of impedance during spoilage progression of seafood. The result revealed that impedance of liquid decreased overtime. This coincides with the fact that spoilage of seafood generates ammonium ion which causes decrease in the impedance of the liquid. The designed sensor was then merged with the proposed system which consists of a sine wave circuit, a voltage control current source circuit, an interdigital sensor, an amplifier circuit, a rectifier circuit, a low-pass filter circuit, a comparator circuit and a display circuit. These circuits adjusted the signals received from the sensor to be proper to the application. The experiment was designed to have conditions similar to the real-world situation of how seafood is stored. The experimental results showed that the proposed system effectively indicated the change of seafood quality.
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    Prediction of Flying Height Using Deep Neural Network Based on Particle Swarm Optimization in Hard Disk Drive Manufacturing Process
    (2024-01-01)
    Kanjanapruthipong, Worawit
    ;
    Prasitmeeboon, Pitcha
    ;
    Konghuayrob, Poom
    In contemporary hard disk drive (HDD) manufacturing processes, after the assembly of the HDD from the production line, a series of diverse calibration procedures are necessary to ensure standardization. These include capacity calibration, which determines the storage space in terabytes (TB) presently available, and flying height (FH) calibration, which evaluates the distance between the head and the disk by applying electric current to the heater coil element to achieve the desired FH, thus optimizing the writing and reading performance and tailoring it to each HDD. Additionally, electric current is saved in a digital-to-analog converter (DAC) unit for the utilization of a read/write head, while a preamp collaborates with the drive firmware to convert the electric current in the DAC unit to milliwatts. In the present scenario, multiple calibrations of flying heights (FHs), specifically flying height 1 (FH1) and flying height 2 (FH2), are performed. Each FH calibration requires a testing time of approximately 5 h owing to the separation of measurement points into 240 locations across the disk surface, referred to as test zones, with a total of 20 heads. The primary objective of this study is to reduce the testing time by using a combination of deep neural network (DNN) and particle swarm optimization techniques to predict the DAC profiles of FH2 as it approaches FH1, where FH1 is the input for the DNN model.
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    Mushroom spawn quality classification with machine learning
    (2020-12-01)
    Tongcham, Phongsakhon
    ;
    Supa, Pichaya
    ;
    Pornwongthong, Peerapong
    ;
    Prasitmeeboon, Pitcha
    During spawn incubation in mushroom cultivation, spawns can be contaminated by a variety of pests and pathogenic molds, which cause virulent crop damage. Contaminated spawns must be classified and discarded before they are delivered to the fruiting stage. In most mushroom farms, humans visually classify spawn, which is labor-intensive and susceptible to human errors. To solve this problem, we designed a machine learning algorithm to classify oyster mushroom (Pleurotus ostreatus) spawns. Spawn samples were collected from a farm in Thailand. Sample regions of interest of spawns, in polypropylene polypropylene bags, were extracted and filtered to reduce noise. Trivariate histograms of these regions were used as a feature. We analyzed the effects of two techniques, including feature scaling and feature compression, using principal components analysis (PCA) in a pre-processing step. We measured performance of five machine learning classifiers: support vector machines (SVMs), nearest centroid classifier (NCC), k-nearest neighbor (KNN), deep neural network (DNN) and decision trees. Parameters of the methods were optimized and overall performances were compared. Although the number samples obtained was limited and unbalanced, a 4-fold cross validation showed that the DNN classifier had the highest accuracy at 98.8%, with residual variance of 2.5%. Thus, our algorithm can be effectively used to create a model for further application in an embedded system for mushroom spawn classification.
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    Repetitive Control Compensator Design for Frequency Response near Singularities
    (2021-12-01)
    Prasitmeeboon, Pitcha
    ;
    Longman, Richard W.
    Repetitive control (RC) aims to achieve zero error from a feedback control system that is subject to a periodic disturbance of known period, or that is executing a periodic command. It can be used in spacecraft for jitter mitigation, for creating active vibration isolation mounts that theoretically can produce complete cancellation of periodic jitter. RC is a feedback loop around an existing feedback control system that adjusts the control system’s command aiming for that command that produces zero error. The design requires creating a compensator that cancels the phase lag through the feedback controller within a tolerance of less that ±90 degrees. The phase behavior of digital systems is presented in detail, exhibiting the possibility of step discontinuities in phase that approach ± 90 degrees. The issue of whether stable RC systems can be designed for sample rates near such discontinuities is addressed. It is shown in numerical studies that compensators that use as few as 2 or 4 gains times previously recorded errors can be sufficient for not only stability, but can give rather fast convergence to zero error for nearly all frequencies except approaching Nyquist frequency. For systems with even pole excess, there can be a different kind of phase singularity that occurs as the sample rate tends to infinity. When designing for fast sample rate it is best to use a larger set of gains to obtain good convergence rate for all frequencies except approaching Nyquist. It was expected that these discontinuities might make it hard to design the RC compensators, but the results indicate that the singularities do not cause serious difficulty in RC design.