Estimation of Voltage Regulator Stable Region Using Radial Basis Function Neural Network

Disturbance to the voltage regulator (VR) output caused by the abrupt change in load current can be compensated using an output capacitor with an internal parasitic element called the equivalent series resistance (ESR). However, the ESR value changes due to aging and temperature change factors, thereby creating a VR stable region in terms of ESR. In practice, time-consuming and high-expertise manual characterization is required to characterize the VR stable region during the design and manufacturing phases. Therefore, this research aims to develop an efficient and effective VR characterization method. In this work, the radial basis function neural network (RBFNN) approach was implemented to estimate the stable region. Results show that the RBFNN approach yields a stable region with higher estimation accuracy and faster characterization time than those of manual characterization. VR characterization using the RBFNN approach can efficiently and effectively estimate the VR stable region

Critical Equivalent Series Resistance Estimation for Voltage Regulator Stability Using Hybrid System Identification and Neural Network

Output capacitor in the voltage regulator (VR) circuit ensures stability especially during fast load transients. However, the capacitor parasitic, namely equivalent series resistance (ESR), may cause unstable VR operation. VR characterization in terms of ESR suggests stable range of capacitor ESR based on the ESR tunnel graph in the VR datasheet. Specifically, the stable ESR range is the critical ESR value, which lies on the failure region boundary of ESR tunnel graph. New or updated ESR tunnel graph through characterization is required for new product development or quality assurance purpose. However, the characterization is typically conducted manually in industry, thereby increases the manufacturing time and cost. Therefore, this work proposed a characterization approach that can reduce the time to determine the ESR tunnel graph based on the hybrid system identification and neural network (SI-NN) approach. This method utilised system identification (SI) to estimate the VR circuit model for certain operating points before predicting the transfer function coefficients for the remaining points using radial basis function neural network (RBFNN). Eventually, the critical ESR of failure region boundary was estimated. This hybrid SI-NN approach able to reduce the number of data that would be acquired manually to 25% compared to manual characterization, while provides critical ESR estimation with error less than 2%

Grey-Level Cooccurrence Matrix Performance Evaluation for Heading Angle Estimation of Moveable Vision System in Static Environment

A method of extracting information in estimating heading angle of vision system is presented. Integration of grey-level cooccurrence matrix (GLCM) in an area of interest selection is carried out to choose a suitable region that is feasible for optical flow generation. The selected area is employed for optical flow generation by using Horn-Schunck method. From the generated optical flow, heading angle is estimated and enhanced via moving median filter (MMF). In order to ascertain the effectiveness of GLCM, we compared the result with a different estimation method of optical flow which is generated directly from untouched greyscale images. The performance of GLCM is compared to the true heading, and the error is evaluated through mean absolute deviation (MAE). The result ensured that GLCM can improve the estimation result of the heading angle of vision system significantly

Algorithms Development in Detection of the Gelatinization Process during Enzymatic âDodolâ Processing

Computer vision systems have found wide application in foods processing industry to perform quality evaluation. The systems enable to replace human inspectors for the evaluation of a variety of quality attributes. This paper describes the implementation of the Fast Fourier Transform and Kalman filtering algorithms to detect the glutinous rice flour slurry (GRFS) gelatinization in an enzymatic âdodol. processing. The onset of the GRFS gelatinization is critical in determining the quality of an enzymatic âdodol.. Combinations of these two algorithms were able to detect the gelatinization of the GRFS. The result shows that the gelatinization of the GRFS was at the time range of 11.75 minutes to 14.75 minutes for 24 batches of processing. This paper will highlight the capability of computer vision using our proposed algorithms in monitoring and controlling of an enzymatic âdodol. processing via image processing technology

Machine Vision System for Automatic Weeding Strategy in Oil Palm Plantation using Image Filtering Technique

Non

Robust Controller for a Vision Feedback Based Telepointer

Telepointer is a very useful tool for teleconsultation and teleproctoring, whereby a telepointer via teleconferencing is a perfect example of computer-supported cooperative work (CSCW) and digital telepresence. To this end, many telepointers are introduced for digital telepresence. However, there are still concerns regarding the speed of response and robustness of the system. It is rather difficult to model the actual system in order to design the controller. This paper described the development of a telepointer and its controller for a real time communication using vision feedback. The main focus of this study was to control the Laser Pointer (LP) with a discrete time PID (proportional–integral–derivative) controller which was tuned using Ziegler-Nichols (ZN) method. The results indicated that the tuned controller bring about fast response with no overshoot and steady state errors at the output response. The controller was shown to be robust against changes in sampling time and external disturbance

Weed detection utilizing quadratic polynomial and ROI techniques

Machine vision for selective weeding or selective herbicide spraying relies substantially on the ability of the system to analyze weed images and process the extracted knowledge for decision making prior to implementing the identified control action. To control weed, different weed type would require different herbicide formulation. Consequently the weed must be identified and classified accordingly. In this work, weed images were classified as either broad or narrow weed type. A fundamental problem in weed image recognition using planar curve analysis is to detect curve. It is difficult to successfully extract curve from the image of weed edges since the appropriate scale to use for extraction is not known a priori As such, this paper considers a curve detection method based on the quadratic polynomial technique which include the use of the region-of-interests (ROI) technique. The ROI technique creates image subsets by selecting regions of the displayed image. The ROIs are typically used to extract statistics for image operations such as classification. As such, the objective of this paper is to present a novel application of curve detection feature extraction technique in weed classification

Shape Transformation of Left Ventricle Wall for Cardiac Global Hypokinetic Evaluation

A method to evaluate left ventricular (LV) wall motion in two-dimensional (2D) echocardiographic image is proposed. It is used to investigate posterior and anterior septum wall shape change while pumping the blood in human cardiac. Shape transformation parameters of myocardial wall boundary were extracted to indicate the LV wall movement from end diastole to end systole. Quantitative parameters represent the movement of LV wall segment whatever translation, rotation, expansion or combination of them. Initial myocardial boundary is drawn manually on end diastole cycle and then tracked to all frames by computing the speckle motion estimation in each frame of cardiac cycle. The motion vector of myocardial boundary movement is computed using warping optical flow on wavelet multi-resolution. The method was applied to parasternal long axis view of 2D echocardiographic from normal subject and patients with global hypokinetic. The results show that quantitative evaluation parameters gave a potential indication in evaluating and diagnosing myocardial wall motion abnormalities