• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.5
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• pp.1109-1122
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• 1996

Generally, moving images contain the various components in motions, which reange from a static object and background to a fast moving object. To extract the accurate motion parameters, we must consider the various motions. That requires a wide search egion in motion estimation. The wide search, however, causes a high computational complexity. If we have a few knowledge about the motion direction and magnitude before motion estimation, we can determine the search location and search window size using the already-known information about the motion. In this paper, we present a local adaptive motion estimation approach that predicts a block motion based on spatio-temporal neighborhood blocks and adaptively defines the search location and search window size. This paper presents a technique for reducing computational complexity, while having high accuracy in motion estimation. The proposed algorithm is introduced the forward and backward projection techniques. The search windeo size for a block is adaptively determined by previous motion vectors and prediction errors. Simulations show significant improvements in the qualities of the motion compensated images and in the reduction of the computational complexity.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.14-17
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• 1995

Various methods and techniques have been proposed for solving optimization problems; the methods have been applied to various practical problems. However the methods have demerits. The demerits which should be covered are, for example, falling into local minima, or, a slow convergence speed to optimal points. In this paper, Likelihood Search Method (L.S.M.) is proposed for searching for a global optimum systematically and effectively in a single framework, which is not a combination of different methods. The L.S.M. is a sort of a random search method (R.S.M.) and thus can get out of local minima. However exploitation of gradient information makes the L.S.M. superior in convergence speed to the commonly used R.S.M..

• Journal of Power Electronics
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• v.16 no.3
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• pp.1097-1109
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• 2016

Fault detection and isolation are related to system monitoring, identifying when a fault has occurred, and determining the type of fault and its location. Fault detection is utilized to determine whether a problem has occurred within a certain channel or area of operation. Fault detection and diagnosis have become increasingly important for many technical processes in the development of safe and efficient advanced systems for supervision. This paper presents an integrated technique for fault diagnosis and classification for open- and short-circuit faults in three-phase inverter circuits. Discrete wavelet transform and principal component analysis are utilized to detect the discontinuity in currents caused by a fault. The features of fault diagnosis are then extracted. A fault dictionary is used to acquire details about transistor faults and the corresponding fault identification. Fault classification is performed with a fuzzy logic system and relevance vector machine (RVM). The proposed model is incorporated with a set of optimization techniques, namely, evolutionary particle swarm optimization (EPSO) and cuckoo search optimization (CSO), to improve fault detection. The combination of optimization techniques with classification techniques is analyzed. Experimental results confirm that the combination of CSO with RVM yields better results than the combinations of CSO with fuzzy logic system, EPSO with RVM, and EPSO with fuzzy logic system.

• Journal of Hydrospace Technology
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• v.2 no.2
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• pp.24-35
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• 1996

This paper describes parameter identification techniques formulated for the estimation of maneuvering coefficients of a submerged body. The first part of this paper is concerned with the identifiability of the system parameters. The relationship between a stochastic linear time-invariant system and the equivalent dynamic system is investigated. The second is concerned with the development of the numerically stable identification technique. Two identification techniques are tested; one is the ma7mum likelihood (ML) methods using the Holder & Mead simplex search method and using the modified Newton-Raphson method, and the other is the modified extended Kalman filter (MEKF) method with a square-root algorithm, which can improve the numerical accuracy of the extended Kalman filter. As a results, it is said that the equations of motion for a submerged body have higher probability to generate simultaneous drift phenomenon compared to general state equations and only the ML method using the Holder & Mead simplex search method and the MEKF method with a square-root algorithm gives acceptable results.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.14
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• pp.6033-6038
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• 2015

Background: Experience of lung cancer includes negative impacts on both physical and psychological health. Pain is one of the negative experiences of lung cancer. Cognitive behavioral therapy techniques are often recommended as treatments for lung cancer pain. The objective of this review was to synthesize the evidence on the effectiveness of cognitive behavioral therapy techniques in treating lung cancer pain. This review considered studies that included lung cancer patients who were required to 1) be at least 18 years old; 2) speak and read English or Thai; 3) have a life expectancy of at least two months; 4) experience daily cancer pain requiring an opioid medication; 5) have a positive response to opioid medication; 6) have "average or usual" pain between 4 and 7 on a scale of 0-10 for the day before the clinic visit or for a typical day; and 7) able to participate in a pain evaluation and treatment program. This review considered studies to examine interventions for use in treatment of pain in lung cancer patients, including: biofeedback, cognitive/attentional distraction, imagery, hypnosis, and meditation. Any randomized controlled trials (RCTs) that examined cognitive behavioral therapy techniques for pain specifically in lung cancer patients were included. In the absence of RCTs, quasi-experimental designs were reviewed for possible conclusion in a narrative summary. Outcome measures were pain intensity before and after cognitive behavioural therapy techniques. The search strategy aimed to find both published and unpublished literature. A three-step search was utilised by using identified keywords and text term. An initial limited search of MEDLINE and CINAHL was undertaken followed by analysis of the text words contained in the title and abstract, and of the index terms used to describe the article. A second search using all the identified keywords and index terms was then undertaken across all included databases. Thirdly, the reference list of all identified reports and articles were searched for additional studies. Searches were conducted during January 1991- March 2014 limited to English and Thai languages with no date restriction. Materials and Methods: All studies that met the inclusion criteria were assessed for methodological quality by three reviewers using a standardized critical appraisal tool from the Joanna Briggs Institute (JBI). Three reviewers extracted data independently, using a standardized data extraction tool from the Joanna Briggs Institute (JBI). Ideally for quantitative data meta-analysis was to be conducted where all results were subject to double data entry. Odds ratios (for categorical data) and weighted mean differences (for continuous data) and their 95% confidence intervals were to be calculated for analysis and heterogeneity was to be assessed using the standard Chi-square. Where statistical pooling was not possible the finding were be presented in narrative form. Results: There were no studies located that met the inclusion requirements of this review. There were also no text and opinion pieces that were specific to cognitive behavioral therapy techniques pain and lung cancer patients.Conclusions: There is currently no evidence available to determine the effectiveness of cognitive behavioural therapy techniques for pain in lung cancer patients.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.7
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• pp.1231-1237
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• 2022

In the present study, the optimization of the main particulars of a ship using AI-based design search techniques was investigated. For the design search techniques, the SHERPA algorithm by HEEDS was applied, and CFD analysis using STAR-CCM+ was applied for the calculation of resistance performance. Main particulars were automatically transformed by modifying the main particulars of the ship at the stage of preprocessing using JAVA script and Python. Small catamaran was chosen for the present study, and the main dimensions of the length, breadth, draft of demi-hull, and distance between demi-hulls were considered as design variables. Total resistance was considered as an objective function, and the range of displaced volume considering the arrangement of the outfitting system was chosen as the constraint. As a result, the changes in the individual design variables were within ±5%, and the total resistance of the optimized hull form was decreased by 11% compared with that of the existing hull form. Throughout the present study, the resistance performance of small catamaran could be improved by the optimization of the main dimensions without direct modification of the hull shape. In addition, the application of optimization using design search techniques is expected for the improvement in the resistance performance of a ship.

• Sleep Medicine and Psychophysiology
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• v.1 no.2
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• pp.145-155
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• 1994

The neurophysiological study has been widely used in the search of the relation between brain and behavior. The basic techniques for the animal experiments of this kind such as stereotaxic techniques, lesioning methods, the methods of electrical stimulation and confirmation of histological location were simply reviewed. Nevertheless, the importance of complementary neurochemical, neuroanatomical and behavioral studies can not be neglected.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.188-191
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• 2002

To reduce the bit-rate of video sequences by removing temporal redundancy, motion estimation techniques have been developed. However, the high computational complexity of the problem makes such techniques very difficult to be applied to high-resolution applications in a real time environment. For this reason, low computational complexity motion estimation algorithms are viable solutions. If a priori knowledge about the motion of the current block is available before the motion estimation, a better starting point for the search of n optimal motion vector on be selected and also the computational complexity will be reduced. In this paper, we present an adaptive block matching algorithm based on temporal correlations of consecutive image frames that defines the search pattern and the location of initial starting point adaptively to reduce computational complexity. Experiments show that, comparing with DS(Diamond Search) algorithm, the proposed algorithm is about 0.1∼0.5(㏈) better than DS in terms of PSNR and improves as much as 50% in terms of the average number of search points per motion estimation.

• Transactions of the Korean Society of Automotive Engineers
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• v.6 no.4
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• pp.76-85
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• 1998

Recently, Genetic Algorithm(GA) is widely adopted into a search procedure for structural optimization, which is a stochastic direct search strategy that mimics the process of genetic evolution. This methods consist of three genetics operations maned selection, crossover and mutation. Contrast to traditional optimal design techniques which use design sensitivity analysis results, GA, being zero-order method, is very simple. So, they can be easily applicable to wide area of design optimization problems. Also, owing to multi-point search procedure, they have higher probability of converge to global optimum compared to traditional techniques which take one-point search method. In this study, a method of finding the optimum values of suspension parameters is proposed by using the GA. And vehicle is modelled as planar vehicle having 5 degree-of-freedom. The generalized coordinates are vertical motion of passenger seat, sprung mass and front and rear unsprung mass and rotate(pitch) motion of sprung mass. For rapid converge and precluding local optimum, share function which distribute chromosomes over design bound is introduced. Elitist survival model, remainder stochastic sampling without replacement method, multi-point crossover method are adopted. In the sight of the improvement of ride comfort, good result can be obtained in 5-D.O.F. vehicle model by using GA.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.10B
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• pp.1732-1743
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• 2000

In this paper a fixed-delay trellis search with decision feedback (FDTrS/DF) for high-density DVD systems (4.7-15GB) is proposed and implemented with FPGA. The proposed FDTrS/DF is derived by transforming the binary tree search structure into trellis search structure implying that FDTrS/DF performs better than the singnal detection techniques based on tree search structure such as FDTS/DF and SSD/DF. Advantages of FDTrS/DF are significant reductions in hardware complexity due to the unique structure of FDTrS composed of only one trellis stage requiring no traceback procedure usually implemented in the Viterbi detector. Also in this paper the PDFS/DF and SSD/DF orginally proposed for high-density magnetic recording systems are modified for the DVD system and compared with the proposed FDTrS/DF. In order to increase speed in the FPGA implementation the pipelining technique and absolute branch metric (instead of square branch metric) are applied. The proposed FDTrS/DF is shown to provide the best performance among various signal detection techniques such as PRML, DFE, FDTS/DF and SSD/DF even with a small hardware complexity.