• Journal of KIISE:Computer Systems and Theory
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• v.26 no.11
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• pp.1333-1343
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• 1999

경성 실시간 시스템의 태스크들은 논리적으로 올바른 결과를 산출해야 하지만 또한 각자의 시간 제한 조건을 만족하여야 한다. 간격제한 스케줄링은 시간 제한 조건이 시간 간격 제한으로 주어지는 실시간 태스크들을 스케줄하기 위하여 도입되었다. 간격제한 스케줄링에서의 각 태스크들은 시간 간격 제한 조건을 갖는데, 이것은 태스크의 두 연속적인 수행의 종료시간에 대해 제한을 가한다. 다시 말해, 간격제한 스케줄링에서의 각 태스크 수행은 그 태스크의 직전 수행 완료 시간으로부터 발생하는 데드라인을 갖는다. 간격제한 태스크 스케줄링에 관한 많은 연구는 단순화 방법에 기초하고 있다. 그러나, 우리는 이 논문에서 단순화 방법을 사용하지 않고, 정적 우선순위 및 정적 분리 제한 정책을 채용한 새로운 간격제한 태스크 스케줄링 방법을 제안한다. 제안된 정적 할당 방법은 스케줄링 분석 및 구현을 매우 간단히 할 수 있으며, 또한 스케줄러의 실행시간 오버헤드를 줄일 수 있다.Abstract Tasks in hard real-time systems must not only be logically correct but also meet their timing constraints. The distance-constrained scheduling has been introduced to schedule real-time tasks whose timing constraints are characterized by temporal distance constraints. Each task in the distance-constrained scheduling has a temporal distance constraint which imposes restriction on the finishing times of two consecutive executions of the task. Thus, each execution of a task in the distance-constrained scheduling has a deadline relative to the finishing time of the previous execution of the task.Much work on the distance-constrained task scheduling has been based on the reduction technique. In this paper, we propose a new scheme for the distance-constrained task scheduling which does not use the reduction technique but adopts static priority and static separation constraint assignment policy. We show that our static assignment approach can simplify the scheduling analysis and its implementation, and can also reduce the run-time overhead of the scheduler.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.5
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• pp.817-824
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• 2021

The SDN(Software Defined Networking) technology, which appeared to overcome the limitations of the existing network system, resolves the rigidity of the existing system through the separation of HW and SW in network equipment. These characteristics of SDN provide wide scalability beyond hardware-oriented network equipment, and provide flexible load balancing policies in data centers of various sizes. In the meantime, many studies have been conducted to apply the advantages of SDN to data centers and have shown their effectiveness. The method mainly used in previous studies was to periodically check the server load and perform load balancing based on this. In this method, the more the number of servers and the shorter the server load check cycle, the more traffic increases. In this paper, we propose a new load balancing technique that can eliminate unnecessary traffic and manage server resources more efficiently by reporting to the controller when a specific level of load occurs in the server to solve this limitation.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2723-2725
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• 2000

It is sometimes necessary to observe the working environment of a robot to control it in the remote location. The remote sensing data and control commands are transmitted via various media such as radio, microwave, and computer network. In this paper we propose an advanced technique of the remote control of mobile robots on the web. The image separation is included in the proposed algorithm to control mobile robots in the real-time. We transmit the positions of a mobile robot and obstacles instead of transmitting the full frame image. An experiment is performed to show the efficiency of the proposed algorithm.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.31 no.3
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• pp.50-57
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• 2003

In the paper, the design and evaluation of a helicopter trajectory tracking controller are presented. The control algorithm is implemented using the feedback linearization technique and the two time-scale separation architecture. In addition, and on-line adaptive architecture that employs a neural network compensating the model inversion error caused by the deficiency of full knowledge of helicopter dynamic is applied to augment the attitude control system. Trajectory tracking performance of the control system in evaluated using modified TMAN simulation program representing as Apache helicopter. It is show that the on-line neural network in an adaptive control architecture is very effective in dealing with the performance depreciation problem of the trajectory tracking control caused by insufficient information of dynamics.

• International journal of advanced smart convergence
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• v.9 no.2
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• pp.20-27
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• 2020

Sound event detection in real-world environments suffers from the interference of non-stationary and time-varying noise. This paper presents an adaptive noise reduction method for sound event detection based on non-negative matrix factorization (NMF). In this paper, we proposed a deep learning model that integrates Convolution Neural Network (CNN) with Non-Negative Matrix Factorization (NMF). To improve the separation quality of the NMF, it includes noise update technique that learns and adapts the characteristics of the current noise in real time. The noise update technique analyzes the sparsity and activity of the noise bias at the present time and decides the update training based on the noise candidate group obtained every frame in the previous noise reduction stage. Noise bias ranks selected as candidates for update training are updated in real time with discrimination NMF training. This NMF was applied to CNN and Hidden Markov Model(HMM) to achieve improvement for performance of sound event detection. Since CNN has a more obvious performance improvement effect, it can be widely used in sound source based CNN algorithm.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.5
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• pp.423-431
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• 2016

In general LPR(License Plate Recognition) in outdoor image is not so simple differently from in the image captured from manmade environment, because of geometric shape distortion and large illumination changes. this paper proposes three techniques for LPR in outdoor images captured from CCTV. At first, a serially connected multi-stage Adaboost LP detector is proposed, in which different complementary features are used. In the proposed detector the performance is increased by the Haar-like Adaboost LP detector consecutively connected to the MB-LBP based one in serial manner. In addition the technique is proposed that makes image processing easy by the prior determination of LP type, after correction of geometric distortion of LP image. The technique is more efficient than the processing the whole LP image without knowledge of LP type in that we can take the appropriate color to gray conversion, accurate location for separation of text/numeric character sub-images, and proper parameter selection for image processing. In the proposed technique we use DBN(Deep Belief Network) to achieve a robust character recognition against stroke loss and geometric distortion like slant due to the incomplete image processing.

• International Journal of Computer Science & Network Security
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• v.24 no.1
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• pp.163-177
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• 2024

The fingerprint image quality relies on the clearness of separated ridges by valleys and the uniformity of the separation. The condition of skin still dominate the overall quality of the fingerprint. However, the identification performance of such system is very sensitive to the quality of the captured fingerprint image. Fingerprint image quality analysis and enhancement are useful in improving the performance of fingerprint identification systems. A fuzzy technique is introduced in this paper for both fingerprint image quality analysis and enhancement. First, the quality analysis is performed by extracting four features from a fingerprint image which are the local clarity score (LCS), global clarity score (GCS), ridge_valley thickness ratio (RVTR), and the Global Contrast Factor (GCF). A fuzzy logic technique that uses Mamdani fuzzy rule model is designed. The fuzzy inference system is able to analyse and determinate the fingerprint image type (oily, dry or neutral) based on the extracted feature values and the fuzzy inference rules. The percentages of the test fuzzy inference system for each type is as follow: For dry fingerprint the percentage is 81.33, for oily the percentage is 54.75, and for neutral the percentage is 68.48. Secondly, a fuzzy morphology is applied to enhance the dry and oily fingerprint images. The fuzzy morphology method improves the quality of a fingerprint image, thus improving the performance of the fingerprint identification system significantly. All experimental work which was done for both quality analysis and image enhancement was done using the DB_ITS_2009 database which is a private database collected by the department of electrical engineering, institute of technology Sepuluh Nopember Surabaya, Indonesia. The performance evaluation was done using the Feature Similarity index (FSIM). Where the FSIM is an image quality assessment (IQA) metric, which uses computational models to measure the image quality consistently with subjective evaluations. The new proposed system outperformed the classical system by 900% for the dry fingerprint images and 14% for the oily fingerprint images.

• Journal of Information Processing Systems
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• v.9 no.1
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• pp.117-140
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• 2013

Hindi is the most widely spoken language in India, with more than 300 million speakers. As there is no separation between the characters of texts written in Hindi as there is in English, the Optical Character Recognition (OCR) systems developed for the Hindi language carry a very poor recognition rate. In this paper we propose an OCR for printed Hindi text in Devanagari script, using Artificial Neural Network (ANN), which improves its efficiency. One of the major reasons for the poor recognition rate is error in character segmentation. The presence of touching characters in the scanned documents further complicates the segmentation process, creating a major problem when designing an effective character segmentation technique. Preprocessing, character segmentation, feature extraction, and finally, classification and recognition are the major steps which are followed by a general OCR. The preprocessing tasks considered in the paper are conversion of gray scaled images to binary images, image rectification, and segmentation of the document's textual contents into paragraphs, lines, words, and then at the level of basic symbols. The basic symbols, obtained as the fundamental unit from the segmentation process, are recognized by the neural classifier. In this work, three feature extraction techniques-: histogram of projection based on mean distance, histogram of projection based on pixel value, and vertical zero crossing, have been used to improve the rate of recognition. These feature extraction techniques are powerful enough to extract features of even distorted characters/symbols. For development of the neural classifier, a back-propagation neural network with two hidden layers is used. The classifier is trained and tested for printed Hindi texts. A performance of approximately 90% correct recognition rate is achieved.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.2
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• pp.112-124
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• 2004

A neural network based adaptive reconfigurable flight controller is presented for a class of discrete-time nonlinear flight systems in the presence of variations of aerodynamic coefficients and control effectiveness decrease caused by control surface damage. The proposed adaptive nonlinear controller is developed making use of the backstepping technique for the angle of attack, sideslip angle, and bank angle command following without two time separation assumption. Feedforward multilayer neural networks are implemented to guarantee reconfigurability for control surface damage as well as robustness to the aerodynamic uncertainties. The main feature of the proposed controller is that the adaptive controller is developed under the assumption that all of the nonlinear functions of the discrete-time flight system are not known accurately, whereas most previous works on flight system applications even in continuous time assume that only the nonlinear functions of fast dynamics are unknown. Neural networks learn through the recursive weight update rules that are derived from the discrete-time version of Lyapunov control theory. The boundness of the error states and neural networks weight estimation errors is also investigated by the discrete-time Lyapunov derivatives analysis. To show the effectiveness of the proposed control law, the approach is i]lustrated by applying to the nonlinear dynamic model of the high performance aircraft.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.4
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• pp.389-394
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• 2006

Inaccurate localization exposes a robot to many dangerous conditions. It could make a robot be moved to wrong direction or damaged by collision with surrounding obstacles. There are numerous approaches to self-localization, and there are different modalities as well (vision, laser range finders, ultrasonic sonars). Since sensor information is generally uncertain and contains noise, there are many researches to reduce the noise. But, the correctness is limited because most researches are based on statistical approach. The goal of our research is to measure more exact robot location by matching between built VRML 3D model and real vision image. To determine the position of mobile robot, landmark-localization technique has been applied. Landmarks are any detectable structure in the physical environment. Some use vertical lines, others use specially designed markers, In this paper, specially designed markers are used as landmarks. Given known focal length and a single image of three landmarks it is possible to compute the angular separation between the lines of sight of the landmarks. The image-processing and neural network pattern matching techniques are employed to recognize landmarks placed in a robot working environment. After self-localization, the 2D scene of the vision is overlaid with the VRML scene.