• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.4
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• pp.491-501
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• 1990

Recently, four speech coding techniques, namely, SBC-APCM(sub-band coding adaptive PCM), RPE-LPC(regualr pulse excitation linear predictive codec), MPE-LTP(multi-pulse excited long-term prediction) and CELP (code-excited linear prediction) are proposed for digital mobile radio applications. However, a performance comparison of these coders in the Rayleigh fading environment has not been made yet. In this paper, the performances of the four spech coders in the random bit error and burst error environment are investigated. For the channel coding of SBC-APCM, RPE-LPC and MPE-LTP, the sensitivity of output bit stream is measured and a bit selective forward error correction is provided acording to the measured bit sensitivity. And for an attempt to improve the performance of CELP, an optimum quantizer is applied for transmitting scalar quantities in CELP. However, an improvement over the conventional approach is found to be negligible. For the channel coding of CELP, Reed-Solomon code, Golay code, convolutional code of rate 1/2 shows the best performance. Finally, from the simulation results, it is concluded that CELP is the best candidate for digital mobile radio and is followed by MPE-LTP, SBC-APCM and RPE-LPC.

• Journal of Microbiology and Biotechnology
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• v.27 no.2
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• pp.207-218
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• 2017

Clustered regulatory interspaced short palindromic repeats (CRISPR) in association with CRISPR-associated protein (Cas) is an adaptive immune system, playing a pivotal role in the defense of bacteria and archaea. Ease of handling and cost effectiveness make the CRISPR-Cas system an ideal programmable nuclease tool. Recent advances in understanding the CRISPR-Cas system have tremendously improved its efficiency. For instance, it is possible to recapitulate the chronicle CRISPR-Cas from its infancy and inaugurate a developed version by generating novel variants of Cas proteins, subduing off-target effects, and optimizing of innovative strategies. In summary, the CRISPR-Cas system could be employed in a number of applications, including providing model systems, rectification of detrimental mutations, and antiviral therapies.

• Journal of KIISE:Software and Applications
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• v.26 no.12
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• pp.1507-1518
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• 1999

본 논문은 제어대상체의 지식을 이용하여 적절한 의사결정을 내리거나 또는 지속적으로 변화하는 주변환경에 적응해 나갈 수 있는 지능시스템 설계를 위한 내배엽성 모델링 방법론을 제시한다. 이러한 지능적 내배엽성 시스템은 의사결정 모델, 지식기반의 내부모델, 그리고 내부모델의 구축모델 등을 기반으로 달성될 수 있다. 학습기능의 모델링을 위하여 수정된 귀납추론 방법과 적응형 전문가 시스템 방법이 제안되었다. 제시된 방법론은 지능적 학습 및 의사결정 기능을 갖춘 지능적 카드경기자 모델링의 예를 통하여 그 가능성을 검증하였다. Abstract This paper presents an endomorphic modeling methodology for designing intelligent systems that can determine by itself using its knowledge of the world and adapt itself to continuously changing circumstances. We have developed such an intelligent endomorphic system by integrating the decision making component and knowledge based internal model with internal model construction model. Learning capabilities are established using the modified inductive reasoning and adaptive expert system techniques we developed. Proposed methodology has been successfully applied to a design of intelligent card game players capable of supporting the intelligent learning and decision making.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.11
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• pp.925-931
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• 2016

The BLDC motor is used widely in industry due to its controllability and freedom from maintenance because there is no mechanical brush in the BLDC motor. Furthermore, it is suitable for high-speed applications, such as compressors and air blowers. For instance, for a compressor with a small impeller due to miniaturizing, the BLDC motor has to rotate at a very high speed to maintain the compression ratio of the compressor. Typically, to reach an ultra-high speed, airfoil bearings must be used in place of ball bearings because of their friction. Unfortunately, the characteristics of airfoil bearings change drastically depending on the revolution speed. In this paper, a BLDC motor with airfoil bearings is controlled with a PID controller. To analyze and determine the PID coefficients, the relay-feedback method is used. Additionally, for adaptive control, a fuzzy logic controller is used. Furthermore, the auto-tuning and self-tuning techniques are combined to control the BLDC motor. The proposed method is able to control the airfoil-bearing BLDC motor efficiently.

• Journal of Information Processing Systems
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• v.7 no.1
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• pp.53-62
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• 2011

With the steady growth of mobile technology and applications, demand for more storage in mobile devices has also increased. A lightweight block-level protocol, Internet Advanced Technology Attachment (iATA), has been developed to deliver a cost-effective storage network solution for mobile devices to obtain more storage. This paper seeks to contribute to designing and implementing Load Balancing (LB), Network Monitoring (NM) and Write Replication (WR) modules to improve the protocol's scalability and data availability. LB and NM modules are invoked to collect system resources states and current network status at each associate node (server machine). A dynamic weight factor is calculated based on the collected information and sent to a referral server. The referral server is responsible to analyze and allocate the most ideal node with the least weight to serve the client. With this approach, the client can avoid connecting to a heavily loaded node that may cause delays in subsequent in-band I/O operations. Write replication is applied to the remaining nodes through a WR module by utilizing the Unison file synchronization program. A client initially connected to node IP A for write operations will have no hindrances in executing the relevant read operations at node IP B in new connections. In the worst case scenario of a node crashing, data remain recoverable from other functioning nodes. We have conducted several benchmark tests and our results are evaluated and verified in a later section.

• Computers and Concrete
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• v.16 no.3
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• pp.399-414
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• 2015

Nowadays, fiber reinforced polymer (FRP) composites are widely used for rehabilitation, repair and strengthening of reinforced concrete (RC) structures. Also, recent advances in concrete technology have led to the production of high strength concrete, HSC. Such concrete due to its very high compression strength is less ductile; so in seismic areas, ductility is an important factor in design of HSC members (especially FRP strengthened members) under flexure. In this study, the Adaptive Neuro-Fuzzy Inference System (ANFIS) and multiple regression analysis are used to predict the curvature ductility factor of FRP strengthened reinforced HSC (RHSC) beams. Also, the effects of concrete strength, steel reinforcement ratio and externally reinforcement (FRP) stiffness on the complete moment-curvature behavior and the curvature ductility factor of the FRP strengthened RHSC beams are evaluated using the analytical approach. Results indicate that the predictions of ANFIS and multiple regression models for the curvature ductility factor are accurate to within -0.22% and 1.87% error for practical applications respectively. Finally, the effects of height to wide ratio (h/b) of the cross section on the proposed models are investigated.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.4
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• pp.35-44
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• 1989

In this paper, a precisely speed-controlled DC servo motor system utilizing direct adaptive control (DAC) algorithm which require that neither satisfaction of the perfect model following conditions (PMFC) nor explicit parameter identification is proposed. Computer simulation as well as experiments using MC-68000 are implemened with the above input trajectory fairly well in spite of load disturbances and parameter variations. Presented algorithm is simple and effective both in software and hardware applications.

• IEMEK Journal of Embedded Systems and Applications
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• v.10 no.5
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• pp.325-333
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• 2015

Vision based night-time vehicle detection has been an emerging research field in various advanced driver assistance systems(ADAS) and automotive vehicle as well as automatic head-lamp control. In this paper, we propose night-time vehicle detection method based on multi-class support vector machine(SVM) that consists of thresholding, labeling, feature extraction, and multi-class SVM. Vehicle light candidate blobs are extracted by local mean based thresholding following by labeling process. Seven geometric and stochastic features are extracted from each candidate through the feature extraction step. Each candidate blob is classified into vehicle light or not by multi-class SVM. Four different multi-class SVM including one-against-all(OAA), one-against-one(OAO), top-down tree structured and bottom-up tree structured SVM classifiers are implemented and evaluated in terms of vehicle detection performances. Through the simulations tested on road video sequences, we prove that top-down tree structured and bottom-up tree structured SVM have relatively better performances than the others.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.361-367
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• 1998

The main two applications of the Genetic Algorithms(GA) are the optimization and the machine learning. Machine Learning has two objectives that make the complex system learn its environment and produce the proper output of a system. The machine learning using the Genetic Algorithms is called GA machine learning or genetic-based machine learning (GBML). The machine learning is different from the optimization problems in finding the rule set. In optimization problems, the population of GA should converge into the best individual because optimization problems, the population of GA should converge into the best individual because their objective is the production of the individual near the optimal solution. On the contrary, the machine learning systems need to find the set of cooperative rules. There are two methods in GBML, Michigan method and Pittsburgh method. The former is that each rule is expressed with a string, the latter is that the set of rules is coded into a string. Th classifier system of Holland is the representative model of the Michigan method. The classifier systems arrange the strength of classifiers of classifier list using the message list. In this method, the real time process and on-line learning is possible because a set of rule is adjusted on-line. A classifier system has three major components: Performance system, apportionment of credit system, rule discovery system. In this paper, we solve the food search problem with the learning and evolution of an artificial ant using the learning classifier system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.7C
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• pp.704-711
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• 2002

This paper proposes a DFE (Decision Feedback Equalizer) equalizer ASIC using the Multi-Modulus Algorithm (MMA) for cable modem applications. We believe that it is the first effort to combine the DFE structure and the MMA algorithm. The proposed equalizer has been designed for 64/256 QAM modems. The existing MMA equalizer uses two transversal filters and updates two tap weights while the proposed equalizer uses two DFE filter banks to improve the channel adaptive performance and to reduce the number of taps and updates only one tap weights. We have used the 0.35 $\mu\textrm{m}$ standard cell library. The implemented equalizer ASIC operates at 8 MHz and provides 64 Mbps which is higher than existing equalizers. The total number of gates are about 160,000.