• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.449-449
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• 2000

Many researchers have studied architectures for non-Neumann's computers because of escaping its bottleneck. To avoid the bottleneck, a neuron-based computer has been developed. The computer has only neurons and their connections, which are constructed of the learning. But still it has information processing facilities, and at the same time, it is like as a simplified brain to make inference; it is called "neuron-computer". No instructions are considered in any neural network usually; however, to complete complex processing on restricted computing resources, the processing must be reduced to primitive actions. Therefore, we introduce the instructions to the neuron-computer, in which the most important function is implications. There is an implication represented by binary-operators, but general implications for multi-value or fuzzy logics can't be done. Therefore, we need to use Lukasiewicz' operator at least. We investigated a neuron-computer having instructions for general implications. If we use the computer, the effective inferences base on multi-value logic is executed rapidly in a small logical unit.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.3
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• pp.199-208
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• 2014

At present, the effect of oriental treatment system is proved in the west and using the data of tongue and pulse of body, the doctor can decide the patient's body state without Xray and CT data of large machines. In this paper, the patient's medical data is transmitted to the doctor and the real time decision algorithm is developed and so the doctor can decide the medical treatments. Using the mobile phone, the pulse data and bio data can be sent to the doctor and therefore the patients, who can't care in real time, can be treated in real time in the impossible medical treatment areas. Therefore in this paper, the oriental medical treatment system algorithm and artificial intelligence electrical needle simulation are processed for real time and checked and treated, so anyone can decide patient's state using mobile phone.

• Geomechanics and Engineering
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• v.9 no.1
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• pp.57-81
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• 2015

Water inrush and mud outburst always restricts the tunnel constructions in mountain area, which becomes a major geological barrier against the development of underground engineering. In view of the complex disaster-causing mechanism and difficult quantitative predictions of water inrush and mud outburst, several theoretical methods are adopted to realize dynamic assessment of water inrush in the progressive process of tunnel construction. Concerning both the geological condition and construction situation, eleven risk factors are quantitatively described and an assessment system is developed to evaluate the water inrush risk. In the static assessment, the weights of eight risk factors about the geological condition are determined using Analytic Hierarchy Process (AHP). Each factor is scored by experts and the synthesis scores are weighted. The risk level is ultimately determined based on the scoring outcome which is derived from the sum of products of weights and comprehensive scores. In the secondary assessment, the eight risk factors in static assessment and three factors about construction situation are quantitatively analyzed using fuzzy evaluation method. Subordinate levels and weight of factors are prepared and then used to calculate the comprehensive subordinate degree and risk level. In the dynamic assessment, the classical field of the eleven risk factors is normalized by using the extension evaluation method. From the input of the matter-element, weights of risk factors are determined and correlation analysis is carried out to determine the risk level. This system has been applied to the dynamic assessment of water inrush during construction of the Yuanliangshan tunnel of Yuhuai Railway. The assessment results are consistent with the actual excavation, which verifies the rationality and feasibility of the software. The developed system is believed capable to be back-up and applied for risk assessment of water inrush in the underground engineering construction.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.8
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• pp.1947-1954
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• 2013

Previous works for detecting arrhythmia have mostly used nonlinear method such as artificial neural network, fuzzy theory, support vector machine to increase classification accuracy. Most methods require accurate detection of P-QRS-T point, higher computational cost and larger processing time. But it is difficult to detect the P and T wave signal because of person's individual difference. Therefore it is necessary to design efficient algorithm that classifies different arrhythmia in realtime and decreases computational cost by extrating minimal feature. In this paper, we propose arrhythmia detection based on binary coding using QRS feature varibility. For this purpose, we detected R wave, RR interval, QRS width from noise-free ECG signal through the preprocessing method. Also, we classified arrhythmia in realtime by converting threshold variability of feature to binary code. PVC, PAC, Normal, BBB, Paced beat classification is evaluated by using 39 record of MIT-BIH arrhythmia database. The achieved scores indicate the average of 97.18%, 94.14%, 99.83%, 92.77%, 97.48% in PVC, PAC, Normal, BBB, Paced beat classification.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.10a
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• pp.748-753
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• 2005

Among processes to manufacture parts from footwear materials like upper leathers, one of the most essential processes is the cutting one optimally arranging lots of parts on raw footwear materials and cutting. A new nesting strategy was proposed for the 2-dimensional part layout by using a two-stage approach, where which can be effectively used for water jet cutting. In the initial layout stage, a SOAL(Self-Organization Assisted Layout) based on the combination of FCM(Fuzzy C-Means) and SOM was adopted. In the layout improvement stage, SA(Simulated Annealing) based approach was adopted for a finer layout. The proposed approach saves much CPU time through a two-stage approach scheme, while other annealing-based algorithm so far reported fur a nesting problem are computationally expensive. The proposed nesting approach uses the stochastic process, and has a much higher possibility to obtain a global solution than the deterministic searching technique. We developed the automatic nesting software of NST(ver.1.1) software for footwear industry by implementing of these proposed algorithms. The NST software was applied by the optimized automatic arrangement algorithm to cut without the loss of leathers. if possible, after detecting damage areas. Also, NST software can consider about several features in not only natural loathers but artificial ones. Lastly, the NST software can reduce a required time to implement generation of NC code. cutting time, and waste of raw materials because the NST software automatically performs parts arrangement, cutting paths generation and finally NC code generation, which are needed much effect and time to generate them manually.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.7
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• pp.1694-1704
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• 2015

In this paper, a robust trajectory tracking control method of a wheeled mobile robot is newly proposed combining the PDC and the ISMC. The PDC is a relatively simple and easy control method for nonlinear system compared to the other non-linear control methods. And the ISMC can have robust and stable control characteristics against model uncertainties and disturbances from the initial time by placing the states on the sliding plane with desired nominal dynamics. Therefore, the proposed PDC+ISMC trajectory tracking control method shows robust trajectory tracking performance in spite of external disturbance. The tracking performance of the proposed method is verified through simulations. Even though the disturbance increases, the proposed method keeps the performance of the PDC method when there is no disturbance. However, the PDC trajectory tracking control method has increasing tracking error unlike the proposed method when the disturbance increases.

• Smart Structures and Systems
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• v.19 no.4
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• pp.341-350
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• 2017

Based on the super elastic properties of the shape memory alloy (SMA) and the inverse piezoelectric effect of piezoelectric (PZT) ceramics, a kind of hybrid semi-active control device was designed and made, its mechanical properties test was done under different frequency and different voltage. The local search ability of genetic algorithm is poor, which would fall into the defect of prematurity easily. A kind of adaptive immune memory cloning algorithm(AIMCA) was proposed based on the simulation of clone selection and immune memory process. It can adjust the mutation probability and clone scale adaptively through the way of introducing memory cell and antibody incentive degrees. And performance indicator based on the modal controllable degree was taken as antigen-antibody affinity function, the optimization analysis of damper layout in a space truss structure was done. The structural seismic response was analyzed by applying the neural network prediction model and T-S fuzzy logic. Results show that SMA and PZT friction composite damper has a good energy dissipation capacity and stable performance, the bigger voltage, the better energy dissipation ability. Compared with genetic algorithm, the adaptive immune memory clone algorithm overcomes the problem of prematurity effectively. Besides, it has stronger global searching ability, better population diversity and faster convergence speed, makes the damper has a better arrangement position in structural dampers optimization leading to the better damping effect.

• Proceedings of the KIEE Conference
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• 2006.07e
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• pp.57-58
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• 2006

과거 2003년 북미 대 정전 이후 전력기기의 사고 발생 후 얼마나 빨리 사고를 제거하고 피해가 적도록 신속하게 복구하는 개념에서 사고이전에 사고를 미연에 방지하는 예방개념으로 관심이 높아지고 있다. 전력기기를 사고로부터 보호하는 보호기기도 중요하지만 사고이전의 상태를 감시하여 미연에 사고를 방지할 수 있도록 하는 예방진단시스템의 중요성도 높아지고 있다. 이렇듯 관심이 높아짐에 따라 각종 진단알고리즘의 개발이 신속히 이루어지고 있다. 보호기기처럼 어떤 설정된 정정 값 이상의 값이 입력되면 보호동작을 수행하는 단순 동작과는 달리 예방진단 시스템은 입력되는 신호의 패턴을 인식하여 열화/노화 등의 진행상황 및 정비조치에 대한 정보를 만들므로 인공지능적인 요소가 많이 적용되고 있다. 따라서 각종 Fuzzy, Neural Network, Expert 등 각종 판단 알고리즘과 패턴을 인식하는 확률통계, 프랙탈 기하학 등이 적용되고 있다. 모두가 틀리다는 것은 아니지만 보다 정확한 예방진단을 위해 각종 알고리즘이 추가 및 수정이 자주이루어지고 있는 실정이다. 그러나 새로운 알고리즘을 적용하기 위해서 기 개발되어 운영 중이거나 설치된 예방진단시스템을 멈추고 전반적으로 수정을 수행하는 것은 감시진단시스템의 본래 모습을 무시하는 행동이라고 할 수 있다. 본 연구에서는 이런 문제를 해결하기 위하여 온라인 상태에서 장비를 감시하는 예방진단 시스템의 알고리즘 변형 시 시스템의 운영이 문제되지 않도록하는 다이나믹 인터페이스를 개발하였다.

• Journal of Korean Society of Transportation
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• v.26 no.2
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• pp.121-133
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• 2008

Conventional car following models are controlled when the velocity of following vehicle is equal to preceding vehicle without consideration of relative distance. Also, since the car following models are hardly consider the driver's behaviors and the environmental factors in driving, the models can't be adopted in reality. Hence, we developed the car following model applying Human Factors to consider driver's safety and comfortness. We simulated to compare the suggested model with the existing model, GGM(General GM). As results of simulations, the GGM model followed the preceding vehicle when the velocity of following vehicle was equal to preceding vehicle without relation of relative range. The other side, when the relative range was less or over than safety range, the suggested model made the relative range equal to safety range. Accordingly, we could be sure that the model would decrease the driver's discomfort and intensify the safety on driving without unnecessary waste of road. We identified that the suggested model is more realistic than the conventional GGM model.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.4
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• pp.825-832
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• 2014

Previous works for detecting arrhythmia have mostly used nonlinear method such as artificial neural network, fuzzy theory, support vector machine to increase classification accuracy. Most methods require accurate detection of P-QRS-T point, higher computational cost and larger processing time. Even if some methods have the advantage in low complexity, but they generally suffer form low sensitivity. Also, it is difficult to detect PVC accurately because of the various QRS pattern by person's individual difference. Therefore it is necessary to design an efficient algorithm that classifies PVC based on QRS pattern in realtime and decreases computational cost by extracting minimal feature. In this paper, we propose PVC classification based on QRS pattern using QS interval and R wave amplitude. For this purpose, we detected R wave, RR interval, QRS pattern from noise-free ECG signal through the preprocessing method. Also, we classified PVC in realtime through QS interval and R wave amplitude. The performance of R wave detection, PVC classification is evaluated by using 9 record of MIT-BIH arrhythmia database that included over 30 PVC. The achieved scores indicate the average of 99.02% in R wave detection and the rate of 93.72% in PVC classification.