• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.9
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• pp.36-43
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• 2019

Turning, a main machining process, is a widespread process in metal cutting industries. Many researchers have investigated the effects of process parameters on the machining process. In the turning process, input variables including cutting speed, feed, and depth of cut are generally used. Surface roughness and electric current consumption are used as output variables in this study. We construct a simulation model for the turning process using a neural network, which predicts the output values based on input values. In the neural network, obtaining the appropriate set of weights, which is called training, is crucial. In general, back propagation (BP) is widely used for training. In this study, techniques such as ant colony optimization (ACO) and particle swarm optimization (PSO) as well as BP were used to obtain the weights in the neural network. Particularly, two combined techniques of ACO_BP and PSO_BP were utilized for training the neural network. Finally, the performances of the two techniques are compared with each other.

• Proceedings of the KIEE Conference
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• 2005.11a
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• pp.9-11
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• 2005

본 연구는 GIS 진단신뢰성 향상기술 개발을 목적으로, 16개의 인위적 결함을 이용하여 부분방전 신호를 발생시키고 검출하여 그 패턴인식 확률을 높이기 위하여 신경망에 Genetic Algorithm (GA) 을 적용하였다. 이를 위하여 다음과 같은 5가지 서로 다른 신경망 모델을 선택하였다: Back Propagation (BP), Jordan-Elman Network (JEN), Principal Component Analysis (PCA), Self-Organizing Feature Map (SOFM) 및 Support Vector Machine (SVM). 이와 같이 선택된 모델에 동일한 데이터를 학습 시키고 패턴인식 확률을 비교 및 분석하였다. 실험 결과에 의하면, BP의 인식률이 가장 높고 다음으로 JEN의 인식률이 높이 나타났으며, 후자의 경우 모든 결함에 대하여 정확한 패턴분류를 한 반면에 전자의 경우 1.8% 의 분류 오차가 발생하였다. 따라서 인식률이 높은 신경망이 더 정확한 패턴분류를 보장하지 못한다는 실험적 결과를 고려 할 때, 인식률이 높은 두 개의 모델을 선정하여 각각의 출력에 일정한 가중치를 주고 합산하여 새로운 출력을 얻는 방법을 제안한다.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10a
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• pp.181-183
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• 2003

최근 대용량의 데이터베이스로부터 유용한 정보를 발견하고 데이터간에 존재하는 연관성을 탐색하고 분석하는 데이터 마이닝에 관한 많은 연구들이 진행되고 있다. 다양한 생체 신호를 분석하기 위하여 데이터 마이닝 기법을 이용할 수 있다. 본 논문에서는 심전도 신호의 패턴을 분류하기 위하여 신경망 기법을 적용하였다. 최근 패턴분류에 있어서 각광을 받고 있는 SVM 모델은 학습과정에서 얻어진 확률분포를 이용하여 의사결정함수를 추정한 후 이 함수에 따라 새로운 데이터를 이원분류 하는 것으로 분류 문제에 있어서 일반화 기능이 매우 높다. 기존에 많이 이용되던 BP 모델과 비교평가 하였다.

• Journal of the Institute of Convergence Signal Processing
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• v.22 no.2
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• pp.51-56
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• 2021

The brachial systolic blood pressure and pulse pressure are the predictors of cardiovascular disease in individuals over 50 years of age. As the stiffness increases, the reflex amplitude and pressure in the late systole increase, resulting in an increase in left ventricular load and myocardial oxygen demand. Therefore, it is necessary to study how stiffness affects blood pressure. In this study, the blood pressure pulse waves were measured before and after taking the drug, and the blood pressure pulse wave was measured before and after myocardial heart transplantation in patients with heart failure. The correlation between R, L, and C components of the Windkessel model was estimated by increasing blood pressure. As a result of modeling the parameters of the Windkessel model using the curve fitting method, the increase in blood pressure and decrease in systolic rise time were due to the increase in the L component in the RLC Windkessel model. Among the various mechanical characteristics of blood vessels, the most important parameter affecting high BP waveform is the inertance.

• Tunnel and Underground Space
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• v.17 no.6
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• pp.438-447
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• 2007

This technical report is to introduce the research on SRM (Synthetic Rock Mass) which was presented in 2007 ISRM Congress at Lisbon by Prof, Fairhurst who speak with emphasis on its importance and potential in rock engineering. The Synthetic Rock Mass approach to jointed rock mass characterization (Pierce et al. 2007) is reviewed relative to existing empirical approaches and current understanding of jointed rock mass behaviour. The review illustrates how the key factors affecting the mechanical behaviour of jointed rock masses may be considered and demonstrates that the SRM approach constitutes a significant step forward in this field. This technique, based on two well-established methods, Bonded Particle Modelling in PFC-3D (Potyondy and Cundall, 2004) and Discrete Fracture Network simulation, employs a new sliding joint model that allows for large rock volumes containing thousands of pre-existing joints to be subjected to any non-trivial stress path. Output from SRM testing includes rock mass brittleness and strength, evolution of the full compliance matrix and primary fragmentation.

• Journal of Acupuncture Research
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• v.22 no.4
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• pp.55-63
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• 2005

목적 : 만성 염증성 질환에 대한 전침효과를 알아보기 위해 complete Freund's adjuvant (CA) 유발 관절염 모델에서 염증관련 단백질의 변화를 살펴보았다. 방법 : Sprague-Dawley계 흰쥐의 족부에 CFA를 주사한 다음 3일 간격으로 2 Hz, 15 Hz 및 120 Hz 전침 자극을 주며 부종 형성여부를 plethysmometer로 측정하여 판정하였으며 30일 째 거퇴관절을 취하여 4% paraformaldehyde에 고정하고 EDTA용액에서 탈회시켜 파라핀연속 절편을 얻어 $NF-{\kappa}B$를 비롯한 5종의 염증관련 단백질의 발현을 면역조직화학적으로 살펴보았다. 결과 : 관절연골내 면역반응 중 연골기질은 반응이 없거나 약하고 연골세포는 $NF-{\kappa}Bp65,\;I-{\kappa}B{\alpha},\;iNOS$반응이 강하며 특히 유리연골층에서 더 현저하였으나 염증 및 전침자극에 따른 변화는 없었다. 관절낭에서 면역반응을 살펴보면 염증유발시 활액세포의 면역반응세포는 $I-{\kappa}B{\alpha}$가 감소한 반면 iNOS, $IL-1{\beta}$는 증가하며 특히 iNOS 증가가 현저하였으며 전침자극에 의해 iNOS가 감소하였다. 활액막조직에서 모든 면역반응이 증가하며 특히 $NF-{\kappa}Bp65,\;I-{\kappa}B{\alpha},\;iNOS$ 반응이 현저한데 전침자극에 의해 $IL-1{\beta}$를 제외한 모든 반응이 감소하였다. 결론 : 만성 염증성 동물모델의 거퇴관절 내 염증관련 단백질은 관절연골보다 관절낭에서 큰 변화를 보이며 전침처치에 의해 이들 단백질 발현이 억제되는 것으로 보아 전침이 만성 염증성 질환에 효과적임을 알 수 있다.

• Advanced Industrial SCIence
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• v.3 no.2
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• pp.8-16
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• 2024

This paper shows the system of drug classification, the goal of this is to foretell the apt drug for the patients based on their demographic and physiological traits. The dataset consists of various attributes like Age, Sex, BP (Blood Pressure), Cholesterol Level, and Na_to_K (Sodium to Potassium ratio), with the objective to determine the kind of drug being given. The models used in this paper are K-Nearest Neighbors (KNN), Logistic Regression and Random Forest. Further to fine-tune hyper parameters using 5-fold cross-validation, GridSearchCV was used and each model was trained and tested on the dataset. To assess the performance of each model both with and without hyper parameter tuning evaluation metrics like accuracy, confusion matrices, and classification reports were used and the accuracy of the models without GridSearchCV was 0.7, 0.875, 0.975 and with GridSearchCV was 0.75, 1.0, 0.975. According to GridSearchCV Logistic Regression is the most suitable model for drug classification among the three-model used followed by the K-Nearest Neighbors. Also, Na_to_K is an essential feature in predicting the outcome.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.7
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• pp.1513-1517
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• 2005

In this paper, I make use of a Multi-Channel skin color model with Hue, Cb, Cg using Red, Blue, Green channel altogether which remove bight component as being consider the characteristics of skin color to do modeling more effective to a facial skin color for extracting a facial area. 1 used efficient HOLA(Higher order local autocorrelation function) using 26 feature vectors to obtain both feature vectors of a facial area and the edge image extraction using Harr wavelet in image which split a facial area. Calculated feature vectors are used of date for the facial recognition through learning of neural network It demonstrate improvement in both the recognition rate and speed by proposed algorithm through simulation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.5 no.1
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• pp.1-6
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• 2004

Air qualify monitoring if a primary activity for industrial and social environment. Especially, the VOCs(Volatile Organic Compounds) are very harmful for human and environment. Throughout this research. we designed sensor array with various kinds of gas sensor, and the recognition algorithm with ANN(Artificial Neural Network : BP), respectively. We have designed system to recognize various kinds and quantities of VOCs, such as benzene, tolylene, and xylene.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.11
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• pp.1186-1193
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• 1991

This paper proposed a trajectory controlmethod for a robot manipulator by using neural networks. The total torque for a manipulator is a sum of the linear feedback controller torque and the neural network feedfoward controller torque. The proposed neural network is a multilayer neural network with time delay elements, and learns the inverse dynamics of manipulator by means of PD(propotional denvative)controller error torque. The error backpropagation (BP) learning neural network controller does not directly require manipulator dynamics information. Instead, it learns the information by training and stores the information and connection weights. The control effects of the proposed system are verified by computer simulation.