• ETRI Journal
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• 제42권3호
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• pp.411-419
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• 2020

In real-world intelligent transportation systems, accuracy in vehicle license plate detection and recognition is considered quite critical. Many algorithms have been proposed for still images, but their accuracy on actual videos is not satisfactory. This stems from several problematic conditions in videos, such as vehicle motion blur, variety in viewpoints, outliers, and the lack of publicly available video datasets. In this study, we focus on these challenges and propose a license plate detection and recognition scheme for videos based on a temporal matching prior network. Specifically, to improve the robustness of detection and recognition accuracy in the presence of motion blur and outliers, forward and bidirectional matching priors between consecutive frames are properly combined with layer structures specifically designed for plate detection. We also built our own video dataset for the deep training of the proposed network. During network training, we perform data augmentation based on image rotation to increase robustness regarding the various viewpoints in videos.

• Nuclear Engineering and Technology
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• 제33권5호
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• pp.457-467
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• 2001

It is well known that the performance of a fuzzy neural network strongly depends on the input features selected for its training. In its applications to sensor signal estimation, there are a large number of input variables related with an output As the number of input variables increases, the training time of fuzzy neural networks required increases exponentially. Thus, it is essential to reduce the number of inputs to a fuzzy neural network and to select the optimum number of mutually independent inputs that are able to clearly define the input-output mapping. In this work, principal component analysis (PCA), genetic algorithms (CA) and probability theory are combined to select new important input features. A proposed feature selection method is applied to the signal estimation of the steam generator water level, the hot-leg flowrate, the pressurizer water level and the pressurizer pressure sensors in pressurized water reactors and compared with other input feature selection methods.

• 한국컴퓨터정보학회논문지
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• 제22권7호
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• pp.31-37
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• 2017

There are lots of combined battlefield elements which complete the war. It looks problematic when collecting and analyzing these elements and then predicting the situation of war. Commander's experience and military power assessment have widely been used to come up with these problems, then simulated combat training program recently supplements the war-game models through recording real-time simulated combat data. Nevertheless, there are challenges to assess winning factors of combat. In this paper, we characterize the combat element (ce) by clustering simulated combat data, and then suggest multi-layered artificial neural network (ANN) model, which can comprehend non-linear, cross-connected effects among ces to assess mission completion degree (MCD). Through our ANN model, we have the chance of analyzing and predicting winning factors. Experimental results show that our ANN model can explain MCDs through networking ces which overperform multiple linear regression model. Moreover, sensitivity analysis of ces will be the basis of predicting combat situation.

• Journal of Welding and Joining
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• 제32권2호
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• pp.43-47
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• 2014

Famous artificial neural network (ANN) is applied to predict proper process window of arc welding. Target weldment is variously combined lap joint fillet welding of automotive steel plates. ANN's system variable such as number of hidden layers, perceptrons and transfer function are carefully selected through case by case test. Input variables are welding condition and steel plate combination, for example, welding machine type, shield gas composition, current, speed and strength, thickness of base material. The number of each input variable referred in welding experiment is counted and provided to make it possible to presume the qualitative precision and limit of prediction. One of experimental process windows is excluded for predictability estimation and the rest are applied for neural network training. As expected from basic ANN theory, experimental condition composed of frequently referred input variables showed relatively more precise prediction while rarely referred set showed poorer result. As conclusion, application of ANN to arc welding process window derivation showed comparatively practical feasibility while it still needs more training for higher precision.

• Journal of Power Electronics
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• 제11권6호
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• pp.922-930
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• 2011

In this paper, two radial basis function neural networks (RBFNNs) are used to dynamically identify harmonics content in converter waveforms based on the p-q (real power-imaginary power) theory. The converter waveforms are analyzed and the types of harmonic content are identified over a wide operating range. Constant power and sinusoidal current compensation strategies are investigated in this paper. The RBFNN filtering training algorithm is based on a systematic and computationally efficient training method called the hybrid learning method. In this new methodology, the RBFNN is combined with the p-q theory to extract the harmonics content in converter waveforms. The small size and the robustness of the resulting network models reflect the effectiveness of the algorithm. The analysis is verified using MATLAB simulations.

• 한국정보처리학회논문지
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• 제3권3호
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• pp.429-438
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• 1996

본 논문에서는 주어진 문제를 해결하기 위해 사용될 최적의 다충 퍼센트론을 구성 하기 위한 하나의 점진적 학습 방법을 제시한다. 고정된 크기의 트레이닝 패턴 집합을 반복적으로 사용하는 기존의 알고리즘들과는 달리, 제시되는 방법에서는 학습 패턴의 수를 점차 증가시키면서 전체 데이터를 학습하기 위해 필요하고도 충분한 은닉뉴런의 수를 찾는다. 이와 같이 신경망 크기의 최적화에 학습 패턴을 점차적으로 선택하여 늘려나감으로써 일반화 능력과 학습 속도가 기존의 방법에서보다 향상됨을 필기체 숫자인식 문제에 있어서 실험적으로 보여준다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권3호
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• pp.754-774
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• 2023

Wrestling is one of the popular events for modern sports. It is difficult to quantitatively describe a wrestling game between athletes. And deep learning can help wrestling training by human recognition techniques. Based on the characteristics of latest wrestling competition rules and human recognition technologies, a set of wrestling competition video analysis and retrieval system is proposed. This system uses a combination of literature method, observation method, interview method and mathematical statistics to conduct statistics, analysis, research and discussion on the application of technology. Combined the system application in targeted movement technology. A deep learning-based facial recognition psychological feature analysis method for the training and competition of classical wrestling after the implementation of the new rules is proposed. The experimental results of this paper showed that the proportion of natural emotions of male and female wrestlers was about 50%, indicating that the wrestler's mentality was relatively stable before the intense physical confrontation, and the test of the system also proved the stability of the system.

• The Korean Journal of Physiology and Pharmacology
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• 제4권2호
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• pp.91-97
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• 2000

The purpose of the present study was to determine the preventive effects of combined interventional trial of fish oil treatment and exercise training on insulin resistance of skeletal muscle in high-fat fed rats. Male Wistar rats were randomly divided into chow diet (CD), high-fat diet (HF), high-fat diet with fish oil (FO), high-fat diet with exercise training (EX), and FO+EX groups. The rats in control group were fed chow diet containing, as percents of calories, 58.9% carbohydrate, 12.4% fat, and 28.7% protein. High-fat diet provided 32% energy as lard, 18% as corn oil, 27% as carbohydrate and 23% as casein. The fish oil diet had the same composition as the high fat diet except that 100 g menhaden oil was substituted for corn oil. Insulin sensitivity was assessed by in vitro glucose transport in the soleus muscle after diet treatment and treadmill running for 4 weeks. While the FO or EX only partially prevented insulin resistance on glucose transport and visceral obesity induced by high-fat diet, these interventions completely corrected hyperinsulinemia and hyperglycemia from the high-fat diet. The rats in the FO+EX showed normalized insulin action on glucose transport, plasma chemicals and visceral fat mass. Insulin-mediated glucose transport was negatively associated with total visceral fat mass (r=－0.734; p＜0.000), plasma triglyceride (r=－0.403; p＜0.05) and lepin (r=－0.583; p＜0.001) concentrations with significance. Multiple stepwise regression analysis showed that only total visceral fat mass was independently associated with insulin-mediated glucose transport (r=－0.668; p＜0.000). In conclusion, combined interventional trial of FO+EX recovered insulin resistance on glucose transport of skeletal muscle induced by high-fat diet. Visceral fat mass might be more important factor than plasma TG and leptin to induce insulin resistance on glucose transport of skeletal muscle in high-fat fed rats.

• 디지털융복합연구
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• 제14권4호
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• pp.471-478
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• 2016

본 연구의 목적은 12주간 복합운동이 비만 여대생의 신체조성 및 기초체력 향상에 미치는 영향을 규명하는데 있다. 본 연구의 취지를 충분히 설명하고 자발적 참여의 동의서를 작성한 D대학교의 여대생 12명이었으며, 과거병력과 현재 특별한 질환이 없고, 규칙적인 운동경험이 없는 자들로 구성하였다. 이들은 실험 전 신체조성검사 및 기초체력검사인 좌 우악력, 배근력, 유연성, 순발력, 근지구력을 측정하고, 12주간 복합운동 실시 후 평균 표준편차를 산출하기 위하여 기술통계를 실시하였으며, 실험 전, 후 차이 검증은 paired t-test를 이용하여 분석하였다. 통계적 유의수준은 p<.05로 설정하여 다음과 같은 결론을 얻었다. 12주간 복합트레이닝 운동집단에서 참여 후 체중, 골격근, 체지방량 및 좌 우악력 및 배근력, 순발력은 통계적으로 유의(p.<05)한 감소와 증가를 보였고, 유연성은 약간의 증가를 보였으나 통계적으로 유의한 수준의 변화를 보이지 않았으며, 통제집단은 유의한 변화가 없었다. 이상을 종합해 보면 12주간 복합운동은 여대생의 근육을 증가시켜 기초대사량이 증가하여 체중 및 체지방을 감소시켜 건강관련 기초체력 향상에 의미 있는 운동프로그램이 될 수 있을 것이라고 사료된다.

• 융합정보논문지
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• 제9권10호
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• pp.234-243
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• 2019

본 연구는 호흡저항이 병행된 전신진동자극 훈련이 뇌졸중환자의 호흡기능 및 균형기능에 미치는 영향을 알아보기 위하여 실시하였다. 만성 뇌졸중환자 17명을 호흡저항이 병행된 전신진동자극 훈련을 적용한 실험군(n=8)과 일반 진동운동프로그램을 적용한 대조군(n=9)으로 무작위 배정하였다. 중재는 30분간 1일 1회 주 3회, 4주간 실시하였다. 중재 전후 폐활량과 호흡근력 그리고 동적/정적 균형능력을 측정하여 비교하였다. 실험군에서 정 동적 균형능력, 폐활량과 호흡근력의 전 후 비교 결과 유의한 향상을 보였다(p<.05). 그리고 실험군과 대조군을 비교하였을 때 동적 균형능력, 폐활량과 호흡근력의 결과값에서 유의한 차이를 보였다(p<.05).실험군의 폐활량과 호흡근력 또한 실험전후 유의한 향상을 보였으며, 대조군에 비해 유의한 변화량의 차이를 나타내었다(p<.05). 본 연구결과를 통하여 호흡저항이 병행된 전신진동자극훈련은 뇌졸중환자의 호흡기능 및 균형능력 향상 프로그램으로 유익할 것으로 사료된다.