• Clinics in Shoulder and Elbow
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• 제24권4호
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• pp.209-214
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• 2021

Background: The treatment for acromioclavicular joint injuries (ACJI) ranges from a conservative approach to extensive surgical reconstruction, and the decision on how to manage these injuries depends on the grade of acromioclavicular (AC) joint separation, resources, and skill availability. After a thorough review of the literature, the researchers adopted a simple cost-effective technique of AC joint reconstruction for acute ACJI requiring surgery. Methods: This was a prospective single-center study conducted between April 2017 and April 2018. For patients with acute ACJI more than Rockwood grade 3, the researchers performed open coracoclavicular ligament reconstruction using synthetic sutures along with an Endobutton and a figure of 8 button plate. This was followed by AC ligament repair augmenting it with temporary percutaneous AC K-wires. Clinical outcomes were evaluated using the Constant Murley shoulder score. Results: Seventeen patients underwent surgery. The immediate postoperative radiograph showed an anatomical reduction of the AC joint dislocation in all patients. During follow-up, one patient developed subluxation but was asymptomatic. The mean follow-up period was 30 months (range, 24-35 months). The mean Constant score at 24 months was 95. No AC joint degeneration was noted in follow-up X-rays. The follow-up X-rays showed significant infra-clavicular calcification in 11 of the 17 patients, which was an evidence of a healed coracoclavicular ligament post-surgery. Conclusions: This study presents a simple cost-effective technique with a short learning curve for anatomic reconstruction of acute ACJI. The preliminary results have been very encouraging.

• 한국게임학회 논문지
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• 제17권5호
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• pp.133-142
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• 2017

최근 스크린 클라이밍용 콘텐츠로 클라이밍 학습 프로그램과 스크린 클라이밍 게임이 등장하였으며, 특히 스크린 클라이밍 게임에 대한 연구가 활발히 진행되고 있다. 본 논문에서는 스크린 클라이밍 콘텐츠 구현의 핵심 기술인 자세 인식 성능의 개선을 위하여 등반자의 신체영역을 기반으로 하는 스켈레톤 보정 방법을 제안한다. 스켈레톤 보정 과정은 비정상적인 스켈레톤 정보를 걸러내는 스켈레톤 프레임 안정화와 신체 영역을 관절부위별로 나누어 각 관절부위의 중점을 보정위치로 하는 신체영역 기반 스켈레톤 수정 과정으로 이루어진다. 이렇게 보정한 스켈레톤 정보는 클라이밍 콘텐츠에서 등반자의 자세가 이상적인 자세와 얼마나 유사한지 판단하는 데 사용될 수 있다.

• 제어로봇시스템학회논문지
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• 제6권8호
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• pp.710-718
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• 2000

In this paper we described an approach to automation of visual inspection of solder joint defects of SMC(Surface Mounted Components) on PCBs(Printed Circuit Board) by using neural network and fuzzy rule-based classification method. Inherently the surface of the solder joints is curved tiny and specular reflective it induces difficulty of taking good image of the solder joints. And the shape of the solder joints tends to greatly vary with the soldering condition and the shapes are not identical to each other even though the solder joints belong to a set of the same soldering quality. This problem makes it difficult to classify the solder joints according to their qualities. Neural network and fuzzy rule-based classification method is proposed to effi-ciently make human-like classification criteria of the solder joint shapes. The performance of the proposed approach is tested on numerous samples of commercial computer PCB boards and compared with the results of the human inspector performance and the conventional Kohonen network.

• 방송공학회논문지
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• 제28권1호
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• pp.141-144
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• 2023

최근에 심층신경망(DNN)을 활용하여 채널 추정, 채널 양자화, 피드백, 프리코딩 과정을 통합하여 모델링하는 연구가 진행되었다. 해당연구는 기존에 이론적으로 어렵던 통합 최적화를 deep learning (DL)을 기반으로 수행하여 기존의 실제 codebook을 활용하는 프리코딩기법에 비해 높은 잠재력이 있음을 보였다. 하지만 기존의 기법은 랜덤하게 정해진 소수의 사용자만을 대상으로하며, 기존의 기법과 다르게 스케줄링이 포함된 환경에는 적응이 어렵다. 따라서 본 연구에서는 심층신경망기반의 프리코딩기법이 활용가능한 스케줄링 방식을 연구하여 기존의 결과와 비교한다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 22-24 Oct. 1991
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• pp.1022-1027
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• 1991

A new method to control a robot manipulator by neural networks is proposed. The controller is composed of both a PD controller and a neural network-based feedforward controller. MLP(multi-layer perceptron) neural network is used for the feedforward controller and trained by BP(back-propagation) learning rule. Error terms for BP learning rule are composed of the outputs of a PD controller and the acceleration errors of manipulator joints. We compare the proposed method with existing ones and contrast performances of them by simulation. Also, We discuss the real application of the proposed method in consideration of the learning time of the neural network and the time required for sensing the joint acceleration.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1988년도 한국자동제어학술회의논문집(국내학술편); 한국전력공사연수원, 서울; 21-22 Oct. 1988
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• pp.58-62
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• 1988

Recently many adaptive control schemes for the industrial robot manipulator have been developed. Especially, learning control utilizing the repetitive motion of robot and based on iterative signal synthesis attracts much interests. However, since most of these approaches excludes the boundness of the input torque supplied to the manipulator, its effectiveness may be limited and also the full dynamic capacity of the robot manipulator can not be utilized. To overcome the above-mentioned difficulties and meet the desired performance, we propose an approach which yields the effective learning control schemes in this paper. In this study, some stability conditions derived from applying the Lyapunov theory to the discrete linear time-varying dynamic system are established and also an optimization scheme considering the bounded input torque is introduced. These results are simulated on a digital computer using a three-joint revolute manipulator to show their effectiveness.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1995년도 Proceedings of the Korea Automation Control Conference, 10th (KACC); Seoul, Korea; 23-25 Oct. 1995
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• pp.65-68
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• 1995

This paper describes trajectory generation of a riobot arm by self-organizing neural networks. These neural networks are based on competitive learning without a teacher and this algorithm which is suitable for problems in which solutions as teaching signal cannot be defined-e.g. inverse dynamics analysis-is adopted to the trajectory generation problem of a robot arm. Utility of unsupervised learning algorithm is confirmed by applying the approximated solution of each joint calculated through learning to an actual robot arm in giving the experiment of tracking for reference trajectory.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2008년도 추계학술대회A
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• pp.745-750
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• 2008

Uncertain dynamic parameters and joint flexibility have been problem to control robot manipulator precisely. Hence, even if the controller tracks the desired trajectory well with the feedback of the motor encoders, it is hard to achieve the desired behavior at the end-effector. In this paper, robot trajectory is taught by a general heuristic iterative learning control (ILC) algorithm in order to reduce tracking error of the tool center point (TCP) and the results of tracking with 6 DOF industrial robot manipulator are presented. The performance is verified based on ISO 9283.

• 대한음성학회:학술대회논문집
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• 대한음성학회 2000년도 7월 학술대회지
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• pp.7-16
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• 2000

It is a great honour for me to speak to you today on the Korean's problems in learning English pronunciation. First of all I would like to thank Prof. H. B. Lee, President of the Phonetic Society of Korea for calling upon me to make a keynote speech at this International Conference on Phonetic Sciences. The year before last when the 1 st Joint Summit on English Phonetics was held at Aichi Gakuin University in Japan, the warm hospitality given to me and my colleagues by the English Phonetic Society of Japan was so great that I would like to take this opportunity to express my sincere gratitude to the members of the English Phonetic Society of Japan and especially to Prof. Masaki Tsuzuki, President of the Society. Korean learners of English have a lot of problems in learning English pronunciation. Some vowel problems seem to be shared by Japanese learners but other problems, especially in consonants, are peculiar to Koreans owing to the nature of phonological rules peculiar to the Korean language. Of course, there are other important problems like speech rhythm and intonation besides vowels and consonants. But they will not be included here because of limited time.

• 전자공학회논문지B
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• 제32B권9호
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• pp.1137-1153
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• 1995

It is not yet possible to solve the optimal number of neurons in hidden layer at neural networks. However, it has been proposed and proved by experiments that there is a limit in increasing the number of neuron in hidden layer, because too much incrememt will cause instability,local minima and large error. This paper proposes a module neural controller with pattern recognition ability to solve the above trade-off problems and to obtain fast learning convergence speed. The proposed neural controller is composed of several module having Multi-layer Perrceptron(MLP). Each module have the less neurons in hidden layer, because it learns only input patterns having a similar learning directions. Experiments with six joint robot manipulator have shown the effectiveness and the feasibility of the proposed the parallel module neural controller with pattern recognition perceptron.