• Clinics in Shoulder and Elbow
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• v.24 no.4
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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.

• Journal of Korea Game Society
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• v.17 no.5
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• pp.133-142
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• 2017

Recently, screen climbing contents such as sports climbing learning program and screen climbing games. Especially, there are many researches on screen climbing games. In this paper, we propose the skeleton correction method based on the body area of a climber to improve the posture recognition accuracy. The correction method consists of the modified skeletal frame normalization with abnormal skeleton joint filtering, the classification of body area into joint parts, and the final skeleton joint correction. The skeletal information obtained by the proposed method can be used to compare the climber's posture and the ideal climbing posture.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.8
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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.

• Journal of Broadcast Engineering
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• v.28 no.1
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• pp.141-144
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• 2023

Recently, a joint channel estimation, channel quantization, feedback, and precoding system based on deep-neural network (DNN) was proposed. The corresponding system achieved a joint optimization based on deep learning such that it achieved a higher sum rate than the existing codebook-based precoding systems. However, this DNN-based procoding system is not directly applicable for the environments with many users such that a specific user selection can potentially increase the sum rate of the system. Thus, in this letter, we study an appropriate user selection method suitable for DNN-based precoding.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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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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• 1988.10a
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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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• 1995.10a
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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.

• Proceedings of the KSME Conference
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• 2008.11a
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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.

• Proceedings of the KSPS conference
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• 2000.07a
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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.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.9
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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.