• The Journal of Korean Medicine
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• v.42 no.4
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• pp.40-60
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• 2021

Objectives: This study aimed to evaluate the impact of a training course on pharmacovigilance for future doctors of Korean medicine (DKM). Methods: In 2020, a pharmacovigilance training course was conducted for 57 senior students of a Korean medicine (KM) college, and its impact to the students were assessed in terms of the knowledge, attitudes, and perceptions of these students at three-time points: pre-training, post-training, and 4-6 months after the end of the training. Results: A total of 38 students completed the survey. The average score from the knowledge test increased significantly after training compared to prior to the training (5.47±2.140, 6.61±1.001, respectively, p<0.001) and was maintained until the final survey (6.61±1.220). The rate of correct answers to most of the knowledge test questions increased after the training but decreased in the final survey. In terms of attitudes, self-confidence in causality assessment (2.63±1.025, 4.58±0.826, p<0.001) and spontaneous reporting (2.08±1.050, 4.74±0.446, p<0.001) significantly increased after the training and then slightly decreased (3.92±1.171, 4.40±0.755). The perception level was high prior to the training, and this pattern was maintained throughout the study period. Students responded that pharmacovigilance education was necessary for DKM after training, and for the undergraduates of KM colleges. Conclusions: This study shows that this pharmacovigilance training course is effective for students majoring in KM but that retraining is required at least 6 months after the initial training. Further follow-up studies are needed to ensure that students actively participate in spontaneous reporting after graduation, and continuous education should be provided to graduates.

• Journal of the Korea Society of Computer and Information
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• v.10 no.5 s.37
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• pp.95-102
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• 2005

A Parallel Processing model by considering a spatiotemporal parallelism is presented for the training procedure of the MLP neural network. We tried to design the flexible Parallel Processing model by simultaneously applying both of the training-set decomposition for a temporal parallelism and the network decomposition for a spatial parallelism. The analytical Performance evaluation model shows that when the problem size is extremely large, the speedup of each implementation depends, in the extreme, on whether the problem size is pattern-size intensive or pattern-quantify intensive.

• International Journal of Industrial Entomology and Biomaterials
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• v.7 no.1
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• pp.69-73
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• 2003

Specific leaf weight (SLW), defined as the mass of tissue per unit leaf area has been found to be an important physiological parameter as it indicates the relative thickness of leaves. Greater SLW provides more photosynthetic potential per unit area of leaf and hence it is frequently been considered as correlated with photosynthesis in several plant species. Collections of 165 mulberry (Morus sp.) germplasm accessions, both Indian and exotic in origin were evaluated for their variability with respect to SLW. The mean specific leaf weight ranged from 35.3 to $72.3 g/m^{-2}$. The distribution of SLW was found to be normal. High heritability (97.08%) and a small difference between genotypic and phenotypic variance demonstrates the genetic control over SLW. Significant heterotic effect with respect to SLW was observed in crosses when parents with high and low SLW were chosen.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.4 no.3
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• pp.57-62
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• 2005

Neural networks, which have learning and self-organizing abilities, can be advantageously used in the pattern recognition. Neural network techniques have been widely used in monitoring and diagnosis, and compare favourable with traditional statistical pattern recognition algorithms, heuristic rule-based approaches, and fuzzy logic approaches. In this study the fault diagnosis of the face-milling using the artificial neural network was investigated. After training, the sample which measure load current was monitored by constant output results.

• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.10-13
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• 1988

An isolated-word recognition method using adaptively partitioned multisection codebooks is proposed. Each training utterance was divided into several sections according to its pattern extracted by labeling technique. For each pattern, reference codebooks were generated by clustering the training vectors of the same section. In recognition procedure, input speech was divided into the sections by the same method used in codebook generation procedure, and recognized to the reference word whose codebook represented the smallest average distortion. The proposed method was tested for 100 Korean words and attained recognition rate about 96 percent.

• Structural Engineering and Mechanics
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• v.31 no.2
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• pp.153-162
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• 2009

The reduction of the dynamic response of an offshore structure subjected to wind-generated random ocean waves is of extreme significance in the aspects of serviceability, fatigue life and safety of the structure. In this study, a new neuro-control scheme is applied to the vibration control of a fixed offshore platform under random wave loads to examine the applicability of the proposed method. It is called the Lattice Probabilistic Neural Network (LPNN), as it utilizes lattice pattern of state vectors as the training data of PNN. When control results of the LPNN are compared with those of the NN and PNN, LPNN showed better performance in effectively suppressing the structural responses in a shorter computational time.

• The Transactions of the Korea Information Processing Society
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• v.3 no.3
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• pp.429-438
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• 1996

An incremental learning algorithm is presented that constructs a multilayer perceptron whose size is optimal for solving a given problem. Unlike conventional algorithms in which a fixed size training set is processed repeat-edly, the method uses an increasing number of critical examples to find a necessary and sufficient number of hidden units for learning the entire data. Experimental results in hand- writtern digit recognition shows that the network size optimization combined with incremental pattern selection generalizes significantly better and converges faster than conventional methods.

• Journal of Biomedical Engineering Research
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• v.28 no.5
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• pp.590-600
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• 2007

We performed experimental studies on the muscle activities in the lower limbs for the different movement patterns on an unstable platform. A training system for postural control using an unstable platform that we previously developed was applied for the experiments. This unstable platform provides 360 degrees of movement allowing for training of posture in various directions and provides simultaneous excitations to visual sensory, somatic sensation and vestibular organs. Compare with the stable platform, keeping body balance on the unstable platform requests more effective sensation from vision, vestibular sense and somatic sense. Especially, the somatosensory inputs from the muscle proprioceptors and muscle force are crucial. To study the muscle activities for the different movement patterns and find the best training method for improving the ability of postural control through training and improving the lower extremity muscular strength, fifteen young healthy participants went through trainings and experiments. The participants were instructed to move the center of pressure following the appointed movement pattern while standing on the unstable platform. The electromyographies of the muscles in the lower limbs were recorded and analyzed in the time and the frequency domain. Our experimental results showed the significant differences in muscle activities for the different movement patterns. Especially, the spectral energy of electromyography signals in muscle for the movement pattern in anterior-posterior direction was significantly higher than those occurred in the other patterns. The muscles in the lower leg, especially tibialis anterior and gastrocnemius were more activated compared to the others for controlling the balance of body on the unstable platform. The experimental results suggest that, through the choice of different movement pattern, the training for lower extremity strength could be performed on specific muscles in different intensity. And, the ability of postural control could be improved by the training for lower extremity strength.

• Journal of the Korean Academy of Clinical Electrophysiology
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• v.7 no.1
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• pp.43-48
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• 2009

Purpose : The purpose of this study was to study the effect of rectus abdominal muscle contraction by proprioceptive neuromuscular facilitation trunk stabilization training using extremity simultaneous pattern (PNF trunk stabilization training) and traditional trunk stabilization training methods. Methods : A group of 24 adults male and female, healthy, with no previous medical history nor disability in neuromuscular system and musculoskeletal system was chosen as subjects, and was divided into a control group, a PNF trunk stabilization training group and a traditional trunk stabilization training group. Experiments were performed on the last two groups, 3 times a week for 6 weeks, totaling 18 times. Using a dynamometer, muscle strength and endurance time on trunk flexion were measured before and after each experiment, and surface electromyography in left and right rectus abdominis were measured. Results : following results were obtained; 1. As for the change in the maximal voluntary isometric contraction (MVIC), all subjects in the trunk stabilization training group showed significant difference from those in the control group. 2. As for surface electromyography measurement and the changes in root mean square at the time of trunk flexion, in the left rectus abdominis, PNF trunk stabilization training group showed significant difference from the control group, while in the right rectus abdominis, traditional trunk stabilization training group showed significant difference. Conclusion : To sum up the results, both trunk stabilization training groups showed improvement in the MVIC of abdominal muscle, motor unit action potential activity, but the difference between two trunk stabilization training groups was not significant. Therefore, while trunk stabilization training significantly improved abdominal muscle contraction, but the difference attributable to training methods was found to be insignificant.

• Structural Engineering and Mechanics
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• v.17 no.3_4
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• pp.483-492
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• 2004

This paper presents an efficient algorithm for the estimation of damage location and severity in bridge structures using Probabilistic Neural Network (PNN). Generally, the Back Propagation Neural Network (BPNN)-based damage detection methods need a lot of training patterns for neural network learning process and the optimum architecture of a BPNN is selected by trial and error. In this paper, the PNN instead of the conventional BPNN is used as a pattern classifier. The modal properties of damaged structure are somewhat different from those of undamaged one. The basic idea of proposed algorithm is that the PNN classifies a test pattern which consists of the modal characteristics from damaged structure, how close it is to each training pattern which is composed of the modal characteristics from various structural damage cases. In this algorithm, two PNNs are sequentially used. The first PNN estimates the damage location using mode shape and the results of the first PNN are put into the second PNN for the damage severity estimation using natural frequency. The proposed damage assessment algorithm using the PNN is applied to a cable-stayed bridge to verify its applicability.