• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.55 no.1
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• pp.74-81
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• 2019

According to the recent presentation by the Korean Maritime Safety Tribunal, about 70% of marine accident occurs from fishing vessel, and 90% of cause of entire marine accidents attributes to human error. As fishing vessels require basic operations, fishing operations, other additional operations and techniques such as fish handling, cultivating excellent marine officer to prevent marine accident and develop industry is very important. A fisheries training ship is still very difficult to satisfy the demand for diversity of fishery training and sense of realism of the industry. As the result of employment expectation by category of business survey targeting 266 marine industry high school graduates who hope to board fishing vessels for the last four years, tuna purse seine was the highest with 132 cadets (49.6%), followed by offshore large purse seine (65 cadets, 22.4%), and tuna long line (35 cadets, 13.2%). The Korea Institute of Maritime and Fisheries Technology (KIMFT) has replaced old jigging and fish pot fishery training ships and proceeded developing and building multi-purpose fisheries training ships considering the demand of industry and the promotion of employment; however, the basic fishing method was set for a tuna purse seine. As a result of seakeeping model test, it can conduct the satisfiable operation at sea state 5, and survive at sea state 8.

• Journal of Korea Multimedia Society
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• v.25 no.3
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• pp.493-501
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• 2022

This paper is a study on context-sensitive spelling error correction and uses the Korean WordNet (KorLex)[1] that defines the relationship between words as a graph to improve the performance of the correction[2] based on the vector information of the word embedded in the correction technique. The Korean WordNet replaced WordNet[3] developed at Princeton University in the United States and was additionally constructed for Korean. In order to learn a semantic network in graph form or to use it for learned vector information, it is necessary to transform it into a vector form by embedding learning. For transformation, we list the nodes (limited number) in a line format like a sentence in a graph in the form of a network before the training input. One of the learning techniques that use this strategy is Deepwalk[4]. DeepWalk is used to learn graphs between words in the Korean WordNet. The graph embedding information is used in concatenation with the word vector information of the learned language model for correction, and the final correction word is determined by the cosine distance value between the vectors. In this paper, In order to test whether the information of graph embedding affects the improvement of the performance of context- sensitive spelling error correction, a confused word pair was constructed and tested from the perspective of Word Sense Disambiguation(WSD). In the experimental results, the average correction performance of all confused word pairs was improved by 2.24% compared to the baseline correction performance.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.12
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• pp.995-1004
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• 2020

This study aims to analyze the effect of transformational leadership (TL), procedural justice (PJ), and training (T) on employee performance (EP) mediated by self-efficacy (SE). The object of this research is Rumah Sakit Umum Daerah (RSUD) M.Th. Djaman, a hospital in Sanggau Regency, while the subjects are the institution's staff. Data collection search uses purposive sampling with a total of 120 samples. Data are obtained through questionnaires distributed directly to respondents using the Google Form application. Data analysis techniques used in this study include standard error of mean (SEM) with AMOS software version 24.00. Methods use to test validity and reliability of data include Confirmatory Factor Analysis (CFA), Construct Reliability (CR) and VE. The results of the analysis show that only training has a significant effect on self-efficacy, and self-efficacy has a significant effect on employee performance. Also, self-efficacy is proven to mediate the role of training on employee performance; the other hypotheses are not significant. Training is the most prominent positive factor affecting self-efficacy and self-efficacy has a significant effect on employee performance at RSUD M.Th. Djaman. The results of this study can be used as a reference by management in determining what policy priorities should take precedence.

• Journal of the Korea Society of Computer and Information
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• v.23 no.4
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• pp.147-153
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• 2018

In this paper, we propose an efficient WBC 14-Diff classification which performs using the WBC-ResNet-152, a type of CNN model. The main point of view is to use Super-pixel for the segmentation of the image of WBC, and to use ResNet for the classification of WBC. A total of 136,164 blood image samples (224x224) were grouped for image segmentation, training, training verification, and final test performance analysis. Image segmentation using super-pixels have different number of images for each classes, so weighted average was applied and therefore image segmentation error was low at 7.23%. Using the training data-set for training 50 times, and using soft-max classifier, TPR average of 80.3% for the training set of 8,827 images was achieved. Based on this, using verification data-set of 21,437 images, 14-Diff classification TPR average of normal WBCs were at 93.4% and TPR average of abnormal WBCs were at 83.3%. The result and methodology of this research demonstrates the usefulness of artificial intelligence technology in the blood cell image classification field. WBC-ResNet-152 based morphology approach is shown to be meaningful and worthwhile method. And based on stored medical data, in-depth diagnosis and early detection of curable diseases is expected to improve the quality of treatment.

• Journal of Korea Multimedia Society
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• v.25 no.11
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• pp.1621-1633
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• 2022

The present study aims to design an application for anger control training of individuals and test its practical usability with the goal of encouraging preventive training in daily life. This study also investigates, through usability evaluation, whether users can use the application to carry out the actual anger management training program, whether it is useful and convenient, and whether it produces adequate learning effects. In order to conduct usability evaluation, a usability evaluation scale comprised of six factors-utility, reuse intention, learning, error, and reflectivity-was derived, and survey items tailored to each factor were produced. The association between usability evaluation elements, user demographic parameters, mobile usage behavior, and state anger was also examined. The result demonstrated that additional menus and features are necessary to increase the usability of the application for anger management. The result also revealed that it is vital to build an intuitive application interface that users unfamiliar with mobile app functionality can easily navigate, as well as to add entertaining components in the content, as users may be somewhat bored. On the basis of the findings, ideas of modifying and creating anger management training programs were discussed.

• Structural Engineering and Mechanics
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• v.90 no.2
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• pp.189-208
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• 2024

Artificial intelligence is one of the efficient methods that can be developed to simulate nonlinear behavior and predict the response of building structures. In this regard, an adaptive method based on optimization algorithms is used to train the TSK model of the fuzzy inference system to estimate the seismic behavior of building structures based on analytical data. The optimization algorithm is implemented to determine the parameters of the TSK model based on the minimization of prediction error for the training data set. The adaptive training is designed on the feedback of the results of previous time steps, in which three training cases of 2, 5, and 10 previous time steps were used. The training data is collected from the results of nonlinear time history analysis under 100 ground motion records with different seismic properties. Also, 10 records were used to test the inference system. The performance of the proposed inference system is evaluated on two 3 and 20-story models of nonlinear steel moment frame. The results show that the inference system of the TSK model by combining the optimization method is an efficient computational method for predicting the response of nonlinear structures. Meanwhile, the multi-vers optimization (MVO) algorithm is more accurate in determining the optimal parameters of the TSK model. Also, the accuracy of the results increases significantly with increasing the number of previous steps.

• Journal of Power System Engineering
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• v.20 no.4
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• pp.19-25
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• 2016

It is needed to use the gait training system for the rehabilitation of the disabled and old people. In this study, a gait training system of turn roller type is proposed for the purpose of helping the rehabilitation. A driving mechanism with the turn roller is designed by using the RecurDyn which is the dynamic analysis program. RecurDyn is used to analyze the dynamic behavior of the gait training system. The static load analysis is carried out to investigate the safety of this system. From the operating test of this system, it is noted that the driving error is little and the load capacity is 130 kgf.

• Phonetics and Speech Sciences
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• v.4 no.2
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• pp.79-86
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• 2012

In this paper, we propose a novel approach to improve histogram equalization for speaker identification. Our method collects all speech features of UBM training data to make a reference distribution. The ranks of the feature vectors are calculated in the sorted list of the collection of the UBM training data and the test data. We use the ranks to perform order-based histogram equalization. The proposed method improves the accuracy of the speaker recognition system with short utterances. We use four kinds of speech databases to evaluate the proposed speaker recognition system and compare the system with cepstral mean normalization (CMN), mean and variance normalization (MVN), and histogram equalization (HEQ). Our system reduced the relative error rate by 33.3% from the baseline system.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.4
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• pp.338-344
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• 2002

In this paper, neural network force tracking control is proposed. The conventional impedance function is reformulated to have direct farce tracking capability. Neural network is used to compensate for all the uncertainties such as unknown robot dynamics, unknown environment stiffness, and unknown environment position. On line training signal of farce error for neural network is formulated. A large x-y table is built as a test-bed and neural network loaming algorithm is implemented on a DSP board mounted in a PC. Experimental studies of farce tracking on unknown environment for x-y table robot are presented to confirm the performance of the proposed technique.

• Physical Therapy Korea
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• v.21 no.2
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• pp.18-27
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• 2014

The purpose of this study was to examine the effects of dual-task training (cognitive and exercise tasks) on the balance and gait performance of chronic stroke patients. Eighteen subjects with chronic stroke were divided equally into two groups, an experimental group and a control group. Subjects in both groups participated in an exercise program, performing the same tasks, for 45 minutes per day, three times per week for four weeks. The experimental group also performed additional cognitive task. The experimental group showed a more significant improvement than the control group on the Berg Balance Scale, the Timed Up and Go Test, the Korean Activities-Specific Balance Confidence Scale, and the Functional Gait Assessment (p<.05). The cognitive task error rates in the final week were significantly less than in the first week in the experimental group (p<.01). These results suggest that dual-task training for chronic stroke patients is effective in improving balance, gait, and cognitive abilities.