• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2001.10a
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• pp.59-64
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• 2001

The objectives of the thesis are to propose a pattern classification method for remote sensing data using artificial neural network. First, we apply the error back propagation algorithm to classify the remote sensing data. In this case, the classification performance depends on a training data set. Using the training data set and the error back propagation algorithm, a layered neural network is trained such that the training pattern are classified with a specified accuracy. After training the neural network, some pixels are deleted from the original training data set if they are incorrectly classified and a new training data set is built up. Once training is complete, a testing data set is classified by using the trained neural network. The classification results of Landsat TM data show that this approach produces excellent results which are more realistic and noiseless compared with a conventional Bayesian method.

• Proceedings of the KSPS conference
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• 2000.07a
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• pp.255-270
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• 2000

Because the accuracy is essential, in order to get the fluency in speaking, both of them are very important in English education and in-service training programs. To get the accuracy and the fluency, the causes and phenomena of the unnatural pronunciation have to be surveyed first of all. Therefore, this article surveyed the problematic and unnatural pronunciation of Korean English teachers in elementary and secondary schools using CSL and Multi-speech. And also, tried to pinpoint what the causes of unnatural pronunciation are\ulcorner Next a procedure or steps were offered for them to speak naturally through in-service training programs. Through this analysis, it was found that elementary teachers have unnatural pronunciation below, within and beyond word level, and the secondary teacher has unnatural pronunciation within and beyond word level. Therefore, pronunciation training courses have to put emphasis on segment features first, and move to suprasegmental features for elementary teachers. For secondary teachers, pronunciation training courses have to focus on word level and move to suprasegmental features, in other words beyond word level. And these pronunciation training courses have to be run integrated.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.11
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• pp.2720-2736
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• 2013

Multi-view face detection has become an active area for research in the last few years. In this paper, a novel multi-view human face detection algorithm based on improved real Adaboost is presented. Real Adaboost algorithm is improved by weighted combination of weak classifiers and the approximately best combination coefficients are obtained. After that, we proved that the function of sample weight adjusting method and weak classifier training method is to guarantee the independence of weak classifiers. A coarse-to-fine hierarchical face detector combining the high efficiency of Haar feature with pose estimation phase based on our real Adaboost algorithm is proposed. This algorithm reduces training time cost greatly compared with classical real Adaboost algorithm. In addition, it speeds up strong classifier converging and reduces the number of weak classifiers. For frontal face detection, the experiments on MIT+CMU frontal face test set result a 96.4% correct rate with 528 false alarms; for multi-view face in real time test set result a 94.7 % correct rate. The experimental results verified the effectiveness of the proposed approach.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.446-448
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• 2021

In training a deep learning model, it is crucial to tune various hyperparameters and gain speed and accuracy. While hyperparameters that mathematically induce convergence impact training speed, system parameters that affect host-to-device transfer are also crucial. Therefore, it is important to properly tune and select parameters that influence the data loader as a system parameter in overall time acceleration. We propose an automated framework called Num Worker Tuner (NWT) to address this problem. This method finds the appropriate number of multi-processing subprocesses through the search space and accelerates the learning through the number of subprocesses. Furthermore, this method allows memory efficiency and speed-up by tuning the system-dependent parameter, the number of multi-process spawns.

• The KIPS Transactions:PartB
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• v.13B no.6 s.109
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• pp.615-624
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• 2006

The objective of this paper is to implement parallel multi-layer ANN(Artificial Neural Network) simulator based on the mobile agent system which is executed in parallel in the virtual parallel distributed computing environment. The Multi-Layer Neural Network is classified by training session, training data layer, node, md weight in the parallelization-level. In this study, We have developed and evaluated the simulator with which it is feasible to parallel the ANN in the training session and training data parallelization because these have relatively few network traffic. In this results, we have verified that the performance of parallelization is high about 3.3 times in the training session and training data. The great significance of this paper is that the performance of ANN's execution on virtual parallel computer is similar to that of ANN's execution on existing super-computer. Therefore, we think that the virtual parallel computer can be considerably helpful in developing the neural network because it decreases the training time which needs extra-time.

• 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 the Korea Institute of Information and Communication Engineering
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• v.9 no.3
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• pp.473-479
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• 2005

A key issue toward mobile multimedia communications is to create technologies for broadband signal transmission that ran support high quality services. Such a broadband mobile communications system should be able to overcome severe distortion caused by time-varying multi-path fading channel, while providing high spectral efficiency and low power consumption. For these reasons, an adaptive suboptimum decision feedback equalize. (DFE) for the single-carrier short-burst transmissions system is considered as one of the feasible solutions. For the performance improvement of the system with the short-burst format including the short training sequence, in this paper, the multiple-training least mean square (MTLMS) based DFE scheme with soft decision feedback is proposed and its performance is investigated in mobile wireless channels throughout computer simulation.

• Journal of Engineering Education Research
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• v.23 no.2
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• pp.49-60
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• 2020

The purpose of this study is to empirically verify whether there are differences according to group characteristics in the effect of job characteristic requirements on practice performance in university long-term on-site training. Specifically, the relationship between job characteristics (job scope, job content, coaching, benefits), practical satisfaction, and occupational competencies was examined according to the group characteristics (gender types, major types, corporation types). For this purpose, the survey data were collected and analyzed for 752 students who participated in K university long-term on-site training. As a result of the analysis, first, it was found that the job characteristics (job scope job content, coaching, benefits) had structural relationship affecting occupational competence through mediation of practice satisfaction. Second, As for the differences according to the group characteristics, there were differences in the relations. Based on the result, theoretical and practical implications and follow-up studies were proposed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.5179-5196
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• 2019

To explore an effective non-invasion medical imaging diagnostics approach for hepatocellular carcinoma (HCC), we propose a method based on adopting the multiple technologies with the multi-parametric data fusion, transfer learning, and multi-scale deep feature extraction. Firstly, to make full use of complementary and enhancing the contribution of different modalities viz. multi-parametric MRI images in the lesion diagnosis, we propose a data-level fusion strategy. Secondly, based on the fusion data as the input, the multi-scale residual neural network with SPP (Spatial Pyramid Pooling) is utilized for the discriminative feature representation learning. Thirdly, to mitigate the impact of the lack of training samples, we do the pre-training of the proposed multi-scale residual neural network model on the natural image dataset and the fine-tuning with the chosen multi-parametric MRI images as complementary data. The comparative experiment results on the dataset from the clinical cases show that our proposed approach by employing the multiple strategies achieves the highest accuracy of 0.847±0.023 in the classification problem on the HCC differentiation. In the problem of discriminating the HCC lesion from the non-tumor area, we achieve a good performance with accuracy, sensitivity, specificity and AUC (area under the ROC curve) being 0.981±0.002, 0.981±0.002, 0.991±0.007 and 0.999±0.0008, respectively.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.1
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• pp.17-21
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• 2016

This paper investigates the importance of the computational overhead when machine learning methods, such as SVM, LASSO, AdaBoosting and AdaBagging, are used for automatic security classification.