• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.2051-2054
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• 2002

In this research, we had two experiments. In the first experiment we focused on the comparison of loaming between two groups of hearing impaired students (multimedia training group and traditional print-based method group). The results from the first experiment indicated that there was no numerical difference in test scores between the two groups of students but the students enjoyed learning with computer. We then carried out the second experiment. This time, we focused more on measuring the qualitative nature of the learning using multimedia technology. The results of the second experiment indicated that the two methods of teaching and learning affected students similarly since the average scores of both groups showed no statistically significant difference. About 89% of the students in the second experiment enjoyed learning from the CD-ROM. This result was based not just on the CD-ROM Life in Saskatchewan, but included any kinds and subjects of CD-ROM used in the classroom. Although multimedia training is as good as, but no better than, the traditional print-based method, multimedia can be used as a valuable supplement in adaptive technology.

• Journal of Information Processing Systems
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• v.16 no.6
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• pp.1293-1308
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• 2020

In fine-tuning-based transfer learning, the size of the dataset may affect learning accuracy. When a dataset scale is small, fine-tuning-based transfer-learning methods use high computing costs, similar to a large-scale dataset. We propose a mid-level feature extractor that retrains only the mid-level convolutional layers, resulting in increased efficiency and reduced computing costs. This mid-level feature extractor is likely to provide an effective alternative in training a small-scale medical image dataset. The performance of the mid-level feature extractor is compared with the performance of low- and high-level feature extractors, as well as the fine-tuning method. First, the mid-level feature extractor takes a shorter time to converge than other methods do. Second, it shows good accuracy in validation loss evaluation. Third, it obtains an area under the ROC curve (AUC) of 0.87 in an untrained test dataset that is very different from the training dataset. Fourth, it extracts more clear feature maps about shape and part of the chest in the X-ray than fine-tuning method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.3
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• pp.1348-1375
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• 2018

Support Vector Machine (SVM) is a well-known machine learning classification algorithm, which has been widely applied to many data mining problems, with good accuracy. However, SVM classification speed decreases with increase in dataset size. Some applications, like video surveillance and intrusion detection, requires a classifier to be trained very quickly, and on large datasets. Hence, this paper introduces two filter-based instance selection techniques for optimizing SVM training speed. Fast classification is often achieved at the expense of classification accuracy, and some applications, such as phishing and spam email classifiers, are very sensitive to slight drop in classification accuracy. Hence, this paper also introduces two wrapper-based instance selection techniques for improving SVM predictive accuracy and training speed. The wrapper and filter based techniques are inspired by Cuckoo Search Algorithm and Bat Algorithm. The proposed techniques are validated on three popular e-fraud types: credit card fraud, spam email and phishing email. In addition, the proposed techniques are validated on 20 other datasets provided by UCI data repository. Moreover, statistical analysis is performed and experimental results reveals that the filter-based and wrapper-based techniques significantly improved SVM classification speed. Also, results reveal that the wrapper-based techniques improved SVM predictive accuracy in most cases.

• Speech Sciences
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• v.11 no.1
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• pp.7-20
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• 2004

In this paper, we propose a two-step noise compensation algorithm in feature extraction for achieving robust speech recognition. The proposed method frees us from requiring a priori information on noisy environments and is simple to implement. First, in frequency domain, the Harmonics-based Spectral Subtraction (HSS) is applied so that it reduces the additive background noise and makes the shape of harmonics in speech spectrum more pronounced. We then apply a judiciously weighted variance Feature Vector Normalization (FVN) to compensate for both the channel distortion and additive noise. The weighted variance FVN compensates for the variance mismatch in both the speech and the non-speech regions respectively. Representative performance evaluation using Aurora 2 database shows that the proposed method yields 27.18% relative improvement in accuracy under a multi-noise training task and 57.94% relative improvement under a clean training task.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.9
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• pp.526-532
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• 2003

This paper describes a recognition method of a road direction for an autonomous vehicle based on magnetic sensors. Using the sensors mounted on a vehicle and the magnetic markers embedded along the center of road, the autonomous vehicle can recognize a road direction and control a steering angle. Using the front lateral deviation of a vehicle and the rear one, the road direction is calculated. The analysis of magnetic field, the acquisition technique of training data, the training method of neural network and the computer simulation are presented. According to the computer simulation, the proposed method is simulated, and its performance is verified. Also, the experimental test is confirmed its reliability.

• Proceedings of KOSOMES biannual meeting
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• 2017.04a
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• pp.62-62
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• 2017

• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.25-32
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• 2022

The current research aims to identify the effectiveness of a training program based on digital stories to develop writing skills for students with learning difficulties. The research sample consisted of (12) students with learning difficulties in the fifth and sixth grades, who were chosen intentionally. The results showed the effectiveness of the program and the maintenance of this improvement over time as results showed that there were statistically significant differences at the level (α = 0.05) between the two measurements, before and after, in favor of the post-measurement. The results also showed that there were no statistically significant differences at the level (α = 0.05) between the post and follow-up measurements on the writing skills scale. This indicates the long-term impact of the program. The researchers recommend the need to expand educational programs' adoption of digital stories to develop the skills of students with learning difficulties.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.4
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• pp.433-440
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• 2016

In order to construct a system for big data processing, one needs to configure the node by using network equipments to connect multiple computers or establish cloud environments through virtual hosts on a single computer. However, there are many restrictions on constructing the big data analysis system including complex system configuration and cost. These constraints are becoming a major obstacle to professional manpower training for big data areas which is emerging as one of the most important national competitiveness. As a result, for professional manpower training of big data areas, this paper proposes a Raspberry Pi Board based educational big data processing system which is capable of practical training at an affordable price.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.18 no.36
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• pp.175-182
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• 1995

Industrial safety management program can be divided three part that is education, technology, and management. The effectiveness of a industrial safety management program depends on the ability to manage hardware which is technology and software, education and management, In this research, it will be described that how to design and develop Computer Integrated Safety System and Computer Based Training System for Press operations which is how to integrated industrial safety program wi th production planning and control in order to control efficiently using personnel computer system.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.4
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• pp.18-29
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• 1994

For the efficient recognition of Korean Alphabets, we proposed the tree structure algorithm which was based on K-tuple NRF-SDF using representative images as training images. Representative images consisted of ECP-SDF images of several consonants or vowels. To reduce the effect of sidelobe in the output correlation plane, we used the representative images as training images and obtained the elements of a vector inner product matrix using the peak value of AMPOF correlation of training images with one another. The proposed algorithm consisted of three main-step containing several substeps. In filter synthesis of each step, representative images were used as training images in the first and the second main-step and each consonant or vowel was used as training images in the third main-step. The performance of this algorithm is demonstrated by computer simulation and optical experiment.