• Journal of IKEEE
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• v.23 no.2
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• pp.448-453
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• 2019

Under the conflicting objectives of maximum user satisfaction and fast launching, there exist great needs for automated mobile app testing. In automated app testing, detection of usage-state changes is one of the most important issues for minimizing human intervention and testing of various usage scenarios. Because conventional approaches utilizing pre-collected training examples can not handle the rapid evolution of apps, we propose a novel method detecting changes in usage-state through graph-entropy. In the proposed method, widgets in a screen shot are recognized through DNNs and 'onverted graphs. We compared the performance of the proposed method with a SIFT (Scale-Invariant Feature Transform) based method on 20 real-world apps. In most cases, our method achieved superior results, but we found some situations where further improvements are required.

• MALSORI
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• no.52
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• pp.161-169
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• 2004

This paper describes the automated call routing for train ticket reservation aid system based on speech recognition. We focus on the task of automatically routing telephone calls based on user's fluently spoken response instead of touch tone menus in an interactive voice response system. Vector-based call routing algorithm is investigated and mapping table for key term is suggested. Korail database collected by KT is used for call routing experiment. We evaluate call-classification experiments for transcribed text from Korail database. In case of small training data, an average call routing error reduction rate of 14% is observed when mapping table is used.

• Journal of the Korea Safety Management & Science
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• v.1 no.1
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• pp.69-77
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• 1999

One of the objectives of any task design is to provide a safe and helpful workplace for the employees. The safety and health module may include means for confronting the design with safety and health regulations and standards as well as tools for obstacles and collisions detection (such as error models and simulators), Virtual Reality is a leading edge technology which has only very recently become available on platforms and at prices accessible to the majority of simulation engineers. The design of an automated manufacturing system is a complicated, multidisciplinary task that requires involvement of several specialists. In this paper, a design procedure that facilitates the safety and ergonomic considerations of an automated manufacturing system are described. The procedure consists of the following major steps. Data collection and analysis of the data, creation of a three-dimensional simulation model of the work environment, simulation for safety analysis and risk assessment, development of safety solutions, selection of the preferred solutions, implementation of the selected solutions, reporting, and training. When improving the safety of an existing system the three-dimensional simulation model helps the designer to perceive the work from operators point of view objectively and safely without the exposure to hazards of the actual system.

• Journal of the Korea Safety Management & Science
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• v.2 no.1
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• pp.99-116
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• 2000

Accident statistics cite the flightcrew as a primary contributor in about 70 percent of accidents involving transport category airplanes. The introduction of modem flight deck designs, which have automated many piloting tasks, has reduced or eliminated some types of flightcrew errors, but other types of errors have been introduced. To identify the impedimental factors in highly automated modem airplane cockpit systems, this study used readily available information sources and case study, From the evidence, this study identified issues that show vulnerabilities in pilot management of automation, situation awareness, communication between pilots and controllers, pilot's training and evaluation methods. The next step will require the aviation community to solve these problems for the safety improvement.

• IE interfaces
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• v.8 no.1
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• pp.5-13
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• 1995

Automated vision inspection has become a vital part of computer related industries. Most of the existing inspection systems mainly utilize black and white images. In this paper, we consider an application of automated vision inspection in which cable color has to be recognized in order to detect the quality status of assembled wire harness. A back-propagation neural network is proposed to classify seven different cable colors. To represent a single point in image space, we use the ($L^*,\;a^*,\;b^*$) model which is one of commonly used color-coordinate systems in image processing. After training the neural network with ($L^*,\;a^*,\;b^*$) data obtained from color image, we tested its performance. The results show that the neural network is able to classify cable colors with high performance.

• Journal of Korea Multimedia Society
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• v.24 no.10
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• pp.1403-1413
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• 2021

In this paper, we investigate automated data validation techniques for artificial intelligence training, and also disclose open-source frameworks, such as Google's TensorFlow Data Validation (TFDV), that support automated data validation in the AI model development process. We also introduce an experimental study using public data sets to demonstrate the effectiveness of the open-source data validation framework. In particular, we presents experimental results of the data validation functions for schema testing and discuss the limitations of the current open-source frameworks for semantic data. Last, we introduce the latest studies for the semantic data validation using machine learning techniques.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.2
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• pp.156-163
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• 2016

This study examined the level of knowledge, attitude, and performance ability of automated external defibrillator (AED) and cardiopulmonary resuscitation (CPR) among university students who were not in health-related majors and to explore the relationships among these variables. In this study, 291 students were recruited from 3 universities in D city. Among them, 77.0% had ever seen or heard of AED and 61.9% did not know how to use it. The levels of knowledge, attitudes and performance ability differed significantly according to the CPR training experience and AED awareness (p<.002~.001). The performance ability showed significant correlations with knowledge (r=. 42, p<.001) and attitude (r=. 55, p<.001) of AED and CPR. These findings suggest that future AED and CPR training programs should be developed to promote a positive attitude towards the willingness to perform AED and CPR as well as to increase the participants' confidence by repeated training.

• Korean Journal of Radiology
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• v.21 no.6
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• pp.660-669
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• 2020

Objective: To evaluate the accuracy of a deep learning-based automated segmentation of the left ventricle (LV) myocardium using cardiac CT. Materials and Methods: To develop a fully automated algorithm, 100 subjects with coronary artery disease were randomly selected as a development set (50 training / 20 validation / 30 internal test). An experienced cardiac radiologist generated the manual segmentation of the development set. The trained model was evaluated using 1000 validation set generated by an experienced technician. Visual assessment was performed to compare the manual and automatic segmentations. In a quantitative analysis, sensitivity and specificity were calculated according to the number of pixels where two three-dimensional masks of the manual and deep learning segmentations overlapped. Similarity indices, such as the Dice similarity coefficient (DSC), were used to evaluate the margin of each segmented masks. Results: The sensitivity and specificity of automated segmentation for each segment (1-16 segments) were high (85.5-100.0%). The DSC was 88.3 ± 6.2%. Among randomly selected 100 cases, all manual segmentation and deep learning masks for visual analysis were classified as very accurate to mostly accurate and there were no inaccurate cases (manual vs. deep learning: very accurate, 31 vs. 53; accurate, 64 vs. 39; mostly accurate, 15 vs. 8). The number of very accurate cases for deep learning masks was greater than that for manually segmented masks. Conclusion: We present deep learning-based automatic segmentation of the LV myocardium and the results are comparable to manual segmentation data with high sensitivity, specificity, and high similarity scores.

• Proceedings of the KSPS conference
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• 2000.07a
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• pp.338-349
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• 2000

Pronunciation teaching is once again coming into the foreground of ELT. Japan is, however, lagging far behind many countries in the development of pronunciation curricula and in the actual speech performance of the Japanese learners of English. The reasons for this can be found in the prevalence of communicative methodologies unfavorable for pronunciation teaching, in the lack of trained professionals, and in the large numbers of students in Japanese foreign language classes. This paper offers a way to promote foreign language pronunciation teaching in Japan and other countries by means of employing computer and internet facilities. The paper outlines the major directions of using modem speech technologies in pronunciation classes, like EVF (electronic visual feedback) training at segmental and prosodic levels; automated error detection, testing, grading and fluency assessment. The author discusses the applicability of some specific software packages (CSLU, SUGIspeech, Multispeech, Wavesurfer, etc.) for the needs of pronunciation teaching. Finally, the author talks about the globalization of pronunciation education via internet resources, such as computer corpora and speech and pronunciation training related web pages.

• Journal of Information Processing Systems
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• v.13 no.1
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• pp.8-25
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• 2017

This survey paper explores the application of multimodal feedback in automated systems for motor learning. In this paper, we review the findings shown in recent studies in this field using rehabilitation and various motor training scenarios as context. We discuss popular feedback delivery and sensing mechanisms for motion capture and processing in terms of requirements, benefits, and limitations. The selection of modalities is presented via our having reviewed the best-practice approaches for each modality relative to motor task complexity with example implementations in recent work. We summarize the advantages and disadvantages of several approaches for integrating modalities in terms of fusion and frequency of feedback during motor tasks. Finally, we review the limitations of perceptual bandwidth and provide an evaluation of the information transfer for each modality.