• Journal of the Ergonomics Society of Korea
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• v.28 no.1
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• pp.37-51
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• 2009

The nuclear power industry in the world has recognized the importance of integrating non-technical and team skills training with the technical training given to its control room operators to reduce human errors since the Three Mile Island and Chernobyl accidents. The Nuclear power plant (NPP) industry in Korea has been also making efforts to reduce the human errors which largely have contributed to 120 nuclear reactor trips from the year 2001 to 2006. The Crew Resource Management (CRM) training was one of the efforts to reduce the human errors in the nuclear power industry. The CRM was developed as a response to new insights into the causes of aircraft accidents which followed from the introduction of flight recorders and cockpit voice recorders into modern jet aircraft. The CRM first became widely used in the commercial airline industry, but military aviation, shipboard crews, medical and surgical teams, offshore oil crews, and other high-consequence, high-risk, time-critical industry teams soon followed. This study aims to develop a CRM training program that helps to improve plant performance by reducing the number of reactor trips caused by the operators' errors in Korean NPP. The program is; firstly, based on the work we conducted to develop a human factors training from the applications to the Nuclear Power Plant; secondly, based on a number of guidelines from the current practicable literature; thirdly, focused on team skills, such as leadership, situational awareness, teamwork, and communication, which have been widely known to be critical for improving the operational performance and reducing human errors in Korean NPPs; lastly, similar to the event-based training approach that many researchers have applied in other domains: aircraft, medical operations, railroads, and offshore oilrigs. We conducted an experiment to test effectiveness of the CRM training program in a condition of simulated control room also. We found that the program made the operators' attitudes and behaviors be improved positively from the experimental results. The more implications of the finding were discussed further in detail.

• Speech Sciences
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• v.15 no.2
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• pp.55-65
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• 2008

Recently, the multi-model based speech recognizer has been used quite successfully for noisy speech recognition. For the selection of the reference HMM (hidden Markov model) which best matches the noise type and SNR (signal to noise ratio) of the input testing speech, the estimation of the SNR value using the VAD (voice activity detection) algorithm and the classification of the noise type based on the GMM (Gaussian mixture model) have been done separately in the multi-model framework. As the SNR estimation process is vulnerable to errors, we propose an efficient method which can classify simultaneously the SNR values and noise types. The KL (Kullback-Leibler) distance between the single Gaussian distributions for the noise signal during the training and testing is utilized for the classification. The recognition experiments have been done on the Aurora 2 database showing the usefulness of the model compensation method in the multi-model based speech recognizer. We could also see that further performance improvement was achievable by combining the probability density function of the MCT (multi-condition training) with that of the reference HMM compensated by the D-JA (data-driven Jacobian adaptation) in the multi-model based speech recognizer.

• Speech Sciences
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• v.9 no.4
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• pp.237-252
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• 2002

MIT has been applied for nonfluent aphasic patients on the basis of lateralization of brain hemisphere. However, its applications for different languages have some inquiry for aphasic patients because of prosodic and rhythmic differences. The purpose of this study is to develop the Korean Melodic Intonation Therapy program using personal computer and its clinical effects for nonfluent aphasic patients. The algorithm was composed to voice analog signal, PCM, AMDF, Short-time autocorrelation function and center clipping. The main menu contains pitch, waveform, sound intensity and speech files on window. Aphasic patients' intonation patterns overlay on selected kMIT patterns. Three aphasic patients with or without kMIT training participated in this study. Four affirmative sentences and two interrogative sentences were uttered on CSL by stimulus of ST. VOT, VD, Hold and TD were measured on Spectrogram. In addition, articulation disorders and intonation patterns were evaluated objectively on spectrogram. The results indicated that nonfluent aphasic patients with kMIT training group showed some clinical effects of speech intelligibility based on VOT, TD values, articulation evaluation and prosodic pattern changes.

• Journal of English Language & Literature
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• v.56 no.5
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• pp.871-905
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• 2010

This paper is motivated by Watanabe's (2001) observation that English byphrase passives are sometimes translated into Japanese object topicalization constructions. That is, the original English sentence in the passive may be translated into the active voice with the logical object topicalized. A number of scholars, including Chomsky (1981) and Baker (1992), have remarked that languages have various ways to avoid focusing on the logical subject. The aim of the present study is to examine the translation equivalents of the English by-phrase passives in an English-Korean parallel corpus compiled by the author. A small sample of articles from Newsweek magazine and its published Korean translation reveals that there are indeed many ways to translate English by-phrase passives, including object topicalization (12.5%). Among the 64 translated sentences analyzed and classified, 12 (18.8%) examples were problematic in terms of agent defocusing, which is the primary function of passives. Of these 12 instances, five cases were identified where an alternative translation would be more suitable. The results suggest that the functional characteristics of English by-phrase passives should be highlighted in translator training as well as language teaching.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.167-170
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• 2015

In this paper, we propose the Micturition training and Automatic feeding system to enable busy modern people can grow pets more efficiently. This system will be able to operate a smart phone application with a Bluetooth, furthermore, by using the Wifi access the Internet from anywhere and to operate remotely. This system is divided micturition board feeding unit. micturition board consists of a water pressure sensor for detecting micturition and recorder module, speaker for voice output, feeding unit consists of two servo motor for auto feeding and Bluetooth sensor for remote control. Both micturition board and feeding unit produced Arduino board and C language-based Arduino sketch program, feeding unit was able to communicate with the library to support Bluetooth communication.

• Journal of the Korea Society of Computer and Information
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• v.26 no.9
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• pp.57-64
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• 2021

In this paper, we propose a device that can control the primary control surface of an aircraft by recognizing speech commands. The speech command consists of 19 commands, and a learning model is constructed based on a total of 2,500 datasets. The training model is composed of a CNN model using the Sequential library of the TensorFlow-based Keras model, and the speech file used for training uses the MFCC algorithm to extract features. The learning model consists of two convolution layers for feature recognition and Fully Connected Layer for classification consists of two dense layers. The accuracy of the validation dataset was 98.4%, and the performance evaluation of the test dataset showed an accuracy of 97.6%. In addition, it was confirmed that the operation was performed normally by designing and implementing a Raspberry Pi-based control device. In the future, it can be used as a virtual training environment in the field of voice recognition automatic flight and aviation maintenance.

• Journal of Cadastre & Land InformatiX
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• v.52 no.1
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• pp.81-93
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• 2022

Recently, there have been many studies on object recognition around the vehicle and recognition of traffic signs and traffic lights in autonomous driving. In particular, such the recognition of traffic lights is one of the core technologies in autonomous driving. Therefore, many studies for such the recognition of traffic lights have been performed, the studies based on various deep learning models have increased significantly in recent. In addition, as a high-quality AI training data set for voice, vision, and autonomous driving is released on AIHub, it makes it possible to develop a recognition model for traffic lights suitable for the domestic environment using the data set. In this study, we developed a recognition model for traffic lights that can be used in Korea using the AIHub's training data set. In particular, in order to improve the recognition performance, we used various models of YOLOv4 and YOLOv5, and performed our recognition experiments by defining various classes for the training data. In conclusion, we could see that YOLOv5 shows better performance in the recognition than YOLOv4 and could confirm the reason from the architecture comparison of the two models.

• 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 Chest Surgery
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• v.7 no.2
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• pp.175-178
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• 1974

The patient was 21-year old male who had gastrostomy and tracheostomy after swallowing lye-stuff in July 1971. He could restore his normal voice and breathing after removal of his destructed epiglottis obstructing his upper airway two years later. Pharyngogastrostomy was performed in Nov 1973. The esophagus which was totally obliterated in its full length was removed and the stomach was brought high up to the level of pharynx where it was anastomosed to the posterior wall of pharynx. His postoperative course was temporarily complicated by aspiration of small food into trachea which could be completely relieved with training, and he is doing his normal life quite well on the follow-up.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.522-526
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• 1994

There are great needs for checking machine operation status precisely in the iron and steel plants. Rotating machines such as pumps, compressors, and motors are the most important objects in the plant maintenance. In this paper back-propagation neural network is utilized in diagnosing rotating machines. Like the finger print or the voice print of human, the abnormal vibrations due to axis misalignment, shaft bending, rotor unbalance, bolt loosening, and faults in gear and bearing have their own spectra. Like the pattern recognition technique, characteristic. feature vectors are obtained from the power spectra of vibration signals. Then we apply the characteristic feature vectors to a back propagation neural net for the weight training and pattern recognition.