• Journal of Biomedical Engineering Research
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• 제18권3호
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• pp.233-241
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• 1997

The research on chromosomes is very significant in cytogenetics since genes of the chromosomes control revelation of the inheritance plasma. The human chromosome analysis is widely used to study leukemia, malignancy, radiation hazard, and mutagen dosimetry as well as various congenital anomalies such as Down's, Klinefelter's, Edward's, and Patau's syndrome. The framing and analysis of the chromosome karyogram, which requires specific cytogenetic knowledge is most important in this field. Many researches on automated chromosome karyotype analysis methods have been carried out, some of which produced commercial systems. However, there still remains much room to improve the accuracy of chromosome classification and to reduce the processing time in real clinic environments. In this paper, we proposed a hierarchical artificial neural network(HANN) to classify the chromosome karyotype. We extracted three or four chromosome morphological feature parameters such as centromeric index, relative length ratio, relative area ratio, and chromosome length by preprocessing from ten human chromosome images. The feature parameters of five human chromosome images were used to learn HANN and the rest of them were used to classify the chromosome images. The experiment results show that the chromosome classification error is reduced much more than that of the other researchers using less feature parameters.

• Journal of the Korean Society of Safety
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• 제39권1호
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• pp.82-95
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• 2024

Analyzing the aftermath of events at domestic nuclear power plants brings in the question: "Why do workers not comply with the prescribed procedures?" The current investigation of nuclear power plant events identifies their reasons considering the factors affecting the workers' behaviors. However, there are some complications to it: in addition to confirming the action such as an error or a violation, there is a limit to identifying the intention of the actor. To overcome this limitation, the study analyzed and examined the reasons for non-compliance identified in nuclear power plant events by Reason's rule-related behavior classification. For behavior analysis, I selected unit behaviors for events that are related to human and organizational factors and occurred at domestic nuclear power plants since 2017, and then I applied the rule-related behavior classification introduced by Reason (2008). This allowed me to identify the intentions by classifying unit behaviors according to quality and compliance with the rules. I also identified the factors that influenced unit behaviors. The analysis showed that most often, non-compliance only pursued personal goals and was based on inadequate risk appraisal. On the other hand, the analysis identified cases where it was caused by such factors as poorly written procedures or human system interfaces. Therefore, the probability of non-compliance can be reduced if these factors are properly addressed. Unlike event investigation techniques that struggle to identify the reasons for employee behavior, this study provides a new interpretation of non-compliance in nuclear power plant events by examining workers' intentions based on the concept of rule-related behavior classification.

• Journal of Biosystems Engineering
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• 제24권1호
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• pp.67-74
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• 1999

A prototype cut-flower sorter was developed and tested for its performance with five varieties of roses. Support plates driven by a chain mechanism transported the roses into an image inspection chamber. Color image processing algorithms were developed to evaluate the length, thickness, and straightness of stem and color, height, and maturity of bud. The average absolute errors of the system for the measurements of stem length, stem thickness, and height of bud were 19.7 mm, 0.5 mm, and 3.8 mm, respectively. The results of classification by the sorter were compared with those of a human inspector for straightness of stem and maturity of bud. The classification error for the straightness of stem was 8.6%, when both direct image and reflected image by a mirror were analyzed. The accuracy in classifying the maturity of bud varied among the varieties, the smallest for‘Nobless’(1.5%) and the largest for‘Rote Rose’(13.5%). The time required to process a rose averaged 2.06 seconds, equivalent to the capacity of 1,600 roses per hour.

• Safety and Health at Work
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• 제8권3호
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• pp.267-275
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• 2017

Background: The mining industry is known worldwide for its highly risky and hazardous working environment. Technological advancement in ore extraction techniques for proliferation of production levels has caused further concern for safety in this industry. Research so far in the area of safety has revealed that the majority of incidents in hazardous industry take place because of human error, the control of which would enhance safety levels in working sites to a considerable extent. Methods: The present work focuses upon the analysis of human factors such as unsafe acts, preconditions for unsafe acts, unsafe leadership, and organizational influences. A modified human factor analysis and classification system (HFACS) was adopted and an accident predictive fuzzy reasoning approach (FRA)-based system was developed to predict the likelihood of accidents for manganese mines in India, using analysis of factors such as age, experience of worker, shift of work, etc. Results: The outcome of the analysis indicated that skill-based errors are most critical and require immediate attention for mitigation. The FRA-based accident prediction system developed gives an outcome as an indicative risk score associated with the identified accident-prone situation, based upon which a suitable plan for mitigation can be developed. Conclusion: Unsafe acts of the worker are the most critical human factors identified to be controlled on priority basis. A significant association of factors (namely age, experience of the worker, and shift of work) with unsafe acts performed by the operator is identified based upon which the FRA-based accident prediction model is proposed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 한국정보통신학회 2019년도 춘계학술대회
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• pp.395-397
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• 2019

Despite technical advance, human error is the main reason for maritime accidents. To ensure a safety of maritime transporting environment, technical and methodological improvement to react to various types of maritime accidents should be developed instead of ambiguously anticipating maritime accidents due to human errors. Survey, questionnaires, and interview have been routinely applied to understand objective human lookout pattern differences in various navigational situations. Although the descriptive methodology helps systematically categorizing different patterns of human behavior to avoid accidents, the subjective methods limit to objectively recognize physical behavior patterns during navigation. The purpose of the study is to develop an objective lookout pattern detection system using wearable sensors in the simulated navigation environment. In the simulated maritime navigation environment, each participant performed a given navigational situation by wearing the wearable sensors on the wrist, trunk, and head. Activity classification algorithm that was developed in the previous navigation activity classification research was applied. The physical lookout behavior patterns before and after situation-aware showed distinctive patterns, and the results are expected to reduce human errors of navigators.

• Journal of the Korea Society of Computer and Information
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• 제26권3호
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• pp.19-28
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• 2021

Esophagogastroduodenoscopy is a method commonly used for early diagnosis of upper gastrointestinal lesions. However, 10-20 percent of the gastric lesions are reported to be missed, due to human error. And countries including the US, the UK, and Japan, the World Endoscopy Organization (WEO) suggested guidelines about essential gastrointestinal parts to take pictures of so that all gastric lesions are observed. In this paper, we propose deep learning techniques for classification of anatomical sites, aiming for the system that informs practitioners whether they successfully did the gastroscopy without blind spots. The proposed model uses pre-processing modules and data augmentation techniques suitable for gastroscopy images. Not only does the experiment result with a maximum F1 score of 99.6%, but it also shows a error rate of less than 4% based on the actual data. Given the performance results, we found the model to be explainable with the potential to be utilized in the clinical area.

• Annual Conference on Human and Language Technology
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• 한국정보과학회언어공학연구회 2017년도 제29회 한글 및 한국어 정보처리 학술대회
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• pp.56-59
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• 2017

CNN(Convolutional Neural Network)을 이용하여 발화 주제 다중 분류 task를 multi-labeling 방법과, cluster 방법을 이용하여 수행하고, 각 방법론에 MSE(Mean Square Error), softmax cross-entropy, sigmoid cross-entropy를 적용하여 성능을 평가하였다. Network는 음절 단위로 tokenize하고, 품사정보를 각 token의 추가한 sequence와, Naver DB를 통하여 얻은 named entity 정보를 입력으로 사용한다. 실험결과 cluster 방법으로 문제를 변형하고, sigmoid를 output layer의 activation function으로 사용하고 cross entropy cost function을 이용하여 network를 학습시켰을 때 F1 0.9873으로 가장 좋은 성능을 보였다.

• Annual Conference on Human and Language Technology
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• 한국정보과학회언어공학연구회 2015년도 제27회 한글 및 한국어 정보처리 학술대회
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• pp.157-162
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• 2015

자동통역(Speech-to-speech translation)의 최우선 단계인 음성인식과정에서 발생한 오류문장은 대부분 비문법적 구조를 갖거나 의미를 이해할 수 없는 문장들이다. 이러한 문장으로 자동번역을 할 경우 심각한 통역오류가 발생하게 되어 이에 대한 개선이 반드시 필요한 상황이다. 이에 본 논문에서는 음성인식 오류문장이 정상적인 인식문장에 비해 비문법적이거나 무의미하다는 특징을 이용하여 DNN(Deep Neural Network) 기반 음성인식오류 판별기를 구현하였으며 84.20%의 오류문장 분류성능결과를 얻었다.

• The Transactions of The Korean Institute of Electrical Engineers
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• 제61권9호
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• pp.1336-1339
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• 2012

In this paper, we focus on solving the classification problem by using semisupervised learning strategy. Traditional classifiers are constructed based on labeled data in supervised learning. Labeled data, however, are often difficult, expensive or time consuming to obtain, as they require the efforts of experienced human annotators. Unlabeled data are significantly easier to obtain without human efforts. Thus, we use AdaBoost algorithm with SVM-KNN classifier to apply semisupervised learning problem and improve the classifier performance. Experimental results on both artificial and UCI data sets show that the proposed methodology can reduce the error rate.

• Healthcare Informatics Research
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• 제24권4호
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• pp.309-316
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• 2018

Objectives: Both the valence and arousal components of affect are important considerations when managing mental healthcare because they are associated with affective and physiological responses. Research on arousal and valence analysis, which uses images, texts, and physiological signals that employ deep learning, is actively underway; research investigating how to improve the recognition rate is needed. The goal of this research was to design a deep learning framework and model to classify arousal and valence, indicating positive and negative degrees of emotion as high or low. Methods: The proposed arousal and valence classification model to analyze the affective state was tested using data from 40 channels provided by a dataset for emotion analysis using electrocardiography (EEG), physiological, and video signals (the DEAP dataset). Experiments were based on 10 selected featured central and peripheral nervous system data points, using long short-term memory (LSTM) as a deep learning method. Results: The arousal and valence were classified and visualized on a two-dimensional coordinate plane. Profiles were designed depending on the number of hidden layers, nodes, and hyperparameters according to the error rate. The experimental results show an arousal and valence classification model accuracy of 74.65 and 78%, respectively. The proposed model performed better than previous other models. Conclusions: The proposed model appears to be effective in analyzing arousal and valence; specifically, it is expected that affective analysis using physiological signals based on LSTM will be possible without manual feature extraction. In a future study, the classification model will be adopted in mental healthcare management systems.