• Korean Journal of Occupational Health Nursing
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• 제7권2호
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• pp.143-154
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• 1998

The purpose of this research is to suggest basic materials for the practical infection precaution program to protect health care workers from hospital infection by grasping their compliance level of Universal Precautions and examining the factors affecting them. The number of the health care workers we studied were 486, including the doctors, the nurses, and the lab technicians who were working in a university hospital. The period of this research was from Aug. 18th, 1997 to Aug. 30th, 1997. The method of the study was to measure the compliance level of Universal Precautions with the item of "Universal Precautions" established by CDC in 1987, and examine the questionnaire of 52 questions dividing related factors into socio-populational, individual, socio-psychological and organizational management ones. The data was analyzed by t-test. ANOVA, and chi-square test. The results were as follows : 1. An the compliance level of Universal Precautions, hand washing had the highest score(85.4%), and doctors(18.9%), nurses(44.0%), and lab technicians(7.6%), had a low compliance level in the safe handling of an injection syringe, and item not to handle patients and their samples when the subject suffered from dermatitis or injury had the lowest score of 17.1%. 23.3% of them said that they wear protection gown, goggles and mask. 2. Female's Compliance level of Universal Precautions Was higher than male. 3. The health care workers who had high recognition on Universal Precautions got significantly higher compliance level of Universal Precautions than those have low recognition on Universal Precautions(P<0.001). 4. The health care workers experienced a needle stick injury had a significantly higher compliance level of Universal Precautions than those who had not(P<0.000). 5. The health care workers who had infection protection education got a significantly higher compliance level of Universal Precautions than those who didn't(P<0.000). 6. The health care workers who had a firm belief in the effect of Universal Precautions got a higher compliance level of Universal Precautions than those who didn't. 7. The health care workers who had less conflicts between treating patient arid protecting them-selves got a higher compliance level of Universal Precautions than others with many conflicts. 8. The health care workers who had a high score in organizational management factors got a significantly higher compliance level of Universal Precautions than those with a low score(P<0.000). 9. Only 16.9 percent of the all respondents(82 in number) answered that they knew well or a little about the Universal Precautions, which is very low rate of recognition. 10. The variables which affected the score in organizational management factors were age, sex, education period, work experience, the kind of work, recognition on Universal Precautions, the experience of needle stick injury, revealing dangerous circumstance related to infection, and training on precaution again infection. According to the result above, compliance level of Universal Precautions showed high correlation with sex, the recognition on Universal Precautions, the experience of needle stick injury, training on precaution against infection, the belief in the effect of Universal Precautions, the recognition degree of conflicts and organizatinal management factors. These results could be used as the basic materials for the developing infection protection programs. Also, There should have a systematic training course to elevate a effective compliance level of Universal Precautions as well as the manageeent of infection protection programs.

• Journal of the Institute of Electronics and Information Engineers
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• 제52권12호
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• pp.174-182
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• 2015

In this paper, we developed the small size RGB sensor that recognizes a long distance using a low-cost color sensor. Light receiving portion of the sensor was used as a camera lens for far distance recognition, and illuminating unit was increased the strength of the light by using a high-power white LED and a lens mounted on the reflector. RGB color recognition algorithm consists of the learning process and the realtime recognition process. We obtain a normalized RGB color reference data in the learning process using the specimens painted with target colors, and classifies the three colors using the Mahalanobis distance in recognition process. We apply the developed the RGB color recognition sensor to a prototype of the part classification system and evaluate the performance of its.

• Journal of Information Processing Systems
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• 제16권5호
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• pp.1145-1157
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• 2020

A convolution neural networks (CNNs) has demonstrated outstanding performance compared to other algorithms in the field of face recognition. Regarding the over-fitting problem of CNN, researchers have proposed a residual network to ease the training for recognition accuracy improvement. In this study, a novel face recognition model based on game theory for call-over in the classroom was proposed. In the proposed scheme, an image with multiple faces was used as input, and the residual network identified each face with a confidence score to form a list of student identities. Face tracking of the same identity or low confidence were determined to be the optimisation objective, with the game participants set formed from the student identity list. Game theory optimises the authentication strategy according to the confidence value and identity set to improve recognition accuracy. We observed that there exists an optimal mapping relation between face and identity to avoid multiple faces associated with one identity in the proposed scheme and that the proposed game-based scheme can reduce the error rate, as compared to the existing schemes with deeper neural network.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권6호
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• pp.1981-1995
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• 2021

When a person tries to conceal emotions, real emotions will manifest themselves in the form of micro-expressions. Research on facial micro-expression recognition is still extremely challenging in the field of pattern recognition. This is because it is difficult to implement the best feature extraction method to cope with micro-expressions with small changes and short duration. Most methods are based on hand-crafted features to extract subtle facial movements. In this study, we introduce a method that incorporates optical flow and deep learning. First, we take out the onset frame and the apex frame from each video sequence. Then, the motion features between these two frames are extracted using the optical flow method. Finally, the features are inputted into an improved MobileNetV2 model, where SVM is applied to classify expressions. In order to evaluate the effectiveness of the method, we conduct experiments on the public spontaneous micro-expression database CASME II. Under the condition of applying the leave-one-subject-out cross-validation method, the recognition accuracy rate reaches 53.01%, and the F-score reaches 0.5231. The results show that the proposed method can significantly improve the micro-expression recognition performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권10호
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• pp.3211-3229
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• 2022

To solve the problems existing in the process of Weibo disaster rumor recognition, such as lack of corpus, poor text standardization, difficult to learn semantic information, and simple semantic features of disaster rumor text, this paper takes Sina Weibo as the data source, constructs a dataset for Weibo disaster rumor recognition, and proposes a deep learning model BERT_AT_Stacked LSTM for Weibo disaster rumor recognition. First, add adversarial disturbance to the embedding vector of each word to generate adversarial samples to enhance the features of rumor text, and carry out adversarial training to solve the problem that the text features of disaster rumors are relatively single. Second, the BERT part obtains the word-level semantic information of each Weibo text and generates a hidden vector containing sentence-level feature information. Finally, the hidden complex semantic information of poorly-regulated Weibo texts is learned using a Stacked Long Short-Term Memory (Stacked LSTM) structure. The experimental results show that, compared with other comparative models, the model in this paper has more advantages in recognizing disaster rumors on Weibo, with an F1_Socre of 97.48%, and has been tested on an open general domain dataset, with an F1_Score of 94.59%, indicating that the model has better generalization.

• Journal of the Ergonomics Society of Korea
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• 제31권4호
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• pp.525-531
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• 2012

Objective: The aim of this study is to investigate learnability of gestures-commands pair when people use gestures to control a device. Background: In vision-based gesture recognition system, selecting gesture-command pairing is critical for its usability in learning. Subjective preference and its agreement score, used in previous study(Lim et al., 2012) was used to group four gesture-command pairings. To quantify the learnability, two learning models, average time model and marginal time model, were used. Method: Two sets of eight gestures, total sixteen gestures were listed by agreement score and preference data. Fourteen participants divided into two groups, memorized each set of gesture-command pair and performed gesture. For a given command, time to recall the paired gesture was collected. Results: The average recall time for initial trials were differed by preference and agreement score as well as the learning rate R driven by the two learning models. Conclusion: Preference rate agreement score showed influence on learning of gesture-command pairs. Application: This study could be applied to any device considered to adopt gesture interaction system for device control.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권3호
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• pp.1100-1118
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• 2021

In a wide range of ML applications, the training data contains privacy-sensitive information that should be kept secure. Training the ML systems by privacy-sensitive data makes the ML model inherent to the data. As the structure of the model has been fine-tuned by training data, the model can be abused for accessing the data by the estimation in a reverse process called model inversion attack (MIA). Although, MIA has been applied to shallow neural network models of recognizers in literature and its threat in privacy violation has been approved, in the case of a deep learning (DL) model, its efficiency was under question. It was due to the complexity of a DL model structure, big number of DL model parameters, the huge size of training data, big number of registered users to a DL model and thereof big number of class labels. This research work first analyses the possibility of MIA on a deep learning model of a recognition system, namely a face recognizer. Second, despite the conventional MIA under the white box scenario of having partial access to the users' non-sensitive information in addition to the model structure, the MIA is implemented on a deep face recognition system by just having the model structure and parameters but not any user information. In this aspect, it is under a semi-white box scenario or in other words a gray-box scenario. The experimental results in targeting five registered users of a CNN-based face recognition system approve the possibility of regeneration of users' face images even for a deep model by MIA under a gray box scenario. Although, for some images the evaluation recognition score is low and the generated images are not easily recognizable, but for some other images the score is high and facial features of the targeted identities are observable. The objective and subjective evaluations demonstrate that privacy cyber-attack by MIA on a deep recognition system not only is feasible but also is a serious threat with increasing alert state in the future as there is considerable potential for integration more advanced ML techniques to MIA.

• Journal of the East Asian Society of Dietary Life
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• 제10권4호
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• pp.321-334
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• 2000

This study was performed by questionnaire to investigation the nutrition knowledge, the recognition, the preference and the actual state of use of sea mustard. The subjects of this study were consisted of 901 people (426 males and 475 females) in the Daegu and Kyungpook area. The results were summarized as follows: The nutrition knowledge score of sea mustard was 11.1 for male and 12.8 for female, respectively. The recognition non sea mustard dishes showed a high mean value of 4.11 to "healthy food". 54% of the respondent liked sea mustard and favorite dish was in the order of soup, fresh, cold soup, Wrapped, salted, fried sea mustard. Soup of sea mustard was the best favorite dish, followed by fresh sea mustard, cold soup, wrapped, salted, fried sea mustard, in descending order. Soaking time of sea mustard was 11~20 minutes and its percentage is 31.1%, 39.6% of responders suggested ′good quality′ as facts that has been improved in the commercial sea mustard.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 한국방송공학회 2011년도 하계학술대회
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• pp.85-88
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• 2011

This paper describes a single channel speech enhancement as the pre-processor of automatic speech recognition system. The improvements are based on using optimally modified log-spectra (OM-LSA) gain function with a non-causal a priori signal-to-noise ratio (SNR) estimation. Experimental results show that the proposed method gives better perceptual evaluation of speech quality score (PESQ) and lower log-spectral distance, and also better word accuracy. In the enhancement system, parameters was turned for automatic speech recognition.

• International conference on construction engineering and project management
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.847-854
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• 2022

The aim of this study is to develop a Named Entity Recognition (NER) model to automatically identify construction-related organizations from news articles. This study collected news articles using web crawling technique and construction-related organizations were labeled within a total of 1,000 news articles. The Bidirectional Encoder Representations from Transformers (BERT) model was used to recognize clients, constructors, consultants, engineers, and others. As a pilot experiment of this study, the best average F1 score of NER was 0.692. The result of this study is expected to contribute to the establishment of international business strategies by collecting timely information and analyzing it automatically.