• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.7
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• pp.61-67
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• 2019

The purpose of this study is to apply self-leadership program using films to nursing students and to confirm the effects of self-leadership, empowerment, self-directed learning and happiness. The study participants were 60 nursing students, the data was collected from March 7, to June 13, 2017. The data was analyzed by frequency, percentage, mean, standard deviation, and paired t-test using SPSS WIN 24.0 computer program. The self-leadership program consisted of 13 sessions, and 14 films were used for self-management, self-training, and self-branding. The self-leadership of nursing students was significantly increased after education(t=-4.38, p<.001). In details, behavior-focused strategies, natural-reward strategies, and constructive thought pattern strategies were all significant. Empowerment also increased significantly after education(t=-5.83, p<.001), and personal skills, collective recognition, and self-determination were all significant. Self-directed learning were high after education(t=-3.31, p=.002), and learning plans and learning practices were significant. In addition, the happiness of nursing college students was significantly higher after education(t=-4.49, p<.001). As a result of this study, self-leadership program using movies can improve self-leadership, empowerment, self-directed learning and happiness of nursing students and It will be possible to apply as educational intervention in the future.

• Journal of Microbiology and Biotechnology
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• v.31 no.7
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• pp.942-948
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• 2021

Canine influenza virus (CIV) induces acute respiratory disease in dogs. In this study, we aimed to determine the signaling pathways leading to the induction of IFN-β in a canine respiratory epithelial cell line (KU-CBE) infected with the H3N2 subtype of CIV. Small interfering RNAs (siRNAs) specific to pattern recognition receptors (PRRs) and transcription factors were used to block the IFN-β induction signals in H3N2 CIV-infected KU-CBE cells. Among the PRRs, only the TLR3 and RIG-I expression levels significantly (p < 0.001) increased in CIV-infected cells. Following transfection with siRNA specific to TLR3 (siTLR3) or RIG-I (siRIG-I), the mRNA expression levels of IFN-β significantly (p < 0.001) decreased, and the protein expression of IFN-β also decreased in infected cells. In addition, co-transfection with both siTLR3 and siRIG-I significantly reduced IRF3 (p < 0.001) and IFN-β (p < 0.001) mRNA levels. Moreover, the protein concentration of IFN-β was significantly (p < 0.01) lower in cells co-transfected with both siTLR3 and siRIG-I than in cells transfected with either siTLR3 or siRIG-I alone. Also, the antiviral protein MX1 was only expressed in KU-CBE cells infected with CIV or treated with IFN-β or IFN-α. Thus, we speculate that IFN-β further induces MX1 expression, which might suppress CIV replication. Taken together, these data indicate that TLR3 and RIG-I synergistically induce IFN-β expression via the activation of IRF3, and the produced IFN-β further induces the production of MX1, which would suppress CIV replication in CIV-infected cells.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.6
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• pp.461-469
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• 2020

In this paper, we propose a PCA-based feature feedback method for extracting important areas of handwritten numeric data sets and face data sets. A PCA-based feature feedback method is proposed by extending the previous LDA-based feature feedback method. In the proposed method, the data is reduced to important feature dimensions by applying the PCA technique, one of the dimension reduction machine learning algorithms. Through the weights derived during the dimensional reduction process, the important points of data in each reduced dimensional axis are identified. Each dimension axis has a different weight in the total data according to the size of the eigenvalue of the axis. Accordingly, a weight proportional to the size of the eigenvalues of each dimension axis is given, and an operation process is performed to add important points of data in each dimension axis. The critical area of the data is calculated by applying a threshold to the data obtained through the calculation process. After that, induces reverse mapping to the original data in the important area of the derived data, and selects the important area in the original data space. The results of the experiment on the MNIST dataset are checked, and the effectiveness and possibility of the pattern recognition method based on PCA-based feature feedback are verified by comparing the results with the existing LDA-based feature feedback method.

• Journal of the Korea Convergence Society
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• v.12 no.2
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• pp.45-50
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• 2021

In this paper, we propose the development of a smart space security device that can be opened and closed remotely using IoT. Existing space security devices can control opening and closing by breaking hardware or only using button devices or replicated keys. The recent COVID-19 crisis has created several applications for non-contact devices. In this study, we propose the development of a small space security device that has the function of unlocking through an app without touching the device. By transferring the control authority to a smartphone, device that cannot be opened or closed by only operating hardware at the user's option. It is convenient and hygienic because it can be opened and closed using an app without touching the locking device. Multiple security is possible because security can be released using an app after user authentication by fingerprint recognition and pattern input on a smartphone. If the user wishes, after using the app security, the security is released by directly touching a button installed in the safe or space or opening it with a key. In addition, by adding an inactive function to the app, it is designed so that the door of the safe cannot be opened when the key is lost or the small safe is lost. This study is expected to be able to effectively expand the security system by applying variously to objects that require security.

• Journal of the Korean Society of Industry Convergence
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• v.24 no.6_2
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• pp.729-735
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• 2021

Recently Artificial Intelligence(AI) has been developed and used in various fields. Especially AI recognition technology can perceive and distinguish images so it should plays a significant role in quality inspection process. For stability of autonomous driving technology, semiconductors inside automobiles must be protected from external electromagnetic wave(EM wave). As a shield film, a thin polymeric material with hole shaped micro-patterns created by a laser processing could be used for the protection. The shielding efficiency of the film can be increased by the hole structure with appropriate pitch and size. However, since the sensitivity of micro-machining for some parameters, the shape of every single hole can not be same, even it is possible to make defective patterns during process. And it is absolutely time consuming way to inspect all patterns by just using optical microscope. In this paper, we introduce a AI inspection system which is based on web site AI tool. And we evaluate the usefulness of AI model by calculate Area Under ROC curve(Receiver Operating Characteristics). The AI system can classify the micro-patterns into normal or abnormal ones displaying the text of the result on real-time images and save them as image files respectively. Furthermore, pressing the running button, the Hardware of robot arm with two Arduino motors move the film on the optical microscopy stage in order for raster scanning. So this AI system can inspect the entire micro-patterns of a film automatically. If our system could collect much more identified data, it is believed that this system should be a more precise and accurate process for the efficiency of the AI inspection. Also this one could be applied to image-based inspection process of other products.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.41-46
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• 2023

Human urine is a process of excreting waste products in the blood, and it is easy to collect and contains various substances. Urinalysis is used to check for diseases, health conditions, and urinary tract infections. There are three methods of urinalysis: physical property test, chemical test, and microscopic test, and chemical test results can be easily confirmed using urine test strips. A variety of items can be tested on the urine test strip, through which various diseases can be identified. Recently, with the spread of smart phones, research on reading urine test strips using smart phones is being conducted. There is a method of detecting and reading the color change of a urine test strip using a smartphone. This method uses the RGB values and the color difference formula to discriminate. However, there is a problem in that accuracy is lowered due to various environmental factors. This paper applies a deep learning model to solve this problem. In particular, color discrimination of a urine test strip is improved in a smartphone using a lightweight CNN (Convolutional Neural Networks) model. CNN is a useful model for image recognition and pattern finding, and a lightweight version is also available. Through this, it is possible to operate a deep learning model on a smartphone and extract accurate urine test results. Urine test strips were taken in various environments to prepare deep learning model training images, and a urine test service application was designed using MobileNet V3.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.5
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• pp.239-246
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• 2023

In this paper, we propose an open-loop algorithm to classify speech and music signals using the spectral flux parameters and Mel Frequency Cepstral Coefficients(MFCC) parameters for the audio coder. To increase responsiveness, the MFCC was used as a short-term feature parameter and spectral fluxes were used as a long-term feature parameters to improve accuracy. The overall voice/music signal classification decision is made by combining the short-term classification method and the long-term classification method. The Gaussian Mixed Model (GMM) was used for pattern recognition and the optimal GMM parameters were extracted using the Expectation Maximization (EM) algorithm. The proposed long-term and short-term combined speech/music signal classification method showed an average classification error rate of 1.5% on various audio sound sources, and improved the classification error rate by 0.9% compared to the short-term single classification method and 0.6% compared to the long-term single classification method. The proposed speech/music signal classification method was able to improve the classification error rate performance by 9.1% in percussion music signals with attacks and 5.8% in voice signals compared to the Unified Speech Audio Coding (USAC) audio classification method.

• The Journal of the Acoustical Society of Korea
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• v.25 no.5
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• pp.213-221
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• 2006

Phone segmentation of speech waveform is especially important for concatenative text to speech synthesis which uses segmented corpora for the construction of synthetic units. because the quality of synthesized speech depends critically on the accuracy of the segmentation. In the beginning. the phone segmentation was manually performed. but it brings the huge effort and the large time delay. HMM-based approaches adopted from automatic speech recognition are most widely used for automatic segmentation in speech synthesis, providing a consistent and accurate phone labeling scheme. Even the HMM-based approach has been successful, it may locate a phone boundary at a different position than expected. In this paper. we categorized adjacent phoneme pairs and analyzed the mismatches between hand-labeled transcriptions and HMM-based labels. Then we described the dominant error patterns that must be improved for the speech synthesis. For the experiment. hand labeled standard Korean speech DB from ETRI was used as a reference DB. Time difference larger than 20ms between hand-labeled phoneme boundary and auto-aligned boundary is treated as an automatic segmentation error. Our experimental results from female speaker revealed that plosive-vowel, affricate-vowel and vowel-liquid pairs showed high accuracies, 99%, 99.5% and 99% respectively. But stop-nasal, stop-liquid and nasal-liquid pairs showed very low accuracies, 45%, 50% and 55%. And these from male speaker revealed similar tendency.

• Informatization Policy
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• v.31 no.2
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• pp.3-38
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• 2024

Hallucination is a significant barrier to the utilization of large-scale language models or multimodal models. In this study, we collected 654 computer science papers with "hallucination" in the abstract from arXiv from December 2022 to January 2024 following the advent of Chat GPT and conducted frequency analysis, knowledge network analysis, and literature review to explore the latest trends in hallucination research. The results showed that research in the fields of "Computation and Language," "Artificial Intelligence," "Computer Vision and Pattern Recognition," and "Machine Learning" were active. We then analyzed the research trends in the four major fields by focusing on the main authors and dividing them into data, hallucination detection, and hallucination mitigation. The main research trends included hallucination mitigation through supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF), inference enhancement via "chain of thought" (CoT), and growing interest in hallucination mitigation within the domain of multimodal AI. This study provides insights into the latest developments in hallucination research through a technology-oriented literature review. This study is expected to help subsequent research in both engineering and humanities and social sciences fields by understanding the latest trends in hallucination research.

• Journal of the Korean Society of Food Science and Nutrition
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• v.39 no.7
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• pp.1056-1063
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• 2010

The purpose of this study is to investigate current nutrition labeling status, levels of recognition and patterns of consumption of domestically processed meat products. The survey results show that 47.4% of products (81 out of 171) were labeled with nutrition information. Where general product labeling and nutrition labeling were provided, it was read by 84.9% and 66.8% of the survey subjects, respectively. The most common reasons for not reading product labeling were 'hard to understand it' (46.2%) and 'not concerned' (30.8%). This was attributed to respondents finding it 'useless' (39.3%) and 'hard to understand the nutrition contents' (32.8%). As for the positive effect of enforcing a nutrition labeling system, 62% of respondents affirmed 'ease of selecting products which are good for health'. The reading of general product labeling showed a significant positive correlation (p<0.01) with the reading of nutrition labeling. The amount the nutrition labeling was read showed a negative correlation (p<0.05) with comprehension of the information on the nutrition labeling contained. Therefore, providing more information on the nutrition labeling for the consumers of processed meat products and also educating them more comprehensively about the nutrition, which would ultimately help them improve their dietary life, is needed.