• Clean Technology
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• v.15 no.2
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• pp.81-90
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• 2009

In this study various cleaning evaluation methods were tested and comparatively evaluated to help cleaning industry. In order to select alternative cleaning agents objectively and systematically, various cleaning evaluation methods such as gravimetric, optically simulated electron emission (OSEE), contact angle, and analytical instrument methods were employed for cleaning contaminants such as flux, solder and grease. The analytical instruments used in this work were Fourier transform infrared spectroscopy (FTIR), ultraviolet visible spectroscopy (UV-VIS) and high performance liquid chromatography (HPLC). The gravimetric method was able to measure cleaning efficiencies easily and simply, but it was not easy to analyze them precisely because of its limitation in the gravimetric measurement. However, the OSEE technique was able to measure quickly and precisely the clean ability of cleaning agents in comparison with the gravimetric method. The contact angle method was found to be necessary for taking special precaution in its application to the cleaning evaluation due to possible formation of tiny organic film on the substrate surface which might be generated from contaminants and cleaning agents. In case of precision analysis that cannot be done by gravimetric method, fine analytical instruments such as UV-VIS, FTIR and HPLC could be used in analyzing trace amount of flux, solder and grease quantitatively, which were extracted from the surface by special solvents.

• Journal of Korean Society for Quality Management
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• v.50 no.1
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• pp.169-181
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• 2022

Purpose: This paper addresses a case study of developing a flame retardant wood-plastic composites (WPC) by adding tannic acid to the existing synthetic wood. The optimal mixing ratios of six components are explored to minimize the burning time using two mixture designs. Methods: In the preliminary experiment, six components are considered to find important components and their ranges. Seven D-optimal mixture design points are generated. Two points are removed for the balance of plastic components to be maintained, and the remaining five points are augmented with two basic compositions. Four components are selected to be considered in the main experiment. In the main experiment, pellets are extruded at the eight mixture design points. In-house testing of burning time is executed three times. Specimens made of pellets from two promising flame retardant compositions are sent to the accredited laboratories and tested. Results: The test results are as follows: 1) The best composition (Wood flour, Tannic acid, PE, Lubricant) = (25, 41, 10, 2) (wt%) shows the burning time of 1 second, which is 9-fold improvement compared to the the burning time of 9 seconds from the existing composition (58, 0, 10, 2) (wt%). 2) The second best composition (41, 25, 10, 2) (wt%) results in the burning time of 2 seconds. This composition is inferior to the best composition in terms of the flame retardancy, but more economical since it needs less tannic acid which is 100-fold expensive than the wood flour. Conclusion: Flame retardant compositions are found by adding tannic acid to the existing WPC employing optimal mixture designs. This case study will be helpful to practitioners who try to develop new products with additional physical properties with as small number of experimental trials as possible. Future research direction includes exploring conditions which satisfy both performance level and cost limitation simultaneously.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.4
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• pp.593-601
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• 2017

In this paper, we constructed the model of production/investment, distribution, and screening by using variables that can be considered at each stage according to the value chain stage of the movie industry. To increase the predictive power of the model, a regression analysis was used to derive meaningful variables. Based on the given variables, we compared the difference in predictive power between the artificial neural network, which is a machine learning analysis method, and the decision tree analysis method. As a result, the accuracy of artificial neural network was higher than that of decision trees when all variables were added in production/ investment model and distribution model. However, decision trees were more accurate when selected variables were applied according to regression analysis results. In the screening model, the accuracy of the artificial neural network was higher than the accuracy of the decision tree regardless of whether the regression analysis result was reflected or not. This paper has an implication which we tried to improve the performance of movie prediction model by using machine learning analysis. In addition, we tried to overcome a limitation of linear approach by reflecting the results of regression analysis to ANN and decision tree model.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.1D
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• pp.135-143
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• 2009

Integrating the Global Positioning System (GPS) and Inertial Navigation System (INS) sensor technologies using the precise GPS Carrier phase measurements is a methodology that has been widely applied in those application fields requiring accurate and reliable positioning and attitude determination; ranging from 'kinematic geodesy', to mobile mapping and imaging, to precise navigation. However, such integrated system may not fulfil the demanding performance requirements when the baseline length between reference and mobil user GPS receiver is grater than a few tens of kilometers. This is because their positioning/attitude determination is still very dependent on the errors of the GPS observations, so-called "baseline dependent errors". This limitation can be remedied by the integration of GPS and INS sensors, using multiple reference stations. Hence, in order to derive the GPS distance dependent errors, this research proposes measurement processing algorithms for multiple reference stations, such as a reference station ambiguity resolution procedure using linear combination techniques, a error estimation based on Kalman filter and a error interpolation. In addition, all the algorithms are evaluated by processing real observations and results are summarized in this paper.

• The Korean Journal of Franchise Management
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• v.9 no.3
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• pp.31-43
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• 2018

Purpose - This study examines the role of foodservice franchisee's perceived justice(distributive, procedural, and interactional) in developing long-term orientation to franchisor and investigate the mediating role of cohesiveness and relationship satisfaction in the relationship between franchisee's perceived justice and long-term orientation to franchisor. Research design, data, and methodology - We collected data from managers and owners in foodservice franchisees located in Seoul, Korea. Among a total of 500 questionnaires, 500 questionnaires were returned. After excluding 36 invalid respondent questionnaires, 496 valid questionnaires(response rate of 99.2%) were analyzed using frequency, confirmatory factor analysis, correlations analysis, and structural equation modeling with SPSS 21 and SmartPLS 3.0. Result - The findings of this study are as follows: First, distributive justice and interactional justice had positive effects on cohesiveness, but procedural justice did not. Second, distributive justice and interactional justice had positive effects on relationship satisfaction, but procedural justice did not. Third, cohesiveness and relationship satisfaction had positive effects on franchisee's long-term orientation to franchisor. Conclusions - The implications of this study are as follows. First, this study found that procedural justice can create a high cohesiveness and identification of franchisee and also maintain a cooperative relationship with the franchisor. Second, this findings suggest that the perceived distributional and interactional justice can improve the satisfaction with the franchisor and thus positively influence the intention to maintain the relationship and the intention to recontract. Third, the results of this study indicate that the cohesiveness of franchisees can play a pivotal role to improve their satisfaction with the franchisor and pursue mutual development by continuously maintaining stable business relationship with franchisor. The findings of this study are subject to at least three limitations. First, the research subject is limited to the food service franchise shops in Seoul area, so the sample was not nationally representative of the franchise stores. Second, the perceived fairness is measured only from the point of view of the franchisee, and this study has a limitation to examine the difference between the perceived franchisee's and franchisor's justice. Third, Future research needs to identify more closely the relationships between perceived fairness and long-term orientation by gathering specific quantitative data such as the renewal rate and the business performance.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.687-692
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• 2023

We analyze the accuracy of emotion analysis of natural language processing deep learning model and propose to use it for emotional content development. After looking at the outline of the GPT-3 model, about 6,000 pieces of dialogue data provided by Aihub were input to 9 emotion categories: 'joy', 'sadness', 'fear', 'anger', 'disgust', and 'surprise'. ', 'interest', 'boredom', and 'pain'. Performance evaluation was conducted using the evaluation indices of accuracy, precision, recall, and F1-score, which are evaluation methods for natural language processing models. As a result of the emotion analysis, the accuracy was over 91%, and in the case of precision, 'fear' and 'pain' showed low values. In the case of reproducibility, a low value was shown in negative emotions, and in the case of 'disgust' in particular, an error appeared due to the lack of data. In the case of previous studies, emotion analysis was mainly used only for polarity analysis divided into positive, negative, and neutral, and there was a limitation in that it was used only in the feedback stage due to its nature. We expand emotion analysis into 9 categories and suggest its use in the development of emotional content considering it from the planning stage. It is expected that more accurate results can be obtained if emotion analysis is performed by additionally collecting more diverse daily conversations through follow-up research.

• Convergence Security Journal
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• v.22 no.3
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• pp.25-32
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• 2022

Recently, the image processing industry has been activated as deep learning-based technology is introduced in the image recognition and detection field. With the development of deep learning technology, learning model vulnerabilities for adversarial attacks continue to be reported. However, studies on countermeasures against poisoning attacks that inject malicious data during learning are insufficient. The conventional countermeasure against poisoning attacks has a limitation in that it is necessary to perform a separate detection and removal operation by examining the training data each time. Therefore, in this paper, we propose a technique for reducing the attack success rate by applying modifications to the training data and inference data without a separate detection and removal process for the poison data. The One-shot kill poison attack, a clean label poison attack proposed in previous studies, was used as an attack model. The attack performance was confirmed by dividing it into a general attacker and an intelligent attacker according to the attacker's attack strategy. According to the experimental results, when the proposed defense mechanism is applied, the attack success rate can be reduced by up to 65% compared to the conventional method.

• Smart Structures and Systems
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• v.32 no.4
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• pp.267-279
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• 2023

Data modelling and interpretation for structural health monitoring (SHM) field data are critical for evaluating structural performance and quantifying the vulnerability of infrastructure systems. In order to improve the data modelling accuracy, and extend the application range from data regression analysis to out-of-sample forecasting analysis, an improved most likely heteroscedastic Gaussian process (iMLHGP) methodology is proposed in this study by the incorporation of the outof-sample forecasting algorithm. The proposed iMLHGP method overcomes this limitation of constant variance of Gaussian process (GP), and can be used for estimating non-stationary typhoon-induced response statistics with high volatility. The first attempt at performing data regression and forecasting analysis on structural responses using the proposed iMLHGP method has been presented by applying it to real-world filed SHM data from an instrumented cable-stay bridge during typhoon events. Uncertainty quantification and correlation analysis were also carried out to investigate the influence of typhoons on bridge strain data. Results show that the iMLHGP method has high accuracy in both regression and out-of-sample forecasting. The iMLHGP framework takes both data heteroscedasticity and accurate analytical processing of noise variance (replace with a point estimation on the most likely value) into account to avoid the intensive computational effort. According to uncertainty quantification and correlation analysis results, the uncertainties of strain measurements are affected by both traffic and wind speed. The overall change of bridge strain is affected by temperature, and the local fluctuation is greatly affected by wind speed in typhoon conditions.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.165-184
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• 2023

Recently, Deep learning analysis of unstructured text data using language models, such as Google's BERT and OpenAI's GPT has shown remarkable results in various applications. Most language models are used to learn generalized linguistic information from pre-training data and then update their weights for downstream tasks through a fine-tuning process. However, some concerns have been raised that privacy may be violated in the process of using these language models, i.e., data privacy may be violated when data owner provides large amounts of data to the model owner to perform fine-tuning of the language model. Conversely, when the model owner discloses the entire model to the data owner, the structure and weights of the model are disclosed, which may violate the privacy of the model. The concept of offsite tuning has been recently proposed to perform fine-tuning of language models while protecting privacy in such situations. But the study has a limitation that it does not provide a concrete way to apply the proposed methodology to text classification models. In this study, we propose a concrete method to apply offsite tuning with an additional classifier to protect the privacy of the model and data when performing multi-classification fine-tuning on Korean documents. To evaluate the performance of the proposed methodology, we conducted experiments on about 200,000 Korean documents from five major fields, ICT, electrical, electronic, mechanical, and medical, provided by AIHub, and found that the proposed plug-in model outperforms the zero-shot model and the offsite model in terms of classification accuracy.

• Korean Journal of Radiology
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• v.20 no.1
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• pp.114-125
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• 2019

Objective: Segmented cardiac cine magnetic resonance imaging (MRI) is the gold standard for cardiac ventricular volumetric assessment. In patients with difficulty in breath-holding or arrhythmia, this technique may generate images with inadequate quality for diagnosis. Real-time cardiac cine MRI has been developed to address this limitation. We aimed to assess the performance of retrospective electrocardiography-gated real-time cine MRI at 3T for left ventricular (LV) volume and mass measurement. Materials and Methods: Fifty-one patients were consecutively enrolled. A series of short-axis cine images covering the entire left ventricle using both segmented and real-time balanced steady-state free precession cardiac cine MRI were obtained. End-diastolic volume (EDV), end-systolic volume (ESV), stroke volume (SV), ejection fraction (EF), and LV mass were measured. The agreement and correlation of the parameters were assessed. Additionally, image quality was evaluated using European CMR Registry (Euro-CMR) score and structure visibility rating. Results: In patients without difficulty in breath-holding or arrhythmia, no significant difference was found in Euro-CMR score between the two techniques (0.3 ± 0.7 vs. 0.3 ± 0.5, p > 0.05). Good agreements and correlations were found between the techniques for measuring EDV, ESV, EF, SV, and LV mass. In patients with difficulty in breath-holding or arrhythmia, segmented cine MRI had a significant higher Euro-CMR score (2.3 ± 1.2 vs. 0.4 ± 0.5, p < 0.001). Conclusion: Real-time cine MRI at 3T allowed the assessment of LV volume with high accuracy and showed a significantly better image quality compared to that of segmented cine MRI in patients with difficulty in breath-holding and arrhythmia.