• Journal of Digital Convergence
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• v.11 no.1
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• pp.99-106
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• 2013

Personal information protection has become one of the most impending business issues because leakage of personal information can cause tremendous financial losses and image degradation. Consequently, personal information protection initiatives have been recognized widely in business. To invigorate personal information protection investments, performance measurement method such as cost benefits analysis or qualitative analyses are needed, which have not been studied enough in the previous studies. This study proposes a performance measurement model which can include quantitative and qualitative analyses in the context of personal information protection investments. A comparative analysis has been performed on security investment and IT investment performance measurements, which leads to choose the WiBe method (developed by the German Interior Ministry), considering the privacy characteristics and the method's applicability. In particular, the quantitative effect measured how proactive threat assessment based on the way according to the nature of the businesses and organizations of privacy and possible investment decisions. This study proposes the 16 performance indicators, which turn out to be meaningful in terms of their materiality and feasibility by conducting focus group interviews of 25 experts on personal information protection.

• Journal of Internet of Things and Convergence
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• v.8 no.6
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• pp.55-63
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• 2022

In recent years, with the rapid development of large and medium-sized urban rail transit in China, the total operating mileage of high-speed railway and the total number of EMUs(Electric Multiple Units) are rising. The system complexity of high-speed EMU is constantly increasing, which puts forward higher requirements for the safety of equipment and the efficiency of maintenance.At present, the maintenance mode of high-speed EMU in China still adopts the post maintenance method based on planned maintenance and fault maintenance, which leads to insufficient or excessive maintenance, reduces the efficiency of equipment fault handling, and increases the maintenance cost. Based on the intelligent operation and maintenance technology of PHM(prognostics and health management). This thesis builds an integrated PHM platform of "vehicle system-communication system-ground system" by integrating multi-source heterogeneous data of different scenarios of high-speed EMU, and combines the equipment fault mechanism with artificial intelligence algorithms to build a fault prediction model for traction motors of high-speed EMU.Reliable fault prediction and accurate maintenance shall be carried out in advance to ensure safe and efficient operation of high-speed EMU.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.3
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• pp.239-247
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• 2022

Cities are becoming more complex due to rapid industrialization and population growth in modern times. In particular, urban areas are rapidly changing due to housing site development, reconstruction, and demolition. Thus accurate road information is necessary for various purposes, such as High Definition Map for autonomous car driving. In the case of the Republic of Korea, accurate spatial information can be generated by making a map through the existing map production process. However, targeting a large area is limited due to time and money. Road, one of the map elements, is a hub and essential means of transportation that provides many different resources for human civilization. Therefore, it is essential to update road information accurately and quickly. This study uses Semantic Segmentation algorithms Such as LinkNet, D-LinkNet, and NL-LinkNet to extract roads from drone images and then apply hyperparameter optimization to models with the highest performance. As a result, the LinkNet model using pre-trained ResNet-34 as the encoder achieved 85.125 mIoU. Subsequent studies should focus on comparing the results of this study with those of studies using state-of-the-art object detection algorithms or semi-supervised learning-based Semantic Segmentation techniques. The results of this study can be applied to improve the speed of the existing map update process.

• Journal of the Korean Chemical Society
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• v.63 no.6
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• pp.459-472
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• 2019

The purpose of this study was to investigate the effect of science writing for different audiences on preservice chemistry teachers' chemistry concept understanding and modeling ability in general chemistry experiment activities using Argument-based Modeling (AbM) strategy. And we also examined preservice chemistry teachers' perceptions of modeling in different audience groups. The participants of the study were 18 university students in the first grade of preservice chemistry teachers taking a general chemistry experiment course. They completed eleven topics of general chemistry experiment using argument-based modeling strategy. The understanding of chemistry concept was compared with the effect size of pre- and post-chemistry concept test scores. To find out modeling ability, we analyzed level of model by each preservice chemistry teacher. Analytical framework for the modeling ability was composed of three elements, explanation, representation, and communication. The questionnaire was conducted to check up on preservice chemistry teacher's recognition of modeling. The result of analyzing the effect of modeling for different audience on the understanding of chemistry concept and modeling ability, the preservice chemistry teachers' were found to be more effective when the level of audience was low. There was no difference in the recognition of modeling between the groups for audience. However, we could confirm that the responses of preservice chemistry teachers are changed in concrete when they have an experience in succession on modeling.

• Journal of Industrial Convergence
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• v.17 no.4
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• pp.77-86
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• 2019

The purpose of this study was to identify the effect of Home Health Nursing based Respiratory Management Program (HHNbRMP) on unmet healthcare need and healthcare resource utilizations of patients applying the home mechanical ventilator in the home with amyotrophic lateral sclerosis. The subjects of this study were 40 patients placed in an experimental group(n=19) and a control group(n=21), respectively. This HHNbRMP based on Cox's interaction model was consisted of cognitive assent (education, specialized medical care, case management), internal motivation (airway clearance, thoracic and air accumulated exercise) and psychological response (meditation & active listening). The intervention was applied to experimental group during 12 weeks. As variables was measured at baseline, twelve, twenty-four weeks and healthcare unmet need, resource utilizations (admission, out patient department, emergency room) was measured at 24 weeks. The data were analyzed by t-test, ANOVA and Repeated Measures ANCOVA. This intervention was not effective the unmet healthcare need. But the admission in to the hospital among the healthcare resource utilizations variables showed a significant difference at twenty-four weeks(t=4.17, p=.049). This results suggest that applying this program tailored to patients condition, utility of medical resource would be decreased, specially admission.

• Proceedings of the KWS Conference
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• 2009.11a
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• pp.71-71
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• 2009

전자제품의 다양한 기능들의 융복합화 및 휴대 편의성 경향은 이제 더 이상 새로운 것이 아니다. 이러한 추세에 따라 전자부품들은 모듈화 되고, 휴대하기 용이해 지고 있다. 또한 다양한 제품 디자인에 적용하기 위해 제품에 장착되는 부품의 기구적 위치 배열의 한계 또한 제약 받고 있다. 따라서 최근의 전자부품은 모듈화 되고 있으며, 기구적 한계를 극복하기 위한 Flexible 모듈의 사용이 증가하고 있다. 또한 양산측면에서 Roll-to-Roll(R2R) 방식을 적용함으로써 생산성을 극대화 하고 있다. 이때 R2R 적용을 위해서는 제품이 굴곡 될 수 있도록 유연성이 보장되는 Bendable 전자모듈의 개발이 필수적으로 요구되고 있다. Flexible 기판은 더 이상 새로운 기술이 아니지만, Felxible 기판 내부에 칩이 내장되고, 회로가 형성되어 자체적으로 기능을 수행할 수 있도록 한 Bendable 전자모듈을 R2R 방식으로 제조하는 기술은 매우 새로운 접근이라 할 수 있다. 이러한 기술개발이 현실화 된다면, Wearable Electronics 및 Flexible Display 등 다양한 전자제품에 응용될 수 있을 것으로 기대된다. 그러나 이러한 제품의 상용화를 위해서는 Bendable 전자모듈에 대한 신뢰성이 확보되고, 제품으로써의 수명이 보증되어야 한다. 신규 개발되는 제품의 신뢰성 검증항목이나 수명평가 모델은 현재까지 제안되지 않고 있는 실정이다. 또한 다양한 사용 환경에서 고장(Failure) 발생을 유발하는 스트레스 인자(Stress Factor)를 도출함으로써, 가속시험 또는 신뢰성 검증을 위한 인가 스트레스를 선정할 수 있다. 그러나 이러한 고장물리를 기반으로 스트레스 인자를 해석한 결과는 아직 보고되고 있지 않다. 따라서 본 연구에서는 $50{\mu}m$ 두께의 Si Chip에 저항변화를 관찰하기 위한 회로를 형성한 후 폴리이미드 기판을 이용하여 Si Chip이 임베딩된 Bendable 전자모듈을 제작하였다. 전자모듈의 실사용 환경에서의 수명예측을 위한 사전단계로써 고장물리에 기반한 고장모드와 고장메카니즘을 해석하는 것이 최우선 수행되어야 하며, 이를 바탕으로 고장을 유발하는 스트레스 인자를 도출 하였다. 고장도출을 위해 시제품은 JEDEC J-STD-020C의 MSL시험, 고온가압시험, 열충격시험 및 고온저장시험을 각각 수행하였으며, 이로부터 발생된 각각의 고장유형을 분석함으로써 스트레스 인자를 도출하였다. 또한 모아레(Moire) 간섭계를 이용하여 제작된 샘플의 온도변화에 따른 변형해석을 수행하였고, 동시에 Half Symetry Model을 이용한 유한요소해석(FEA)을 수행하여 변형해석 및 스트레스 유발원인을 도출하였다. 이 결과로 부터 고장물리 기반의 고장해석과 Moire 분석 그리고 시뮬레이션 해석 결과를 바탕으로 Bendable 전자모듈의 고장유발 스트레스 인자를 해석할 수 있었다.

• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.87-94
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• 2022

Recently, with the development of information and communication infrastructure, the number of Internet access devices is rapidly increasing. Smartphones, laptops, computers, and even IoT devices are receiving information and communication services through Internet access. Since most of the device operating environment consists of web (WEB), it is vulnerable to web cyber attacks using web shells. When the web shell is uploaded to the web server, it is confirmed that the attack frequency is high because the control of the web server can be easily performed. As the damage caused by the web shell occurs a lot, each company is responding to attacks with various security devices such as intrusion prevention systems, firewalls, and web firewalls. In this case, it is difficult to detect, and in order to prevent and cope with web shell attacks due to these characteristics, it is difficult to respond only with the existing system and security software. Therefore, it is an automated defense system through the collection and analysis of web shells based on artificial intelligence machine learning that can cope with new cyber attacks such as detecting unknown web shells in advance by using artificial intelligence machine learning and deep learning techniques in existing security software. We would like to propose about. The machine learning-based web shell defense system model proposed in this paper quickly collects, analyzes, and detects malicious web shells, one of the cyberattacks on the web environment. I think it will be very helpful in designing and building a security system.

• Therapeutic Science for Rehabilitation
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• v.12 no.2
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• pp.69-83
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• 2023

Objective : This study, as a preliminary study, applied an Information & Communication Technology (ICT) home-based program to elderly people aged 65 years or older to confirm the effect of the cognitive enhancement program and to find the possibility of remote rehabilitation. Methods : This study from August to October 2022, three subjects were selected and the intervention was conducted for about 2 months. This intervention was conducted using Korean version of Mini-Mental State Examination, Korean version of Montreal Cognitive Assessment (MoCA-K), Computer Cognitive Senior Assessment System, and the Center for Epidemiologic Studies Depression scale to evaluate cognitive improvement before and after the program. The therapist remotely set the level of cognitive training according to the subject's level through weekly feedback. Results : After the intervention, all subjects showed improved scores in most items of the MoCA-K conducted before and after the intervention. In addition, among the items of Cotras-pro, upper cognition, language ability, attention, visual perception, and memory were improved. Conclusion : Cognitive rehabilitation training using an ICT home-based program not only prevented dementia but also made it habitual. Through this study, it was confirmed that remote rehabilitation for the elderly could be possible.

• Composites Research
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• v.36 no.3
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• pp.205-210
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• 2023

In this study, we developed a transfer learning framework based on homogenization data for efficient prediction of the effective mechanical properties and thermal conductivity of cellular foam structures. Mean-field homogenization (MFH) based on the Eshelby's tensor allows for efficient prediction of properties in porous structures including ellipsoidal inclusions, but accurately predicting the properties of cellular foam structures is challenging. On the other hand, finite element homogenization (FEH) is more accurate but comes with relatively high computational cost. In this paper, we propose a data-driven transfer learning framework that combines the advantages of mean-field homogenization and finite element homogenization. Specifically, we generate a large amount of mean-field homogenization data to build a pre-trained model, and then fine-tune it using a relatively small amount of finite element homogenization data. Numerical examples were conducted to validate the proposed framework and verify the accuracy of the analysis. The results of this study are expected to be applicable to the analysis of materials with various foam structures.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.1-25
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• 2020

Sentiment Analysis (SA) is a Natural Language Processing (NLP) task that analyzes the sentiments consumers or the public feel about an arbitrary object from written texts. Furthermore, Aspect-Based Sentiment Analysis (ABSA) is a fine-grained analysis of the sentiments towards each aspect of an object. Since having a more practical value in terms of business, ABSA is drawing attention from both academic and industrial organizations. When there is a review that says "The restaurant is expensive but the food is really fantastic", for example, the general SA evaluates the overall sentiment towards the 'restaurant' as 'positive', while ABSA identifies the restaurant's aspect 'price' as 'negative' and 'food' aspect as 'positive'. Thus, ABSA enables a more specific and effective marketing strategy. In order to perform ABSA, it is necessary to identify what are the aspect terms or aspect categories included in the text, and judge the sentiments towards them. Accordingly, there exist four main areas in ABSA; aspect term extraction, aspect category detection, Aspect Term Sentiment Classification (ATSC), and Aspect Category Sentiment Classification (ACSC). It is usually conducted by extracting aspect terms and then performing ATSC to analyze sentiments for the given aspect terms, or by extracting aspect categories and then performing ACSC to analyze sentiments for the given aspect category. Here, an aspect category is expressed in one or more aspect terms, or indirectly inferred by other words. In the preceding example sentence, 'price' and 'food' are both aspect categories, and the aspect category 'food' is expressed by the aspect term 'food' included in the review. If the review sentence includes 'pasta', 'steak', or 'grilled chicken special', these can all be aspect terms for the aspect category 'food'. As such, an aspect category referred to by one or more specific aspect terms is called an explicit aspect. On the other hand, the aspect category like 'price', which does not have any specific aspect terms but can be indirectly guessed with an emotional word 'expensive,' is called an implicit aspect. So far, the 'aspect category' has been used to avoid confusion about 'aspect term'. From now on, we will consider 'aspect category' and 'aspect' as the same concept and use the word 'aspect' more for convenience. And one thing to note is that ATSC analyzes the sentiment towards given aspect terms, so it deals only with explicit aspects, and ACSC treats not only explicit aspects but also implicit aspects. This study seeks to find answers to the following issues ignored in the previous studies when applying the BERT pre-trained language model to ACSC and derives superior ACSC models. First, is it more effective to reflect the output vector of tokens for aspect categories than to use only the final output vector of [CLS] token as a classification vector? Second, is there any performance difference between QA (Question Answering) and NLI (Natural Language Inference) types in the sentence-pair configuration of input data? Third, is there any performance difference according to the order of sentence including aspect category in the QA or NLI type sentence-pair configuration of input data? To achieve these research objectives, we implemented 12 ACSC models and conducted experiments on 4 English benchmark datasets. As a result, ACSC models that provide performance beyond the existing studies without expanding the training dataset were derived. In addition, it was found that it is more effective to reflect the output vector of the aspect category token than to use only the output vector for the [CLS] token as a classification vector. It was also found that QA type input generally provides better performance than NLI, and the order of the sentence with the aspect category in QA type is irrelevant with performance. There may be some differences depending on the characteristics of the dataset, but when using NLI type sentence-pair input, placing the sentence containing the aspect category second seems to provide better performance. The new methodology for designing the ACSC model used in this study could be similarly applied to other studies such as ATSC.