• Journal of Software Assessment and Valuation
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• v.15 no.2
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• pp.111-119
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• 2019

The existing smart grid device authentication system is concentrated on DCU, meter reading FEP and MDMS, and the authentication system for smart meters is not established. Although some cryptographic chips have been developed at present, it is difficult to complete the PKI authentication scheme because it is at the low level of simple encryption. Unlike existing power grids, smart grids are based on open two-way communication, increasing the risk of accidents as information security vulnerabilities increase. However, PKI is difficult to apply to smart meters, and there is a possibility of accidents such as system shutdown by sending manipulated packets and sending false information to the operating system. Issuing an existing PKI certificate to smart meters with high hardware constraints makes authentication and certificate renewal difficult, so an ultra-lightweight password authentication protocol that can operate even on the poor performance of smart meters (such as non-IP networks, processors, memory, and storage space) was designed and implemented. As a result of the experiment, lightweight cryptographic authentication protocol was able to be executed quickly in the Cortex-M3 environment, and it is expected that it will help to prepare a more secure authentication system in the smart grid industry.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11a
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• pp.19-21
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• 2005

현대의 산업화 사회에서는 자동인식을 통해서 사람과 사물을 식별하고자 하는 연구들이 진행되고 있다. 그 대표적인 예로 바코드를 이용한 접촉식 판별기술이 있고, 라디오 주파수를 이용한 RFID(Radio Frequency Identification) 기술을 들 수 있다. RFID의 경우는 무선 주파수를 이용하기 때문에 대량의 사물을 동시에 인식 할 수 있다는 장점이 있다. 하지만. 어떠한 상황에서 리더의 요청에 응답을 하는 리더-태그 시스템이기 때문에 사용자의 프라이버시 침해 문제를 야기 할 수 있다. 사용자의 프라이버시 침해문제를 막기 위해서 많은 연구들이 진행되고 있다. 그 중에서, Miyako Ohkubo의 Hash체인을 이용한 프라이버시 보호 기법은 정보유출, 위치추적공격(Location Tracking Attack), 전방위보안성(Forward Security)과 같은 프라이버시 침해문제들로부터 사용자의 프라이버시를 보호 할 수 있는 프로토콜이다. 그러나 Hash함수를 태그에 구현하는 것은 현재까지는 불가능한 상황이다. 또, Martin Feldhofer의 AES(Advanced Encryption Standard)를 사용한 프로토콜은 실제로 태그에 구현 가능하면서 내부구조가 8bit인 AES를 사용함으로써 암호학적인 강도를 높였으나, 프라이버시 침해 문제에서 단점을 드러냈다. 이러한 단점을 보완한 AES기반에서의 개선된 RFID 프라이버시 보호 프로토콜은 실제적으로 태그에 구현 가능한 AES를 이용한 암호화 체인을 통해서 프라이버시 보호에 우수하면서 실제 사용이 가능한 프로토콜을 제안하였다[1]. 그러나, 이 프로토콜은 생성되는 키 값들이 물리적 공격을 통해서 노출이 되었을 때, 이전의 seed값과 키 값들이 노출 되는 단점이 있다. 본 논문에서는 이러한 문제들을 해결하고자 프라이버시보호에 새로운 키 생성 방법을 통한 강력한 프로토콜을 제안 한다.하였으나 사료효율은 증진시켰으며, 후자(사양, 사료)와의 상호작용은 나타나지 않았다. 이상의 결과는 거세비육돈에서 1) androgen과 estrogen은 공히 자발적인 사료섭취와 등지방 침적을 억제하고 IGF-I 분비를 증가시키며, 2) 성선스테로이드호르몬의 이 같은 성장에 미치는 효과의 일부는 IGF-I을 통해 매개될 수도 있을을 시사한다. 약 $70 {\~} 90\%$의 phenoxyethanol이 유상에 존재하였다. 또한, 미생물에 대한 항균력도 phenoxyethanol이 수상에 많이 존재할수록 증가하는 경향을 나타내었다. 따라서, 제형 내 oil tomposition을 변화시킴으로써 phenoxyethanol의 사용량을 줄일 수 있을 뿐만 아니라, 피부 투과를 감소시켜 보다 피부 자극이 적은 저자극 방부시스템 개발이 가능하리라 보여 진다. 첨가하여 제조한 curd yoghurt는 저장성과 관능적인 면에서 우수한 상품적 가치가 인정되는 새로운 기능성 신제품의 개발에 기여할 수 있을 것으로 사료되었다. 여자의 경우 0.8이상이 되어서 심혈관계 질환의 위험 범위에 속하는 수준이었다. 삼두근의 두겹 두께는 남녀 각각 $20.2\pm8.58cm,\;22.2\pm4.40mm$으로 남녀간에 유의한 차이는 없었다. 조사대상자의 식습관 상태는 전체 대상자의 $84.4\%$가 대부분이 하루 세끼 식사를 규칙적으로 하고 있었으며 식사속도는 허겁지겁 빨리 섭취하는 경우가 남자는 $31.0\%$, 여자는 $21.4\%$로 나타났고 이들을 제외한 나머지 사람들은 보통 속도 혹은 충분한 시간을 가지고 식사를 하였다. 평소 식사량은 조금 적게 혹은 적당하게 섭취하는 사람이 대부분이었으며 남자가 여자보다는 배부르게 먹는 경 향이 유의적으로 높았다(p<0.05). 식사는 혼자 하는 경우가 남자

• Journal of dental hygiene science
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• v.17 no.6
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• pp.523-532
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• 2017

The purpose of this study was to provide basic data to standardize the clinical dental hygiene curriculum, based on analysis of current clinical dental hygiene curricula in Korea. We emailed questionnaires to 12 schools to investigate clinical dental hygiene curricula, from February to March, 2017. We analyzed the clinical dental hygiene curricula in 5 schools with a 3-year program and in 7 schools with a 4-year program. The questionnaire comprised nine items on topics relating to clinical dental hygiene, and four items relating to the dental hygiene process and oral prophylaxis. The questionnaire included details regarding the subject name, the grade/semester/credit system, course content and class hours, the number of senior professors, and the number of patients available for dental hygiene clinical training purposes. In total, there were 96 topics listed in the curricula relating to clinical dental hygiene training, and topics varied between the schools. There was an average of 20.4 topic credits, and more credits and hours were allocated to the 4-year program than to the 3-year program. On average, the ratio of students to professors was 21.4:1. Course content included infection control, concepts for dental hygiene processes, dental hygiene assessment, intervention and evaluation, case studies, and periodontal instrumentation. An average of 2 hours per patient was spent on dental hygiene practice, with an average of 1.9 visits. On average, student clinical training involved 19 patients and 26.6 patients in the 3-year and 4-year programs, respectively. The average participation time per student per topic was 38.0 hours and 53.1 hours, in the 3-year and 4-year programs, respectively. Standardizing the clinical dental hygiene curricula in Korea will require consensus guidelines on topics, the number of classes required to achieve core competencies as a dental hygienist, and theory and practice time.

• Journal of Intelligence and Information Systems
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• v.27 no.4
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• pp.23-48
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• 2021

The Korean economy has achieved continuous economic growth for the past several decades thanks to the government's export strategy policy. This increase in exports is playing a leading role in driving Korea's economic growth by improving economic efficiency, creating jobs, and promoting technology development. Traditionally, the main factors affecting Korea's exports can be found from two perspectives: economic factors and industrial structural factors. First, economic factors are related to exchange rates and global economic fluctuations. The impact of the exchange rate on Korea's exports depends on the exchange rate level and exchange rate volatility. Global economic fluctuations affect global import demand, which is an absolute factor influencing Korea's exports. Second, industrial structural factors are unique characteristics that occur depending on industries or products, such as slow international division of labor, increased domestic substitution of certain imported goods by China, and changes in overseas production patterns of major export industries. Looking at the most recent studies related to global exchanges, several literatures show the importance of cultural aspects as well as economic and industrial structural factors. Therefore, this study attempted to develop a forecasting model by considering cultural factors along with economic and industrial structural factors in calculating the import volume of each country from Korea. In particular, this study approaches the influence of cultural factors on imports of Korean products from the perspective of PUSH-PULL framework. The PUSH dimension is a perspective that Korea develops and actively promotes its own brand and can be defined as the degree of interest in each country for Korean brands represented by K-POP, K-FOOD, and K-CULTURE. In addition, the PULL dimension is a perspective centered on the cultural and psychological characteristics of the people of each country. This can be defined as how much they are inclined to accept Korean Flow as each country's cultural code represented by the country's governance system, masculinity, risk avoidance, and short-term/long-term orientation. The unique feature of this study is that the proposed final prediction model can be selected based on Design Principles. The design principles we presented are as follows. 1) A model was developed to reflect interest in Korea and cultural characteristics through newly added data sources. 2) It was designed in a practical and convenient way so that the forecast value can be immediately recalled by inputting changes in economic factors, item code and country code. 3) In order to derive theoretically meaningful results, an algorithm was selected that can interpret the relationship between the input and the target variable. This study can suggest meaningful implications from the technical, economic and policy aspects, and is expected to make a meaningful contribution to the export support strategies of small and medium-sized enterprises by using the import forecasting model.

• Korean Chemical Engineering Research
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• v.60 no.3
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• pp.334-346
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• 2022

There is a lack of knowledge and information on the understanding and application of the Process Safety Management (PSM) system, recognized as a major cause of industrial accidents in small-and medium-sized workplaces. Hence, it is necessary to prepare a protocol to secure the practical and continuous levels of implementation for PSM and eliminate human errors through tracking management. However, insufficient research has been conducted on this. Therefore, this study investigated and analyzed the various violations in the administrative measures, based on the regulations announced by the Ministry of Employment and Labor, in approximately 200 small-and medium-sized PSM workplaces with fewer than 300 employees across in korea. This study intended to contribute to the prevention of major industrial accidents by developing a facility maintenance web program that removed human errors in small-and medium-sized workplaces. The major results are summarized as follows. First, It accessed the web via a QR code on a smart device to check the equipment's specification search function, cause of failure, and photos for the convenience of accessing the program, which made it possible to make requests for the it inspection and maintenance in real time. Second, it linked the identification of the targets to be changed, risk assessment, worker training, and pre-operation inspection with the program, which allowed the administrator to track all the procedures from start to finish. Third, it made it possible to predict the life of the equipment and verify its reliability based on the data accumulated through the registration of the pictures for improvements, repairs, time required, cost, etc. after the work was completed. It is suggested that these research results will be helpful in the practical and systematic operation of small-and medium-sized PSM workplaces. In addition, it can be utilized in a useful manner for the development and dissemination of a facility maintenance web program when establishing future smart factories in small-and medium-sized PSM workplaces under the direction of the government.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.173-198
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• 2020

For a long time, many studies have been conducted on predicting the success of campaigns for customers in academia, and prediction models applying various techniques are still being studied. Recently, as campaign channels have been expanded in various ways due to the rapid revitalization of online, various types of campaigns are being carried out by companies at a level that cannot be compared to the past. However, customers tend to perceive it as spam as the fatigue of campaigns due to duplicate exposure increases. Also, from a corporate standpoint, there is a problem that the effectiveness of the campaign itself is decreasing, such as increasing the cost of investing in the campaign, which leads to the low actual campaign success rate. Accordingly, various studies are ongoing to improve the effectiveness of the campaign in practice. This campaign system has the ultimate purpose to increase the success rate of various campaigns by collecting and analyzing various data related to customers and using them for campaigns. In particular, recent attempts to make various predictions related to the response of campaigns using machine learning have been made. It is very important to select appropriate features due to the various features of campaign data. If all of the input data are used in the process of classifying a large amount of data, it takes a lot of learning time as the classification class expands, so the minimum input data set must be extracted and used from the entire data. In addition, when a trained model is generated by using too many features, prediction accuracy may be degraded due to overfitting or correlation between features. Therefore, in order to improve accuracy, a feature selection technique that removes features close to noise should be applied, and feature selection is a necessary process in order to analyze a high-dimensional data set. Among the greedy algorithms, SFS (Sequential Forward Selection), SBS (Sequential Backward Selection), SFFS (Sequential Floating Forward Selection), etc. are widely used as traditional feature selection techniques. It is also true that if there are many risks and many features, there is a limitation in that the performance for classification prediction is poor and it takes a lot of learning time. Therefore, in this study, we propose an improved feature selection algorithm to enhance the effectiveness of the existing campaign. The purpose of this study is to improve the existing SFFS sequential method in the process of searching for feature subsets that are the basis for improving machine learning model performance using statistical characteristics of the data to be processed in the campaign system. Through this, features that have a lot of influence on performance are first derived, features that have a negative effect are removed, and then the sequential method is applied to increase the efficiency for search performance and to apply an improved algorithm to enable generalized prediction. Through this, it was confirmed that the proposed model showed better search and prediction performance than the traditional greed algorithm. Compared with the original data set, greed algorithm, genetic algorithm (GA), and recursive feature elimination (RFE), the campaign success prediction was higher. In addition, when performing campaign success prediction, the improved feature selection algorithm was found to be helpful in analyzing and interpreting the prediction results by providing the importance of the derived features. This is important features such as age, customer rating, and sales, which were previously known statistically. Unlike the previous campaign planners, features such as the combined product name, average 3-month data consumption rate, and the last 3-month wireless data usage were unexpectedly selected as important features for the campaign response, which they rarely used to select campaign targets. It was confirmed that base attributes can also be very important features depending on the type of campaign. Through this, it is possible to analyze and understand the important characteristics of each campaign type.