• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권6호
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• pp.2168-2187
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• 2021

The smart grid replaces the traditional power structure with information inventiveness that contributes to a new physical structure. In such a field, malicious information injection can potentially lead to extreme results. Incorrect, FDI attacks will never be identified by typical residual techniques for false data identification. Most of the work on the detection of FDI attacks is based on the linearized power system model DC and does not detect attacks from the AC model. Also, the overwhelming majority of current FDIA recognition approaches focus on FDIA, whilst significant injection location data cannot be achieved. Building on the continuous developments in deep learning, we propose a Deep Learning based Locational Detection technique to continuously recognize the specific areas of FDIA. In the development area solver gap happiness is a False Data Detector (FDD) that incorporates a Convolutional Neural Network (CNN). The FDD is established enough to catch the fake information. As a multi-label classifier, the following CNN is utilized to evaluate the irregularity and cooccurrence dependency of power flow calculations due to the possible attacks. There are no earlier statistical assumptions in the architecture proposed, as they are "model-free." It is also "cost-accommodating" since it does not alter the current FDD framework and it is only several microseconds on a household computer during the identification procedure. We have shown that ANN-MLP, SVM-RBF, and CNN can conduct locational detection under different noise and attack circumstances through broad experience in IEEE 14, 30, 57, and 118 bus systems. Moreover, the multi-name classification method used successfully improves the precision of the present identification.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 26-27 Oct. 1990
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• pp.181-185
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• 1990

In design of the plastic injection mold, most of drawings are composed of basic entities. It is very easy to produce many kinds of drawings by Group Technology. Group Technology is a technique In which part similarities are used to classify parts into part families according to either geometric shape and size or processing requirements. Almost data for the mold are decided during the assembly design. A system which shows a good interfaces between the design stage and producing part exploding Is developed using AutoCAD system and data conversion technique.

• 한국연소학회:학술대회논문집
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• 한국연소학회 2012년도 제44회 KOSCO SYMPOSIUM 초록집
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• pp.77-78
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• 2012

To predict combustion instability in actual full-scale combustion chamber of rocket engines, air-injection test is proposed with scaling techniques. From the data, damping factors have been obtained as a function of hydraulic parameter and the data give us instability map. Two instability regions are presented and it is found that they coincide reasonably with them from hot-fire test with full-scale flow rates. Accordingly, the proposed approach can be applied cost-effectively to stability rating of jet injectors when mixing of fuel and oxidizer jets is the dominant process in instability triggering.

• 대한산업공학회지
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• 제19권4호
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• pp.71-86
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• 1993

In design stage of a plastic injection mold, basic entities of various kinds are introduced under the concept of Group Technology to produce a full drawings of the mold more effectively Most of the entity data should be extracted from the assembly drawing by pattern matching technique. An automated design system is developed using the extracted data of entities and thus shows a good interfaces between the design stage and producing part explosion without any manual conversion procedure under the AutoCAD environment.

• 정보보호학회논문지
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• 제30권3호
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• pp.455-464
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• 2020

데이터 활용의 다양성이 높아짐에 따라 비관계형 데이터베이스 사용이 증가했으며, 이에 대한 NoSQL 삽입 공격 또한 증가했다. 전통적으로 NoSQL 삽입 공격을 탐지하기 위해 규칙 기반 탐지 방법론이 제안돼왔으나, 이 방식은 규칙의 범위를 벗어나 발생하는 삽입 공격에의 대응이 어렵다는 한계점이 있다. 이에 본 논문에서는 CNN 알고리즘을 이용해 특징을 추출하고, RNN 알고리즘을 활용해 NoSQL 삽입 공격을 탐지하는 기법을 제시한다. 또한, 실험을 통하여 본 논문에서 제시한 모델이 기존의 지도학습을 이용한 가장 우수한 모델보다 정확도는 10%, 정밀도는 4%, 재현율은 14%, F2-score는 0.082만큼 더 높은 비율로 NoSQL 삽입 공격을 탐지함을 보인다.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2005년도 춘계학술대회 논문집
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• pp.1601-1604
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• 2005

The demand increasing of optical applications like as display devices derives interest for fabrication process. The product s development is apt to fabricate numerous thin and wide surfaces with micro pattern. Naturally that needs injection molding fabrication for the mass production. In existing manufacturing, the product quality is controlled by input fabrication condition from the outside. That can be called as a try and error method and not fundamentally solve the troubles; imperfect replication, war page, short shot, etc. To understand the cause and bring a solution, it is needed that check of changing in the cavity. This study can catch them. Data acquisition system about temperature and pressure distribution is settled and can get some data. From this research, other studies related with DAQ in cavity can start on the easier step.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2007년도 추계학술대회 논문집
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• pp.325-326
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• 2007

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2012년도 추계학술대회 논문집
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• pp.437-438
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• 2012

• 한국콘텐츠학회논문지
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• 제15권3호
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• pp.265-271
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• 2015

본 연구는 보건소에서 예방접종을 실시한 2012년 출생아 397명을 연구대상으로 하였으며 예방접종등록관리 보건정보시스템 전산등록 데이터에 축적된 2012년~2014년까지의 수집된 자료를 SPSS win 18.0 프로그램을 이용하여 빈도분석을 실시하였다. 연구대상자의 출생월별, 백신별, 접종시기별 미접종 현황을 분석한 결과 89명(22.4%)이 예방접종 전산 미등록, 미접종 영유아로 SMS 문자전송을 통해, 예방접종 안내 및 예방접종 실시를 하였으며 오류로 인한 전산등록 누락자, 연락처 부재로 인한 연락불가 20명을 제외한 69명(17.4%)의 미접종 영유아들로 39명(9.8%)이 기본접종을 완료하여 92.4%의 높은 예방접종률을 확인하였다. 예방접종을 통해 지역사회 내 감염병을 예방하고, 예방접종사업비 지원과 보건정책 방안을 마련하는데 기초 자료로 활용하기 위한 것이다.

• 한국정밀공학회지
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• 제8권1호
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• pp.105-115
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• 1991

This paper describes an integrated CAD and CAPP system for prismatic parts of injection mold which generates a complete process plan automatically from CAD data of a part without human intervention. This system employs Auto CAD as a CAD model and GS-CAPP as an automatic process planning system for injection mold. The proposed CAD/CAPP system consists of three modules such as CAD data conversion module, manufacturing feature recognition module, and CAD/CAPP interface module. CAD data conversion module transforms design data of AutoCAD into three dimensional part data. Manufacturing feature recognition module extracts specific manufacturing features of a part using feature recognition rule base. Each feature can be recognized by combining geometry, position and size of the feature. CAD/CAPP interface module links manufacturing feature codes and other head data to automatic process planning system. The CAD/CAPP system can improve the efficiency of process planning activities and reduce the time required for process planning. This system can provide a basis for the development of part feature based design by analyzing manufacturing features.