• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권4호
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• pp.1436-1457
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• 2018

Secure routing services in Wireless Sensor Networks (WSNs) are essential, especially in mission critical fields such as the military and in medical applications. Additionally, they play a vital role in the current and future Internet of Things (IoT) services. Lightness and efficiency of a routing protocol are not the only requirements that guarantee success; security assurance also needs to be enforced. This paper proposes a Secure-Fuzzy Energy Aware Routing Protocol (S-FEAR) for WSNs. S-FEAR applies a security model to an existing energy efficient FEAR protocol. As part of this research, the S-FEAR protocol has been analyzed in terms of the communication and processing costs associated with building and applying this model, regardless of the security techniques used. Moreover, the Qualnet network simulator was used to implement both FEAR and S-FEAR after carefully selecting the following security techniques to achieve both authentication and data integrity: the Cipher Block Chaining-Message Authentication Code (CBC-MAC) and the Elliptic Curve Digital Signature Algorithm (ECDSA). The performance of both protocols was assessed in terms of complexity and energy consumption. The results reveal that achieving authentication and data integrity successfully excluded all attackers from the network topology regardless of the percentage of attackers. Consequently, the constructed topology is secure and thus, safe data transmission over the network is ensured. Simulation results show that using CBC-MAC for example, costs 0.00064% of network energy while ECDSA costs about 0.0091%. On the other hand, attacks cost the network about 4.7 times the cost of applying these techniques.

• 전자공학회논문지
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• 제54권4호
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• pp.50-58
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• 2017

최근 들어, 효과적인 화재감지를 위해 이종 화재센서 데이터들을 융합하는 방안들이 제안되었으나, 룰 기반의 방법의 경우 적응성과 정밀도가 낮고, 퍼지추론의 경우 영상에 대한 고려 미흡으로 검출 속도와 정밀도가 떨어지는 등의 문제점들이 있다. 더불어 영상기반 딥러닝 기술들도 제안되었으나, 실제 상황에서 카메라가 없거나 카메라 영역 밖의 화재 발생에 대한 신속한 탐지가 어렵다. 이에 본 논문에서는 CNN 기반의 딥러닝 알고리즘과 온도 습도 가스 연기를 포함하는 이종 화재센서 데이터기반의 퍼지추론엔진을 결합시킨 새로운 방식의 화재 감지 시스템을 제안한다. 이로써 영상 데이터를 활용한 신속한 화재 감지와 이종 센서 데이터들을 이용한 신뢰성 있는 화재 감지가 가능해짐을 보인다. 또한, 대규모 시스템에서 컴퓨팅 파워의 지나친 서버 집중을 방지하기 위해 화재 인식 알고리즘에 분산 컴퓨팅 구조를 채택하여 확장성을 높인다. 마지막으로, NIST 화재 동역학 시뮬레이터를 이용한 화재 시뮬레이션 데이터와 화재영상을 활용하여 화재가 점진적으로 번지는 환경과 급작스럽게 폭발이 발생하는 환경에서 실험을 수행함으로써 시스템의 성능을 검증한다.

• 조명전기설비학회논문지
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• 제13권2호
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• pp.63-70
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• 1999

본 논문은 여러 설비시스템의 프로세스 제어에 사용되는 PID제어기의 최적 자동동조에 관한 새로운 방법을 제안하고자 한다. 이 방법은 먼저. 제어대상의 계단응답으로부터 모델링 된 1차 지연계를 Pad 근사화하고, Ziefler-Nichols의 한계감도법으로 초기값을 정한 후, 최대 오버슈트, 감쇠비, 상승시간, 정정시간에 대한 퍼지 평가함수를 초대로 하는 최적화되 PID 계수를 목표치로 하여 신경회로망의 역전파 알고리즘을 통해 충분히 반복, 학습시켜 새로운 K, L, T값을 입력하였을 때 근사적으로 최적화된 PID 계수를 구함으로써 퍼지추론에 의한 제어 규칙이 불필요하여 자동 동조시간이 짧다는 장점을 가지고 있다.

• 한국정보처리학회논문지
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• 제7권6호
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• pp.1753-1765
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• 2000

As the areas of computer application are expanded, real-time application environments that must process as many transactions as possible within their deadlines, such as a stock transaction systems, ATM switching systems etc, have been increased recently. The reason why the conventional database systems can't process soft real-time applications is the lack of prediction and poor performance on processing transaction's deadline. If transactions want to access data stored at the secondary storage, they can not satisfy requirements of real-time applications because of the disk delay time. This paper designs a main-memory database prototype systems to be suitable to real-time applications and then this system can produce rapid results without disk i/o as all of the information are loaded in main memory database. In thesis proposed the improved techniques with respect to logging, checkpointing, and recovering in our environment. In order to improve the performance of the system, a) the frequency of log analysis and redo processing is reduced by the proposed redo technique at system failure, b) database consistency is maintained by improved fuzzy checkpointing. The performance model is proposed which consists of two parts. The first part evaluates log processing time for recovery and compares with other research activities. The second part examines checkpointing behavior.

• International Journal of Safety
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• 제6권2호
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• pp.27-32
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• 2007

For last several decades with the achievement of fast economic development, the electrical fires occupies over 30 percent of total fire incidents almost every year in Korea and not decreased in spite of much times and efforts. Electrical fire cause diagnostics are to confirm a cause for the fire by examination of fire scene. Cause diagnosis methods haven't been systematized yet, because of limits for available information, investigator's biased knowledge, etc. Therefore, in order to assist the investigators and to find out the exact causes of electrical fires, required is research for an electrical fire cause diagnosis system using DB, computer programming and some mathematical tools. The electrical fire cause diagnosis system has two functions of DB and electrical fire cause diagnosis. The cause diagnosis is conducted by a case-based reasoning on a case base and rule-based reasoning on a rule base. For the diagnosis with high reliability, a mixed reasoning approach of a case-based reasoning and fuzzy rule-based reasoning has been adopted. The electrical fire cause diagnosis system proposes the electrical fire causes inferred from the diagnosis processes, and possibility of the causes as well.

• 반도체디스플레이기술학회지
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• 제2권1호
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• pp.7-9
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• 2003

In order to predict the moldability of an injection molded part, a simulation of filling is needed. Short shot is one of the most frequent troubles encountered during injection molding process. The adjustment of process conditions is the most economic way to troubleshoot the problematic short shot in cost and time since the mold doesn't need to be modified at all. But it is difficult to adjust the process conditions appropriately in no times since it requires an empirical knowledge of injection molding. In this paper, the intelligent CAE system synergistically combines fuzzy-neural network(FNN) for heuristic knowledge with CAE programs for analytical knowledge. To evaluate the intelligent algorithms, a cellular phone flip has been chosen as a finite element model and filling analyses have been performed with a commercial CAE software. As the results, the intelligent CAE system drastically reduces the troubleshooting time of short shot in comparison with the expert's conventional way which is similar to the golden section search algorithm.

• 한국반도체및디스플레이장비학회:학술대회논문집
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• 한국반도체및디스플레이장비학회 2002년도 추계학술대회 발표 논문집
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• pp.127-129
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• 2002

In order to predict the moldability of an injection molded part, a simulation of filling is needed. Short shot is one of the most frequent troubles encountered during injection molding process. The adjustment of process conditions is the most economic way to troubleshoot the problematic short shot in cost and time since the mold doesn't need to be modified at all. But it is difficult to adjust the process conditions appropriately in no times since it requires an empirical knowledge of injection molding. In this paper, the intelligent CAE system synergistically combines fuzzy-neural network(FNN) for heuristic knowledge with CAE programs for analytical knowledge. To evaluate the intelligent algorithms, a cellular phone flip has been chosen as a finite element model and filling analyses have been performed with a commercial CAE software. As the results, the intelligent CAE system drastically reduces the troubleshooting time of short shot in comparison with the expert's conventional way which is similar to the golden section search algorithm.

• International Journal of Internet, Broadcasting and Communication
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• 제15권2호
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• pp.144-156
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• 2023

Smart agriculture is a rapidly growing field that seeks to optimize crop yields and reduce risk through the use of advanced technology. A key challenge in this field is the need to create a comprehensive smart farm system that can effectively monitor and control the growth environment of crops, particularly when cultivating new varieties. This is where fuzzy theory comes in, enabling the collection and analysis of external environmental factors to generate a rule-based system that considers the specific needs of each crop variety. By doing so, the system can easily set the optimal growth environment, reducing trial and error and the user's risk burden. This is in contrast to existing systems where parameters need to be changed for each breed and various factors considered. Additionally, the type of house used affects the environmental control factors for crops, making it necessary to adapt the system accordingly. While developing such a framework requires a significant investment of labour and time, the benefits are numerous and can lead to increased productivity and profitability in the field of smart agriculture. We developed an AI platform for optimal control of facility houses by integrating data from mushroom crops and environmental factors, and analysing the correlation between optimal control conditions and yield. Our experiments demonstrated significant performance improvement compared to the existing system.

• 한국멀티미디어학회논문지
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• 제3권4호
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• pp.339-346
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• 2000

의료 영상은 환자에 대한 해부학적인 진단 정보를 얻기 위한 영상으로 정확한 병변 인식과 판단을 위해서는 조직별 분할이 선행되어야 한다. 본 논문에서는 T1 강조 영상 그리고 T2 강조 영상, PD 영상의 특징을 상호보완적으로 이용한 자동적인 영상 분할 방법을 제안한다. 제안한 분할 알고리듬은 PD 영상으로부터 대뇌마스크를 획득하고, 대뇌마스크를 T1 과 T2, PD의 입력 영상에 씌워 각각의 대뇌 영상을 획득하여 T1과 T2, PD를 축으로 하는 3차원 공간상에서 스케일 스페이스 필터링과, 3차원 클러스터링을 이용하여 대뇌 내부조직에 해당하는 클러스터를 찾아서 분할에 이용한다. 대뇌 영상분할은 이들 클러스터의 중심 값을 FCM 알고리듬의 초기 중심 값으로 두고 FCM 알고리듬을 이용하여 분할한다. 제안한 분할 알고리듬은 정확한 클러스터의 중심 값을 계산함으로 초기 값의 영향을 많이 받는 FCM 알고리듬의 단점을 보완하였고 다중 스펙트럼 영상의 특성을 조합하여 분할에 이용함으로 단일 스펙트럼 영상만을 이용하는 방법보다 향상된 분할 결과를 얻을 수 있었다.

• 대한토목학회논문집
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• 제31권5B호
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• pp.405-414
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• 2011

본 연구에서는 유역에서 관측되는 강우량과 유출량의 시계열 자료를 바탕으로 최근 시계열 예측 및 시스템 제어 분야에서 성공적으로 적용되고 있는 적응형 네트워크 기반 퍼지추론 시스템(ANFIS)을 갑천 유역에 적용하여 시유출량을 모델링하였다. 입력구조, 소속함수 종류와 개수 등을 다양하게 변화시켜 ANFIS 모형을 학습하고, 평균제곱근오차(RMSE), 평균첨두유량오차(PE) 및 평균첨두시간오차(TE)를 이용하여 ANFIS의 유출해석에 대한 적용성을 평가하였다. 현재시간의 시유출량 Q(t)에 대한 ANFIS의 적용성은 우수한 것으로 평가되었으며, ANFIS 모형은 관측유출량을 적절히 모의하였다. 입력구조가 다른 입력모형을 구성하여 최대 8시간까지 ANFIS의 유출예측 적용성을 평가하였다. 예측시간 증가에 따라서 ANFIS의 유출예측 정확도는 감소하여 예측시간 4시간 이상의 시유출량에 대한 ANFIS의 유출예측 적용성은 제한적이었다. ANFIS는 입력과 출력 자료들만 이용하므로 물리기반 모형에 비교하여 모형구축이 비교적 손쉽기 때문에 홍수 유출모델링에 ANFIS을 유용하게 적용할 수 있을 것으로 판단된다.