• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.524-529
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• 2004

In this paper, We described the method which developed the optimal control system for air separation unit to change production rates frequently and rapidly. Control models of the process were developed from actual plant data using subspace identification method which is developed by many researchers in resent years. The model consist of a series connection of linear dynamic block and static nonlinear block (Wiener model). The model is controlled by model based predictive controller. In MPC the input is calculated by on-line optimization of a performance index based on predictions by the model, subject to possible constraints. To calculate the optimal the performance index, conditions are expressed by LMI(Linear Matrix Inequalities).In order to access at the Bailey DCS system, we applied the OPC server and developed the Client program. The OPC sever is a device which can access Bailey DCS system.The Client program is developed based on the Matlab language for easy calculation,data simulation and data logging. Using this program, we can apply the optimal input to the DCS system at real time.

• Structural Engineering and Mechanics
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• 제52권1호
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• pp.149-172
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• 2014

Four algorithms for damage detection of trusses are presented in this paper. These approaches can detect damage by using both complete and incomplete measurements. The suggested methods are based on the minimization of the difference between the measured and analytical static responses of structures. A non-linear constrained optimization problem is established to estimate the severity and location of damage. To reach the responses, the successive quadratic method is used. Based on the objective function, the stiffness matrix of the truss should be estimated and inverted in the optimization procedure. The differences of the proposed techniques are rooted in the strategy utilized for inverting the stiffness matrix of the damaged structure. Additionally, for separating the probable damaged members, a new formulation is proposed. This scheme is employed prior to the outset of the optimization process. Furthermore, a new tactic is presented to select the appropriate load pattern. To investigate the robustness and efficiency of the authors' method, several numerical tests are performed. Moreover, Monte Carlo simulation is carried out to assess the effect of noisy measurements on the estimated parameters.

• Computers and Concrete
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• 제13권2호
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• pp.209-220
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• 2014

Vibration based damage detection is very popular in the civil engineering area. Especially, special structures like dams, long-span bridges and high-rise buildings, need continues monitoring in terms of mechanical properties of material, static and dynamic behavior. It has been stated in the International Commission on Large Dams that more than half of the large concrete dams were constructed more than 50 years ago and the old dams have subjected to repeating loads such as earthquake, overflow, blast, etc.,. So, some unexpected failures may occur and catastrophic damages may be taken place because of theloss of strength, stiffness and other physical properties of concrete. Therefore, these dams need repairs provided with global damage evaluation in order to preserve structural integrity. The paper aims to show the effectiveness of the model updating method for global damage detection on a laboratory arch dam model. Ambient vibration test is used in order to determine the experimental dynamic characteristics. The initial finite element model is updated according to the experimentally determined natural frequencies and mode shapes. The web thickness is selected as updating parameter in the damage evaluation. It is observed from the study that the damage case is revealed with high accuracy and a good match is attained between the estimated and the real damage cases by model updating method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권9호
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• pp.3258-3273
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• 2021

Malware is a severe threat to the computing system and there's a long history of the battle between malware detection and anti-detection. Most traditional detection methods are based on static analysis with signature matching and dynamic analysis methods that are focused on sensitive behaviors. However, the usual detections have only limited effect when meeting the development of malware, so that the manual update for feature sets is essential. Besides, most of these methods match target samples with the usual feature database, which ignored the characteristics of the sample itself. In this paper, we propose a new malware detection method that could combine the features of a single sample and the general features of malware. Firstly, a structure of Directed Cyclic Graph (DCG) is adopted to extract features from samples. Then the sensitivity of each API call is computed with Markov Chain. Afterward, the graph is merged with the chain to get the final features. Finally, the detectors based on machine learning or deep learning are devised for identification. To evaluate the effect and robustness of our approach, several experiments were adopted. The results showed that the proposed method had a good performance in most tests, and the approach also had stability with the development and growth of malware.

• 한국정보컨버전스학회:학술대회논문집
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• 한국정보컨버전스학회 2008년도 International conference on information convergence
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• pp.47-52
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• 2008

A radio-frequency identification(RFID) tag is a small, inexpensive microchip that emits an identifier in response to a query from a nearby reader. The price of these tags promises to drop to the range of $0.05 per unit in the next several years, offering a viable and powerful replacement for barcodes. The challenge in providing security for low-cost RFID tags is that they are computationally weak devices, unable to perform even basic symmetric-key cryptographic operations. Security researchers often therefore assume that good privacy protection in RFID tags is unattainable. In this paper, we explore a notion of minimalist cryptography suitable for RFID tags. We consider the type of security obtainable in RFID devices with a small amount of rewritable memory, but very limited computing capability. Our aim is to show that standard cryptography is not necessary as a starting point for improving security of very weak RFID devices. Our contribution is threefold: 1. We propose a new formal security model for authentication and privacy in RFID tags. This model takes into account the natural computational limitations and the likely attack scenarios for RFID tags in real-world settings. It represents a useful divergence from standard cryptographic security modeling, and thus a new view of practical formalization of minimal security requirements for low-cost RFID-tag security. 2. We describe protocol that provably achieves the properties of authentication and privacy in RFID tags in our proposed model, and in a good practical sense. Our proposed protocol involves no computationally intensive cryptographic operations, and relatively little storage. 3. Of particular practical interest, we describe some reduced-functionality variants of our protocol. We show, for instance, how static pseudonyms may considerably enhance security against eavesdropping in low-cost RFID tags. Our most basic static-pseudonym proposals require virtually no increase in existing RFID tag resources.

• 정보보호학회논문지
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• 제29권4호
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• pp.775-784
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• 2019

신규 및 변종 악성코드의 발생으로 모바일, IoT, windows, mac 등 여러 환경에서 악성코드 침해 공격이 지속적으로 증가하고 있으며, 시그니처 기반 탐지의 대응만으로는 악성코드 탐지에 한계가 존재한다. 또한, 난독화, 패킹, Anti-VM 기법의 적용으로 분석 성능이 저하되고 있는 실정이다. 이에 유사성 해시 기반의 패턴 탐지 기술과 패킹에 따른 파일 분류 후의 정적 분석 적용으로 기계학습 기반 악성코드 식별이 가능한 시스템을 제안한다. 이는 기존에 알려진 악성코드의 식별에 강한 패턴 기반 탐지와 신규 및 변종 악성코드 탐지에 유리한 기계학습 기반 식별 기술을 모두 활용하여 보다 효율적인 탐지가 가능하다. 본 연구 결과물은 정보보호 R&D 데이터 챌린지 2018 대회의 AI기반 악성코드 탐지 트랙에서 제공하는 정상파일과 악성코드를 대상으로 95.79% 이상의 탐지정확도를 도출하여 분석 성능을 확인하였다. 향후 지속적인 연구를 통해 패킹된 파일의 특성에 맞는 feature vector와 탐지기법을 추가 적용하여 탐지 성능을 높이는 시스템 구축이 가능할 것으로 기대한다.

• 한국식품저장유통학회지
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• 제21권6호
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• pp.903-907
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• 2014

식초 제조용 종초 선발을 위해 다양한 초산균을 분리한 후, 그들의 발효특성 분석하여 종초 가능성을 검토하였다. 수집된 식초 시료에서 분리된 균주의 16S rRNA 염기서열을 분석한 결과, A. pasteurianus, A. malorum, Ga. entanii, Ga. intermedius, Ga. xylinus로 동정되었다. 식초 제조용 초산균의 발효특성을 분석한 결과, 초산의 과산화능은 모든 시험 균주에서 음성으로 나타나 과산화를 보이지 않았으며, 초산 내성은 Gluconacetobacter 속 균주만 관찰되었다. pH 내성은A. malorum V5-7 균주가 가장 높았다. 식초 품질에 해로운 영향을 주는 섬유질상의 세포외 다당체인 콜로이드 생성 균주는 Ga. intermedius V11-5, Ga. xylinus V8-1 균주로 나타났다. 초산 생성능은 A. malorum V5-7, A. pasteurianus Gam2, Ga. intermedius V11-5에서 가장 높은 산 생성능을 나타내었다. 본 연구에서는 종초용 초산균의 발효 특성 연구를 통해 국내 발효 식초의 품질 향상을 기대한다.

• 한국지진공학회논문집
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• 제14권2호
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• pp.67-74
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• 2010

비닐하우스 아치구조에 고정햄머 및 이동가속도계 형식을 취한 충격진동실험을 수행하여 획득한 일련의 진동기록으로 부터 고유진동수, 감쇠율 및 모드형태 등과 같은 모달계수를 추출하기 위하여 최신 고급 주파수영역 시스템판별법인 PolyMAX 및 FDD를 적용하였다. 전자는 입력-출력 데이터 모두를 사용하며, 후자는 출력 데이터 만 을 사용한다. 본 연구의 비닐하우스 강재 파이프 아치와 같이 매우 세장한 구조물에 진동계측 등과 같은 비파괴 실험기법을 적용하여 정적좌굴 하중을 결정할 수 있는 지 여부 및 손상을 감지할 수 있는지 등에 대하여 중점적으로 조사하였다. 대체로 추출한 모달계수는 유한요소해석으로부터 획득한 결과와 좋은 일치를 나타냈으며, 지속적으로 수행 할 후속연구에 가능성을 제시하였다.

• 한국생산제조학회지
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• 제19권1호
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• pp.145-155
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• 2010

This paper presents a new approach to obstacle avoidance for mobile robot in unknown or partially unknown environments. The method combines two navigation subsystems: low level and high level. The low level subsystem takes part in the control of linear, angular velocities using a multivariable PI controller, and the nonlinear position control. The high level subsystem uses ultrasonic and IR sensors to detect the unknown obstacle include static and dynamic obstacle. This approach provides both obstacle avoidance and target-following behaviors and uses only the local information for decision making for the next action. Also, we propose a new algorithm for the identification and solution of the local minima situation during the robot's traversal using the set of fuzzy rules. The system has been successfully demonstrated by simulations and experiments.

• International Journal of Control, Automation, and Systems
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• 제2권2호
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• pp.189-200
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• 2004

In this paper, we introduce a new soft computing technique that dwells on the ideas of combining fuzzy rules in a fuzzy system with polynomial neural networks (PNN). The PNN is a flexible neural architecture whose structure is developed through the modeling process. Unfortunately, the PNN has a fatal drawback in that it cannot be constructed for nonlinear systems with only a small amount of input variables. To overcome this limitation in the conventional PNN, we employed one of three principal soft computing components such as a fuzzy system. As such, a space of input variables is partitioned into several subspaces by the fuzzy system and these subspaces are utilized as new input variables to the PNN architecture. The proposed soft computing technique is achieved by merging the fuzzy system and the PNN into one unified framework. As a result, we can find a workable synergistic environment and the main characteristics of the two modeling techniques are harmonized. Thus, the proposed method alleviates the problems of PNN while providing superb performance. Identification results of the three-input nonlinear static function and nonlinear system with two inputs will be demonstrated to demonstrate the performance of the proposed approach.