Earticle

현재 스마트폰의 급격한 보급과 IoT을 대상으로 활성화로 인해 소셜네트워크 서비스를 이용하여 악성코드 를 유포하거나 지능화된 APT와 랜섬웨어 등과 같은 지능적인 침입이 진행되고 있고 이로 인한 피해도 이전의 침입 보다는 많이 심각해지고 커지고 있는 실정이다. 따라서 본 논문에서는 이런 지능적인 악성 코드로 이루어지는 침입 행위를 탐지하기 위하여 지능적인 침입 상황 인식 추론 시스템을 제안하고, 제안한 시스템을 이용하여 지능적으로 진행되는 다양한 침입 행위를 조기에 탐지하고 대응하게 하였다. 제안 시스템은 이벤트 모니터와 이벤트 관리기, 상황 관리기, 대응 관리기, 데이터베이스로 구성되어 있으며 각 구성 요소들 사이에 긴밀한 상호 작용을 통해 기존 에 인식하고 있는 침입 행위를 탐지하게 하고 새로운 침입 행위에 대해서는 학습을 통해 추론 엔진의 성능을 개선 하는 기능을 통하여 탐지하게 하였다. 또한, 지능적인 침입 유형인 랜섬웨어를 탐지하는 시나리오 통하여 제안 시 스템이 지능적인 침입을 탐지하고 대응함을 알 수 있었다.

At present, due to the rapid spread of smartphones and activation of IoT, malicious codes are disseminated using SNS, or intelligent intrusions such as intelligent APT and ransomware are in progress. The damage caused by the intelligent intrusion is also becoming more consequential, threatening, and emergent than the previous intrusion. Therefore, in this paper, we propose an intelligent intrusion situation-aware reasoning system to detect transgression behavior made by such intelligent malicious code. The proposed system was used to detect and respond to various intelligent intrusions at an early stage. The anticipated system is composed of an event monitor, event manager, situation manager, response manager, and database, and through close interaction between each component, it identifies the previously recognized intrusive behavior and learns about the new invasive activities. It was detected through the function to improve the performance of the inference device. In addition, it was found that the proposed system detects and responds to intelligent intrusions through the state of detecting ransomware, which is an intelligent intrusion type.

Recent ubiquitous computing environments increasingly impact on our lives using the current technologies of sensor network and ubiquitous services. In this paper, we propose ubiquitous architectural framework for ubiquitous sleep aid service(UbiSAS) in the subset of ubiquitous computing for refreshing of human's sleep. And we examine technical feasibility. Human can recover his health through refreshing sleep from fatigue. Ubiquitous architectural framework for UbiSAS in digital home offers agreeable sleeping environment and improves recovery from fatigue. So we present new concept of ubiquitous architectural framework dissolving stress. Specially, we apply context to context-aware framework module. This context is transferred to context adaptive inference engine which has service invocation function in intelligent agent module. Ubiquitous architectural framework for UbiSAS using context adaptive rule inference engine without user intervention is technical issue. That is to say, we should take sleep comfortably during our sleeping. And sensed information during sleeping is changed to context-aware information. This presents significant information in context adaptive rule inference engine for UbiSAS. This information includes all sleeping state during sleeping in context-aware computing technique. So we propose more effective and most suitable ubiquitous architectural framework using context adaptive rule inference engine for refreshing sleep in this paper.

Smart home services for users require precise and reliable context information to guarantee credibility. To do this, system should have ability of context-awareness. Most of the context-aware systems have adopted rule-based inference but it did not fulfill users’ demand. To support valuable services in smart home environment, we present the rule-based inference engine which uses user profiles to let the system be aware of users’ context much accurately. Since the physical and logical size of the domain is relatively small, we also introduce inference algorithm optimized for limited size of memory space of the system.

맨홀, 지하정화조, 저장탱크, 밀폐공간 등의 유해 작업장은 환기가 불충분한 상태에서 산소결핍, 유해가스로 인한 건강장해와 인화성 물질에 의한 화재, 폭발 등의 위험이 있다. 이와 같은 유해환경 정보를 작업장 내의 센서를 통해서 실시간으로 모니터링하고, 위험으로부터 작업자의 안전을 보장할 수 있는 시스템이 필요하다. 이 논문에서는 작업장의 유해환경을 모니터링하기 위한 추론엔진을 설계한다. 제안하는 추론엔진은 규칙기반 시스템의 구조를 가지며 JESS를 활용한다. 제안 시스템은 특정 컴퓨팅 플랫폼에 제약되지 않으며 OSGi 기반의 미들웨어와 연동이 쉬운 특징을 가진다.

The risk of health impairment due to poor ventilation, fire and explosion by inflammable materials, and other unintended occurrences is always present in dangerous workplaces such as manholes, underground septic tanks, storage tanks and confined areas. Therefore, it a system which can monitor harmful working environment through sensors in workplace on a realtime basis and keep workers safe from the risk is needed. This paper has attempted to design an inference engine to monitor harmful environments in the workplace. The proposed inference engine has a rule-based system structure using JESS. This system is not confined to a particular computing platform and is easily interlocked with OSGi-based middleware.

In this research, we propose a mechanism to develop an inference engine and expert systems based on relational database and SQL (structured query language). Generally, former researchers had tried to develop an expert systems based on text-oriented knowledge base and backward/forward (chaining) inference engine. In these researches, however, the speed of inference was remained as a tackling point in the development of agile expert systems. Especially, the forward inference needs more times than backward inference. In addition, the size of knowledge base, complicate knowledge expression method, expansibility of knowledge base, and hierarchies among rules are the critical limitations to develop an expert systems. To overcome the limitations in speed of inference and expansibility of knowledge base, we proposed a relational database-oriented knowledge base and forward inference engine. Therefore, our proposed mechanism could manipulate the huge size of knowledge base efficiently, and inference with the large scaled knowledge base in a short time. To this purpose, we designed and developed an SQL-based forward inference engine using relational database. In the implementation process, we also developed a prototype expert system and presented a real-world validation data set collected from medical diagnosis field.

Although many methods of knowledge acquisition has been developed in the exper systems field, such a need form causal knowledge acquisition hs not been stressed relatively. In this respect, this paper is aimed at suggesting a causal knowledge acquisition process, and then investigate the causal knowledge-based inference process. A vehicle for causal knowledge acquisition is FCM (Fuzzy Cognitive Map), a fuzzy signed digraph with causal relationships between concept variables found in a specific application domain. Although FCM has a plenty of generic properties for causal knowledge acquisition, it needs some theoretical improvement for acquiring a more refined causal knowledge. In this sense, we refine fuzzy implications of FCM by proposing fuzzy causal relationship and fuzzy partially causal relationship. To test the validity of our proposed approach, we prototyped a causal knowledge-driven inference engine named CAKES and then experimented with some illustrative examples.

This paper presents a method on applying Genetric Algorithms(GA), which is a well-know high performance optimizing algorithm, to construct the self-organizing fuzzy logic controller. Fuzzy logic controller considered in this paper utilized Sugeno's hybrid inference method. which has an advantage of simple defuzzification process in the inference engine. Genetic algorithm is used to find the iptimal parameters in the FLC. The proposed approach will be demonstrated using 2 d. o. f robot manipulator to verify its effectiveness.

In this research, we propose the mechanism to develop self-evolving expert systems (SEES) based on data mining (DM), fuzzy neural networks (FNN), and relational database (RDB)-driven forward/backward inference engine. Most researchers had tried to develop a text-oriented knowledge base (KB) and inference engine (IE). However, this approach had some limitations such as 1) automatic rule extraction, 2) manipulation of ambiguousness in knowledge, 3) expandability of knowledge base, and 4) speed of inference. To overcome these limitations, knowledge engineers had tried to develop an automatic knowledge extraction mechanism. As a result, the adaptability of the expert systems was improved. Nonetheless, they didn't suggest a hybrid and generalized solution to develop self-evolving expert systems. To this purpose, we propose an automatic knowledge acquisition and composite inference mechanism based on DM, FNN, and RDB-driven inference engine. Our proposed mechanism has five advantages. First, it can extract and reduce the specific domain knowledge from incomplete database by using data mining technology. Second, our proposed mechanism can manipulate the ambiguousness in knowledge by using fuzzy membership functions. Third, it can construct the relational knowledge base and expand the knowledge base unlimitedly with RDBMS (relational database management systems) module. Fourth, our proposed hybrid data mining mechanism can reflect both association rule-based logical inference and complicate fuzzy relationships. Fifth, RDB-driven forward and backward inference time is shorter than the traditional text-oriented inference time.

A fuzzy inference logic system is proposed for gas turbine engine fault isolation. The gas path measurements used for fault isolation are exhaust gas temperature, low and high rotor speed, and fuel flow. The fuzzy inference logic uses rules developed from a model of performance influence coefficients to isolate engine faults while accounting for uncertainty in gas path measurements. Inputs to the fuzzy inference logic system are measurement deviations of gas path parameters which are transferred directly from the ECM(Engine Control Monitoring) program and outputs are engine module faults. The proposed fuzzy inference logic system is tested using simulated data developed from the ECM trend plot reports and the results show that the proposed fuzzy inference logic system isolates module faults with high accuracy rate in the environment of high level of uncertainty.

본 논문은 온톨로지를 분석할 때 사용하는 추론엔진의 대한 연구와 비교를 서술한다. 온톨로지를 분석할 때 메타데이터로 검색하고 이를 바탕으로 데이터를 추론한다. 이러한 온톨로지는 시멘틱 웹에서 중요하며 논리를 바탕으로 추론을 적용하여 데이터를 분석한다. 이러한 추론엔진의 적용 사례는 사물 인터넷, 상황인식, 전문가 시스템 등 다양한 곳에 적용이 가능하다. 추론엔진의 연구와 비교를 통하여 추론 기술에 대한 연구와 조사를 서술한다.

In this paper, an algorithm for PLC(Programmable Logic Controller) fault diagnosis system is proposed and experimentation is conducted with a PLC and a virtual plant. Wrong output backward tracking algorithm is proposed in order to find the external faults of PLC. And query with keywords of the fault systems and specially designed test sequence programs are used. We lay emphasis on the backward tracking algorithm to diagnose the faults of PLC. It is shown experimentally that the proposed algorithm can find the faults which a typical self diagnostics in the-commercially available PLC cannot.

퍼지제어가 서보시스템에 응용할 때 퍼지정보 시스템의 하드웨어 실현은 고속연산, 짧은 실시간의 제어 및 시스템의 소형 경량화가 요구되고 있다. 본 연구의 목적은 서보시스템에 적용하기 위한 퍼지정보 시스템의 하드웨어를 개발하는 것이다. 본 연구에서는 $\alpha$-레벨 집합의 분해 원리에 기초하여 $\alpha$-컷 양자화에 따라 퍼지추론의 계산 방법을 제안하려다. 이 방법은 퍼지제어기의 아날로그 하드웨어의 구성을 쉽게 행할 수 있다. $\alpha$-컷 양자화에 따른 회지추론 엔진의 출력으로 $\alpha$-컷 양자화에 따른 퍼지추론 영향을 조사하였고 $\alpha$-컷 양자화 4 단계에서 DC 서보시스템의 퍼지제어에 양호한 결과를 얻었다. 제안한 퍼지추론 연산과 중심법에 의한 비퍼지화에서 직접 PWM 조작 신호를 얻는 방법을 나타내었다. 서보계의 실험을 행하여 유용성을 구명하였다.

As the fuzzy control is applied to servo system, the hardware implementation of the fuzzy information systems requires the high speed operations, short real time control and the small size systems. The aims of this study is to develop hardware of the fuzzy information systems to be apply to servo system. In this paper, we propose a calculation method of approximate reasoning for fuzzy control based on $\alpha$ -level set decomposition of fuzzy sets by quantize $\alpha$ -cuts. This method can be easily implemented with analog hardware. The influence of quantization Bevels of $\alpha$-cuts on output from fuzzy inference engine is investigated. It is concluded that 4 quantization levels give sufficient result for fuzzy control performance of dc servo system. The hardware implementation of proposed operation method and of the defuzzification by gravity center method which is directly converted to PWM actuating signal is also presented. It is verified useful with experiment for dc servo system.

현재 일선고등학교에서 사용하고 있는 학생생활기록부의 효율적 이용 및 명가방법에 대한 논의와 연구가 활발한 실정이다. 본 논문에서는 학생생활기록부의 13개 항목을 분석하여 몇 개의 영역으로 일반화시킨 후 각 영역에 해당하는 확정계약조건들을 결정한다. 또한 이를 기반으로 일반화시킨 영역들을 추론하고, 교사들의 관찰내용과 같은 불확정계약조건들을 상담형태로 지식베이스화시켜 추론할 수 있도록 한다. 제안된 추론엔진은 교사들이 학생들에 대한 학습 및 생활지도시 이용함으로써 보다 정확하고 효율적인 지도를 유도할 수 있으며, 대학입시에서도 비교과 영역평가시 변별력을 높일 수 있다.

가용 센서를 내장하고 있는 모바일 디바이스의 사용이 많아지고 자동화, 자율화, 맞춤식 서비스의 요구가 커짐에 따라 컨텍스트 인지 모바일 컴퓨팅(Context-Aware Mobile Computing)의 필요성이 증대하고 있다. 하지만 추론 시스템 설계는 컨텍스트 분석, 인지하고자 하는 상황분석 등의 복잡한 과정을 요구한다. 본 눈문에서는 이러한 과정을 간결하고 정확하게 표현하기 위한 컨텍스트-상황 추론 요소의 범용적 정형 모델을 제안한다. 또한 추론 요소들의 정형 모델을 실사례에 적용하여 본 논문에서 제안하고 있는 추론 요소들의 정형 모델이 실효성을 가지고 있으며 범용적임을 보여준다.

가용 센서를 내장하고 있는 모바일 디바이스의 사용이 많아지고 자동화, 자율화, 맞춤식 서비스의 요구가 커짐에 따라 컨텍스트 인지 모바일 컴퓨팅(Context-Aware Mobile Computing)의 필요성이 증대하고 있다. 하지만 추론 시스템 설계는 컨텍스트 분석, 인지하고자 하는 상황분석 등의 복잡한 과정을 요구한다. 본 눈문에서는 이러한 과정을 간결하고 정확하게 표현하기 위한 컨텍스트-상황 추론 요소의 범용적 정형 모델을 제안한다. 또한 추론 요소들의 정형 모델을 실사례에 적용하여 본 논문에서 제안하고 있는 추론 요소들의 정형 모델이 실효성을 가지고 있으며 범용적임을 보여준다.