Earticle

본 연구는 콰욜라 작품에서 컴퓨터 알고리즘 기술이 주체로서, 인간이 재현한 이미지를디지털 이미지로 재창조하는 데서 발생한 숭고적 의미와 특성을 고찰한다. 그리고 이를 통해콰욜라 작품이 인간중심성을 비판적으로 성찰한다는 점을 살펴본다. 숭고에서 주체는 변형되고 비결정화되어 이전과 다른 창조적 자아로 거듭난다. 숭고에서 주체의 이런 양상은 주체로서 알고리즘 기술이 대상에 야기하는 현상과 유사하다. 알고리즘 기술은 대상을 데이터화함으로써 대상에 창조적 잠재력을 준다. 데이터로서 비결정화 된 대상은 다른 존재들로 다시변형되어 이전과 불일치할 가능성을 갖기 때문이다. ≪포로들 #B06 Captives #B06≫ (2014)과 ≪유적: 프로방스 Remains: Provance≫(2016)에서 알고리즘 기술은 대상을 변형, 비결정화 하고 이전과 불일치하게 만들어 대상에 창조적 잠재력과 숭고적 특성을 준다. 그래서 인간 시각에 대한 차이를 발생시켜 인간 시각과 근본적으로 같지만 다른 형태를 지니는 기계의 시각을 제시한다. 그리하여 알고리즘 기술은 인간이 기계의 시각을 통해 자신에대한 메타적 보기를 가능케 한다.

This study explores the meaning and characteristics of the sublimeness brought forth by computer algorithm technology while it transforms human-created images into digital images. In the process, this paper will examine Davide Quayola’s works and show that they are indeed an attempt to critically reflect on anthropocentrism. In the sublime theory, the subject is metamorphosed into an indeterminate entity, and thus creatively reborn. This parallels the phenomenon that algorithm technology, as subject, induces to objects. Algorithm technology affords creative potential to objects by turning them into digital data. In Captive #B06 and Remains: Provence, algorithm technology metamorphoses objects and makes them indeterminate and disagreeing with their previous status, giving them creative potential and the characteristics of the sublimeness. The mechanical view that algorithm technology creates is fundamentally the same as the human view, but has a different form. Therefore, algorithm technology enables humans to meta-view themselves through the mechanical views.

The essence of PHM technology is to process the collected information with the help of the system information collected by sensors, using information fusion, artificial intelligence, big data, reasoning algorithms and other technologies, and realize the monitoring management, status evaluation and fault prediction functions of the target system. PHM is an important part of the intelligent equipment detection and maintenance system. Its application and realization in the railway field is the key link of the intelligent operation and maintenance of multiple units, and is an important means to realize the shift from planned preventive maintenance to digital and accurate condition maintenance. It is of great significance for China's high-speed railway to maintain the world's advanced level and move towards higher quality, efficiency and efficiency. With the improvement of operation speed and the growth of application scale of High-Speed Electric Multiple Units in China, hereinafter referred to as EMU, the technical challenges of operation safety and security of EMUs are increasingly prominent. As a kind of equipment health management technology, PHM can realize equipment status monitoring, abnormal prediction, fault diagnosis, maintenance prediction and maintenance decision-making. In order to improve the safety assurance capability of high-speed EMU, reduce the maintenance cost and improve the maintenance efficiency, this paper deeply integrates big data technology, algorithm model and PHM technology, and explores the theory and method of intelligent fault prediction of key components of high-speed EMU based on PHM technology. Focus on the research of EMU condition monitoring and fault diagnosis technology based on HSMM and DBN algorithms, as well as the component maintenance prediction and maintenance decision-making technology based on fixed repair schedule prevention, so as to transfer the theoretical basis and technical support for the maintenance mode of EMU from "planned repair" to "planned repair predictive maintenance".

The hollow letter CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) is an optimized version of solid CAPTCHA, specifically designed to weaken characteristic information and increase the difficulty of machine recognition. Although convolutional neural networks can solve CAPTCHA in a single step, a good attack result heavily relies on sufficient training data. To address this challenge, we propose a seed filling algorithm that converts hollow characters to solid ones after contour line restoration and applies three rounds of detection to remove noise background by eliminating noise blocks. Subsequently, we utilize a support vector machine to construct a feature vector for recognition. Security analysis and experiments show the effectiveness of this algorithm during the pre-processing stage, providing favorable conditions for subsequent recognition tasks and enhancing the accuracy of recognition for hollow CAPTCHA.

최근 포털 및 소셜미디어를 통한 뉴스가 유통되면서 디지털 플랫폼 환경에서의 저널 리즘에 대한 고찰과 미래 저널리즘에 대한 논의가 심화되고 있다. 빅데이터, 인공지능, 실감미디어 등 4차산업혁명 기술은 뉴스 생산자와 이용자에게 많은 영향을 주고 있다. 디지털 뉴스 플랫폼의 증가와 온라인 뉴스 유통은 뉴스 산업에 긍정적인 기대와 부정적 인 우려가 동시에 나오고 있다. 이에 본 연구에서는 디지털 플랫폼 환경이 가져온 뉴스 의 변화 측면을 뉴스의 사회적 영향력, 여론 형성, 언론의 신뢰도, 뉴스 생산, 뉴스의 공 정성·객관성·중립성, 뉴스 만족도, 뉴스 품질, 뉴스의 역할 등을 주요 개념으로 저널 리즘에 미치는 영향을 중심으로 살펴보았다. 응답자는 ‘보도 윤리를 우려하는 유형’, ‘참여 저널리즘을 통한 언론의 품질 향상’, ‘거짓 뉴스와 다양성 훼손에 대한 우려’ 등 3개 유형이 발견되었다.

Recently, as news is circulated through portals and social media, considerations on journalism in a digital platform environment and discussions on future journalism are intensifying. The 4th industrial revolution technologies such as big data, artificial intelligence, and immersive media have a great impact on news producers and users. ‧Objectivity/neutrality, news satisfaction, news quality, and the role of news are the main concepts, and the effect on journalism is examined. As a result of the analysis, three types were found: ‘types of concern about reporting ethics’, ‘improvement of media quality through participatory journalism’, and ‘concerns about false news and damage to diversity’.

본 논문은 소모 전력 계산을 위해 회로를 구현하고자 FPGA 기술 매핑을 위한 소모 전력을 고려한 재사용 모듈 생성 알고리즘과 FPGA 기술 매핑을 위해 사용되는 재사용 모듈에 대해 소모 전력을 고려하여 RT 라이브러리를 생성하는 알고리즘을 제안한다. 소모 전력 계산을 위해 회로를 구현하고자 하는 FPGA를 선정한다. 선정된 FPGA를 구성하고 있는 LUT의 조건을 고려하여 전체 소모 전력이 최소가 되도록 기술 매핑을 수행한다. 이러한 정보를 이용하여 할당된 결과의 모듈들 중에서 주어진 소모 전력에 맞는 모듈을 선정하여 회로를 구현한다.

In this paper, we present the reuse module library generating algorithm and register-transfer (RT) library generating algorithm considering the power consumption of reuse module for field-programmable gate array (FPGA) technology mapping in order to implement into the circuit for calculating power consumption. To realize the circuit of calculation of power consumption, the FPGA is selected. Considering lookup table (LUT) conditions of selected FPGA, technology mapping process is conducted to minimize the total power consumption. With these information, the circuit is realized using suitable given power consumption among allocated results of modules.

계단 보행 시 로봇의 최적 궤도 계산은 유전자 알고리즘과 계산 토크 컨트롤러를 사용하여 수행되었다. 첫째, 생식, 교배, 돌연변이로 이루어진 실시간 유전 알고리즘 (RCGA)을 사용하여 총 에너지 효율이 최소화되었다. 보폭의 시작과 끝, 그리고 조인트, 각도, 각속도 위치 어셈블리 관련 재현성 조건은 선형 제약이다. 다음은 고르지 못한 제약은 코너 스윙 다리와 계단의 외부와의 충돌을 막기 위한 조건, 운동 학적 특이성을 막기 위한 무릎 관절의 조건 및 진행 방향의 안전은 보장되지 않음 이란 조건을 따른다. 마지막으로, 각 관절의 각도 궤도는 염색체를 근사 계수를 가지는 4차 다항식에 의해 정의된다. 이것은 보통 도보를 의미한다. 이 연구에서는 최적의 궤도의 에너지 효율을 7개의 링크로 구성된 7자유도의 2족 로봇을 통한 컴퓨터 시뮬레이션을 통해 분석했다.

The calculation of the optimal trajectory of the stepped top-down robot was made using a genetic algorithm and a computational torque controller. First, the total energy efficiency was minimized using the Red-Cold Generic Algorithm (RCGA) consisting of reproductive, cross, and mutation. The reproducibility condition related to the position assembly of the start and end of the stride and the joints, angles, and angular velocities are linear constraints. Next, the unequal constraint accompanies the condition for preventing the collision of the swing leg at the corner with the outer surface of the stairs, the condition of the knee joint for preventing kinematic peculiarity, and the condition of no moment in safety in the traveling direction. Finally, the angular trajectory of each joint is defined by fourth-order polynomial whose coefficient is to approximate chromosomes. This is to approximate walking. In this study, the energy efficiency of the optimal trajectory was analyzed by computer simulation through a biped robot with seven degrees of freedom composed of seven links.

미래 전장의 전쟁수행 역량은 AICBMS(AI, Cloud, Bigdata, Mobile, Security)라 일컫는 4차 산업혁명의 차세대 기술을 적 용하여 혁신적인 국방력을 확보할 수 있는가에 달려 있다 해도 과언이 아니다. 또한, 미래의 군 작전환경은 네트워크를 기반 으로 모든 무기체계가 하나의 통합된 정보통신망 내에서 실시간으로 전장정보를 상호공유하며 작전을 수행하게 되는 네트워 크 중심전(NCW)으로 급변하고, 유·무인 복합전투체계 운용범위로 확대되고 있다. 특히, 초고속, 초연결성을 책임지는 통신 네 트워크는 여러 전투 요소를 연결하고 정보의 원활한 유통을 위해 높은 생존성과 다계층(국방 모바일, 위성, M/W, 유선) 네트 워크 기반의 전력 운용의 효율성을 요구한다. 이러한 관점에서 본 연구는 제원이 고정된 기존의 단일매체, 단일경로 전송과는 달리, 가용한 통신 유무선 인프라 다매체를 동시 사용하여 통신량 폭주시 부하분산과 RNN(Recurrent Neural Networks) 알고 리즘을 이용한 인공지능 기반의 전송기술로 다매체다중경로(MMMP-Multi-Media Multi-Path) 적응적 네트워크 기술 개발하는 것이다.

The performance capability of the future battlefield depends on whether the next-generation technology of the Fourth Industrial Revolution, called ABCMS (AI, Bigdata, Cloud, Mobile, Security), can be applied to secure innovative defense ca pabilities It is no exaggeration to say. In addition, the future military operation environment is rapidly changing into a net work-oriented war (NCW) in which all weapon systems mutually share battlefield information and operate in real-time wi thin a single integrated information and communication network based on the network and is expanding to the scope of o peration of the manned and unmanned complex combat system. In particular, communication networks responsible for hig h-speed and hyperconnectivity require high viability and efficiency in power operation based on multi-tier (defense mobile, satellite, M/W, wired) networks for the connection of multiple combat elements and smooth distribution of information. Fro m this point of view, this study is different from conventional single-media, single-path transmission with fixed specificati ons, It is an artificial intelligence-based transmission technology using RNN (Recurrent Neural Networks) algorithm and lo ad distribution during traffic congestion using available communication wired and wireless infrastructure multimedia simult aneously and It is the development of MMMP-Multi-Media Multi-Path adaptive network technology.

산업의 급격한 발전은 화석에너지의 고갈을 야기하였고, 이러한 이유로 화석연료를 대체하기 위한 에너지로 태양광에너지가 각광받기 시작하였다. 하지만 기술발전에 있어 전체적인 연구방향 및 향후 연구 방향에 대한 논의가 부족하였다. 이에 보다 효과적인 기술개발을 위해 특허 데이터와 논문 데이터를 활용하여 태양광 에너지의 기술 동향을 파악하고 논의를 진행하였다. 분석방법으로는 토픽 모델링과 텍스트 마이닝을 활용하여 1997년도부터 2015년까지의 데이터를 토대로 기술 동향과 연구의 방향성에 대하여 분석한다. LDA알고리즘을 통하여 토픽을 선정하고, 선정된 기술 범주에 포함된 키워드의 증가량을 살펴보고, 태양광 기술의 발전방향에 대하여 분석하였다. 태양광 발전 기술에 대한 연구는 꾸준히 진행될 것으로 예상되며, 특히 고효율화 및 고성능화 기술에 대하여 집중적으로 연구가 이루어질 것으로 분석된다. 향후 연구로는 해외의 특허데이터와 다양한 논문데이터를 추가하여 연구를 진행할 수 있을 것이다.

Solar energy is attracting attention as an alternative to fossil fuels. However, there was a lack of discussion on the overall research direction and future direction of research in technology development. In order to develop more effective technology, we analyzed and discussed the technology trend of solar energy using patent data and thesis data. As an analysis method, topics were selected by using topic modeling and text mining, the increase of included keywords was analyzed, and the direction of development of solar technology was analyzed. Research on solar power generation technology is expected to proceed steadily, and it is analyzed that intensive research will be done especially on high efficiency and high performance technology. Future studies could be conducted by adding overseas patent data and various paper data.

본 논문에서는 유비쿼터스 시대가 도래함에 따라 사회적으로 실내 위치 추적 시스템에 대한 관심이 증가되었다. 하지만 기존 실내 위치 추적 시스템은 실내 환경의 빈번한 변화에 능동적으로 대응하지 못하고, 실내 환경의 NLOS 특성으로 인하여 이동 객체의 정확한 위치 측정이 어려운 문제점이 있다. 본 연구의 목적은 앞으로 유비쿼터스 시대가 도래함에 따라 다양한 사용자의 요구를 만족시키기 위한 서비스를 제공하는데 필수 요소인 실내 이동 객체의 위치를 효과적으로 파악하고, 이동 객체의 위치 정확도를 향상시킨 환경 적응형 위치 추적 시스템을 제안한다.

In this paper, we proposed a method of improving the location estimation error existing in RTLS (Real Time Location Service) system for the mobility individual. According as Ubiquitous comes, interest for indoor location tracking system was more increased socially. However, existing indoor location tracking system doesn't correspond actively in frequent change of indoor environment, and there is a problem that correct location measurement of transfer object is difficult by NLOS property of indoor environment. Purpose of this paper proposes environment accommodation location tracking system that is improved location precision of transfer object and grasps location of indoor transfer object effectively that is essential element effectively to provide service to satisfy various user's request according as Ubiquitous comes.

전훈이란 군사용어로서 전투발전분야의 교육과 현실에서 문제점이 확인되거나 개선이 필요한 요소를 찾아서 미래의 발전 을 도모하는 모든 활동이다. 이 논문에서는 전훈활동을 추진하는데 드러나는 문제점, 즉 분석시 장기간 소요, 예산 문제, 전문 가 필요성 등을 해결하고자 실제 사례를 제시하고 인공지능 분석 추론기술을 적용하는 데 초점을 맞춘다. 이미 실용화되어 사 용 중인, 인지 컴퓨팅 관련 기술을 활용한 인공지능 법률자문 서비스가 전훈의 문제점을 해결하는데 가장 적합한 사례로 판단 했다. 이 논문은 인공지능을 활용한 지능형 전훈분석 추론기술의 효과적인 적용방안을 제시한다. 이를 위해, 전훈분석 정의 및 사례, 인공지능의 머신러닝으로 진화, 인지 컴퓨팅 등 이론적 배경을 살펴보고, 새롭게 제안한 L2-OODA 앙상블 알고리즘을 이용해 국방분야 신기술에 적용함으로써 현존전력 개선 및 최적화를 구현하는데 기여하고자 한다.

Lessons Learned(LL) is a military term defined as all activities that promote future development by finding problems and need improvement in education and reality in the field of warfare development. In this paper, we focus on presenting actual examples and applying AI analysis inference techniques to solve revealed problems in promoting LL activities, such as long-term analysis, budget problems, and necessary expertise. AI legal advice services using cognitive computing-related technologies that have already been practical and in use, were judged to be the best examples to solve the problems of LL. This paper presents intelligent LL inference techniques, which utilize AI. To this end, we want to explore theoretical backgrounds such as LL analysis definitions and examples, evolution of AI into Machine Learning, cognitive computing, and apply it to new technologies in the defense sector using the newly proposed L2-OODA ensemble algorithm to contribute to implementing existing power improvement and optimization.

최근 입체영상 기술은 신 성장 동력 산업으로서 잠재성 있는 블루오션으로 자리매김하고 있으며, 가상 및 증강현실 기술의 발전과 함께 관심이 꾸준히 증가하고 있다. 입체영상 표현방 식은 양안시차, 편광안경 등 다양한 형태의 기술이 개발되어 활용되고 있으나, 눈의 피해나, 두통, 혼선, 해상도 저하 등 한계점을 지닌다. 본 논문에서는 기존의 한계점을 극복하고자 중첩된 투명 디스플레이를 활용한 새로운 방식의 입체영상 표현 기법을 제시하였으며, Grabcut 알고리즘을 활용한 전경 추출 실험과 아크릴과 스마트 디바이스를 활용한 새로운 방식의 입체감 표현 실험을 통해 적용 가능성을 확인하였다. 또한, 이미지 프로세싱 소요 시간 등 분석을 통해 실시간 스트리밍 적용도 가능함을 검증하였다.

Recently, stereoscopic technology has become a potential for blue ocean as a new growth power industry, and interest in it has been steadily increasing with the development of virtual and augmented reality technologies. Various methods such as binocular parallax and polarized glasses have been developed and used for stereoscopic image expression, but they have limitations such as eye damage, headache, crosstalk and resolution degradation. In this paper, we present a new method of stereoscopic image representation that can overcome the limitations and verify its applicability through basic experiments for object extraction and real - time image representation.

Recently, nano-methodology is increasingly important as the ruler for measuring nano-technology, and we applied the linear encoder to nano-methodology. The quadrature output in the linear encoder has an effect on increasing the resolution in some techniques. Already, various interpolation techniques based on the quadrature signal have applied to the precision servo system. In this paper, we propose a new interpolation algorithm for ultra-precision positioning in the low speed with simulation by MATLAB SIMULINK. This method modified previous methods and was properly designed for some given control system. To verify, we first fulfilled the encoder signal test to find main parameters fer the signal transformation, then we proved the proposed interpolation algorithm by experiments, which show that the result of the interpolation algorithm corresponds with the measurement of the laser interferometer in 100 nm unit approximately. In addition, we can get more precise measurement by more accurate and noise-free signal. So we need to compensate imperfections in the encoder signal. After that, we will apply this algorithm to nano positioning system.

실세계에는 다양한 최적화 문제가 존재하고 이를 해결하기 위한 연구가 지속되고 있다. 최적화 문제는 목적 함수의 결과 값을 최대 혹은 최소로 만드는 파라미터의 조합을 찾는 문제이다. 하모니 탐색은 이러한 최적화 문제 해결을 위한 인구 기반 메타휴리스틱 알고리즘으로 재즈 음악의 즉흥 연주를 모방하여 고안되었다. 하모니 탐색은 현재 토목, 컴퓨터, 에너지, 의료, 수질 공학 등 다양한 분야의 최적화 문제에 활발히 적용되고 있다. 하모니 탐색은 동작 원리가 간단하고 제약조건이 있는 최적화 문제에서 빠르게 동작한다는 장점이 있다. 특히 경험적 도함수를 통해 해를 개선하여 낮은 반복 횟수로 높은 정확도를 보인 사례들이 존재한다. 본 논문에서는 하모니 탐색의 동작 원리를 설명하고 최근 3년간 수행된 주요 연구들을 분류, 각 분류에 따라 요약 및 소개, 향후 연구 방향을 제시한다. 분류는 분야별 리뷰, 알고리즘 분석 및 이론, 실세계 문제에 대한 적용으로 나누고 실세계 문제에 대한 적용은 다른 메타휴리스틱 알고리즘과의 결합 여부, 최적화 목적에 따라 분류하여 설명한다.

There are various optimization problems in real world and research continues to solve them. An optimization problem is the problem of finding a combination of parameters that maximizes or minimizes the objective function. Harmony search is a population-based metaheuristic algorithm for solving optimization problems and it is designed to mimic the improvisation of jazz music. Harmony search has been actively applied to optimization problems in various fields such as civil engineering, computer science, energy, medical science, and water quality engineering. Harmony search has a simple working principle and it has the advantage of finding good solutions quickly in constrained optimization problems. Especially there are various application cases showing high accuracy with a low number of iterations by improving the solution through the empirical derivative. In this paper, we explain working principle of Harmony search and classify the leading research in recent 3 years, review them according to category, and suggest future research directions. The research is divided into review by field, algorithmic analysis and theory, and application to real world problems. Application to real world problems is classified according to the purpose of optimization and whether or not they are hybridized with other metaheuristic algorithms.

과학기술콘텐츠가 증가함에 따라 과학기술콘텐츠의 효율적인 검색을 지원하는 서비스가 요구되고 있다. 저자의 소속기관명을 키워드로 사용할 경우 한 기관에서 생산된 콘텐츠를 확인할 수 있을 뿐만 아니라 저자, 용어를 키워드로 사용한 검색 결과의 식별율을 향상 시킬 수 있다. 검색 키워드로 사용되는 데이터들의 중의성과 모호성으로 인해 검색 결과에 false negative, false positive가 포함될 수 있으므로 데이터의 식별을 통한 통제는 중요하다. 저자의 소속기관명의 식별을 통한 통제 역시 기관의 이명, 약어 검색을 지원가능하게 하므로 매우 중요하지만 기존의 데이터 식별을 통한 통제에 대한 연구는 저자, 용어에 대한 연구가 주를 이루었다. 본 연구에서는 기관명 식별 알고리즘을 제안하고, 한국과학기술정보연구원에서 보유하고 있는 국내 과학기술콘텐츠들에 대한 데이터를 이용한 실험 결과를 보인다.

As the number of scientific and technical contents increases, services that support efficient search of scientific and technical contents are required. When an author's affiliation is used as a keyword, not only the contents produced by the affiliation can be searched, but also the identification rate of the search result using the author and the term as keyword can be improved. Because of the ambiguity and vagueness of the data used as a search keyword, the search result may include false negative or false positive. However, the previous research on the control through identification of the search keyword is mainly focused on the author data and terminology data. In this paper, we propose the algorithm to identify affiliations and experiment with show the experiment with scientific and technological contents held by the Korea Institute of Science and Technology Information.

A location fingerprint positioning technology method is proposed based on bat algorithm to improve the defect of traditional indoor location fingerprint positioning technology. These shortcomings include offline heavy workload, limited accuracy and poor robustness. The fingerprint database can generate using the middle point interpolation method and the method that channel attenuation model which instead of offline training stage. This stage realize the function of timing automatic databased updates. After that the database combines K nearest neighbor algorithm with bat algorithm in the stage of matching algorithm to realize the positioning function. Compared with the traditional method, location fingerprint positioning technology using bat algorithm reduces the overall positioning of workload and rapidly respond to the effect of the changing environment. Finally, localization performance test is carried out under the given simulation environment. The result indicates that this method improves the average precision than other algorithms about 23.14%, the vast most of the blind node position error range within 1.5 meters, which shows the advantages of positioning accuracy, robustness and adaptation to the changing environment.

Computer network virus has been manufactured in bulk, which can affect the normal work and study of people, and therefore the defense and detection of computer network virus should be concerned, advanced defense ability of computer network virus under the environment of computer internet can be improved. Firstly, the basic characteristics of computer network virus are analyzed. Secondly, main computer network virus propagation models are studied. Thirdly, active defense technology and detection model of computer network virus are established based on supported vector machine, and corresponding algorithm procedure is constructed. Finally, the simulation experiments are carried out, and results show that the detection model can obtain better detection effect of the computer network virus.

Aiming at the problem of resource scheduling in cloud computing, we constructed a new scheduling method based on trust model and LB algorithm. Firstly, on the basis of matching tasks and resources, we established three calculation modes on security requirements and reliable demand benefits. Secondly, based on the benefit function of security requirements and reliable demand, we built the trust benefit function of total. Finally, we got a cloud resource scheduling algorithm which combined with load balancing algorithm. The experimental results show that proposed method has obvious advantages comparing with the maximum and minimum trust driving method in the execution time span, load balancing performance and average waiting time, and this method can carry out fast and efficient resources scheduling for cloud computing.

The computer system is becoming more complex and massive network data, which brings great difficulties to the traditional intrusion detection system. Intrusion detection system is an important part of the network and information security architecture, which is mainly used to distinguish the normal activities of the system and the suspicious and intrusion patterns. But the challenge is how to effectively detect network intrusion behavior in order to reduce the false alarm rate and false negative rate. Based on the shortcomings of existing intrusion detection methods, the fuzzy C- means clustering method is proposed to analyze the intrusion detection data, so as to find out the abnormal network behavior patterns. By testing the CUP99 data set, the results show that the IFCA is not only feasible but also accurate and efficient. The improved fuzzy clustering algorithm proposed in this paper can improve the detection rate of intrusion detection and reduce the false detection rate, and can be widely used in intrusion detection system.

In this particular work, we have done power analysis of DES algorithm implemented on 28nm FPGA using HTML (H-HSUL, T-TTL, M-MOBILE_DDR, L-LVCMOS) technology. In this research, we have used high performance software Xilinx ISE where we have selected four different IO Standards i.e. MOBILE_DDR, HSUL_12, LVTTL and LVCMOS (LVCMOS_15, LVCMOS_18, LVCMOS_25 and LVCMOS_33). We have done power analysis of on-chip power like clock power, signals power, IO power, leakage power and supply power. We notified our analysis at five different voltages like 0.5V, 0.8V, 1.0V, 1.2V and 1.5V.

The machine-part cell formation problem explains the topics surrounding the creation of part families based on the processing needs of the components, and the classification of machine groups based on their capacity to process exact part families in manufacturing. Allocate the specified machines and parts into cells so that the grouping efficiency is maximized. The objective of the paper is to cluster the parts based upon self-organizing map (SOM). After clustering, Minkowski distance is used to sort the machines and some bottlenecks are introduced during the clustering process which can be removed manually by rearranging. We are going to implement self-organizing map (SOM) which is a type of artificial neural network technique to solve this problem. The proposed Minkowski distance approach produced solution with a good group efficiency.