• 한국멀티미디어학회:학술대회논문집
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• 한국멀티미디어학회 2004년도 춘계학술발표대회논문집
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• pp.199-202
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• 2004

데이터 방송은 지상파, 위성, 케이블 둥의 방송망을 이용하여 하나의 송신자가 다수의 수용자에게 디지털 멀티미디어 컨텐츠를 여러 종류의 단말기에 전송하는 것으로 컨텐츠는 프로그램에 연동되는 서비스 및 비연동형 서비스, 또는 대화형 서비스 헝태로 제공되는 다양한 서비스를 포함한다. 방송망을 이용한 데이터방송의 전송 규약은 DSM-CC에서 정의한 캐로셀(carousel) 형태로 전송된다. 캐로셀은 주기적으로 반복 전송되는 데이터 모듈을 가리키며, 캐로셀로 전송되는 데이터 중에는 MPEG-2 비디오로 인코딩되어 광고와 배경 이미지 등으로 사용할 수 있는 영상 데이터가 있다. 이 MPEG-2 비디오 영상 데이터를 TV화면에 출력하기 위해 셋톱박스의 미들웨어는 썬 마이크로시스템즈(Sun Microsystems)에서 만든 자바 기반의 JMF(Java Media Framework) 플레이어를 통해 화면에 출력한다 이렇게 제공되는 MPEG-2 비디오 영상 데이터를 비디오 드립(drips)이라 하고, 비디오 드립을 JMF 플레이어를 통해 화면에 출력하는 모드를 드립피드(drip-feed) 모드라 한다. 그러나 MHP용 셋톱박스를 위한 JMF 버전 1.0은 그대로 사용 할 수 없다. 비디오 드립 모드를 위한 구현이 없기 때문에 별도의 확장을 통해 비디오 드립을 지원하거나, 네이티브(Native) 메소드를 만들어 JMF를 확장하는 방식을 제공해야 한다. 본 논문에서는 데이터 캐로셀로 전송되는 비디오 드림 모드를 구현하기 위해 네이티브 코드로 JMF를 확장해서 비디오 드립을 JMF 플레이어를 통해 TV 화면에 출력하는 MPEG-2 비디오 드립 디코더를 설계하고 구현하였다.

• Nuclear Engineering and Technology
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• 제40권4호
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• pp.269-276
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• 2008

Operational license of WWER-440/213 units at Paks NPP, Hungary is limited to the design lifetime of 30 years. Prolongation by additional 20 years of the operational lifetime is feasible. Moreover, enhancement of the reactor thermal power by 8% will increase both the net power output and the competitiveness of the plant. Paks NPP is a pioneer considering the power up-rate and preparation of long-term operation of WWER-440/213 design. Systematic preparatory work for long-term operation of Paks NPP has been started in 2000. A regulatory framework and a comprehensive engineering practice have been developed. According to the authors view, creation of a gapless engineering system via consequent application of best practices, and feed-back of experiences together with proper consideration of WWER-440/V213 features are the decisive elements of ensuring the safety of long-term operation. That systematic engineering approach is in the focus of recent paper. Key elements of justification and measures for ensuring the safety of long-term operation of Paks NPP WWER-440/213 units are identified and discussed. These are the assessment of plant condition and review of adequacy of ageing management programmes, also the review, validation and reconstitution of time limited ageing analyses as core tasks of licence renewal.

• 한국응용곤충학회지
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• 제61권1호
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• pp.129-142
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• 2022

영양은 모든 생명활동의 근본이며, 생물의 진화적 적응도를 결정하는 가장 중요한 요인이다. 곤충영양학은 곤충생리학의 전통적인 연구영역이며, 최근 산업곤충의 대량사육 필요성이 증가함에 따라 그 중요성이 부각되고 있다. 이러한 중요성에도 불구하고, 곤충의 영양현상을 정확히 이해하기란 어려운데, 이는 영양의 다변량적 특성, 영양소 간의 교호작용 등으로 설명되는 영양적 복잡성에 기인한다. 영양기하학(Nutritional Geometry)은 이러한 난점을 극복하기 위해 고안된 통합적이고 다차원적인 분석모형으로서, 최근 곤충영양학이 급격하게 발전할 수 있는 이론적 및 실험적 기반을 제공하였다. 본 종설은 영양기하학의 기본개념을 소개하고, 이러한 방법론이 어떻게 최근 곤충영양학의 급속한 학문적 진보를 가능케 하였는지, 그리고 영양이 어떻게 생리학, 생태학, 진화생물학을 통합하는 구심점이 될 수 있었는지를, 최신 연구사례를 중심으로 살펴볼 것이다. 또한 본 종설은 향후 영양기하학을 적용함으로써 발전할 가능성이 높은 연구분야를 고찰할 것이다.

• Smart Structures and Systems
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• 제22권5호
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• pp.561-574
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• 2018

In this paper, for efficiently reducing the computational cost of the model updating during the optimization process of damage detection, the structural response is evaluated using properly trained surrogate model. Furthermore, in practice uncertainties in the FE model parameters and modelling errors are inevitable. Hence, an efficient approach based on Monte Carlo simulation is proposed to take into account the effect of uncertainties in developing a surrogate model. The probability of damage existence (PDE) is calculated based on the probability density function of the existence of undamaged and damaged states. The current work builds a framework for Probability Based Damage Detection (PBDD) of structures based on the best combination of metaheuristic optimization algorithm and surrogate models. To reach this goal, three popular metamodeling techniques including Cascade Feed Forward Neural Network (CFNN), Least Square Support Vector Machines (LS-SVMs) and Kriging are constructed, trained and tested in order to inspect features and faults of each algorithm. Furthermore, three wellknown optimization algorithms including Ideal Gas Molecular Movement (IGMM), Particle Swarm Optimization (PSO) and Bat Algorithm (BA) are utilized and the comparative results are presented accordingly. Furthermore, efficient schemes are implemented on these algorithms to improve their performance in handling problems with a large number of variables. By considering various indices for measuring the accuracy and computational time of PBDD process, the results indicate that combination of LS-SVM surrogate model by IGMM optimization algorithm have better performance in predicting the of damage compared with other methods.

• Asian-Australasian Journal of Animal Sciences
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• 제26권1호
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• pp.1-18
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• 2013

The elements that determine the success of development projects on goats and the prerequisites for ensuring this are discussed in the context of the bewildering diversity of goat genetic resources, production systems, multifunctionality, and opportunities for responding to constraints for productivity enhancement. Key determinants for the success of pro-poor projects are the imperatives of realistic project design, resolution of priorities and positive impacts to increase investments and spur agricultural growth, and appropriate policy. Throughout the developing world, there exist 97% of the total world population of 921 million goats across all agroecological zones (AEZs), including 570 breeds and 64% share of the breeds. They occupy a very important biological and socioeconomic niche in farming systems making significant multifunctional contributions especially to food, nutrition and financial security, stability of farm households, and survival of the poor in the rural areas. Definitions are given of successful and failed projects. The analyses highlighted in successful projects the value of strong participatory efforts with farmers and climate change. Climate change effects on goats are inevitable and are mediated through heat stress, type of AEZ, water availability, quantity and quality of the available feed resources and type of production system. Within the prevailing production systems, improved integrated tree crops - ruminant systems are underestimated and are an important pathway to enhance C sequestration. Key development strategies and opportunities for research and development (R and D) are enormous, and include inter alia defining a policy framework, resolution of priority constraints using systems perspectives and community-based participatory activities, application of yield-enhancing technologies, intensification, scaling up, and impacts. The priority for development concerns the rainfed areas with large concentrations of ruminants in which goats, with a capacity to cope with heat tolerance, can be the entry point for development. Networks and networking are very important for the diffusion of information and can add value to R and D. Well formulated projects with clear priority setting and participatory R and D ensure success and the realisation of food security, improved livelihoods and self-reliance in the future.

• 한국통신학회논문지
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• 제35권5B호
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• pp.816-823
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• 2010

정보 검색서비스 모델 도출 영역에서는 정보 검색시스템을 각 센서 네트워크의 서비스로의 추상화를 기반으로 동적으로 필요한 센서 네트워크 서비스의 검색서비스로 제공하는 시스템으로 정의하였다. 이어 효율적인 정보 검색서비스의 요구사항을 정의하였으며 이러한 요구사항을 만족시킬 수 있는 성능 분석 모델을 개발하였으며 마지막으로 효과적인 설계 공간을 도출하였다. USN 기술 현황 분석 영역에서는 USN 시스템 기술, USN 네트워킹 기술, 그리고 USN 미들웨어 및 서비스플랫폼 부문으로 분류한 후 각 기술부분별로 세부기술에 대하여 국내외 대표적인 기술개발의 내용과 현황을 조사. 분석 결과를 제시한다. 이러한 기술 분석의 결과를 바탕으로 USN 소프트웨어 모델 도출 영역에서는 센서 네트워크의 센싱, 데이터 저장, 데이터 네이밍, 네임서비스 부문에 대하여 모델을 도출한다. 이러한 모델 도출은 USN 하드웨어 및 소프트웨어의 참조 모델을 제시한다. 정보 검색서비스 아키텍처 프레임워크 개발 영역에서는 입력 쿼리 명세, Lookup, 분산 레졸루션을 위한 성능확장성 측면에서 효과적인 시스템 구축 프레임워크로 DNS 기반 정보 검색서비스 아키텍처와 DHT 기반 정보 검색서비스 아키텍처 프레임워크를 제시하였다.

• 한국지능시스템학회논문지
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• 제1권1호
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• pp.9-25
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• 1991

This tutorial paper has been written for biologists, physicians or beginners in fuzzy sets theory and applications. This field is introduced in the framework of medical diagnosis problems. The paper describes and illustrates with practical examples, a general methodology of special interest in the processing of borderline cases, that allows a graded assignment of diagnoses to patients. A pattern of medical knowledge consists of a tableau with linguistic entries or of fuzzy propositions. Relationships between symptoms and diagnoses are interpreted as labels of fuzzy sets. It is shown how possibility measures (soft matching) can be used and combined to derive diagnoses after measurements on collected data. The concepts and methods are illustrated in a biomedical application on inflammatory protein variations. In the case of poor diagnostic classifications, it is introduced appropriate ponderations, acting on the characterizations of proteins, in order to decrease their relative influence. As a consequence, when pattern matching is achieved, the final ranking of inflammatory syndromes assigned to a given patient might change to better fit the actual classification. Defuzzification of results (i.e. diagnostic groups assigned to patients) is performed as a non fuzzy sets partition issued from a "separating power", and not as the center of gravity method commonly employed in fuzzy control. It is then introduced a model of fuzzy connectionist expert system, in which an artificial neural network is designed to build the knowledge base of an expert system, from training examples (this model can also be used for specifications of rules in fuzzy logic control). Two types of weights are associated with the connections: primary linguistic weights, interpreted as labels of fuzzy sets, and secondary numerical weights. Cell activation is computed through MIN-MAX fuzzy equations of the weights. Learning consists in finding the (numerical) weights and the network topology. This feed forward network is described and illustrated in the same biomedical domain as in the first part.

• 한국산업정보학회논문지
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• 제23권4호
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• pp.81-92
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• 2018

Bitcoin is a cryptographic digital currency and has been given a significant amount of attention in literature since it was first introduced by Satoshi Nakamoto in 2009. It has become an outstanding digital currency with a current market capitalization of approximately $60 billion. By 2019, it is expected to have over 5 million users. Nowadays, investing in Bitcoin is popular, and along with the advantages and disadvantages of Bitcoin, learning how to forecast is important for investors in their decision-making so that they are able to anticipate problems and earn a profit. However, most investors are reluctant to invest in bitcoin because it often fluctuates and is unpredictable, which may cost a lot of money. In this paper, we focus on solving the Bitcoin forecasting prediction problem based on deep learning structures and neural decomposition. First, we propose a deep learning-based framework for the bitcoin forecasting problem with deep feed forward neural network. Forecasting is a time-dependent data type; thus, to extract the information from the data requires decomposition as the feature extraction technique. Based on the results of the experiment, the use of neural decomposition and deep neural networks allows for accurate predictions of around 89%.

• Smart Structures and Systems
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• 제12권5호
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• pp.501-521
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• 2013

This paper is concerned with static and dynamic shape control of a laminated Bernoulli-Euler beam hosting a uniformly distributed array of resistively interconnected piezoelectric patches. We present an analytical one-dimensional model for a laminated piezoelectric beam with material discontinuities within the framework of Bernoulli-Euler and extent the model by a network of resistors which are connected to several piezoelectric patch actuators. The voltage of only one piezoelectric patch is prescribed: we answer the question how to design the interconnected resistive electric network in order to annihilate lateral vibrations of a cantilever. As a practical example, a cantilever with eight patch actuators under the influence of a tip-force is studied. It is found that the deflection at eight arbitrary points along the beam axis may be controlled independently, if the local action of the piezoelectric patches is equal in magnitude, but opposite in sign, to the external load. This is achieved by the proper design of the resistive network and a suitable choice of the input voltage signal. The validity of our method is exact in the static case for a Bernoulli-Euler beam, but it also gives satisfactory results at higher frequencies and for transient excitations. As long as a certain non-dimensional parameter, involving the number of the piezoelectric patches, the sum of the resistances in the electric network and the excitation frequency, is small, the proposed shape control method is approximately fulfilled for dynamic load excitations. We evaluate the feasibility of the proposed shape control method with a more refined model, by comparing the results of our one-dimensional calculations based on the extended Bernoulli-Euler equations to three-dimensional electromechanically coupled finite element results in ANSYS 12.0. The results with the simple Bernoulli-Euler model agree well with the three-dimensional finite element results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권7호
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• pp.1726-1748
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• 2024

Trajectory planning is vital for autonomous systems like robotics and UAVs, as it determines optimal, safe paths considering physical limitations, environmental factors, and agent interactions. Recent advancements in trajectory planning and future location prediction stem from rapid progress in machine learning and optimization algorithms. In this paper, we proposed a novel framework for Spatial-temporal transformer-based feed-forward neural networks (STTFFNs). From the traffic flow local area point of view, skip-gram model is trained on trajectory data to generate embeddings that capture the high-level features of different trajectories. These embeddings can then be used as input to a transformer-based trajectory planning model, which can generate trajectories for new objects based on the embeddings of similar trajectories in the training data. In the next step, distant regions, we embedded feedforward network is responsible for generating the distant trajectories by taking as input a set of features that represent the object's current state and historical data. One advantage of using feedforward networks for distant trajectory planning is their ability to capture long-term dependencies in the data. In the final step of forecasting for future locations, the encoder and decoder are crucial parts of the proposed technique. Spatial destinations are encoded utilizing location-based social networks(LBSN) based on visiting semantic locations. The model has been specially trained to forecast future locations using precise longitude and latitude values. Following rigorous testing on two real-world datasets, Porto and Manhattan, it was discovered that the model outperformed a prediction accuracy of 8.7% previous state-of-the-art methods.