• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제8권4호
• /
• pp.254-259
• /
• 2008

Uncertainty of result of context awareness always exists in any context-awareness computing. This falling-off in accuracy of context awareness result is mostly caused by the imperfectness and incompleteness of sensed data, because of this reasons, we must improve the accuracy of context awareness. In this article, we propose a novel approach to model the uncertain context by using ontology and context reasoning method based on Bayesian Network. Our context aware processing is divided into two parts; context modeling and context reasoning. The context modeling is based on ontology for facilitating knowledge reuse and sharing. The ontology facilitates the share and reuse of information over similar domains of not only the logical knowledge but also the uncertain knowledge. Also the ontology can be used to structure learning for Bayesian network. The context reasoning is based on Bayesian Networks for probabilistic inference to solve the uncertain reasoning in context-aware processing problem in a flexible and adaptive situation.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제13권1호
• /
• pp.152-171
• /
• 2019

In opportunistic networks, nodes may appear some selfishness while transmitting the message, however, most of the existing research works consider the individual selfishness or social selfishness respectively, and these two types of selfishness may coexist in opportunistic networks. In this paper, we propose an Incentive Aware Routing based on Game Theory for selfish OPPNETs, named IAR-GT, which uses Rubinstein-Stahl bargaining game model to incentivize selfish nodes cooperation in data forwarding. IAR-GT scheme not only considers the resources of nodes, but also uses a new method to calculate the social ties between them. Trace-driven simulations show that our incentive aware routing scheme achieves better performances than comparing schemes under two types of selfishness coexistence environments.

• Journal of the Korean Physical Society
• /
• 제73권12호
• /
• pp.1873-1878
• /
• 2018

We have investigated the ionic conductivity and dielectric relaxation in $Li_2B_4O_7$ (LBO) and $Li_2O-B_2O_3-Ta_2O_5$ (LBTO) glasses. The sample was synthesized by using the melt quenching method. The frequency dependence of the electrical data from the LBO and LBTO glasses has been analyzed in the frameworks of the impedance Cole-Cole formalism and the universal power-law representation driven by the modified fractional Rayleigh equation. The potential barriers in the LBO and the LBTO glasses turn out to be the same. Comparing with the dc and ac activation energies of the LBO glass, these energies of the LBTO glass decrease due to the increasing Coulomb interaction of inter-cationic interaction.

• International Journal of Computer Science & Network Security
• /
• 제21권4호
• /
• pp.223-228
• /
• 2021

As a consequence of sudden outbreak of COVID-19 pandemic worldwide, educational institutes around the globe are forced to switch from traditional learning systems to e-learning systems. This has led to a variety of technology-driven pedagogies in e-teaching as well as e-learning. In order to take the best advantage, an appropriate understanding of the cognitive capability is of prime importance. This paper presents an intelligent cognitive maturity recognition system for confidence-based e-learning. We gather the data from actual test environment by involving a number of students and academicians to act as experts. Then a Genetic Programming based simulation and modeling is applied to generate a generalized classifier in the form of a mathematical expression. The simulation is derived towards an optimal space by carefully designed fitness function and assigning a range to each of the class labels. Experimental results validate that the proposed method yields comparative and superior results which makes it feasible to be used in real world scenarios.

• 국제학술발표논문집
• /
• The 6th International Conference on Construction Engineering and Project Management
• /
• pp.294-298
• /
• 2015

In the United States, the completion of Construction Work Zone (CWZ) impact assessments for all federally-funded highway infrastructure improvement projects is mandated, yet it is regarded as a daunting task for state transportation agencies, due to a lack of standardized analytical methods for developing sounder Transportation Management Plans (TMPs). To circumvent these issues, this study aims to create a spatiotemporal modeling framework, dubbed "SWAT" (Spatiotemporal Work zone Assessment for TMPs). This study drew a total of 43,795 traffic sensor reading data collected from heavily trafficked highways in U.S. metropolitan areas. A multilevel-cluster-driven analysis characterized traffic patterns, while being verified using a measurement system analysis. An artificial neural networks model was created to predict potential 24/7 traffic demand automatically, and its predictive power was statistically validated. It is proposed that the predicted traffic patterns will be then incorporated into a what-if scenario analysis that evaluates the impact of numerous alternative construction plans. This study will yield a breakthrough in automating CWZ impact assessments with the first view of a systematic estimation method.

• 한국음향학회지
• /
• 제23권2호
• /
• pp.173-179
• /
• 2004

음소 질의어 집합은 문맥 속에서 비슷한 조음 효과를 보이는 음소들을 분류해 놓은 것으로서, 음성 인식 시스템 학습 시 결정트리를 기반으로 HMM (hidden Markov model)의 상태들을 클러스터링할 때 사용된다. 현재까지의 음소 질의어 집합은 대부분 음성학자나 언어학자들에 의해 수작업으로 제시되어 왔는데, 이러한 지식 기반음소 질의어들은 언어 또는 유사음소 단위 (PLU: phone like unit)에 종속될 뿐 아니라 생성된 클러스터 내의 동질성을 저하시킬 수 있다는 단점이 있다. 본 논문에서는 이와 같은 문제점들을 해결하기 위해 음성 데이터를 사용하여 측정한 음소들 사이의 유사도를 기반으로 언어나 유사음소단위에 상관없이 자동으로 음소 질의어 집합을 생성하는 알고리즘을 제안한다. 실험결과, 제안한 방법으로 생성된 음소 질의어들을 사용한 인식기의 에러율이 약 14.3%감소하여 데이터 기반의 음소 질의어 집합이 상태 클러스터링에 효율적임을 관측하였다.

• Journal of the Korean Data and Information Science Society
• /
• 제27권5호
• /
• pp.1285-1291
• /
• 2016

추상적 개념을 계량화 하기 위해 상대적으로 구체적인 여러 개의 문항을 조사한 후 이들 점수의 합 또는 이들 점수를 표준화한 후 합을 구하는 리커드 (Likert) 척도 (합산등급척도법)를 많이 사용한다. 합산등급척도법은 각 항목의 크기가 차이가 많이 나는 경우에 원자료가 아닌 표준화 값을 사용하여 합하기도 한다. 이와 같은 상황은 평가 시스템에서도 발생한다. 예를 들어 기초지방자치단체들을 발전정도에 따라 분류하기 위해 인구, 세수현황 등의 값을 표준화하고 이를 단순합산하여 분류의 기초로 사용할 수 있다. 본 연구에서는 위의 같은 추상적 개념의 수치화 또는 평가 시스템에 많이 적용되는 합산등급척도법의 문제점을 개선하는 한 방법으로 가중치를 자료에서 계산하는 데이터 구동 방식의 평가 시스템을 제안하고, 이 시스템을 실자료에 적용한다.

• 한국멀티미디어학회논문지
• /
• 제22권1호
• /
• pp.44-54
• /
• 2019

Recently, image-based object detection has made great progress with the introduction of Convolutional Neural Network (CNN). Many trials such as Region-based CNN, Fast R-CNN, and Faster R-CNN, have been proposed for achieving better performance in object detection. YOLO has showed the best performance under consideration of both accuracy and computational complexity. However, these data-driven detection methods including YOLO have the fundamental problem is that they can not guarantee the good performance without a large number of training database. In this paper, we propose a data sampling method using CycleGAN to solve this problem, which can convert styles while retaining the characteristics of a given input image. We will generate the insufficient data samples for training more robust object detection without efforts of collecting more database. We make extensive experimental results using the day-time and night-time road images and we validate the proposed method can improve the object detection accuracy of the night-time without training night-time object databases, because we converts the day-time training images into the synthesized night-time images and we train the detection model with the real day-time images and the synthesized night-time images.

• 디지털융복합연구
• /
• 제10권6호
• /
• pp.297-302
• /
• 2012

본 논문은 이미지 상황분석을 기반으로 하여 객체 검출 및 추적 방법을 제안한다. 제안하는 방법은 배경이 복잡한 형태이거나 배경이 동적으로 움직일 때에도 일관성 있는 결과를 얻을 수 있다. 입력 영상의 상황분석은 K-means와 RBF의 하이브리드 네트워크를 이용하여 수행되어진다. 제안된 객체 검출은 일정하지 않은 객체 이미지 때문에 생기는 영향을 감소시키기 위해 상황 기반 적응적 베이지안 네트워크를 이용한다. 본 논문에서는 학습 속도를 높이기 위해 2D Haar 웨이블릿 변형을 이용한 특징 벡터 생성기와 베이지안 판별식 방법을 이용하여 학습 시간이 적게 걸리며 학습 데이터의 변화에 일정한 성능을 갖는 방법론을 제안하였다. 제안하는 방법을 개발하여 실환경에 적용한 결과 검출하고자 하는 물체가 예측 영역을 넘나들거나 다른 불확실한 변화에도 안정적으로 반응함을 알 수 있었다. 실험 결과는 기존의 방법들에서 사용되었던 다양한 데이터 집합에 적용하였을 때 우수한 성능을 보여준다.

• 한국기계가공학회지
• /
• 제16권2호
• /
• pp.88-93
• /
• 2017

Due to the requirement of the development of the precision manufacturing industry, the accuracy of machine tools has become a key issue in this field. A critical factor that affects the accuracy of machine tools is the feed system, which is generally driven by a ball screw. Basically, to improve the performance of the feed drive system, which will be thermally extended lengthwise by continuous usage, a thermal error compensation system that is highly dependent on the feedback temperature or positioning data is employed in the machine tool system. Due to the overdependence on measuring technology, the cost of the compensation system and low productivity level are inevitable problems in the machine tool industry. This paper presents a novel feed drive thermal error compensation system method that could compensate for thermal error without positioning or temperature feedback. Regarding this thermal error compensation system, the heat generation of components, principal of compensation, thermal model, mathematic model, and calculation method are discussed. As a result, the test data confirm the correctness of the developed feed drive thermal error compensation system very well.