• 한국소음진동공학회논문집
• /
• 제12권7호
• /
• pp.542-549
• /
• 2002

Computer simulation is essential to design the suspension elements of railway vehicle. By computer simulation, engineers can assess the feasibility of given design variables and chance them to get a bettor design. Even though commercial simulation codes are used, the computational time and cost remains non-trivial. Therefore, malty researchers have used a mesa model made by sampling data through simulation. In this paper, four mesa-models for each index group such as ride comfort, derailment Quotient, unloading radio and stability index, are constructed by use of neural network. After these meta models are constructed, multi-objective optimization are achieved by using the differential evolution. This paper shows that the optimization of design variables using the neural network model is very efficient to solve the complex optimization Problem.

• Nuclear Engineering and Technology
• /
• 제54권1호
• /
• pp.310-317
• /
• 2022

The mass attenuation coefficient is the primary physical parameter to model narrow beam gamma-ray attenuation. A new machine learning based approach is proposed to model gamma-ray shielding behavior of composites alternative to theoretical calculations. Two fuzzy logic algorithms and a neural network algorithm were trained and tested with different mixture ratios of vanadium slag/epoxy resin/antimony in the 0.05 MeV-2 MeV energy range. Two of the algorithms showed excellent agreement with testing data after optimizing adjustable parameters, with root mean squared error (RMSE) values down to 0.0001. Those results are remarkable because mass attenuation coefficients are often presented with four significant figures. Different training data sizes were tried to determine the least number of data points required to train sufficient models. Data set size more than 1000 is seen to be required to model in above 0.05 MeV energy. Below this energy, more data points with finer energy resolution might be required. Neuro-fuzzy models were three times faster to train than neural network models, while neural network models depicted low RMSE. Fuzzy logic algorithms are overlooked in complex function approximation, yet grid partitioned fuzzy algorithms showed excellent calculation efficiency and good convergence in predicting mass attenuation coefficient.

• International Journal of Computer Science & Network Security
• /
• 제22권3호
• /
• pp.220-228
• /
• 2022

The directed tests produce an expectation model to assist the organization's heads and professionals with settling on the right and speedy choice. A directed deep learning strategy has been embraced and applied for SCADA information. In this paper, for the load shedding expectation overall power organization of Libya, a convolutional neural network with multi neurons is utilized. For contributions of the neural organization, eight convolutional layers are utilized. These boundaries are power age, temperature, stickiness and wind speed. The gathered information from the SCADA data set were pre-handled to be ready in a reasonable arrangement to be taken care of to the deep learning. A bunch of analyses has been directed on this information to get a forecast model. The created model was assessed as far as precision and decrease of misfortune. It tends to be presumed that the acquired outcomes are promising and empowering. For assessment of the outcomes four boundary, MSE, RMSE, MAPE and R2 are determined. The best R2 esteem is gotten for 1-overlap and it was 0.98.34 for train information and for test information is acquired 0.96. Additionally for train information the RMSE esteem in 1-overlap is superior to different Folds and this worth was 0.018.

• Steel and Composite Structures
• /
• 제46권6호
• /
• pp.731-744
• /
• 2023

Vehicle load information is an important role in operating and ensuring the structural health of cable-stayed bridges. In this regard, an efficient and economic method is proposed for vehicle load detection based on the observed cable tension and vehicle position using a graph neural network (GNN). Datasets are first generated using the practical advanced analysis program (PAAP), a robust program for modeling and considering both geometric and material nonlinearities of bridge structures subjected to vehicle load with low computational costs. With the superiority of GNN, the proposed model is demonstrated to precisely capture complex nonlinear correlations between the input features and vehicle load in the output. Four popular machine learning methods including artificial neural network (ANN), decision tree (DT), random forest (RF), and support vector machines (SVM) are refereed in a comparison. A case study of a cable-stayed bridge with the typical truck is considered to evaluate the model's performance. The results demonstrate that the GNN-based model provides high accuracy and efficiency in prediction with satisfactory correlation coefficients, efficient determination values, and very small errors; and is a novel approach for vehicle load detection with the input data of the existing monitoring system.

• 한국통신학회논문지
• /
• 제29권2B호
• /
• pp.192-202
• /
• 2004

2본 논문에서는 인터넷 QoS 지원 이동 IP 망에서의 정책기반 네트워크 시스템 설계 및 망관리 시스템 구현 방법에 대해 제안한다. 본 논문의 망관리 시스템은 정책기반 네트워크의 정책서버로서의 역할을 하게 된다. 인터넷 QoS 지원 이동 IP 망에서의 정책기반 네트워크의 전체적인 프레임워크는 크게 응용계층, 정보관리계층, 정책제어 계층, 디바이스계층의 네 계층으로 나뉘어 통합된 관리를 수행하는 구조를 가지고 있으며, 이러한 통합된 망관리 시스템에 적용할 네 가지 범주(access control, mobile IP operation, QoS control, network monitoring)의 정책 구조를 정의하고 이에 따른 동작 절차의 예를 보인다. 실제 QoS 지원 이동 IP 망에서의 정책기반 망관리 시스템의 구현을 위한 설계 방법 및 S/W의 구조와 각 모듈 별 기능에 대해 제시하고 이 망관리 시스템과 각각의 에이전트들과의 원활한 통신을 위해 개발한 SCOPS(Simple Common Open Policy Service)프로토콜의 구조 및 기능에 대해 정의한다. 마지막으로 제안된 인터넷 QoS 지원 이동 IP 망에서의 정책기반 망관리 시스템을 실험실 규모의 테스트 베드에 적용하여 구성하는 방법에 대해 자세히 설명하고 성능평가한다.

• 방송공학회논문지
• /
• 제23권2호
• /
• pp.186-196
• /
• 2018

본 논문에서는 객체를 추적하기 위해 합성곱 신경망 모델을 이용한 온라인 추적 기법을 제안한다. 오프라인에 모델을 학습시키기 위해서는 많은 수의 훈련 샘플이 필요하다. 이러한 문제를 해결하기 위해, 학습되지 않은 모델을 사용하고, 실험 영상으로부터 직접 훈련 샘플을 수집하여 모델을 갱신한다. 기존의 방법들은 많은 훈련 샘플을 획득하여 모델의 학습에 사용하였지만, 본 논문에서는 적은 수의 훈련 샘플만으로도 객체의 추적이 가능함을 증명한다. 또한 컬러 정보를 활용하여 새로운 손실 함수를 정의하였고 이로부터 잘못 수집된 훈련 샘플로 인해 모델이 잘못된 방향으로 학습되는 문제를 방지한다. 실험을 통해 4가지 비교 방법과 동등하거나 개선된 추적 성능을 보임을 증명하였다.

• Journal of Information Science Theory and Practice
• /
• 제9권2호
• /
• pp.83-98
• /
• 2021

Research collaboration is an important strategy to improve research output, and virtual academic communities (VACs) have become an important platform to collaborate on. This paper reveals the influencing factors of researchers' collaboration intention in VACs from two attributes: individual, and inter-members. On the basis of the Social Cognitive Theory, Social Exchange Theory, social network theory, and Five-Factor Model, this paper constructed a model demonstrating the influencing factors of VACs researchers' collaboration intention. A self-administered questionnaire was employed on members of four VACs in China to collect data; subsequently, 558 usable responses were analyzed using structural equation modeling. The result showed that openness, conscientiousness, reciprocity, trust, and the social network characteristic had a significant influence on the collaboration intention of researchers in VACs, while self-efficacy, agreeableness, extroversion, neuroticism, and experience had no significant effects on the collaboration intention of researchers in VACs. This model plays a positive role in promoting the research collaboration intention of Chinese VACs researchers and in guiding the construction of VAC platforms.

• International Journal of Computer Science & Network Security
• /
• 제22권5호
• /
• pp.294-302
• /
• 2022

Detection of fake news is a complex and a challenging task. Generation of fake news is very hard to stop, only steps to control its circulation may help in minimizing its impacts. Humans tend to believe in misleading false information. Researcher started with social media sites to categorize in terms of real or fake news. False information misleads any individual or an organization that may cause of big failure and any financial loss. Automatic system for detection of false information circulating on social media is an emerging area of research. It is gaining attention of both industry and academia since US presidential elections 2016. Fake news has negative and severe effects on individuals and organizations elongating its hostile effects on the society. Prediction of fake news in timely manner is important. This research focuses on detection of fake news spreaders. In this context, overall, 6 models are developed during this research, trained and tested with dataset of PAN 2020. Four approaches N-gram based; user statistics-based models are trained with different values of hyper parameters. Extensive grid search with cross validation is applied in each machine learning model. In N-gram based models, out of numerous machine learning models this research focused on better results yielding algorithms, assessed by deep reading of state-of-the-art related work in the field. For better accuracy, author aimed at developing models using Random Forest, Logistic Regression, SVM, and XGBoost. All four machine learning algorithms were trained with cross validated grid search hyper parameters. Advantages of this research over previous work is user statistics-based model and then ensemble learning model. Which were designed in a way to help classifying Twitter users as fake news spreader or not with highest reliability. User statistical model used 17 features, on the basis of which it categorized a Twitter user as malicious. New dataset based on predictions of machine learning models was constructed. And then Three techniques of simple mean, logistic regression and random forest in combination with ensemble model is applied. Logistic regression combined in ensemble model gave best training and testing results, achieving an accuracy of 72%.

• 문화기술의 융합
• /
• 제9권3호
• /
• pp.1-10
• /
• 2023

이 연구의 목적은 ANT를 중심으로 창의ˑ융합형 인재 양성 방안을 탐색한 것이다. ANT를 중심으로 창의ˑ융합형 인재를 양성하기 위해서는 첫째, 네트워크 형성단계에서 카오의 법칙을 활용할 필요가 있다. 둘째, 네트워크 형성단계에서 약한 유대의 강한 힘을 활용할 필요가 있다. 셋째, 번역의 4단계 중 문제 제기 단계에서 파문을 일으키기 위해서는 질문하는 능력을 키워야 한다. 넷째, 파문을 던지기 위해서는 다양한 창의적 문제해결 기법 교육이 필요하다. 다섯째, 번역의 4단계 중 2단계인 관심 끌기 단계에서 끼어들기를 성공시키기 위해서는 의사소통 역량과 비판적사고 역량을 교육해야 한다. 후속 연구를 위한 제언은 첫째, ANT에서 제시한 번역의 4단계와 창의적 문제해결 모형단계를 비교 분석해 볼 필요가 있다. 둘째, ANT를 중심으로 구체적인 사례를 적용한 후속 연구들이 있기를 기대한다.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2002년도 하계학술대회 논문집 B
• /
• pp.1241-1243
• /
• 2002

This study presents a AC electric railway system analysis using PSCAD/EMTDC for circuit analysis and fault studies. This PSCAD/EMTDC model includes feeder, contact line, rails. Scott-transformer. Auto-transformer and so on. This model is based on four-port network which is an extension of two-port network theory. In order to verify the proposed model, fault studies of a test system are performed.