• Title/Summary/Keyword: Computer Model

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Development of Simulation Model for Power Tiller Motion (동력경운기(動力耕耘機)의 운동(運動) 시뮬레이션을 위한 모델 개발(開發))

  • Kim, K.U.
    • Journal of Biosystems Engineering
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    • v.12 no.2
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    • pp.1-15
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    • 1987
  • A mathematical model is developed and computer programmed for simulation of power tiller motion. The model consists of a main body and two driving wheels resulting in an 8 degrees of freedom system. Drawbar loading is also included by coupling the model with a sub-model representing the implement to be used. The computer program SIMPTL can predict motion characteristics and static stability of power tiller under a given set of ground and operation conditions.

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Nonlinear Behavior in Love Model with Discontinuous External Force

  • Bae, Youngchul
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.16 no.1
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    • pp.64-71
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    • 2016
  • This paper proposes nonlinear behavior in a love model for Romeo and Juliet with an external force of discontinuous time. We investigated the periodic motion and chaotic behavior in the love model by using time series and phase portraits with respect to some variable and fixed parameters. The computer simulation results confirmed that the proposed love model with an external force of discontinuous time shows periodic motion and chaotic behavior with respect to parameter variation.

A New Stochastic Binary Neural Network Based on Hopfield Model and Its Application

  • Nakamura, Taichi;Tsuneda, Akio;Inoue, Takahiro
    • Proceedings of the IEEK Conference
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    • 2002.07a
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    • pp.34-37
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    • 2002
  • This paper presents a new stochastic binary neural network based on the Hopfield model. We apply the proposed network to TSP and compare it with other methods by computer simulations. Furthermore, we apply 2-opt to the proposed network to improve the performance.

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A environments study for the model construction of risk scenario (위험 시나리오 모델 구성을 위한 환경 연구)

  • Park, Sangjoon;Lee, Jongchan
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2019.07a
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    • pp.123-124
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    • 2019
  • 본 논문에서는 안전관리를 시나리오 모델의 환경을 고려한다. 안전관리에 대해서 보통의 경우 사고의 재발 환경이 높은 곳에서 그 필요성이 절실히 요구된다. 따라서 안전관리의 필요성이 제시된 곳에서 그 환경에 적용될 수 있는 시나리오의 모델을 추출해야 한다. 시나리오 모델들의 추출은 시나리오 적용 지역의 환경 분석을 통하여 대상 요소를 분석하여 구축하여야 한다.

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A study of DEVS model for safety management environments (안전관리 환경을 위한 DEVS 모델 연구)

  • Park, Sangjoon;Lee, Jongchan
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2019.07a
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    • pp.125-126
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    • 2019
  • 본 논문에서는 위험 관리 지역에 대한 시나리오 구축을 위한 분석 모델을 고려한다. 위험 요소에 대한 분석은 외부 상황에 대한 요소뿐만 아니라 내부 상황에 의한 요소도 제공된다. 대응 모델에 대한 반응은 요소분석을 통하여 도출될 수 있는 결과를 통하여 결정된다. 위험 관리 지역에 대한 시나리오 분석 모델을 위하여 DEVS 방안을 고려하며, 이에 대한 모델 설계를 고려한다.

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Suggestions for learning design patterns based on the Dreyfus model (드라이퍼스 모델 기반 디자인 패턴 학습 모델 제안)

  • Moon, HyunJun;Kim, Jungsun
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2021.01a
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    • pp.335-336
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    • 2021
  • 디자인 패턴은 클래스와 객체를 활용한 23가지의 개발자 의도를 만족시켜주는 최선의 실천법들을 정리한 것이다. 디자인 패턴은 설계 경험과 객체지향 패러다임의 기반 지식들을 필요하므로 실질적인 패턴 학습에 어려움이 있다. 디자인 패턴 학습에 대한 도움을 제시해 줄 수 있는 가이드라인으로 기술 습득 모델에 활용하는 드라이퍼스 모델을 적용하는 것을 제안하고자 한다. 드라이퍼스 단계별 모델을 기반으로 단계 별 디자인 패턴 학습 단계를 제시한다.

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Collaborative Filtering Recommendation Algorithm Based on LDA2Vec Topic Model (LDA2Vec 항목 모델을 기반으로 한 협업 필터링 권장 알고리즘)

  • Xin, Zhang;Lee, Scott Uk-Jin
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2020.07a
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    • pp.385-386
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    • 2020
  • In this paper, we propose a collaborative filtering recommendation algorithm based on the LDA2Vec topic model. By extracting and analyzing the article's content, calculate their semantic similarity then combine the traditional collaborative filtering algorithm to recommend. This approach may promote the system's recommend accuracy.

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Differential Evolution with Numerous Strategies (수많은 전략을 가진 차등 진화)

  • Oh, Suk-Kyong;Shin, Seong-Yoon
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2020.01a
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    • pp.243-244
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    • 2020
  • 본 논문에서는 SIM(Soft Island Model)을 통해 소집단 정보를 이동시키기 위한 KSDE라고 하는 수많은 전략을 제안한다. 먼저, 전체 모집단은 k- 평균 군집 알고리즘에 의해 k 개의 하위 모집단으로 분리된다. 둘째, 소집단에 돌연변이 조작을 수행하기 위해 전략 풀에서 돌연변이 전략을 무작위로 선택한다. 마지막으로, 이 알고리즘의 모집단 다양성을 개선하기 위해 하위 집단 정보가 SIM을 통해 마이그레이션 된다.

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Quality Assessment Model for Practical Wearable Computers (실용적 웨어러블 컴퓨터 품질평가모델)

  • Oh, Cheon-Seok;Choi, Jae-Hyun;Kim, Jong-Bae;Park, Jea-Won
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.39B no.12
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    • pp.842-855
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    • 2014
  • Recently, the progress of smart phone market has retarded by oversupply therefore wearable computer has been the focus of new growth engine. Wearable computing system is a complex fusion of a variety of technologies such as wireless network, embedded, sensor and new material. Because these technologies involves utilization and mobility in addition to quality characteristic in existing software, application of ISO/IEC 9126 is not perfect when assessing quality of wearable computer. In this study, author suggested new quality assessment model for wearable computer by sorting quality attribute in ISO/IEC 9126 and adding new quality attribute. For this, author investigated features and functional requirements related to wearable computer. and then author suggested quality standard and metrics by identifying quality characteristic. Author confirmed practicality of quality assessment model by using suggested model in scenario and comparing quality assessment of three goods such as company S, L, G. This quality assessment model is expected to use guidelines for assessing quality of wearable computer.

Remote Sensing Image Classification for Land Cover Mapping in Developing Countries: A Novel Deep Learning Approach

  • Lynda, Nzurumike Obianuju;Nnanna, Nwojo Agwu;Boukar, Moussa Mahamat
    • International Journal of Computer Science & Network Security
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    • v.22 no.2
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    • pp.214-222
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    • 2022
  • Convolutional Neural networks (CNNs) are a category of deep learning networks that have proven very effective in computer vision tasks such as image classification. Notwithstanding, not much has been seen in its use for remote sensing image classification in developing countries. This is majorly due to the scarcity of training data. Recently, transfer learning technique has successfully been used to develop state-of-the art models for remote sensing (RS) image classification tasks using training and testing data from well-known RS data repositories. However, the ability of such model to classify RS test data from a different dataset has not been sufficiently investigated. In this paper, we propose a deep CNN model that can classify RS test data from a dataset different from the training dataset. To achieve our objective, we first, re-trained a ResNet-50 model using EuroSAT, a large-scale RS dataset to develop a base model then we integrated Augmentation and Ensemble learning to improve its generalization ability. We further experimented on the ability of this model to classify a novel dataset (Nig_Images). The final classification results shows that our model achieves a 96% and 80% accuracy on EuroSAT and Nig_Images test data respectively. Adequate knowledge and usage of this framework is expected to encourage research and the usage of deep CNNs for land cover mapping in cases of lack of training data as obtainable in developing countries.