• Title/Summary/Keyword: Artificial life algorithm

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The Genetic Algorithm using Variable Chromosome with Chromosome Attachment for decision making model (의사결정 모델을 위한 염색체 비분리를 적용한 가변 염색체 유전 알고리즘)

  • Park, Kang-Moon;Shin, Suk-Hoon;Chi, Sung-Do
    • Journal of the Korea Society for Simulation
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    • v.26 no.4
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    • pp.1-9
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    • 2017
  • The Genetic Algorithm(GA) is a global search algorithm based on biological genetics. It is widely used in various fields such as industrial applications, artificial neural networks, web applications and defense industry. However, conventional Genetic Algorithm has difficulty maintaining feasibility in complicated situations due to its fixed number of chromosomes. This study proposes the Genetic Algorithm using variable chromosome with chromosome attachment. And in order to verify the implication of changing number of chromosomes in the simulation, it applies the Genetic Algorithm using variable chromosome with chromosome attachment to antisubmarine High Value Unit(HVU) escort mission simulation. As a result, the Genetic Algorithm using variable chromosome has produced complex strategies faster than the conventional method, indicating the increase of the number of chromosome during the process.

Multilayer Perceptron Model to Estimate Solar Radiation with a Solar Module

  • Kim, Joonyong;Rhee, Joongyong;Yang, Seunghwan;Lee, Chungu;Cho, Seongin;Kim, Youngjoo
    • Journal of Biosystems Engineering
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    • v.43 no.4
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    • pp.352-361
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    • 2018
  • Purpose: The objective of this study was to develop a multilayer perceptron (MLP) model to estimate solar radiation using a solar module. Methods: Data for the short-circuit current of a solar module and other environmental parameters were collected for a year. For MLP learning, 14,400 combinations of input variables, learning rates, activation functions, numbers of layers, and numbers of neurons were trained. The best MLP model employed the batch backpropagation algorithm with all input variables and two hidden layers. Results: The root-mean-squared error (RMSE) of each learning cycle and its average over three repetitions were calculated. The average RMSE of the best artificial neural network model was $48.13W{\cdot}m^{-2}$. This result was better than that obtained for the regression model, for which the RMSE was $66.67W{\cdot}m^{-2}$. Conclusions: It is possible to utilize a solar module as a power source and a sensor to measure solar radiation for an agricultural sensor node.

Numerical evaluation of gamma radiation monitoring

  • Rezaei, Mohsen;Ashoor, Mansour;Sarkhosh, Leila
    • Nuclear Engineering and Technology
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    • v.51 no.3
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    • pp.807-817
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    • 2019
  • Airborne Gamma Ray Spectrometry (AGRS) with its important applications such as gathering radiation information of ground surface, geochemistry measuring of the abundance of Potassium, Thorium and Uranium in outer earth layer, environmental and nuclear site surveillance has a key role in the field of nuclear science and human life. The Broyden-Fletcher-Goldfarb-Shanno (BFGS), with its advanced numerical unconstrained nonlinear optimization in collaboration with Artificial Neural Networks (ANNs) provides a noteworthy opportunity for modern AGRS. In this study a new AGRS system empowered by ANN-BFGS has been proposed and evaluated on available empirical AGRS data. To that effect different architectures of adaptive ANN-BFGS were implemented for a sort of published experimental AGRS outputs. The selected approach among of various training methods, with its low iteration cost and nondiagonal scaling allocation is a new powerful algorithm for AGRS data due to its inherent stochastic properties. Experiments were performed by different architectures and trainings, the selected scheme achieved the smallest number of epochs, the minimum Mean Square Error (MSE) and the maximum performance in compare with different types of optimization strategies and algorithms. The proposed method is capable to be implemented on a cost effective and minimum electronic equipment to present its real-time process, which will let it to be used on board a light Unmanned Aerial Vehicle (UAV). The advanced adaptation properties and models of neural network, the training of stochastic process and its implementation on DSP outstands an affordable, reliable and low cost AGRS design. The main outcome of the study shows this method increases the quality of curvature information of AGRS data while cost of the algorithm is reduced in each iteration so the proposed ANN-BFGS is a trustworthy appropriate model for Gamma-ray data reconstruction and analysis based on advanced novel artificial intelligence systems.

Development of Data Mining Algorithm for Implementation of Fine Dust Numerical Prediction Model (미세먼지 수치 예측 모델 구현을 위한 데이터마이닝 알고리즘 개발)

  • Cha, Jinwook;Kim, Jangyoung
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.22 no.4
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    • pp.595-601
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    • 2018
  • Recently, as the fine dust level has risen rapidly, there is a great interest. Exposure to fine dust is associated with the development of respiratory and cardiovascular diseases and has been reported to increase death rate. In addition, there exist damage to fine dusts continues at industrial sites. However, exposure to fine dust is inevitable in modern life. Therefore, predicting and minimizing exposure to fine dust is the most efficient way to reduce health and industrial damages. Existing fine dust prediction model is estimated as good, normal, poor, and very bad, depending on the concentration range of the fine dust rather than the concentration value. In this paper, we study and implement to predict the PM10 level by applying the Artificial neural network algorithm and the K-Nearest Neighbor algorithm, which are machine learning algorithms, using the actual weather and air quality data.

Hybrid Learning Architectures for Advanced Data Mining:An Application to Binary Classification for Fraud Management (개선된 데이터마이닝을 위한 혼합 학습구조의 제시)

  • Kim, Steven H.;Shin, Sung-Woo
    • Journal of Information Technology Application
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    • v.1
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    • pp.173-211
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    • 1999
  • The task of classification permeates all walks of life, from business and economics to science and public policy. In this context, nonlinear techniques from artificial intelligence have often proven to be more effective than the methods of classical statistics. The objective of knowledge discovery and data mining is to support decision making through the effective use of information. The automated approach to knowledge discovery is especially useful when dealing with large data sets or complex relationships. For many applications, automated software may find subtle patterns which escape the notice of manual analysis, or whose complexity exceeds the cognitive capabilities of humans. This paper explores the utility of a collaborative learning approach involving integrated models in the preprocessing and postprocessing stages. For instance, a genetic algorithm effects feature-weight optimization in a preprocessing module. Moreover, an inductive tree, artificial neural network (ANN), and k-nearest neighbor (kNN) techniques serve as postprocessing modules. More specifically, the postprocessors act as second0order classifiers which determine the best first-order classifier on a case-by-case basis. In addition to the second-order models, a voting scheme is investigated as a simple, but efficient, postprocessing model. The first-order models consist of statistical and machine learning models such as logistic regression (logit), multivariate discriminant analysis (MDA), ANN, and kNN. The genetic algorithm, inductive decision tree, and voting scheme act as kernel modules for collaborative learning. These ideas are explored against the background of a practical application relating to financial fraud management which exemplifies a binary classification problem.

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Prediction of Net Irrigation Water Requirement in paddy field Based on Machine Learning (머신러닝 기법을 활용한 논 순용수량 예측)

  • Kim, Soo-Jin;Bae, Seung-Jong;Jang, Min-Won
    • Journal of Korean Society of Rural Planning
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    • v.28 no.4
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    • pp.105-117
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    • 2022
  • This study tested SVM(support vector machine), RF(random forest), and ANN(artificial neural network) machine-learning models that can predict net irrigation water requirements in paddy fields. For the Jeonju and Jeongeup meteorological stations, the net irrigation water requirement was calculated using K-HAS from 1981 to 2021 and set as the label. For each algorithm, twelve models were constructed based on cumulative precipitation, precipitation, crop evapotranspiration, and month. Compared to the CE model, the R2 of the CEP model was higher, and MAE, RMSE, and MSE were lower. Comprehensively considering learning performance and learning time, it is judged that the RF algorithm has the best usability and predictive power of five-days is better than three-days. The results of this study are expected to provide the scientific information necessary for the decision-making of on-site water managers is expected to be possible through the connection with weather forecast data. In the future, if the actual amount of irrigation and supply are measured, it is necessary to develop a learning model that reflects this.

Helping People with Visual Disability Using AI

  • Naif Al Otaibi;Tariq S Almurayziq
    • International Journal of Computer Science & Network Security
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    • v.24 no.1
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    • pp.205-208
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    • 2024
  • Artificial Intelligence (AI) technology has evolved rapidly in recent years and is used in everything from banking to email management to surgery, but without the help of the visible, most of the fun features of the Internet include visual impairment. It benefits people with disabilities. The main purpose of this study is to find ways to help people with visual impairments using AI technology. A visually impaired request is made for the visually impaired. For example, when a message arrives that the program will notify you by voice (reads the sender's name, read the message, and replies to it if necessary), this is a special program installed on your mobile phone. This program uses a customized algorithm developed in Python to convert written text to voice, read text, and convert voice to written text on a message when a visually impaired person wants to respond. Then it sends the response in the form of a text message. Therefore, the research should lead to programs for people with visual impairments. This program makes mobile phones easier and more comfortable to use and makes the daily life easier for visual impairments.

Fast Search Algorithm for Determining the Optimal Number of Clusters using Cluster Validity Index (클러스터 타당성 평가기준을 이용한 최적의 클러스터 수 결정을 위한 고속 탐색 알고리즘)

  • Lee, Sang-Wook
    • The Journal of the Korea Contents Association
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    • v.9 no.9
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    • pp.80-89
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    • 2009
  • A fast and efficient search algorithm to determine an optimal number of clusters in clustering algorithms is presented. The method is based on cluster validity index which is a measure for clustering optimality. As the clustering procedure progresses and reaches an optimal cluster configuration, the cluster validity index is expected to be minimized or maximized. In this Paper, a fast non-exhaustive search method for finding the optimal number of clusters is designed and shown to work well in clustering. The proposed algorithm is implemented with the k-mean++ algorithm as underlying clustering techniques using CB and PBM as a cluster validity index. Experimental results show that the proposed method provides the computation time efficiency without loss of accuracy on several artificial and real-life data sets.

Implementation of Customized Variable Insurance Management System Using Data Crawling and Fund Management Algorithm

  • Nam, Sung-hyun;Kwon, Soon-kak
    • Journal of Multimedia Information System
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    • v.8 no.1
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    • pp.69-74
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    • 2021
  • This paper accumulates the product structure data such as bond obligation ratio and investment ratio for variable insurance using crawling from the insurance company's API, also accumulates variable insurance income and project expenses for variable insurance using crawling from the API of life insurance association. From these accumulated data, the correlation coefficient between fund product and customer preference is calculated with an investment algorithm, and variable insurance funds by customer investment preference and product structure are recommended according to market conditions. From the simulation results, it is shown that the proposed variable insurance management system properly recommends and manages variable insurance according to customer preferences.

Robust Extraction of Lean Tissue Contour From Beef Cut Surface Image

  • Heon Hwang;Lee, Y.K.;Y.r. Chen
    • Proceedings of the Korean Society for Agricultural Machinery Conference
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    • 1996.06c
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    • pp.780-791
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    • 1996
  • A hybrid image processing system which automatically distinguished lean tissues in the image of a complex beef cut surface and generated the lean tissue contour has been developed. Because of the in homegeneous distribution and fuzzy pattern of fat and lean tissue on the beef cut, conventional image segmentation and contour generation algorithm suffer from a heavy computing requirement, algorithm complexity and poor robustness. The proposed system utilizes an artificial neural network enhance the robustness of processing. The system is composed of pre-network , network and post-network processing stages. At the pre-network stage, gray level images of beef cuts were segmented and resized to be adequate to the network input. Features such as fat and bone were enhanced and the enhanced input image was converted tot he grid pattern image, whose grid was formed as 4 X4 pixel size. at the network stage, the normalized gray value of each grid image was taken as the network input. Th pre-trained network generated the grid image output of the isolated lean tissue. A training scheme of the network and the separating performance were presented and analyzed. The developed hybrid system showed the feasibility of the human like robust object segmentation and contour generation for the complex , fuzzy and irregular image.

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