• Journal of Electrical Engineering and Technology
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• 제13권6호
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• pp.2511-2520
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• 2018

In this study, Multi-Radial Basis Function Support Vector Machine (Multi-RBF SVM) classifier is introduced based on a composite kernel function. In the proposed multi-RBF support vector machine classifier, the input space is divided into several local subsets considered for extremely nonlinear classification tasks. Each local subset is expressed as nonlinear classification subspace and mapped into feature space by using kernel function. The composite kernel function employs the dual RBF structure. By capturing the nonlinear distribution knowledge of local subsets, the training data is mapped into higher feature space, then Multi-SVM classifier is realized by using the composite kernel function through optimization procedure similar to conventional SVM classifier. The original training data set is partitioned by using some unsupervised learning methods such as clustering methods. In this study, three types of clustering method are considered such as Affinity propagation (AP), Hard C-Mean (HCM) and Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA). Experimental results on benchmark machine learning datasets show that the proposed method improves the classification performance efficiently.

• Journal of Computing Science and Engineering
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• 제5권4호
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• pp.305-313
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• 2011

Due to the expansion of high-speed Internet access, the need for secure and reliable networks has become more critical. The sophistication of network attacks, as well as their severity, has also increased recently. As such, more and more organizations are becoming vulnerable to attack. The aim of this research is to classify network attacks using neural networks (NN), which leads to a higher detection rate and a lower false alarm rate in a shorter time. This paper focuses on two classification types: a single class (normal, or attack), and a multi class (normal, DoS, PRB, R2L, U2R), where the category of attack is also detected by the NN. Extensive analysis is conducted in order to assess the translation of symbolic data, partitioning of the training data and the complexity of the architecture. This paper investigates two engines; the first engine is the back-propagation neural network intrusion detection system (BPNNIDS) and the second engine is the radial basis function neural network intrusion detection system (BPNNIDS). The two engines proposed in this paper are tested against traditional and other machine learning algorithms using a common dataset: the DARPA 98 KDD99 benchmark dataset from International Knowledge Discovery and Data Mining Tools. BPNNIDS shows a superior response compared to the other techniques reported in literature especially in terms of response time, detection rate and false positive rate.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2006년도 춘계학술대회 학술발표 논문집 제16권 제1호
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• pp.56-58
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• 2006

Camera calibration in machine vision is the process of determining the intrinsic cameara parameters and the three-dimensional (3D) position and orientation of the camera frame relative to a certain world coordinate system. On the other hand, Takagi-Sugeno-Kang (TSK) fuzzy system is a very popular fuzzy system and approximates any nonlinear function to arbitrary accuracy with only a small number of fuzzy rules. It demonstrates not only nonlinear behavior but also transparent structure. In this paper, we present a novel and simple technique for camera calibration for machine vision using TSK fuzzy model. The proposed method divides the world into some regions according to camera view and uses the clustered 3D geometric knowledge. TSK fuzzy system is employed to estimate the camera parameters by combining partial information into complete 3D information. The experiments are performed to verify the proposed camera calibration.

• 산업경영시스템학회지
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• 제4권4호
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• pp.63-69
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• 1981

As enterprises are getting bigger and bigger and more competecious, an engineering economy for the maximization of profit based on basic theory must be considered. This thesis present dynamic computer model for the decision which controls complicated and various man- machine system optimally. This model occur in general stage can be adaptable to every kind of enterprises. So, any one who has no expert knowledge is able to get the optimal solution. And decision tree used in this paper can be applied in every kinds of academic circles as well as whole the industrial world. This paper studied optimal management of engineering project based upon basic theory of engineering economy. It introduces and functionizes the variables which generalize every possible elements, set up a model in order to find out the variable which maximize the calculated value among many other variables. And the selected values ate used as decision- marking variables for the optimal management of engineering projects. It found out some problem of this model. They are : 1. In some kinds of man-machine system it refers to Probability, but other case, it depends on only experimental probability. 2. Unless decision making process (decision tree) goes on, this model can not be applied. So these cases, this paper says, can be solved by adapting finite decision tree which is analyzed by using the same technic as those in product introduction problem. And this paper set up the computer model in order to control every procedure quickly and optimally, using Fortran IV.

• 한국게임학회 논문지
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• 제6권4호
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• pp.79-87
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• 2006

본 논문은 기계학습 기법을 이용한 게임 캐릭터를 제어하는 엔진을 설계하고 구현하는 것을 제안한다. 제안된 엔진은 실제 게임에서 상황 데이터를 추출하여 지식 데이터로 사용하므로 지능 캐릭터의 행동 패턴을 게이머들이 쉽게 인식하지 못하는 장점이 있다. 이를 위하여 상황 데이터를 추출하여 학습하는 모듈과 임의의 상황 데이터에 대하여 최적의 상황 제어를 판단하는 시험 모듈을 개발하는 것을 제안하였다. 구현된 엔진은 FEAR에 이식되고 Quake2 게임에 적용되었다. 또한 개발된 엔진의 올바른 동작과 효율성을 위하여 다양한 실험을 하였다. 실험으로부터 개발된 엔진이 올바르게 동작할 뿐만 아니라 제한된 시간 내에 효율적으로 동작함을 알 수 있었다.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2005년도 춘계학술대회 논문집
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• pp.48-53
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• 2005

The world, coming into the 21st century, is preparing a new revolution called a knowledge-based society after the industrial society. The interest of the world is concentrated on information technology, nano-technology and biotechnology. In particular, the nano-technology of which study was originally started from an alternative for overcoming semiconductor micro-technology. It can be applied to most industry subject such as electronics, information and communication, machinery, chemistry, bioengineering, energy, etc. They are emerging into the technology that can change civilization of human beings. Specially, ultra precision machining is quickly applied to nano-technology in the field of machinery. Lately, with rapid development of electronics industry and optic industry, there are needs for super precision finishing of various core parts required in such related apparatuses. This paper handles stability of a super precision micro cutting machine that is a core unit of such a super precision finisher, and analyzes the results depending on the hinge type and material change, using FEM analysis. By reviewing the stability, it is possible to achieve the effect of basic data collection for unit control and to reduce trials and errors in unit design and manufacturing.

• 한국산학기술학회:학술대회논문집
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• 한국산학기술학회 2001년도 추계산학기술 심포지엄 및 학술대회 발표논문집
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• pp.107-110
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• 2001

Design process is the essential technology for development of industry in nation, but contrary to its significance the trial for development of design technology is not so active because it requires a lot of time and efforts to educate design engineers. For that reason, most of enterprises concentrated their efforts for improving product technologies to get instant effects in short periods, and through these trials considerable results could be achieved. Recently, however, many people realized that industrial development through only product technology without design technology has limits, accordingly, a lot of efforts, to educate machine designers whom have enough knowledge and ability on design through advanced design technology, concentrated for industrial development. In general, the curriculum of conventional education for machine design in most universities is mainly compose of three subjects, the theory for elements design, geometric modeling practice for mating engineering drawings using CAD software, and analysis of elements using CAE software fur determining whether proposed solution is correct or rational. Furthermore, because these three subject are provided for students as the completely separated subjects, most of students who have educated with this method have no enough ability to Integrate all design process into a comprehensive whole process. This paper proposes a new design education methodology through reverse engineering that can overcome these problems of conventional education method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권4호
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• pp.1128-1145
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• 2022

Owing to the rapid development of information science, data analysis based on machine learning has become an interdisciplinary and strategic area. Marine predators algorithm (MPA) is a novel metaheuristic algorithm inspired by the foraging strategies of marine organisms. Considering the randomness of these strategies, an improved algorithm called co-evolutionary cultural mechanism-based marine predators algorithm (CECMPA) is proposed. Through this mechanism, search agents in different spaces can share knowledge and experience to improve the performance of the native algorithm. More specifically, CECMPA has a higher probability of avoiding local optimum and can search the global optimum quickly. In this paper, it is the first to use CECMPA to perform feature subset selection and optimize hyperparameters in support vector machine (SVM) simultaneously. For performance evaluation the proposed method, it is tested on twelve datasets from the university of California Irvine (UCI) repository. Moreover, the coronavirus disease 2019 (COVID-19) can be a real-world application and is spreading in many countries. CECMPA is also applied to a COVID-19 dataset. The experimental results and statistical analysis demonstrate that CECMPA is superior to other compared methods in the literature in terms of several evaluation metrics. The proposed method has strong competitive abilities and promising prospects.

• International Journal of Computer Science & Network Security
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• 제22권5호
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• pp.330-334
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• 2022

In light of the decreasing crop production and shortage of food across the world, one of the crucial criteria of agriculture nowadays is selecting the right crop for the right piece of land at the right time. First problem is that How Farmers can predict the right crop for cultivation because famers have no knowledge about prediction of crop. Second problem is that which algorithm is best that provide the maximum accuracy for crop prediction. Therefore, in this research Author proposed a method that would help to select the most suitable crop(s) for a specific land based on the analysis of the affecting parameters (Temperature, Humidity, Soil Moisture) using machine learning. In this work, the author implemented Random Forest Classifier, Support Vector Machine, k-Nearest Neighbor, and Decision Tree for crop selection. The author trained these algorithms with the training dataset and later these algorithms were tested with the test dataset. The author compared the performances of all the tested methods to arrive at the best outcome. In this way best algorithm from the mention above is selected for crop prediction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권2호
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• pp.504-519
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• 2023

Heart disease is becoming the top reason of death all around the world. Diagnosing cardiac illness is a difficult endeavor that necessitates both expertise and extensive knowledge. Machine learning (ML) is becoming gradually more important in the medical field. Most of the works have concentrated on the prediction of cardiac disease, however the precision of the results is minimal, and data integrity is uncertain. To solve these difficulties, this research creates an Integrated Accurate-Secure Heart Disease Prediction (IAS) Model based on Deep Convolutional Neural Networks. Heart-related medical data is collected and pre-processed. Secondly, feature extraction is processed with two factors, from signals and acquired data, which are further trained for classification. The Deep Convolutional Neural Networks (DCNN) is used to categorize received sensor data as normal or abnormal. Furthermore, the results are safeguarded by implementing an integrity validation mechanism based on the hash algorithm. The system's performance is evaluated by comparing the proposed to existing models. The results explain that the proposed model-based cardiac disease diagnosis model surpasses previous techniques. The proposed method demonstrates that it attains accuracy of 98.5 % for the maximum amount of records, which is higher than available classifiers.