• Journal of KIISE:Software and Applications
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• v.29 no.3
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• pp.192-201
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• 2002

Automated text classification is considered as an important method to manage and process a huge amount of documents in digital forms that are widespread and continuously increasing. Recently, text classification has been addressed with machine learning technologies such as k-nearest neighbor, decision tree, support vector machine and neural networks. However, only few investigations in text classification are studied on real problems but on well-organized text corpus, and do not show their usefulness. This paper proposes and analyzes text classification methods for a real application, email document classification task. First, we propose a combining method of multiple neural networks that improves the performance through the combinations with maximum and neural networks. Second, we present another strategy of combining multiple machine learning classifiers. Voting, Borda count and neural networks improve the overall classification performance. Experimental results show the usefulness of the proposed methods for a real application domain, yielding more than 90% precision rates.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.279-286
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• 2013

This paper introduces a new method for identification of building energy performance problems. The presented method is based on automated analysis and visualization of deviations between actual and expected energy performance of the building using EPAR (Energy Performance Augmented Reality) models. For generating EPAR models, during building inspections, energy auditors collect a large number of digital and thermal imagery using a consumer-level single thermal camera that has a built-in digital lens. Based on a pipeline of image-based 3D reconstruction algorithms built on GPU and multi-core CPU architecture, 3D geometrical and thermal point cloud models of the building under inspection are automatically generated and integrated. Then, the resulting actual 3D spatio-thermal model and the expected energy performance model simulated using computational fluid dynamics (CFD) analysis are superimposed within an augmented reality environment. Based on the resulting EPAR models which jointly visualize the actual and expected energy performance of the building under inspection, two new algorithms are introduced for quick and reliable identification of potential performance problems: 1) 3D thermal mesh modeling using k-d trees and nearest neighbor searching to automate calculation of temperature deviations; and 2) automated visualization of performance deviations using a metaphor based on traffic light colors. The proposed EPAR v2.0 modeling method is validated on several interior locations of a residential building and an instructional facility. Our empirical observations show that the automated energy performance analysis using EPAR models enables performance deviations to be rapidly and accurately identified. The visualization of performance deviations in 3D enables auditors to easily identify potential building performance problems. Rather than manually analyzing thermal imagery, auditors can focus on other important tasks such as evaluating possible remedial alternatives.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.2
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• pp.835-838
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• 2016

Background: Lung cancer remains one of the most common cancers in the world, both in terms of new cases (about 13% of total per year) and deaths (nearly one cancer death in five), because of the high case fatality. Errors in lung cancer type or malignant growth determination lead to degraded treatment efficacy, because anticancer strategy depends on tumor morphology. Materials and Methods: We have made an attempt to evaluate effectiveness of machine learning algorithms in the task of lung cancer classification based on gene expression levels. We processed four publicly available data sets. The Dana-Farber Cancer Institute data set contains 203 samples and the task was to classify four cancer types and sound tissue samples. With the University of Michigan data set of 96 samples, the task was to execute a binary classification of adenocarcinoma and non-neoplastic tissues. The University of Toronto data set contains 39 samples and the task was to detect recurrence, while with the Brigham and Women's Hospital data set of 181 samples it was to make a binary classification of malignant pleural mesothelioma and adenocarcinoma. We used the k-nearest neighbor algorithm (k=1, k=5, k=10), naive Bayes classifier with assumption of both a normal distribution of attributes and a distribution through histograms, support vector machine and C4.5 decision tree. Effectiveness of machine learning algorithms was evaluated with the Matthews correlation coefficient. Results: The support vector machine method showed best results among data sets from the Dana-Farber Cancer Institute and Brigham and Women's Hospital. All algorithms with the exception of the C4.5 decision tree showed maximum potential effectiveness in the University of Michigan data set. However, the C4.5 decision tree showed best results for the University of Toronto data set. Conclusions: Machine learning algorithms can be used for lung cancer morphology classification and similar tasks based on gene expression level evaluation.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.9
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• pp.965-970
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• 2014

In an effort to improve the convergence speed in k-means clustering, we introduce the notion of the birds' movement in a flock. Their motion is characterized by the observation that each bird runs after his nearest neighbor. We utilize this feature in clustering procedure. Once the class of a vector is determined, then a number of vectors in the vicinity of it are assigned to the same class. Experiments have shown that the required number of iterations for termination is significantly lower in the proposed method than in the conventional one. Furthermore, the time of calculation per iteration is more than 5% shorter in the proposed case. The quality of the clustering, as determined from the total accumulated distance between the vector and its centroid vector, was found to be practically the same. It might be phrased that we may acquire practically the same clustering result with shorter computational time.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.19 no.3
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• pp.29-44
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• 2016

The purpose of this study is to quantitatively and visually analyze the degree of green biotope fragmentation caused by road construction and other development work using FRAGSTATS and GUIDOS tool. Moreover, linking of the endangered species research, we mapped "Biotope Fragmentation Map" of Daejeon-city. The findings of the study are summarized as follows: First, as the result of FRAGSTATS, landscape indices : number of patch(NP), mean patch size (MPS), edge length(TE), mean nearest neighbor distance(MNN), edge shape(LSI) showed meaningful change from fragmentation. Moreover, the result of GUIDOS analysis, middle core-small core-bridge-branch-edge-islet-perforation showed increase of area percentage without large core. Lastly, analysis result of 'Biotope Fragmentation Map' revealed that changing site of large core's size appeared eighteen-site and designated as the special protection area appeared forty-one site. As the result of the two data, overlapping areas that showed both change of core size and revealed special protection areas revealed four site. For example, five species of endangered species appeared on the NO. 4 site in 'Biotope Fragmentation Map'. The findings of this study as summarized above are considered to play an important role in basic data preventing green biotope fragmentation at the planned level from various development work.

• Journal of Advanced Navigation Technology
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• v.22 no.5
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• pp.472-477
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• 2018

In this paper, we develop interactive content services for preventing depression of users through an intelligent Internet of Things(IoT) mirror system. For interactive content services, an IoT mirror device measures attention and meditation data from an EEG headset device and also measures facial expression data such as "sad", "angery", "disgust", "neutral", " happy", and "surprise" classified by a multi-layer perceptron algorithm through an webcam. Then, it sends the measured data to an oneM2M-compliant IoT server. Based on the collected data in the IoT server, a machine learning model is built to classify three levels of depression (RED, YELLOW, and GREEN) given by a proposed merge labeling method. It was verified that the k-nearest neighbor (k-NN) model could achieve about 93% of accuracy by experimental results. In addition, according to the classified level, a social network service agent sent a corresponding alert message to the family, friends and social workers. Thus, we were able to provide an interactive content service between users and caregivers.

• Journal of Acupuncture Research
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• v.28 no.3
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• pp.85-100
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• 2011

Objectives : The purpose of this thesis is to help the preparation of oriental medicine clinical guidelines for drawing up the standards of oriental medicine demonstration and diagnosis classification about the neck pain. Methods : Statistical analysis about Gyeonghangtong(頸項痛), Nakchim(落枕), Sagyeong(斜頸), Hanggang (項强) classified experts' opinions about neck pain patients by Delphi method is conducted by using oriental medicine diagnosis questionnaire. The result was classified by using linear discriminant analysis (LDA), diagonal linear discriminant analysis (DLDA), diagonal quadratic discriminant analysis (DQDA), K-nearest neighbor classification (KNN), classification and regression trees (CART), support vector machines (SVM). Results : The results are summarized as follows. 1. The result analyzed by using LDA has a hit rate of 84.47% in comparison with the original diagnosis. 2. High hit rate was shown when the test for three categories such as Gyeonghangtong and Hanggang category, Sagyeong caterogy and Nakchim caterogy was conducted. 3. The result analyzed by using DLDA has a hit rate of 58.25% in comparison with the original diagnosis. The result analyzed by using DQDA has a accuracy of 57.28% in comparison with the original diagnosis. 4. The result analyzed by using KNN has a hit rate of 69.90% in comparison with the original diagnosis. 5. The result analyzed by using CART has a hit rate of 69.60% in comparison with the original diagnosis. There was a hit rate of 70.87% When the test of selected 8 significant questions based on analysis of variance was performed. 6. The result analyzed by using SVM has a hit rate of 80.58% in comparison with the original diagnosis. Conclusions : Statistical analysis using oriental medicine diagnosis questionnaire on neck pain generally turned out to have a significant result.

• Journal of Ocean Engineering and Technology
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• v.23 no.2
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• pp.58-68
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• 2009

The increased use of unmanned underwater vehicles (UUV) has led to the development of alternative navigational methods that do not employ acoustic beacons and dead reckoning sensors. This paper describes a simultaneous localization and mapping (SLAM) scheme that uses range sonars mounted on a small UUV. A SLAM scheme is an alternative navigation method for measuring the environment through which the vehicle is passing and providing the relative position of the UUV. A technique for a SLAM algorithm that uses several ranging sonars is presented. This technique utilizes an unscented Kalman filter to estimate the locations of the UUV and surrounding objects. In order to work efficiently, the nearest neighbor standard filter is introduced as the data association algorithm in the SLAM for associating the stored targets returned by the sonar at each time step. The proposed SLAM algorithm was tested by experiments under various three degrees of freedom motion conditions. The results of these experiments showed that the proposed SLAM algorithm was capable of estimating the position of the UUV and the surrounding objects and demonstrated that the algorithm will perform well in various environments.

• The KIPS Transactions:PartD
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• v.19D no.2
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• pp.151-160
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• 2012

Most of the distributed high-dimensional indexing structures provide a reasonable search performance especially when the dataset is uniformly distributed. However, in case when the dataset is clustered or skewed, the search performances gradually degrade as compared with the uniformly distributed dataset. We propose a method of improving the k-nearest neighbor search performance for the distributed vector approximation-tree based on the strongly clustered or skewed dataset. The basic idea is to compute volumes of the leaf nodes on the top-tree of a distributed vector approximation-tree and to assign different number of bits to them in order to assure an identification performance of vector approximation. In other words, it can be done by assigning more bits to the high-density clusters. We conducted experiments to compare the search performance with the distributed hybrid spill-tree and distributed vector approximation-tree by using the synthetic and real data sets. The experimental results show that our proposed scheme provides consistent results with significant performance improvements of the distributed vector approximation-tree for strongly clustered or skewed datasets.

• Journal of Korean Tunnelling and Underground Space Association
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• v.22 no.5
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• pp.575-589
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• 2020

In recent years, Shield TBM construction has been continuously increasing in domestic tunnels. The main excavation tool in the shield TBM construction is a disc cutter which naturally wears during the excavation process and significantly degrades the excavation efficiency. Therefore, it is important to know the appropriate time of the disc cutter replacement. In this study, it is proposed a predictive model that can determine yes/no of disc cutter replacement using machine learning algorithm. To do this, the shield TBM machine data which is highly correlated to the disc cutter wears and the disc cutter replacement from the shield TBM field which is already constructed are used as the input data in the model. Also, the algorithms used in the study were the support vector machine, k-nearest neighbor algorithm, and decision tree algorithm are all classification methods used in machine learning. In order to construct an optimal predictive model and to evaluate the performance of the model, the classification performance evaluation index was compared and analyzed.