• Journal of Information Processing Systems
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• v.11 no.3
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• pp.421-437
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• 2015

This paper aims to present a supervised classification algorithm based on data fusion for the segmentation of the textured images. The feature extraction method we used is based on discrete wavelet transform (DWT). In the segmentation stage, the estimated feature vector of each pixel is sent to the support vector machine (SVM) classifier for initial labeling. To obtain a more accurate segmentation result, two strategies based on information fusion were used. We first integrated decision-level fusion strategies by combining decisions made by the SVM classifier within a sliding window. In the second strategy, the fuzzy set theory and rules based on probability theory were used to combine the scores obtained by SVM over a sliding window. Finally, the performance of the proposed segmentation algorithm was demonstrated on a variety of synthetic and real images and showed that the proposed data fusion method improved the classification accuracy compared to applying a SVM classifier. The results revealed that the overall accuracies of SVM classification of textured images is 88%, while our fusion methodology obtained an accuracy of up to 96%, depending on the size of the data base.

• Journal of Information Processing Systems
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• v.7 no.4
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• pp.717-732
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• 2011

Classification in biomedical applications is an important task that predicts or classifies an outcome based on a given set of input variables such as diagnostic tests or the symptoms of a patient. Traditionally the classification algorithms would have to digest a stationary set of historical data in order to train up a decision-tree model and the learned model could then be used for testing new samples. However, a new breed of classification called stream-based classification can handle continuous data streams, which are ever evolving, unbound, and unstructured, for instance--biosignal live feeds. These emerging algorithms can potentially be used for real-time classification over biosignal data streams like EEG and ECG, etc. This paper presents a pioneer effort that studies the feasibility of classification algorithms for analyzing biosignals in the forms of infinite data streams. First, a performance comparison is made between traditional and stream-based classification. The results show that accuracy declines intermittently for traditional classification due to the requirement of model re-learning as new data arrives. Second, we show by a simulation that biosignal data streams can be processed with a satisfactory level of performance in terms of accuracy, memory requirement, and speed, by using a collection of stream-mining algorithms called Optimized Very Fast Decision Trees. The algorithms can effectively serve as a corner-stone technology for real-time classification in future biomedical applications.

• International Journal of Computer Science & Network Security
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• v.23 no.6
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• pp.115-120
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• 2023

Plant leaf diseases and destructive insects are major challenges that affect the agriculture production of the country. Accurate and fast prediction of leaf diseases in crops could help to build-up a suitable treatment technique while considerably reducing the economic and crop losses. In this paper, Convolutional Neural Network based model is proposed to detect leaf diseases of a plant in an efficient manner. Convolutional Neural Network (CNN) is the key technique in Deep learning mainly used for object identification. This model includes an image classifier which is built using machine learning concepts. Tensor Flow runs in the backend and Python programming is used in this model. Previous methods are based on various image processing techniques which are implemented in MATLAB. These methods lack the flexibility of providing good level of accuracy. The proposed system can effectively identify different types of diseases with its ability to deal with complex scenarios from a plant's area. Predictor model is used to precise the disease and showcase the accurate problem which helps in enhancing the noble employment of the farmers. Experimental results indicate that an accuracy of around 93% can be achieved using this model on a prepared Data Set.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.107-112
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• 2024

Plant leaf diseases and destructive insects are major challenges that affect the agriculture production of the country. Accurate and fast prediction of leaf diseases in crops could help to build-up a suitable treatment technique while considerably reducing the economic and crop losses. In this paper, Convolutional Neural Network based model is proposed to detect leaf diseases of a plant in an efficient manner. Convolutional Neural Network (CNN) is the key technique in Deep learning mainly used for object identification. This model includes an image classifier which is built using machine learning concepts. Tensor Flow runs in the backend and Python programming is used in this model. Previous methods are based on various image processing techniques which are implemented in MATLAB. These methods lack the flexibility of providing good level of accuracy. The proposed system can effectively identify different types of diseases with its ability to deal with complex scenarios from a plant's area. Predictor model is used to precise the disease and showcase the accurate problem which helps in enhancing the noble employment of the farmers. Experimental results indicate that an accuracy of around 93% can be achieved using this model on a prepared Data Set.

• Journal of The Geomorphological Association of Korea
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• v.24 no.3
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• pp.105-118
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• 2017

The debris slope landforms which are existent in Korean mountains is generally on the steep slopes and mostly covered by vegetation, it is difficult to investigate the landform. Therefore a scientific method is required to come up with an effective field investigation plan. For this purpose, the use of Remote Sensing and GIS technologies for a spatial analysis is essential. This study has extracted the potential area of debrisslope landform formation using Fuzzy set and Bayesian Predictive Discriminate Model as mathematical data integration methods. The first step was to obtain information about debris locations and their related factors. This information was verified through field investigation and then used to build a database. In the second step, the map that zoning the study area based on the degree of debris formation possibility was generated using two modeling methods, and then cross validation technique was applied. In order to quantitatively analyze the accuracy of two modeling methods, the calculated potential rate of debrisformation within the study area was evaluated by plotting SRC(Success Rate Curve) and calculating AUC(Area Under the Curve). As a result, the prediction accuracy of Fuzzy set model wes 83.1% and Bayesian Predictive Discriminate Model wes 84.9%. It showed that two models are accurate and reliable and can contribute to efficient field investigation and debris landform management.

• Wind and Structures
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• v.10 no.2
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• pp.99-119
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• 2007

Estimations of wind flow over terrain are often needed for applications such as pollutant dispersion, transport safety or wind farm location. Whilst field studies offer very detailed information regarding the wind potential over a small region, the cost of instrumenting a natural fetch alone is prohibitive. Wind tunnels offer one alternative although wind tunnel simulations can suffer from scale effects and high costs as well. Computational Fluid Dynamics (CFD) offers a second alternative which is increasingly seen as a viable one by wind engineers. There are two issues associated with CFD however, that of accuracy of the predictions and set-up and simulation times. This paper aims to address the two issues by demonstrating, by way of an investigation of wind potential for the Askervein Hill, that a good level of accuracy can be obtained with CFD (10% for the speed up ratio) and that it is possible to automate the simulations in order to compute a full wind rose efficiently. The paper shows how a combination of script and session files can be written to drive and automate CFD simulations based on commercial software. It proposes a general methodology for the automation of CFD applied to the computation of wind flow over a region of interest.

• MALSORI
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• no.50
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• pp.139-150
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• 2004

A Phonetic Tied-Mixture (PTM) model has been proposed as a way of efficient decoding in large vocabulary continuous speech recognition systems (LVCSR). It has been reported that PTM model shows better performance in decoding than triphones by sharing a set of mixture components among states of the same topological location[5]. In this paper we propose a Phonetic Tied-Mixture Syllable (PTMS) model which extends PTM technique up to syllables. The proposed PTMS model shows 13% enhancement in decoding speed than PTM. In spite of difference in context dependent modeling (PTM : cross-word context dependent modeling, PTMS : word-internal left-phone dependent modeling), the proposed model shows just less than 1% degradation in word accuracy than PTM with the same beam width. With a different beam width, it shows better word accuracy than in PTM at the same or higher speed.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.7 no.3
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• pp.41-46
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• 2008

Generally optical components are fabricated by grinding, lapping and polishing processes. Those processes take long time to obtain optical high surface quality. In the case of large optical components, the on-machine measurement is strongly recommended because the workpiece is fragile and difficult to set up for fabricating and measuring. This paper is concerned about a swing-arm mechanism which can be used for on-machine measurement of a surface profile with a sensing probe end-effect, and also for grinding or lapping the surface with a corresponding tool. The measuring accuracy and uncertainty using a swing arm type profilometer have been studied. The experimental results show that this method is useful specially in lapping process with the accuracy of $5{\mu}m$. Those inspection data are provided for correcting the residual figuring error in next processes.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 1998.10a
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• pp.827-834
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• 1998

This paper describes a multi-level simulation technique and its implementation, which accurately solve voltages and currents of circuits descreibed at mixed levels of abstractions. A metho to form a tightly coupled simulation environment is proposed and, starting from a description of a circuit, simulation set-up and analysis procedure of the multi-level simulator for a transient response are presented. Circuit and behavioral simulation techniques and their implementations composing the multi-level simulation are explained in detail. Most of the algorithms implemented in the simulation are based upon the standard simulation techniques in order to obtain the reliability and accuracy of conventinoal simulators. Simulation examples show that the multi-level simulator can analyze circuits containing highly nonlinear behavioral models without loss of accuracy provided the behavioral models are accurate enough.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.11b
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• pp.434-439
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• 2002

A diagnostic methodology, which utilizes only the remotely-measurable signals, has been requested to be developed in order to evaluate and monitor conditions of MOVs. It is proven that the stem thrust are the most important variables which provide the operability of MOVs. Therefore the stem thrust estimator was developed and validated, which estimates stem thrust by use of the motor torque. The motor torque is calculated using electrical signals which can be measured in Motor Control Center(MCC). The procedures to evaluate the accuracy of the diagnostic variables were set up and the accuracy of each variable was obtained through the experiments under various conditions. In addition, the applicability of the stem thrust estimator was tested in the plants.