• Korean Journal of Remote Sensing
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• v.28 no.4
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• pp.357-367
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• 2012

This paper presents a two-stage methodology for anomaly detection from hyperspectral imagery that consists of transform-based feature extraction and selection, and computation of a local spatial auto-correlation statistic. First, principal component transform and 3D wavelet transform are applied to reduce redundant spectral information from hyperspectral imagery. Then feature selection based on global skewness and the portion of highly skewed sub-areas is followed to find optimal features for anomaly detection. Finally, a local indicator of spatial association (LISA) statistic is computed to account for both spectral and spatial information unlike traditional anomaly detection methodology based only on spectral information. An experiment using airborne CASI imagery is carried out to illustrate the applicability of the proposed anomaly detection methodology. From the experiments, anomaly detection based on the LISA statistic linked with the selection of optimal features outperformed both the traditional RX detector which uses only spectral information, and the case using major principal components with large eigen-values. The combination of low- and high-frequency components by 3D wavelet transform showed the best detection capability, compared with the case using optimal features selected from principal components.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.6
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• pp.1828-1847
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• 2014

Coordinated multi-point (CoMP) transmission has been regarded as a potential technology for LTE-Advanced. In frequency division duplexing systems, channel quantization is applied for reporting channel state information (CSI). Considering the dynamic number of cooperation base stations (BSs), asymmetry feature of CoMP channels and high searching complexity, simply increasing the size of the codebook used in traditional multiple antenna systems to quantize the global CSI of CoMP systems directly is infeasible. Per-cell codebook based channel quantization to quantize local CSI for each BS separately is an effective method. In this paper, the theoretical upper bounds of system throughput are derived for two codeword selection schemes, independent codeword selection (ICS) and joint codeword selection (JCS), respectively. The feedback overhead and selection complexity of these two schemes are analyzed. In the simulation, the system throughput of ICS and JCS is compared. Both analysis and simulation results show that JCS has a better tradeoff between system throughput and feedback overhead. The ICS has obvious advantage in complexity, but it needs additional phase information (PI) feedback for obtaining the approximate system throughput with JCS. Under the same number of feedback bits constraint, allocating the number of bits for channel direction information (CDI) and PI quantization can increase the system throughput, but ICS is still inferior to JCS. Based on theoretical analysis and simulation results, some recommendations are given with regard to the application of each scheme respectively.

• IE interfaces
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• v.24 no.4
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• pp.351-361
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• 2011

Forecasting of electricity demand have difficulty in adapting to abrupt weather changes along with a radical shift in major regional and global climates. This has lead to increasing attention to research on the immediate and accurate forecasting model. Technically, this implies that a model requires only a few input variables all of which are easily obtainable, and its predictive performance is comparable with other competing models. To meet the ends, this paper presents an energy demand forecasting model that uses the variable selection or extraction methods of data mining to select only relevant input variables, and employs support vector regression method for accurate prediction. Also, it proposes a novel performance measure for time-series prediction, shift index, followed by description on preprocessing procedure. A comparative evaluation of the proposed method with other representative data mining models such as an auto-regression model, an artificial neural network model, an ordinary support vector regression model was carried out for obtaining the forecast of monthly electricity demand from 2000 to 2008 based on data provided by Korea Energy Economics Institute. Among the models tested, the proposed method was shown promising results than others.

• Journal of the Ergonomics Society of Korea
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• v.35 no.5
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• pp.449-464
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• 2016

Objective: The objective of this study is to identify essential requirements of the instrument cluster's features and layout for elderly drivers through interview and paper prototyping. Background: Recent updates implemented in passenger vehicles require more complex information to be processed by drivers. Concurrently, a large portion of the US population, the baby boomer generation has aged, causing their physical and cognitive abilities to deter. Thus it is crucial that new methods be implemented into vehicle design in order to accommodate for the deterioration of mental and physical abilities. Method: Forty elderly drivers and twenty young drivers participated in this study. The test included three sessions including: 1) location value assessment to identify the priority of areas within the instrument cluster; 2) component value assessment to capture rankings of the degree of importance and frequency of use for possible instrument cluster components; and 3) paper prototyping to collect self-designed cluster with selection of designs for each component and location of features from each participant. Results: Results revealed differences in the area priority of the instrument cluster as well as the shape and location of component features for age and gender groups. Conclusion: The study provided insights on instrument cluster layout guidelines by proving elderly driver's mental model and preferred cluster design configurations to improve driving safety. Application: LCD-based vehicle instrument cluster design, with an adaptable feature configuration for cluster components and layouts.

• Journal of Digital Convergence
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• v.17 no.3
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• pp.107-117
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• 2019

In the era of SNS, many people relies on it to express their emotions about various kinds of products and services. Therefore, for the companies eagerly seeking to investigate how their products and services are perceived in the market, emotion mining tasks using dataset from SNSs become important much more than ever. Basically, emotion mining is a branch of sentiment analysis which is based on BOW (bag-of-words) and TF-IDF. However, there are few studies on the emotion mining which adopt feature selection (FS) methods to look for optimal set of features ensuring better results. In this sense, this study aims to propose FS methods to conduct emotion mining tasks more effectively with better outcomes. This study uses Twitter and SemEval2007 dataset for the sake of emotion mining experiments. We applied three FS methods such as CFS (Correlation based FS), IG (Information Gain), and ReliefF. Emotion mining results were obtained from applying the selected features to nine classifiers. When applying DT (decision tree) to Tweet dataset, accuracy increases with CFS, IG, and ReliefF methods. When applying LR (logistic regression) to SemEval2007 dataset, accuracy increases with ReliefF method.

• IEIE Transactions on Smart Processing and Computing
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• v.1 no.3
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• pp.133-142
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• 2012

This paper presents a salient region detection algorithm for auto-focusing based on the characteristics of a human's visual attention. To describe the saliency at the local, regional, and global levels, this paper proposes a set of novel features including multi-scale local contrast, variance, center-surround entropy, and closeness to the center. Those features are then prioritized to produce a saliency map. The major advantage of the proposed approach is twofold; i) robustness to changes in focus and ii) low computational complexity. The experimental results showed that the proposed method outperforms the existing low-level feature-based methods in the sense of both robustness and accuracy for auto-focusing.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.12
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• pp.4513-4532
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• 2014

Motivated by the Weber's Law, this paper proposes an efficient and robust shape descriptor based on the perceptual stimulus model, called Weber's Law Shape Descriptor (WLSD). It is based on the theory that human perception of a pattern depends not only on the change of stimulus intensity, but also on the original stimulus intensity. Invariant to scale and rotation is the intrinsic properties of WLSD. As a global shape descriptor, WLSD has far lower computation complexity while is as discriminative as state-of-art shape descriptors. Experimental results demonstrate the strong capability of the proposed method in handling shape retrieval.

• Journal of the Korea Society of Computer and Information
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• v.17 no.2
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• pp.31-40
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• 2012

Iris recognition is a biometric technology which can identify a person using the iris pattern. It is important for the iris recognition system to extract the feature which is invariant to changes in iris patterns. Those changes can be occurred by the influence of lights, changes in the size of the pupil, and head tilting. In this paper, we propose a novel method based on Zernike Moment which is robust to rotations of iris patterns. we utilized a selection of Zernike moments for the fast and effective recognition by selecting global optimum moments and local optimum moments for optimal matching of each iris class. The proposed method enables high-speed feature extraction and feature comparison because it requires no additional processing to obtain the rotation invariance, and shows comparable performance to the well-known previous methods.

• Journal of Microbiology and Biotechnology
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• v.28 no.11
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• pp.1771-1781
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• 2018

The emergence of multidrug-resistant microorganisms, as well as fungal infectious diseases that further threaten health, especially in immunodeficient populations, is a major global problem. The development of new antifungal agents in clinical trials is inferior to the incidence of drug resistance, and the available antifungal agents are restricted. Their mechanisms aim at certain characteristics of the fungus in order to avoid biological similarities with the host. Synthesis of the cell wall and ergosterol are mainly targeted in clinical use. The need for new approaches to antifungal therapeutic agents or development alternatives has increased. This review explores new perspectives on mechanisms to effectively combat fungal infections and effective antifungal activity. The clinical drug have a common feature that ultimately causes caspase-dependent cell death. The drugs-induced cell death pathway is associated with mitochondrial dysfunction, including mitochondrial membrane depolarization and cytochrome c release. This mechanism of action also reveals antimicrobial peptides, the primary effector molecules of innate systems, to highlight new alternatives. Furthermore, drug combination therapy is suggested as another strategy to combat fungal infection. The proposal for a new approach to antifungal agents is not only important from a basic scientific point of view, but will also assist in the selection of molecules for combination therapy.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.4
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• pp.262-268
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• 2017

Intelligent video surveillance systems have been developed to monitor global areas and find specific target objects using a large-scale database. However, person re-identification presents some challenges, such as pose change and occlusions. To solve the problems, this paper presents an improved person re-identification method using sparse representation and saliency-based dictionary construction. The proposed method consists of three parts: i) feature description based on salient colors and textures for dictionary elements, ii) orthogonal atom selection using cosine similarity to deal with pose and viewpoint change, and iii) measurement of reconstruction error to rank the gallery corresponding a probe object. The proposed method provides good performance, since robust descriptors used as a dictionary atom are generated by weighting some salient features, and dictionary atoms are selected by reducing excessive redundancy causing low accuracy. Therefore, the proposed method can be applied in a large scale-database surveillance system to search for a specific object.