Go to the main menu
Skip to content
Go to bottom
REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
> Journal Vol & Issue
International Journal of Fuzzy Logic and Intelligent Systems
Journal Basic Information
Journal DOI :
Korean Institute of Intelligent Systems
Editor in Chief :
Volume & Issues
Volume 13, Issue 4 - Dec 2013
Volume 13, Issue 3 - Sep 2013
Volume 13, Issue 2 - Jun 2013
Volume 13, Issue 1 - Mar 2013
Selecting the target year
Landmark Initialization for Unscented Kalman Filter Sensor Fusion in Monocular Camera Localization
Hartmann, Gabriel ; Huang, Fay ; Klette, Reinhard ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 13, issue 1, 2013, Pages 1~11
DOI : 10.5391/IJFIS.2013.13.1.1
The determination of the pose of the imaging camera is a fundamental problem in computer vision. In the monocular case, difficulties in determining the scene scale and the limitation to bearing-only measurements increase the difficulty in estimating camera pose accurately. Many mobile phones now contain inertial measurement devices, which may lend some aid to the task of determining camera pose. In this study, by means of simulation and real-world experimentation, we explore an approach to monocular camera localization that incorporates both observations of the environment and measurements from accelerometers and gyroscopes. The unscented Kalman filter was implemented for this task. Our main contribution is a novel approach to landmark initialization in a Kalman filter; we characterize the tolerance to noise that this approach allows.
Discriminative Power Feature Selection Method for Motor Imagery EEG Classification in Brain Computer Interface Systems
Yu, XinYang ; Park, Seung-Min ; Ko, Kwang-Eun ; Sim, Kwee-Bo ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 13, issue 1, 2013, Pages 12~18
DOI : 10.5391/IJFIS.2013.13.1.12
Motor imagery classification in electroencephalography (EEG)-based brain-computer interface (BCI) systems is an important research area. To simplify the complexity of the classification, selected power bands and electrode channels have been widely used to extract and select features from raw EEG signals, but there is still a loss in classification accuracy in the state-of- the-art approaches. To solve this problem, we propose a discriminative feature extraction algorithm based on power bands with principle component analysis (PCA). First, the raw EEG signals from the motor cortex area were filtered using a bandpass filter with
bands. This research considered the power bands within a 0.4 second epoch to select the optimal feature space region. Next, the total feature dimensions were reduced by PCA and transformed into a final feature vector set. The selected features were classified by applying a support vector machine (SVM). The proposed method was compared with a state-of-art power band feature and shown to improve classification accuracy.
Approximate Dynamic Programming-Based Dynamic Portfolio Optimization for Constrained Index Tracking
Park, Jooyoung ; Yang, Dongsu ; Park, Kyungwook ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 13, issue 1, 2013, Pages 19~30
DOI : 10.5391/IJFIS.2013.13.1.19
Recently, the constrained index tracking problem, in which the task of trading a set of stocks is performed so as to closely follow an index value under some constraints, has often been considered as an important application domain for control theory. Because this problem can be conveniently viewed and formulated as an optimal decision-making problem in a highly uncertain and stochastic environment, approaches based on stochastic optimal control methods are particularly pertinent. Since stochastic optimal control problems cannot be solved exactly except in very simple cases, approximations are required in most practical problems to obtain good suboptimal policies. In this paper, we present a procedure for finding a suboptimal solution to the constrained index tracking problem based on approximate dynamic programming. Illustrative simulation results show that this procedure works well when applied to a set of real financial market data.
Electrocardiogram Signal Compression with Reconstruction via Radial Basis Function Interpolation Based on the Vertex
Ryu, Chunha ; Kim, Tae-Hun ; Kim, Jungjoon ; Choi, Byung-Jae ; Park, Kil-Houm ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 13, issue 1, 2013, Pages 31~38
DOI : 10.5391/IJFIS.2013.13.1.31
Patients with heart disease need long-term monitoring of the electrocardiogram (ECG) signal using a portable electrocardiograph. This trend requires the miniaturization of data storage and faster transmission to medical doctors for diagnosis. The ECG signal needs to be utilized for efficient storage, processing and transmission, and its data must contain the important components for diagnosis, such as the P wave, QRS-complex, and T wave. In this study, we select the vertex which has a larger curvature value than the threshold value for compression. Then, we reconstruct the compressed signal using by radial basis function interpolation. This technique guarantees a lower percentage of root mean square difference with respect to the extracted sample points and preserves all the important features of the ECG signal. Its effectiveness has been demonstrated in the experiment using the Massachusetts Institute of Technology and Boston`s Beth Israel Hospital arrhythmia database.
Daily Electric Load Forecasting Based on RBF Neural Network Models
Hwang, Heesoo ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 13, issue 1, 2013, Pages 39~49
DOI : 10.5391/IJFIS.2013.13.1.39
This paper presents a method of improving the performance of a day-ahead 24-h load curve and peak load forecasting. The next-day load curve is forecasted using radial basis function (RBF) neural network models built using the best design parameters. To improve the forecasting accuracy, the load curve forecasted using the RBF network models is corrected by the weighted sum of both the error of the current prediction and the change in the errors between the current and the previous prediction. The optimal weights (called "gains" in the error correction) are identified by differential evolution. The peak load forecasted by the RBF network models is also corrected by combining the load curve outputs of the RBF models by linear addition with 24 coefficients. The optimal coefficients for reducing both the forecasting mean absolute percent error (MAPE) and the sum of errors are also identified using differential evolution. The proposed models are trained and tested using four years of hourly load data obtained from the Korea Power Exchange. Simulation results reveal satisfactory forecasts: 1.230% MAPE for daily peak load and 1.128% MAPE for daily load curve.
Novel Backprojection Method for Monocular Head Pose Estimation
Ju, Kun ; Shin, Bok-Suk ; Klette, Reinhard ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 13, issue 1, 2013, Pages 50~58
DOI : 10.5391/IJFIS.2013.13.1.50
Estimating a driver`s head pose is an important task in driver-assistance systems because it can provide information about where a driver is looking, thereby giving useful cues about the status of the driver (i.e., paying proper attention, fatigued, etc.). This study proposes a system for estimating the head pose using monocular images, which includes a novel use of backprojection. The system can use a single image to estimate a driver`s head pose at a particular time stamp, or an image sequence to support the analysis of a driver`s status. Using our proposed system, we compared two previous pose estimation approaches. We introduced an approach for providing ground-truth reference data using a mannequin model. Our experimental results demonstrate that the proposed system provides relatively accurate estimations of the yaw, tilt, and roll angle. The results also show that one of the pose estimation approaches (perspective-n-point, PnP) provided a consistently better estimate compared to the other (pose from orthography and scaling with iterations, POSIT) using our proposed system.
Model-based Clustering of DOA Data Using von Mises Mixture Model for Sound Source Localization
Dinh, Quang Nguyen ; Lee, Chang-Hoon ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 13, issue 1, 2013, Pages 59~66
DOI : 10.5391/IJFIS.2013.13.1.59
In this paper, we propose a probabilistic framework for model-based clustering of direction of arrival (DOA) data to obtain stable sound source localization (SSL) estimates. Model-based clustering has been shown capable of handling highly overlapped and noisy datasets, such as those involved in DOA detection. Although the Gaussian mixture model is commonly used for model-based clustering, we propose use of the von Mises mixture model as more befitting circular DOA data than a Gaussian distribution. The EM framework for the von Mises mixture model in a unit hyper sphere is degenerated for the 2D case and used as such in the proposed method. We also use a histogram of the dataset to initialize the number of clusters and the initial values of parameters, thereby saving calculation time and improving the efficiency. Experiments using simulated and real-world datasets demonstrate the performance of the proposed method.
Cloud-Type Classification by Two-Layered Fuzzy Logic
Kim, Kwang Baek ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 13, issue 1, 2013, Pages 67~72
DOI : 10.5391/IJFIS.2013.13.1.67
Cloud detection and analysis from satellite images has been a topic of research in many atmospheric and environmental studies; however, it still is a challenging task for many reasons. In this paper, we propose a new method for cloud-type classification using fuzzy logic. Knowing that visible-light images of clouds contain thickness related information, while infrared images haves height-related information, we propose a two-layered fuzzy logic based on the input source to provide us with a relatively clear-cut threshold in classification. Traditional noise-removal methods that use reflection/release characteristics of infrared images often produce false positive cloud areas, such as fog thereby it negatively affecting the classification accuracy. In this study, we used the color information from source images to extract the region of interest while avoiding false positives. The structure of fuzzy inference was also changed, because we utilized three types of source images: visible-light, infrared, and near-infrared images. When a cloud appears in both the visible-light image and the infrared image, the fuzzy membership function has a different form. Therefore we designed two sets of fuzzy inference rules and related classification rules. In our experiment, the proposed method was verified to be efficient and more accurate than the previous fuzzy logic attempt that used infrared image features.
Hybrid Feature Selection Using Genetic Algorithm and Information Theory
Cho, Jae Hoon ; Lee, Dae-Jong ; Park, Jin-Il ; Chun, Myung-Geun ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 13, issue 1, 2013, Pages 73~82
DOI : 10.5391/IJFIS.2013.13.1.73
In pattern classification, feature selection is an important factor in the performance of classifiers. In particular, when classifying a large number of features or variables, the accuracy and computational time of the classifier can be improved by using the relevant feature subset to remove the irrelevant, redundant, or noisy data. The proposed method consists of two parts: a wrapper part with an improved genetic algorithm(GA) using a new reproduction method and a filter part using mutual information. We also considered feature selection methods based on mutual information(MI) to improve computational complexity. Experimental results show that this method can achieve better performance in pattern recognition problems than other conventional solutions.
Some Characterizations of the Choquet Integral with Respect to a Monotone Interval-Valued Set Function
Jang, Lee-Chae ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 13, issue 1, 2013, Pages 83~90
DOI : 10.5391/IJFIS.2013.13.1.83
Intervals can be used in the representation of uncertainty. In this regard, we consider monotone interval-valued set functions and the Choquet integral. This paper investigates characterizations of monotone interval-valued set functions and provides applications of the Choquet integral with respect to monotone interval-valued set functions, on the space of measurable functions with the Hausdorff metric.