• The KIPS Transactions:PartB
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• v.18B no.4
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• pp.213-220
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• 2011

This paper proposes an effective, four-stage smoke detection method based on video that provides emergency response in the event of unexpected hazards in early fire-alarming systems. In the first phase, an approximate median method is used to segment moving regions in the present frame of video. In the second phase, a color segmentation of smoke is performed to select candidate smoke regions from these moving regions. In the third phase, a feature extraction algorithm is used to extract five feature parameters of smoke by analyzing characteristics of the candidate smoke regions such as area randomness and motion of smoke. In the fourth phase, extracted five parameters of smoke are used as an input for a K-nearest neighbor (KNN) algorithm to identify whether the candidate smoke regions are smoke or non-smoke. Experimental results indicate that the proposed four-stage smoke detection method outperforms other algorithms in terms of smoke detection, providing a low false alarm rate and high reliability in open and large spaces.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.2
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• pp.191-196
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• 2015

There have been many studies on statistical forecasting on firm's performance and stock price by applying various financial indicators such as debt ratio and sales growth rate. Selecting predictors for constructing a prediction model among the various financial indicators is very important for precise prediction. Most of the previous studies applied variable selection algorithms for selecting predictors. However, the variable selection algorithm is considered to be at risk of eliminating certain amount of information from the indicators that were excluded from model construction. Therefore, we propose a firm's performance prediction model which principal component analysis is applied instead of the variable selection algorithm, in order to reduce dimensionality of input variables of the prediction model. In this study, we constructed the proposed prediction model by using financial data of American IT companies to empirically analyze prediction performance of the model.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.6
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• pp.621-628
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• 2015

This paper describes an algorithm for detecting vehicles detection in real time. The proposed algorithm has the technique based on computer vision and image processing. In real, complex environment such as one with road traffic, many algorithms have great difficulty such as low detection rate and increasing computational time due to complex backgrounds and rapid changes. To overcome this problem in this paper, the proposed algorithm consists of the following methods. First, to effectively separate the candidate regions, we use vertical and horizontal edge information, and shadow values from input image sequences. Second, we extracts features by using HOG from the selected candidate regions. Finally, this paper uses the OS fuzzy-ELM based on SLFN to classify the extracted features. The experimental results show that the proposed method perform well for detecting vehicles and improves the accuracy and the computational time of detecting.

• Journal of KIISE:Databases
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• v.29 no.5
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• pp.358-366
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• 2002

Feature-based similarity retrieval become an important research issue in image database systems. The features of image data are useful to discrimination of images. In this paper, we propose the high speed k-Nearest Neighbor search algorithm based on Self-Organizing Maps. Self-Organizing Maps(SOM) provides a mapping from high dimensional feature vectors onto a two-dimensional space and generates a topological feature map. A topological feature map preserves the mutual relations (similarities) in feature spaces of input data, and clusters mutually similar feature vectors in a neighboring nodes. Therefore each node of the topological feature map holds a node vector and similar images that is closest to each node vector. We implemented a k-NN search for similar image classification as to (1) access to topological feature map, and (2) apply to pruning strategy of high speed search. We experiment on the performance of our algorithm using color feature vectors extracted from images. Promising results have been obtained in experiments.

• The KIPS Transactions:PartB
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• v.12B no.2 s.98
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• pp.123-130
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• 2005

In this paper, fingerprint identification algorithm using pixel direction factor in blocks is proposed to minimize false acceptance ratio and to apply security system. The proposed algorithm is that a fingerprint image is divided by 16 blocks, then feature parameters which have direct factors of $0^{\circ},\;45^{\circ},\;90^{\circ}\;and\;135^{\circ}$ is extracted for each block. Membership function of a reference fingerprint and an input fingerprint for the extracted parameters is calculated, then identification of two fingerprint is distinguished using fuzzy inference. False acceptance ratio is evaluated about different fingerprints of In kinds regardless of sex and shape which are obtained from adults, and false rejection ratio is evaluated about fingerprints which are obtained by adding fingerprints of 10 kinds on different fingerprints of 100 kinds. The experiment results is that false acceptance ratio is average $0.34\%$ about experiment of 4,950 times, and false rejection ratio is average $3.7\%$ about experiment of 1,000 times. The proposed algerian is excellent for recognition rate and security.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.11
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• pp.4473-4482
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• 2010

The examination of pulse, which is a typical palpation technique in the oriental medicine, has been used conventional analog system for discrimination of 28 pulses. However, the clipping phenomenon in the pulses, which used same feature extraction technique with ECG signals, has been occurred in analog system due to feature extraction method and over amplification from the input signals. It caused inaccurate to analyze the pulse signals. In this paper, we propose a digital filter design technique based on Prony's method for signal modeling and C-spline interpolation for feature extraction from pulse signal to compensate analog pulse detection system. In addition, we suggest a compensated electronic pulse detection system comprising new pulse analyzing algorithm and shape analysis technique for pulses, which were difficult to use in analog system. The feasibility for new proposed system has been confirmed comparing output signals between electronic pulse detection system having proposed filter design techniques with pulse analyzing algorithm and conventional analog system.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.3
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• pp.195-198
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• 2013

Many libraries utilize RFID tags for checking in and out of books. However, the recognition rate of this automatic process may depend on the orientation of antennas and RFID tags. Therefore we need supplemental systems to improve the recognition rate. The proposed algorithm sets up the ROI of the book existing area from the input image and then performs Canny edge detection algorithm to extract edges of books. Finally Hough line transform algorithm allows to detect the number of books from the extracted edges. To evaluate the performance of the proposed method, we applied our method to 350 book images under various circumstances. We then analyzed the performance of proposed method from results using recognition and mismatch ratio. The experimental result gave us 97.1% accuracy in book counting.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.735-740
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• 2012

In this paper, we introduce design methodologies of polynomial radial basis function neural network classifier with the aid of Principal Component Analysis(PCA) and Linear Discriminant Analysis(LDA). By minimizing the information loss of given data, Feature data is obtained through preprocessing of PCA and LDA and then this data is used as input data of RBFNNs. The hidden layer of RBFNNs is built up by Fuzzy C-Mean(FCM) clustering algorithm instead of receptive fields and linear polynomial function is used as connection weights between hidden and output layer. In order to design optimized classifier, the structural and parametric values such as the number of eigenvectors of PCA and LDA, and fuzzification coefficient of FCM algorithm are optimized by Artificial Bee Colony(ABC) optimization algorithm. The proposed classifier is applied to some machine learning datasets and its result is compared with some other classifiers.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.11
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• pp.251-262
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• 2019

In this paper, we propose a simulator that provides various algorithms of RBF networks on neuromorphic chips. To develop algorithms based on neuromorphic chips, the disadvantages of using simulators are that it is difficult to test various types of algorithms, although time is fast. This proposed simulator can simulate four times more types of network architecture than existing simulators, and it provides an additional a two-layer structure algorithm in particular, unlike RBF networks provided by existing simulators. This two-layer architecture algorithm is configured to be utilized for multiple input data and compared to the existing RBF for performance analysis and validation of utilization. The analysis showed that the two-layer structure algorithm was more accurate than the existing RBF networks.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.5
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• pp.169-178
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• 2021

Recently, a bike-sharing system (BSS) has become popular as a convenient "last mile" transportation. Rebalancing of bikes is a critical issue to manage BSS because the rents and returns of bikes are not balanced by stations and periods. For efficient and effective rebalancing, accurate traffic prediction is important. Recently, cluster-based traffic prediction has been utilized to enhance the accuracy of prediction at the station-level and the clustering step is very important in this approach. In this paper, we propose a k-means based clustering algorithm that overcomes the drawbacks of the existing clustering methods for BSS; indeterministic and hardly converged. By employing the centroid initialization and using the temporal proportion of the rents and returns of stations as an input for clustering, the proposed algorithm can be deterministic and fast.