• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.10
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• pp.1997-2003
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• 2009

Methods for effective musical score recognition and efficient editing of musical scores are demanded because functions of computers for researches on musical activities become more and more important parts in recent days. In the conventional methods for handling musical scores manually, there are weak points such as incorrect score symbols in input process and requirement of much time to adjust the incorrect symbols. And also there is another weak point that the scores edited by each application program can be remodified by a specific application program only. In this paper, we proposed a method for automatic musical score recognition of printed musical scores in order to make up for the weak points. In the proposed method, staffs in a scanned score image are eliminated by horizontal histogram, noises are removed by 4 directional edge tracking algorithm, and then musical score symbols are extracted by using Grassfire algorithm. The extracted symbols are recognized by hierarchical ART2 algorithm. In order to evaluate the performance of the proposed method, we used 100 musical scores for experiment. In the experiment, we verified that the proposed method using hierarchical ART2 algorithm is efficient.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.7
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• pp.100-106
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• 2015

In this paper, we propose a fast algorithm for low-power image enhancement method for mobile LCD. In the proposed fast algorithm, the spatial resolution of the input image is significantly reduced, and the image characteristics are analyzed on the reduced resolution image to find a dimming rate adaptive to the image content, thereby saving power. The proposed fast adaptive dimming and image enhancement algorithm is implemented as an application that runs on an Android device. Image quality evaluation and running time analysis experiments on the device indicate that the proposed fast algorithm jointly minimizes the quality degradation and power consumption, reducing the required computation load by over 95%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.12
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• pp.1625-1631
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• 2020

With the development of IoT technology, various digital equipment is being spread, and accordingly, the importance of data processing is increasing. The importance of data processing is increasing as it greatly affects the reliability of equipment, and various studies are being conducted. In this paper, we propose an algorithm to remove AWGN according to the characteristics of the fuzzy membership function. The proposed algorithm calculates the estimated value according to the correlation between the value of the fuzzy membership function between the input image and the pixel value inside the filtering mask, and obtains the final output by adding or subtracting the output of the spatial weight filter. In order to evaluate the proposed algorithm, it was simulated with existing AWGN removal algorithms, and analyzed using difference image and PSNR comparison. The proposed algorithm minimizes the effect of noise, preserves the important characteristics of the image, and shows the performance of efficiently removing noise.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1635-1641
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• 2021

Portable systems with built-in active noise control is required low power operation. Excessive anti noise search operation can lead to rapid battery consumption. A method that can adaptively cancel noise according to the operating conditions of the system is required and the methods of reducing power are becoming very important key feature in today's portable systems. In this paper, we propose the method of active noise control(ANC) using binary search algorithm in noisy systems. The implemented architecture detects a frequency component considered as noise from the input signal and by using the binary search algorithm, the system find out an appropriate amplitude value for anti-noise in a much faster time than the general linear search algorithm. Through the experimental results, it was confirmed that the proposed algorithm performs a successful functional operation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.210-212
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• 2021

In modern society, various digital equipment are being distributed due to the influence of the 4th industrial revolution, and they are used in a wide range of fields such as automated processes, intelligent CCTV, medical industry, robots, and drones. Accordingly, the importance of the preprocessing process in a system operating based on an image is increasing, and an algorithm for effectively reconstructing an image is drawing attention. In this paper, we propose a filter algorithm based on a combined weight value to reconstruct an image in a complex noise environment. The proposed algorithm calculates the weight according to the spatial distance and the weight according to the difference between the pixel values for the input image and the pixel values inside the filtering mask, respectively. The final output was filtered by applying the join weights calculated based on the two weights to the mask. In order to verify the performance of the proposed algorithm, we simulated it by comparing it with the existing filter algorithm.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.39 no.4
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• pp.188-200
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• 2002

The WRR scheduling algorithm is widely used in ATM networks due to its simplicity and the low cost of hardware implementation. It guarantees minimum cell rate according to the weight of each queue. The fairness is a important factor for ABR service. That is, scheduling algorithm allocates network resources fairly to each VC. However, WRR algorithm shows worse performance on bursty traffic. Because it schedules input traffics according to predetermined weight, it can not satisfies fairness criteria, MCR plus equal share and Maximum of MCR or Max-Min share, defined by ATM Forum TM 4.1 specification. The Nabeshima et al algorithm is not adapt to network status rapidly because it is not compensate the weights of unused bandwidth to VCs and assign the unused bandwidth to VCs by RR method. In this paper, we propose a scheduling algorithm for satisfying the two fairness criteria, MCR plus equal share and Maximum of MCR or Max-Min share, among the six criteria defined by ATM Forum TM 4.1 specification. The WRR, Nabeshima et al, and the proposed scheduling algorithms are compared with respect to fairness and convergence time throughout experimental simulation. According to the simulation results, the proposed algorithm shows higher fairness and more rapid convergence than other algorithms.

• Journal of the Korean Society of Safety
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• v.27 no.5
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• pp.219-223
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• 2012

For an off-site consequence analysis at nuclear power plant, MELCOR Accident Consequence Code System(MACCS) II code is widely used as a software tool. In this study, the algorithm of web-based off-site consequence analysis program(OSCAP) using the MACCS II code was developed for an Integrated Leak Rate Test (ILRT) interval extension and Level 3 probabilistic safety assessment(PSA), and verification and validation(V&V) of the program was performed. The main input data for the MACCS II code are meteorological, population distribution and source term information. However, it requires lots of time and efforts to generate the main input data for an off-site consequence analysis using the MACCS II code. For example, the meteorological data are collected from each nuclear power site in real time, but the formats of the raw data collected are different from each site. To reduce the efforts and time for risk assessments, the web-based OSCAP has an automatic processing module which converts the format of the raw data collected from each site to the input data format of the MACCS II code. The program also provides an automatic function of converting the latest population data from Statistics Korea, the National Statistical Office, to the population distribution input data format of the MACCS II code. For the source term data, the program includes the release fraction of each source term category resulting from modular accident analysis program(MAAP) code analysis and the core inventory data from ORIGEN. These analysis results of each plant in Korea are stored in a database module of the web-based OSCAP, so the user can select the defaulted source term data of each plant without handling source term input data.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.433-436
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• 2004

In this paper, we introduce a new Fuzzy Polynomial Neural Networks (FPNNS)-like structure whose neuron is based on the Fuzzy Set-based Fuzzy Inference System (FS-FIS) and is different from that of FPNNS based on the Fuzzy relation-based Fuzzy Inference System (FR-FIS) and discuss the ability of the new FPNNS-like structure named Fuzzy Set-based Polynomial Neural Networks (FSPNN). The premise parts of their fuzzy rules are not identical, while the consequent parts of the both Networks (such as FPNN and FSPNN) are identical. This difference results from the angle of a viewpoint of partition of input space of system. In other word, from a point of view of FS-FIS, the input variables are mutually independent under input space of system, while from a viewpoint of FR-FIS they are related each other. The proposed design procedure for networks architecture involves the selection of appropriate nodes with specific local characteristics such as the number of input variables, the order of the polynomial that is constant, linear, quadratic, or modified quadratic functions being viewed as the consequent part of fuzzy rules, and a collection of the specific subset of input variables. On the parameter optimization phase, we adopt Information Granulation (IC) based on HCM clustering algorithm and a standard least square method-based learning. Through the consecutive process of such structural and parametric optimization, an optimized and flexible fuzzy neural network is generated in a dynamic fashion. To evaluate the performance of the genetically optimized FSPNN (gFSPNN), the model is experimented with using the time series dataset of gas furnace process.

• Transactions on Control, Automation and Systems Engineering
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• v.3 no.4
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• pp.283-288
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• 2001

In this paper, we proposed a method for extracting facial characteristics of human being in an image. Given a pair of gray level sample images taken with and without human being, the face of human being is segmented from the image. Noise in the input images is removed with the help of Gaussian filters. Edge maps are found of the two input images. The binary edge differential image is obtained from the difference of the two input edge maps. A mask for face detection is made from the process of erosion followed by dilation on the resulting binary edge differential image. This mask is used to extract the human being from the two input image sequences. Features of face are extracted from the segmented image. An effective recognition system using the discrete wave let transform (DWT) is used for recognition. For extracting the facial features, such as eyebrows, eyes, nose and mouth, edge detector is applied on the segmented face image. The area of eye and the center of face are found from horizontal and vertical components of the edge map of the segmented image. other facial features are obtained from edge information of the image. The characteristic vectors are extrated from DWT of the segmented face image. These characteristic vectors are normalized between +1 and -1, and are used as input vectors for the neural network. Simulation results show recognition rate of 100% on the learned system, and about 92% on the test images.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.2
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• pp.51-64
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• 2004

This paper proposes an algerian of generating a tuner-template from multiple fingerprint impressions using a data fusion technique for fingerprint enrollment. The super-template is considered as a single fingerprint template which contains most likely true minutiae based on multiple fingerprint images. The proposed algorithm creates the super template by utilizing a recursive Bayesian estimation method (RBEM), which assumes a sequential fingerprint input model and estimates the credibility of the minutiae in previous input templates froma current input template. Consequently. the RBEM assigns a higher credibility to commonly detectable minutiae from several input templates and a lower credibility to rarely found minutiae from other input templates. Likewise, the RBEM is able to estimate a credibility of the minutia type (ridge ending or bifurcation). Preliminary experiments demonstrate that, as the number of fingerfrint images increases, the performance of recognition can be improved while maintaining the processing time and the size of memory storage for tile super-template almost constant.