• Journal of IKEEE
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• v.11 no.4
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• pp.219-226
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• 2007

An efficient block based two-layer JPEG encoding algorithm is proposed to compress high dynamic range (HDR) images in this work. The proposed algorithm separates an input HDR image into a tone-mapped low dynamic range (LDR) image and a ratio image, which represents the quotients of the original HDR pixels divided by the tone-mapped LDR pixels. Then, the tone-mapped LDR image is compressed using the standard JPEG scheme to preserve backward compatibility and the ratio image is encoded to minimize a cost function that models the perception of each block with different quantization parameters in the human visual system (HVS). Simulation results show that the proposed algorithm provides better performance than the conventional method, which encodes the ratio image without any prior information of blocks.

• Journal of KIISE:Databases
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• v.32 no.3
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• pp.228-242
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• 2005

The projected clustering seeks to find clusters in different subspaces within a high dimensional dataset. We propose an algorithm to discover near optimal projected clusters without user specified parameters such as the number of output clusters and the average cardinality of subspaces of projected clusters. The objective function of the algorithm computes projected energy, quality, and the number of outliers in each process of clustering. In order to minimize the projected energy and to maximize the quality in clustering, we start to find best subspace of each cluster on the density of input points by comparing standard deviations of the full dimension. The weighting factor for each dimension of the subspace is used to get id of probable error in measuring projected distances. Our extensive experiments show that our algorithm discovers projected clusters accurately and it is scalable to large volume of data sets.

• Journal of Satellite, Information and Communications
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• v.12 no.2
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• pp.56-60
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• 2017

This paper describes the design and implementation of fall-down detection algorithm based on image processing. The fall-down detection algorithm separates objects by using background subtraction and binarization after grayscale conversion of the input image acquired by the camera, and recognizes the human body by using labeling operation. The recognized human body can be monitored on the display image, and an alarm is generated when fall-down is detected. By using computer simulation, the proposed algorithm has shown a detection rate of 90%. We verify the feasibility of the proposed system by verifying the function by using the prototype test implemented on the DSP image processing board.

• Journal of Korea Multimedia Society
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• v.24 no.8
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• pp.1114-1121
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• 2021

In this paper, we implement a recipe recommendation system using ingredient harmonization analysis based on recipe data. The proposed system receives an image of a food ingredient purchase receipt to recommend ingredients and recipes to the user. Moreover, it performs preprocessing of the receipt images and text extraction using the OCR algorithm. The proposed system can recommend recipes based on the combined data of ingredients. It collects recipe data to calculate the combination for each food ingredient and extracts the food ingredients of the collected recipe as training data. And then, it acquires vector data by learning with a natural language processing algorithm. Moreover, it can recommend recipes based on ingredients with high similarity. Also, the proposed system can recommend recipes using replaceable ingredients to improve the accuracy of the result through preprocessing and postprocessing. For our evaluation, we created a random input dataset to evaluate the proposed recipe recommendation system's performance and calculated the accuracy for each algorithm. As a result of performance evaluation, the accuracy of the Word2Vec algorithm was the highest.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.48 no.4
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• pp.6-14
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• 2011

The fast growing of the number of users requires the development of reliable communication systems able to provide higher data rates. In order to meet those requirements, techniques such as Multiple Input Multiple Out (MIMO) and Orthogonal Frequency Division multiplexing (OFDM) have been developed in the recent years. In order to combine the benefits of both techniques, the research activity is currently focused on MIMO-OFDM systems. In addition, for a fast wireless channel environment, the data rate and reliability can be optimized by setting the modulation and coding adaptively according to the channel conditions; and using sub-carrier frequency, and power allocation techniques. Depending on how accurate the feedback-based system obtain the channel state information (CSI) and feed it back to the transmitter without delay, the overall system performance would be poor or optimal. In this paper, we propose a Signal to Noise Ratio (SNR) estimation algorithm where the preamble is known for both sides of the transciever. Through simulations made over several channel environments, we prove that our proposed SNR estimation algorithm is more accurate compared with the traditional SNR estimation. Also, We applied AMC on several channel environments using the parameters of IEEE 802.11n, and compared the Throughput performance when using each of the different SNR Estimation Algorithms. The results obtained in the simulation confirm that the proposed algorithm produces the highest Throughput performance.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.2B
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• pp.36-43
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• 2005

This paper suggests a method to effectively apply an application model of fuzzy-neural network to the optimal load distribution algorithm, considering the complication and non-linearity of the web server environment. We use the clustering web server in the linux system and it consists of a load balancer that distributes the network loads and some of real servers that processes the load and responses to the client. The previous works considered only with the scrappy decision information such as the connections. That is, since the distribution algorithm depends on the input of the whole network throughput, it was proved inefficient in terms of performance improvement of the web server. With the proposed algorithm, it monitors the whole states of both network input and output. Then, it infers CPU and memory states of each real server and effectively distributes the requests of the clients. In this paper, the proposed model is compared with the previous method through simulations and we analysis the results to develop the optimal and intelligent load balancing model.

• Journal of Internet Computing and Services
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• v.17 no.6
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• pp.17-23
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• 2016

In the future network such as Internet of Things (IoT), the number of computing devices are expected to grow exponentially, and each of the things communicates with the others and acquires information by itself. Due to the growing interest in IoT applications, the broadcasting in Opportunistic ad-hoc networks such as Machine-to-Machine (M2M) is very important transmission strategy which allows fast data dissemination. In distributed networks for IoT, the energy efficiency of the nodes is a key factor in the network performance. In this paper, we propose a fuzzy logic based probabilistic multi-hop broadcast (FPMCAST) algorithm which statistically disseminates data accordingly to the remaining energy rate, the replication density rate of sending node, and the distance rate between sending and receiving nodes. In proposed FPMCAST, the inference engine is based the fuzzy rule base which is consists of 27 if-then rules. It maps input and output parameters to membership functions of input and output. The output of fuzzy system defines the fuzzy sets for rebroadcasting probability, and defuzzification is used to extract a numeric result from the fuzzy set. Here Center of Gravity (COG) method is used to defuzzify the fuzzy set. Then, the performance of FPMCAST is evaluated through a simulation study. From the simulation, we demonstrate that the proposed FPMCAST algorithm significantly outperforms flooding and gossiping algorithms. Specially, the FPMCAST algorithm has longer network lifetime because the residual energy of each node consumes evenly.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.6
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• pp.1126-1139
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• 1994

This paper proposes an effective recognition system which recognizes the mixed document consisting of handwritten korean/alphanumeric texts and graphic images. In the preprocessing step, an input image is binarized by the proposed thresholding scheme, then graphic and character regions are separated by using connected components and chain codes. Separated Korean characters are merged based on partial recognition and their character types and sized. In the character recognition step, we use the branch and bound algorithm based on DP matching costs to recognize Korean characters. Also we recognize alphanumeric characters using several robust features. Finally we use a dictionary and information of a recognition step to correct wrong recognition results. Computer simulation with several test documents shows what the proposed algorithm recognized effectively handwritten mixed texts.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.7
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• pp.3010-3025
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• 2016

Lattice reduction (LR) has been used widely in conventional multiple-input multiple-output (MIMO) systems to enhance the performance. However, LR is hard to be applied to the relay systems which are important but more complicated in the wireless communication theory. This paper introduces a new viewpoint for utilizing LR in multiuser MIMO relay systems. The vector precoding (VP) is designed along with zero force (ZF) criterion and minimum mean square error (MMSE) criterion and enhanced by LR algorithm. This implementable precoder design combines nonlinear processing at the base station (BS) and linear processing at the relay. This precoder is capable of avoiding multiuser interference (MUI) at the mobile stations (MSs) and achieving excellent performance. Moreover, it is shown that the amount of feedback information is much less than that of the singular value decomposition (SVD) design. Simulation results show that the proposed scheme using the complex version of the Lenstra--Lenstra--Lovász (LLL) algorithm significantly improves system performance.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.3
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• pp.42-50
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• 2010

It is necessary to implementation of system contain intelligent decision making algorithm considering marine feature because existing vessel USN system is simply monitoring obtained data from vessel USN. In this paper, we designed inference system using case based reasoning method and implemented knowledge base that case for fire and demage of digital marine vessel. We used K-Nearest Neighbor algorithm for recommend best similar case and input 3.000 EA by case for fire and demage context case base. As a result, we obtained about 82.5% average accuracy for fire case and about 80.1% average accuracy for demage case. We implemented digital marine vessel monitoring system using inference result.