• Journal of the Institute of Electronics Engineers of Korea SC
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• v.39 no.3
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• pp.228-238
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• 2002

This paper presents a digital controller of the electronic ballast using high frequency modulation method for the metal halide lamp. The proposed controller includes the control algorithm for soft starting, no load protection, over current protection and power control. The proposed digital controller, moreover, has the high frequency modulation scheme and the tracking algorithm to avoid acoustic resonance phenomena. For the math production with the low cost using the ASICs (Application Specific Integrated Circuit), the proposed digital controller has been designed with the FPGAs(Field Programmable Gate array) only, without any microprocessor. In this paper, the detail digital control algorithms are described and the experimental results of prototype 150w metal halide electronic ballast are presented.

• Journal of the Society of Naval Architects of Korea
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• v.51 no.2
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• pp.99-106
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• 2014

Weather routing of a ship provides an optimal route to the destination by using minimal time or fuel in a given sea condition. These days, weather routing came into a spotlight with soaring fuel price and the environmental regulations of IMO and several countries. This study presents three scenarios of voyaging strategies for a ship and compared them in terms of the fuel consumption. The first strategy fixes the speed of a ship as a constant value for entire sailing course, the second fixes the RPM of the ship as constant for entire course, and the third determines the RPMs of the ship for each segment of the course. For each strategy, a ship route is optimized by using the $A^*$ search method. Wind, ocean current and wave are considered as ocean environment factors when seeking the optimal routes. Based on 7000 TEU container ship's sea trial records, simulation has been conducted for three scenarios, and the most efficient routing scenario is determined in the view of fuel consumption.

• The Journal of the Korea Contents Association
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• v.9 no.7
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• pp.102-109
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• 2009

In this paper, we present an algorithm which generates the impossible drawings after the manner of M.C. Escher. A class of the impossible drawings, focused on this paper, depicts the non-realistic configuration such that an ascent (or a descent) looks like keeping on permanently with a height-deceptive loop. We analyze the fact that the ascending direction in the non-realistic illustrations comes not from the physical heights of the objects but from the artist's intended forwarding direction about the loop, which does not have any physical sense of depths. The basic idea to support such impossible drawings is to use a loop of layered depth images (LDIs), where several LDIs are arranged along with the forwarding direction of the loop while having the physically constant heights. The height-deception between two adjacent objects comes from the layer values in the LDIs. In this paper, we propose a NPR system which can manipulate a shape of the loop and layer values of the LDIs and demonstrate several impossible drawings results generated by using our system.

• Journal of Digital Convergence
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• v.11 no.11
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• pp.367-372
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• 2013

In general, Levenshtein distance algorithm have a problem with not distinguish the consideration of vacabulary retrieve, because Levenshtein methode is used to vocabulary order are not defined. In this paper, we propose a improved Levenshtein methode, it effectively manage the vocabulary retrieve by frequency use of a vocabulary, and it gives the weight number which have a order between vocabularies. Therefore proposed methode have a advantage of solve the defect of perception rate in the case of increase the vocabulary, improve the recognition time become higher and it can be effectively retrieval space management.. System performance as a result of represent vocabulary dependence recognition rate of 97.81%, vocabulary independence recognition rate of 96.91% in indoor environment. Also, vocabulary dependence recognition rate of 91.11%, vocabulary independence recognition rate of 90.01% in outdoor environment.

• Journal of the Korean Operations Research and Management Science Society
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• v.33 no.3
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• pp.17-28
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• 2008

The Steiner arborescence problem is known to be NP-hard. The objective of this problem is to find a minimal Steiner tree which starts from a designated node and spans all given terminal nodes. This paper proposes a method based on a two-step procedure to solve this problem efficiently. In the first step, graph reduction rules eliminate useless nodes and arcs which do not contribute to make an optimal solution. In the second step. ant colony algorithm with use of Prim's algorithm is used to solve the Steiner arborescence problem in the reduced graph. The proposed method based on a two-step procedure is tested in the five test problems. The results show that this method finds the optimal solutions to the tested problems within 50 seconds. The algorithm can be applied to undirected Steiner tree problems with minor changes. 18 problems taken from Beasley are used to compare the performances of the proposed algorithm and Singh et al.'s algorithm. The results show that the proposed algorithm generates better solutions than the algorithm compared.

• Journal of the Korea Society of Computer and Information
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• v.25 no.4
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• pp.113-121
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• 2020

With the rapid development of information and communication technology (ICT), various sensors are being embedded in wearable devices. Consequently, these devices can continuously collect data including health data from individuals. The collected health data can be used not only for healthcare services but also for analyzing an individual's lifestyle by combining with other external data. This helps in making an individual's life more convenient and healthier. However, collecting health data may lead to privacy issues since the data is personal, and can reveal sensitive insights about the individual. Thus, in this paper, we present a method to collect an individual's health data from a smart band in a privacy-preserving manner. We leverage the local differential privacy to achieve our goal. Additionally, we propose a way to find feature points from health data. This allows for an effective trade-off between the degree of privacy and accuracy. We carry out experiments to demonstrate the effectiveness of our proposed approach and the results show that, with the proposed method, the error rate can be reduced upto 77%.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.3
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• pp.1-8
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• 2022

Recently, Google MedeiaPipe presents a novel approach for neural network-based hair segmentation from a single camera input specifically designed for real-time, mobile application. Though neural network related to hair segmentation is relatively small size, it produces a high-quality hair segmentation mask that is well suited for AR effects such as a realistic hair recoloring. However, it has undesirable segmentation effects according to hair styles or in case of containing noises and holes. In this study, the energy function of the test image is constructed according to the estimated prior distributions of hair location and hair color likelihood function. It is further optimized according to graph cuts algorithm and initial hair region is obtained. Finally, clustering algorithm and image post-processing techniques are applied to the initial hair region so that the final hair region can be segmented precisely. The proposed method is applied to MediaPipe hair segmentation pipeline.

• Journal of Internet of Things and Convergence
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• v.9 no.3
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• pp.13-19
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• 2023

In this paper, we investigated the implementation of a hairstyle recommendation system based on big data and deepfake technology. The proposed hairstyle recommendation system recognizes the facial shapes based on the user's photo (image). Facial shapes are classified into oval, round, and square shapes, and hairstyles that suit each facial shape are synthesized using deepfake technology and provided as videos. Hairstyles are recommended based on big data by applying the latest trends and styles that suit the facial shape. With the image segmentation map and the Motion Supervised Co-Part Segmentation algorithm, it is possible to synthesize elements between images belonging to the same category (such as hair, face, etc.). Next, the synthesized image with the hairstyle and a pre-defined video are applied to the Motion Representations for Articulated Animation algorithm to generate a video animation. The proposed system is expected to be used in various aspects of the beauty industry, including virtual fitting and other related areas. In future research, we plan to study the development of a smart mirror that recommends hairstyles and incorporates features such as Internet of Things (IoT) functionality.

• Journal of Internet Computing and Services
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• v.5 no.5
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• pp.129-138
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• 2004

In the telecommunications network, multicasting is widely used recently. Multicast tree problem is modeled as the NP-complete Steiner problem in the networks. In this paper, we study algorithms for finding efficient multicast trees with hop and node degree constraints. Multimedia service is an application of multicasting and it is required to transfer a large volume of multimedia data with QoS(Quality of Service). Though heuristics for solving the multicast tree problems with one constraint have been studied. however, there is no optimum algorithm that finds an optimum multicast tree with hop and node degree constraints up to now. In this paper, an approach for finding an efficient multicast tree that satisfies hop and node degree constraints is presented and the experimental results explain how the hop and node degree constraints affect to the total cost of a multicast tree.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.93-104
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• 2024

Demand response is a strategy that encourages customers to adjust their consumption patterns at times of peak demand with the aim to improve the reliability of the power grid and minimize expenses. The integration of renewable energy sources into smart grids poses significant challenges due to their intermittent and unpredictable nature. Demand response strategies, coupled with reinforcement learning techniques, have emerged as promising approaches to address these challenges and optimize grid operations where traditional methods fail to meet such kind of complex requirements. This research focuses on investigating the application of reinforcement learning algorithms in demand response for renewable energy integration. The objectives include optimizing demand-side flexibility, improving renewable energy utilization, and enhancing grid stability. The results emphasize the effectiveness of demand response strategies based on reinforcement learning in enhancing grid flexibility and facilitating the integration of renewable energy.