• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.3
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• pp.417-422
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• 2022

It is always desirable that the continuation of power flow through the lines should not be interrupted for a long time. The optimized guideline of reclosers on distribution lines is known to improve the reliability of power systems, the protection functions on distribution systems heavily rely on the number and placement of such reclosers. This study reviewed the effect of using protection settings methodology with the number of reclosing operations to reduce the damage sustained during faults on distribution networks. The aim of the study is to determine the number of reclosing operations and fault current conditions based on simulation data of PSCAD/EMTDC for standard distribution networks. It is found that the determination of the number of operations on reclosers, which are the protection function of feeders, helped to optimize the operation and reliability of distribution networks.

• Journal of Broadcast Engineering
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• v.28 no.1
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• pp.90-99
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• 2023

In this paper, we propose a super resolution network for raw sensor image which data size is lower comparatively to RGB image. But the actual capabilities of raw image super resolution depends on color correction because its absent of camera post processing that leads to unintended result having different white balance, saturation, etc. Thus, we introduce novel color corrector attention network by adopting the idea of precedent raw super resolution research, and tune to the our faced problem from data specification. The result is not superior to former researches but shows decent output on certain performance matrix. In the same time, we encounter new challenging problem of unexpected shadowing artifact around image objects that cause performance declination despite its good result overall. This problem remains a task to be solved in the future research.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.41 no.1
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• pp.1-12
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• 2004

In this paper, we take advantage of the characteristics of optical burst switching (OBS) to support service-differentiation in optical networks. With the offset time between control packet and burst data, the proposed scheme uses different offset time of each service class. As contrasted with the Previous method, in which the high Priority service use only long offset time, it derives the burst loss rate as a QoS parameter in consideration of conservation law and given service-differential ratios and decides a reasonable offset time for this QoS finally Firstly proposed method classifies services into one of high or low class and is an algorithm deciding the offset time for supporting the required QoS of high class. In order to consider the multi-classes environment, we expand the analysis method of first algorithm and propose the second algorithm. It divides services into one of high or low group according to their burst loss rate and decides the offset time for high group, and lastly cumulates the offset time of each class. The proposed algorithms are evaluated through simulation. The result of simulation is compared with that of analysis to verify the proposed scheme.

• Korean Journal of Remote Sensing
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• v.39 no.3
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• pp.283-295
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• 2023

With the development of sensor and satellite technology, numerous high-resolution and multi-spectral satellite images have been available. Due to their wavelength-dependent reflection, transmission, and scattering characteristics, multi-spectral satellite images can provide complementary information for earth observation. In particular, the short-wave infrared (SWIR) band can penetrate certain types of atmospheric aerosols from the benefit of the reduced Rayleigh scattering effect, which allows for a clearer view and more detailed information to be captured from hazed surfaces compared to the visible band. In this study, we proposed a multi-resolution transform-based image fusion method to combine visible and SWIR satellite images. The purpose of the fusion method is to generate a single integrated image that incorporates complementary information such as detailed background information from the visible band and land cover information in the haze region from the SWIR band. For this purpose, this study applied the Laplacian pyramid-based multi-resolution transform method, which is a representative image decomposition approach for image fusion. Additionally, we modified the multiresolution fusion method by combining a haze-guided weight map based on the prior knowledge that SWIR bands contain more information in pixels from the haze region. The proposed method was validated using very high-resolution satellite images from Worldview-3, containing multi-spectral visible and SWIR bands. The experimental data including hazed areas with limited visibility caused by smoke from wildfires was utilized to validate the penetration properties of the proposed fusion method. Both quantitative and visual evaluations were conducted using image quality assessment indices. The results showed that the bright features from the SWIR bands in the hazed areas were successfully fused into the integrated feature maps without any loss of detailed information from the visible bands.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37B no.9
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• pp.806-813
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• 2012

In a cloud computing environment, various solutions were introduced to provide the service to users such as Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Software as a Service (SaaS) and Desktop as a Service (DaaS). Nowadays, Mobile as a Service (MaaS) to provide the mobility in a cloud environment. In other words, users must have access to data and applications even when they are moving. Thus, to support the mobility to a mobile Thin-Client is the key factor. Related works to support the mobility for mobile devices were Mobile IPv6 and Proxy Mobile IPv6 which showed performance drawbacks such as packet loss during hand-over which could be very critical when collaborating with cloud computing environment. The proposed model in this paper deploys middleware and replica servers to support the data transmission among cloud and PMIPv6 domain. It supports efficient mobility during high-speed movement as well as high-density of mobile nodes in local mobility anchor. In this paper, through performance evaluation, the proposed scheme shows the cost comparison between previous PMIPv6 and verifies its significant efficiency.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.16 no.12 s.103
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• pp.1171-1178
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• 2005

Multi-code code division multiple access(MC-CDMA) has been proposed for providing the various service rates with different quality of service requirement by assigning multiple codes and increasing the capacity. However, it suffers from the serious problem of high peak to average power ratio(PAPR). So, it requires large input back-off, which causes poor power consumption in high power amplifier(HPA). In this paper, we propose a new method that can reduce PAPR efficiently by constraint codes based on the opposite correlation to the incoming information data in MC-CDMA. PAPR reduction depends on the length and indices of constraint codes in MC-CDMA system. There is a trade-off between PAPR reduction and the length of constraint codes. From the simulation results, we also investigate the BER improvement in AWGN channel with HPA. The simulation results show that BER performance can be similar with linear amplifier in two cases: 1) Using exact constraint codes without input back-off and 2) a few constraint codes with small input back-off.

• The Journal of the Acoustical Society of Korea
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• v.22 no.5
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• pp.375-382
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• 2003

Lip-reading technique based on bimodal is to enhance speech recognition rate in noisy environment. It is most important to detect the correct lip-image. But it is hard to estimate stable performance in dynamic environment, because of many factors to deteriorate Lip-reading's performance. There are illumination change, speaker's pronunciation habit, versatility of lips shape and rotation or size change of lips etc. In this paper, we propose the IIR filtering in time-domain for the stable performance. It is very proper to remove the noise of speech, to enhance performance of recognition by digital filtering in time domain. While the lip-reading technique in whole lip image makes data massive, the Principal Component Analysis of pre-process allows to reduce the data quantify by detection of feature without loss of image information. For the observation performance of speech recognition using only image information, we made an experiment on recognition after choosing 22 words in available car service. We used Hidden Markov Model by speech recognition algorithm to compare this words' recognition performance. As a result, while the recognition rate of lip-reading using PCA is 64%, Time-domain filter applied to lip-reading enhances recognition rate of 72.4%.

• Journal of the Korea Society for Simulation
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• v.18 no.2
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• pp.29-38
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• 2009

In the computing environment with heterogeneous resources, a job scheduling model is necessary for effective resource utilization and high-speed data processing. And, the job scheduling model has to cope with a dynamic change in the condition of resources. There have been lots of researches on resource estimation methods and heuristic algorithms about how to distribute and allocate jobs to heterogeneous resources. But, existing researches have a weakness for system compatibility and scalability because they do not support the standard language. Also, they are impossible to process jobs effectively and deal with a variety of computing situations in which the condition of resources is dynamically changed in real-time. In order to solve the problems of existing researches, this paper proposes a semantic computing-based dynamic job scheduling model that defines various knowledge-based rules for job scheduling methods adaptable to changes in resource condition and allocate a job to the best suited resource through inference. This paper also constructs a resource ontology to manage information about heterogeneous resources without difficulty as using the OWL, the standard ontology language established by W3C. Experimental results shows that the proposed scheduling model outperforms existing scheduling models, in terms of throughput, job loss, and turn around time.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.7
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• pp.303-314
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• 2023

Voice conversion, a technology that allows an individual's speech data to be regenerated with the acoustic properties(tone, cadence, gender) of another, has countless applications in education, communication, and entertainment. This paper proposes an approach based on the StarGAN-VC model that generates realistic-sounding speech without requiring parallel utterances. To overcome the constraints of the existing StarGAN-VC model that utilizes one-hot vectors of original and target speaker information, this paper extracts feature vectors of target speakers using a pre-trained version of Rawnet3. This results in a latent space where voice conversion can be performed without direct speaker-to-speaker mappings, enabling an any-to-any structure. In addition to the loss terms used in the original StarGAN-VC model, Wasserstein distance is used as a loss term to ensure that generated voice segments match the acoustic properties of the target voice. Two Time-Scale Update Rule (TTUR) is also used to facilitate stable training. Experimental results show that the proposed method outperforms previous methods, including the StarGAN-VC network on which it was based.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.27-35
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• 2023

In this paper, we propose a process of increasing productivity by applying a deep learning-based defect detection and classification system to the prepreg fiber manufacturing process, which is in high demand in the field of producing composite materials. In order to apply it to toe prepreg manufacturing equipment that requires a solution due to the occurrence of a large amount of defects in various conditions, the optimal environment was first established by selecting cameras and lights necessary for defect detection and classification model production. In addition, data necessary for the production of multiple classification models were collected and labeled according to normal and defective conditions. The multi-classification model is made based on CNN and applies pre-learning models such as VGGNet, MobileNet, ResNet, etc. to compare performance and identify improvement directions with accuracy and loss graphs. Data augmentation and dropout techniques were applied to identify and improve overfitting problems as major problems. In order to evaluate the performance of the model, a performance evaluation was conducted using the confusion matrix as a performance indicator, and the performance of more than 99% was confirmed. In addition, it checks the classification results for images acquired in real time by applying them to the actual process to check whether the discrimination values are accurately derived.