• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.7
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• pp.12-19
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• 2020

The clustered heterogeneous wireless sensor network is comprised of sensor nodes and cluster heads, which are hierarchically organized for different objectives. In the network, we should especially take care of managing node resources to enhance network performance based on memory and battery capacity constraints. For instances, if some interesting events occur frequently in the vicinity of particular sensor nodes, those nodes might receive massive amounts of data. Data congestion can happen due to a memory bottleneck or link disconnection at cluster heads because the remaining memory space is filled with those data. In this paper, we utilize drones as mobile sinks to resolve data congestion and model the network, sensor nodes, and cluster heads. We also design a cost function and a congestion indicator to calculate the degree of congestion. Then we propose a data congestion map index and a data congestion mapping scheme to deploy drones at optimal points. Using control variable, we explore the relationship between the degree of congestion and the number of drones to be deployed, as well as the number of drones that must be below a certain degree of congestion and within communication range. Furthermore, we show that our algorithm outperforms previous work by a minimum of 20% in terms of memory overflow.

• Journal of Broadcast Engineering
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• v.27 no.3
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• pp.378-390
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• 2022

With the recent development of deep composite multiplication neural network learning, deep learning techniques applied to single-image super-resolution have shown good results, and the strong expression ability of deep networks has enabled complex nonlinear mapping between low-resolution and high-resolution images. However, there are limitations in applying it to real-time or low-power devices with increasing parameters and computational amounts due to excessive use of composite multiplication neural networks. This paper uses blocks that extract hierarchical characteristics little by little using information distillation and suggests the Recursive Distillation Super Resolution Network (RDSRN), a lightweight network that improves performance by making more accurate high frequency components through high frequency residual purification blocks. It was confirmed that the proposed network restores images of similar quality compared to RDN, restores images 3.5 times faster with about 32 times fewer parameters and about 10 times less computation, and produces 0.16 dB better performance with about 2.2 times less parameters and 1.8 times faster processing time than the existing lightweight network CARN.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.12
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• pp.1111-1122
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• 2020

The purpose of this research aims to develop the creative economy sector of rural communities in Trenggalek Regency, East Java, Indonesia by mapping the creative economy sub-sectors in Trenggalek Regency. The creative economy industry has been shown to have a positive effect on GDP in many developing countries and developed countries. In urban areas, the creative economy is often associated with the development of advanced technology, such that it is unlikely to evolve in rural communities. However, the rural environmental landscape, which not only has a wealth of creative human capital but also a cultural heritage, also encourages the growth of the creative economy sector in rural areas. This qualitative research was conducted for 60 working days through in-depth interviews, focus group discussions, observation and literature review. More precisely, first, it identified the potential of the creative economy both sectorally and regionally. Second, it identified the leading creative economy subsectors and compiled a creative economic development road map. After defining and mapping the potential of the creative economy, four of the sixteen sub-sectors of the creative economy were identified to be developed through a strategic plan for the growth of the creative economy using urban branding, innovative industrial centers and renewable resources.

• Proceedings of the Korea Contents Association Conference
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• 2006.11a
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• pp.706-710
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• 2006

Recently, several studies have been carried on knowledge flow of science, technology, industry to establish STI policy. Still it is lack of studies on relationship of science-industry although there have been studied only in aspect of science-technology relationship or technology-industry relationship. This paper's purpose is to propose method to measure knowledge flow among science, technology, and industry by means of patent citation. After gathering knowledge flow data between science and technology through mapping citing patent and cited paper, it gets knowledge flows data between technology-industry by using OTC (OECD Technology Concordance), technology-industry mapping program. Basd on 2 types of knowledge flow data, it propose method to examine knowledge flow from science to industry by applying overlap function is a network link weight function.

• Journal of the Korean Data and Information Science Society
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• v.8 no.2
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• pp.163-171
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• 1997

Function approximation from a set of input-output pairs has numerous applications in scientific and engineering areas. Multilayer feedforward neural networks have been proposed as a good approximator of nonlinear function. The back propagation(BP) algorithm allows multilayer feedforward neural networks to learn input-output mappings from training samples. It iteratively adjusts the network parameters(weights) to minimize the sum of squared approximation errors using a gradient descent technique. However, the mapping acquired through the BP algorithm may be corrupt when errorneous training data we employed. When errorneous traning data are employed, the learned mapping can oscillate badly between data points. In this paper we propose a robust BP learning algorithm that is resistant to the errorneous data and is capable of rejecting gross errors during the approximation process, that is stable under small noise perturbation and robust against gross errors.

• Journal of Broadcast Engineering
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• v.25 no.2
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• pp.242-251
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• 2020

Recently, many deep convolutional neural networks for image super-resolution have been studied. Existing deep learning-based super-resolution algorithms are architecture that up-samples the resolution at the end of the network. The post-upsampling architecture has an inefficient structure at large scaling factor result of predicting a lot of information for mapping from low-resolution to high-resolution at once. In this paper, we propose a single image super-resolution using Channel Attention Residual Dense Block based on an iterative up-down sampling architecture. The proposed algorithm efficiently predicts the mapping relationship between low-resolution and high-resolution, and shows up to 0.14dB performance improvement and enhanced subjective image quality compared to the existing algorithm at large scaling factor result.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.05a
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• pp.165-170
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• 2003

This paper presents an integrated approach to unconstrained face recognition in arbitrary scenes. The front end of the system comprises of a scale and pose tolerant face detector. Scale normalization is achieved through novel combination of a skin color segmentation and log-polar mapping procedure. Principal component analysis is used with the multi-view approach proposed in[10] to handle the pose variations. For a given color input image, the detector encloses a face in a complex scene within a circular boundary and indicates the position of the nose. Next, for recognition, a radial grid mapping centered on the nose yields a feature vector within the circular boundary. As the width of the color segmented region provides an estimated size for the face, the extracted feature vector is scale normalized by the estimated size. The feature vector is input to a trained neural network classifier for face identification. The system was evaluated using a database of 20 person's faces with varying scale and pose obtained on different complex backgrounds. The performance of the face recognizer was also quite good except for sensitivity to small scale face images. The integrated system achieved average recognition rates of 87% to 92%.

• The Journal of Korean Physical Therapy
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• v.15 no.3
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• pp.295-345
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• 2003

This study investigated activation of cerebral cortex in patients with hemiplegia that was caused by neural damage. Key-point control movement therapy of Bobath was performed for 9 weeks in 3 subjects with hemiplegia and fMRI was used to compare and analyze activated degree of cerebral cortex in these subjects. fMRI was conducted using the blood oxygen level-dependent(BOLD) technique at 3.0T MR scanner with a standard head coil. The motor activation task consisted of finger flexion-extension exercise in six cycles(one half-cycles = 8 scans = $3\;sec{\times}\;8\;=\;24\;sec$). Subjects performed this task according to visual stimulus that sign of right hand or left hand twinkled(500ms on, 500ms off). After mapping activation of cerebral motor cortex on hand motor function, below results were obtained. 1. Activation decreased in primary motor area, whereas it increased in supplementary motor area and visual association area(p<.001). 2. Activation was observed in bilateral medial frontal gyrus, middle frontal gyrus of left cerebrum, inferior frontal gyrus, inter-hemispheric, fusiform gyrus of right cerebrum, superior parietal lobule of parietal lobe and precuneus in subjedt 1, parahippocampal gyrus of limbic lobe and cingulate gyrus in subject 2, and inferior frontal gyrus of right frontal lobe, middle frontal gyrus, and inferior parietal lobule of left cerebrum in subject 3 (p<.001). 3. Activation cluster extended in declive of right cellebellum posterior lobe in subject 1, culmen of anterior lobe and declive of posterior lobe in subject 2, and dentate gyrus of anterior lobe, culmen and tuber of posterior lobe in subject 3 (p<.001). In conclusion, these data showed that Key-point control movement therapy of Bobath after stroke affect cerebral cortex activation by increasing efficiency of cortical networks. Therefore mapping of brain neural network activation is useful for plasticity and reorganization of cerebral cortex and cortico-spinal tract of motor recovery mechanisms after stroke.

• Korean Business Review
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• v.18 no.2
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• pp.197-223
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• 2005

The industrial cluster policy of 21st century rise to the focus method of regional economic promotion, therefore, the importance of study in cluster identification and mapping as policy task will bring into relief. This paper will confirms the estimate index and policy of industrial clusters with regional industry. The result in this case study, Cluster development should embrace the pursuit of competitive advantage and specialization rather than simply imitate successful clusters in other locations. This requires building on local sources of uniqueness. Government should reinforce and building on existing and emerging clusters rather than attempt to create entirely new ones. This sort of role for government is very different from industrial policy. The aim of cluster policy is to reinforce the development of all clusters. Not all clusters will succeed, but market forces should determine the outcomes. In other words, government should build on market- oriented system and innovative infra. The result of this study is meaning to the development of objectivity estimate index and derivation of cluster-focused policy with a case study of industrial clusters.

• Journal of Environmental Science International
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• v.20 no.9
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• pp.1205-1212
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• 2011

As serving people's activities, pedestrian road systems are basic and necessary facilities in urban structure. To provide and utilize these pedestrian road systems in residential area would enhance urban environment as well as quality of life. For this reason, pedestrian road should be planned by consideration of people's activity in residential area. Evaluation of existing pedestrian road should be also oriented how people use it and what people do in it. This study amis to investigate functions of pedestrian road system throughout evaluation of user's satisfaction in order to improve better pedestrian road system in residential area. The purposes of this study are to analyze components of factors affecting on user's satisfaction, and to find the relationship among affecting factors. For this study, the on-site questionnaire method was applied to 267 individuals who were collected as the study areas where locate Toyogaoka and Kaidori, Japan. The collected data were clarified exploratory factors, and analyzed relationship between the factors and satisfaction by applying quantitative statistical techniques for the mapping investigation, Mann-Whitney u-test, and correlation. The results of this study are follows. The pedestrian road system is more preferred than surroundings of vehicle roads in residential district area where maintains pedestrian road as open space. In addition, satisfactions of the pedestrian road for each purpose were highly evaluated, because of conformability and convenience for usage. Consequently, the pedestrian road which is secure and greening as an open space is well carried out for the living circulation of residents. It would suggest that pedestrian road have to be managed and planned not a function of circulation but an open space system.