• Journal of the Economic Geographical Society of Korea
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• v.19 no.4
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• pp.764-781
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• 2016

During the last two decades, industrial cluster policies for promoting regional economic growth and industrial development have been flourishing all over the world. Even though cluster policies have partly contributed to regional industrial growth and innovation capabilities, they have long been blamed for regional industrial lock-ins and declining regional industrial resilience because of applying homeogenous cluster policies and regional specialized strategic industrial promotion policies for various localities, which are based on so-called 'selection and concentration' principle. This paper suggests postcluster policy focused on placed-based smart specialization and regional business platform strategies.

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.3
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• pp.60-71
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• 2017

Smart devices such as smart phones, smart TVs, and smart pads have become essential devices in recent years. As the popularity and demand grows, the performance of smart devices is also getting better and users are dealing with a lot of things such as education and business using smart devices instead of desktop. However, smart devices that still have poor performance compared to desktop, even with improved performance, have difficulty running high performance applications due to limited resources. In this paper, we propose a load balancing algorithm applying the characteristics of smart devices to overcome the resource limitations of devices. in order to verify the algorithm, we implemented the algorithm after adding the distributed processing system service in Android platform. After constructing the cluster on the smart device, various experiments were conducted. Through the analysis of the test results, it is confirmed that the proposed algorithm efficiently improves the overall distributed processing performance by effectively aggregating different amounts of computing resources in heterogeneous smart devices.

• Journal of the Society of Disaster Information
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• v.18 no.4
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• pp.922-929
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• 2022

Purpose: The purpose of this paper is to develop a deep learning model to detect fire extinguishers in images taken from CCTVs in underground utility tunnels. Method: Various fire extinguisher images were collected for detection of fire extinguishers in the running-based underground utility tunnel, and a model applying the One-stage Detector method was developed based on the CNN algorithm. Result: The detection rate of fire extinguishers photographed within 10m through CCTV video in the underground common area is over 96%, showing excellent detection rate. However, it was confirmed that the fire extinguisher object detection rate drops sharply at a distance of 10m or more, in a state where it is difficult to see with the naked eye. Conclusion: This paper develops a model for detecting fire extinguisher objects in underground common areas, and the model shows high performance, and it is judged that it can be used for underground common area digital twin model synchronizing.

• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.11-16
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• 2023

Vehicular networks are part of the next generation wireless and smart Intelligent Transportation Systems (ITS). In the future, autonomous vehicles will be an integral part of ITS and will provide safe and reliable traveling features to the users. The reliability and security of data transmission in vehicular networks has been a challenging task. To manage data transmission in vehicular networks, road networks are divided into clusters and a cluster head is selected to handle the data. The selection of cluster heads is a challenge as vehicles are mobile and their connectivity is dynamically changing. In this paper, a novel secure cluster head selection algorithm is proposed for secure and reliable data sharing. The idea is to use the secrecy rate of each vehicle in the cluster and adaptively select the most secure vehicle as the cluster head. Simulation results show that the proposed scheme improves the reliability and security of the transmission significantly.

• Smart Media Journal
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• v.8 no.4
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• pp.46-52
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• 2019

The size of data generated and collected at industrial sites or in public institutions is growing rapidly. The existing data processing server often handles the increasing data by increasing the performance by scaling up. However, in the big data era, when the speed of data generation is exploding, there is a limit to data processing with a conventional server. To overcome such limitations, a distributed cluster computing system has been introduced that distributes data in a scale-out manner. However, because distributed cluster computing systems distribute data, inefficient use of network bandwidth can degrade the performance of the cluster as a whole. In this paper, we propose a scheme that compresses data when transmitting data in a Hadoop cluster considering network bandwidth. The proposed scheme considers the network bandwidth and the characteristics of the compression algorithm and selects the optimal compression transmission scheme before transmission. Experimental results show that the proposed scheme reduces data transfer time and size.

• Journal of Industrial Convergence
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• v.14 no.1
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• pp.39-45
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• 2016

This paper deals with the consideration of mathematical models with regards to growth of cluster and firms by reviewing the Metcalf and Breuner's articles. prior studies have been argued the phenomenon of local industrial clusters and districts. Several concepts have been adopted to support the success of and changes to these clusters and firm growth. Through the review of two papers, evolution of both cluster and firm growth may be achieved in terms of utilizations of the different local aspects and mechanisms. This paper supports the theoretical back bone with regards to the regional cluster policy implementing in Korea for the purpose of regional developments. In particular, a mathematical model that, on a more abstract level, captures the fundamental dynamic structure of all the observed mechanisms. On the basis of this model, the emergence and evolution of local clusters can be described. Also this model has given that the knowledge sharing between firms has an important role to firms and cluster' growth.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.10
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• pp.277-284
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• 2021

Recently, the smart home environment is expected to be a platform that collects, integrates, and utilizes various data through convergence with wireless information and communication technology. In fact, the number of smart devices with various sensors is increasing inside smart homes. The amount of data that needs to be processed by the increased number of smart devices is also increasing, and big data processing systems are actively being introduced to handle it effectively. However, traditional big data processing systems have all requests directed to cluster drivers before they are allocated to distributed nodes, leading to reduced cluster-wide performance sharing as cluster drivers managing segmentation tasks become bottlenecks. In particular, there is a greater delay rate on smart home devices that constantly request small data processing. Thus, in this paper, we design a Apriori-based big data system for effective data processing in smart home environments where frequent requests occur at the same time. According to the performance evaluation results of the proposed system, the data processing time was reduced by up to 38.6% from at least 19.2% compared to the existing system. The reason for this result is related to the type of data being measured. Because the amount of data collected in a smart home environment is large, the use of cache servers plays a major role in data processing, and association analysis with Apriori algorithms stores highly relevant sensor data in the cache.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.3
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• pp.68-84
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• 2020

This study analyzed the relationship between spatial characteristics and heat waves in the distribution area of the elderly population in Changwon, Gyeongsangnam-do. For analysis, the Statistics Census data, the Ministry of Environment land cover, Landsat 8 surface temperature, and the Meteorological Agency's heat wave days data were used. The spatial characteristics of the distribution of the elderly population was classified into 5 types through K-mean cluster analysis considering the land use types. The characteristics of the elderly population by spatial type were higher in the urbanized type(cluster-3), but the proportion of the elderly population was higher in the agricultural and forest area types(cluster-1, cluster-2). In the characteristics of the surface temperature and the heat wave days, the surface temperature was the highest in the urban area, but heat wave days were the highest in the rural area. As a result of analyzing the heat wave characteristics according to the spatial type of the distribution area of elderly population, cluster-2 with the largest area in agricultural areas was highest at 15.95 days, and cluster-3 with a large area in urbanized types was the lowest at 9.41 days and 9.18 days. In other words, the elderly population living in rural areas is more exposed to heat waves than the elderly population living in urban areas, and the damage is expected to increase. The results of this study could be used as basic data to prepare various policy measures for effective management and prevention of vulnerable areas in summer.

• Smart Media Journal
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• v.7 no.1
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• pp.9-15
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• 2018

As an organization or organization grows in size, many different types of data are being generated in different systems. There is a need for a way to improve efficiency by processing data smarter in different systems. Just like DataLake, we are creating a single domain model that accurately describes the data and can represent the most important data for the entire business. In order to realize the benefits of a DataLake, it is import to know how a DataLake may be expected to work and what components architecturally may help to build a fully functional DataLake. DataLake components have a life cycle according to the data flow. And while th data flows into a DataLake from the point of acquisition, its meta-data is captured and managed along with data traceability, data lineage, and security aspects based on data sensitivity across its life cycle. According to this reason, we have designed the DataLake Framework based on Abyss Storage Cluster.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.3
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• pp.207-215
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• 2013

This paper is channel assignment and scheduling techniques for efficient wireless AMI network. In AMI system, the multi-channel cluster network to be proposed defines the communication channel between NC (Network Coordinator) and CDA (Clustered Data Aggregator) as the network channel. CDA and OMD(Out Meter display) and communication channel between SMD(Smart Meter Device) are defined as the group channel. AMI network of the multi-channel cluster based in which the network channel and group channel is mixed increases the administration efficiency through the physical/logical consumer channel clustering. The reliability of inspection data through the channel use distinguished between the adjacent cluster is enhanced. In addition, the fast aggregation of data is possible and the size of a metering network is increased through the channel allocation of the multichannel cluster based.