• The Journal of the Acoustical Society of Korea
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• v.28 no.6
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• pp.499-505
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• 2009

Main design issues for MAC (Medium Access Control) protocol in underwater sensor networks are long propagation delay caused by the low speed of sound, difficult synchronization, and energy-limited node's life. We aimed to mitigate the problems of strict synchronization and channel inefficiency of TDMA and also the throughput degradation induced by unavoidable collisions in contention based MAC protocols. This proposed protocol improved not only the energy efficiency by adopting a sleep-mode, but also the throughput by reducing collisions and increasing channel efficiency.

• Journal of KIISE:Information Networking
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• v.32 no.3
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• pp.415-426
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• 2005

Wireless internet, which becomes a hot social issue, has limitations due to the following characteristics, as different from wired internet. It has low bandwidth, frequent disconnection, low computing power, and small screen in user terminal. Also, it has technical issues to Improve in terms of user mobility, network protocol, security, and etc. Wireless internet server should be scalable to handle a large scale traffic due to rapidly growing users. In this paper, wireless internet proxy server clusters are used for the wireless Internet because their caching, distillation, and clustering functions are helpful to overcome the above limitations and needs. TranSend was proposed as a clustering based wireless internet proxy server but it has disadvantages; 1) its scalability is difficult to achieve because there is no systematic way to do it and 2) its structure is complex because of the inefficient communication structure among modules. In our former research, we proposed the All-in-one structure which can be scalable in a systematic way but it also has disadvantages; 1) data sharing among cache servers is not allowed and 2) its communication structure among modules is complex. In this paper, we proposed its improved scheme which has an efficient communication structure among modules and allows data to be shared among cache servers. We performed experiments using 16 PCs and experimental results show 54.86$\%$ and 4.70$\%$ performance improvement of the proposed system compared to TranSend and All-in-one system respectively Due to data sharing amount cache servers, the proposed scheme has an advantage of keeping a fixed size of the total cache memory regardless of cache server numbers. On the contrary, in All-in-one, the total cache memory size increases proportional to the number of cache servers since each cache server should keep all cache data, respectively.

• 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.

• Information Systems Review
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• v.19 no.4
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• pp.43-62
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• 2017

This study investigates the effect of bonding and bridging social capital on the technological innovation performance in the Pangyo Techno Valley. In particular, we consider the information technology (IT) usage in industrial cluster as an antecedent of social capital. IT instigates the intra and extra communication and information sharing between employees, thereby promoting the formation of a network of various members. Results show that the IT usage factor positively affects both bridging and bonding social capital, but an evident difference exists among the effects of social capital on the technological innovation performance. In case of Pangyo industrial cluster, bridging social capital exerts significant effect on the technological innovation performance, whereas bonding social capital shows insignificance. Bridging social capital is composed of the interactions of various networks. Bonding social capital is based on the strong tie from trust and internal cooperation. Results are related with the characteristics of Pangyo Techno Valley, where various IT ventures need active communication and information sharing with other organizations for technological innovation performance.

• The KIPS Transactions:PartA
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• v.12A no.7 s.97
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• pp.627-636
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• 2005

This paper presents a PC cluster-based distributed video on demand (VOD) server that minimizes the load of the interconnection network by adopting channel bonding-based MVIA and the interval cache algorithm Video data is distributed to the disks of each server node of the distributed VOD server and each server node receives the data through the interconnection network and sends it to clients. The load of the interconnection network increases because of the large volume of video data transferred. We adopt two techniques to reduce the load of the interconnection network. First, an Msupporting channel bonding technique is adopted for the interconnection network. n which is a user-level communication protocol that reduces the overhead of the TCP/IP protocol in cluster systems, minimizes the time spent in communicating. We increase the bandwidth of the interconnection network using the channel bonding technique with MThe channel bonding technique expands the bandwidth by sending data concurrently through multiple network cards. Second, the interval cache reduces traffic on the interconnection network by caching the video data transferred from the remote disks in main memory Experiments using the distributed VOD server of this paper showed a maximum performance improvement of $30\%$ compared with a distributed VOD server without channel bonding-based MVIA and the interval cache, when used with a four-node PC cluster.

• Journal of the Korean association of regional geographers
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• v.18 no.4
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• pp.388-399
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• 2012

Jinju City has been often called as the largest agglomeration of the silk industry in Korea. However Jinju's silk industry has experienced a continuous decline in employment and to production outputs after the late 1980s. This paper aims to explore innovation capacities of Jinju's silk industry and evaluate the government policy to promote the competitiveness of the silk industry in Jinju. The main findings are as follows. First, the survey shows that the major sources of innovation tend to come from customer firms in the Capital area and the innovation supporting agency and universities in Jinju City. Second, local silk production firms tend to by and large evaluate that the industrial policy projects to promote the competitiveness of Jinju's silk industry have been successful, particularly in terms of a joint branding project and the marketing support program.

• Journal of Digital Convergence
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• v.19 no.3
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• pp.211-216
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• 2021

Information from various heterogeneous devices is steadily increasing in distributed cloud environments. This is because high-speed network speeds and high-capacity multimedia data are being used. However, research is still underway on how to minimize information errors in big data sent and received by heterogeneous devices. In this paper, we propose a deep learning-based asymmetric storage management technique for minimizing bandwidth and data errors in networks generated by information sent and received in cloud environments. The proposed technique applies deep learning techniques to optimize the load balance after asymmetric hash of the big data information generated by each device. The proposed technique is characterized by allowing errors in big data collected from each device, while also ensuring the connectivity of big data by grouping big data into groups of clusters of dogs. In particular, the proposed technique minimizes information errors when storing and managing big data asymmetrically because it used a loss function that extracted similar values between big data as seeds.

• Convergence Security Journal
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• v.23 no.4
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• pp.61-70
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• 2023

Clustering is an unsupervised learning method that involves grouping data based on features such as distance metrics, using data without known labels or ground truth values. This method has the advantage of being applicable to various types of data, including images, text, and audio, without the need for labeling. Traditional clustering techniques involve applying dimensionality reduction methods or extracting specific features to perform clustering. However, with the advancement of deep learning models, research on deep clustering techniques using techniques such as autoencoders and generative adversarial networks, which represent input data as latent vectors, has emerged. In this study, we propose a deep clustering technique based on deep learning. In this approach, we use an autoencoder to transform the input data into latent vectors, and then construct a vector space according to the cluster structure and perform k-means clustering. We conducted experiments using the MNIST and Fashion-MNIST datasets in the PyTorch machine learning library as the experimental environment. The model used is a convolutional neural network-based autoencoder model. The experimental results show an accuracy of 89.42% for MNIST and 56.64% for Fashion-MNIST when k is set to 10.

• Journal of the Korea Convergence Society
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• v.8 no.8
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• pp.9-15
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• 2017

With the development of semiconductor integration technology, central processing units and storage devices have been miniaturized and performance has been rapidly developed, interconnection network technology is becoming a more important factor in terms of the performance of high performance computing system. In this paper, we analyze the trend of interconnection network technology used in high performance computing. Interconnect technology, which is the most widely used in the Supercomputer Top 500(2017. 06.), is an Infiniband. Recently, Ethernet is the second highest share after InfiniBand due to the emergence of 40/100Gbps Gigabit Ethernet technology. Gigabit Ethernet, where latency performance is lower than InfiniBand, is preferred in cost-effective medium-sized data centers. In addition, top-end HPC systems that demand high performance are devoting themselves from Ethernet and InfiniBand technologies and are attempting to maximize system performance by introducing their own interconnect networks. In the future, high-performance interconnects are expected to utilize silicon-based optical communication technology to exchange data with light.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.6
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• pp.152-161
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• 2014

In this paper, a multi-node real-time diagnostic and management system based on zigbee sensor network is proposed, which is to monitor and diagnose multiple nodes as well as to control the data generated from the various multiple sensors collectively. The proposed system is designed to transmit the collected wireless and wired data to the server for monitoring and controling efficiently the condition for multi-nodes by taking the corresponding actions according to the analysis. The system is implemented to make it possible to manage the sensor data by classifying them, of which data are issued from the clustered sources with a number of the remote sensors. In order to evaluate the performance of the proposed system, we measure and analyze both the transmission delay time according to the distance and the data loss rate issued from multiple sensors. The results shows that the proposed system has a good performance.