• Journal of Korean Society of Transportation
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• v.1 no.1
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• pp.28-42
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• 1983

In recent years, significant advances have been made enabling travel demand analysis and network design methods to be used as increasingly realistic evaluation tools. What has been lacking is the integration of travel demand analysis with network design models. This paper reviews some of advanced (integrated) modeling approaches and presents future research directions of integrated modeling system. To design urban transportation networks, it is argued that the travelers' free choice of mode, destination and route should be introduced into transportation network design procedure instead of assuming that trips from a zone to a workplace are fixed or deriving them in a normative procedure to achieve hypothetical system optima.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.1
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• pp.1-8
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• 1992

Hopfield neural network has been applied to the problem of economic load dispatch(ELD) of electric power. The optimum values of neuron potentials are represented in terms of large numbers. The differential synchronous transition mode is used in this simulation. Through case studies, we have shown the possibility of the application of neural network to ELD. In case of including the transmission losses, the proposed method has an advantage that the problem can be solved simply with one neural network, without calculating incremental fuel costs and incremental losses required by traditional method.

• ETRI Journal
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• v.28 no.4
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• pp.513-516
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• 2006

Experimental demonstration of bipolar spectral encoding code-division multiple-access with modified pseudorandom noise codes is presented. Bipolar spectral encoding is achieved with an erbium-doped fiber amplifier amplified spontaneous emission source and arrayed waveguide gratings. The bit-error rate performance of 1.25 Gbps signal transmission over 80 km single mode fiber is measured in a multiple-user environment.

• 한국정보통신설비학회:학술대회논문집
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• 2009.08a
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• pp.115-116
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• 2009

Home-network gateway always powered on, while network data is not transferred. And the Power consumption of networked electric devices are greatly increased, to support higher speed data transfer. To reduce the power consumption of networked electric devices, some ideas were proposed. Such as ALR, Proxying, Subset-PHY. In this paper we propose a scheme to reduce power consumption of Home-Network Gateway. Proposed Home-network Gateway architecture consumes less than 1W in passive standby mode.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.1
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• pp.11-14
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• 2015

WSN (Wireless Sensor Network) based on low-power is one of the core technologies in the ubiquitous society. In this paper, we present a border surveillance and control system in WSN environment. The system consists of static sensor node, mobile sensor node, static gateway, mobile gateway, server and mobile application. Mobile applications are divided into user mode and manager mode. So users monitor border surveillance through mobile phone and get information of border network environment without time and space constraints. In manager mode, for the flexible operation of nodes, manager can update to the software remotely and adjust the position of the mobile node. And also we implement a suitable multi-hop routing protocol for scalable low-power sensor nodes and confirm that the system operates well in WSN environment.

• IE interfaces
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• v.11 no.1
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• pp.145-153
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• 1998

In this paper, two parts of neural networks were proposed for determination of optimal EDM parameters. One is pattern recognition neural network that can be selecting expert neural network suitable to the EDM mode. The other is expert neural network that can be determining optimal EDM parameters such as pulse on time and pulse off time. Prior to determination of EDM parameters, Peak current, which is related to the EDM area closely, determined base on EDM area that is calculated from CAD data, firstly. Then, the other EDM parameters determined by the expert neural network that is selected to the EDM mode.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.93-107
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• 2014

In this paper, we report what we have observed with regard to user-perspective issue clustering based on multi-layered two-mode network analysis. This work is significant in the context of data collection by companies about customer needs. Most companies have failed to uncover such needs for products or services properly in terms of demographic data such as age, income levels, and purchase history. Because of excessive reliance on limited internal data, most recommendation systems do not provide decision makers with appropriate business information for current business circumstances. However, part of the problem is the increasing regulation of personal data gathering and privacy. This makes demographic or transaction data collection more difficult, and is a significant hurdle for traditional recommendation approaches because these systems demand a great deal of personal data or transaction logs. Our motivation for presenting this paper to academia is our strong belief, and evidence, that most customers' requirements for products can be effectively and efficiently analyzed from unstructured textual data such as Internet news text. In order to derive users' requirements from textual data obtained online, the proposed approach in this paper attempts to construct double two-mode networks, such as a user-news network and news-issue network, and to integrate these into one quasi-network as the input for issue clustering. One of the contributions of this research is the development of a methodology utilizing enormous amounts of unstructured textual data for user-oriented issue clustering by leveraging existing text mining and social network analysis. In order to build multi-layered two-mode networks of news logs, we need some tools such as text mining and topic analysis. We used not only SAS Enterprise Miner 12.1, which provides a text miner module and cluster module for textual data analysis, but also NetMiner 4 for network visualization and analysis. Our approach for user-perspective issue clustering is composed of six main phases: crawling, topic analysis, access pattern analysis, network merging, network conversion, and clustering. In the first phase, we collect visit logs for news sites by crawler. After gathering unstructured news article data, the topic analysis phase extracts issues from each news article in order to build an article-news network. For simplicity, 100 topics are extracted from 13,652 articles. In the third phase, a user-article network is constructed with access patterns derived from web transaction logs. The double two-mode networks are then merged into a quasi-network of user-issue. Finally, in the user-oriented issue-clustering phase, we classify issues through structural equivalence, and compare these with the clustering results from statistical tools and network analysis. An experiment with a large dataset was performed to build a multi-layer two-mode network. After that, we compared the results of issue clustering from SAS with that of network analysis. The experimental dataset was from a web site ranking site, and the biggest portal site in Korea. The sample dataset contains 150 million transaction logs and 13,652 news articles of 5,000 panels over one year. User-article and article-issue networks are constructed and merged into a user-issue quasi-network using Netminer. Our issue-clustering results applied the Partitioning Around Medoids (PAM) algorithm and Multidimensional Scaling (MDS), and are consistent with the results from SAS clustering. In spite of extensive efforts to provide user information with recommendation systems, most projects are successful only when companies have sufficient data about users and transactions. Our proposed methodology, user-perspective issue clustering, can provide practical support to decision-making in companies because it enhances user-related data from unstructured textual data. To overcome the problem of insufficient data from traditional approaches, our methodology infers customers' real interests by utilizing web transaction logs. In addition, we suggest topic analysis and issue clustering as a practical means of issue identification.

• Journal of Information Processing Systems
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• v.11 no.1
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• pp.39-56
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• 2015

Continuous multi-interval prediction (CMIP) is used to continuously predict the trend of a data stream based on various intervals simultaneously. The continuous integrated hierarchical temporal memory (CIHTM) network performs well in CMIP. However, it is not suitable for CMIP in real-time mode, especially when the number of prediction intervals is increased. In this paper, we propose a real-time integrated hierarchical temporal memory (RIHTM) network by introducing a new type of node, which is called a Zeta1FirstSpecializedQueueNode (ZFSQNode), for the real-time continuous multi-interval prediction (RCMIP) of data streams. The ZFSQNode is constructed by using a specialized circular queue (sQUEUE) together with the modules of original hierarchical temporal memory (HTM) nodes. By using a simple structure and the easy operation characteristics of the sQUEUE, entire prediction operations are integrated in the ZFSQNode. In particular, we employed only one ZFSQNode in each level of the RIHTM network during the prediction stage to generate different intervals of prediction results. The RIHTM network efficiently reduces the response time. Our performance evaluation showed that the RIHTM was satisfied to continuously predict the trend of data streams with multi-intervals in the real-time mode.

• Journal of Sensor Science and Technology
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• v.22 no.1
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• pp.18-27
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• 2013

Real-time monitoring is one of the prime necessities in a weapon flight test that is required for the efficient and timely collection of large amounts of high-rate sampled data acquired by an event-trigger. The wireless sensor network is a good candidate to resolve this requirement, especially considering the inhospitable environment of a weapon flight test. In this paper, we propose a priority based multi-channel MAC protocol with CSMA/CA over a single radio for a real-time monitoring of a weapon flight test. Multi-channel transmissions of nodes can improve the network performance in wireless sensor networks. Our proposed MAC protocol has two operation modes: Normal mode and Priority Mode. In the normal mode, the node exploits the normal CSMA/CA mechanism. In the priority mode, the node has one of three grades - Class A, B, and C. The node uses a different CSMA/CA mechanism according to its grade that is determined by a signal level. High grade nodes can exploit more channels and lower backoff exponents than low ones, which allow high grade nodes to obtain more transmission opportunities. In addition, it can guarantee successful transmission of important data generated by high grade nodes. Simulation results show that the proposed MAC exhibits excellent performance in an event-triggered real-time application.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.48 no.1
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• pp.7-13
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• 2011

A mixed-mode QPSK demodulator for 60-GHz wireless personal area network application is demonstrated. In this work, mixed-mode QPKS demodulation scheme achieving low power consumption and small area is employed. The prototype chip realized by 60-nm CMOS Logic process can demodulate up to 4.8-Gb/s QPSK signals at 4.8-GHz carrier frequency. At this carrier frequency, the demodulator core consumes 54 mW from 1.2-V power supply while the chip area is $150{\times}150{\mu}m^2$. Using the fabricated chip, transmission and demodulation of 1.7-GSymbol/s QPSK signal in 60-GHz link is demonstrated.