• Magazine of the Korean Society of Agricultural Engineers
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• v.43 no.4
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• pp.69-78
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• 2001

In this study optimal analysis of pipe network was performed using linear and non linear analysis method for complex real pipe network system of Mungyeong water purification field system which consists of 70 nodes and 86 elements. From the examination result of total flow which is distributed to each pipe, it is found that KYPIPE2 Model supplies less amount than NLAM. It is known that dynamic water level and pressure head of KYPIPE2 Model and NLAM are nearly in accordance with each other from each method of the pipe network analyses, and appeared that both methods of analysis shows high reliable result since the distribution of dynamic water level for every node is the short range of EL. 205.0m~EL. 210.0m besides the pressed dynamic water level. The analysis results of pressure in the methods of pipe network analysis for KYPIPE2 Model and NLAM are similar and it is satisfactory result that the pressure distributions of the tab water design criterion of 5.0kgf/cm$^2$ besides the small part of highland.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.10
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• pp.4117-4135
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• 2020

Due to the angle of view, illumination and scene diversity, real-time dynamic face detection and recognition is no small difficulty in those unrestricted environments. In this study, we used the intrinsic correlation between detection and calibration, using a multi-task cascaded convolutional neural network(MTCNN) to improve the efficiency of face recognition, and the output of each core network is mapped in parallel to a compact Euclidean space, where distance represents the similarity of facial features, so that the target face can be identified as quickly as possible, without waiting for all network iteration calculations to complete the recognition results. And after the angle of the target face and the illumination change, the correlation between the recognition results can be well obtained. In the actual application scenario, we use a multi-camera real-time monitoring system to perform face matching and recognition using successive frames acquired from different angles. The effectiveness of the method was verified by several real-time monitoring experiments, and good results were obtained.

• Structural Engineering and Mechanics
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• v.12 no.5
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• pp.527-540
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• 2001

A new sort of learning algorithm named whole learning algorithm is proposed to simulate the nonlinear and dynamic behavior of RC members for the estimation of structural integrity. A mathematical technique to solve the multi-objective optimization problem is applied for the learning of the feedforward neural network, which is formulated so as to minimize the Euclidean norm of the error vector defined as the difference between the outputs and the target values for all the learning data sets. The change of the outputs is approximated in the first-order with respect to the amount of weight modification of the network. The governing equation for weight modification to make the error vector null is constituted with the consideration of the approximated outputs for all the learning data sets. The solution is neatly determined by means of the Moore-Penrose generalized inverse after summarization of the governing equation into the linear simultaneous equations with a rectangular matrix of coefficients. The learning efficiency of the proposed algorithm from the viewpoint of computational cost is verified in three types of problems to learn the truth table for exclusive or, the stress-strain relationship described by the Ramberg-Osgood model and the nonlinear and dynamic behavior of RC members observed under an earthquake.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2369-2373
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• 2001

Usually the static scheduling algorithms such as DMS (Deadline Monotonic Scheduling) or RMS(Rate Monotonic Scheduling) are used for CAN scheduling due to its ease with implementation. However, due to their inherently low utilization of network media, some dynamic scheduling approaches have been studied to enhance the utilization. In case of dynamic scheduling algorithms, two considerations are needed. The one is a priority inversion due to rough deadline encoding into stricted arbitration fields of CAN. The other is an arbitration delay due to the non-preemptive feature of CAN. In this paper, an extended algorithm is proposed from an existing EDS(Earliest Deadline Scheduling) approach of CAN scheduling algorithm haying a solution to the priority inversion. In the proposed algorithm, the available bandwidth of network media can be checked dynamically by all nodes. Through the algorithm, arbitration delay causing the miss of their deadline can be avoided in advance. Also non real-time messages can be processed with their bandwidth allocation. The proposed algorithm can achieve full network utilization and enhance aperiodic responsiveness, still guaranteeing the transmission of periodic messages.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.7B
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• pp.620-627
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• 2003

Ethernet passive optical networks (EPONs) are an emerging access network technology that provide a low-cost method of deploying optical access lines between a carrier's central office and customer sites. Dynamic bandwidth allocation (DBA) provides statistical multiplexing between the optical network units for efficient upstream channel utilization. To support dynamic bandwidth distribution, 1 propose an cyclic polling-based DBA algorithm for differentiated classes of service in EPONs. And, I show that an interleaved polling scheme severely decreases downstream channel capacity for user traffics when the upstream network load is low. To obtain realistic simulation results, I used synthetic traffic that exhibits the properties of self-similarity and long-range dependence I then analyzed the network performance under various loads, specifically focusing on packet delays for different classes of traffic.

• The KIPS Transactions:PartC
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• v.9C no.6
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• pp.841-846
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• 2002

We applies a seasonal ARIMA model to the timely forecasting in a line utilization and its confidence interval on the base of the past data of the line utilization that QoS of the network is greatly influenced by. And this paper proposes the learning algorithm of dynamic threshold in line utilization using the SARIMA model. We can find the proper dynamic threshold in timely line utilization on the various network environments and provide the confidence based on probability. Also, we have evaluated the validity of the proposed model and estimated the value of a proper threshold on real network. Network manager can overcome a shortcoming of original threshold method and maximize the performance of this algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.6
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• pp.1128-1133
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• 2008

Context-aware computing has been attracting the attention as an approach to alleviating the inconvenience in human-computer interactions. This paper proposes a context-aware system architecture to be implemented on an UoC (Ubiquitous system on Chip). A new proposed technology of CRS (Context Recognition Switch) and DOS (Dynamic and Optimal Standard) based on Context-awareness system architecture with pre-processor, HPSP(High Performance Signal Processor) in this paper. And proposed a new algorithm using in network topology processor shows for Ubiquitous Computing System. implementing in UoC (Ubiquitous System on Chip) base on the IEEE 802.15.4 WPAN (Wireless Personal Area Network) standard. Also, This context-aware based UoC architecture has been developed to apply to mobile intelligent robots which would support human in a context-aware manner.

• Journal of Distribution Science
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• v.14 no.4
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• pp.47-53
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• 2016

Purpose - The objective of this study is to investigate the dynamic relationships among Advertising Cost (AD), Newly Registered Users(NRU), and Buying Users(BU) of Social Network Game(SNG). SNG is getting pervasive mainly due to the rapid growth of mobile game and Social Network Service(SNS). It would be helpful for marketing researchers interested in SNG and related practitioners to understand the changes in AD, NRU, and BU with time as well as the effects on one another in mutual and dynamic way. Research Design, Data, and Methodology - Necessary data were collected from Social Network Game(SNG) company. AD, NRU, and BU are endogenous variables, but new event such as launching (event) and holidays(holiday) are exogenous dummy variables. Vector Auto regression (VAR) model is generally used to examine and capture the dynamic relationships among endogenous variables. VAR model can easily capture dynamic and endogenous relationships among time-series variables. Vector Auto regression with Exogenous variables(VARX) is a model in which exogenous variables are added to VAR. To investigate this study, VARX is applied. Result - By estimating the VARX model, the author finds that the past periods' NRU affect negatively and significantly the present AD, and past periods' BU have a positive and significant impact on the increase of AD. In addition, the author shows that the past periods' AD and BU have a positive and significant effect on the increase of NRU, and the past periods' AD affect positively and significantly BU. While the impact of AD on NRU happens after 3 or 4 days (carryover effect), that of AD on BU comes about within just 1 or 2 days (immediate effect). The effect of BU on NRU can be considered as word of mouth (WOM effect). Therefore, SNG companies can obtain not only the growth of revenue but also the increase of NRU by increasing BU. Through those results, the author can also find that there are significant interactions between endogenous variables. Conclusion - This study intends to investigate endogenous and dynamic relationships between AD, NRU, and BU. They also give managerial implications to practitioners for SNS and SNG firms. Through this study, it is found that there exist significant interactions and dynamic relationships between those three endogenous variables. The results of this study can have meaningful implications for practitioners and researchers of SNG. This research is unique in that it deals with "actual" field data and intend to find "actual" relationships among variables unlike other related existing studies which intend to investigate psychological factors affecting the intention of game usage and the intention of purchasing game items. This study is also meaningful by showing that the increase of BU can be a good strategy for "killing birds with one stone" (i.e., revenue growth and NRU increase). Although there are some limitations related with future research topics, this research contributes to the current research on SNG marketing in the above mentioned ways.

• Journal of Korean Society of Transportation
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• v.22 no.5
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• pp.123-137
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• 2004

The study on traffic assignment is actively being performed which reflect networks status using time. Its background is increasing social needs to use traffic assignment models in not only hardware area of road network plan but also software area of traffic management or control. In addition, multi-class traffic assignment model is receiving study in order to fill a gap between theory and practice of traffic assignment model. This model is made up of two, one of which is multi-driver class and the other multi-vehicle class. The latter is the more realistic because it can be combined with dynamic model. On this background, this study is to build multidynamic model combining the above-mentioned two areas. This has been a theoretic pillar of ITS in which dynamic user equilibrium assignment model is now made an issue, therefore more realistic dynamic model is expected to be built by combining it with multi-class model. In case of multi-vehicle, FIFO would be violated which is necessary to build the dynamic assignment model. This means that it is impossible to build multi-vehicle dynamic model with the existing dynamic assignment modelling method built under the conditions of FIFO. This study builds dynamic network model which could relieve the FIFO conditions. At the same time, simulation method, one of the existing network loading method, is modified to be applied to this study. Also, as a solution(algorithm) area, time dependent shortest path algorithm which has been modified from existing shortest path algorithm and the existing MSA modified algorithm are built. The convergence of the algorithm is examined which is built by calculating dynamic user equilibrium solution adopting the model and algorithm and grid network.

• Journal of Distribution Science
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• v.17 no.3
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• pp.85-95
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• 2019

Purpose - The existing marketing studies using Social Network Analysis have assumed that network structure variables are time-invariant. However, a node's network position can fluctuate considerably over time and the node's network structure can be changed dynamically. Hence, if such a dynamic structural network characteristics are not specified for virtual goods purchase model, estimated parameters can be biased. In this paper, by comparing a time-invariant network structure specification model(base model) and time-varying network specification model(proposed model), the authors intend to prove whether the proposed model is superior to the base model. In addition, the authors also intend to investigate whether coefficients of network structure variables are random over time. Research design, data, and methodology - The data of this study are obtained from a Korean social network provider. The authors construct a monthly panel data by calculating the raw data. To fit the panel data, the authors derive random effects panel tobit model and multi-level mixed effects model. Results - First, the proposed model is better than that of the base model in terms of performance. Second, except for constraint, multi-level mixed effects models with random coefficient of every network structure variable(in-degree, out-degree, in-closeness centrality, out-closeness centrality, clustering coefficient) perform better than not random coefficient specification model. Conclusion - The size and importance of virtual goods market has been dramatically increasing. Notwithstanding such a strategic importance of virtual goods, there is little research on social influential factors which impact the intention of virtual good purchase. Even studies which investigated social influence factors have assumed that social network structure variables are time-invariant. However, the authors show that network structure variables are time-variant and coefficients of network structure variables are random over time. Thus, virtual goods purchase model with dynamic network structure variables performs better than that with static network structure model. Hence, if marketing practitioners intend to use social influences to sell virtual goods in social media, they had better consider time-varying social influences of network members. In addition, this study can be also differentiated from other related researches using survey data in that this study deals with actual field data.