• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.146-146
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• 2021

A contamination event occurring in water distribution networks (WDNs) needs to be handled with the appropriate mitigation strategy to protect public health safety and ensure water supply service continuation. Typically the mitigation phase consists of contaminant sensing, public warning, network inspection, and recovery. After the contaminant source has been detected and treated, contaminants still exist in the network, and the contaminated water should be flushed out. The recovery period is critical to remove any lingering contaminant in a rapid and non-detrimental manner. The contaminant flushing can be done in several ways. Conventionally, the opening of hydrants is applied to drain the contaminant out of the system. Relying on advanced information and communication technology (ICT) on WDN management, warning and information can be distributed fast through electronic media. Water utilities can inform their customers to participate in the contaminant flushing by opening and closing their house faucets to drain the contaminated water. The household draining strategy consists of determining sectors and timeslots of the WDN users based on hydraulic simulation. The number of sectors should be controlled to maintain sufficient pressure for faucet draining. The draining timeslot is determined through hydraulic simulation to identify the draining time required for each sector. The effectiveness of the strategy is evaluated using three measurements, such as Wasted Water (WW), Flushing Duration (FD), and Pipe Erosion (PE). The optimal draining strategy (i.e., group and timeslot allocation) in the WDN can be determined by minimizing the measures.

• The Journal of Korean Association of Computer Education
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• v.19 no.1
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• pp.111-118
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• 2016

Measuring similarity in collaborative filtering-based recommender systems greatly affects system performance. This is because items are recommended from other similar users. In order to overcome the biggest problem of traditional similarity measures, i.e., data sparsity problem, this study suggests a new similarity measure that is the optimal combination of previous similarity and the value reflecting the number of co-rated items. We conducted experiments with various conditions to evaluate performance of the proposed measure. As a result, the proposed measure yielded much better performance than previous ones in terms of prediction qualities, specifically the maximum of about 7% improvement over the traditional Pearson correlation and about 4% over the cosine similarity.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.6A
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• pp.643-651
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• 2008

In this paper, we analyze the performance of normalized SNR based proportional fair scheduling with partial feedback information for multiuser MIMO-OFDMA systems. The closed form expression on the downlink capacity of the selective partial CQI feedback scheme is derived and its asymptotic behavior is investigated. From the performance analysis and numerical results, it is found that the optimal growth rate of downlink capacity can be achieved with bounded average feedback overhead irrespective of the number of users.

• Proceedings of the KSR Conference
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• 1998.05a
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• pp.23-30
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• 1998

The construction of the transportation history database system is to serve the scheduling and seat inventory controling. Recently, lots of countries have been faced with the advance era because of the new railway transportation system, like the high speed railway and/or magnetic levitation vehicle system. This can be reasonably translated as those of operators are willing to provide the more various and high quality schedule to the customer. Those operators' these ideas make possible to forecast that scheduling process is going to be complicated more and more The seat inventory control, so to speak Yield Management System(YMS), goes a long way to improve the total passenger revenue at the railway business. The YMS forecasts the number of the last reservation value(DCP# END) and recommends the optimal values on the seat sales. The history database system contains infra-data(ie, train, seat, sales) that will be the foundation of scheduling and seat inventory control application programs. The development of the application programs are reserved to the next step. The database system is installed on the pc platform(IBM compatible), using the DB2(RDBMS). And at next step, the platform and DBMS will be considered whether they can meet the users' requirement or not.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.57-60
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• 2015

Recently, a multi-user massive MIMO (MU-Massive MIMO) network has been attracting tremendous interest as one of technologies to accommodate explosively increasing mobile data traffic. The MU-Massive MIMO network can significantly enhance the network capacity because a base station (BS) equipped with large-scale transmit antennas can transmit high-rate data to multiple users simultaneously. In the MU-Massive MIMO network, transmit antenna selection schemes are generally used to decrease the computational complexity and cost of the BS. In this paper, we investigate the energy efficiency of the transmit antenna selection scheme in the MU-Massive MIMO network and the optimal number of selected transmit antennas for maximizing the energy efficiency.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.4
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• pp.1568-1589
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• 2021

Deployment of access point (AP) is a problem that must be considered in network planning. However, this problem is usually a NP-hard problem which is difficult to directly reach optimal solution. Thus, improved AP deployment optimization scheme based on swarm intelligence algorithm is proposed to research on this problem. First, the scheme estimates the number of APs. Second, the multi-objective particle swarm optimization (MOPSO) algorithm is used to optimize the location and transmit power of APs. Finally, the greedy algorithm is used to remove the redundant APs. Comparing with multi-objective whale swarm optimization algorithm (MOWOA), particle swarm optimization (PSO) and grey wolf optimization (GWO), the proposed deployment scheme can reduce AP's transmit power and improves energy efficiency under different numbers of users. From the experimental results, the proposed deployment scheme can reduce transmit power about 2%-7% and increase energy efficiency about 2%-25%, comparing with MOWOA. In addition, the proposed deployment scheme can reduce transmit power at most 50% and increase energy efficiency at most 200%, comparing with PSO and GWO.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.48 no.4
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• pp.69-76
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• 2011

In this paper, we present the analytic model to quantify the system capacity with delay Quality of Service (QoS) constraints, and analyze the effect of user-multiplexing on the delay QoS performance in multiuser low-power wireless communication systems. For this purpose, we define the degree of multiplexing as the number of scheduled users to be served in a frame, and investigate the effect of degree of multiplexing (DoM) on the trade-off of throughput and delay QoS constraints. Through this analysis, we characterize the optimal DoM maximizing the energy efficiency in low-power communication environments. Finally, through the simulation results, we verify that our approach with its optimal DoM yields substantial capacity gain.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1325-1344
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• 2019

Recently, to satisfy mobile users' increasing data transmission requirement, energy efficiency (EE) resource allocation in distributed antenna systems (DASs) has become a hot topic. In this paper, we aim to maximize EE in DASs subject to constraints of the minimum data rate requirement and the maximum transmission power of distributed antenna units (DAUs) with different density distributions. Virtual cell is defined as DAUs selected by the same user equipment (UE) and the size of virtual cells is dependent on the number of subcarriers and the transmission power. Specifically, the selection rule of DAUs is depended on different scenarios. We develop two scenarios based on the density of DAUs, namely, the sparse scenario and the dense scenario. In the sparse scenario, each DAU can only be selected by one UE to avoid co-channel interference. In order to make the original non-convex optimization problem tractable, we transform it into an equivalent fractional programming and solve by the following two sub-problems: optimal subcarrier allocation to find suitable DAUs; optimal power allocation for each subcarrier. Moreover, in the dense scenario, we consider UEs could access the same channel and generate co-channel interference. The optimization problem could be transformed into a convex form based on interference upper bound and fractional programming. In addition, an energy-efficient DAU selection scheme based on the large scale fading is developed to maximize EE. Finally, simulation results demonstrate the effectiveness of the proposed algorithm for both sparse and dense scenarios.

• Journal of Information Technology Services
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• v.7 no.2
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• pp.1-12
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• 2008

The development of IT and explosively growing number of Internet users are rapidly spreading and developing e-commerce, while creating diverse on-line transaction methods such as a negotiation, a reverse auction, and a bid. Among these transaction methods, the transactions by means of a negotiation are being made for goods that have no posted price. In particular, the transactions by means of a negotiation are expected to be widely used in the B2B. In order to determine their transportation costs, shippers usually make negotiations with many transporters and logistics companies. And before long, these negotiations are expected to be made on an on-line automated negotiation system. Because of this, this study has tried to develop an automated negotiation methodology that is absolutely necessary for an on-line automated negotiation. This study has estimated and selected the evaluation functions for multi-lateral negotiators' proposals, thus developing an automated negotiation methodology. As a result of this study, a new direction for an automated negotiation has been suggested. Also we expect that this study will be widely used in the automated negotiation of diverse fields.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.334-349
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• 2022

The effectiveness of Wi-Fi networks is greatly influenced by the optimization of contention window (CW) parameters. Unfortunately, the conventional approach employed by IEEE 802.11 wireless networks is not scalable enough to sustain consistent performance for the increasing number of stations. Yet, it is still the default when accessing channels for single-users of 802.11 transmissions. Recently, there has been a spike in attempts to enhance network performance using a machine learning (ML) technique known as reinforcement learning (RL). Its advantage is interacting with the surrounding environment and making decisions based on its own experience. Deep RL (DRL) uses deep neural networks (DNN) to deal with more complex environments (such as continuous state spaces or actions spaces) and to get optimum rewards. As a result, we present a new approach of CW control mechanism, which is termed as contention window threshold (CW_Threshold). It uses the DRL principle to define the threshold value and learn optimal settings under various network scenarios. We demonstrate our proposed method, known as a smart exponential-threshold-linear backoff algorithm with a deep Q-learning network (SETL-DQN). The simulation results show that our proposed SETL-DQN algorithm can effectively improve the throughput and reduce the collision rates.