• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.2
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• pp.305-314
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• 2017

Most of the existing researches on systemwide optimization of generator maintenance scheduling do not consider the equivalent operating hours(EOHs) mainly due to the difficulties of calculating the EOHs of the CCGTs in the large scale system. In order to estimate the EOHs not only the operating hours but also the number of start-up/shutdown during the planning period should be estimated, which requires the mathematical model to incorporate the economic dispatch model and unit commitment model. The model is inherently modelled as a large scale mixed-integer nonlinear programming problem and the computation time increases exponentially and intractable as the system size grows. To make the problem tractable, this paper proposes an EOH calculation based on demand grouping by K-means clustering algorithm. Network congestion is also considered in order to improve the accuracy of EOH calculation. This proposed method is applied to the actual Korean electricity market and compared to other existing methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.6
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• pp.1849-1876
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• 2022

Wireless Sensor Network (WSN) is considered as an integral part of the Internet of Things (IoT) for collecting real-time data from the site having many applications in industry 4.0 and smart cities. The task of nodes is to sense the environment and send the relevant information over the internet. Though this task seems very straightforward but it is vulnerable to certain issues like energy consumption, delay, throughput, etc. To efficiently address these issues, this work develops a cross-layer model for the optimization between MAC and the Network layer of the OSI model for WSN. A high value of duty cycle for nodes is selected to control the delay and further enhances data transmission reliability. A node measurement prediction system based on the Kalman filter has been introduced, which uses the constraint based on covariance value to decide the scheduling scheme of the nodes. The concept of duty cycle for node scheduling is employed with a greedy data forwarding scheme. The proposed Duty Cycle-based Greedy Routing (DCGR) scheme aims to minimize the hop count, thereby mitigating the energy consumption rate. The proposed algorithm is tested using a real-world wastewater treatment dataset. The proposed method marks an 87.5% increase in the energy efficiency and reduction in the network latency by 61% when validated with other similar pre-existing schemes.

• Journal of Korea Multimedia Society
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• v.11 no.12
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• pp.1716-1721
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• 2008

Currently, Wireless Networks have some nice characteristics such as multi-hop, multi-channel, multi-radio, etc but these kinds of resources are not fully used. The most difficulty to solve this issue is to solve mixed integer optimization. This paper proposes a method to solve a class of mixed integer optimization for wireless networks by using AMPL modeling language with CPLEX solver. The result of method is scheduling and congestion control in multi-channel multi-radio wireless networks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.11
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• pp.4016-4027
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• 2014

Understanding network latency is important for providing consistent and acceptable levels of services in network-based applications. However, due to the difficulty of estimating applications' network demands and the difficulty of network latency modeling the management of network resources has often been ignored. We expect that, since network latency repeats cycles of congested states, a systematic classification method for network status would be helpful to simplify issues in network resource managements. This paper presents a simple empirical method to classify network status with a real operational network. By observing oscillating behavior of end-to-end latency we determine networks' status in run time. Five typical network statuses are defined based on a long-term stability and a short-term burstiness. By investigating prediction accuracies of several simple numerical models we show the effectiveness of the network status classification. Experimental results show that around 80% reduction in prediction errors depending on network status.

• Journal of the Korea Society of Computer and Information
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• v.29 no.9
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• pp.169-177
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• 2024

Industrial Wireless Sensor Network (IWSN) is a key feature of Industrial IoT that enables industrial automation through process monitoring and control by connecting industrial equipment such as sensors, robots, and machines wirelessly, and must support the strict requirements of modern industrial environments such as real-time, reliability, and energy efficiency. To achieve these goals, IWSN uses reliable communication methods such as multipath routing, fixed redundant resource allocation, and non-contention-based scheduling. However, the issue of wasting redundant resources that are not utilized for communication degrades not only the efficiency of limited radio resources but also the energy efficiency. In this paper, we propose a scheme that utilizes reinforcement learning in communication scheduling to periodically identify unused wireless resources and reallocate them to save energy consumption of the entire industrial network. The experimental performance evaluation shows that the proposed approach achieves about 30% improvement of resource efficiency in scheduling compared to the existing method while supporting high reliability. In addition, the energy efficiency and latency are improbed by more than 21% and 38%, respectively, by reducing unnecessary communication.

• ETRI Journal
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• v.25 no.6
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• pp.475-488
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• 2003

In this paper, we propose an efficient and simple fair queuing algorithm, called new starting potential fair queuing (NSPFQ), which has O(1) complexity for virtual time computation and also has good delay and fairness properties. NSPFQ introduces a simpler virtual time recalibration method as it follows a rate-proportional property. The NSPFQ algorithm recalibrates the system virtual time to the minimum virtual start time among all possible virtual start times for head-of-line packets in backlogged sessions. Through analysis and simulation, we show that the proposed algorithm has good delay and fairness properties. We also propose a hardware implementation framework for the scheduling algorithm.

• IE interfaces
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• v.11 no.1
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• pp.75-84
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• 1998

The important issue for intra-city vehicle scheduling is to measure and store actual vehicle travel speeds between customer locations. Travel speeds(and times) in nearly all metropolitan areas change drastically during the day because of congestion in certain parts of the city road network. We propose three models for estimating departure time-dependent travel speeds between locations that relieve much burden for the data collection and computer storage requirements. Two of the three models use a least squares method and the rest one employs a neural network trained with the back-propagation rule. On a real-world study using the travel speed data collected in Seoul, we found out that the neural network model is more accurate than the other two models.

• The Journal of the Korea Contents Association
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• v.10 no.9
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• pp.45-56
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• 2010

This paper reports a new mechanism for congestion controls. The proposed congestion detection algorithm can be provided with delay and unnecessary energy consumption. Conventional congestion control methods decide congestion by queue occupancy or mean packet arrival rate of MAC layer only, however, our method can perform precise detection by considering queue occupancy and mean packet arrival rate. In addition, the congestion avoiding method according to congestion degree and scheduling method using priority for real time packets are proposed. Finally, simulation results show that proposed congestion detection and control methods outperforms conventional scheduling schemes for wireless sensor network.

• The Journal of the Acoustical Society of Korea
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• v.37 no.6
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• pp.512-517
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• 2018

Improving voice quality is a major concern in telecommunications. In this paper, we propose a robust speech quality enhancement against background noise and packet loss at VoIP (Voice-over-IP) receiver. The proposed method combines network jitter estimation based on hybrid Markov chain, adaptive playout scheduling using the estimated jitter, and speech enhancement based on restoration of amplitude and phase to enhance the quality of the speech signal arriving at the VoIP receiver over IP network. The experimental results show that the proposed method removes the background noise added to the speech signal before encoding at the sender side and provides the enhanced speech quality in an unstable network environment.

• ETRI Journal
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• v.35 no.4
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• pp.677-686
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• 2013

In this paper, we first introduce a general architecture of an energy management system in a home area network based on a smart grid. Then, we propose an efficient scheduling method for home power usage. The home gateway (HG) receives the demand response (DR) information indicating the real-time electricity price, which is transferred to an energy management controller (EMC). Referring to the DR, the EMC achieves an optimal power scheduling scheme, which is delivered to each electric appliance by the HG. Accordingly, all appliances in the home operate automatically in the most cost-effective way possible. In our research, to avoid the high peak-to-average ratio (PAR) of power, we combine the real-time pricing model with the inclining block rate model. By adopting this combined pricing model, our proposed power scheduling method effectively reduces both the electricity cost and the PAR, ultimately strengthening the stability of the entire electricity system.