• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1527-1534
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• 2016

Online voltage stability monitoring using real-time measurements is one of the most important tasks in a smart grid system to maintain the grid stability. Loading margin is a good indicator for assessing the voltage stability level. This paper presents an Extreme Learning Machine (ELM) approach for estimation of voltage stability level under credible contingencies using real-time measurements from Phasor Measurement Units (PMUs). PMUs enable a much higher data sampling rate and provide synchronized measurements of real-time phasors of voltages and currents. Depth First (DF) algorithm is used for optimally placing the PMUs. To make the ELM approach applicable for a large scale power system problem, Mutual information (MI)-based feature selection is proposed to achieve the dimensionality reduction. MI-based feature selection reduces the number of network input features which reduces the network training time and improves the generalization capability. Voltage magnitudes and phase angles received from PMUs are fed as inputs to the ELM model. IEEE 30-bus test system is considered for demonstrating the effectiveness of the proposed methodology for estimating the voltage stability level under various loading conditions considering single line contingencies. Simulation results validate the suitability of the technique for fast and accurate online voltage stability assessment using PMU data.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2007.12a
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• pp.255-256
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• 2007

In case of inappropriate stacking position of the container taking in container yard, the working time for the container would be delayed in taking out because of the occurrence of the re-handle and the increase of the crane moving time. We have to take into account a variety of elements like the crane interference, the container group and stacking height in order to determine the optimal stacking position and decide the weight reflecting the importance of these criteria. We propose the dynamic weight adjustment algorithm for the stacking policy criteria employing the online search in this research.

• Journal of the Korean Society of Safety
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• v.33 no.3
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• pp.27-32
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• 2018

Nowadays, to prevent electrical accidents in Korea, inspectors directly performed checking general electrical facilities as a cycle from every one to three years. It is difficult to presuppose an omen because intact conditions of electrical equipments are not kept at the time of inspection. In this paper, in order to ensure effectiveness of an online basis electric inspection, we developed an electrical safety IoT system using LoRa communication technology to enable monitoring mainly electrical safety components such as overcurrent, overvoltage, resistive leakage current, and power. Then we proposed a method for verifying performances of the prosed electrical safety IoT system. Resistive leakage currents are calculated by using difference of phase between voltages and currents. We verified that average errors are 0.97%, which reference goal is ${\pm}5%$ for a device, through reliability test according to conditions. Results of this research can be used as basic study materials to develop technologies for measuring three phase leakage current and for implementing public electrical safety. platform.

• Journal of KIISE:Information Networking
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• v.37 no.3
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• pp.228-233
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• 2010

During wireless network operations, adaptive power control is an effective way to enhance the network performance. In this paper, a new online power control scheme is proposed based on the evolutionary game theory. To converge a desirable network equilibrium, the proposed scheme adaptively adjusts a transmit power level in a distributed online manner. With a simulation study, we demonstrate that the proposed scheme improves network performance under widely diverse network environments.

• Smart Structures and Systems
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• v.21 no.5
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• pp.537-548
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• 2018

A tunnel deformation monitoring system is developed with the use of fiber Bragg grating (FBG) sensing technique, aiming at providing continuous monitoring of railway tunnel deformation in the long term, and early warning for the rail service maintainers and authorities to avoid catastrophic consequences when significant deformation occurs. Specifically, a set of FBG bending gauges with the ability of angle measurement and temperature compensation is designed and manufactured for the purpose of online monitoring of tunnel deformation. An overall profile of lateral tunnel displacement along the longitudinal direction can be obtained by implementing an array of the FBG bending gauges interconnected by rigid rods, in conjunction with a proper algorithm. The devised system is verified in laboratory experiments with a test setup enabling to imitate various patterns of tunnel deformation before the implementation of this system in an in-service high-speed railway (HSR) tunnel.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.9
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• pp.3496-3514
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• 2015

One of the challenging tasks in Mobile Ad hoc Network is to discover precise optimal routing solution due to the infrastructure-less dynamic behavior of wireless mobile nodes. Ant Colony Optimization, a swarm Intelligence technique, inspired by the foraging behaviour of ants in colonies was used in the past research works to compute the optimal path. In this paper, we propose a Recurrent Ant Colony Optimization (RECACO) that executes the actual Ant Colony Optimization iteratively based on recurrent value in order to obtain an optimal path convergence. Each iteration involves three steps: Pheromone tracking, Pheromone renewal and Node selection based on the residual energy in the mobile nodes. The novelty of our approach is the inclusion of new pheromone updating strategy in both online step-by-step pheromone renewal mode and online delayed pheromone renewal mode with the use of newly proposed metric named ELD (Energy Load Delay) based on energy, Load balancing and end-to-end delay metrics to measure the performance. RECACO is implemented using network simulator NS2.34. The implementation results show that the proposed algorithm outperforms the existing algorithms like AODV, ACO, LBE-ARAMA in terms of Energy, Delay, Packet Delivery Ratio and Network life time.

• Journal of Information Technology Services
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• v.16 no.3
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• pp.113-125
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• 2017

Customers have been affected by others' opinions when they make a purchase. Thanks to the development of technologies, people are sharing their experiences such as reviews or ratings through online or social network services, However, although ratings are intuitive information for others, many reviews include only texts without ratings. Also, because of huge amount of reviews, customers and companies can't read all of them so they are hard to evaluate to a product without ratings. Therefore, in this study, we propose a methodology to predict ratings based on reviews for a product. In a methodology, we first estimate the topic-review matrix using the Latent Dirichlet Allocation technic which is widely used in topic modeling. Next, we predict ratings based on the topic-review matrix using the artificial neural network model which is based on the backpropagation algorithm. Through experiments with actual reviews, we find that our methodology can predict ratings based on customers' reviews. And our methodology performs better with reviews which include certain opinions. As a result, our study can be used for customers and companies that want to know exactly a product with ratings. Moreover, we hope that our study leads to the implementation of future studies that combine machine learning and topic modeling.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.5
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• pp.2346-2362
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• 2016

As one of the most important issues in computer vision and image processing, online object tracking plays a key role in numerous areas of research and in many real applications. In this study, we present a novel tracking method based on the proposed structured sparse representation model, in which the tracked object is assumed to be sparsely represented by a set of object and background templates. The contributions of this work are threefold. First, the structure information of all the candidate samples is utilized by a joint sparse representation model, where the representation coefficients of these candidates are promoted to share the same sparse patterns. This representation model can be effectively solved by the simultaneous orthogonal matching pursuit method. In addition, we develop a tracking algorithm based on the proposed representation model, a discriminative candidate selection scheme, and a simple model updating method. Finally, we conduct numerous experiments on several challenging video clips to evaluate the proposed tracker in comparison with various state-of-the-art tracking algorithms. Both qualitative and quantitative evaluations on a number of challenging video clips show that our tracker achieves better performance than the other state-of-the-art methods.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.2
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• pp.232-241
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• 2010

This paper describes the development of robust contingency analysis program for Korean Energy Management System. The important function of contingency analysis is to determine the bus/branch model for contingency, and to calculate the state of the power network based on the network model and topology output. In the proposed method, the bus/branch models for contingencies are determined exactly using a fast linked-list method based on the application common model database. To calculate the state of the power system included contingency, the full-decoupled powerflow approach, the partial powerflow method for contingencies and the proposed contingency screening algorithm are also used to contingency analysis. To verify the performance of the developed processor, we performed a file-based test using several structured input data and online test using the database which resides on memory. The results of these comprehensive tests showed that the developed processors can accurately calculate the power system contingency state from online data and can be applied to Korea Power Exchange system.

• Journal of Electrical Engineering and Technology
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• v.9 no.2
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• pp.550-558
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• 2014

Direct Torque controlled induction motor (DTC-IM) drives use stator resistance of the motor for stator flux estimation. So, stator resistance estimation properly is very important for a stable and effective operation of the induction motor. Stator resistance variations because of changing in temperature make DTC operation difficult mainly at low speed. A method based on artificial neural network (ANN) to estimate the stator resistance online of IM for DTC drive is modeled and verified in this paper. To train the neural network a back propagation algorithm is used. Weight adjustment of neural network is done by back propagating the error signal between measured and estimated stator current. An extensive simulation has been carried out in MATLAB/SIMULINK to prove the efficacy of the proposed stator resistance estimator. The simulation & experimental result reveals that proposed method is able to obtain precise torque and flux control at low speed.