• Journal of Institute of Control, Robotics and Systems
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• v.5 no.5
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• pp.550-557
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• 1999

This paper describes a road-following controller using the proposed neural network for autonomous vehicle. Road-following with visual sensor like camera requires intelligent control algorithm because analysis of relation from road image to steering control is complex. The proposed neural network, relative similarity modular network(RSMN), is composed of some learning networks and a partitioniing network. The partitioning network divides input space into multiple sections by similarity of input data. Because divided section has simlar input patterns, RSMN can learn nonlinear relation such as road-following with visual control easily. Visual control uses two criteria on road image from camera; one is position of vanishing point of road, the other is slope of vanishing line of road. The controller using neural network has input of two criteria and output of steering angle. To confirm performance of the proposed neural network controller, a software is developed to simulate vehicle dynamics, camera image generation, visual control, and road-following. Also, prototype autonomous electric vehicle is developed, and usefulness of the controller is verified by physical driving test.

• Computers and Concrete
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• v.20 no.1
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• pp.111-118
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• 2017

The present study established prediction models based on multiple nonlinear regressions (MNLRs) and backpropagation neural networks (BPNs) for the expansion of cement mortar caused by oxidization slag that was used as a replacement of the aggregate. The data used for the models were obtained from actual laboratory tests on specimens that were produced with water/cement ratios of 0.485 or 1.5, within which 0%, 10%, 20%, 30%, 40%, or 50% of the cement had been replaced by oxidization slag from electric-arc furnaces; the samples underwent high-temperature curing at either $80^{\circ}C$ or $100^{\circ}C$ for 1-4 days. The varied mixing ratios, curing conditions, and water/cement ratios were all used as input parameters for the expansion prediction models, which were subsequently evaluated based on their performance levels. Models of both the MNLR and BPN groups exhibited $R^2$ values greater than 0.8, indicating the effectiveness of both models. However, the BPN models were found to be the most accurate models.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.9B
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• pp.1407-1415
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• 2010

In recent years, the smart grid technique for maximizing the energy efficiency of power networks has received a great deal of attentions. In particular, the Demand Response is a core technology differentiated from the present power network under the smart grid paradigm. To minimize the electric cost and maximize users' satisfaction, this paper proposes a unique scheduling algorithm derived by using optimization where the characteristics of various home appliances are taken into account. For this goal, we represent mathematical consumption patterns of the electric loads and propose the optimal scheduling scheme based on the importance factor of each device during one day. In the simulation results, we demonstrate the effectiveness of the proposed algorithm in the viewpoint of the minimal electric costs utilizing real statistical figures.

• Computers and Concrete
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• v.16 no.3
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• pp.343-356
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• 2015

1078 sets of mixtures in total that include fly ash, slag, and/or silica fume have been collected for prediction on concrete properties. A new database platform (Compos) has been developed, by which the stepwise multiple linear regression (SMLR) and BP artificial neural networks (BP ANNs) programs have been applied respectively to identify correlations between the concrete properties (strength, workability, and durability) and the dosage and/or quality of raw materials'. The results showed obvious nonlinear relations so that forecasting by using nonlinear method has clearly higher accuracy than using linear method. The forecasting accuracy rises along with the increasing of age and the prediction on cubic compressive strength have the best results, because the minimum average relative error (MARE) for 60-day cubic compressive strength was less than 8%. The precision for forecasting of concrete workability takes the second place in which the MARE is less than 15%. Forecasting on concrete durability has the lowest accuracy as its MARE has even reached 30%. These conclusions have been certified in a ready-mixed concrete plant that the synthesized MARE of 7-day/28-day strength and initial slump is less than 8%. The parameters of BP ANNs and its conformation have been discussed as well in this study.

• Journal of Communications and Networks
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• v.6 no.1
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• pp.19-25
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• 2004

In this paper, we present a trellis-based blind channel estimation and equalization technique coupling two kinds of adaptive Viterbi algorithms. First, the initial blind channel estimation is accomplished by incorporating the list parallel Viterbi algorithm with the least mean square (LMS) updating approach. In this operation, multiple trellis mappings are preserved simultaneously and ranked in terms of path metrics. Equivalently, multiple channel estimates are maintained and updated once a single symbol is received. Second, the best channel estimate from the above operation will be adopted to set up the whole trellis. The conventional adaptive Viterbi algorithm is then applied to detect the signal and further update the channel estimate alternately. A small delay is introduced for the symbol detection and the decision feedback to smooth the noise impact. An automatic switch between the above two operations is also proposed by exploiting the evolution of path metrics and the linear constraint inherent in the trellis mapping. Simulation has shown an overall excellent performance of the proposed scheme in terms of mean square error (MSE) for channel estimation, robustness to the initial channel guess, computational complexity, and channel equalization.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.102-103
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• 2007

In order to apply resistive superconducting fault current limiters into electric power systems, the urgent issues to be settled are as follows, such as initial installation price of SFCL, operation and maintenance cost due to ac loss of superconductor and the life of cryostat, and high voltage and high current problems. The ac loss and high cost of superconductor and cryostat system are main bottlenecks for real application. Furthermore in order to increase voltage and current ratings of SFCL, a lot of superconductor components should be connected in series and parallel which resulted in extreme high cost. Thus, in order to make practical SFCL, we designed novel hybrid SFCL which combines superconductor and conventional electric equipment including vacuum interrupter, power fuse and current limiting reactor. The main purpose of hybrid SFCL is to drastically reduce total usage of superconductor by adopting current commutation method by use of superconductor and high fast switch. Consequently, it was possible to get the satisfactory test results using this method, and further works for practical applications are in the process.

• Proceedings of the KIEE Conference
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• 2006.07e
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• pp.19-22
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• 2006

The CMD(Condition Monitoring & Diagnosis) System is used to monitor and analyze the PD(Partial Discharge), Arc, Temperature, Pressure, Gas in Oil and so on for reliability and availability of the substation. Although this system comes into the spotlight as the forecast and management of the failure and fault, there are some problems. For example, the unified standard was not defined, and the effective management of the layed communication network does not performed, limitation of physical space, etc. To resolve above problems, this paper suggests the IEC61850 compatible eCMD system architecture for monitoring and analyzing the CMD factor in substations. The suggested eCMD system consists of CMD-LU(Condition Monitoring & Diagnosis-Local Unit), IED(Intelligent Electronic Device), and engineering centers. The IEC61850 is the international standard that defines communication networks and system in substation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.3
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• pp.551-569
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• 2024

With the rapid development of electric vehicles (EVs) industry, EV charging service becomes more and more important. Especially, in the case of suddenly drop of air temperature or open holidays that large-scale EVs seeking for charging devices (CDs) in a short time. In such scenario, inefficient EV charging scheduling algorithm might lead to a bad service quality, for example, long queueing times for EVs and unreasonable idling time for charging devices. To deal with this issue, this paper propose a Deep-Q-Network (DQN) based two-stage scheduling method for the large-scale EVs charging service. Fine-grained states with two delicate neural networks are proposed to optimize the sequencing of EVs and charging station (CS) arrangement. Two efficient algorithms are presented to obtain the optimal EVs charging scheduling scheme for large-scale EVs charging demand. Three case studies show the superiority of our proposal, in terms of a high service quality (minimized average queuing time of EVs and maximized charging performance at both EV and CS sides) and achieve greater scheduling efficiency. The code and data are available at THE CODE AND DATA.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2009.11a
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• pp.244-244
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• 2009

Carbon nanotubes (CNTs) have excellent electrical, chemical stability, mechanical and thermal properties. In this paper, networks of Multi-walled carbon nanotube (MWCNT) materials were investigated as transparent electrode. Sensor films were fabricated by air spray method using the multi-walled CNTs solution on glass substrates. The film that was sprayed with the MWCNT dispersion for 60 sec, was 300nm thick. And the electric resistivity and the light transmittance rate are $2{\times}10^2{\Omega}cm$ and 60%, respectively.

• Proceedings of the KIEE Conference
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• 2007.11b
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• pp.46-47
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• 2007

SCADA (Supervisory Control and Data Acquisition) system has been used fur remote measurement and control on the critical infrastructures as well as modem industrial facilities. As cyber attacks increase on communication networks. SCADA network has been also exposed to cyber security problems. Especially, SCADA systems of energy industry such as electric power, gas and oil are vulnerable to targeted cyber attack and terrorism. Recently, many research efforts to solve the problems have made progress on SCADA network security. In this paper, we introduce recent security issue of SCADA network and propose the application of encryption method to Korea SCADA network.