• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.12
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• pp.3632-3637
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• 2009

In this paper, we describe the performance results of the field installation test which is required to practicalize the single-phase MJ81 Switch Point Machine. This product has passed the certified test through performance improvement of driving parts in order to use 3 phase MJ81 Switch Point Machine, which is localized by taking over technology from Alstom and Cogifer when constructing Seoul-Busan rapid-transit railway, without change of the electrical equipment at track-side in domestic existing lines which single-phase 220V is used. KRRI and Samsung SDS have localized the single-phase MJ81 Switch Point Machine to improve the speed and safety of the conventional lines through the existing railway technology development project. For practicalization of this, we should, however, verify the performance through not only field installation test in real lines but also interface test with the interlocking. In this paper we verify the practicality of the domestic single-phase MJ81 Switch Point Machine through analysis on the performance result of the field installation test as well as the research contents for this test. Thereby, in Feb 2009 we have received an order from the Korea Rail Network Authority and are currently installing the single-phase MJ81 Switch Point Machine.

• Journal of the Korea Society of Computer and Information
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• v.17 no.1
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• pp.211-218
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• 2012

Fossil fuel is used all over the world and it produces greenhouse gases due to fossil fuel use. Therefore, it cause global warming and is serious environmental pollution. In order to decrease the environmental pollution, we should use renewable energy which is clean energy. Among several renewable energy, wind energy is the most promising one. Wind power generation is does not produce environmental pollution and could not be exhausted. However, due to wind power generation has irregular power output, it is important to predict generated electrical energy accurately for smoothing wind energy supply. There, we consider use ramp characteristic to forecast accurate wind power output. The ramp increase and decrease rapidly wind power generation during in a short time. Therefore, it can cause problem of unbalanced power supply and demand and get damaged wind turbine. In this paper, we make prediction models using power ramp rate as well as wind speed and wind direction to increase prediction accuracy. Prediction model construction algorithm used multilayer neural network. We built four prediction models with PRR, wind speed, and wind direction and then evaluated performance of prediction models. The predicted values, which is prediction model with all of attribute, is nearly to the observed values. Therefore, if we use PRR attribute, we can increase prediction accuracy of wind power generation.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.2
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• pp.8-14
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• 2020

Recently, there was an increasing demand for an integrated access control system which is capable of user recognition, door control, and facility operations control for smart buildings automation. The market available door lock access control solutions need to be improved from the current level security of door locks operations where security is compromised when a password or digital keys are exposed to the strangers. At present, the access control system solution providers focusing on developing an automatic access control system using (RF) based technologies like bluetooth, WiFi, etc. All the existing automatic door access control technologies required an additional hardware interface and always vulnerable security threads. This paper proposes the user identification and authentication solution for automatic door lock control operations using camera based visible light communication (VLC) technology. This proposed approach use the cameras installed in building facility, user smart devices and IoT open source controller based LED light sensors installed in buildings infrastructure. The building facility installed IoT LED light sensors transmit the authorized user and facility information color grid code and the smart device camera decode the user informations and verify with stored user information then indicate the authentication status to the user and send authentication acknowledgement to facility door lock integrated camera to control the door lock operations. The camera based VLC receiver uses the artificial intelligence (AI) methods to decode VLC data to improve the VLC performance. This paper implements the testbed model using IoT open-source based LED light sensor with CCTV camera and user smartphone devices. The experiment results are verified with custom made convolutional neural network (CNN) based AI techniques for VLC deciding method on smart devices and PC based CCTV monitoring solutions. The archived experiment results confirm that proposed door access control solution is effective and robust for automatic door access control.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.8 s.350
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• pp.47-58
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• 2006

Due to the movement of nodes, ad-hoc networks suffer from the problems such as the decrease of data delivery ratio, the increase of end-to-end delay, and the increase of routing overhead. The backup routing schemes try to solve these problems by finding the backup routes during the route discovery phase and using them when a route fails. Generally the backup routing schemes outperform the single-path routing schemes in terms of data delivery ratio, end-to-end delay, and routing overhead when the nodes move rapidly. But when the nodes don't move rapidly, the backup routing schemes generate more routing traffics than the single-path routing schemes because they need to exchange packets to find the backup route. In addition, when the backup route fails earlier than the main route, it can not use the backup route because in many backup route algorithms, the backup route is found only at the initial route discovery phase. RBR(Reactive Backup Routing Algorithm) proposed in this paper is an algorithm that provides more stable data delivery than the previous backup routing schemes through the selective maintenance of backup route and the backup route rediscovery. To do that RBR prioritize the backup routes, and maintain and use them selectively Thus it can also decrease the routing overheads. Also, RBR can increase data delivery ratio and decrease delay because it reestablishes the backup route when the network topology changes. For the performance evaluation, OPNET simulator is used to compare RBR with the single-path routing scheme and some of the well known backup routing schemes.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.3
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• pp.1-8
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• 2011

The parking guidance system can increase driver's convenience with detailed parking information service, but it continuously consumes electrical energy with large amount of sensors, displays and control modules. With the increase of the demand for green and sustainable building design, it becomes a meaningful issue for parking guidance system to reduce operating power. This paper presents the preliminary design and estimated results of a parking guidance system which is optimized to reduce the power consumption mainly on detectors and displays. The system design is based on commercial wireless parking detectors, wireless-loop-detector and earth-magnetic-detector. We have performed system architecture design, communication network design, parking information service scenario planning, battery life regulation and at last operating power estimation. With the 7 years of battery replace cycle, the estimated result for power consumption of designed system was 0.33W/slot, which is 13% of the traditional system's estimation result. The estimated annual maintain cost was similar to the traditional ultrasonic sensor based system's. The low power operable designed system can be expected to reduce CO2 emission.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.2
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• pp.185-195
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• 2018

As machines have been automated in the field of industries in recent years, it is a paramount importance to manage and maintain the automation machines. When a fault occurs in sensors attached to the machine, the machine may malfunction and further, a huge damage will be caused in the process line. To prevent the situation, the fault of sensors should be monitored, diagnosed and classified in a proper way. In the paper, we propose a sensor fault detection scheme based on SVM and CNN to detect and classify typical sensor errors such as erratic, drift, hard-over, spike, and stuck faults. Time-domain statistical features are utilized for the learning and testing in the proposed scheme, and the genetic algorithm is utilized to select the subset of optimal features. To classify multiple sensor faults, a multi-layer SVM is utilized, and ensemble technique is used for CNN. As a result, the SVM that utilizes a subset of features selected by the genetic algorithm provides better performance than the SVM that utilizes all the features. However, the performance of CNN is superior to that of the SVM.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.5
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• pp.19-24
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• 2016

In Long Term Evolution-Advanced (LTE-A), small cell enhancement(SCE) has been developed as a cost-effective way of supporting exponentially increasing demand of wireless data services and satisfying the user quality of service(QoS). However, due to the dense and irregular distribution of a large number of small cells, the offloading scheme should be applied in the small cell network. In this paper, we propose an user offloading scheme for SCE in LTE-Advanced system. We divide the small cells into different clusters according to the reference signal received power(RSRP) from user equipment(UE). Within a cluster, We apply the user offloading scheme with the consideration of the number of users and interference conditions. Simulation results show that proposed scheme can improve the throughput, and spectral efficiency of small cell users. Eventually, proposed scheme can improve overall cell performance.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.8
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• pp.74-85
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• 2015

PCI Express is a well-known and widely used de-facto system bus standard for connecting among a processor and IO devices. PCI Express is originated from old PCI standard, and its most of applications are limited to be used within a PC or server system. But, because of its fast speed, low power consumption, and good protocol efficiency, it is considered as one of a good candidate for an alternate system interconnect for many years. In this paper, we present design, implementation and early evaluation of an alternate system interconnect by utilizing PCI Express. The developed alternate system interconnect using PCI Express (named PCIeLINK) utilizes non-transparent bridging (NTB) technic which generally used in fail-over system in PCI and PCI Express. By using NTB technic, PCI Express device can be extended to outside of a system without electrical and logical problems arising during system boot and enumeration. To build up an alternate system interconnect, we designed and implemented a network interface card having multiple PCI Express ${\times}4$ connections (theoretically 20 Gbps) and tested, The early test results revealed that an ${\times}4$ port in the card showed 8.6 Gbps peak performance for bulk transmission and 5.1 Gbps peak for normal TCP/IP transfer.

• Journal of IKEEE
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• v.22 no.3
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• pp.829-835
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• 2018

Recently, studies have been actively conducted to reduce spare power that is unnecessarily generated or wasted in existing power systems and to improve energy use efficiency. In this study, smart meter, which is one of the element technologies of smart grid, is implemented to improve the efficiency of energy use by controlling power of electric devices, and predicting trends of energy usage based on deep learning. We propose and develop an algorithm that controls the power of the electric devices by comparing the predicted power consumption with the real-time power consumption. To verify the performance of the proposed smart meter based on the deep running, we constructed the actual power consumption environment and obtained the power usage data in real time, and predicted the power consumption based on the deep learning model. We confirmed that the unnecessary power consumption can be reduced and the energy use efficiency increases through the proposed deep learning-based smart meter.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.6
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• pp.639-645
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• 2018

This paper is intended to develop a decision model that can be applied to autonomous vehicles and autonomous mobile vehicles. The developed module has an independent configuration for application in various driving environments and is based on a platform for organically operating them. Each module is studied for decision making on lane changes and for securing safety through reinforcement learning using a deep learning technique. The autonomous mobile moving body operating to change the driving state has a characteristic where the next operation of the mobile body can be determined only if the definition of the speed determination model (according to its functions) and the lane change decision are correctly preceded. Also, if all the moving bodies traveling on a general road are equipped with an autonomous driving function, it is difficult to consider the factors that may occur between each mobile unit from unexpected environmental changes. Considering these factors, we applied the decision model to the platform and studied the lane change decision system for implementation of the platform. We studied the decision model using a modular learning method to reduce system complexity, to reduce the learning time, and to consider model replacement.