• Journal of the Korea Society of Computer and Information
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• v.16 no.11
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• pp.245-253
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• 2011

Due to the imprudent spending of the fossil fuels, the environment was contaminated seriously and the exhaustion problems of the fossil fuels loomed large. Therefore people become taking a great interest in alternative energy resources which can solve problems of fossil fuels. The wind power energy is one of the most interested energy in the new and renewable energy. However, the plants of wind power energy and the traditional power plants should be balanced between the power generation and the power consumption. Therefore, we need analysis and prediction to generate power efficiently using wind energy. In this paper, we have performed a research to predict power generation patterns using the wind power data. Prediction approaches of datamining area can be used for building a prediction model. The research steps are as follows: 1) we performed preprocessing to handle the missing values and anomalous data. And we extracted the characteristic vector data. 2) The representative patterns were found by the MIA(Mean Index Adequacy) measure and the SOM(Self-Organizing Feature Map) clustering approach using the normalized dataset. We assigned the class labels to each data. 3) We built a new predicting model about the wind power generation with classification approach. In this experiment, we built a forecasting model to predict wind power generation patterns using the decision tree.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.311-316
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• 2005

The power saving mechanism of IEEE 802.16e operates in two modes; awake mode and sleep mode. While the user terminal transmits and receives packets in awake mode, it sleeps for a given interval to save the power consumption in sleep mode. The IEEE 802.16e specifies that the user terminal increases the sleep interval exponentially unless it has to wake up. In this paper, we analyze the performance of IEEE 802.16e power saving mechanism by considering down link traffic conditions. With the extensive simulations, we observe the trade-off between the power saving performance and the average packet delay. In addition, we observe that various performance parameters of IEEE 802.16e power saving mechanism are affected by the traffic patterns.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.45 no.6
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• pp.43-48
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• 2008

Power consumption during test can be much higher than that during normal operation since test vectors are determined independently. In order to reduce the power consumption during test process, a new BIST(Built-In Self Test) architecture is proposed. In the proposed architecture, test vectors generated by an LFSR(Linear Feedback Shift Resister) are transformed into the new patterns with low transitions using Bit Generator and Bit Dropper. Experiments performed on ISCAS'89 benchmark circuits show that transition reduction during scan testing can be achieved by 62% without loss of fault coverage. Therefore the new architecture is a viable solution for reducing both peak and average power consumption.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.7
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• pp.1721-1726
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• 2014

The sensor network technology for core technology of ubiquitous computing is in the spotlight recently, the research on sensor network is proceeding actively which is composed many different sensor node. The major traffic patterns of plenty of sensor networks are composed of collecting types of single directional data, which is transmitting packets from several sensor nodes to sink node. One of the important condition for design of sensor node is to extend for network life which is to minimize power-consumption under the limited resources of sensor network. In this paper analysis used routing protocols using the network simulation that was used second level cluster structure to reduce delay and power-consumption of sensor node.

• The Korean Journal of Applied Statistics
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• v.30 no.5
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• pp.633-645
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• 2017

This study proposes a statistical procedure for analyzing container ship operation data that can help determine fuel consumption patterns. We first investigate the features that affect fuel consumption and develop the prediction model to find current fuel consumption. The ship data can be divided into two-type data. One set of operation data includes sea route, voyage information, longitudinal water speed, longitudinal ground speed, and wind, the other includes machinery data such as engine power, rpm, fuel consumption, temperature, and pressure. In this study, we separate the effects of external force on ships according to Beaufort Scale and apply a partial least squares regression to develop a prediction model.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.11
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• pp.369-382
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• 2015

Energy aware server clusters aim to reduce power consumption at maximum while keeping QoS(quality of service) compared to energy non-aware server clusters. They adjust the power mode of each server in a fixed or variable time interval to activate only the minimum number of servers needed to handle current user requests. Previous studies on energy aware server cluster put efforts to reduce power consumption or heat dissipation, but they do not consider energy efficiency well. In this paper, we propose an energy efficient cluster management method to improve not only performance per watt but also QoS of the existing server power mode control method based on autonomous learning. Our proposed method is to adjust server power mode based on a hybrid approach of autonomous learning method with multi level thresholds and power consumption prediction method. Autonomous learning method with multi level thresholds is applied under normal load situation whereas power consumption prediction method is applied under abnormal load situation. The decision on whether current load is normal or abnormal depends on the ratio of the number of current user requests over the average number of user requests during recent past few minutes. Also, a dynamic shutdown method is additionally applied to shorten the time delay to make servers off. We performed experiments with a cluster of 16 servers using three different kinds of load patterns. The multi-threshold based learning method with prediction and dynamic shutdown shows the best result in terms of normalized QoS and performance per watt (valid responses). For banking load pattern, real load pattern, and virtual load pattern, the numbers of good response per watt in the proposed method increase by 1.66%, 2.9% and 3.84%, respectively, whereas QoS in the proposed method increase by 0.45%, 1.33% and 8.82%, respectively, compared to those in the existing autonomous learning method with single level threshold.

• Proceedings of the KIPE Conference
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• 2010.07a
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• pp.240-241
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• 2010

Research on modification and replacement of conventional AC distribution system to DC distribution system has been widely conducted. When DC system is applied, it is possible to improve energy transferring efficiency because most of the home appliances are electric loads which require DC input voltage. Furthermore, compatibility with renewable energy sources and secondary batteries should be improved as they are DC based power sources. To design energy efficient DC system, it is important to understand the load characteristics of the electric devices. In this paper, the electric appliances are classified to 3 types: motor, heating, and electric loads and their typical power consumptions are shown. Load patterns of load which can be used in analyzing the designed system are modeled according the statistics. Feasibility of the developed load patterns are verified by applying it in distribution system design tool.

• Transactions of the Korean Society of Automotive Engineers
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• v.18 no.4
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• pp.31-39
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• 2010

Although driving patterns strongly influence greenhouse gas and air pollutant emission rate from light duty vehicles, emission measurements have been mainly based on chassis dynamometer testing with one standard driving pattern. And there has been limited work on quantifying the independent effect of driving parameters on emission rate because of multidimensional nature of real-world driving pattern. The objective of this study is to obtain the quantitative effect of relative positive acceleration (RPA) on vehicle emission rate. RPA has been used to define the occurrence of acceleration demanding large amounts of power in certain driving distance and shown to be a significant affecting parameter for real-world emission rate. 40 driving patterns have been developed with fixed driving parameters to investigate independent effect of RPA. For the same values of average vehicle speed and power, the trend in carbon dioxide emission rate and fuel consumption with respect to RPA is very clear. Emission rate of nitrogen oxide and particulate matter also increase with respect to RPA, but the trend is less clear. Carbon dioxide emission from diesel vehicle appear to be more affected by high accelerations compared to that from gasoline vehicle because of high intake air restriction during acceleration caused by turbocharger and intercooler. The results have implications for the possible reduction of environmental effects through better traffic planning and management, driver education and car design.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.733-736
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• 2013

The sensor network technology for core technology of ubiquitous computing is in the spotlight recently, the research on sensor network is proceeding actively which is composed many different sensor node. The major traffic patterns of plenty of sensor networks are composed of collecting types of single directional data, which is transmitting packets from several sensor nodes to sink node. One of the important condition for design of sensor node is to extend for network life which is to minimize power-consumption under the limited resources of sensor network. In this paper analysis used routing protocols using the network simulation that was used second level cluster structure to reduce delay and power-consumption of sensor node.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.3
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• pp.273-281
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• 2019

The energy consumed by buildings among the total national energy consumption is more than 10% of the total. For this reason, Korea has adopted the zero energy building policy since 2025, and research on the energy saving technology of buildings has been demanded. Analysis of buildings' energy consumption patterns shows that lighting, heating and cooling energy account for more than 60% of total energy consumption, which is directly related to solar power acquisition and window opening and closing operation. In this paper, we have developed a low - power IoT sensor module for window system to transfer acquired information to building energy management system. This module transmits the external environment and window opening / closing status information to the building energy management system in real time, and constructs the network to actively take energy saving measures. The power used in the module is designed as an independent power source using solar power among the harvest energy. The topology of the power supply is a Buck converter, which is charged at 4V to the lithium ion battery through MPPT control, and the efficiency is about 85.87%. Communication is configured to be able to transmit in real time by applying WiFi. In order to reduce the power consumption of the module, we analyzed the hardware and software aspects and implemented a low power IoT sensor module.