• Journal of Technology Innovation
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• v.23 no.3
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• pp.137-167
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• 2015

Based on the stakeholder theory, this paper analyzes a clash of standards in Korea's Electric Vehicle(EV) market, particularly between an EV charging standard and a smart grid communication standard in 2012~2013. For charging, EV is connected with the electric power grid and simultaneously exchanges data regarding the charging status. When EV is connected with the power grid, a clash between two standards may arise. It actually happened when BMW entered into the Korean EV market with the DC Combo charging system. In that course, the frequency interference occurred between the EV data communication technology adopted by BMW and the AMI(Advanced Metering Infrastructure) for the smart grid system in Korea. Standardization of Korea's EV charging systems was required to solve this problem. However, it had been delayed due to the confrontation between various stakeholders involved in the process of standardization. It lasted until the DC combo was accepted as one of the Korea EV charging standards(KSAE SAE 1772-2040, 2014.1) by KSAE(The Korea Society of Automotive Engineers) in January 2014. This is an interesting case in the age of convergence. As it deals with the standard competition not among EV standards, but a clash between the EV industry and the smart grid, i.e. electric power industry, it addresses the necessity to consider standardization processes between different industries. This study draws on the stakeholder theory to analyse the dynamics of the standard clash between EV charging systems and the smart grid system, which is a unique example of standard clash between different industries. We expect such clashes to increase in the age of convergence.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.49 no.7
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• pp.56-64
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• 2012

A smart grid is the next generation power grid which combines the existing power grid with information technology, so an energy efficient power grid can be provided. In this paper, in order to build an efficient smart grid an AMI system, which gears with the existing home network and provides an user friendly management function, is proposed. The proposed AMI system, which is based on an extended home network, consists of various functional units; smart meters, communication modules, home gateway, security modules, meter data management modules (MDMM), electric power application modules and so on. The proposed home network system, which can reduce electric power consumption and transmit data more effectively, is designed by using IEEE 802.15.4. The extended home gateway can exchange energy consumption information with the outside management system via web services. The proposed AMI system is designed to enable two-way communication between the home gateway and MDMM via the Internet. The AES(Advanced Encryption Standard) algorithm, which is a symmetric block cipher algorithm, is used to ensure secure information exchange. Even though the results in this study could be limited to our experimental environment, the result of the simulation test shows that the proposed system reduces electric power consumption by 4~42% on average compared to the case of using no control.

• 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 Navigation and Port Research
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• v.39 no.6
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• pp.457-463
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• 2015

A gas turbine engine plays an important role as a prime mover that is used in the marine transportation field as well as the space/aviation and power plant fields. However, it has a complicated structure and there is a time delay element in the combustion process. Therefore, an elaborate mathematical model needs to be developed to control a gas turbine engine. In this study, a modeling technique for a gas generator, a PLA actuator, and a metering valve, which are major components of a gas turbine engine, is explained. In addition, sub-models are obtained at several operating points in a steady state based on the trial running data of a gas turbine engine, and a method for controlling the engine speed is proposed by designing an NPID controller for each sub-model. The proposed NPID controller uses three kinds of gains that are implemented with a nonlinear function. The parameters of the NPID controller are tuned using real-coded genetic algorithms in terms of minimizing the objective function. The validity of the proposed method is examined by applying to a gas turbine engine and by conducting a simulation.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.163-176
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• 2014

Social media is becoming the platform for users to communicate their activities, status, emotions, and experiences to other people. In recent years, microblogs, such as Twitter, have gained in popularity because of its ease of use, speed, and reach. Compared to a conventional web blog, a microblog lowers users' efforts and investment for content generation by recommending shorter posts. There has been a lot research into capturing the social phenomena and analyzing the chatter of microblogs. However, measuring television ratings has been given little attention so far. Currently, the most common method to measure TV ratings uses an electronic metering device installed in a small number of sampled households. Microblogs allow users to post short messages, share daily updates, and conveniently keep in touch. In a similar way, microblog users are interacting with each other while watching television or movies, or visiting a new place. In order to measure TV ratings, some features are significant during certain hours of the day, or days of the week, whereas these same features are meaningless during other time periods. Thus, the importance of features can change during the day, and a model capturing the time sensitive relevance is required to estimate TV ratings. Therefore, modeling time-related characteristics of features should be a key when measuring the TV ratings through microblogs. We show that capturing time-dependency of features in measuring TV ratings is vitally necessary for improving their accuracy. To explore the relationship between the content of microblogs and TV ratings, we collected Twitter data using the Get Search component of the Twitter REST API from January 2013 to October 2013. There are about 300 thousand posts in our data set for the experiment. After excluding data such as adverting or promoted tweets, we selected 149 thousand tweets for analysis. The number of tweets reaches its maximum level on the broadcasting day and increases rapidly around the broadcasting time. This result is stems from the characteristics of the public channel, which broadcasts the program at the predetermined time. From our analysis, we find that count-based features such as the number of tweets or retweets have a low correlation with TV ratings. This result implies that a simple tweet rate does not reflect the satisfaction or response to the TV programs. Content-based features extracted from the content of tweets have a relatively high correlation with TV ratings. Further, some emoticons or newly coined words that are not tagged in the morpheme extraction process have a strong relationship with TV ratings. We find that there is a time-dependency in the correlation of features between the before and after broadcasting time. Since the TV program is broadcast at the predetermined time regularly, users post tweets expressing their expectation for the program or disappointment over not being able to watch the program. The highly correlated features before the broadcast are different from the features after broadcasting. This result explains that the relevance of words with TV programs can change according to the time of the tweets. Among the 336 words that fulfill the minimum requirements for candidate features, 145 words have the highest correlation before the broadcasting time, whereas 68 words reach the highest correlation after broadcasting. Interestingly, some words that express the impossibility of watching the program show a high relevance, despite containing a negative meaning. Understanding the time-dependency of features can be helpful in improving the accuracy of TV ratings measurement. This research contributes a basis to estimate the response to or satisfaction with the broadcasted programs using the time dependency of words in Twitter chatter. More research is needed to refine the methodology for predicting or measuring TV ratings.