• The KIPS Transactions:PartC
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• v.11C no.5
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• pp.621-632
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• 2004

Recent measurement studies of real teletraffic data in modern telecommunication networks have shown that self-similar (or fractal) processes may provide better models of teletraffic in modern telecommunication networks than Poisson processes. If this is not taken into account, it can lead to inaccurate conclusions about performance of telecommunication networks. Thus, an important requirement for conducting simulation studies of telecommunication networks is the ability to generate long synthetic stochastic self-similar sequences. A new generator of pseu-do-random self-similar sequences, based on the fractional Gaussian nois and a wavelet transform, is proposed and analysed in this paper. Specifically, this generator uses Daubechies wavelets. The motivation behind this selection of wavelets is that Daubechies wavelets lead to more accurate results by better matching the self-similar structure of long range dependent processes, than other types of wavelets. The statistical accuracy and time required to produce sequences of a given (long) length are experimentally studied. This generator shows a high level of accuracy of the output data (in the sense of the Hurst parameter) and is fast. Its theoretical algorithmic complexity is 0(n).

• Journal of the Korean Solar Energy Society
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• v.28 no.2
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• pp.64-69
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• 2008

As the grid-connected photovoltaic power conditioning systems (PVPCS) are installed in many residential areas, these have raised potential problems of network protection on electrical power system. One of the numerous problems is an Islanding phenomenon. There has been an argument that it may be a non-issue in practice because the probability of islanding is extremely low. However, there are three counter-arguments: First, the low probability of islanding is based on the assumption of 100% power matching between the PVPCS and the islanded local loads. In fact, an islanding can be easily formed even without 100% power matching (the power mismatch could be up to 30% if only traditional protections are used, e.g. under/over voltage/frequency). The 30% power-mismatch condition will drastically increase the islanding probability. Second, even with a larger power mismatch, the time for voltage or frequency to deviate sufficiently to cause a trip, plus the time required to execute a trip (particularly if conventional switchgear is required to operate), can easily be greater than the typical re-close time on the distribution circuit. Third, the low-probability argument is based on the study of PVPCS. Especially, if the output power of PVPCS equals to power consumption of local loads, it is very difficult for the PVPCS to sustain the voltage and frequency in an islanding. Unintentional islanding of PVPCS may result in power-quality issues, interference to grid-protection devices, equipment damage, and even personnel safety hazards. Therefore the verification of anti-islanding performance is strongly needed. In this paper, improved RPV method is proposed through considering power quality and anti-islanding capacity of grid-connected single-phase PVPCS in IEEE Std 1547 ("Standard for Interconnecting Distributed Resources to Electric Power Systems"). And the simulation results are verified.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.21 no.2
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• pp.121-128
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• 2010

This paper presents a design of a 100 W high-efficiency power amplifier, whose operational frequency band expands from 30 to 512 MHz, using negative feedback network, push-pull structure, broadband RF choke, and transmission line transformer for balun configuration. The push-pull amplifier has been tuned for higher output power using a shunt capacitor as a matching component at its load especially for high-frequency region. The implemented power amplifier exhibited a very flat power gain of $18.34{\pm}0.9\;dB$ throughout the operating frequency band and very high power-added efficiency(PAE) of greater than 40% at an output power of 100 W. It also showed second- and third-harmonic distortion levels of below -34 dBc and -12 dBc, respectively, through the entire operating frequency band.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.6D
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• pp.569-575
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• 2010

The occupancy based O/D is essential for measuring efficiency of various transportation policies like HOV/HOT lane, ramp metering, and public parking station. There has been many studies on occupancy survey methodology and O/D estimation using TCS (Toll Collection System) data separately. The occupancy O/D estimation methodology using TCS data has not been attempted thus far. An overall process from data collection stage to the occupancy O/D estimation stage has been suggested. Field survey was performed at the northbound Seoul toll station of Gyeongbu Expressway by each 2 hours of AM peak, PM non-peak, PM peak, midnight periods on a day. The process of matching the TCS data and field survey data classified by tollbooth ID, car type/mode, and arrival time was also performed. One typical output of the results showed that the ratio of single occupancy vehicles bounding for Seoul during the AM peak amounted to 60%. With the key output of this study and the specific O/D estimation methodology suggested, the whole centroid-to-centroid occupancy O/D of the country could be available, and then various applications in which the occupancy information is required could be possible.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.109-122
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• 2014

People are nowadays creating a tremendous amount of data on Social Network Service (SNS). In particular, the incorporation of SNS into mobile devices has resulted in massive amounts of data generation, thereby greatly influencing society. This is an unmatched phenomenon in history, and now we live in the Age of Big Data. SNS Data is defined as a condition of Big Data where the amount of data (volume), data input and output speeds (velocity), and the variety of data types (variety) are satisfied. If someone intends to discover the trend of an issue in SNS Big Data, this information can be used as a new important source for the creation of new values because this information covers the whole of society. In this study, a Twitter Issue Tracking System (TITS) is designed and established to meet the needs of analyzing SNS Big Data. TITS extracts issues from Twitter texts and visualizes them on the web. The proposed system provides the following four functions: (1) Provide the topic keyword set that corresponds to daily ranking; (2) Visualize the daily time series graph of a topic for the duration of a month; (3) Provide the importance of a topic through a treemap based on the score system and frequency; (4) Visualize the daily time-series graph of keywords by searching the keyword; The present study analyzes the Big Data generated by SNS in real time. SNS Big Data analysis requires various natural language processing techniques, including the removal of stop words, and noun extraction for processing various unrefined forms of unstructured data. In addition, such analysis requires the latest big data technology to process rapidly a large amount of real-time data, such as the Hadoop distributed system or NoSQL, which is an alternative to relational database. We built TITS based on Hadoop to optimize the processing of big data because Hadoop is designed to scale up from single node computing to thousands of machines. Furthermore, we use MongoDB, which is classified as a NoSQL database. In addition, MongoDB is an open source platform, document-oriented database that provides high performance, high availability, and automatic scaling. Unlike existing relational database, there are no schema or tables with MongoDB, and its most important goal is that of data accessibility and data processing performance. In the Age of Big Data, the visualization of Big Data is more attractive to the Big Data community because it helps analysts to examine such data easily and clearly. Therefore, TITS uses the d3.js library as a visualization tool. This library is designed for the purpose of creating Data Driven Documents that bind document object model (DOM) and any data; the interaction between data is easy and useful for managing real-time data stream with smooth animation. In addition, TITS uses a bootstrap made of pre-configured plug-in style sheets and JavaScript libraries to build a web system. The TITS Graphical User Interface (GUI) is designed using these libraries, and it is capable of detecting issues on Twitter in an easy and intuitive manner. The proposed work demonstrates the superiority of our issue detection techniques by matching detected issues with corresponding online news articles. The contributions of the present study are threefold. First, we suggest an alternative approach to real-time big data analysis, which has become an extremely important issue. Second, we apply a topic modeling technique that is used in various research areas, including Library and Information Science (LIS). Based on this, we can confirm the utility of storytelling and time series analysis. Third, we develop a web-based system, and make the system available for the real-time discovery of topics. The present study conducted experiments with nearly 150 million tweets in Korea during March 2013.