• KIPS Transactions on Computer and Communication Systems
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• v.8 no.4
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• pp.87-92
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• 2019

This paper describes the pattern analysis for data of the factory energy consumption by using machine learning method. While usual statistical methods or approaches require specific equations to represent the physical characteristics of the plant, machine learning based approach uses historical data and calculate the result effectively. Although rule-based approach calculates energy usage with the physical equations, it is hard to identify the exact equations that represent the factory's characteristics and hidden variables affecting the results. Whereas the machine learning approach is relatively useful to find the relations quickly between the data. The factory has several components directly affecting to the electricity consumption which are machines, light, computers and indoor systems like HVAC (heating, ventilation and air conditioning). The energy loads from those components are generated in real-time and these data can be shown in time-series. The various sensors were installed in the factory to construct the database by collecting the energy usage data from the components. After preliminary statistical analysis for data mining, time-series clustering techniques are applied to extract the energy load pattern. This research can attributes to develop Factory Energy Management System (FEMS).

• Journal of the Society of Naval Architects of Korea
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• v.60 no.2
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• pp.95-109
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• 2023

In the 4th industrial revolution, changes in the technological paradigm have had a direct impact on the maintenance system of ships. The 2-stroke low speed engine system integrates with the core equipment required for propulsive power. The Condition Based Management (CBM) is defined as a technology that predictive maintenance methods in existing calender-based or running time based maintenance systems by monitoring the condition of machinery and diagnosis/prognosis failures. In this study, we have established a framework for CBM technology development on our own, and are engaged in engineering-based failure analysis, data development and management, data feature analysis and pre-processing, and verified the reliability of failure mode DB using LSTM algorithms. We developed various simulated failure mode scenarios for 2-stroke low speed engine and researched to produce data on onshore basis test_beds. The analysis and pre-processing of normal and abnormal status data acquired through failure mode simulation experiment used various Exploratory Data Analysis (EDA) techniques to feature extract not only data on the performance and efficiency of 2-stroke low speed engine but also key feature data using multivariate statistical analysis. In addition, by developing an LSTM classification algorithm, we tried to verify the reliability of various failure mode data with time-series characteristics.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.40 no.11
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• pp.51-62
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• 2003

This paper presents a novel survivor memeory management and decoding techniques with sequential backward state transition control in the trace back Viterbi decoder. The Viterbi algorithm is an maximum likelihood decoding scheme to estimate the likelihood of encoder state for channel error detection and correction. This scheme is applied to a broad range of digital communication such as intersymbol interference removing and channel equalization. In order to achieve the area-efficiency VLSI chip design with high throughput in the Viterbi decoder in which recursive operation is implied, more research is required to obtain a simple systematic parallel ACS architecture and surviver memory management. As a method of solution to the problem, this paper addresses a progressive decoding algorithm with sequential backward state transition control in the trace back Viterbi decoder. Compared to the conventional trace back decoding techniques, the required total memory can be greatly reduced in the proposed method. Furthermore, the proposed method can be implemented with a simple pipelined structure with systolic array type architecture. The implementation of the peripheral logic circuit for the control of memory access is not required, and memory access bandwidth can be reduced Therefore, the proposed method has characteristics of high area-efficiency and low power consumption with high throughput. Finally, the examples of decoding results for the received data with channel noise and application result are provided to evaluate the efficiency of the proposed method.

• Journal of Digital Convergence
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• v.19 no.9
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• pp.217-228
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• 2021

The purpose of this study is to analyze drum performance techniques, focusing on the representative artists of funk music, who had a revival starting in the 1970s. The purpose of this study is to analyze the creativity shown in David Garibaldi's playing technique, which had a great influence on drum majors with his funk drum playing technique. As a research method, 'James Brown', 'Earth, Wind & Fire' and 'Tower of Power' with David Garibaldi as drummer were selected as representative bands. For the study period, the drum performance was examined by selecting two representative songs from among the songs released between 1965 and 1975, when the development of the corresponding funk music began. David Garibaldi's creative performance in this study is as follows. First, we tried to create a new rhythm in the form of changing the beat or resting the beat out of the frame of contemporary drum performance. Second, the 'Paradiddle', 'Accent', and 'Swiss Army triplet' rudiment techniques were applied to the entire drum kit, bringing innovation to the rhythm. Third, the samba pattern of Latin rhythm and the form of 'Afro-Cuban' were grafted onto funk music. Fourth, the idea of Unison Rhythm obtained from the structure of Latin music was applied to funk music. Based on this study, it is hoped that research on drum performance techniques of various genres will be conducted.

• Journal of Industrial Convergence
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• v.21 no.7
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• pp.29-39
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• 2023

In the recent period, there have been numerous earthquakes both domestically and internationally, and buildings in South Korea are particularly vulnerable to seismic design and earthquake damage. Therefore, the objective of this study is to discover an effective method for assessing the seismic vulnerability of buildings and conducting a density analysis of high-risk structures. The aim is to model this approach and validate it using data from pilot area(Seoul). To achieve this, two modeling techniques were employed, of which the predictive accuracy of the statistical analysis technique was 87%. Among the machine learning techniques, Random Forest Model exhibited the highest predictive accuracy, and the accuracy of the model on the Test Set was determined to be 97.1%. As a result of the analysis, the district rating revealed that Gwangjin-gu and Songpa-gu were relatively at higher risk, and the density analysis of at-risk buildings predicted that Seocho-gu, Gwanak-gu, and Gangseo-gu were relatively at higher risk. Finally, the result of the statistical analysis technique was predicted as more dangerous than those of the machine learning technique. However, considering that about 18.9% of the buildings in Seoul are designed to withstand the Seismic intensity of 6.5 (MMI), which is the standard for seismic-resistant design in South Korea, the result of the machine learning technique was predicted to be more accurate. The current research is limited in that it only considers buildings without taking into account factors such as population density, police stations, and fire stations. Considering these limitations in future studies would lead to more comprehensive and valuable research.

• Journal of the Korean Society for Marine Environment & Energy
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• v.18 no.4
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• pp.310-316
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• 2015

The intertidal area in Lake Sihwa formed after operation of Sihwa-Lake tidal power plant and the change of the area in Lake Sihwa by period were analyzed. For computation of the intertidal area, remote sensing techniques were applied and high resolution Digital Elevation Model (DEM) was generated with root mean square (rms) error 14.4 cm. The intertidal area was $165.1km^2$ in 1910s, $115.2km^2$ in 1991 before completion of Sihwa dyke, $5.6km^2$ in 2010 during the period on operation of sluice gate, and $20.3km^2$ in 2013 after operation of Sihwa tidal power plant. Intertidal in Lake Sihwa was nearly dissipated after completion of Sihwa dyke, but significantly increased with operation of Sihwa tidal power plant from April 2012 as developing a regular tide environment and increasing of sea water flux. The re-formation of tidal flat of Sihwa Lake is an uncommon case. This study that precisely analyzed on the area of artificially formed Sihwa tidal flat would be applicable for management and making conservation plan.

• Clean Technology
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• v.23 no.1
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• pp.54-63
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• 2017

Volatile organic compounds (VOCs) are widely used in both industrial and domestic activities. VOCs are one of the most unpleasant, frequently complaint-rousing factors of pollution around the world. It is now necessary to research and develop an alternative technology that could overcome the problems of the existing odor-control and VOC-eliminating techniques. In this study, essential oil and photocatalytic process was applied in the removal of benzene and toluene, typical VOCs in petrochemistry plant. therefore, this study conducted experiments on the selection of appropriate essential oil, photodegradation, hydroxyl radical generation capacity. The removal efficiency and reaction rate were performed to selecte the type and concentration of essential oil. As a result, removal efficiency of Hinoki Cypress oil was approximately 70% and reaction rate of Hinoki Cypress was high. The results of photolysis experiment, photocatalytic oxidation process showed that the decomposition efficiency of VOCs increased considerably with increasing UV lamp power. In addition, the conversion of VOCs was increased up to $0.1gL^{-1}$ photocatalysts. The hydroxyl radicals measure was performed to determine the ability to generate hydroxyl radicals. The analytical result showed that high $TiO_2$ concentration and lamp power was produced many hydroxyl radical. Experiments of the removal efficiency and reaction rate were performed using essential oil and photooxidation. As a result, the removal efficiency showed that the removal efficiency was increased high temperature and reaction time. The activation energy was calculated from the reaction rate equation at various temperature condition. Activation energy was approximately $18kJmol^{-1}$.

• Journal of the Korea Society of Computer and Information
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• v.16 no.7
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• pp.1-11
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• 2011

Many researchers have studied on the methods to improve the processor performance. However, high integrated semiconductor technology for improving the processor performance causes many problems such as battery life, high power density, hotspot, etc. Especially, as hotspot has critical impact on the reliability of chip, thermal problems should be considered together with performance and power consumption when designing high-performance processors. To alleviate the thermal problems of processors, there have been various researches. In the past, mechanical cooling methods have been used to control the temperature of processors. However, up-to-date microprocessors causes severe thermal problems, resulting in increased cooling cost. Therefore, recent studies have focused on architecture-level thermal-aware design techniques than mechanical cooling methods. Even though architecture-level thermal-aware design techniques are efficient for reducing the temperature of processors, they cause performance degradation inevitably. Therefore, if the mechanical cooling methods can manage the thermal problems of processors efficiently, the performance can be improved by reducing the performance degradation due to architecture-level thermal-aware design techniques such as dynamic thermal management. In this paper, we analyze the cooling efficiency of high-performance multicore processors according to mechanical cooling methods. According to our experiments using air cooler and liquid cooler, the liquid cooler consumes more power than the air cooler whereas it reduces the temperature more efficiently. Especially, the cost for reducing $1^{\circ}C$ is varied by the environments. Therefore, if the mechanical cooling methods can be used appropriately, the temperature of high-performance processors can be managed more efficiently.

• Journal of the Korean Institute of Landscape Architecture
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• v.49 no.2
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• pp.113-127
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• 2021

Although planning techniques linking parks, green areas, and waterways have become common, there are frequent disruptions in the operation and management of landscape-use artificial channels (LuAC). Therefore, this study examined a design to promote the sustainable management and operation of a LuAC using rainwater for the streamlets of the Jeonju-Wanju Innovative City. In order to accomplish the purpose of this study, scenarios were set up by dividing the design into waterhead and waterway portions. First, the scenario regarding the waterhead was analyzed to calculate the water supply and storage required for the waterway and waterhead. The analysis showed that the waterway requires a water supply of 676.8 tons/months, 3,018 tons to 5,512 tons of storage space, and a water depth of 0.75 m to 1.37 m considering the ecological and landscape aspects. The second scenario is to select an effective system of facilities for the operation and management of the LuAC. To accomplish this, a single-circulation system (SCS), which transports water to a highland location was compared to a multi-circulation system (MCS), which supplied water separately to each water space and operated independently. The results showed that the MCS, which was operated independently by small power units, was more effective owing to the vast difference in water supply operation times.

• Smart Media Journal
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• v.12 no.10
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• pp.63-70
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• 2023

Recently, energy consumption for heating costs, which is 35% of smart farm energy costs, has increased, requiring energy consumption efficiency, and the importance of new and renewable energy is increasing due to concerns about the realization of electricity bills. Renewable energy belongs to hydropower, wind, and solar power, of which solar energy is a power generation technology that converts it into electrical energy, and this technology has less impact on the environment and is simple to maintain. In this study, based on the greenhouse heat storage tank and heat pump data, the factors that affect the heat storage tank are selected and a heat storage tank supply temperature prediction model is developed. It is predicted using Long Short-Term Memory (LSTM), which is effective for time series data analysis and prediction, and XGBoost model, which is superior to other ensemble learning techniques. By predicting the temperature of the heat pump heat storage tank, energy consumption may be optimized and system operation may be optimized. In addition, we intend to link it to the smart farm energy integrated operation system, such as reducing heating and cooling costs and improving the energy independence of farmers due to the use of solar power. By managing the supply of waste heat energy through the platform and deriving the maximum heating load and energy values required for crop growth by season and time, an optimal energy management plan is derived based on this.