• Journal of Digital Convergence
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• v.18 no.12
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• pp.525-531
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• 2020

In today's IT environment where various pieces of information are distributed in large volumes, recommendation systems are in the spotlight capable of figuring out users' needs fast and helping them with their decisions. The current recommendation systems, however, have a couple of problems including that user preference may not be reflected on the systems right away according to their changing tastes or interests and that items with no relations to users' preference may be recommended, being induced by advertising. In an effort to solve these problems, this study set out to propose a Fuzzy-AHP-based movie recommendation system by applying the BRNN(Bidirectional Recurrent Neural Network) language model. Applied to this system was Fuzzy-AHP to reflect users' tastes or interests in clear and objective ways. In addition, the BRNN language model was adopted to analyze movie-related data collected in real time and predict movies preferred by users. The system was assessed for its performance with grid searches to examine the fitness of the learning model for the entire size of word sets. The results show that the learning model of the system recorded a mean cross-validation index of 97.9% according to the entire size of word sets, thus proving its fitness. The model recorded a RMSE of 0.66 and 0.805 against the movie ratings on Naver and LSTM model language model, respectively, demonstrating the system's superior performance in predicting movie ratings.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.45-52
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• 2023

The power generation method using expensive diesel has operation problems such as high cost diesel generator and a lack of reserved power due to increase of power demand in some islands, requiring expansion of power generation facilities. To solve this problems, it is necessary to improve the efficiency of power generation facilities through an ORC(Organic Rankin Cycle) system application that uses waste heat as a heat source. Therefore, localized application technology of price competitive and highly reliable ORC power generation system is needed, and optimization technology of generators is having great effect, so this study performed two generator designs to get a high-efficiency generator with an optimized 30kW output. The comparison of simulation data for two designed models showed that a generator with SPM factor of 46.2% had an efficiency of 92.1% and a power ouput of about 23.2kW based on 12,000rpm, a generator with SPM factor of 44.46%, had a power output of 27.9kW and efficiency of 93.6% based on above rpm. For the verification of improved design model with SPM factor of 44.46%, the prototype test system with 110kW motor dynamometer was installed and got to the efficiency of 92.08% with conditions of the rated capacity 25kW at 12,000rpm, the test results of prototype generator showed the validity of generator design.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.6B
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• pp.503-512
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• 2009

Watershed models, which are a tool for water cycle mechanism, are classified as the distributed model and the lumped model. Currently, the distributed models have been more widely used than lumped model for many researches and applications. The lumped model estimates the parameters in the conceptual and empirical sense, on the other hand, in the case of distributed model the first-guess value is estimated from the grid-based watershed characteristics and rainfall data. Therefore, the distributed model needs more detailed parameter adjustment in its calibration and also one should precisely understand the model parameters' characteristics and sensitivity. This study uses Jungnang basin as a study area and $Vflo^{TM}$ model, which is a physics-based distributed hydrologic model, is used to analyze its parameters' sensitivity. To begin with, 100 years frequency-design rainfall is derived from Huff's method for rainfall duration of 6 hours, then the discharge is simulated using the calibrated parameters of $Vflo^{TM}$ model. As a result, hydraulic conductivity and overland's roughness have an effect on runoff depth and peak discharge, respectively, while channel's roughness have influence on travel time and peak discharge.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.187-204
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• 2016

Document classification based on emotional polarity has become a welcomed emerging task owing to the great explosion of data on the Web. In the big data age, there are too many information sources to refer to when making decisions. For example, when considering travel to a city, a person may search reviews from a search engine such as Google or social networking services (SNSs) such as blogs, Twitter, and Facebook. The emotional polarity of positive and negative reviews helps a user decide on whether or not to make a trip. Sentiment analysis of customer reviews has become an important research topic as datamining technology is widely accepted for text mining of the Web. Sentiment analysis has been used to classify documents through machine learning techniques, such as the decision tree, neural networks, and support vector machines (SVMs). is used to determine the attitude, position, and sensibility of people who write articles about various topics that are published on the Web. Regardless of the polarity of customer reviews, emotional reviews are very helpful materials for analyzing the opinions of customers through their reviews. Sentiment analysis helps with understanding what customers really want instantly through the help of automated text mining techniques. Sensitivity analysis utilizes text mining techniques on text on the Web to extract subjective information in the text for text analysis. Sensitivity analysis is utilized to determine the attitudes or positions of the person who wrote the article and presented their opinion about a particular topic. In this study, we developed a model that selects a hot topic from user posts at China's online stock forum by using the k-means algorithm and self-organizing map (SOM). In addition, we developed a detecting model to predict a hot topic by using machine learning techniques such as logit, the decision tree, and SVM. We employed sensitivity analysis to develop our model for the selection and detection of hot topics from China's online stock forum. The sensitivity analysis calculates a sentimental value from a document based on contrast and classification according to the polarity sentimental dictionary (positive or negative). The online stock forum was an attractive site because of its information about stock investment. Users post numerous texts about stock movement by analyzing the market according to government policy announcements, market reports, reports from research institutes on the economy, and even rumors. We divided the online forum's topics into 21 categories to utilize sentiment analysis. One hundred forty-four topics were selected among 21 categories at online forums about stock. The posts were crawled to build a positive and negative text database. We ultimately obtained 21,141 posts on 88 topics by preprocessing the text from March 2013 to February 2015. The interest index was defined to select the hot topics, and the k-means algorithm and SOM presented equivalent results with this data. We developed a decision tree model to detect hot topics with three algorithms: CHAID, CART, and C4.5. The results of CHAID were subpar compared to the others. We also employed SVM to detect the hot topics from negative data. The SVM models were trained with the radial basis function (RBF) kernel function by a grid search to detect the hot topics. The detection of hot topics by using sentiment analysis provides the latest trends and hot topics in the stock forum for investors so that they no longer need to search the vast amounts of information on the Web. Our proposed model is also helpful to rapidly determine customers' signals or attitudes towards government policy and firms' products and services.

• Journal of the Korean Institute of Landscape Architecture
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• v.40 no.6
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• pp.148-160
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• 2012

The main purpose of this paper is to deliver a climate analysis and evaluation method based on GIS by using airborne LiDAR data and Biotop type map and to provide spatial information of climate analysis and evaluation based on Biotop type Map. At first stage, the area, slope, slope length, surface, wind corridor function and width, and obstacle factors were analyzed to obtain cold/fresh air production and wind corridor evaluation. In addition, climate evaluation was derived from those two results in the second stage. Airborne LiDAR data are useful in wind corridor analysis during the study. Correlation analysis results show that ColdAir_GRD grade was highly correlated with Surface_GRD (-0.967461139) and WindCorridor_ GRD was highly correlated with Function_GRD (-0.883883476) and Obstacle_GRD (-0.834057656). Climate Evaluation GRID was highly correlated with WindCorridor_GRD (0.927554516) than ColdAir_GRD (0.855051646). Visual validations of climate analysis and evaluation results were performed by using aerial ortho-photo image, which shows that the climate evaluation results were well related with in-situ condition. At the end, we applied climate analysis and evaluation by using Biotop map and airborne LiDAR data in Gwangmyung-Shiheung City, candidate for the Bogeumjari Housing District. The results show that the aerial percentile of the 1st Grade is 18.5%, 2nd Grade is 18.2%, 3rd Grade is 30.7%, 4th Grade is 25.2%, and 5th Grade is 7.4%. This study process provided both the spatial analysis and evaluation of climate information and statistics on behalf of each Biotop type.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1505-1514
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• 2022

Image matching is a crucial preprocessing step for effective utilization of multi-temporal and multi-sensor very high resolution (VHR) satellite images. Deep learning (DL) method which is attracting widespread interest has proven to be an efficient approach to measure the similarity between image pairs in quick and accurate manner by extracting complex and detailed features from satellite images. However, Image matching of VHR satellite images remains challenging due to limitations of DL models in which the results are depending on the quantity and quality of training dataset, as well as the difficulty of creating training dataset with VHR satellite images. Therefore, this study examines the feasibility of DL-based method in matching pair extraction which is the most time-consuming process during image registration. This paper also aims to analyze factors that affect the accuracy based on the configuration of training dataset, when developing training dataset from existing multi-sensor VHR image database with bias for DL-based image matching. For this purpose, the generated training dataset were composed of correct matching pairs and incorrect matching pairs by assigning true and false labels to image pairs extracted using a grid-based Scale Invariant Feature Transform (SIFT) algorithm for a total of 12 multi-temporal and multi-sensor VHR images. The Siamese convolutional neural network (SCNN), proposed for matching pair extraction on constructed training dataset, proceeds with model learning and measures similarities by passing two images in parallel to the two identical convolutional neural network structures. The results from this study confirm that data acquired from VHR satellite image database can be used as DL training dataset and indicate the potential to improve efficiency of the matching process by appropriate configuration of multi-sensor images. DL-based image matching techniques using multi-sensor VHR satellite images are expected to replace existing manual-based feature extraction methods based on its stable performance, thus further develop into an integrated DL-based image registration framework.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.229-256
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• 2023

The increase in telecommuting and household electricity demand due to the pandemic has led to significant changes in electricity demand patterns. This has led to difficulties in identifying KEPCO's PPA (power purchase agreements) and residential solar power generation and has added to the challenges of electricity demand forecasting and grid operation for power exchanges. Unlike other energy resources, electricity is difficult to store, so it is essential to maintain a balance between energy production and consumption. A shortage or overproduction of electricity can cause significant instability in the energy system, so it is necessary to manage the supply and demand of electricity effectively. Especially in the Fourth Industrial Revolution, the importance of data has increased, and problems such as large-scale fires and power outages can have a severe impact. Therefore, in the field of electricity, it is crucial to accurately predict the amount of power generation, such as renewable energy, along with the exact demand for electricity, for proper power generation management, which helps to reduce unnecessary power production and efficiently utilize energy resources. In this study, we reviewed the renewable energy generation forecasting system, its objectives, and practical applications to construct optimal aggregated power resources using data from 169 power plants provided by the Ministry of Trade, Industry, and Energy, developed an aggregation algorithm considering the settlement of the forecasting system, and applied it to the analytical logic to synthesize and interpret the results. This study developed an optimal aggregation algorithm and derived an aggregation configuration (Result_Number 546) that reached 80.66% of the maximum settlement amount and identified plants that increase the settlement amount (B1783, B1729, N6002, S5044, B1782, N6006) and plants that decrease the settlement amount (S5034, S5023, S5031) when aggregating plants. This study is significant as the first study to develop an optimal aggregation algorithm using aggregated power resources as a research unit, and we expect that the results of this study can be used to improve the stability of the power system and efficiently utilize energy resources.

• Journal of Korean Society of Transportation
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• v.19 no.4
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• pp.35-47
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• 2001

Traffic represents one of the largest sources of primary air pollutants in urban area. As a consequence. numerous abatement strategies are being pursued to decrease the ambient concentration of pollutants. A characteristic of most of the these strategies is a requirement for accurate data on both the quantity and spatial distribution of emissions to air in the form of an atmospheric emission inventory database. In the case of traffic pollution, such an inventory must be compiled using activity statistics and emission factors for vehicle types. The majority of inventories are compiled using passive data from either surveys or transportation models and by their very nature tend to be out-of-date by the time they are compiled. The study of current trends are towards integrating urban traffic control systems and assessments of the environmental effects of motor vehicles. In this study, a methodology of motor vehicle emission calculation by using real-time traffic data was studied. A methodology for estimating emissions of CO at a test area in Seoul. Traffic data, which are required on a street-by-street basis, is obtained from induction loops of traffic control system. It was calculated speed-related mass of CO emission from traffic tail pipe of data from traffic system, and parameters are considered, volume, composition, average velocity, link length. And, the result was compared with that of a method of emission calculation by VKT(Vehicle Kilometer Travelled) of vehicles of category.

• Journal of Internet Computing and Services
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• v.13 no.1
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• pp.37-44
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• 2012

Numerous scientists who are engaged in compute-intensive researches require more computing facilities than before, while the computing resource and techniques are increasingly becoming more advanced. For this reason, many works for e-Science environment have been actively invested and established around the world, but still the scientists look for an intuitive experimental environment, which is guaranteed the improved environmental facilities without additional configurations or installations. In this paper, we present an integrated scientific workflow execution environment for Scientific applications supporting workflow design with high performance computing infrastructure and accessibility for web browser. This portal supports automated consecutive execution of computation jobs in order of the form defined by workflow design tool and execution service concerning characteristics of each job to batch over distributed grid resources. Workflow editor of the portal presents a high-level frontend and easy-to-use interface with monitoring service, which shows the status of workflow execution in real time so that user can check the intermediate data during experiments. Therefore, the scientists can take advantages of the environment to improve the productivity of study based on HTC.

• Korean Journal of Agricultural and Forest Meteorology
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• v.5 no.3
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• pp.200-207
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• 2003

'||'||'||'&'||'||'||'quot;Overheating index'||'||'||'&'||'||'||'quot;, the normalized difference in incident solar energy between a target surface and a level surface, is helpful in estimating the spatial variation in daily maximum temperature at the landscape scale. It can be computed as the ratio of the 4-hour cumulative solar irradiance surplus or deficit from that over a level surface to the maximum possible deviation (15 MJ $m^{-2}$ ) during the midafternoon. Ecosystem models may, for simplicity, use an empirical proxy (exposure index) variable combining slope and aspect in place of the overheating index to account for the variation of midafternoon solar irradiance. A comparative study with real-world landscape data was carried out to evaluate the performance of exposure index in replacing the overheating index. Overheating indices for summer solstice, fall equinox and winter solstice were calculated at 573,650 grid cells constituting the land surface of Donggye-Myun, Sunchang County in Korea, based on a 10-m DEM. Exposure index was also calculated for the same area and fitted for the variation of overheating index to derive a 2$^{nd}$ -order linear regression equation. The coefficient of determination ($R^2$) was 0.50 on summer solstice, 0.56 on fall equinox, and 0.44 on winter solstice, respectively. These are much lower than the theoretically calculated $R^2$ values ranging from 0.7 in summer to 0.9 in autumn. According to our study, exposure index failed to accurately predict the cumulative solar irradiance over a complex terrain, hindering its application to daily maximum temperature estimation. We suggest direct calculation of the overheating index in preference to using the exposure index.