• The Journal of the Acoustical Society of Korea
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• v.21 no.4
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• pp.339-438
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• 2002

This paper presents a method to simulate noise propagation by a computer for outdoor environment. Sound propagated in 3 dimensional space generates reflected waves whenever it hits boundary surfaces. If a receiver is away from a sound source, it receives multiple sound waves which are reflected from various boundary surfaces in space. The algorithm being developed in this paper is based on a ray sound theory. If we get 3 dimensional geometry input as well as sound sources, we can compute sound effects all over the boundary surfaces. In this paper, we present two approaches to compute sound: the first approach, called forward tracing, traces sounds forwards from sound sources. while the second approach, called geometry based computation, computes possible propagation routes between sources and receivers. We compare two approaches and suggest the geometry based sound computation for outdoor simulation. Also this approach is very efficient in the sense we can save computational time compared to the forward sound tracing. Sound due to impulse-response is governed by physical environments. When a sound source waveform and numerically computed impulse in time is convoluted, the result generates a synthetic sound. This technique can be easily generalized to synthesize realistic stereo sounds for virtual reality, while the simulation result is visualized using VRML.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.111-126
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• 2017

Recommender systems based on association rule mining significantly contribute to seller's sales by reducing consumers' time to search for products that they want. Recommendations based on the frequency of transactions such as orders can effectively screen out the products that are statistically marketable among multiple products. A product with a high possibility of sales, however, can be omitted from the recommendation if it records insufficient number of transactions at the beginning of the sale. Products missing from the associated recommendations may lose the chance of exposure to consumers, which leads to a decline in the number of transactions. In turn, diminished transactions may create a vicious circle of lost opportunity to be recommended. Thus, initial sales are likely to remain stagnant for a certain period of time. Products that are susceptible to fashion or seasonality, such as clothing, may be greatly affected. This study was aimed at expanding association rules to include into the list of recommendations those products whose initial trading frequency of transactions is low despite the possibility of high sales. The particular purpose is to predict the strength of the direct connection of two unconnected items through the properties of the paths located between them. An association between two items revealed in transactions can be interpreted as the interaction between them, which can be expressed as a link in a social network whose nodes are items. The first step calculates the centralities of the nodes in the middle of the paths that indirectly connect the two nodes without direct connection. The next step identifies the number of the paths and the shortest among them. These extracts are used as independent variables in the regression analysis to predict future connection strength between the nodes. The strength of the connection between the two nodes of the model, which is defined by the number of nodes between the two nodes, is measured after a certain period of time. The regression analysis results confirm that the number of paths between the two products, the distance of the shortest path, and the number of neighboring items connected to the products are significantly related to their potential strength. This study used actual order transaction data collected for three months from February to April in 2016 from an online commerce company. To reduce the complexity of analytics as the scale of the network grows, the analysis was performed only on miscellaneous goods. Two consecutively purchased items were chosen from each customer's transactions to obtain a pair of antecedent and consequent, which secures a link needed for constituting a social network. The direction of the link was determined in the order in which the goods were purchased. Except for the last ten days of the data collection period, the social network of associated items was built for the extraction of independent variables. The model predicts the number of links to be connected in the next ten days from the explanatory variables. Of the 5,711 previously unconnected links, 611 were newly connected for the last ten days. Through experiments, the proposed model demonstrated excellent predictions. Of the 571 links that the proposed model predicts, 269 were confirmed to have been connected. This is 4.4 times more than the average of 61, which can be found without any prediction model. This study is expected to be useful regarding industries whose new products launch quickly with short life cycles, since their exposure time is critical. Also, it can be used to detect diseases that are rarely found in the early stages of medical treatment because of the low incidence of outbreaks. Since the complexity of the social networking analysis is sensitive to the number of nodes and links that make up the network, this study was conducted in a particular category of miscellaneous goods. Future research should consider that this condition may limit the opportunity to detect unexpected associations between products belonging to different categories of classification.

• Journal of Practical Agriculture & Fisheries Research
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• v.22 no.2
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• pp.99-114
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• 2020

In this study we examined the topic analysis and correlation analysis by text mining from the self introduction letter of freshman at Korea National College of Agriculture and Fisheries(KNCAF) in 2020. The analysis items of the 3rd question were and the 4th question were the motivation for applying to college, the academic plan and the career plan. The text mining to the 3rd question showed that the frequency of 'friends' was overwhelmingly high, followed by keywords such as 'thought', 'time', 'opinion', 'activity', and 'club'. In the 4th question, keyword frequency such as 'thought', 'agriculture', 'KNCAF', 'farm', 'father' was high. The result of association rules analysis for each question showed that the relationship with the highest support level, which means the frequency and importance of the rule, was the {friend} <=> {thought}, {thought} <=> {KNCAF}. The confidence level of a correlation between keywords was the highest in the rules of {teacher}=>{friend}, {agriculture, KNCAF}=>{thought}. Also the lift level that indicates the closeness of two words was the highest in the rules of {friend} <=> {teacher}, {knowledge} <=> {professional}. These keywords are found to play a very important roles in analyzing betweenness centrality and analyzing degree centrality between keywords. The results of frequency analysis and association analysis were visualized with word cloud and correlation graphs to make it easier to understand all the results.

• Journal of the Korean earth science society
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• v.43 no.2
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• pp.239-252
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• 2022

Quality control methods for the first G-band vapor radiometer (GVR) mounted on a weather aircraft in Korea were developed using the GVR Precipitable Water Vapor (PWV). The aircraft attitude information (degree of pitch and roll) was applied to quality control to select the shortest vertical path of the GVR beam. In addition, quality control was applied to remove a GVR PWV ≥20 mm. It was found that the difference between the warm load average power and sky load average power converged to near 0 when the GVR PWV increased to 20 mm or higher. This could be due to the high brightness temperature of the substratus and mesoclouds, which was confirmed by the Communication, Ocean and Meteorological Satellite (COMS) data (cloud type, cloud top height, and cloud amount), cloud combination probe (CCP), and precipitation imaging probe (PIP). The GVR PWV before and after the application of quality control on a cloudy day was quantitatively compared with that of a local data assimilation and prediction system (LDAPS). The Root Mean Square Difference (RMSD) decreased from 2.9 to 1.8 mm and the RMSD with Korea Local Analysis and Precipitation System (KLAPS) decreased from 5.4 to 4.3 mm, showing improved accuracy. In addition, the quality control effectiveness of GVR PWV suggested in this study was verified through comparison with the COMS PWV by using the GVR PWV applied with quality control and the dropsonde PWV.