• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.19 no.1
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• pp.94-131
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• 2007

A review on the papers published in the Korean Journal of Air-Conditioning and Refrigerating Engineering in 2004 and 2005 has been done. Focus has been put on current status of research in the aspect of heating, cooling, air-conditioning, ventilation, sanitation and building environment. The conclusions are as follows. (1) Most of fundamental studies on fluid flow were related with heat transportation of facilities. Drop formation and rivulet flow on solid surfaces were interesting topics related with condensation augmentation. Research on micro environment considering flow, heat, humidity was also interesting for comfortable living environment. It can be extended considering biological aspects. Development of fans and blowers of high performance and low noise were continuing topics. Well developed CFD and flow visualization(PIV, PTV and LDV methods) technologies were widely applied for developing facilities and their systems. (2) The research trends of the previous two yews are surveyed as groups of natural convection, forced convection, electronic cooling, heat transfer enhancement, frosting and defrosting, thermal properties, etc. New research topics introduced include natural convection heat transfer enhancement using nanofluid, supercritical cooling performance or oil miscibility of $CO_2$, enthalpy heat exchanger for heat recovery, heat transfer enhancement in a plate heat exchanger using fluid resonance. (3) The literature for the last two years($2004{\sim}2005$) is reviewed in the areas of heat pump, ice and water storage, cycle analysis and reused energy including geothermal, solar and unused energy). The research on cycle analysis and experiments for $CO_2$ was extensively carried out to replace the Ozone depleting and global warming refrigerants such as HFC and HCFC refrigerants. From the year of 2005, the Gas Engine Heat Pump(GHP) has been paid attention from the viewpoint of the gas cooling application. The heat pipe was focused on the performance improvement by the parametric analysis and the heat recovery applications. The storage systems were studied on the performance enhancement of the storage tank and cost analysis for heating and cooling applications. In the area of unused energy, the hybrid systems were extensively introduced and the life cycle cost analysis(LCCA) for the unused energy systems was also intensively carried out. (4) Recent studies of various refrigeration and air-conditioning systems have focused on the system performance and efficiency enhancement. Heat transfer characteristics during evaporation and condensation are investigated for several tube shapes and of alternative refrigerants including carbon dioxide. Efficiency of various compressors and expansion devices are also dealt with for better modeling and, in particular, performance improvement. Thermoelectric module and cooling systems are analyzed theoretically and experimentally. (5) According to the review of recent studies on ventilation systems, an appropriate ventilation systems including machenical and natural are required to satisfied the level of IAQ. Also, an recent studies on air-conditioning and absorption refrigeration systems, it has mainly focused on distribution and dehumidification of indoor air to improve the performance were carried out. (6) Based on a review of recent studies on indoor environment and building service systems, it is noticed that research issues have mainly focused on optimal thermal comfort, improvement of indoor air Quality and many innovative systems such as air-barrier type perimeter-less system with UFAC, radiant floor heating and cooling system and etc. New approaches are highlighted for improving indoor environmental condition as well as minimizing energy consumption, various activities of building control and operation strategy and energy performance analysis for economic evaluation.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.173-198
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• 2020

For a long time, many studies have been conducted on predicting the success of campaigns for customers in academia, and prediction models applying various techniques are still being studied. Recently, as campaign channels have been expanded in various ways due to the rapid revitalization of online, various types of campaigns are being carried out by companies at a level that cannot be compared to the past. However, customers tend to perceive it as spam as the fatigue of campaigns due to duplicate exposure increases. Also, from a corporate standpoint, there is a problem that the effectiveness of the campaign itself is decreasing, such as increasing the cost of investing in the campaign, which leads to the low actual campaign success rate. Accordingly, various studies are ongoing to improve the effectiveness of the campaign in practice. This campaign system has the ultimate purpose to increase the success rate of various campaigns by collecting and analyzing various data related to customers and using them for campaigns. In particular, recent attempts to make various predictions related to the response of campaigns using machine learning have been made. It is very important to select appropriate features due to the various features of campaign data. If all of the input data are used in the process of classifying a large amount of data, it takes a lot of learning time as the classification class expands, so the minimum input data set must be extracted and used from the entire data. In addition, when a trained model is generated by using too many features, prediction accuracy may be degraded due to overfitting or correlation between features. Therefore, in order to improve accuracy, a feature selection technique that removes features close to noise should be applied, and feature selection is a necessary process in order to analyze a high-dimensional data set. Among the greedy algorithms, SFS (Sequential Forward Selection), SBS (Sequential Backward Selection), SFFS (Sequential Floating Forward Selection), etc. are widely used as traditional feature selection techniques. It is also true that if there are many risks and many features, there is a limitation in that the performance for classification prediction is poor and it takes a lot of learning time. Therefore, in this study, we propose an improved feature selection algorithm to enhance the effectiveness of the existing campaign. The purpose of this study is to improve the existing SFFS sequential method in the process of searching for feature subsets that are the basis for improving machine learning model performance using statistical characteristics of the data to be processed in the campaign system. Through this, features that have a lot of influence on performance are first derived, features that have a negative effect are removed, and then the sequential method is applied to increase the efficiency for search performance and to apply an improved algorithm to enable generalized prediction. Through this, it was confirmed that the proposed model showed better search and prediction performance than the traditional greed algorithm. Compared with the original data set, greed algorithm, genetic algorithm (GA), and recursive feature elimination (RFE), the campaign success prediction was higher. In addition, when performing campaign success prediction, the improved feature selection algorithm was found to be helpful in analyzing and interpreting the prediction results by providing the importance of the derived features. This is important features such as age, customer rating, and sales, which were previously known statistically. Unlike the previous campaign planners, features such as the combined product name, average 3-month data consumption rate, and the last 3-month wireless data usage were unexpectedly selected as important features for the campaign response, which they rarely used to select campaign targets. It was confirmed that base attributes can also be very important features depending on the type of campaign. Through this, it is possible to analyze and understand the important characteristics of each campaign type.