• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.5
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• pp.185-196
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• 2023

This paper investigates the effects of entrepreneurs' cognitive biases on business opportunity evaluation, given their strong entrepreneurial spirit, which is characterized by innovation, proactivity, and risk-taking. When making decisions related to business activities, entrepreneurs typically make rational judgments based on their knowledge, experience, and the advice of external experts. However, in situations of extreme stress or when quick decisions are required, they often rely on heuristics based on their cognitive biases. In particular, we often see cases where entrepreneurs fail because they make decisions based on heuristics in the process of evaluating and selecting new business opportunities that are planned to guarantee the growth and sustainability of their companies. This study was conducted in response to the need for research to clarify the effects of entrepreneurs' cognitive biases on new business opportunity evaluation, given that the cognitive biases of entrepreneurs, which are formed by repeated successful experiences, can sometimes lead to business failure. Although there have been many studies on the effects of cognitive biases on entrepreneurship and opportunity evaluation among university students and general people who aspire to start a business, there have been few studies that have clarified the relationship between cognitive biases and social networks among entrepreneurs. In contrast to previous studies, this study conducted empirical surveys of entrepreneurs only, and also conducted research on the relationship with social networks. For the study, a survey was conducted using a parallel survey method using online mobile surveys and self-report questionnaires from 150 entrepreneurs of small and medium-sized enterprises. The results of the study showed that 'overconfidence' and 'illusion of control', among the independent variables of entrepreneurs' cognitive biases, had a statistically significant positive(+) effect on business opportunity evaluation. In addition, it was confirmed that the moderating variable, social network, moderates the effect of overconfidence on business opportunity evaluation. This study showed that entrepreneurs' cognitive biases play a role in the process of evaluating and selecting new business opportunities, and that social networks play a role in moderating the structural relationship between entrepreneurs' cognitive biases and business opportunity evaluation. This study is expected to be of great help not only to entrepreneurs, but also to entrepreneur education and policy making, by showing how entrepreneurs can use cognitive biases in a positive way and the influence of social networks.

• Journal of the Society of Disaster Information
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• v.20 no.3
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• pp.705-713
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• 2024

Purpose: Currently, the only routes that enter Yeongjong Island are Yeongjong Bridge and Incheon Bridge, which are private roads. The purpose of this study is to predict and study changes in transportation demand for new routes and two existing routes according to the plan to open the 3rd Bridge, a new route, in December 2025. Method: The basic data for traffic demand forecast were O/D and NETWORK data from 2021.08, KOTI. In order to examine the reliable impact of Yeongjong Bridge and Incheon Bridge on the opening of the 3rd Bridge, it is necessary to correct the traffic distribution of Yeongjong Island and Incheon International Airport to suit reality, and in this study, the trip distribution by region was corrected and applied using Mobile Big Data. Result: As of 2026, the scheduled year of the opening of the 3rd Bridge, two alternatives, Alternative 1 (2,000 won) and Alternative 2 (4,000 won), were established and future transportation demand analysis was conducted, In the case of Alternative 1, which is similar to the existing private road toll restructuring, the traffic volume of the 3rd Bridge was predicted to be 42,836 out of 199,101 veh/day in the Yeongjong area in 2026, and the traffic volume reduction rate of the existing road was analyzed as 21.5%. Conlclusion: As a result of the review (based on Alternative 1), the proportion of convertted traffic on the 3rd Yanji Bridge was estimated to be 70% of Yeongjong Bridge and 30% of Incheon Bridge, and 21.5% of the predicted traffic reduction on the existing road when the 3rd Yanji Bridge was opened is considered appropriate considering the results of the case review and changes in conditions. It is judged that it is a way to secure the reliability of the prediction of traffic demand because communication big data is used to reflect more realistic traffic distribution when predicting future traffic demand.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.57-77
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• 2013

Social media has become one of the most popular media in web and mobile application. In 2011, social networks and blogs are still the top destination of online users, according to a study from Nielsen Company. In their studies, nearly 4 in 5active users visit social network and blog. Social Networks and Blogs sites rule Americans' Internet time, accounting to 23 percent of time spent online. Facebook is the main social network that the U.S internet users spend time more than the other social network services such as Yahoo, Google, AOL Media Network, Twitter, Linked In and so on. In recent trend, most of the companies promote their products in the Facebook by creating the "Facebook Page" that refers to specific product. The "Like" option allows user to subscribed and received updates their interested on from the page. The film makers which produce a lot of films around the world also take part to market and promote their films by exploiting the advantages of using the "Facebook Page". In addition, a great number of streaming service providers allows users to subscribe their service to watch and enjoy movies and TV program. They can instantly watch movies and TV program over the internet to PCs, Macs and TVs. Netflix alone as the world's leading subscription service have more than 30 million streaming members in the United States, Latin America, the United Kingdom and the Nordics. As the matter of facts, a million of movies and TV program with different of genres are offered to the subscriber. In contrast, users need spend a lot time to find the right movies which are related to their interest genre. Recent years there are many researchers who have been propose a method to improve prediction the rating or preference that would give the most related items such as books, music or movies to the garget user or the group of users that have the same interest in the particular items. One of the most popular methods to build recommendation system is traditional Collaborative Filtering (CF). The method compute the similarity of the target user and other users, which then are cluster in the same interest on items according which items that users have been rated. The method then predicts other items from the same group of users to recommend to a group of users. Moreover, There are many items that need to study for suggesting to users such as books, music, movies, news, videos and so on. However, in this paper we only focus on movie as item to recommend to users. In addition, there are many challenges for CF task. Firstly, the "sparsity problem"; it occurs when user information preference is not enough. The recommendation accuracies result is lower compared to the neighbor who composed with a large amount of ratings. The second problem is "cold-start problem"; it occurs whenever new users or items are added into the system, which each has norating or a few rating. For instance, no personalized predictions can be made for a new user without any ratings on the record. In this research we propose a clustering method according to the users' genre interest extracted from social network service (SNS) and user's movies rating information system to solve the "cold-start problem." Our proposed method will clusters the target user together with the other users by combining the user genre interest and the rating information. It is important to realize a huge amount of interesting and useful user's information from Facebook Graph, we can extract information from the "Facebook Page" which "Like" by them. Moreover, we use the Internet Movie Database(IMDb) as the main dataset. The IMDbis online databases that consist of a large amount of information related to movies, TV programs and including actors. This dataset not only used to provide movie information in our Movie Rating Systems, but also as resources to provide movie genre information which extracted from the "Facebook Page". Formerly, the user must login with their Facebook account to login to the Movie Rating System, at the same time our system will collect the genre interest from the "Facebook Page". We conduct many experiments with other methods to see how our method performs and we also compare to the other methods. First, we compared our proposed method in the case of the normal recommendation to see how our system improves the recommendation result. Then we experiment method in case of cold-start problem. Our experiment show that our method is outperform than the other methods. In these two cases of our experimentation, we see that our proposed method produces better result in case both cases.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.143-163
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• 2016

The demographics of Internet users are the most basic and important sources for target marketing or personalized advertisements on the digital marketing channels which include email, mobile, and social media. However, it gradually has become difficult to collect the demographics of Internet users because their activities are anonymous in many cases. Although the marketing department is able to get the demographics using online or offline surveys, these approaches are very expensive, long processes, and likely to include false statements. Clickstream data is the recording an Internet user leaves behind while visiting websites. As the user clicks anywhere in the webpage, the activity is logged in semi-structured website log files. Such data allows us to see what pages users visited, how long they stayed there, how often they visited, when they usually visited, which site they prefer, what keywords they used to find the site, whether they purchased any, and so forth. For such a reason, some researchers tried to guess the demographics of Internet users by using their clickstream data. They derived various independent variables likely to be correlated to the demographics. The variables include search keyword, frequency and intensity for time, day and month, variety of websites visited, text information for web pages visited, etc. The demographic attributes to predict are also diverse according to the paper, and cover gender, age, job, location, income, education, marital status, presence of children. A variety of data mining methods, such as LSA, SVM, decision tree, neural network, logistic regression, and k-nearest neighbors, were used for prediction model building. However, this research has not yet identified which data mining method is appropriate to predict each demographic variable. Moreover, it is required to review independent variables studied so far and combine them as needed, and evaluate them for building the best prediction model. The objective of this study is to choose clickstream attributes mostly likely to be correlated to the demographics from the results of previous research, and then to identify which data mining method is fitting to predict each demographic attribute. Among the demographic attributes, this paper focus on predicting gender, age, marital status, residence, and job. And from the results of previous research, 64 clickstream attributes are applied to predict the demographic attributes. The overall process of predictive model building is compose of 4 steps. In the first step, we create user profiles which include 64 clickstream attributes and 5 demographic attributes. The second step performs the dimension reduction of clickstream variables to solve the curse of dimensionality and overfitting problem. We utilize three approaches which are based on decision tree, PCA, and cluster analysis. We build alternative predictive models for each demographic variable in the third step. SVM, neural network, and logistic regression are used for modeling. The last step evaluates the alternative models in view of model accuracy and selects the best model. For the experiments, we used clickstream data which represents 5 demographics and 16,962,705 online activities for 5,000 Internet users. IBM SPSS Modeler 17.0 was used for our prediction process, and the 5-fold cross validation was conducted to enhance the reliability of our experiments. As the experimental results, we can verify that there are a specific data mining method well-suited for each demographic variable. For example, age prediction is best performed when using the decision tree based dimension reduction and neural network whereas the prediction of gender and marital status is the most accurate by applying SVM without dimension reduction. We conclude that the online behaviors of the Internet users, captured from the clickstream data analysis, could be well used to predict their demographics, thereby being utilized to the digital marketing.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.1-19
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• 2018

Large amount of data is now available for research and business sectors to extract knowledge from it. This data can be in the form of unstructured data such as audio, text, and image data and can be analyzed by deep learning methodology. Deep learning is now widely used for various estimation, classification, and prediction problems. Especially, fashion business adopts deep learning techniques for apparel recognition, apparel search and retrieval engine, and automatic product recommendation. The core model of these applications is the image classification using Convolutional Neural Networks (CNN). CNN is made up of neurons which learn parameters such as weights while inputs come through and reach outputs. CNN has layer structure which is best suited for image classification as it is comprised of convolutional layer for generating feature maps, pooling layer for reducing the dimensionality of feature maps, and fully-connected layer for classifying the extracted features. However, most of the classification models have been trained using online product image, which is taken under controlled situation such as apparel image itself or professional model wearing apparel. This image may not be an effective way to train the classification model considering the situation when one might want to classify street fashion image or walking image, which is taken in uncontrolled situation and involves people's movement and unexpected pose. Therefore, we propose to train the model with runway apparel image dataset which captures mobility. This will allow the classification model to be trained with far more variable data and enhance the adaptation with diverse query image. To achieve both convergence and generalization of the model, we apply Transfer Learning on our training network. As Transfer Learning in CNN is composed of pre-training and fine-tuning stages, we divide the training step into two. First, we pre-train our architecture with large-scale dataset, ImageNet dataset, which consists of 1.2 million images with 1000 categories including animals, plants, activities, materials, instrumentations, scenes, and foods. We use GoogLeNet for our main architecture as it has achieved great accuracy with efficiency in ImageNet Large Scale Visual Recognition Challenge (ILSVRC). Second, we fine-tune the network with our own runway image dataset. For the runway image dataset, we could not find any previously and publicly made dataset, so we collect the dataset from Google Image Search attaining 2426 images of 32 major fashion brands including Anna Molinari, Balenciaga, Balmain, Brioni, Burberry, Celine, Chanel, Chloe, Christian Dior, Cividini, Dolce and Gabbana, Emilio Pucci, Ermenegildo, Fendi, Giuliana Teso, Gucci, Issey Miyake, Kenzo, Leonard, Louis Vuitton, Marc Jacobs, Marni, Max Mara, Missoni, Moschino, Ralph Lauren, Roberto Cavalli, Sonia Rykiel, Stella McCartney, Valentino, Versace, and Yve Saint Laurent. We perform 10-folded experiments to consider the random generation of training data, and our proposed model has achieved accuracy of 67.2% on final test. Our research suggests several advantages over previous related studies as to our best knowledge, there haven't been any previous studies which trained the network for apparel image classification based on runway image dataset. We suggest the idea of training model with image capturing all the possible postures, which is denoted as mobility, by using our own runway apparel image dataset. Moreover, by applying Transfer Learning and using checkpoint and parameters provided by Tensorflow Slim, we could save time spent on training the classification model as taking 6 minutes per experiment to train the classifier. This model can be used in many business applications where the query image can be runway image, product image, or street fashion image. To be specific, runway query image can be used for mobile application service during fashion week to facilitate brand search, street style query image can be classified during fashion editorial task to classify and label the brand or style, and website query image can be processed by e-commerce multi-complex service providing item information or recommending similar item.

• 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.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.143-155
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• 2011

Lately electronic tagging policy for the sexual offenders was introduced in order to reduce and prevent sexual offences. However, most sexual offences against children happening these days are committed by the tagged offenders whose identities have been released. So, for the crime prevention, we need measures with which we could minimize the suffers more promptly and actively. This paper suggests a new system to relieve the sexual abuse related anxiety of the children and solve the problems that electronic bracelet has. Existing bracelets are only worn by serious criminals, and it's only for risk management and positioning, there is no way to protect the children who are the potential victims of sexual abuse and there actually happened some cases. So we suggest also letting the students(children) wear the LBS(Location Based Service) and USN(Ubiquitous Sensor Network) technology based electronic bracelets to monitor and figure out dangerous situations intelligently, so that we could prevent sexual offences against children beforehand, and while a crime is happening, we could judge the situation of the crime intelligently and take swift action to minimize the suffer. And by checking students' attendance and position, guardians could know where their children are in real time and could protect the children from not only sexual offences but also violent crimes against children like kidnapping. The overall system is like follows : RFID Tag for children monitors the approach of offenders. While an offender's RFID tag is approaching, it will transmit the situation and position as the first warning message to the control center and the guardians. When the offender is going far away, it turns to monitoring mode, and if the tag of the child or the offender is taken off or the child and offender stay at one position for 3~5 minutes or longer, then it will consider this as a dangerous situation, then transmit the emergency situations and position as the second warning message to the control center and the guardians, and ask for the dispatch of police to prevent the crime at the initial stage. The RFID module of criminals' electronic bracelets is RFID TAG, and the RFID module for the children is RFID receiver(reader), so wherever the offenders are, if an offender is at a place within 20m from a child, RFID module for children will transmit the situation every certain periods to the control center by the automatic response of the receiver. As for the positioning module, outdoors GPS or mobile communications module(CELL module)is used and UWB, WI-FI based module is used indoors. The sensor is set under the purpose of making it possible to measure the position coordinates even indoors, so that one could send his real time situation and position to the server of central control center. By using the RFID electronic roll book system of educational institutions and safety system installed at home, children's position and situation can be checked. When the child leaves for school, attendance can be checked through the electronic roll book, and when school is over the information is sent to the guardians. And using RFID access control turnstiles installed at the apartment or entrance of the house, the arrival of the children could be checked and the information is transmitted to the guardians. If the student is absent or didn't arrive at home, the information of the child is sent to the central control center from the electronic roll book or access control turnstiles, and look for the position of the child's electronic bracelet using GPS or mobile communications module, then send the information to the guardians and teacher so that they could report to the police immediately if necessary. Central management and control system is built under the purpose of monitoring dangerous situations and guardians' checking. It saves the warning and pattern data to figure out the areas with dangerous situation, and could help introduce crime prevention systems like CCTV with the highest priority. And by DB establishment personal data could be saved, the frequency of first and second warnings made, the terminal ID of the specific child and offender, warning made position, situation (like approaching, taken off of the electronic bracelet, same position for a certain time) and so on could be recorded, and the data is going to be used for preventing crimes. Even though we've already introduced electronic tagging to prevent recurrence of child sexual offences, but the crimes continuously occur. So I suggest this system to prevent crimes beforehand concerning the children's safety. If we make electronic bracelets easy to use and carry, and set the price reasonably so that many children can use, then lots of criminals could be prevented and we can protect the children easily. By preventing criminals before happening, it is going to be a helpful system for our safe life.

• Asia pacific journal of information systems
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• v.23 no.4
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• pp.53-84
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• 2013

With rapid development of ICT (Information and Communications Technology), new services by the convergence of mobile network and application technology began to appear. Today, smart phone with new ICT convergence network capabilities is exceedingly popular and very useful as a new tool for the development of business opportunities. Previous studies based on Technology Acceptance Model (TAM) suggested critical factors, which should be considered for acquiring new customers and maintaining existing users in smart phone market. However, they had a limitation to focus on technology acceptance, not value based approach. Prior studies on customer's adoption of electronic utilities like smart phone product showed that the antecedents such as the perceived benefit and the perceived sacrifice could explain the causality between what is perceived and what is acquired over diverse contexts. So, this research conceptualizes perceived value as a trade-off between perceived benefit and perceived sacrifice, and we need to research the perceived value to grasp user's continuous intention to use of smart phone. The purpose of this study is to investigate the structured relationship between benefit (quality, usefulness, playfulness) and sacrifice (technicality, cost, security risk) of smart phone users, perceived value, and continuous intention to use. In addition, this study intends to analyze the differences between two subgroups of smart phone users by the degree of personal innovativeness. Personal innovativeness could help us to understand the moderating effect between how perceptions are formed and continuous intention to use smart phone. This study conducted survey through e-mail, direct mail, and interview with smart phone users. Empirical analysis based on 330 respondents was conducted in order to test the hypotheses. First, the result of hypotheses testing showed that perceived usefulness among three factors of perceived benefit has the highest positive impact on perceived value, and then followed by perceived playfulness and perceived quality. Second, the result of hypotheses testing showed that perceived cost among three factors of perceived sacrifice has significantly negative impact on perceived value, however, technicality and security risk have no significant impact on perceived value. Also, the result of hypotheses testing showed that perceived value has significant direct impact on continuous intention to use of smart phone. In this regard, marketing managers of smart phone company should pay more attention to improve task efficiency and performance of smart phone, including rate systems of smart phone. Additionally, to test the moderating effect of personal innovativeness, this research conducted multi-group analysis by the degree of personal innovativeness of smart phone users. In a group with high level of innovativeness, perceived usefulness has the highest positive influence on perceived value than other factors. Instead, the analysis for a group with low level of innovativeness showed that perceived playfulness was the highest positive factor to influence perceived value than others. This result of the group with high level of innovativeness explains that innovators and early adopters are able to cope with higher level of cost and risk, and they expect to develop more positive intentions toward higher performance through the use of an innovation. Also, hedonic behavior in the case of the group with low level of innovativeness aims to provide self-fulfilling value to the users, in contrast to utilitarian perspective, which aims to provide instrumental value to the users. However, with regard to perceived sacrifice, both groups in general showed negative impact on perceived value. Also, the group with high level of innovativeness had less overall negative impact on perceived value compared to the group with low level of innovativeness across all factors. In both group with high level of innovativeness and with low level of innovativeness, perceived cost has the highest negative influence on perceived value than other factors. Instead, the analysis for a group with high level of innovativeness showed that perceived technicality was the positive factor to influence perceived value than others. However, the analysis for a group with low level of innovativeness showed that perceived security risk was the second high negative factor to influence perceived value than others. Unlike previous studies, this study focuses on influencing factors on continuous intention to use of smart phone, rather than considering initial purchase and adoption of smart phone. First, perceived value, which was used to identify user's adoption behavior, has a mediating effect among perceived benefit, perceived sacrifice, and continuous intention to use smart phone. Second, perceived usefulness has the highest positive influence on perceived value, while perceived cost has significant negative influence on perceived value. Third, perceived value, like prior studies, has high level of positive influence on continuous intention to use smart phone. Fourth, in multi-group analysis by the degree of personal innovativeness of smart phone users, perceived usefulness, in a group with high level of innovativeness, has the highest positive influence on perceived value than other factors. Instead, perceived playfulness, in a group with low level of innovativeness, has the highest positive factor to influence perceived value than others. This result shows that early adopters intend to adopt smart phone as a tool to make their job useful, instead market followers intend to adopt smart phone as a tool to make their time enjoyable. In terms of marketing strategy for smart phone company, marketing managers should pay more attention to identify their customers' lifetime value by the phase of smart phone adoption, as well as to understand their behavior intention to accept the risk and uncertainty positively. The academic contribution of this study primarily is to employ the VAM (Value-based Adoption Model) as a conceptual foundation, compared to TAM (Technology Acceptance Model) used widely by previous studies. VAM is useful for understanding continuous intention to use smart phone in comparison with TAM as a new IT utility by individual adoption. Perceived value dominantly influences continuous intention to use smart phone. The results of this study justify our research model adoption on each antecedent of perceived value as a benefit and a sacrifice component. While TAM could be widely used in user acceptance of new technology, it has a limitation to explain the new IT adoption like smart phone, because of customer behavior intention to choose the value of the object. In terms of theoretical approach, this study provides theoretical contribution to the development, design, and marketing of smart phone. The practical contribution of this study is to suggest useful decision alternatives concerned to marketing strategy formulation for acquiring and retaining long-term customers related to smart phone business. Since potential customers are interested in both benefit and sacrifice when evaluating the value of smart phone, marketing managers in smart phone company has to put more effort into creating customer's value of low sacrifice and high benefit so that customers will continuously have higher adoption on smart phone. Especially, this study shows that innovators and early adopters with high level of innovativeness have higher adoption than market followers with low level of innovativeness, in terms of perceived usefulness and perceived cost. To formulate marketing strategy for smart phone diffusion, marketing managers have to pay more attention to identify not only their customers' benefit and sacrifice components but also their customers' lifetime value to adopt smart phone.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.29 no.6
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• pp.327-340
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• 2017

This article reviews the papers published in the Korean Journal of Air-Conditioning and Refrigeration Engineering during 2016. It is intended to understand the status of current research in the areas of heating, cooling, ventilation, sanitation, and indoor environments of buildings and plant facilities. Conclusions are as follows. (1) The research works on the thermal and fluid engineering have been reviewed as groups of flow, heat and mass transfer, the reduction of pollutant exhaust gas, cooling and heating, the renewable energy system and the flow around buildings. CFD schemes were used more for all research areas. (2) Research works on heat transfer area have been reviewed in the categories of heat transfer characteristics, pool boiling and condensing heat transfer and industrial heat exchangers. Researches on heat transfer characteristics included the results of the long-term performance variation of the plate-type enthalpy exchange element made of paper, design optimization of an extruded-type cooling structure for reducing the weight of LED street lights, and hot plate welding of thermoplastic elastomer packing. In the area of pool boiling and condensing, the heat transfer characteristics of a finned-tube heat exchanger in a PCM (phase change material) thermal energy storage system, influence of flow boiling heat transfer on fouling phenomenon in nanofluids, and PCM at the simultaneous charging and discharging condition were studied. In the area of industrial heat exchangers, one-dimensional flow network model and porous-media model, and R245fa in a plate-shell heat exchanger were studied. (3) Various studies were published in the categories of refrigeration cycle, alternative refrigeration/energy system, system control. In the refrigeration cycle category, subjects include mobile cold storage heat exchanger, compressor reliability, indirect refrigeration system with $CO_2$ as secondary fluid, heat pump for fuel-cell vehicle, heat recovery from hybrid drier and heat exchangers with two-port and flat tubes. In the alternative refrigeration/energy system category, subjects include membrane module for dehumidification refrigeration, desiccant-assisted low-temperature drying, regenerative evaporative cooler and ejector-assisted multi-stage evaporation. In the system control category, subjects include multi-refrigeration system control, emergency cooling of data center and variable-speed compressor control. (4) In building mechanical system research fields, fifteenth studies were reported for achieving effective design of the mechanical systems, and also for maximizing the energy efficiency of buildings. The topics of the studies included energy performance, HVAC system, ventilation, renewable energies, etc. Proposed designs, performance tests using numerical methods and experiments provide useful information and key data which could be help for improving the energy efficiency of the buildings. (5) The field of architectural environment was mostly focused on indoor environment and building energy. The main researches of indoor environment were related to the analyses of indoor thermal environments controlled by portable cooler, the effects of outdoor wind pressure in airflow at high-rise buildings, window air tightness related to the filling piece shapes, stack effect in core type's office building and the development of a movable drawer-type light shelf with adjustable depth of the reflector. The subjects of building energy were worked on the energy consumption analysis in office building, the prediction of exit air temperature of horizontal geothermal heat exchanger, LS-SVM based modeling of hot water supply load for district heating system, the energy saving effect of ERV system using night purge control method and the effect of strengthened insulation level to the building heating and cooling load.

• Journal of Korea Multimedia Society
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• v.14 no.1
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• pp.54-75
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• 2011

PoC(Push-to-talk Over Cellular) is an integrated technology of group voice calls, video calls and internet based multimedia services. If a PoC user can not participate in the PoC session for various reasons such as an emergency situation, lack of battery capacity, then the user can use the PoC Box which has a similar functionality to the MM Box in the MMS(Multimedia Messaging Service). The RTSP(Real-Time Streaming Protocol) method is recommended to be used when there is a transmission session between the PoC box and a terminal. Since the existing VOD service uses a wired network, the packet size of RTSP-based VOD service is huge, however, the PoC service has wireless communication environments which have general characteristics to be used in RTSP method. Packet loss in a wired communication environments is relatively less than that in wireless communication environment, therefore, a buffering latency occurs in PoC service due to a play-out delay which means an asynchronous play of audio & video contents. Those problems make a user to be difficult to find the information they want when the media contents are played-out. In this paper, the following techniques and methods were proposed and their performance and superiority were verified through testing: cross-over dual reception buffering technique, advance partition multi-reception buffering technique, and on-demand multi-reception buffering technique, which are designed for effective picking up of information in media content being transmitted in short amount of time using RTSP when a user searches for media, as well as for reduction in playback delay; and same-priority packetization transmission method and priority-based packetization transmission method, which are media data packetization methods for transmission. From the simulation of functional evaluation, we could find that the proposed multiple receiving buffering and packetizing methods are superior, with respect to the media retrieval inclination, to the existing single receiving buffering method by 6-9 points from the viewpoint of effectiveness and excellence. Among them, especially, on-demand multiple receiving buffering technology with same-priority packetization transmission method is able to manage the media search inclination promptly to the requests of users by showing superiority of 3-24 points above compared to other combination methods. In addition, users could find the information they want much quickly since large amount of informations are received in a focused media retrieval period within a short time.