The Demand Analysis of Water Purification of Groundwater for the Horticultural Water Supply (시설원예 용수 공급을 위한 지하수 정수 요구도 분석)
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- Journal of the Korea Academia-Industrial cooperation Society
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- v.21 no.12
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- pp.510-523
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- 2020
This study analyzed groundwater quality in hydroponic cultivation facilities. Through this study, the possibility of groundwater use was evaluated according to the quality of the groundwater for hydroponic cultivation facilities. Good groundwater quality, on average, is pH 6.61, EC 0.27 dS/m, NO3-N 7.64 mg/L, NH4+-N 0.80 mg/L, PO4-P 0.09 mg/L, K+ 6.26 mg/L, Ca2+ 18.57 mg/L, Mg2+ 4.38 mg/L, Na+ 20.85 mg/L, etc. All of these satisfy the water quality standard for raw water in nutrient cultivation. But in the case of farmers experiencing problems with groundwater quality, most of the items exceeded the water quality standard. As a result of the analysis, it was judged that purifying groundwater of unsuitable quality for crop cultivation, and using it as raw water, was effective in terms of water quality and soil purification. If an agricultural water purification system is constructed based on the results of this study, it is judged that the design will be easy because the items to be treated can be estimated. If a purification system is established, it can use groundwater directly in the facility and for horticulture. These study results will be available for use in sustainable agriculture and environments.
We frequently use search engines to find relevant information in the Web but still end up with too much information. In order to solve this problem of information overload, ranking algorithms have been applied to various domains. As more information will be available in the future, effectively and efficiently ranking search results will become more critical. In this paper, we propose a ranking algorithm for the Semantic Web resources, specifically RDF resources. Traditionally, the importance of a particular Web page is estimated based on the number of key words found in the page, which is subject to manipulation. In contrast, link analysis methods such as Google's PageRank capitalize on the information which is inherent in the link structure of the Web graph. PageRank considers a certain page highly important if it is referred to by many other pages. The degree of the importance also increases if the importance of the referring pages is high. Kleinberg's algorithm is another link-structure based ranking algorithm for Web pages. Unlike PageRank, Kleinberg's algorithm utilizes two kinds of scores: the authority score and the hub score. If a page has a high authority score, it is an authority on a given topic and many pages refer to it. A page with a high hub score links to many authoritative pages. As mentioned above, the link-structure based ranking method has been playing an essential role in World Wide Web(WWW), and nowadays, many people recognize the effectiveness and efficiency of it. On the other hand, as Resource Description Framework(RDF) data model forms the foundation of the Semantic Web, any information in the Semantic Web can be expressed with RDF graph, making the ranking algorithm for RDF knowledge bases greatly important. The RDF graph consists of nodes and directional links similar to the Web graph. As a result, the link-structure based ranking method seems to be highly applicable to ranking the Semantic Web resources. However, the information space of the Semantic Web is more complex than that of WWW. For instance, WWW can be considered as one huge class, i.e., a collection of Web pages, which has only a recursive property, i.e., a 'refers to' property corresponding to the hyperlinks. However, the Semantic Web encompasses various kinds of classes and properties, and consequently, ranking methods used in WWW should be modified to reflect the complexity of the information space in the Semantic Web. Previous research addressed the ranking problem of query results retrieved from RDF knowledge bases. Mukherjea and Bamba modified Kleinberg's algorithm in order to apply their algorithm to rank the Semantic Web resources. They defined the objectivity score and the subjectivity score of a resource, which correspond to the authority score and the hub score of Kleinberg's, respectively. They concentrated on the diversity of properties and introduced property weights to control the influence of a resource on another resource depending on the characteristic of the property linking the two resources. A node with a high objectivity score becomes the object of many RDF triples, and a node with a high subjectivity score becomes the subject of many RDF triples. They developed several kinds of Semantic Web systems in order to validate their technique and showed some experimental results verifying the applicability of their method to the Semantic Web. Despite their efforts, however, there remained some limitations which they reported in their paper. First, their algorithm is useful only when a Semantic Web system represents most of the knowledge pertaining to a certain domain. In other words, the ratio of links to nodes should be high, or overall resources should be described in detail, to a certain degree for their algorithm to properly work. Second, a Tightly-Knit Community(TKC) effect, the phenomenon that pages which are less important but yet densely connected have higher scores than the ones that are more important but sparsely connected, remains as problematic. Third, a resource may have a high score, not because it is actually important, but simply because it is very common and as a consequence it has many links pointing to it. In this paper, we examine such ranking problems from a novel perspective and propose a new algorithm which can solve the problems under the previous studies. Our proposed method is based on a class-oriented approach. In contrast to the predicate-oriented approach entertained by the previous research, a user, under our approach, determines the weights of a property by comparing its relative significance to the other properties when evaluating the importance of resources in a specific class. This approach stems from the idea that most queries are supposed to find resources belonging to the same class in the Semantic Web, which consists of many heterogeneous classes in RDF Schema. This approach closely reflects the way that people, in the real world, evaluate something, and will turn out to be superior to the predicate-oriented approach for the Semantic Web. Our proposed algorithm can resolve the TKC(Tightly Knit Community) effect, and further can shed lights on other limitations posed by the previous research. In addition, we propose two ways to incorporate data-type properties which have not been employed even in the case when they have some significance on the resource importance. We designed an experiment to show the effectiveness of our proposed algorithm and the validity of ranking results, which was not tried ever in previous research. We also conducted a comprehensive mathematical analysis, which was overlooked in previous research. The mathematical analysis enabled us to simplify the calculation procedure. Finally, we summarize our experimental results and discuss further research issues.
Overview of Research: Product availability is one of important competences of store to fulfill consumer needs. If stock-outs which means a product what consumer wants to buy is not available occurs, consumer will face decision-making uncertainty that leads to consumer's negative responses such as consumer dissatisfaction on store. Stockouts was much studied in the field of academia as well as practice in other countries. However, stock-outs has not been researched at all in Marketing and/or Distribution area in Korea. The main objectives of this study are to find out determinants of consumer responses such as Substitute, Delay, and Leave(SDL) when consumer encounters out-of-stock situation and then to examine the effects of these factors on consumer responses. Specifically, this study focuses on situational characteristics(e.g., purchase urgency and surprise), store characteristics (e.g., product assortment and store convenience), and consumer characteristics (e.g., brand loyalty and store loyalty). Then, this study empirically investigates relationships these factors with consumers behaviors such as product substitution, purchase delay, and store switching.
As Internet and information technology (IT) continues to develop and evolve, the issue of big data has emerged at the foreground of scholarly and industrial attention. Big data is generally defined as data that exceed the range that can be collected, stored, managed and analyzed by existing conventional information systems and it also refers to the new technologies designed to effectively extract values from such data. With the widespread dissemination of IT systems, continual efforts have been made in various fields of industry such as R&D, manufacturing, and finance to collect and analyze immense quantities of data in order to extract meaningful information and to use this information to solve various problems. Since IT has converged with various industries in many aspects, digital data are now being generated at a remarkably accelerating rate while developments in state-of-the-art technology have led to continual enhancements in system performance. The types of big data that are currently receiving the most attention include information available within companies, such as information on consumer characteristics, information on purchase records, logistics information and log information indicating the usage of products and services by consumers, as well as information accumulated outside companies, such as information on the web search traffic of online users, social network information, and patent information. Among these various types of big data, web searches performed by online users constitute one of the most effective and important sources of information for marketing purposes because consumers search for information on the internet in order to make efficient and rational choices. Recently, Google has provided public access to its information on the web search traffic of online users through a service named Google Trends. Research that uses this web search traffic information to analyze the information search behavior of online users is now receiving much attention in academia and in fields of industry. Studies using web search traffic information can be broadly classified into two fields. The first field consists of empirical demonstrations that show how web search information can be used to forecast social phenomena, the purchasing power of consumers, the outcomes of political elections, etc. The other field focuses on using web search traffic information to observe consumer behavior, identifying the attributes of a product that consumers regard as important or tracking changes on consumers' expectations, for example, but relatively less research has been completed in this field. In particular, to the extent of our knowledge, hardly any studies related to brands have yet attempted to use web search traffic information to analyze the factors that influence consumers' purchasing activities. This study aims to demonstrate that consumers' web search traffic information can be used to derive the relations among brands and the relations between an individual brand and product attributes. When consumers input their search words on the web, they may use a single keyword for the search, but they also often input multiple keywords to seek related information (this is referred to as simultaneous searching). A consumer performs a simultaneous search either to simultaneously compare two product brands to obtain information on their similarities and differences, or to acquire more in-depth information about a specific attribute in a specific brand. Web search traffic information shows that the quantity of simultaneous searches using certain keywords increases when the relation is closer in the consumer's mind and it will be possible to derive the relations between each of the keywords by collecting this relational data and subjecting it to network analysis. Accordingly, this study proposes a method of analyzing how brands are positioned by consumers and what relationships exist between product attributes and an individual brand, using simultaneous search traffic information. It also presents case studies demonstrating the actual application of this method, with a focus on tablets, belonging to innovative product groups.
I. Introduction Since the 1970's drug abuse among young people has increasingly become a social problem in Korea. In the 1980's, drug abuse, especially glue sniffing, has become the cause of many unfortunated incidents resulting in harm to others as well as the abusers themselves. Taking into consideration of the seriousness of this problem, the Republic of Korea National Red Cross initiated a nation-wide research programme, to understand the present situation and to raise the level of public awareness. The goal of this research was to begin a nation - wide campaign against drug abuse. The research team was composed of the Advisary Committee members and the staff of the Youth Department of the Republic of Korea National Red Cross. The data were collected in February 1988 with the collaboration of the staff and volunteers in the local Chapters. The respondents were allocated nation-wide by the quota sampling method. The questionnaires were distributed to the respondents in three groups :2, 700 to junior and senior high school students, 605 to working youths, and 916 to delinquent youths. A total of 4, 221 questionnaires were collected. II. Characteristics of the Respondents The respondents in each group were selected evenly from rural and urban areas. The general characteristics of the respondents can be described as follow: in case of students, the proportions between male and female respondents, and between senior high school and junior high school students were almost evenly distributed. In case of working youths, the proportion of females (80.5%) was higher than those of the students and the delinquents groups. Delinquent youths were defined as those currently being under custody of the centers for juvenile delinquents. Of this number, 38.8% and 68.2% were junior and senior high school drop-outs respectively. The majority of them (92.6%) were male. As for the family background of the respondents, the proportion of those residing in poverty - stricken areas, and the proportion of those from broken families were higher in case of working youths and delinquent youths than those in case of students. III. Present Patterns of Drug Abuse The following summarizes the presents of drug abuse, as tabulated from the results of the survey. 1. Smoking The percentage of youths who smoke was 36% in the student group, 32% m the working youths group, and 94.4% in the delinquent youths group. 2. Alcohol 50.3% of students, 71.6% of working youths, and 93.3% of delinquent youths has experienced drinking alcohol beverages. 3. Tonic: non - alcoholic, caffeinated beverages popular in Korea and Japan The percentage of those who have used tonic at least once was over 90% in all of the three groups. 4. Sedative About 70% of each group has used sedative with the proportion of working youths use higher than those in other groups. 5. Stimulants Those who have used stimulants comprised around 15% in each group. 6. Tranquilizers Somewhat less than 5% of students and working youths, and 28% of delinquent youths, have used tranquilizers. 7. Hypnotics The users of hypnotics amounted to 0.4% of students, 2.6% of working youths and 7.1% of delinquent youths. 8. Marihuana Those who have used marihuana indicated 0.7% of students, 0.8% of working youths, and 13% of delinquent youths. 9. Glue-sniffing The percentage of glue-sniffing was 3.7%, 5% in the students group and in the youths group respectively, but the proportion was unusually high, at 40.7% in the delinquent youths group. From the results of the survey the present situation of drug abuse in Korea can be summarized as follows: 1. A high percentage of Korean youths have experienced smoking cigarettes and drinking alcoholic beverages. 2. Tonics (non - alcoholic, caffeinated beverages), antipyretic analgesics and stimulants quite regularly used. 3. Tranquilizers, hypnotics, marihuana and glue-sniffing are more widely used among delinquent youths than the other youths. From this fact, there exists a correlation between drug abuse and juvenile delinquency. IV. Time-series Analysis of the First Experience of Drug Abuse and Deviant Behaviour The respoundents were asked when they were first exposed to drugs and when they committed deviant acts. By calculating the average age of each experience, the following pattern was found (See Figure 1). Youths are first exposed to drugs by abuse of tonic(non - alcoholic, caffeinated beverages). At the age of 13, they amoke cigarettes, the use of antipyretic analgesics begins at 14 year old, while at the age of 15, they use tranquilizers, and at 16 hynotics. The period of drug abuse which starts from drinking caffeinated beverages and smoking cigarettes and ends in the use of hypnotics takes about three years. During this period, other delinquent behaviours begin to surface, that is, at the age of 13 when smoking cigarettes begins, the delinquent behaviour pattern starts with truancy. Next, they start taking money from others by using physical force. Prior to the age of 15, they are suspended from school, become hostile to adults, begin running away from home, and start using stimulants and alcohol. Soon they become involved even in glue-sniffing and in the use of marihuana. At the age of 15, they begin to see adult videos and carry weapons. Sexual promiscuity and usage of tranquilizers follows the viewing of adult videos. Consequently, by the time they reach the age of 16, they visit drinking establishments, and are picked up by police for committing delinquent acts. And finally, they come to use hypnotic - type drugs. From the above descriptions, drug abuse can be assumed to have a close correlation with delinquent behaviour. V. Social Factors Related to Drug Abuse As for the Korean youths, glue-sniffing is found to he related to aggressive delinquency, in such cases as run - aways, being picked up by the police, and taking money by force. Smoking cigarettes and drinking alcohol is found to be related to seeing adult videos and visiting drinking establishments. Hypnotics and marihuana were found to be representive of drugs which are related to degenerational delinquency, irrespective of social delinquency. The social factors connected with these drug abuse are as follows: 1. Individual factors Male students were more heavily involved in the usage of drug than females. Youths who do not attend church were more likely to be involved in drugs than those who attend. 2. Family factors The youths who were displeased with their mothers smoking and those who thought their parents did not love each other, or those whose parents had used drugs without prescription, were more likely to he drug users. 3. School factors Those youths who found school life boring, were unsuccessful in their studies, spend most of their time with friends, feel their teachers smoke too much, those who had a positive perception of their teachers smoking were likely to he drug users. To sum up, drug abusers depend on the influence of their parents, teachers and peers. IV. Reasons for Drug Abuse Korean students have mainly used drugs to release stress (42.8%), to stay awake (19.7%), and because of the easy accessibility of drugs( 16.6%). Other reasons are due to their ignorance of the side effects of the drugs (3.6%), natural curiosity (4.2%), and to increase strength(3.O%). From the above facts, the major reasons for drug abuse among Korean youths are to release stress and to stay awake in order to prepare exams. Furthermore, since drugs are readily available, we can conclude that drug abuse is caused by the school system(such as entrance exams) in Korea. VII. Conclusion Drug usage among Korean youths are relatively less common than those of western youths. In some cases, such as, glue-sniffing and use of stimulants, the pattern of drug abuse is found. Moreover, early drug abuse is evident, and it has a close connection with deviant behaviour, resulting in juvenile delinquency. Drug abuse cannot be attributed to any one social factor. Specifically, drug abuse depends on parents, peers, teachers and other members of the community, and also is influenced by social institutions such as the entrance exam system. Every person and organization concerned with youth must participate collectively in restraining drug abuse. Finally, it is suggested that social agencial working for youth welfare should make every effort to tackle this serious problem confronted by the Korean youths today.
In recent years, one of the major reasons for the fierce competition amongst firms is that they strive to increase their own market shares and customer acquisition rate in the same market with similar and apparently undifferentiated products in terms of quality and perceived benefit. Because of this change in recent marketing environment, the differentiated after-sales service and diversified promotion strategies have become more important to gain competitive advantage. Price promotion is the favorite strategy that most retailers use to achieve short-term sales increase, induce consumer's brand switch, in troduce new product into market, and so forth. However, if marketers apply or copy an identical price promotion strategy without considering the characteristic differences in product and consumer preference, it will cause serious problems because discounted price itself could make people skeptical about product quality, and the changes of perceived value might appear differently depending on other factors such as consumer involvement or brand attitude. Previous studies showed that price promotion would certainly increase sales, and the discounted price compared to regular price would enhance the consumer's perceived values. On the other hand, discounted price itself could make people depreciate or skeptical about product quality, and reduce the consumers' positivity bias because consumers might be unsure whether the current price promotion is the retailer's best price offer. Moreover, we cannot say that discounted price absolutely enhances the consumer's perceived values regardless of product category and purchase situations. That is, the factors that affect consumers' value perceptions and buying behavior are so diverse in reality that the results of studies on the same dependent variable come out differently depending on what variable was used or how experiment conditions were designed. Majority of previous researches on the effect of price-comparison advertising have used consumers' buying behavior as dependent variable. In order to figure out consumers' buying behavior theoretically, analysis of value perceptions which influence buying intentions is needed. In addition, they did not combined the independent variables such as brand loyalty and price discount rate together. For this reason, this paper tried to examine the moderating effect of brand loyalty on relationship between the different levels of discounting rate and buyers' value perception. And we provided with theoretical and managerial implications that marketers need to consider such variables as product attributes, brand loyalty, and consumer involvement at the same time, and then establish a differentiated pricing strategy case by case in order to enhance consumer's perceived values properl. Three research concepts were used in our study and each concept based on past researches was defined. The perceived acquisition value in this study was defined as the perceived net gains associated with the products or services acquired. That is, the perceived acquisition value of the product will be positively influenced by the benefits buyers believe they are getting by acquiring and using the product, and negatively influenced by the money given up to acquire the product. And the perceived transaction value was defined as the perception of psychological satisfaction or pleasure obtained from taking advantage of the financial terms of the price deal. Lastly, the brand loyalty was defined as favorable attitude towards a purchased product. Thus, a consumer loyal to a brand has an emotional attachment to the brand or firm. Repeat purchasers continue to buy the same brand even though they do not have an emotional attachment to it. We assumed that if the degree of brand loyalty is high, the perceived acquisition value and the perceived transaction value will increase when higher discount rate is provided. But we found that there are no significant differences in values between two different discount rates as a result of empirical analysis. It means that price reduction did not affect consumer's brand choice significantly because the perceived sacrifice decreased only a little, and customers are satisfied with product's benefits when brand loyalty is high. From the result, we confirmed that consumers with high degree of brand loyalty to a specific product are less sensitive to price change. Thus, using price promotion strategy to merely expect sale increase is not recommendable. Instead of discounting price, marketers need to strengthen consumers' brand loyalty and maintain the skimming strategy. On the contrary, when the degree of brand loyalty is low, the perceived acquisition value and the perceived transaction value decreased significantly when higher discount rate is provided. Generally brands that are considered inferior might be able to draw attention away from the quality of the product by making consumers focus more on the sacrifice component of price. But considering the fact that consumers with low degree of brand loyalty are known to be unsatisfied with product's benefits and have relatively negative brand attitude, bigger price reduction offered in experiment condition of this paper made consumers depreciate product's quality and benefit more and more, and consumer's psychological perceived sacrifice increased while perceived values decreased accordingly. We infer that, in the case of inferior brand, a drastic price-cut or frequent price promotion may increase consumers' uncertainty about overall components of product. Therefore, it appears that reinforcing the augmented product such as after-sale service, delivery and giving credit which is one of the levels consisting of product would be more effective in reality. This will be better rather than competing with product that holds high brand loyalty by reducing sale price. Although this study tried to examine the moderating effect of brand loyalty on relationship between the different levels of discounting rate and buyers' value perception, there are several limitations. This study was conducted in controlled conditions where the high involvement product and two different levels of discount rate were applied. Given the presence of low involvement product, when both pieces of information are available, it is likely that the results we have reported here may have been different. Thus, this research results explain only the specific situation. Second, the sample selected in this study was university students in their twenties, so we cannot say that the results are firmly effective to all generations. Future research that manipulates the level of discount along with the consumer involvement might lead to a more robust understanding of the effects various discount rate. And, we used a cellular phone as a product stimulus, so it would be very interesting to analyze the result when the product stimulus is an intangible product such as service. It could be also valuable to analyze whether the change of perceived value affects consumers' final buying behavior positively or negatively.
Up to this day, mobile communications have evolved rapidly over the decades, mainly focusing on speed-up to meet the growing data demands of 2G to 5G. And with the start of the 5G era, efforts are being made to provide such various services to customers, as IoT, V2X, robots, artificial intelligence, augmented virtual reality, and smart cities, which are expected to change the environment of our lives and industries as a whole. In a bid to provide those services, on top of high speed data, reduced latency and reliability are critical for real-time services. Thus, 5G has paved the way for service delivery through maximum speed of 20Gbps, a delay of 1ms, and a connecting device of 106/㎢ In particular, in intelligent traffic control systems and services using various vehicle-based Vehicle to X (V2X), such as traffic control, in addition to high-speed data speed, reduction of delay and reliability for real-time services are very important. 5G communication uses high frequencies of 3.5Ghz and 28Ghz. These high-frequency waves can go with high-speed thanks to their straightness while their short wavelength and small diffraction angle limit their reach to distance and prevent them from penetrating walls, causing restrictions on their use indoors. Therefore, under existing networks it's difficult to overcome these constraints. The underlying centralized SDN also has a limited capability in offering delay-sensitive services because communication with many nodes creates overload in its processing. Basically, SDN, which means a structure that separates signals from the control plane from packets in the data plane, requires control of the delay-related tree structure available in the event of an emergency during autonomous driving. In these scenarios, the network architecture that handles in-vehicle information is a major variable of delay. Since SDNs in general centralized structures are difficult to meet the desired delay level, studies on the optimal size of SDNs for information processing should be conducted. Thus, SDNs need to be separated on a certain scale and construct a new type of network, which can efficiently respond to dynamically changing traffic and provide high-quality, flexible services. Moreover, the structure of these networks is closely related to ultra-low latency, high confidence, and hyper-connectivity and should be based on a new form of split SDN rather than an existing centralized SDN structure, even in the case of the worst condition. And in these SDN structural networks, where automobiles pass through small 5G cells very quickly, the information change cycle, round trip delay (RTD), and the data processing time of SDN are highly correlated with the delay. Of these, RDT is not a significant factor because it has sufficient speed and less than 1 ms of delay, but the information change cycle and data processing time of SDN are factors that greatly affect the delay. Especially, in an emergency of self-driving environment linked to an ITS(Intelligent Traffic System) that requires low latency and high reliability, information should be transmitted and processed very quickly. That is a case in point where delay plays a very sensitive role. In this paper, we study the SDN architecture in emergencies during autonomous driving and conduct analysis through simulation of the correlation with the cell layer in which the vehicle should request relevant information according to the information flow. For simulation: As the Data Rate of 5G is high enough, we can assume the information for neighbor vehicle support to the car without errors. Furthermore, we assumed 5G small cells within 50 ~ 250 m in cell radius, and the maximum speed of the vehicle was considered as a 30km ~ 200 km/hour in order to examine the network architecture to minimize the delay.
Titanium miniscrews we being used increasingly as an anchorage for tooth movement, because they ate easy to place and to remove, increase the number of sites available, give minimum strain to patients regarding surgical procedures, and offer uneventful healing alter removal. The use of titanium miniscrews as an orthodontic anchorage has been reported in clinical case reports, but clinicians have experienced screw loosening when using such screws.' To our knowledge, there are no published reports evaluating the stability of miniscrews. Information about the length of miniscrews used in relation to the location is of some importance, as stability will vary depending on bone duality The purpose of this study was to evaluate a variety of Lengths of miniscrews (dimeter: 2mm) which were inserted in maxilla or mandible and to demonstrate in a dog model which miniscrew provides fundamental stability in the jaws. 10 mm long miniscrews in the maxilla and 8mm long: miniscrews in the mandible showed no clinical mobility and retained their position throughout an 8 weeks force (200g) application. The mucosal condition around the screws was healthy in cases in which miniserews were inserted in the alveolar bone between the roots and the head of the screws emerged into the attached gingiva. When the force application was terminated, radiographic analysis revealed neither rent resorption not periodontal pathology around the miniscrews that remained stable during the entire treatment period. This study suggests that if titanium miniscrews with adequate length are properly used depending on the location, they provide sufficient stability for orthodontic anchorage.
In this study, the production potential of alternative coagulant (
In recent years, the rapid development of internet technology and the popularization of smart devices have resulted in massive amounts of text data. Those text data were produced and distributed through various media platforms such as World Wide Web, Internet news feeds, microblog, and social media. However, this enormous amount of easily obtained information is lack of organization. Therefore, this problem has raised the interest of many researchers in order to manage this huge amount of information. Further, this problem also required professionals that are capable of classifying relevant information and hence text classification is introduced. Text classification is a challenging task in modern data analysis, which it needs to assign a text document into one or more predefined categories or classes. In text classification field, there are different kinds of techniques available such as K-Nearest Neighbor, Naïve Bayes Algorithm, Support Vector Machine, Decision Tree, and Artificial Neural Network. However, while dealing with huge amount of text data, model performance and accuracy becomes a challenge. According to the type of words used in the corpus and type of features created for classification, the performance of a text classification model can be varied. Most of the attempts are been made based on proposing a new algorithm or modifying an existing algorithm. This kind of research can be said already reached their certain limitations for further improvements. In this study, aside from proposing a new algorithm or modifying the algorithm, we focus on searching a way to modify the use of data. It is widely known that classifier performance is influenced by the quality of training data upon which this classifier is built. The real world datasets in most of the time contain noise, or in other words noisy data, these can actually affect the decision made by the classifiers built from these data. In this study, we consider that the data from different domains, which is heterogeneous data might have the characteristics of noise which can be utilized in the classification process. In order to build the classifier, machine learning algorithm is performed based on the assumption that the characteristics of training data and target data are the same or very similar to each other. However, in the case of unstructured data such as text, the features are determined according to the vocabularies included in the document. If the viewpoints of the learning data and target data are different, the features may be appearing different between these two data. In this study, we attempt to improve the classification accuracy by strengthening the robustness of the document classifier through artificially injecting the noise into the process of constructing the document classifier. With data coming from various kind of sources, these data are likely formatted differently. These cause difficulties for traditional machine learning algorithms because they are not developed to recognize different type of data representation at one time and to put them together in same generalization. Therefore, in order to utilize heterogeneous data in the learning process of document classifier, we apply semi-supervised learning in our study. However, unlabeled data might have the possibility to degrade the performance of the document classifier. Therefore, we further proposed a method called Rule Selection-Based Ensemble Semi-Supervised Learning Algorithm (RSESLA) to select only the documents that contributing to the accuracy improvement of the classifier. RSESLA creates multiple views by manipulating the features using different types of classification models and different types of heterogeneous data. The most confident classification rules will be selected and applied for the final decision making. In this paper, three different types of real-world data sources were used, which are news, twitter and blogs.
is the estimation results of l\1NL model, and
shows the marginal effects for each determinant to consumer's responses(SDL). Significant statistical results were as follows. Purchase urgency, purchase quantities, pre-purchase plan, product assortment, store price image, brand loyalty, and store loyalty were turned out to be significant determinants to influence consumer alternative behaviors in case of out-of-stock situation. Specifically, first, product substitution behavior was triggered by purchase urgency, surprise, purchase quantities, pre-purchase plan, product assortment, store price image, brand loyalty, and store loyalty. Second, purchase delay behavior was led by purchase urgency, purchase quantities, and brand loyalty. Third, store switching behavior was influenced by purchase urgency, purchase quantities, pre-purchase plan, product assortment, store price image, brand loyalty, and store loyalty. Finally, when out-of-stock situation occurs, store convenience and salesperson service did not have significant effects on consumer alternative responses.
Intelligent Brand Positioning Visualization System Based on Web Search Traffic Information : Focusing on Tablet PC
(웹검색 트래픽 정보를 활용한 지능형 브랜드 포지셔닝 시스템 : 태블릿 PC 사례를 중심으로)
한국 청소년의 약물남용과 비행행위
The Effect of Price Discount Rate According to Brand Loyalty on Consumer's Acquisition Value and Transaction Value
(브랜드애호도에 따른 가격할인율의 차이가 소비자의 획득가치와 거래가치에 미치는 영향)
Edge to Edge Model and Delay Performance Evaluation for Autonomous Driving
(자율 주행을 위한 Edge to Edge 모델 및 지연 성능 평가)
A Study on Titanium Miniscrew as Orthodontic Anchorage : An experimental investigation in dogs
(성견에서 교정적 고정원으로서의 티타늄 미니스크류에 대한 연구)
Production of Alternative Coagulant Using Waste Activated Alumina and Evaluation of Coagulation Activity
(폐촉매 부산물로부터 대체 응집제 제조 및 응집성능 평가)
Improving the Accuracy of Document Classification by Learning Heterogeneity
(이질성 학습을 통한 문서 분류의 정확성 향상 기법)
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