• Journal of Korean Academy of Nursing Administration
• /
• v.3 no.1
• /
• pp.51-64
• /
• 1997

The purpose of the study was to introduce an integrated approach of new Casuistry and specified principlism in resolving ethical problems and studying nursing ethics. In studying clinical ethics and nursing ethics, there is no systematic research method. While nurses often experience ethical dilemmas in practice, much of previous research on nursing ethics has focused merely on describing the existing problems. In addition, ethists presented theoretical analysis and critics rather than providing the specific problems solving strategies. There is a need in clinical situations for an integrated method which can provide the objective description for existing problem situations as well as specific problem solving methods. We inherit two distinct ways of discussing ethical issues. One of these frames these issues in terms of principles, rules, and other general ideas; the other focuses on the specific features of particular kinds of moral cases. In the first way general ethical rules relate to specific moral cases in a theoretical manner, with universal rules serving as "axioms" from which particular moral judgments are deduced as theorems. In the seconds, this relation is frankly practical. with general moral rules serving as "maxims", which can be fully understood only in terms of the paradigmatic cases that define their meaning and force. Theoretical arguments are structured in ways that free them from any dependence on the circumstances of their presentation and ensure them a validity of a kind that is not affected by the practical context of use. In formal arguments particular conclusions are deduced from("entailed by") the initial axioms or universal principles that are the apex of the argument. So the truth or certainty that attaches to those axioms flows downward to the specific instances to be "proved". In the language of formal logic, the axioms are major premises, the facts that specify the present instance are minor premises, and the conclusion to be "proved" is deduced (follows necessarily) from the initial presises. Practical arguments, by contrast, involve a wider range of factors than formal deductions and are read with an eye to their occasion of use. Instead of aiming at strict entailments, they draw on the outcomes of previous experience, carrying over the procedures used to resolve earlier problems and reapply them in new problmatic situations. Practical arguments depend for their power on how closely the present circumstances resemble those of the earlier precedent cases for which this particular type of argument was originally devised. So. in practical arguments, the truths and certitudes established in the precedent cases pass sideways, so as to provide "resolutions" of later problems. In the language of rational analysis, the facts of the present case define the gounds on which any resolution must be based; the general considerations that carried wight in similar situations provide warrants that help settle future cases. So the resolution of any problem holds good presumptively; its strengh depends on the similarities between the present case and the prededents; and its soundness can be challenged (or rebutted) in situations that are recognized ans exceptional. Jonsen & Toulmin (1988), and Jonsen (1991) introduce New Casuistry as a practical method. The oxford English Dictionary defines casuistry quite accurately as "that part of ethics which resolves cases of conscience, applying the general rules of religion and morality to particular instances in which circumstances alter cases or in which there appears to be a conflict of duties." They modified the casuistry of the medieval ages to use in clinical situations which is characterized by "the typology of cases and the analogy as an inference method". A case is the unit of analysis. The structure of case was made with interaction of situation and moral rules. The situation is what surrounds or stands around. The moral rule is the essence of case. The analogy can be objective because "the grounds, the warrants, the theoretical backing, the modal qualifiers" are identified in the cases. The specified principlism was the method that Degrazia (1992) integrated the principlism and the specification introduced by Richardson (1990). In this method, the principle is specified by adding information about limitations of the scope and restricting the range of the principle. This should be substantive qualifications. The integrated method is an combination of the New Casuistry and the specified principlism. For example, the study was "Ethical problems experienced by nurses in the care of terminally ill patients"(Um, 1994). A semi-structured in-depth interview was conducted for fifteen nurses who mainly took care of terminally ill patients. The first stage, twenty one cases were identified as relevant to the topic, and then were classified to four types of problems. For instance, one of these types was the patient's refusal of care. The second stage, the ethical problems in the case were defined, and then the case was analyzed. This was to analyze the reasons, the ethical values, and the related ethical principles in the cases. Then the interpretation was synthetically done by integration of the result of analysis and the situation. The third stage was the ordering phase of the cases, which was done according to the result of the interpretation and the common principles in the cases. The first two stages describe the methodology of new casuistry, and the final stage was for the methodology of the specified principlism. The common principles were the principle of autonomy and the principle of caring. The principle of autonomy was specified; when competent patients refused care, nurse should discontinue the care to respect for the patients' decision. The principle of caring was also specified; when the competent patients refused care, nurses should continue to provide the care in spite of the patients' refusal to preserve their life. These specification may lead the opposite behavior, which emphasizes the importance of nurse's will and intentions to make their decision in the clinical situations.

• Journal of Intelligence and Information Systems
• /
• v.17 no.2
• /
• pp.39-60
• /
• 2011

Since the introduction of web 2.0 technology, social network service has been recognized as the foundation of an important future information technology. The advent of web 2.0 has led to the change of content creators. In the existing web, content creators are service providers, whereas they have changed into service users in the recent web. Users share experiences with other users improving contents quality, thereby it has increased the importance of social network. As a result, diverse forms of social network service have been emerged from relations and experiences of users. Social network is a network to construct and express social relations among people who share interests and activities. Today's social network service has not merely confined itself to showing user interactions, but it has also developed into a level in which content generation and evaluation are interacting with each other. As the volume of contents generated from social network service and the number of connections between users have drastically increased, the social network extraction method becomes more complicated. Consequently the following problems for the social network extraction arise. First problem lies in insufficiency of representational power of object in the social network. Second problem is incapability of expressional power in the diverse connections among users. Third problem is the difficulty of creating dynamic change in the social network due to change in user interests. And lastly, lack of method capable of integrating and processing data efficiently in the heterogeneous distributed computing environment. The first and last problems can be solved by using FOAF, a tool for describing ontology-based user profiles for construction of social network. However, solving second and third problems require a novel technology to reflect dynamic change of user interests and relations. In this paper, we propose a novel method to overcome the above problems of existing social network extraction method by applying FOAF (a tool for describing user profiles) and RSS (a literary web work publishing mechanism) to OLAP system in order to dynamically innovate and manage FOAF. We employed data interoperability which is an important characteristic of FOAF in this paper. Next we used RSS to reflect such changes as time flow and user interests. RSS, a tool for literary web work, provides standard vocabulary for distribution at web sites and contents in the form of RDF/XML. In this paper, we collect personal information and relations of users by utilizing FOAF. We also collect user contents by utilizing RSS. Finally, collected data is inserted into the database by star schema. The system we proposed in this paper generates OLAP cube using data in the database. 'Dynamic FOAF Management Algorithm' processes generated OLAP cube. Dynamic FOAF Management Algorithm consists of two functions: one is find_id_interest() and the other is find_relation (). Find_id_interest() is used to extract user interests during the input period, and find-relation() extracts users matching user interests. Finally, the proposed system reconstructs FOAF by reflecting extracted relationships and interests of users. For the justification of the suggested idea, we showed the implemented result together with its analysis. We used C# language and MS-SQL database, and input FOAF and RSS as data collected from livejournal.com. The implemented result shows that foaf : interest of users has reached an average of 19 percent increase for four weeks. In proportion to the increased foaf : interest change, the number of foaf : knows of users has grown an average of 9 percent for four weeks. As we use FOAF and RSS as basic data which have a wide support in web 2.0 and social network service, we have a definite advantage in utilizing user data distributed in the diverse web sites and services regardless of language and types of computer. By using suggested method in this paper, we can provide better services coping with the rapid change of user interests with the automatic application of FOAF.

• Journal of Intelligence and Information Systems
• /
• v.24 no.1
• /
• pp.167-181
• /
• 2018

Over the past decade, deep learning has been in spotlight among various machine learning algorithms. In particular, CNN(Convolutional Neural Network), which is known as the effective solution for recognizing and classifying images or voices, has been popularly applied to classification and prediction problems. In this study, we investigate the way to apply CNN in business problem solving. Specifically, this study propose to apply CNN to stock market prediction, one of the most challenging tasks in the machine learning research. As mentioned, CNN has strength in interpreting images. Thus, the model proposed in this study adopts CNN as the binary classifier that predicts stock market direction (upward or downward) by using time series graphs as its inputs. That is, our proposal is to build a machine learning algorithm that mimics an experts called 'technical analysts' who examine the graph of past price movement, and predict future financial price movements. Our proposed model named 'CNN-FG(Convolutional Neural Network using Fluctuation Graph)' consists of five steps. In the first step, it divides the dataset into the intervals of 5 days. And then, it creates time series graphs for the divided dataset in step 2. The size of the image in which the graph is drawn is $40(pixels){\times}40(pixels)$, and the graph of each independent variable was drawn using different colors. In step 3, the model converts the images into the matrices. Each image is converted into the combination of three matrices in order to express the value of the color using R(red), G(green), and B(blue) scale. In the next step, it splits the dataset of the graph images into training and validation datasets. We used 80% of the total dataset as the training dataset, and the remaining 20% as the validation dataset. And then, CNN classifiers are trained using the images of training dataset in the final step. Regarding the parameters of CNN-FG, we adopted two convolution filters ($5{\times}5{\times}6$ and $5{\times}5{\times}9$) in the convolution layer. In the pooling layer, $2{\times}2$ max pooling filter was used. The numbers of the nodes in two hidden layers were set to, respectively, 900 and 32, and the number of the nodes in the output layer was set to 2(one is for the prediction of upward trend, and the other one is for downward trend). Activation functions for the convolution layer and the hidden layer were set to ReLU(Rectified Linear Unit), and one for the output layer set to Softmax function. To validate our model - CNN-FG, we applied it to the prediction of KOSPI200 for 2,026 days in eight years (from 2009 to 2016). To match the proportions of the two groups in the independent variable (i.e. tomorrow's stock market movement), we selected 1,950 samples by applying random sampling. Finally, we built the training dataset using 80% of the total dataset (1,560 samples), and the validation dataset using 20% (390 samples). The dependent variables of the experimental dataset included twelve technical indicators popularly been used in the previous studies. They include Stochastic %K, Stochastic %D, Momentum, ROC(rate of change), LW %R(Larry William's %R), A/D oscillator(accumulation/distribution oscillator), OSCP(price oscillator), CCI(commodity channel index), and so on. To confirm the superiority of CNN-FG, we compared its prediction accuracy with the ones of other classification models. Experimental results showed that CNN-FG outperforms LOGIT(logistic regression), ANN(artificial neural network), and SVM(support vector machine) with the statistical significance. These empirical results imply that converting time series business data into graphs and building CNN-based classification models using these graphs can be effective from the perspective of prediction accuracy. Thus, this paper sheds a light on how to apply deep learning techniques to the domain of business problem solving.

• Journal of the Korean Institute of Landscape Architecture
• /
• v.39 no.2
• /
• pp.40-51
• /
• 2011

Recently, the public's interest in quality of life and good design has increased, and the opportunities for their participation in space planning and the design process are expanding. However, the public still lacks understanding of the role(and importance) of space and environment and is not experienced in expressing their opinion on improving the urban environment. At this point, 'Built Environment Education for Kids' will be the key to understanding space and environment as future citizens and to developing the ability of problem-solving and expressing their opinions. This study aims to change the awareness of the public as well as experts, and to make a better urban space through comparison and analysis of domestic and foreign 'Built Environment Education.' In 27 countries around the world(more than 110 institutions), 'Built Environment Education' from childhood is being implemented. Such movements aim to make people participate in the space design and decision-making process by understanding a fundamental element of the built environment and space perception. In this study, the United States, Britain, Finland, Japan and South Korea's 'Built Environment Education' are discussed Above all, the definition, range and target of 'Built Environment Education' are discussed For each case, the purpose and effect, laws and educational processes, systems and roles, and examples of programs are analyzed. Through reviewing each attribute and their implications, a conclusion is drawn on the aspects we have to consider in laying the foundation for implementing the 'Built Environment Education' in Korea, such as consideration of the locality, organizing systematic networks and composing a pool of experts, building proper institutions, and establishing the role of the government. This case study of 'Built Environment Education' can help increase the awareness of the public and build their strength in establishing a better future space. Through the analysis of the purpose, laws, systems, and contents, this case study is expected to provide and build the foundation for an educational system and develop an appropriate program that best suits our society.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
• /
• v.13 no.1
• /
• pp.15-23
• /
• 2002

Knowledge regarding the resilience factors and risk factors of the childhood trauma on the developental trajectory is in its infancy due to the lack of prospective follow-up studies in the childhood trauma and limited understanding of the complex reciprocal interactions between childhood trauma, develop-ent and various aspects of children's environment. These difficulties in the conceptual framework and research methods in the childhood trauma are partly reflected in the inconsistencies, even controversies, of the results in the childhood trauma researches. Despite these difficulties, common aspects of the risk factors and resilience of the childhood trauma on the development can be identified from the previous studies. The resilience to the negative outcome on the development by childhood trauma includes：sex female before puberty, male after puberty or infancy), high socioeconomic status, no organic problem, easy temperament, no previous experience with early loss or separation, younger age at the trauma, better problem solving capacity, high self-esteem, internal locus of control, high coping skills, ability to identify interpersonal relationships, ability to play, sense of humor, having capable parents, having a warm relaionship with at least one of the parents, high education and participating in the organized religious activities. These commonalities of the results suggest that risk and resilient factors of the childhood trauma are interdependent, each factor has multiplicity in the impacts on the children's development according to the developmental stage of the child, family and children's other environment, trauma and stressor have diverse effects according to their intensity and risk and resilience factors could have synergistic or antagonistic effects to each other. To develop comprehensive understanding on the relationship between childhood trauma and developmental psychopathology, risk and resilience factors and to develop effective and efficient prevention and intervention, research on the effect of the stress on the neurodevelopment, on the individual differences of the response to the trauma including genetic factors and constitution, and on the brain plasticity should be accompanied in the future.

• Asia pacific journal of information systems
• /
• v.19 no.2
• /
• pp.139-155
• /
• 2009

For the last few decades, many studies have tried to explore and unveil venture companies' success factors and unique features in order to identify the sources of such companies' competitive advantages over their rivals. Such venture companies have shown tendency to give high returns for investors generally making the best use of information technology. For this reason, many venture companies are keen on attracting avid investors' attention. Investors generally make their investment decisions by carefully examining the evaluation criteria of the alternatives. To them, credit rating information provided by international rating agencies, such as Standard and Poor's, Moody's and Fitch is crucial source as to such pivotal concerns as companies stability, growth, and risk status. But these types of information are generated only for the companies issuing corporate bonds, not venture companies. Therefore, this study proposes a method for evaluating venture businesses by presenting our recent empirical results using financial data of Korean venture companies listed on KOSDAQ in Korea exchange. In addition, this paper used multi-class SVM for the prediction of DEA-based efficiency rating for venture businesses, which was derived from our proposed method. Our approach sheds light on ways to locate efficient companies generating high level of profits. Above all, in determining effective ways to evaluate a venture firm's efficiency, it is important to understand the major contributing factors of such efficiency. Therefore, this paper is constructed on the basis of following two ideas to classify which companies are more efficient venture companies: i) making DEA based multi-class rating for sample companies and ii) developing multi-class SVM-based efficiency prediction model for classifying all companies. First, the Data Envelopment Analysis(DEA) is a non-parametric multiple input-output efficiency technique that measures the relative efficiency of decision making units(DMUs) using a linear programming based model. It is non-parametric because it requires no assumption on the shape or parameters of the underlying production function. DEA has been already widely applied for evaluating the relative efficiency of DMUs. Recently, a number of DEA based studies have evaluated the efficiency of various types of companies, such as internet companies and venture companies. It has been also applied to corporate credit ratings. In this study we utilized DEA for sorting venture companies by efficiency based ratings. The Support Vector Machine(SVM), on the other hand, is a popular technique for solving data classification problems. In this paper, we employed SVM to classify the efficiency ratings in IT venture companies according to the results of DEA. The SVM method was first developed by Vapnik (1995). As one of many machine learning techniques, SVM is based on a statistical theory. Thus far, the method has shown good performances especially in generalizing capacity in classification tasks, resulting in numerous applications in many areas of business, SVM is basically the algorithm that finds the maximum margin hyperplane, which is the maximum separation between classes. According to this method, support vectors are the closest to the maximum margin hyperplane. If it is impossible to classify, we can use the kernel function. In the case of nonlinear class boundaries, we can transform the inputs into a high-dimensional feature space, This is the original input space and is mapped into a high-dimensional dot-product space. Many studies applied SVM to the prediction of bankruptcy, the forecast a financial time series, and the problem of estimating credit rating, In this study we employed SVM for developing data mining-based efficiency prediction model. We used the Gaussian radial function as a kernel function of SVM. In multi-class SVM, we adopted one-against-one approach between binary classification method and two all-together methods, proposed by Weston and Watkins(1999) and Crammer and Singer(2000), respectively. In this research, we used corporate information of 154 companies listed on KOSDAQ market in Korea exchange. We obtained companies' financial information of 2005 from the KIS(Korea Information Service, Inc.). Using this data, we made multi-class rating with DEA efficiency and built multi-class prediction model based data mining. Among three manners of multi-classification, the hit ratio of the Weston and Watkins method is the best in the test data set. In multi classification problems as efficiency ratings of venture business, it is very useful for investors to know the class with errors, one class difference, when it is difficult to find out the accurate class in the actual market. So we presented accuracy results within 1-class errors, and the Weston and Watkins method showed 85.7% accuracy in our test samples. We conclude that the DEA based multi-class approach in venture business generates more information than the binary classification problem, notwithstanding its efficiency level. We believe this model can help investors in decision making as it provides a reliably tool to evaluate venture companies in the financial domain. For the future research, we perceive the need to enhance such areas as the variable selection process, the parameter selection of kernel function, the generalization, and the sample size of multi-class.

• Journal of The Korean Association For Science Education
• /
• v.37 no.5
• /
• pp.825-833
• /
• 2017

The Korean Ministry of Education has emphasized human resource development with creative and convergent ability for future science and technology development. Korean STEAM Education aims to enhance students' interest and their understanding of science and technology as well as to develop students' creative problem-solving skills. Through STEAM R&E project, students experience self-directed research in order to solve the problem in the context of everyday life. In this study, we aim to find out whether the creative leader competency of high school students changed after they experienced the STEAM R&E project. The creative leader competency consisted of three domains: cognitive, affective, and societal domain. We measured the creative leader competency using the questionnaire scales. The questionnaire was administered to 612 high school students who participated in the 2016 STEAM R&E project. Pre- and post- test scores were collected, and we analyzed it. We compared the mean difference between pre- and post- test scores as well as the mean differences among science high school, gifted school, science core school, and general high school. From the result, we found that all student' creative leader competency improved after participating in the STEAM R&E project in all three domains. The result also showed that students' test scores of science high school and gifted school showed no significant mean differences, while student's scores of both science core school and general high school improved significantly. From the results, we concluded that STEAM R&E activities could be an effective tool in cultivating creative leader competency, especially for general high school students and science core school students. We also suggested that further researches are needed to find how we could enhance students' creative leader competency.

• Journal of Global Scholars of Marketing Science
• /
• v.20 no.3
• /
• pp.239-248
• /
• 2010

This paper describes a collaborative project between academia and industry which focused on improving the marketing and product development strategies for two private label apparel brands of a large regional department store chain in the southeastern United States. The goal of the project was to revitalize product lines of the two brands by incorporating student ideas for new solutions, thereby giving the students practical experience with a real-life industry situation. There were a number of key players involved in the project. A privately-owned department store chain based in the southeastern United States which was seeking an academic partner had recognized a need to update two existing private label brands. They targeted middle-aged consumers looking for casual, moderately priced merchandise. The company was seeking to change direction with both packaging and presentation, and possibly product design. The branding and product development divisions of the company contacted professors in an academic department of a large southeastern state university. Two of the professors agreed that the task would be a good fit for their classes - one was a junior-level Intermediate Brand Management class; the other was a senior-level Fashion Product Development class. The professors felt that by working collaboratively on the project, students would be exposed to a real world scenario, within the security of an academic learning environment. Collaboration within an interdisciplinary team has the advantage of providing experiences and resources beyond the capabilities of a single student and adds "brainpower" to problem-solving processes (Lowman 2000). This goal of improving the capabilities of students directed the instructors in each class to form interdisciplinary teams between the Branding and Product Development classes. In addition, many universities are employing industry partnerships in research and teaching, where collaboration within temporal (semester) and physical (classroom/lab) constraints help to increase students' knowledge and experience of a real-world situation. At the University of Tennessee, the Center of Industrial Services and UT-Knoxville's College of Engineering worked with a company to develop design improvements in its U.S. operations. In this study, Because should be lower case b with a private label retail brand, Wickett, Gaskill and Damhorst's (1999) revised Retail Apparel Product Development Model was used by the product development and brand management teams. This framework was chosen because it addresses apparel product development from the concept to the retail stage. Two classes were involved in this project: a junior level Brand Management class and a senior level Fashion Product Development class. Seven teams were formed which included four students from Brand Management and two students from Product Development. The classes were taught the same semester, but not at the same time. At the beginning of the semester, each class was introduced to the industry partner and given the problem. Half the teams were assigned to the men's brand and half to the women's brand. The teams were responsible for devising approaches to the problem, formulating a timeline for their work, staying in touch with industry representatives and making sure that each member of the team contributed in a positive way. The objective for the teams was to plan, develop, and present a product line using merchandising processes (following the Wickett, Gaskill and Damhorst model) and develop new branding strategies for the proposed lines. The teams performed trend, color, fabrication and target market research; developed sketches for a line; edited the sketches and presented their line plans; wrote specifications; fitted prototypes on fit models, and developed final production samples for presentation to industry. The branding students developed a SWOT analysis, a Brand Measurement report, a mind-map for the brands and a fully integrated Marketing Report which was presented alongside the ideas for the new lines. In future if the opportunity arises to work in this collaborative way with an existing company who wishes to look both at branding and product development strategies, classes will be scheduled at the same time so that students have more time to meet and discuss timelines and assigned tasks. As it was, student groups had to meet outside of each class time and this proved to be a challenging though not uncommon part of teamwork (Pfaff and Huddleston, 2003). Although the logistics of this exercise were time-consuming to set up and administer, professors felt that the benefits to students were multiple. The most important benefit, according to student feedback from both classes, was the opportunity to work with industry professionals, follow their process, and see the results of their work evaluated by the people who made the decisions at the company level. Faculty members were grateful to have a "real-world" case to work with in the classroom to provide focus. Creative ideas and strategies were traded as plans were made, extending and strengthening the departmental links be tween the branding and product development areas. By working not only with students coming from a different knowledge base, but also having to keep in contact with the industry partner and follow the framework and timeline of industry practice, student teams were challenged to produce excellent and innovative work under new circumstances. Working on the product development and branding for "real-life" brands that are struggling gave students an opportunity to see how closely their coursework ties in with the real-world and how creativity, collaboration and flexibility are necessary components of both the design and business aspects of company operations. Industry personnel were impressed by (a) the level and depth of knowledge and execution in the student projects, and (b) the creativity of new ideas for the brands.

• Journal of Intelligence and Information Systems
• /
• v.26 no.2
• /
• pp.79-104
• /
• 2020

Recently, as deep learning has attracted attention, the use of deep learning is being considered as a method for solving problems in various fields. In particular, deep learning is known to have excellent performance when applied to applying unstructured data such as text, sound and images, and many studies have proven its effectiveness. Owing to the remarkable development of text and image deep learning technology, interests in image captioning technology and its application is rapidly increasing. Image captioning is a technique that automatically generates relevant captions for a given image by handling both image comprehension and text generation simultaneously. In spite of the high entry barrier of image captioning that analysts should be able to process both image and text data, image captioning has established itself as one of the key fields in the A.I. research owing to its various applicability. In addition, many researches have been conducted to improve the performance of image captioning in various aspects. Recent researches attempt to create advanced captions that can not only describe an image accurately, but also convey the information contained in the image more sophisticatedly. Despite many recent efforts to improve the performance of image captioning, it is difficult to find any researches to interpret images from the perspective of domain experts in each field not from the perspective of the general public. Even for the same image, the part of interests may differ according to the professional field of the person who has encountered the image. Moreover, the way of interpreting and expressing the image also differs according to the level of expertise. The public tends to recognize the image from a holistic and general perspective, that is, from the perspective of identifying the image's constituent objects and their relationships. On the contrary, the domain experts tend to recognize the image by focusing on some specific elements necessary to interpret the given image based on their expertise. It implies that meaningful parts of an image are mutually different depending on viewers' perspective even for the same image. So, image captioning needs to implement this phenomenon. Therefore, in this study, we propose a method to generate captions specialized in each domain for the image by utilizing the expertise of experts in the corresponding domain. Specifically, after performing pre-training on a large amount of general data, the expertise in the field is transplanted through transfer-learning with a small amount of expertise data. However, simple adaption of transfer learning using expertise data may invoke another type of problems. Simultaneous learning with captions of various characteristics may invoke so-called 'inter-observation interference' problem, which make it difficult to perform pure learning of each characteristic point of view. For learning with vast amount of data, most of this interference is self-purified and has little impact on learning results. On the contrary, in the case of fine-tuning where learning is performed on a small amount of data, the impact of such interference on learning can be relatively large. To solve this problem, therefore, we propose a novel 'Character-Independent Transfer-learning' that performs transfer learning independently for each character. In order to confirm the feasibility of the proposed methodology, we performed experiments utilizing the results of pre-training on MSCOCO dataset which is comprised of 120,000 images and about 600,000 general captions. Additionally, according to the advice of an art therapist, about 300 pairs of 'image / expertise captions' were created, and the data was used for the experiments of expertise transplantation. As a result of the experiment, it was confirmed that the caption generated according to the proposed methodology generates captions from the perspective of implanted expertise whereas the caption generated through learning on general data contains a number of contents irrelevant to expertise interpretation. In this paper, we propose a novel approach of specialized image interpretation. To achieve this goal, we present a method to use transfer learning and generate captions specialized in the specific domain. In the future, by applying the proposed methodology to expertise transplant in various fields, we expected that many researches will be actively conducted to solve the problem of lack of expertise data and to improve performance of image captioning.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.19 no.4
• /
• pp.52-62
• /
• 2016

Smart City is urban development for complex problem solving that provides convenience and safety for citizens, and it is a blueprint for future cities. In 2008, the Korean government defined the construction, management, and government support of U-Cities in the legislation, Act on the Construction, Etc. of Ubiquitous Cities (Ubiquitous City Act), which included definitions of terms used in the act. In addition, the Minister of Land, Infrastructure and Transport has established a "ubiquitous city master plan" considering this legislation. The concept of U-Cities is complex, due to the mix of informatization and urban planning. Because of this complexity, the foundation of relevant regulations is inadequate, which is impeding the establishment and implementation of practical plans. Smart City intelligent service facilities are not easy to define and classify, because technology is rapidly changing and includes various devices for gathering and expressing information. The purpose of this study is to complement the legal definition of the intelligent service facility, which is necessary for integrated management and operation. The related laws and regulations on U-City were analyzed using text-mining techniques to identify insufficient legal definitions of intelligent service facilities. Using data gathered from interviews with officials responsible for constructing U-Cities, this study identified problems generated by implementing intelligent service facilities at the field level. This strategy should contribute to improved efficiency management, the foundation for building integrated utilization between departments. Efficiencies include providing a clear concept for establishing five-year renewable plans for U-Cities.