• Journal of the Korea Convergence Society
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• v.9 no.9
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• pp.209-215
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• 2018

The purpose of this study is to grasp the overall policy decision system of sex crime prevention policy and analyze the interaction and pattern of actors in policy formation process. This is a useful way to identify the causes and ways to improve the policy if the sex crime prevention policy fails. As a research method, we used a model of advocacy through case analysis and language network analysis. In the external environment, low reporting of sex offenses, technical improvement and supplement for preventive management, consciousness of victims of sexual crimes, amendment of legislation, and support of the president. The conflicts between the advocacy coalition opposed the strong regulation, the prevention of recidivism, the expansion of the range of objects to be worn, the temporary effect of the system and the retrospective of the bill. As a problem-solving strategy, it was confirmed that the opposing positions of pros and cons of lack of manpower and negligence of management through the extension of the system were acutely opposed. In the context of media reports, this tendency is more likely to be understood as the concern of prevention and management at the central government level to prevent sex crimes. Therefore, although the methods of prevention of sex crimes have been insufficient in the past, it is hoped that this study will be helpful in breaking the link of negative policy vicious cycle.

• Radiation Oncology Journal
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• v.27 no.4
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• pp.240-248
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• 2009

Purpose: To provide a simple research tool that may be used to analyze a dose volume histogram from different radiation therapy planning systems for NTCP (Normal Tissue Complication Probability), OED (Organ Equivalent Dose) and so on. Materials and Metohds: A high-level computing language was chosen to implement Niemierko's EUD, Lyman-Kutcher-Burman model's NTCP, and OED. The requirements for treatment planning analysis were defined and the procedure, using a developed GUI based program, was described with figures. The calculated data, including volume at a dose, dose at a volume, EUD, and NTCP were evaluated by a commercial radiation therapy planning system, Pinnacle (Philips, Madison, WI, USA) for comparison. Results: The volume at a special dose and a dose absorbed in a volume on a dose volume histogram were successfully extracted using DVH data of several radiation planning systems. EUD, NTCP and OED were successfully calculated using DVH data and some required parameters in the literature. Conclusion: A simple DVH analyzer program was developed and has proven to be a useful research tool for radiation therapy.

• The Transactions of the Korea Information Processing Society
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• v.7 no.1
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• pp.166-180
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• 2000

Recommendations of ITU-T and ISO stipulate the managerial abstraction of static and dynamic characteristics of network elements, management functions as well as management communication protocol. The current recommendations provide the formal mechanism for the structural parts of managed objects such as managed object class and attributes. But the current description method does not provide the formal mechanism for the behavioral characteristics of managed objects in clear manner but in natural language form, the complete specification of managed objects is not fully described. Also, the behaviour of managed objects is affected by their temporal and active properties. While the temporal properties representing periodic or repetitive internals are to describe managed objects behaviour in rather strict way, it will be more powerful if more dynamic temporal properties determined by external conditions are added to managed objects. In this paper, we added dynamic features to scheduling managed objects, and described, in GDMO, scheduling managed objects that support dynamic features. We also described behaviour of managed objects in newly defined BDL that has dynamic temporal properties. This paper showed that dynamic temporal managed objects provide a systematic and formal method in agent management function model.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.219-240
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• 2018

Sentiment analysis, which is one of the text mining techniques, is a method for extracting subjective content embedded in text documents. Recently, the sentiment analysis methods have been widely used in many fields. As good examples, data-driven surveys are based on analyzing the subjectivity of text data posted by users and market researches are conducted by analyzing users' review posts to quantify users' reputation on a target product. The basic method of sentiment analysis is to use sentiment dictionary (or lexicon), a list of sentiment vocabularies with positive, neutral, or negative semantics. In general, the meaning of many sentiment words is likely to be different across domains. For example, a sentiment word, 'sad' indicates negative meaning in many fields but a movie. In order to perform accurate sentiment analysis, we need to build the sentiment dictionary for a given domain. However, such a method of building the sentiment lexicon is time-consuming and various sentiment vocabularies are not included without the use of general-purpose sentiment lexicon. In order to address this problem, several studies have been carried out to construct the sentiment lexicon suitable for a specific domain based on 'OPEN HANGUL' and 'SentiWordNet', which are general-purpose sentiment lexicons. However, OPEN HANGUL is no longer being serviced and SentiWordNet does not work well because of language difference in the process of converting Korean word into English word. There are restrictions on the use of such general-purpose sentiment lexicons as seed data for building the sentiment lexicon for a specific domain. In this article, we construct 'KNU Korean Sentiment Lexicon (KNU-KSL)', a new general-purpose Korean sentiment dictionary that is more advanced than existing general-purpose lexicons. The proposed dictionary, which is a list of domain-independent sentiment words such as 'thank you', 'worthy', and 'impressed', is built to quickly construct the sentiment dictionary for a target domain. Especially, it constructs sentiment vocabularies by analyzing the glosses contained in Standard Korean Language Dictionary (SKLD) by the following procedures: First, we propose a sentiment classification model based on Bidirectional Long Short-Term Memory (Bi-LSTM). Second, the proposed deep learning model automatically classifies each of glosses to either positive or negative meaning. Third, positive words and phrases are extracted from the glosses classified as positive meaning, while negative words and phrases are extracted from the glosses classified as negative meaning. Our experimental results show that the average accuracy of the proposed sentiment classification model is up to 89.45%. In addition, the sentiment dictionary is more extended using various external sources including SentiWordNet, SenticNet, Emotional Verbs, and Sentiment Lexicon 0603. Furthermore, we add sentiment information about frequently used coined words and emoticons that are used mainly on the Web. The KNU-KSL contains a total of 14,843 sentiment vocabularies, each of which is one of 1-grams, 2-grams, phrases, and sentence patterns. Unlike existing sentiment dictionaries, it is composed of words that are not affected by particular domains. The recent trend on sentiment analysis is to use deep learning technique without sentiment dictionaries. The importance of developing sentiment dictionaries is declined gradually. However, one of recent studies shows that the words in the sentiment dictionary can be used as features of deep learning models, resulting in the sentiment analysis performed with higher accuracy (Teng, Z., 2016). This result indicates that the sentiment dictionary is used not only for sentiment analysis but also as features of deep learning models for improving accuracy. The proposed dictionary can be used as a basic data for constructing the sentiment lexicon of a particular domain and as features of deep learning models. It is also useful to automatically and quickly build large training sets for deep learning models.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.71-90
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• 2020

Recently, the global insurance industry is rapidly developing digital transformation through the use of artificial intelligence technologies such as machine learning, natural language processing, and deep learning. As a result, more and more foreign insurers have achieved the success of artificial intelligence technology-based InsurTech and platform business, and Ping An Insurance Group Ltd., China's largest private company, is leading China's global fourth industrial revolution with remarkable achievements in InsurTech and Digital Platform as a result of its constant innovation, using 'finance and technology' and 'finance and ecosystem' as keywords for companies. In response, this study analyzed the InsurTech and platform business activities of Ping An Insurance Group Ltd. through the ser-M analysis model to provide strategic implications for revitalizing AI technology-based businesses of domestic insurers. The ser-M analysis model has been studied so that the vision and leadership of the CEO, the historical environment of the enterprise, the utilization of various resources, and the unique mechanism relationships can be interpreted in an integrated manner as a frame that can be interpreted in terms of the subject, environment, resource and mechanism. As a result of the case analysis, Ping An Insurance Group Ltd. has achieved cost reduction and customer service development by digitally innovating its entire business area such as sales, underwriting, claims, and loan service by utilizing core artificial intelligence technologies such as facial, voice, and facial expression recognition. In addition, "online data in China" and "the vast offline data and insights accumulated by the company" were combined with new technologies such as artificial intelligence and big data analysis to build a digital platform that integrates financial services and digital service businesses. Ping An Insurance Group Ltd. challenged constant innovation, and as of 2019, sales reached $155 billion, ranking seventh among all companies in the Global 2000 rankings selected by Forbes Magazine. Analyzing the background of the success of Ping An Insurance Group Ltd. from the perspective of ser-M, founder Mammingz quickly captured the development of digital technology, market competition and changes in population structure in the era of the fourth industrial revolution, and established a new vision and displayed an agile leadership of digital technology-focused. Based on the strong leadership led by the founder in response to environmental changes, the company has successfully led InsurTech and Platform Business through innovation of internal resources such as investment in artificial intelligence technology, securing excellent professionals, and strengthening big data capabilities, combining external absorption capabilities, and strategic alliances among various industries. Through this success story analysis of Ping An Insurance Group Ltd., the following implications can be given to domestic insurance companies that are preparing for digital transformation. First, CEOs of domestic companies also need to recognize the paradigm shift in industry due to the change in digital technology and quickly arm themselves with digital technology-oriented leadership to spearhead the digital transformation of enterprises. Second, the Korean government should urgently overhaul related laws and systems to further promote the use of data between different industries and provide drastic support such as deregulation, tax benefits and platform provision to help the domestic insurance industry secure global competitiveness. Third, Korean companies also need to make bolder investments in the development of artificial intelligence technology so that systematic securing of internal and external data, training of technical personnel, and patent applications can be expanded, and digital platforms should be quickly established so that diverse customer experiences can be integrated through learned artificial intelligence technology. Finally, since there may be limitations to generalization through a single case of an overseas insurance company, I hope that in the future, more extensive research will be conducted on various management strategies related to artificial intelligence technology by analyzing cases of multiple industries or multiple companies or conducting empirical research.