• Journal of Intelligence and Information Systems
• /
• v.23 no.1
• /
• pp.23-46
• /
• 2017

Although there have been cases of evaluating the value of specific companies or projects which have centralized on developed countries in North America and Europe from the early 2000s, the system and methodology for estimating the economic value of individual technologies or patents has been activated on and on. Of course, there exist several online systems that qualitatively evaluate the technology's grade or the patent rating of the technology to be evaluated, as in 'KTRS' of the KIBO and 'SMART 3.1' of the Korea Invention Promotion Association. However, a web-based technology valuation system, referred to as 'STAR-Value system' that calculates the quantitative values of the subject technology for various purposes such as business feasibility analysis, investment attraction, tax/litigation, etc., has been officially opened and recently spreading. In this study, we introduce the type of methodology and evaluation model, reference information supporting these theories, and how database associated are utilized, focusing various modules and frameworks embedded in STAR-Value system. In particular, there are six valuation methods, including the discounted cash flow method (DCF), which is a representative one based on the income approach that anticipates future economic income to be valued at present, and the relief-from-royalty method, which calculates the present value of royalties' where we consider the contribution of the subject technology towards the business value created as the royalty rate. We look at how models and related support information (technology life, corporate (business) financial information, discount rate, industrial technology factors, etc.) can be used and linked in a intelligent manner. Based on the classification of information such as International Patent Classification (IPC) or Korea Standard Industry Classification (KSIC) for technology to be evaluated, the STAR-Value system automatically returns meta data such as technology cycle time (TCT), sales growth rate and profitability data of similar company or industry sector, weighted average cost of capital (WACC), indices of industrial technology factors, etc., and apply adjustment factors to them, so that the result of technology value calculation has high reliability and objectivity. Furthermore, if the information on the potential market size of the target technology and the market share of the commercialization subject refers to data-driven information, or if the estimated value range of similar technologies by industry sector is provided from the evaluation cases which are already completed and accumulated in database, the STAR-Value is anticipated that it will enable to present highly accurate value range in real time by intelligently linking various support modules. Including the explanation of the various valuation models and relevant primary variables as presented in this paper, the STAR-Value system intends to utilize more systematically and in a data-driven way by supporting the optimal model selection guideline module, intelligent technology value range reasoning module, and similar company selection based market share prediction module, etc. In addition, the research on the development and intelligence of the web-based STAR-Value system is significant in that it widely spread the web-based system that can be used in the validation and application to practices of the theoretical feasibility of the technology valuation field, and it is expected that it could be utilized in various fields of technology commercialization.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.20 no.5
• /
• pp.81-87
• /
• 2012

New car assessment program (NCAP) originated from USNCAP in 1979 has been implemented in several countries or markets, for instance USA, Europe, Korea, Japan, China and Australia. NCAP has contributed greatly to reduce accidental tolls. But recently, NCAP performance has no distinction between cars because manufacturer have been continuously developed to improve NCAP performance. Therefore, NHTSA announced new USNCAP protocol becoming effective from MY2011. NHTSA had carried out many NCAP tests based on the new test protocol and announced these test results. In this paper, USNCAP test results were reviewed by statistical method. This review was focused on passenger cars and frontal crash test results in order to investigate effect of changes in new NCAP protocol. There are two key changes, one is sited female dummy in passenger position, the other is enlarged to 4 scoring body regions in each dummy. Results of this review were summarized as followings. Performance in Passenger (12.5%) is lower than Driver's (50%) for number of 5 star vehicle. Neck injury criterion is dominant to NCAP star rating for both dummies in the mean sense. For standard deviation, chest deflection is showed largest value in driver dummy but neck injury criterion is showed for passenger's. DKAB and PKAB were equipped 28.1% and 6.2%, respectively. Consequently, the countermeasure for new USNCAP frontal crash test is essential to control well dummy kinematics with some safety features including KAB to reduce neck injuries.

• Journal of Intelligence and Information Systems
• /
• v.28 no.2
• /
• pp.1-18
• /
• 2022

As digital technology converges into the e-commerce market across industries, online transactions have activated, and the use of online has increased. With the recent spread of infectious diseases such as COVID-19, this market flow is accelerating, and various product information can be provided to customers online. Providing a variety of information provides customers with various opportunities but causes difficulties in decision-making. The recommendation system can help customers to make a decision more effectively. However, the previous research on recommendation systems is limited to only quantitative data and does not reflect detailed factors of products and customers. In this study, we propose an intelligent recommendation system that quantifies the attributes of products and customers by applying text mining techniques to qualitative data based on online reviews and integrates the existing objective indicators of total star rating, sentiment, and emotion. The proposed integrated recommendation model showed superior performance to the overall rating-oriented recommendation model. It expects the new business value to be created through the recommendation result reflecting detailed factors of products and customers.

• Journal of Power Electronics
• /
• v.15 no.1
• /
• pp.256-267
• /
• 2015

Grid-connected transformerless photovoltaic (PV) inverters (TPVIs) are increasingly dominating the market due to their higher efficiency, lower cost, lighter weight, and reduced size when compared to their transformer based counterparts. However, due to the lack of galvanic isolation in the low voltage grid interconnections of these inverters, the PV systems become vulnerable to leakage currents flowing through the grounded star point of the distribution transformer, the earth, and the distributed parasitic capacitance of the PV modules. These leakage currents are prohibitive, since they constitute an issue for safety, reliability, protection coordination, electromagnetic compatibility, and module lifetime. This paper investigates a wide range of multi-kW range power rating TPVI topologies and classifies them in terms of their leakage current attributes. This systematic classification places most topologies under a small number of classes with basic leakage current attributes. Thus, understanding and evaluating these topologies becomes an easy task. In addition, based on these observations, new topologies with reduced leakage current characteristics are proposed in this paper. Furthermore, the important efficiency and cost determining characteristics of converters are studied to allow design engineers to include cost and efficiency as deciding factors in selecting a converter topology for PV applications.

• Journal of the Korean Society for Aviation and Aeronautics
• /
• v.21 no.3
• /
• pp.27-40
• /
• 2013

Rotorcraft operating in the domestic aviation safety techniques are applied CRM training is conducted but, aircraft accidents caused by human factors has not shown a declining trend. Thus, knowledge of aviation safety and human factors for the spread of awareness of improved rotorcraft flight operations department managers to understand the complexity of nature and culture, and to perform high-risk mission helicopter pilot study of local activation and enhance safety awareness research was conducted in order to. In this study, the development direction of CRM training studies in order to identify the leading NOTECHS (Non-Technical Skills; non-technical pilot skills) of the four categories as the independent variable and the dependent variable corresponding to the resulting effect on the key variables awareness of the differences were studied. In addition, the direction and strength of the relationship were analyzed to determine the relationship of each independent variable to assess the impact on the dependent variable regression analysis was performed. Pilot training and evaluation of non-technical skills related to the teaching reflected in the CRM training and assessment must be carried out with 5 star rating scale was preferred. Therefore, to meet our country rotorcraft operating environment 'NOTECHS' aviation safety by developing training programs reflected in the educational process, implementation, and periodic training and assessment is done in future research on this analysis and feedback is done to reflect the specific performance expect.

• Journal of Information Technology Services
• /
• v.21 no.4
• /
• pp.75-90
• /
• 2022

From the past to the present, reviews have had much influence on consumers' purchasing decisions. Companies are making various efforts, such as introducing a review incentive system to increase the number of reviews. Recently, as various types of reviews can be left, reviews have begun to be recognized as interesting new content. This way, reviews have become essential in creating loyal customers. Therefore, research and utilization of reviews are being actively conducted. Some studies analyze reviews to discover customers' needs, studies that upgrade recommendation systems using reviews, and studies that analyze consumers' emotions and attitudes through reviews. However, research that predicts the future using reviews is insufficient. This study used a dataset consisting of two reviews written in pairs with differences in usage periods. In this study, the direction of consumer product evaluation is predicted using KoBERT, which shows excellent performance in Text Deep Learning. We used 7,233 reviews collected to demonstrate the excellence of the proposed model. As a result, the proposed model using the review text and the star rating showed excellent performance compared to the baseline that follows the majority voting.

• Information Systems Review
• /
• v.23 no.3
• /
• pp.51-75
• /
• 2021

Global e-commerce websites offer personalized recommendation services to gain sustainable competitiveness. Existing studies have offered personalized recommendation services using quantitative preferences such as ratings. However, offering personalized recommendation services using only quantitative data has raised the problem of decreasing recommendation performance. For example, a user gave a five-star rating but wrote a review that the user was unsatisfied with hotel service and cleanliness. In such cases, has problems where quantitative and qualitative preferences are inconsistent. Recently, a growing number of studies have considered review data simultaneously to improve the limitations of existing personalized recommendation service studies. Therefore, in this study, we identify review and rating mismatches and build a new user profile to offer personalized recommendation services. To this end, we use deep learning algorithms such as CNN, LSTM, CNN + LSTM, which have been widely used in sentiment analysis studies. And extract sentiment features from reviews and compare with quantitative preferences. To evaluate the performance of the proposed methodology in this study, we collect user preference information using real-world hotel data from the world's largest travel platform TripAdvisor. Experiments show that the proposed methodology in this study outperforms the existing other methodologies, using only existing quantitative preferences.

• Journal of Intelligence and Information Systems
• /
• v.22 no.3
• /
• pp.71-89
• /
• 2016

Sentiment analysis is used for identifying emotions or sentiments embedded in the user generated data such as customer reviews from blogs, social network services, and so on. Various research fields such as computer science and business management can take advantage of this feature to analyze customer-generated opinions. In previous studies, the star rating of a review is regarded as the same as sentiment embedded in the text. However, it does not always correspond to the sentiment polarity. Due to this supposition, previous studies have some limitations in their accuracy. To solve this issue, the present study uses a supervised sentiment classification model to measure a more accurate sentiment polarity. This study aims to propose an advanced sentiment classifier and to discover the correlation between movie reviews and box-office success. The advanced sentiment classifier is based on two supervised machine learning techniques, the Support Vector Machines (SVM) and Feedforward Neural Network (FNN). The sentiment scores of the movie reviews are measured by the sentiment classifier and are analyzed by statistical correlations between movie reviews and box-office success. Movie reviews are collected along with a star-rate. The dataset used in this study consists of 1,258,538 reviews from 175 films gathered from Naver Movie website (movie.naver.com). The results show that the proposed sentiment classifier outperforms Naive Bayes (NB) classifier as its accuracy is about 6% higher than NB. Furthermore, the results indicate that there are positive correlations between the star-rate and the number of audiences, which can be regarded as the box-office success of a movie. The study also shows that there is the mild, positive correlation between the sentiment scores estimated by the classifier and the number of audiences. To verify the applicability of the sentiment scores, an independent sample t-test was conducted. For this, the movies were divided into two groups using the average of sentiment scores. The two groups are significantly different in terms of the star-rated scores.

• The Journal of the Korea Contents Association
• /
• v.18 no.6
• /
• pp.706-718
• /
• 2018

This study analyzed Naver TV users' pattern of video clip watching, and analyzed the factors affecting the popularity of Naver TV's video clip. We selected 572 individual video clips that were ranked 50th in Naver TV rankings from September 10th to September 24th in 2017. We classified video clip's characteristics into several factors, including the number of likes, the number of subscriber, genre, video clip's types, and star appearances. We indexed the popularity of video clip, which implies the degree of popularity for each video clip. The results showed that the number of likes for video clips and the number of subscribers for each video clip were positively related to the popularity of video clip. Video clip's genre, video clip's type and star power positively affected the popularity of video clip. The effect of extras genre on the popularity of video clip was the lowest, followed by entertainment, music, and drama genre. but the difference among entertainment, music and drama genre was not statistically significant. Web-only video and non-broadcast video positively affected the popularity of video clip. Finally, the popularity of video clip was higher when stars appeared in the video clip.

• Journal of Intelligence and Information Systems
• /
• v.22 no.1
• /
• pp.43-61
• /
• 2016

Customer reviews help potential customers make purchasing decisions. However, the prevalence of reviews on websites push the customer to sift through them and change the focus from a mere search to identifying which of the available reviews are valuable and useful for the purchasing decision at hand. To identify useful reviews, websites have developed different mechanisms to give customers options when evaluating existing reviews. Websites allow users to rate the usefulness of a customer review as helpful or not. Amazon.com uses a ratio-type helpfulness, while Yelp.com uses a count-type usefulness index. This usefulness index provides helpful reviews to future potential purchasers. This study investigated the effects of sentiment and readability on useful votes for customer reviews. Similar studies on the relationship between sentiment and readability have focused on the ratio-type usefulness index utilized by websites such as Amazon.com. In this study, Yelp.com's count-type usefulness index for restaurant reviews was used to investigate the relationship between sentiment/readability and usefulness votes. Yelp.com's online customer reviews for stores in the beverage and food categories were used for the analysis. In total, 170,294 reviews containing information on a store's reputation and popularity were used. The control variables were the review length, store reputation, and popularity; the independent variables were the sentiment and readability, while the dependent variable was the number of helpful votes. The review rating is the moderating variable for the review sentiment and readability. The length is the number of characters in a review. The popularity is the number of reviews for a store, and the reputation is the general average rating of all reviews for a store. The readability of a review was calculated with the Coleman-Liau index. The sentiment is a positivity score for the review as calculated by SentiWordNet. The review rating is a preference score selected from 1 to 5 (stars) by the review author. The dependent variable (i.e., usefulness votes) used in this study is a count variable. Therefore, the Poisson regression model, which is commonly used to account for the discrete and nonnegative nature of count data, was applied in the analyses. The increase in helpful votes was assumed to follow a Poisson distribution. Because the Poisson model assumes an equal mean and variance and the data were over-dispersed, a negative binomial distribution model that allows for over-dispersion of the count variable was used for the estimation. Zero-inflated negative binomial regression was used to model count variables with excessive zeros and over-dispersed count outcome variables. With this model, the excess zeros were assumed to be generated through a separate process from the count values and therefore should be modeled as independently as possible. The results showed that positive sentiment had a negative effect on gaining useful votes for positive reviews but no significant effect on negative reviews. Poor readability had a negative effect on gaining useful votes and was not moderated by the review star ratings. These findings yield considerable managerial implications. The results are helpful for online websites when analyzing their review guidelines and identifying useful reviews for their business. Based on this study, positive reviews are not necessarily helpful; therefore, restaurants should consider which type of positive review is helpful for their business. Second, this study is beneficial for businesses and website designers in creating review mechanisms to know which type of reviews to highlight on their websites and which type of reviews can be beneficial to the business. Moreover, this study highlights the review systems employed by websites to allow their customers to post rating reviews.