• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.181-199
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• 2010

As rapid progress of population aging and strong interest in health, the demand for new healthcare service is increasing. Until now healthcare service has provided post treatment by face-to-face manner. But according to related researches, proactive treatment is resulted to be more effective for preventing diseases. Particularly, the existing healthcare services have limitations in preventing and managing metabolic syndrome such a lifestyle disease, because the cause of metabolic syndrome is related to life habit. As the advent of ubiquitous technology, patients with the metabolic syndrome can improve life habit such as poor eating habits and physical inactivity without the constraints of time and space through u-healthcare service. Therefore, lots of researches for u-healthcare service focus on providing the personalized healthcare service for preventing and managing metabolic syndrome. For example, Kim et al.(2010) have proposed a healthcare model for providing the customized calories and rates of nutrition factors by analyzing the user's preference in foods. Lee et al.(2010) have suggested the customized diet recommendation service considering the basic information, vital signs, family history of diseases and food preferences to prevent and manage coronary heart disease. And, Kim and Han(2004) have demonstrated that the web-based nutrition counseling has effects on food intake and lipids of patients with hyperlipidemia. However, the existing researches for u-healthcare service focus on providing the predefined one-way u-healthcare service. Thus, users have a tendency to easily lose interest in improving life habit. To solve such a problem of u-healthcare service, this research suggests a u-healthcare recommender system which is based on collaborative filtering principle and social network. This research follows the principle of collaborative filtering, but preserves local networks (consisting of small group of similar neighbors) for target users to recommend context aware healthcare services. Our research is consisted of the following five steps. In the first step, user profile is created using the usage history data for improvement in life habit. And then, a set of users known as neighbors is formed by the degree of similarity between the users, which is calculated by Pearson correlation coefficient. In the second step, the target user obtains service information from his/her neighbors. In the third step, recommendation list of top-N service is generated for the target user. Making the list, we use the multi-filtering based on user's psychological context information and body mass index (BMI) information for the detailed recommendation. In the fourth step, the personal information, which is the history of the usage service, is updated when the target user uses the recommended service. In the final step, a social network is reformed to continually provide qualified recommendation. For example, the neighbors may be excluded from the social network if the target user doesn't like the recommendation list received from them. That is, this step updates each user's neighbors locally, so maintains the updated local neighbors always to give context aware recommendation in real time. The characteristics of our research as follows. First, we develop the u-healthcare recommender system for improving life habit such as poor eating habits and physical inactivity. Second, the proposed recommender system uses autonomous collaboration, which enables users to prevent dropping and not to lose user's interest in improving life habit. Third, the reformation of the social network is automated to maintain the quality of recommendation. Finally, this research has implemented a mobile prototype system using JAVA and Microsoft Access2007 to recommend the prescribed foods and exercises for chronic disease prevention, which are provided by A university medical center. This research intends to prevent diseases such as chronic illnesses and to improve user's lifestyle through providing context aware and personalized food and exercise services with the help of similar users'experience and knowledge. We expect that the user of this system can improve their life habit with the help of handheld mobile smart phone, because it uses autonomous collaboration to arouse interest in healthcare.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.53-65
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• 2019

Object tracking is one of important steps to achieve video-based surveillance systems. Object tracking is considered as an essential task similar to object detection and recognition. In order to perform object tracking, various machine learning methods (e.g., least-squares, perceptron and support vector machine) can be applied for different designs of tracking systems. In general, generative methods (e.g., principal component analysis) were utilized due to its simplicity and effectiveness. However, the generative methods were only focused on modeling the target object. Due to this limitation, discriminative methods (e.g., binary classification) were adopted to distinguish the target object and the background. Among the machine learning methods for binary classification, total error rate minimization can be used as one of successful machine learning methods for binary classification. The total error rate minimization can achieve a global minimum due to a quadratic approximation to a step function while other methods (e.g., support vector machine) seek local minima using nonlinear functions (e.g., hinge loss function). Due to this quadratic approximation, the total error rate minimization could obtain appropriate properties in solving optimization problems for binary classification. However, this total error rate minimization was based on a batch mode setting. The batch mode setting can be limited to several applications under offline learning. Due to limited computing resources, offline learning could not handle large scale data sets. Compared to offline learning, online learning can update its solution without storing all training samples in learning process. Due to increment of large scale data sets, online learning becomes one of essential properties for various applications. Since object tracking needs to handle data samples in real time, online learning based total error rate minimization methods are necessary to efficiently address object tracking problems. Due to the need of the online learning, an online learning based total error rate minimization method was developed. However, an approximately reweighted technique was developed. Although the approximation technique is utilized, this online version of the total error rate minimization could achieve good performances in biometric applications. However, this method is assumed that the total error rate minimization can be asymptotically achieved when only the number of training samples is infinite. Although there is the assumption to achieve the total error rate minimization, the approximation issue can continuously accumulate learning errors according to increment of training samples. Due to this reason, the approximated online learning solution can then lead a wrong solution. The wrong solution can make significant errors when it is applied to surveillance systems. In this paper, we propose an exactly reweighted technique to recursively update the solution of the total error rate minimization in online learning manner. Compared to the approximately reweighted online total error rate minimization, an exactly reweighted online total error rate minimization is achieved. The proposed exact online learning method based on the total error rate minimization is then applied to object tracking problems. In our object tracking system, particle filtering is adopted. In particle filtering, our observation model is consisted of both generative and discriminative methods to leverage the advantages between generative and discriminative properties. In our experiments, our proposed object tracking system achieves promising performances on 8 public video sequences over competing object tracking systems. The paired t-test is also reported to evaluate its quality of the results. Our proposed online learning method can be extended under the deep learning architecture which can cover the shallow and deep networks. Moreover, online learning methods, that need the exact reweighting process, can use our proposed reweighting technique. In addition to object tracking, the proposed online learning method can be easily applied to object detection and recognition. Therefore, our proposed methods can contribute to online learning community and object tracking, detection and recognition communities.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.125-139
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• 2013

Big data refers to the data that cannot be processes with conventional contemporary data technologies. As smart devices and social network services produces vast amount of data, big data attracts much attention from researchers. There are strong demands form governments and industries for bib data as it can create new values by drawing business insights from data. Since various new technologies to process big data introduced, academic communities also show much interest to the big data domain. A notable advance related to the big data technology has been in various fields. Big data technology makes it possible to access, collect, and save individual's personal data. These technologies enable the analysis of huge amounts of data with lower cost and less time, which is impossible to achieve with traditional methods. It even detects personal information that people do not want to open. Therefore, people using information technology such as the Internet or online services have some level of privacy concerns, and such feelings can hinder continued use of information systems. For example, SNS offers various benefits, but users are sometimes highly exposed to privacy intrusions because they write too much personal information on it. Even though users post their personal information on the Internet by themselves, the data sometimes is not under control of the users. Once the private data is posed on the Internet, it can be transferred to anywhere by a few clicks, and can be abused to create fake identity. In this way, privacy intrusion happens. This study aims to investigate how perceived personal information overload in SNS affects user's risk perception and information privacy concerns. Also, it examines the relationship between the concerns and user resistance behavior. A survey approach and structural equation modeling method are employed for data collection and analysis. This study contributes meaningful insights for academic researchers and policy makers who are planning to develop guidelines for privacy protection. The study shows that information overload on the social network services can bring the significant increase of users' perceived level of privacy risks. In turn, the perceived privacy risks leads to the increased level of privacy concerns. IF privacy concerns increase, it can affect users to from a negative or resistant attitude toward system use. The resistance attitude may lead users to discontinue the use of social network services. Furthermore, information overload is mediated by perceived risks to affect privacy concerns rather than has direct influence on perceived risk. It implies that resistance to the system use can be diminished by reducing perceived risks of users. Given that users' resistant behavior become salient when they have high privacy concerns, the measures to alleviate users' privacy concerns should be conceived. This study makes academic contribution of integrating traditional information overload theory and user resistance theory to investigate perceived privacy concerns in current IS contexts. There is little big data research which examined the technology with empirical and behavioral approach, as the research topic has just emerged. It also makes practical contributions. Information overload connects to the increased level of perceived privacy risks, and discontinued use of the information system. To keep users from departing the system, organizations should develop a system in which private data is controlled and managed with ease. This study suggests that actions to lower the level of perceived risks and privacy concerns should be taken for information systems continuance.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.5
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• pp.581-589
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• 2021

With the recent increase in the introduction of micro-electric vehicles in Korea, interest in micro-electric vehicle user satisfaction is increasing to revitalize related markets. In this paper, a basic study was conducted on the development of public services using micro-electric vehicle based on the constituent factors of user satisfaction. The survey includes: ① 'Analytic Hierarchy Process (AHP) for selecting the priority factors of user satisfaction of micro-electric vehicles', ② 'A survey of micro-electric vehicles image' to collect data in advance for providing users' preferences and transportation services for micro-electric vehicles, ③ In order to investigate the user satisfaction level of users who actually operated micro-electric vehicles, the order of 'user satisfaction survey of micro-electric vehicle drivers' was conducted. In the Analytic Hierarchy Process (AHP) analysis, it was found that users regarded as important in the order of 'user utilization data', 'vehicle movement data', and 'charging service data'. In the micro-electric vehicle image survey, users perceived micro-electric vehicles more positively in terms of "safety", 'durability', 'Ride comfort', 'design', 'MOOE (Maintenance and other operating expense)', and 'environment-friendly' when comparing micro-electric vehicles with electric motorcycles. In the survey on the user satisfaction of micro-electric vehicle drivers, the use of micro-electric vehicle did not directly affect work performance efficiency, and there was an experience of being disadvantaged on the road due to the size of the micro-electric vehicle, and driving in a cluster of micro-electric vehicle for outdoor advertisements. The city's public relations effect was great, but it was concerned about safety. In the future, based on the results of this study, we plan to build a user satisfaction structural equation model, preemptively discover feedback R&D for micro-electric vehicle utilization services in the public field, and actively seek to discover new public mobility support services.

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1361-1371
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• 2021

Among smart city services, the crime and disaster prevention sector accounted for the highest 24% in 2018. The most important platform for providing real-time situation information is CCTV (Closed-Circuit Television). Therefore, it is essential to create the actual CCTV surveillance coverage to maximize the usability of CCTV. However, the amount of CCTV installed in Korea exceeds one million units, including those operated by the local government, and manual identification of CCTV coverage is a time-consuming and inefficient process. This study proposed a method to efficiently construct CCTV's actual surveillance coverage and reduce the time required for the decision-maker to manage the situation. For this purpose, first, the exterior orientation parameters and focal lengths of the pre-installed CCTV cameras, which are difficult to access, were calculated using the point cloud data of the MMS (Mobile Mapping System), and the FOV (Field of View) was calculated accordingly. Second, using the FOV result calculated in the first step, CCTV's actual surveillance coverage area was constructed with 1 m, 2 m, 3 m, 5 m, and 10 m grid interval considering the occluded regions caused by the buildings. As a result of applying our approach to 5 CCTV images located in Uljin-gun, Gyeongsnagbuk-do the average re-projection error was about 9.31 pixels. The coordinate difference between calculated CCTV and location obtained from MMS was about 1.688 m on average. When the grid length was 3 m, the surveillance coverage calculated through our research matched the actual surveillance obtained from visual inspection with a minimum of 70.21% to a maximum of 93.82%.

• Journal of Applied Biological Chemistry
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• v.64 no.3
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• pp.253-261
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• 2021

In this study, the ginseng sprout has produced through smart farm was classified according to its size and divided into above-ground (AG) and below-ground (BG) parts to compare ginsenoside contents and antioxidant activity. In the case of the AG part, the total phenolic contents were the highest at 5.16 mg/g in medium (M) size and the lowest at 2.23 mg/g in largest (L) size. The BG part also showed the highest content in the M size, but there was no significant difference. Also, the total flavonoid contents were also high in the M size in both the AG (5.16 mg/g) and BG (1.28 mg/g) parts. The major ginsenosides in the AG part were Re (20.33-24.15 mg/g) > Rd (11.36-27.42 mg/g) > Rg1 (4.48-5.54 mg/g) and the main ginsenosides in the BG part were Rb1 (5.09-8.61 mg/g) > Re (4.48-5.54 mg/g) > Rc (3.11-4.11 mg/g) in orders. In the case of M size, Re and Rd were approximately 4- and 19-folds higher at 24.15 mg/g and at 27.42 mg/g in the AG part and 5.20 mg/g and 1.43 mg in the BG part, respectively. In addition, F3 and Rh1 were detected in the AG part, but not in the BG part. 2,2-diphenyl-1-picrylhydrazyl (74.95%), 2,4,6-azino-bis (3-ethylbenzothiazoline-6-sulphnoic acid) diammonium salt (94.47%), and hydroxyl (70.39%) radical scavenging activities and FRAP (2.169) assay were the highest in M size than other sizes.

• Journal of Bio-Environment Control
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• v.30 no.4
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• pp.419-428
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• 2021

In order to develope a mobile-based greenhouse energy calculation program, firstly, the overall thermal transmittance of 10 types of major covers and 16 types of insulation materials were measured. In addition, to estimate the overall thermal transmittance when the cover and insulation materials were installed in double or triple layers, 24 combinations of double installations and 59 combinations of triple installations were measured using the hotbox. Also, the overall thermal transmittance value for a single material and the thermal resistance value were used to calculate the overall thermal transmittance value at the time of multi-layer installation of covering and insulating materials, and the linear regression equation was derived to correct the error with the measured values. As a result of developing the model for estimating thermal transmittance when installing multiple layers of coverings and insulating materials based on the value of overall thermal transmittance of a single-material, the model evaluation index was 0.90 (good when it is 0.5 or more), indicating that the estimated value was very close to the actual value. In addition, as a result of the on-site test, it was evaluated that the estimated heat saving rate was smaller than the actual value with a relative error of 2%. Based on these results, a mobile-based greenhouse energy calculation program was developed that was implemented as an HTML5 standard web-based mobile web application and was designed to work with various mobile device and PC browsers with N-Screen support. It had functions to provides the overall thermal transmittance(heating load coefficient) for each combination of greenhouse coverings and thermal insulation materials and to evaluate the energy consumption during a specific period of the target greenhouse. It was estimated that an energy-saving greenhouse design would be possible with the optimal selection of coverings and insulation materials according to the region and shape of the greenhouse.

• Journal of the Korean Applied Science and Technology
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• v.38 no.6
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• pp.1383-1392
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• 2021

It has been reported that emodin, a major pharmacologically active ingredient of herbal medicines such as Polygonum cuspidatum, Polygonum multiflorum, Rheum palmatum, and Aloe vera, is effective in antioxidant, antibacterial, anti-inflammatory, anticancer, and liver protection. In this study, to investigate the potential of emodin to be used as a skin disease and functional material, the activity related to the improvement of inflammation and skin barrier function was confirmed. To observe the anti-inflammatory effect on HaCaT cells, which are human keratinocytes, cytokine inhibition was confirmed by ELISA kit and protein expression by western blot. In HaCaT cells activated with TNF-α (10 ng/mL)/IFN-γ (10 ng/mL), emodin was treated with each concentration (5, 10, 20, 40) µM. As a result, It was confirmed that the production amount of TNF-α, IL-1β and IL-6 decreased as the concentration of emodin increased. In the experimental results on the expression levels of inflammation-related proteins iNOS and COX-2, it was confirmed that 48% of iNOS and 29% of COX-2 were inhibited compared to control at a concentration of 20 µM of emodin. As an indicator of skin barrier function improvement, the mRNA expression level of filaggrin, involucrin, and loricirn and the production amount of filaggrin, involucrin, and loricirn were confirmed. and excellent results were obtained with an emodin concentration-dependent increase. In particular, filaggrin, which was produced twice as much as the control at a concentration of 20 µM, is a protein involved in the formation of NMF, a natural moisturizing factor, and is known to play an important role in moisturizing the stratum corneum. In conclusion, it was confirmed that emodin can be used as a material for improving inflammation and improving skin barrier function, which is part of the potential for use as a skin disease and functional material. It is believed that if additional research is performed in the future, the scope of its application can be further expanded.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.4
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• pp.113-132
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• 2019

The degree of females' participation in corporate activity has been recently increased over the world and females' participation in economic activity may be new dynamic fuel for the Korean economy that falls into the vicious cycle of low growth. Start-up, therefore, has increasingly taken attention as an opportunity for females whose careers were interrupted to re-enter the labor market. The need for studies that examine factors influencing the decision of start-up is also increased along with the increase of the ratio of females' start-up. This study aims to verify effects of the women's characteristics(women discrimination, women's role conflict) and the human networks of females whose careers were interrupted, with the intention for entrepreneurial intention, which are mediated by personal attitudes and subjective norm suggested by Ajzen's Theory of Reasoned Action, based on an empirical research. The findings show that the human networks of females have an effect on attitudes toward start-up activity and subjective norm and the woman discrimination influence the personal attitudes. In contrast, the women's role conflict have no effect on both personal attitude toward start-up activity and subjective norm. This can be supposed as an outcome resulted from the subjects' low level of conflict caused by their sex roles, on their age distribution. The relation between subjective norm and entrepreneurial Intention seemed to be moderated by their perceived strong entrepreneurial supporting policy. Their attitudes toward start-up activity were found to have a mediating effect on the relation between the women discrimination, human networks and entrepreneurial Intention, while the subjective norm only mediated the relation between human networks and entrepreneurial Intention. Based on such results, this study attempts to suggest theoretical suggestions and the direction of various entrepreneurial supporting policy for the increase and the growth of start-up of females whose careers were interrupted, in Korea.

• Science of Emotion and Sensibility
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• v.21 no.4
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• pp.25-36
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• 2018

Nowadays, it is hard to imagine one's life without smart phones or the internet. Furthermore, not only do people form groups offline, but also online. Based on the cognitive dissonance theory, there have been many studies about how an offline group's initiation affects attitudes toward the group. However, there has not been a study about how an online group's initiation can affect attitudes toward the group. Therefore, this study aims to find out how cognitive dissonance aroused by initiation affects the attitudes toward the online community, which represents groups that are formed online. In addition, this study examined how perceived similarity affects changes in attitude aroused by cognitive dissonance. Participants were assigned to a group in three ways as follows: without a registration process, with a simple registration process, and/or with a complex registration process. Perceived similarity was calculated by the difference between the current body mass index (BMI) and the target BMI of the participant. Attitudes toward the online group were measured by perceived source credibility, perceived information quality, satisfaction, information usefulness, and continuance intention. Contrary to the cognitive dissonance theory, the results showed that when applied to offline social groups, there were conflicting results. There were cases where there was no difference in the evaluation between initiation conditions. However, other cases showed that groups with the most complex registration process were found to have the worst evaluation. People were more favorable toward the group when the perceived similarity was larger. Interestingly, people who had higher perceived similarity had more positive attitudes toward the groups that had been assigned with a registration process compared to the group formed without a registration process. Conversely, people with lower perceived similarity had more positive attitudes toward the group when there was no initiation process. Online communities may use the results of this study to design more suitable registration processes for their communities.