• Journal of Global Scholars of Marketing Science
• /
• v.18 no.3
• /
• pp.59-88
• /
• 2008

Digital Convergence means integration between industry, technology, and contents, and in marketing, it usually comes with creation of new types of product and service under the base of digital technology as digitalization progress in electro-communication industries including telecommunication, home appliance, and computer industries. One can see digital convergence not only in instruments such as PC, AV appliances, cellular phone, but also in contents, network, service that are required in production, modification, distribution, re-production of information. Convergence in contents started around 1990. Convergence in network and service begins as broadcasting and telecommunication integrates and DMB(digital multimedia broadcasting), born in May, 2005 is the symbolic icon in this trend. There are some positive and negative expectations about DMB. The reason why two opposite expectations exist is that DMB does not come out from customer's need but from technology development. Therefore, customers might have hard time to interpret the real meaning of DMB. Time is quite critical to a high tech product, like DMB because another product with same function from different technology can replace the existing product within short period of time. If DMB does not positioning well to customer's mind quickly, another products like Wibro, IPTV, or HSPDA could replace it before it even spreads out. Therefore, positioning strategy is critical for success of DMB product. To make correct positioning strategy, one needs to understand how consumer interprets DMB and how consumer's interpretation can be changed via communication strategy. In this study, we try to investigate how consumer perceives a new product, like DMB and how AD strategy change consumer's perception. More specifically, the paper segment consumers into sub-groups based on their DMB perceptions and compare their characteristics in order to understand how they perceive DMB. And, expose them different printed ADs that have messages guiding consumer think DMB in specific ways, either cellular phone or personal TV. Research Question 1: Segment consumers according to perceptions about DMB and compare characteristics of segmentations. Research Question 2: Compare perceptions about DMB after AD that induces categorization of DMB in direction for each segment. If one understand and predict a direction in which consumer perceive a new product, firm can select target customers easily. We segment consumers according to their perception and analyze characteristics in order to find some variables that can influence perceptions, like prior experience, usage, or habit. And then, marketing people can use this variables to identify target customers and predict their perceptions. If one knows how customer's perception is changed via AD message, communication strategy could be constructed properly. Specially, information from segmented customers helps to develop efficient AD strategy for segment who has prior perception. Research framework consists of two measurements and one treatment, O1 X O2. First observation is for collecting information about consumer's perception and their characteristics. Based on first observation, the paper segment consumers into two groups, one group perceives DMB similar to Cellular phone and the other group perceives DMB similar to TV. And compare characteristics of two segments in order to find reason why they perceive DMB differently. Next, we expose two kinds of AD to subjects. One AD describes DMB as Cellular phone and the other Ad describes DMB as personal TV. When two ADs are exposed to subjects, consumers don't know their prior perception of DMB, in other words, which subject belongs 'similar-to-Cellular phone' segment or 'similar-to-TV' segment? However, we analyze the AD's effect differently for each segment. In research design, final observation is for investigating AD effect. Perception before AD is compared with perception after AD. Comparisons are made for each segment and for each AD. For the segment who perceives DMB similar to TV, AD that describes DMB as cellular phone could change the prior perception. And AD that describes DMB as personal TV, could enforce the prior perception. For data collection, subjects are selected from undergraduate students because they have basic knowledge about most digital equipments and have open attitude about a new product and media. Total number of subjects is 240. In order to measure perception about DMB, we use indirect measurement, comparison with other similar digital products. To select similar digital products, we pre-survey students and then finally select PDA, Car-TV, Cellular Phone, MP3 player, TV, and PSP. Quasi experiment is done at several classes under instructor's allowance. After brief introduction, prior knowledge, awareness, and usage about DMB as well as other digital instruments is asked and their similarities and perceived characteristics are measured. And then, two kinds of manipulated color-printed AD are distributed and similarities and perceived characteristics for DMB are re-measured. Finally purchase intension, AD attitude, manipulation check, and demographic variables are asked. Subjects are given small gift for participation. Stimuli are color-printed advertising. Their actual size is A4 and made after several pre-test from AD professionals and students. As results, consumers are segmented into two subgroups based on their perceptions of DMB. Similarity measure between DMB and cellular phone and similarity measure between DMB and TV are used to classify consumers. If subject whose first measure is less than the second measure, she is classified into segment A and segment A is characterized as they perceive DMB like TV. Otherwise, they are classified as segment B, who perceives DMB like cellular phone. Discriminant analysis on these groups with their characteristics of usage and attitude shows that Segment A knows much about DMB and uses a lot of digital instrument. Segment B, who thinks DMB as cellular phone doesn't know well about DMB and not familiar with other digital instruments. So, consumers with higher knowledge perceive DMB similar to TV because launching DMB advertising lead consumer think DMB as TV. Consumers with less interest on digital products don't know well about DMB AD and then think DMB as cellular phone. In order to investigate perceptions of DMB as well as other digital instruments, we apply Proxscal analysis, Multidimensional Scaling technique at SPSS statistical package. At first step, subjects are presented 21 pairs of 7 digital instruments and evaluate similarity judgments on 7 point scale. And for each segment, their similarity judgments are averaged and similarity matrix is made. Secondly, Proxscal analysis of segment A and B are done. At third stage, get similarity judgment between DMB and other digital instruments after AD exposure. Lastly, similarity judgments of group A-1, A-2, B-1, and B-2 are named as 'after DMB' and put them into matrix made at the first stage. Then apply Proxscal analysis on these matrixes and check the positional difference of DMB and after DMB. The results show that map of segment A, who perceives DMB similar as TV, shows that DMB position closer to TV than to Cellular phone as expected. Map of segment B, who perceive DMB similar as cellular phone shows that DMB position closer to Cellular phone than to TV as expected. Stress value and R-square is acceptable. And, change results after stimuli, manipulated Advertising show that AD makes DMB perception bent toward Cellular phone when Cellular phone-like AD is exposed, and that DMB positioning move towards Car-TV which is more personalized one when TV-like AD is exposed. It is true for both segment, A and B, consistently. Furthermore, the paper apply correspondence analysis to the same data and find almost the same results. The paper answers two main research questions. The first one is that perception about a new product is made mainly from prior experience. And the second one is that AD is effective in changing and enforcing perception. In addition to above, we extend perception change to purchase intention. Purchase intention is high when AD enforces original perception. AD that shows DMB like TV makes worst intention. This paper has limitations and issues to be pursed in near future. Methodologically, current methodology can't provide statistical test on the perceptual change, since classical MDS models, like Proxscal and correspondence analysis are not probability models. So, a new probability MDS model for testing hypothesis about configuration needs to be developed. Next, advertising message needs to be developed more rigorously from theoretical and managerial perspective. Also experimental procedure could be improved for more realistic data collection. For example, web-based experiment and real product stimuli and multimedia presentation could be employed. Or, one can display products together in simulated shop. In addition, demand and social desirability threats of internal validity could influence on the results. In order to handle the threats, results of the model-intended advertising and other "pseudo" advertising could be compared. Furthermore, one can try various level of innovativeness in order to check whether it make any different results (cf. Moon 2006). In addition, if one can create hypothetical product that is really innovative and new for research, it helps to make a vacant impression status and then to study how to form impression in more rigorous way.

• Analytical Science and Technology
• /
• v.33 no.3
• /
• pp.151-158
• /
• 2020

Alcohol, which can easily be obtained in the same way as ordinary beverages, is harmful enough to cause death due to excessive drinking and chronic alcohol intake, so it is important to maintain a proper amount of drinking and healthy drinking habits. In addition, the incidence of behavioral disturbances and impaired judgments that can be caused by chronic alcohol drinking of more than adequate amounts of alcohol is also significant. Accordingly it is very useful for forensic science to check whether the person involved is drunken or is alcoholism state in various accidents. Currently, in Korea, alcohol consumption is determined by detecting the level of alcohol or alcohol metabolism 'ethyl glucuronide (EtG)' in blood or urine samples. However, analysis of alcohol or EtG in blood or urine can only provide information about the current state of alcohol consumption because of a narrow window of detection time. Therefore, it is important to analyze the EtG as a long-term direct alcohol metabolite bio-marker in human hair and to investigate relationship between alcohol consumption and EtG concentration for the evaluation of chronic ethanol consumption. In this study, we established an analytical method for the detection of EtG in Korean hair efficiently and validated selectivity, linearity, limits of detection (LOD), limits of quantification (LOQ), matrix effect, recovery, process efficiency, accuracy and precision using liquid chromatography tandem mass spectrometry (LC-MS/MS). In addition, the assay performance was evaluated in Korean social drinker's hair and the postmortem hair of a chronic alcoholism. The results of this study can be useful in monitoring the alcohol abuse of Korean in clinical cases and legal procedures related to custody and provide a useful tool to evaluate postmortem diagnosis of alcoholic ketoacidosis in forensics.

• Journal of the Korean Society of Food Science and Nutrition
• /
• v.41 no.12
• /
• pp.1663-1670
• /
• 2012

The inhibitory effects of rose hip (Rosa canina L.) water extracts from two different manufactures on osteoarthritis was comparatively investigated in primary cultures of rat cartilage cells. To identify the effects of rose hip extracts against $H_2O_2$ (300 ${\mu}M$, 2 hr) treatment, cell survival was measured by the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. Cell survival increased by rose hip extracts in the range of 100 to 600 ${\mu}g/mL$ of $H_2O_2$ treatment. To determine the anti-inflammatory effects of rose hip extracts, tumor necrosis factor alpha (TNF-${\alpha}$), nitric oxide (NO), and Cox-2 expression were measured after lipopolysaccharide (LPS) activation. TNF-${\alpha}$ level with rose hip extract treatment was decreased by 27.4% and 31.9% at 600 ${\mu}g/mL$ of $H_2O_2$ treatment. Nitric oxide was inhibited by rose hip extract at 100~600 ${\mu}g/mL$ of $H_2O_2$ treatment in a dose-dependent manner. In addition, Cox-2 protein expression was dose-dependently decreased while Cox-1 had no change in expression level. The severity of osteoarthritis is controlled by a balance between anabolic and catobolic factors in an articulation, therefore the expression of these factors plays a critical role in preventing osteoarthritis. In measuring anabolic factors, the genetic expression of collagen type I increased with rose hip treatment, while the genetic expression of collagen II did not change. In addition, the genetic expression of aggrecan (proteoglycan core protein) was significantly increased. while the genetic expression of matrix metalloproteinase (MMP) 3, 7 and 13, known catabolic factors, was significantly inhibited by treatment with rose hip extract. The expression of MMP13 was especially highly influenced. In conclusion, rose hip water extracts show inhibitory effects on cell death by $H_2O_2$ mediated oxidative stress, which is related to inhibitory effects on inflammation due to TNF-${\alpha}$, NO, and Cox-2. The ability of rose hip extracts to ameliorate inflammation in primary cultures of cartilage cells seems to associate with an increased genetic expression of specific anabolic factors, collagen type I and aggrecan, and a decreased expression of catabolic factors, MMPs (3, 7, and 13). However, there were no significant differences between rose hip extracts from the two manufacturers.

• Tuberculosis and Respiratory Diseases
• /
• v.79 no.3
• /
• pp.143-152
• /
• 2016

Background: Idiopathic pulmonary fibrosis (IPF) is a progressive and lethal lung disease characterized by the accumulation of excessive fibroblasts and myofibroblasts in the extracellular matrix. The transforming growth factor ${\beta}1$ (TGF-${\beta}1$)-induced epithelial-to-mesenchymal transition (EMT) is thought to be a possible source of fibroblasts/myofibroblasts in IPF lungs. We have previously reported that apolipoprotein A1 (ApoA1) has anti-fibrotic activity in experimental lung fibrosis. In this study, we determine whether ApoA1 modulates TGF-${\beta}1$-induced EMT in experimental lung fibrosis and clarify its mechanism of action. Methods: The A549 alveolar epithelial cell line was treated with TGF-${\beta}1$ with or without ApoA1. Morphological changes and expression of EMT-related markers, including E-cadherin, N-cadherin, and ${\alpha}$-smooth muscle actin were evaluated. Expressions of Smad and non-Smad mediators and TGF-${\beta}1$ receptor type 1 ($T{\beta}RI$) and type 2 ($T{\beta}RII$) were measured. The silica-induced lung fibrosis model was established using ApoA1 overexpressing transgenic mice. Results: TGF-${\beta}1$-treated A549 cells were changed to the mesenchymal morphology with less E-cadherin and more N-cadherin expression. The addition of ApoA1 inhibited the TGF-${\beta}1$-induced change of the EMT phenotype. ApoA1 inhibited the TGF-${\beta}1$-induced increase in the phosphorylation of Smad2 and 3 as well as that of ERK and p38 mitogen-activated protein kinase mediators. In addition, ApoA1 reduced the TGF-${\beta}1$-induced increase in $T{\beta}RI$ and $T{\beta}RII$ expression. In a mouse model of silica-induced lung fibrosis, ApoA1 overexpression reduced the silica-mediated effects, which were increased N-cadherin and decreased E-cadherin expression in the alveolar epithelium. Conclusion: Our data demonstrate that ApoA1 inhibits TGF-${\beta}1$-induced EMT in experimental lung fibrosis.

• Journal of Acupuncture Research
• /
• v.19 no.2
• /
• pp.51-64
• /
• 2002

We have compared(using the same series of experimental tissue samples) the levels of proteolytic enzyme activities and free radical-induced protein damage in synovial fluid from RA and CPH cases. Many protease types showed significantly increased (typically by a factor of approximately 2-3-fold) activity in RA, compared to normal rats. However, CPH significantly reduced the cytoplasmic enzyme activities of arginyl aminopeptidase, leucyl aminopeptidase, pyroglutamyl aminopeptidase, tripeptidyl aminopeptidase, and proline endopeptidase to almost about 1/10 each. For the Iysosomal proteases, synovial fluid samples from RA rats, CPH significantly reduced the enzyme activities of cathepsin B, dipeptidyl aminopeptidase I and dipeptidyl aminopeptidase II. In extracellular matrix degrading(collagenase, tissue elastase) and leukocyte as sociated proteases (leukocyte elastase, cathepsin G), CPH decreased these enzyme activities of collagenase, tissue elastase and leukocyte associated elastase in RA. In cytoplasmic and lysosomal protease activities in plasma from RA. CPH and normal plasma samples were not significantly different, suggesting that altered activity of plasma proteases (particularly those enzymes putatively involved in the immune response) is not a contributory factor in the pathogenesis of RA. In addition, the level of free radical induced damage to synovial fluid proteins was approximately twice that in RA, compared with CPH. CPH significantly decreased the level of ROS induced oxidative damage to synovial fluid proteins (quantified as protein carbonyl derivative). Therefore we conclude that both proteolytic enzymes and free radicals are likely to be of equal potential importance as damaging agents in the pathogenesis of inflammatory joint disease, and that the design of novel therapeutic strategies for patients with the latter disorder should include both protease inhibitory and free radical scavenging elements. In addition, the protease inhibitory element should be designed to inhibit the action of a broad range of protease mechanistic types (i.e. cysteine-, metallo- and serine- proteinases and peptidases). However, increased protein damage induced by ROS could not be rationalised in terms of compromised antioxidant total capacity, since the latter was not significantly altered in RA synovial fluid or plasma compared with CPH.

• Journal of Intelligence and Information Systems
• /
• v.29 no.3
• /
• pp.1-17
• /
• 2023

With the recent rapid development of ICT(Information and Communication Technology) and the popularization of digital devices, the size of the online market continues to grow. As a result, we live in a flood of information. Thus, customers are facing information overload problems that require a lot of time and money to select products. Therefore, a personalized recommender system has become an essential methodology to address such issues. Collaborative Filtering(CF) is the most widely used recommender system. Traditional recommender systems mainly utilize quantitative data such as rating values, resulting in poor recommendation accuracy. Quantitative data cannot fully reflect the user's preference. To solve such a problem, studies that reflect qualitative data, such as review contents, are being actively conducted these days. To quantify user review contents, text mining was used in this study. The general CF consists of the following three steps: user-item matrix generation, Top-N neighborhood group search, and Top-K recommendation list generation. In this study, we propose a recommendation algorithm that applies an extended similarity measure, which utilize quantified review contents in addition to user rating values. After calculating review similarity by applying TF-IDF, Word2Vec, and Doc2Vec techniques to review content, extended similarity is created by combining user rating similarity and quantified review contents. To verify this, we used user ratings and review data from the e-commerce site Amazon's "Health and Personal Care". The proposed recommendation model using extended similarity measure showed superior performance to the traditional recommendation model using only user rating value-based similarity measure. In addition, among the various text mining techniques, the similarity obtained using the TF-IDF technique showed the best performance when used in the neighbor group search and recommendation list generation step.

• Journal of Intelligence and Information Systems
• /
• v.26 no.1
• /
• pp.47-73
• /
• 2020

KTX rolling stocks are a system consisting of several machines, electrical devices, and components. The maintenance of the rolling stocks requires considerable expertise and experience of maintenance workers. In the event of a rolling stock failure, the knowledge and experience of the maintainer will result in a difference in the quality of the time and work to solve the problem. So, the resulting availability of the vehicle will vary. Although problem solving is generally based on fault manuals, experienced and skilled professionals can quickly diagnose and take actions by applying personal know-how. Since this knowledge exists in a tacit form, it is difficult to pass it on completely to a successor, and there have been studies that have developed a case-based rolling stock expert system to turn it into a data-driven one. Nonetheless, research on the most commonly used KTX rolling stock on the main-line or the development of a system that extracts text meanings and searches for similar cases is still lacking. Therefore, this study proposes an intelligence supporting system that provides an action guide for emerging failures by using the know-how of these rolling stocks maintenance experts as an example of problem solving. For this purpose, the case base was constructed by collecting the rolling stocks failure data generated from 2015 to 2017, and the integrated dictionary was constructed separately through the case base to include the essential terminology and failure codes in consideration of the specialty of the railway rolling stock sector. Based on a deployed case base, a new failure was retrieved from past cases and the top three most similar failure cases were extracted to propose the actual actions of these cases as a diagnostic guide. In this study, various dimensionality reduction measures were applied to calculate similarity by taking into account the meaningful relationship of failure details in order to compensate for the limitations of the method of searching cases by keyword matching in rolling stock failure expert system studies using case-based reasoning in the precedent case-based expert system studies, and their usefulness was verified through experiments. Among the various dimensionality reduction techniques, similar cases were retrieved by applying three algorithms: Non-negative Matrix Factorization(NMF), Latent Semantic Analysis(LSA), and Doc2Vec to extract the characteristics of the failure and measure the cosine distance between the vectors. The precision, recall, and F-measure methods were used to assess the performance of the proposed actions. To compare the performance of dimensionality reduction techniques, the analysis of variance confirmed that the performance differences of the five algorithms were statistically significant, with a comparison between the algorithm that randomly extracts failure cases with identical failure codes and the algorithm that applies cosine similarity directly based on words. In addition, optimal techniques were derived for practical application by verifying differences in performance depending on the number of dimensions for dimensionality reduction. The analysis showed that the performance of the cosine similarity was higher than that of the dimension using Non-negative Matrix Factorization(NMF) and Latent Semantic Analysis(LSA) and the performance of algorithm using Doc2Vec was the highest. Furthermore, in terms of dimensionality reduction techniques, the larger the number of dimensions at the appropriate level, the better the performance was found. Through this study, we confirmed the usefulness of effective methods of extracting characteristics of data and converting unstructured data when applying case-based reasoning based on which most of the attributes are texted in the special field of KTX rolling stock. Text mining is a trend where studies are being conducted for use in many areas, but studies using such text data are still lacking in an environment where there are a number of specialized terms and limited access to data, such as the one we want to use in this study. In this regard, it is significant that the study first presented an intelligent diagnostic system that suggested action by searching for a case by applying text mining techniques to extract the characteristics of the failure to complement keyword-based case searches. It is expected that this will provide implications as basic study for developing diagnostic systems that can be used immediately on the site.

• Journal of the Korean Society of Food Science and Nutrition
• /
• v.43 no.5
• /
• pp.631-640
• /
• 2014

The inhibitory effects of Boswellia serrata (BW) extracts on degenerative osteoarthritis were investigated in primary-cultured rat cartilage cells and a monosodium-iodoacetate (MIA)-induced osteoarthritis rat model. To identify the protective effects of BW extract against $H_2O_2$ ($800{\mu}M$, 2 hr) in vitro, cell survival was measured by MTT assay. Cell survival after $H_2O_2$ treatment was elevated by BW extract at a concentration of $20{\mu}g/mL$. In addition, BW extract treatment significantly reduced and normalized the productions of pro-inflammatory factors, nuclear transcription factor ${\kappa}B$, cyclooxygenase-2, tumor necrosis factor-${\alpha}$, and interleukin-6 at a concentration of $20{\mu}g/mL$. Treatment of chondrocytes with BW extract significantly reduced 5-lipoxygenase activity and production of prostaglandin E2, especially at a concentration of $10{\sim}20{\mu}g/mL$. For the in vivo animal study, osteoarthritis was induced by intra-articular injection of MIA into knee joints of rats. Consumption of a diet containing BW extract (100 and 200 mg/kg) for 35 days significantly inhibited the development and severity of osteoarthritis in rats. To determine the genetic expression of arthritic factors in articular cartilage, real-time PCR was applied to measure matrix metalloproteinases (MMP-3, MMP-9, and MMP-13), collagen type I, collagen type II, and aggrecan, and BW extract had protective effects at a concentration of 200 mg/kg. In conclusion, BW extract was able to inhibit articular cartilage degeneration by preventing extracellular matrix degradation and chondrocyte injury. One can consider that BW extract may be a potential therapeutic treatment for degenerative osteoarthritis.

• Journal of Intelligence and Information Systems
• /
• v.20 no.2
• /
• pp.73-92
• /
• 2014

An Adaptive Clustering-based Collaborative Filtering Technique was proposed to solve the fundamental problems of collaborative filtering, such as cold-start problems, scalability problems and data sparsity problems. Previous collaborative filtering techniques were carried out according to the recommendations based on the predicted preference of the user to a particular item using a similar item subset and a similar user subset composed based on the preference of users to items. For this reason, if the density of the user preference matrix is low, the reliability of the recommendation system will decrease rapidly. Therefore, the difficulty of creating a similar item subset and similar user subset will be increased. In addition, as the scale of service increases, the time needed to create a similar item subset and similar user subset increases geometrically, and the response time of the recommendation system is then increased. To solve these problems, this paper suggests a collaborative filtering technique that adapts a condition actively to the model and adopts the concepts of a context-based filtering technique. This technique consists of four major methodologies. First, items are made, the users are clustered according their feature vectors, and an inter-cluster preference between each item cluster and user cluster is then assumed. According to this method, the run-time for creating a similar item subset or user subset can be economized, the reliability of a recommendation system can be made higher than that using only the user preference information for creating a similar item subset or similar user subset, and the cold start problem can be partially solved. Second, recommendations are made using the prior composed item and user clusters and inter-cluster preference between each item cluster and user cluster. In this phase, a list of items is made for users by examining the item clusters in the order of the size of the inter-cluster preference of the user cluster, in which the user belongs, and selecting and ranking the items according to the predicted or recorded user preference information. Using this method, the creation of a recommendation model phase bears the highest load of the recommendation system, and it minimizes the load of the recommendation system in run-time. Therefore, the scalability problem and large scale recommendation system can be performed with collaborative filtering, which is highly reliable. Third, the missing user preference information is predicted using the item and user clusters. Using this method, the problem caused by the low density of the user preference matrix can be mitigated. Existing studies on this used an item-based prediction or user-based prediction. In this paper, Hao Ji's idea, which uses both an item-based prediction and user-based prediction, was improved. The reliability of the recommendation service can be improved by combining the predictive values of both techniques by applying the condition of the recommendation model. By predicting the user preference based on the item or user clusters, the time required to predict the user preference can be reduced, and missing user preference in run-time can be predicted. Fourth, the item and user feature vector can be made to learn the following input of the user feedback. This phase applied normalized user feedback to the item and user feature vector. This method can mitigate the problems caused by the use of the concepts of context-based filtering, such as the item and user feature vector based on the user profile and item properties. The problems with using the item and user feature vector are due to the limitation of quantifying the qualitative features of the items and users. Therefore, the elements of the user and item feature vectors are made to match one to one, and if user feedback to a particular item is obtained, it will be applied to the feature vector using the opposite one. Verification of this method was accomplished by comparing the performance with existing hybrid filtering techniques. Two methods were used for verification: MAE(Mean Absolute Error) and response time. Using MAE, this technique was confirmed to improve the reliability of the recommendation system. Using the response time, this technique was found to be suitable for a large scaled recommendation system. This paper suggested an Adaptive Clustering-based Collaborative Filtering Technique with high reliability and low time complexity, but it had some limitations. This technique focused on reducing the time complexity. Hence, an improvement in reliability was not expected. The next topic will be to improve this technique by rule-based filtering.

• Journal of Internet Computing and Services
• /
• v.16 no.1
• /
• pp.47-55
• /
• 2015

Recently, the environment of a hospital information system is a trend to combine various SMART technologies. Accordingly, various smart devices, such as a smart phone, Tablet PC is utilized in the medical information system. Also, these environments consist of various applications executing on heterogeneous sensors, devices, systems and networks. In these hospital information system environment, applying a security service by traditional access control method cause a problems. Most of the existing security system uses the access control list structure. It is only permitted access defined by an access control matrix such as client name, service object method name. The major problem with the static approach cannot quickly adapt to changed situations. Hence, we needs to new security mechanisms which provides more flexible and can be easily adapted to various environments with very different security requirements. In addition, for addressing the changing of service medical treatment of the patient, the researching is needed. In this paper, we suggest a dynamic approach to medical information systems in smart mobile environments. We focus on how to access medical information systems according to dynamic access control methods based on the existence of the hospital's information system environments. The physical environments consist of a mobile x-ray imaging devices, dedicated mobile/general smart devices, PACS, EMR server and authorization server. The software environment was developed based on the .Net Framework for synchronization and monitoring services based on mobile X-ray imaging equipment Windows7 OS. And dedicated a smart device application, we implemented a dynamic access services through JSP and Java SDK is based on the Android OS. PACS and mobile X-ray image devices in hospital, medical information between the dedicated smart devices are based on the DICOM medical image standard information. In addition, EMR information is based on H7. In order to providing dynamic access control service, we classify the context of the patients according to conditions of bio-information such as oxygen saturation, heart rate, BP and body temperature etc. It shows event trace diagrams which divided into two parts like general situation, emergency situation. And, we designed the dynamic approach of the medical care information by authentication method. The authentication Information are contained ID/PWD, the roles, position and working hours, emergency certification codes for emergency patients. General situations of dynamic access control method may have access to medical information by the value of the authentication information. In the case of an emergency, was to have access to medical information by an emergency code, without the authentication information. And, we constructed the medical information integration database scheme that is consist medical information, patient, medical staff and medical image information according to medical information standards.y Finally, we show the usefulness of the dynamic access application service based on the smart devices for execution results of the proposed system according to patient contexts such as general and emergency situation. Especially, the proposed systems are providing effective medical information services with smart devices in emergency situation by dynamic access control methods. As results, we expect the proposed systems to be useful for u-hospital information systems and services.