• Journal of Digital Convergence
• /
• v.20 no.5
• /
• pp.761-776
• /
• 2022

For fostering the biomedical industry, the formation of a high-tech-based innovation cluster is continuously being promoted. Accordingly, studies dealing with domestic clusters are being conducted based on case studies on major overseas clusters, but they are limited to a single case. In this paper, content analysis was used based on the literature about innovation clusters and bio-medical industry to comprehensively summarize the factors to be considered for the creation and activation of bio-medical clusters. Subsequently, the factors derived through content analysis were applied to the case of the Hongreung Innotown. The requirements for the successful creation of the Hongreung Innotown, it is required to improve settlement conditions, prepare systems to create start-up culture, and revitalize translational research, attract investment, and cooperate and connect with local clusters.

• Journal of Industrial Convergence
• /
• v.21 no.1
• /
• pp.95-102
• /
• 2023

This study was to identify the factors affecting nurses' use intention of digital healthcare and the moderating effect of clinical career based on the UTAUT model. The items were composed by performance expectancy 3 items, facilitation condition 3tiems, and perceived risk 3 items. CFA was performed to verify the construct validity. As a results, average variance extracted (AVE) was .5 or higher, and construct reliability (CR) was .7 or higher. Model fit was confirmed as CMIN/df=1.797, GFI=.955, CFI=.979, TLI=.968, IFI=.979, and RMSEA=.063. The internal reliability was .93 for performance expectancy, .84 for facilitating conditions, and .64 for perceived risk. Performance expectancy, facilitating condition, and perceived risk had a significant effect on use intention, and clinical career showed a moderating effect(t=-2.159, p=.032). Therefore, in order to enhance the use intention of digital health care, performance expectancy, and facilitating conditions should be raised and perceived risk should be reduced.

• Korean Journal of Plant Resources
• /
• v.32 no.6
• /
• pp.669-676
• /
• 2019

Zanthoxylum piperitum D.C. (ZP) peels has been used as a natural spice and herb medicine for hypertension reduction, for strokes, and for its anti-bacterial and anti-oxidant activity. However, the anti-inflammatory mechanisms employed by ZP have yet to be completely understood. In this study, we elucidate the anti-inflammatory mechanism of ZP in lipopolysaccharide (LPS)-induced RAW264.7 cells. We evaluated the effects of ZP in LPS-induced levels of inflammatory cytokines, prostaglandin E₂ (PGE₂), and caspase-1 using ELISA. The expression levels of inflammatory-related genes, including cyclooxygenase (COX)-2 and inducible nitric oxide synthase (iNOS), were assayed by Western blot analysis. We elucidated the effect of ZP on nuclear factor (NF)-κB activation by means of a luciferase activity assay. The findings of this study demonstrated that ZP inhibited the production of inflammatory cytokine and PGE₂ and inhibited the increased levels of COX-2 and iNOS caused by LPS. Additionally, we showed that the anti-inflammatory effect of ZP arises by suppressing the activation of NF-κB and caspase-1 in LPS- induced RAW264.7 cells. These results provide novel insights into the pharmacological actions of ZP as a potential candidate for development of new drugs to treat inflammatory diseases.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.12 no.4
• /
• pp.33-40
• /
• 2016

Previous works for detecting arrhythmia have mostly used nonlinear method to increase classification accuracy. Most methods require accurate detection of ECG signal, higher computational cost and larger processing time. But it is difficult to analyze the ECG signal because of various noise types. Also in the healthcare system based IOT that must continuously monitor people's situation, it is necessary to process ECG signal in realtime. Therefore it is necessary to design efficient algorithm that classifies different arrhythmia in realtime and decreases computational cost by extrating minimal feature. In this paper, we propose R wave detection considering complexity and arrhythmia classification based on binary coding. For this purpose, we detected R wave through SOM and then RR interval from noise-free ECG signal through the preprocessing method. Also, we classified arrhythmia in realtime by converting threshold variability of feature to binary code. R wave detection and PVC, PAC, Normal classification is evaluated by using 39 record of MIT-BIH arrhythmia database. The achieved scores indicate the average of 99.41%, 97.18%, 94.14%, 99.83% in R wave, PVC, PAC, Normal.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.7 no.4
• /
• pp.15-22
• /
• 2011

U-Healthcare service is a real-time service using the vital signs which are continuously transferred from monitoring sensors attached to mobile patients under the wireless network environments. It should monitor the health condition of mobile patients everywhere at any time. In this paper, we have improved two features of the three layered mobile patient monitoring system with load balancing ability. First, the simulation process has been improved by allowing the number of related nodes to be changed. Secondly, we have modified S node to which queue is added to reduce the loss rate of collecting data from patients during the delay of S node process. And the data from the patient with high priority can be transferred to the server immediately through the filtering function. Furthermore, we have solved the problem of redundancy in sharing information among S nodes by differentiating process time to each S node. By performing a DEVS Java-based system simulation, we have verified the efficiency of this improved system.

• Journal of Digital Convergence
• /
• v.11 no.11
• /
• pp.389-395
• /
• 2013

As fusing IT and medical technique, the number of patients who adhere medical equipment inside of them is increasing. However there is a problem of for the third person to tap or modulate the patient's biometric data viciously. This paper suggests quantum encryption-based key distribution model to share key for the third person not to tap or modulate the patient's biometric data between patient and hospital staff. The proposed model uses one-time pad key that shares key sending random bits not direct sending message of quantum data. Also, it guarantees patient's anonymity because the biometric data of injected-device in the body doesn't be exposed unnecessarily.

• Journal of Digital Contents Society
• /
• v.19 no.2
• /
• pp.245-250
• /
• 2018

Ab Image Processing can be applied to the various fields nowadays. The healthcare, which people may have interest, is one of those fields. The healthcare program which fitness equipments are used requires the correct posture of body. However, people usually does exercise by oneself with various reasons such as cost to hiring personal trainer. In this case, the effectiveness of training should not be guaranteed. Moreover, reverse effect may be produced. This paper is an implementing a correcting posture program by using image processing techniques, which people can use exercise training by oneself. It is expected that people can have a personal trainer who coaches the correct postur through this program.

• Journal of Korea Multimedia Society
• /
• v.24 no.7
• /
• pp.849-859
• /
• 2021

Prostate cancer (PCa) is a fatal disease that occurs in men. In general, PCa cells are found in the prostate gland. Early diagnosis is the key to prevent the spreading of cancers to other parts of the body. In this case, deep learning-based systems can detect and distinguish histological patterns in microscopy images. The histological grades used for the analysis were benign, grade 3, grade 4, and grade 5. In this study, we attempt to use transfer learning and fine-tuning methods as well as different model architectures to develop and compare the models. We implemented MobileNet, ResNet50, and DenseNet121 models and used three different strategies of freezing layers techniques of fine-tuning, to get various pre-trained weights to improve accuracy. Finally, transfer learning using MobileNet with the half-layer frozen showed the best results among the nine models, and 90% accuracy was obtained on the test data set.

• Journal of Korea Multimedia Society
• /
• v.22 no.5
• /
• pp.588-600
• /
• 2019

Each year Malaria affects over 200 million people worldwide. Particularly, African continent is highly hit by this disease. According to many researches, this continent is ideal for Anopheles mosquitoes which transmit Malaria parasites to thrive. Rainfall volume is one of the major factor favoring the development of these Anopheles in the tropical Sub-Sahara Africa (SSA). However, the surveillance, monitoring and reporting of this epidemic is still poor and bureaucratic only. In our paper, we proposed a method to fast monitor and report Malaria instances by using Social Network Systems (SNS) and precipitation volume in Nigeria. We used Twitter search Application Programming Interface (API) to live-stream Twitter messages mentioning Malaria, preprocessed those Tweets and classified them into Malaria cases in Nigeria by using Support Vector Machine (SVM) classification algorithm and compared those Malaria cases with average precipitation volume. The comparison yielded a correlation of 0.75 between Malaria cases recorded by using Twitter and average precipitations in Nigeria. To ensure the certainty of our classification algorithm, we used an oversampling technique and eliminated the imbalance in our training Tweets.

• Journal of Korea Multimedia Society
• /
• v.22 no.8
• /
• pp.832-843
• /
• 2019

Prostate cancer is a high-risk with a high incidence and is a disease that occurs only in men. Accurate diagnosis of cancer is necessary as the incidence of cancer patients is increasing. Prostate cancer is also a disease that is difficult to predict progress, so it is necessary to predict in advance through prognosis. Therefore, in this paper, grade classification is attempted based on texture feature extraction. There are two main methods of classification: Uses One-way Analysis of Variance (ANOVA) to determine whether texture features are significant values, compares them with all texture features and then uses only one classification i.e. Benign versus. The second method consisted of more detailed classifications without using ANOVA for better analysis between different grades. Results of both these methods are compared and analyzed through the machine learning models such as Support Vector Machine and K-Nearest Neighbor. The accuracy of Benign versus Grade 4&5 using the second method with the best results was 90.0 percentage.