• International journal of advanced smart convergence
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• v.4 no.2
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• pp.94-102
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• 2015

Optical camera communication (OCC) is an extension of Visible Light Communication. Different from traditional visible light communication, optical camera communications is an almost no additional cost technology by taking the advantage of build-in camera in devices. It was became a candidate for communication protocol for IoT. Camera module can be easy attached to IoT device, because it is small and flexible. Furthermore almost smartphone equip one or two camera for both back and font side with high quality and resolution. It can be utilized for receiving the data from LED or positioning. Actually, OCC combines illumination and communication. It can supply communication for special areas or environment where do not allow Radio frequency such as hospital, airplane etc. There are many concept and experiment be proposed. In this paper we proposed utilizing Android smart-phone camera for receiver and introduce new approach in modulation scheme for LED at transmitter. It also show how Manchester coding can be used encode bits while at the same time being successfully decoded by Android smart-phone camera. We introduce new data frame format for easy decoded and can be achieve high bit rate. This format can be easy to adapt to performance limit of Android operator or embedded system.

• International journal of advanced smart convergence
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• v.7 no.1
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• pp.33-41
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• 2018

This study was designed to provide basic data for developing exercise program that helps correcting posture by knowing the effect of strengthening and elongation exercises of upper extremity muscle to forward head posture correction. In this study determined subjects whether they had forward head posture or not. On the basis of the New York state posture rating, if a subject's posture is match up with the normal standard posture, gives 5 points and if the posture is slightly get out of the normal standard posture, gives 3 points and if the posture is apparently get out of the standard, gives 1 points. When determining the forward head posture, if talus, humerus and outer ear center are on the same line, it is determined as normal and if outer ear center is off the line less than 1.0cm, it is a slight deformation and if outer ear center is off the line more than 1.0cm, it is a high deformation. In the study selected people who have more than 1 cm gap between two vertical lines start from outer ear center and acromion separately as subjects. Length between the ideal alignment line measured by using goniometer and temporal region showed statistically significant decrease as $2.36{\pm}1.07cm$ before the intervention and $1.06{\pm}0.88cm$ after the intervention. After 4 weeks of neck and chest extensor muscle exercise, the group who exercised both showed increase in range of neck joint motion and neck flexion of the forward head posture. Meanwhile the group who only exercised neck extensor muscle only and the group who only exercised chest extensor muscle didn't showed statistically significant result. That only the group who exercised both muscles showed significant result is the different with studies before. Because this study didn't target patient who had a lesion, couldn't compare effect of the conservative manner and exercise. However, this study provides the fact that the group who exercised both neck and chest muscle had more effect than the control group.

• International journal of advanced smart convergence
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• v.7 no.1
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• pp.42-47
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• 2018

Recently, as the demand for limited resources continues to rise and problems of resource depletion rise worldwide, the importance of renewable energy is gradually increasing. In order to solve these problems, various methods such as energy conservation and alternative energy development have been suggested, and biogas, which can utilize the gas produced from biomass as fuel, is also receiving attention as the next generation of innovative renewable energy. New and renewable energy using biogas is an energy production method that is expected to be possible in large scale because it can supply energy with high efficiency in compliance with energy supply method of recycling conventional resources. In order to more efficiently produce and manage these biogas, a biogas plant has emerged. In recent years, a large number of biogas plants have been installed and operated in various locations. Organic wastes corresponding to biogas production resources in a biogas plant exist in a wide variety of types, and each of the incoming raw materials is processed in different processes. Because such a process is required, the case where the biogas plant process is inefficiently operated is continuously occurring, and the economic cost consumed for the operation of the biogas production relative to the generated biogas production is further increased. In order to solve such problems, various attempts such as process analysis and feedback based on the feedstock have been continued but it is a passive method and very limited to operate a medium/large scale biogas plant. In this paper, we propose "CNN-based production yield prediction algorithm for increasing process efficiency of biogas plant" for efficient operation of biogas plant process. Based on CNN-based production yield forecasting, which is one of the deep-leaning technologies, it enables mechanical analysis of the process operation process and provides a solution for optimal process operation due to process-related accumulated data analyzed by the automated process.

• International journal of advanced smart convergence
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• v.10 no.3
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• pp.113-121
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• 2021

This study was conducted on 1,174 retirees in their 50s and 60s nationwide using the 7th data of the National Pension Service's National Elderly Security Panel Survey. We were able to confirm the following results through the SPSS WIN 18.0 program.. First, men showed a higher level of education than women. From these results, it is expected that men will be better prepared for retirement. Second, men had spouses and two or more household members than women. According to previous studies showing that men are better prepared for retirement if they have a spouse, it is expected that men are better prepared for retirement. Second, 38% of men and 62% of women were voluntary retirees. Third, both men and women responded that their own and spouses should play the main role in preparing for retirement. Fourth, both men and women had very low rates of preparation for old age, economic independence, and public and private pensions. Among them, women were lower than men. Fifth, economic problem solving, health, and medical care were the priority as parts to be done for retirement in oneself and society. Based on these results, the directions for preparing retirees for retirement are as follows. First, education on wage peak system, retirement age extension and financial management for involuntary retirees is required, and guidance and management methods on health care and disease should be provided to address needs for health and health care. Women had more voluntary retirees than men, and they need to know why. Second, when both men and women are very poor at preparing for retirement, there should be job creation measures to ensure that they and their spouses are fully prepared for retirement.

• International journal of advanced smart convergence
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• v.11 no.1
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• pp.111-116
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• 2022

After COVID-19, the number of people with sleep disorders around the world is increasing. In particular, in the flow of the 4th industrial revolution, the differentiation of types and characteristics of the sleep industry is accelerating. Therefore, in this study, the characteristics of each type of sleep-related industry were reclassified from an industrial point of view, and based on this, an attempt was made to review the classification system that can help companies develop sleep products and improve related national systems. Based on the 10th standard industry classification, we compared input cost, value, and usability and analyzed common characteristics, treatments, and preventive effects based on this. A comprehensive taxonomy using matrix analysis was reviewed. As a result, in terms of cost (A), the most common sleeping products are general mattresses and general bedding. It is an IOT device (auxiliary device), and the value aspect (B, B/D) included sleep cafe, bedding rental and management service, and sleep consulting. In terms of utility (A/B), a total of 6 product groups including sleep aids (health functional foods) belong to this category, and in terms of treatment (A/C), a total of 3 product groups including sleep clinics (medical services) belong to this category. As for the product group (A/D) with both properties, it was found that non-insurance sleep treatment medical devices, sleep-related over-the-counter drugs, and some sleep monitoring applications belong to this category. Ultimately, it was found that the sleep industry classification enables the most active product development and composition according to the relative relationship between cost and utility, and treatment and utility. appeared to be necessary.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.434-442
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• 2023

The relationship between acute kidney injury (AKI) prediction and nephrotoxic drugs, or drugs that adversely affect kidney function, is one that has yet to be explored in the critical care setting. One contributing factor to this gap in research is the limited investigation of drug modalities in the intensive care unit (ICU) context, due to the challenges of processing prescription data into the corresponding drug representations and a lack in the comprehensive understanding of these drug representations. This study addresses this gap by proposing a novel approach that leverages patient prescription data as a modality to improve existing models for AKI prediction. We base our research on Electronic Health Record (EHR) data, extracting the relevant patient prescription information and converting it into the selected drug representation for our research, the extended-connectivity fingerprint (ECFP). Furthermore, we adopt a unique multimodal approach, developing machine learning models and 1D Convolutional Neural Networks (CNN) applied to clinical drug representations, establishing a procedure which has not been used by any previous studies predicting AKI. The findings showcase a notable improvement in AKI prediction through the integration of drug embeddings and other patient cohort features. By using drug features represented as ECFP molecular fingerprints along with common cohort features such as demographics and lab test values, we achieved a considerable improvement in model performance for the AKI prediction task over the baseline model which does not include the drug representations as features, indicating that our distinct approach enhances existing baseline techniques and highlights the relevance of drug data in predicting AKI in the ICU setting.

• Journal of Internet Computing and Services
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• v.14 no.1
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• pp.31-39
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• 2013

Generally, there can be a interface discrepancy between the legacy hardware and the new software in combining new software component with reused hardware components in the embedded system. This kind of the interface discrepancy may cause various types of faults and also result in declining interface fault tolerance. In this paper we propose a method to improve interface fault tolerance. First of all, the new interface discrepancy fault type which has not been dealt with before is to be defined and next the testing method for generating test paths is proposed by considering the new defined interface discrepancy fault type in this paper. Several tests show that the proposed method detects more fatal faults about 7.9% in comparison with the existing testing method for commercial broadcasting receiver. Since the proposed method can provide software developers with test paths to be available earlier on the software development cycle, in addition, software developers can regard on interface discrepancy fault in advance. Consequently, more efficient test planning can be established to improve the interface fault tolerance.

• Journal of Broadcast Engineering
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• v.14 no.4
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• pp.463-474
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• 2009

By employing the subscriber concept in broadcasting services, IPTV (Internet Protocol Television) operators provide various grades of services to subscribers based on the billing level of the subscribers. With the income from subscribers for a basis, IPTV operators plan to provide high quality services. Since Web browser-based IPTV provides T-commerce and E-commerce services as well as television services, users may frequently visit other service domains to buy goods or content. To provide the user with charged or private services, these service domains request authentication of user. The existing authentication system is not appropriate for the IPTV service environment because the environment unavoidably forces the user to cross from one authentication-based service domain to another. Single sign-on provides a user with transparent authentication services by enabling an authenticated user to move between authentication-based service domains without any re-authentication. Like this distributed environment, since the IPTV service environment also provides a variety of authentication-based services, transparent authentication service needs to be provided to subscribers who want to access charged or private services. In this paper, we propose a new user authentication scheme for the IPTV environment. This scheme integrates the Security Assertion Markup Language (SAML), which is a standard for XML-based single sign on. We validate this scheme using a simple use case scenario.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.2B
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• pp.302-312
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• 2010

Internet Protocol Television (IPTV) service is the convergence service of the telecommunication and broadcasting that provides various bidirectional multimedia contents by IPTV service subscribe's request through the high-speed internet. However, the proposed technologies current do not guarantee the security such as authentication between Set-Top-Box (STB) and the user mobile devices available IPTV service at home domain, and authentication of mobile user device at out of door. This paper proposes the authentication protocol for distributing content securely from STB to the users' mobile devices at home domain and authentication for network access and IPTV service access when the user's mobile device is moved out of the house. The proposed scheme using the proxy signature enables to distribute and protect securely the contents protected through an underlying Conditional Access System (CAS) without re-encrypting then that the existing scheme should employ. Then this protocol supports the authentication scheme to get service access authentication based on network access authentication using the signature, which the STB issued on behalf of the trust authority of IPTV service provider. Also the proposed authentication protocol reduces the total communication overhead and computation time comparing to the other authentication protocol.

• Smart Media Journal
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• v.8 no.1
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• pp.74-81
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• 2019

Recently, machine learning technology has had a significant impact on society, particularly in the medical, manufacturing, marketing, finance, broadcasting, and agricultural aspects of human lives. In this paper, we study how to apply machine learning techniques to foods, which have the greatest influence on the human survival. In the field of Smart Farm, which integrates the Internet of Things (IoT) technology into agriculture, we focus on optimizing the crop growth environment by monitoring the growth environment in real time. KT Smart Farm Solution 2.0 has adopted machine learning to optimize temperature and humidity in the greenhouse. Most existing smart farm businesses mainly focus on controlling the growth environment and improving productivity. On the other hand, in this study, we are studying how to apply machine learning with respect to harvest time so that we will be able to harvest fruits of the highest quality and ship them at an excellent cost. In order to apply machine learning techniques to the field of smart farms, it is important to acquire abundant voluminous data. Therefore, to apply accurate machine learning technology, it is necessary to continuously collect large data. Therefore, the color, value, internal temperature, and moisture of greenhouse-grown fruits are collected and secured in real time using color, weight, and temperature/humidity sensors. The proposed FPSML provides an architecture that can be used repeatedly for a similar fruit crop. It allows for a more accurate harvest time as massive data is accumulated continuously.