• Management & Information Systems Review
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• v.29 no.4
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• pp.305-332
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• 2010

This study was searching for elements affecting satisfaction of customers by comparing health management service businesses such as wellbeing care, yoga, and fitness. The discovered elements were analyzed and verified to find which elements are affecting what businesses through case studies. Multidirectional analysis was implemented for each service type using program, physical environment, and provided service drawn from the previous researches with SERVQUAL criteria and measured values on customer satisfactions. According to the analysis, physical environment in forest wellbeing care, program in yoga, and provided service in fitness were the most affecting elements. Thus, each health management service business must consider the lifestyle and trend of customers, and the specialized service corresponding to its uniqueness must be provided to customers. Surely, modernized exercise equipment, personalized program, and comfortable-luxurious settings are must have in order to be competitive. In addition, the business owners have to realize that customers are moving to quality from quantity. This means exercise must be brought up to the level of social value for relationship and links rather than left at the level of simple physical and mental trainings. To achieve these, other programs to support relationship among customers and circulating system with friendly environment must be considered at the same time.

• Journal of Digital Convergence
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• v.15 no.8
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• pp.511-516
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• 2017

The study conducted a systematic analysis through systematic literature to explore trends in physical activities and trends in the elderly and smart health care sector. Based on the research data collected from 2006 to 2017, the research paper was selected as an analysis data base and collected data from the patented patent data registered to the Patent Office. Based on the combination of the aged, physical activities, IT, IoT, and healthcare, the research identified the research trends and subjects through the analysis and analysis of subjects based on a total of 102 academic journals and 79 patents. First of all, the academic research published a surge in 2010 research in 2006, and it has emerged as an area of continuous interest in academia until 2017. Meanwhile, patents for patents soared in 2012, according to the company's patent. Second, research shows that studies are being conducted in five areas of research. Service design, monitoring systems, systems, policies, and other studies. In the case of patents, three types of patents were classified as patents, devices and information related to information. Subsequent studies will be deemed necessary to verify the effectiveness of the smart health care technology to enhance the health of the elderly.

• Korean Journal of Clinical Laboratory Science
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• v.37 no.3
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• pp.178-184
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• 2005

Carry over contamination has been reduced in some systems by flushing the internal and external surfaces of the sample probe with copious amount of diluent. It between specimens should be kept as small as possible. A built-in, continuous-flow wash reservoir, which allows the simultaneous washing of the interior and exterior of the syringe needles, addresses this issue. In addition, residual contamination can further be prevented through the use of efficient needle rinsing procedures. In discrete systems with disposable reaction vessels and measuring cuvets, any carry over is entirely caused by the pipetting system. In analyzers with reuseable cuvets or flow cells, carry over may arise at every point through which high samples pass sequentially. Therefore, disposable sample probe tips can eliminate both the contamination of one sample by another inside the probe and the carry over of in specimen into the specimen in the cup. The results of the applicative carry over experiment studied on 21 items for total protein (TP), albumin (ALB), total bilirubin (TB), alkaline phosphatase (ALP), aspratate aminotranferase (AST), alanine aminotranferase (ALT), gamma glutamyl transferase (GGT), creatinine kinase (CK), lactic dehydrogenase (LD), creatnine (CRE), blood urea nitrogen (BUN), uric acid (UA), total cholesterol (TC), triglyceride (TG), glucose (GLU), amylase (AMY), calcium (CA), inorganic phosphorus (IP), sodium (Na), potassium (K), chloride (CL) tests in chematology were as follows. Evaluation of process performance less than 1% in all tests was very good, but a percentage of ALB, TP, TB, ALP, CRE, UA, TC, GLU, AMY, IP, K, Na, and CL was 0%, implying no carry over. Other tests were ALT(-0.08%), GGT(-0.09%), CK(0.08%), LD(0.06%), BUN(0.12%), TG (-0.06%), and CA(0.89%).

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.6
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• pp.47-54
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• 2020

The acupressure is a treatment that applies pressure to certain parts of the body and has been mainly used for pain relief in the field of oriental medicine. However, the treatment effect is often different depending on the practitioner's ability, experience, and physical strength, so standardized acupressure is needed. In this regard, the equipment is being released, but this is mainly a rolling massage method, which reduces energy concentration and poses a risk of injury. Therefore, in this study, a device that provides vertical acupressure based on variable bogie (wheel truck) was implemented. As a result of experimenting with load and body pressure distribution and desirability to validate the device's bearing pressure, the acupressure rod held up to 150kg, the body pressure ratio was measured lower than the body pressure ratio of the comparison item in section 0%

• KIPS Transactions on Software and Data Engineering
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• v.10 no.4
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• pp.133-142
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• 2021

With the recent advancements of deep learning, companies such as smart home, healthcare, and intelligent transportation systems are utilizing its functionality to provide high-quality services for vehicle detection, emergency situation detection, and controlling energy consumption. To provide reliable services in such sensitive systems, deep learning models are required to have high accuracy. In order to develop a deep learning model for analyzing previously mentioned services, developers should utilize the state of the art deep learning models that have already been verified for higher accuracy. The developers can verify the accuracy of the referenced model by validating the model on the dataset. For this validation, the developer needs structural information to document and apply deep learning models, including metadata such as learning dataset, network architecture, and development environments. In this paper, we propose a description language that represents the network architecture of the deep learning model along with its metadata that are necessary to develop a deep learning model. Through the proposed description language, developers can easily verify the accuracy of the referenced deep learning model. Our experiments demonstrate the application scenario of a deep learning description document that focuses on the license plate recognition for the detection of illegally parked vehicles.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.1
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• pp.135-144
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• 2024

In this study, we propose an automatic classification model for quantitative multidimensional analysis based on facet theory to understand public opinions and demands on major issues through big data analysis. Civil complaints, as a form of public feedback, are generated by various individuals on multiple topics repeatedly and continuously in real-time, which can be challenging for officials to read and analyze efficiently. Specifically, our research introduces a new classification framework that utilizes facet theory and political analysis models to analyze the characteristics of citizen complaints and apply them to the policy-making process. Furthermore, to reduce administrative tasks related to complaint analysis and processing and to facilitate citizen policy participation, we employ deep learning to automatically extract and classify attributes based on the facet analysis framework. The results of this study are expected to provide important insights into understanding and analyzing the characteristics of big data related to citizen complaints, which can pave the way for future research in various fields beyond the public sector, such as education, industry, and healthcare, for quantifying unstructured data and utilizing multidimensional analysis. In practical terms, improving the processing system for large-scale electronic complaints and automation through deep learning can enhance the efficiency and responsiveness of complaint handling, and this approach can also be applied to text data processing in other fields.

• Information Systems Review
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• v.25 no.1
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• pp.93-110
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• 2023

Recently, attempts have been made to convert unstructured text into vectors and to analyze vast amounts of natural language for various purposes. In particular, the demand for analyzing texts in specialized domains is rapidly increasing. Therefore, studies are being conducted to analyze specialized and general-purpose documents simultaneously. To analyze specific terms with general terms, it is necessary to align the embedding space of the specific terms with the embedding space of the general terms. So far, attempts have been made to align the embedding of specific terms into the embedding space of general terms through a transformation matrix or mapping function. However, the linear transformation based on the transformation matrix showed a limitation in that it only works well in a local range. To overcome this limitation, various types of nonlinear vector alignment methods have been recently proposed. We propose a vector alignment model that matches the embedding space of specific terms to the embedding space of general terms through end-to-end learning that simultaneously learns the autoencoder and regression model. As a result of experiments with R&D documents in the "Healthcare" field, we confirmed the proposed methodology showed superior performance in terms of accuracy compared to the traditional model.

• Journal of Korea Entertainment Industry Association
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• v.14 no.2
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• pp.165-176
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• 2020

Customized medical care and services timely providing effective prevention and treatment by collecting and using individuals' biomedical data are recently possible and utilized for users' health care. They are developed as the real-time health care services and information is provided to individuals by using smart phones, PC, tablet, etc. Interactive communication is supported by informing managers of analysis data and results, through collected data. It is therefore the time for constructing health care. This study attempts to prepare for patent applications of technical development at this time, by analyzing the tendency of smart wearable health care technologies, including biological signal-based health care devices and real-time health care system. Patents regarding smart wearable health care technologies were reported to have the relatively higher concentration of research development. Korea focuses on patent activities for real-time health care systems across the intervals of analysis, while U.S and European countries actively make efforts for patent activities regarding health care devices Japan conduct patent activities across health care devices and systems, based on bio-technologies. Korea has recently dominated the market of patents for bio-technologies-based health care devices and real-time health care devices and also appears to secure patents for the technologies and the market, so entry barriers to the market of smart wearable health care technologies are determined to be higher in Korea. It is important to establish the portfolios of patents, by securing patent rights for the figures of products, manufacturing methods and other related technical systems, if technologies are planned to be commercialized.

• The Journal of the Korea Contents Association
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• v.11 no.4
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• pp.101-111
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• 2011

Due to recent development in wireless communication technologies and mobile information devices, the services on ubiquitous computing technology without time and place restriction have been spotlighted. Moreover, the interest of the Wireless Sensor Networks (WSNs) and context-awareness technologies have largely been escalating and their technologies utilization is active in the various applications such as healthcare, farm management and so on. However, the direct adaption of the existing context-awareness technique to the WSN technology has several drawbacks as follows. First, such systems waste precious energy of sensor nodes, due to unnecessary data transmissions. Secondly, since the existing work was designed to support only specific applications, it is required to implement a new context-awareness application for a specific purpose. Therefore, we, in this paper, propose a new real-time monitoring system based on context-awareness in WSNs. Our system not only enhances energy efficiency by reducing data transmissions by doing context-awareness on a sensor node, but also is scalable in terms of supporting new context-awareness functionalities through modularization.

• Journal of KIISE:Computer Systems and Theory
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• v.35 no.4
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• pp.158-165
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• 2008

LOINC (Logical Observation Identifiers Names and Codes) is a database and universal standard for identifying laboratory and clinical test results that is developed and maintained by Regenstrief Institute. Exchanging laboratory test results is one of the most important area in EHR system and the terminology for laboratory test results has to be standardized. In this paper, we present a pre-preprocessing tool that converts a local database in healthcare organizations to LMOF format LMOF format is required by RELMA and our work helps mapping laboratory test results to LOINC very efficiently Our proposed tool provided user friendly interface and 15% keyword reduction in RELMA search compared to no pre-processing RELMA search.