• Journal of Industrial Convergence
• /
• v.21 no.10
• /
• pp.65-80
• /
• 2023

Through inductive content analysis, this study sought to examine the crisis experienced by the institution, the quality of service, and the distrust of the system and institution based on the experience of distrust in the home-visiting care service of bbeneficiary and guardians. FGI was conducted on five managers of institutions that provide home-visiting care services. As a result, the central phenomenon was found: deterioration of service quality, distrust of systems and institutions, and difficulties in opera-ting long-term care institutions. In order to improve the quality of home-visited care services and build trust in care workers and institutions, first, home-based associations or operating corporations should develop new education program plans and manuals to strengthen the capabilities of care workers and social workers. Second, the NHIS's monitoring system and the professional management system of care workers should be established. Third, it is necessary to improve awareness of the role, expertise, and rights of care workers, and fourth, improvement measures are required to reduce the turnover rate of care workers, which is the cause of the deterioration of the quality of long-term care services.

• Journal of the Korea Society of Computer and Information
• /
• v.28 no.10
• /
• pp.1-8
• /
• 2023

If there is a defect in the wheel bearing, which is a major part of the car, it can cause problems such as traffic accidents. In order to solve this problem, big data is collected and monitoring is conducted to provide early information on the presence or absence of wheel bearing failure and type of failure through predictive diagnosis and management technology. System development is needed. In this paper, to implement such an intelligent wheel hub bearing maintenance system, we develop an embedded system equipped with sensors for monitoring reliability and soundness and algorithms for predictive diagnosis. The algorithm used acquires vibration signals from acceleration sensors installed in wheel bearings and can predict and diagnose failures through big data technology through signal processing techniques, fault frequency analysis, and health characteristic parameter definition. The implemented algorithm applies a stable signal extraction algorithm that can minimize vibration frequency components and maximize vibration components occurring in wheel bearings. In noise removal using a filter, an artificial intelligence-based soundness extraction algorithm is applied, and FFT is applied. The fault frequency was analyzed and the fault was diagnosed by extracting fault characteristic factors. The performance target of this system was over 12,800 ODR, and the target was met through test results.

• Journal of Food Hygiene and Safety
• /
• v.38 no.6
• /
• pp.420-429
• /
• 2023

GC-MS/MS using liquid-liquid extraction (LLE) and C18 cartridges was used to identify and quantify levels of chlorpyrifos, chlorpyrifos-methyl, cypermethrin, deltamethrin and permethrin in bulk raw milk. A calibration curve spanning 10 ng/mL to 200 ng/mL was obtained with a satisfactory correlation coefficient of 0.99. The limits of detection (LOD) and limits of quantitation (LOQ) for chlorpyrifos, chlorpyrifos-methyl, cypermethrin, deltamethrin, and permethrin in the matrix ranged from 0.06 to 1.81 ng/mL and 0.19 to 6.04 ng/mL, respectively. The recoveries of 5 pesticides from spiked samples at 37.5-125 ng/mL ranged from 86.1 to 102.1%. The measurement of uncertainty of the GC-MS/MS method for these five pesticides was developed based on the analytical process and quantification. An analysis method that is easier and faster than the method specified in the Korean food standards codes for analyzing these five pesticides in raw material milk was developed. Moreover, the analytical method for chlorpyrifos, chlorpyrifos-methyl, cypermethrin, deltamethrin, and permethrin in bulk raw milk by GC-MS/MS was established.

• The Journal of Engineering Geology
• /
• v.33 no.4
• /
• pp.531-541
• /
• 2023

The most important thing in the 4th industry, AI era, and smart construction era is digital data. Basic data in the civil engineering field begins with ground investigation. The Ministry of Land, Infrastructure and Transport operates the Geotechnical Information Database Center to manage ground survey data, including drilling but the focus is on data distribution. This study seeks to devise a plan for long-term use of the results of drilling investigation conducted for the design and construction of various construction projects. For this purpose, a pilot area was set up and a 'geotechnical design parameters digital map' was created using some geotechnical design parameters from the drilling investigation data. Using the developed algorithm, a digital map of friction angle and permeability coefficient for the hard rock stratum in the pilot area was created. Geotechnical design parameters digital map can identify the overall condition of the ground, but reliability needs to be improved due to the lack of initial data input. Through additional research, it will be possible to produce a more complete geotechnical design parameters digital map.

• Journal of Internet Computing and Services
• /
• v.24 no.6
• /
• pp.161-169
• /
• 2023

Due to the Fourth Industrial Revolution, innovation in various technologies such as artificial intelligence (AI), big data (Big Data), and cloud (Cloud) is accelerating, and data is considered an important asset. With the innovation of these technologies, various efforts are being made to lead technological innovation in the field of defense science and technology. In Korea, the government also announced the "Defense Innovation 4.0 Plan," which consists of five key points and 16 tasks to foster advanced science and technology forces in March 2023. The plan also includes the establishment of a Condition-Based Maintenance system (CBM+) to improve the operability and availability of weapons systems and reduce defense costs. Condition Based Maintenance (CBM) aims to secure the reliability and availability of the weapon system and analyze changes in equipment's state information to identify them as signs of failure and defects, and CBM+ is a concept that adds Remaining Useful Life prediction technology to the existing CBM concept [1]. In order to establish a CBM+ system for the weapon system, sensors are installed and sensor data are required to obtain condition information of the weapon system. In this paper, we propose a sensor data metadata schema to efficiently and effectively manage sensor data collected from sensors installed in various weapons systems.

• Convergence Security Journal
• /
• v.23 no.3
• /
• pp.13-20
• /
• 2023

This study conducted a digital forensic analysis of Signal and Telegram, two secure messengers widely used in the Android environment. As mobile messengers currently play an important role in daily life, data management and security within these apps have become very important issues. Signal and Telegram, among others, are secure messengers that are highly reliable among users, and they safely protect users' personal information based on encryption technology. However, much research is still needed on how to analyze these encrypted data. In order to solve these problems, in this study, an in-depth analysis was conducted on the message encryption of Signal and Telegram and the database structure and encryption method in Android devices. In the case of Signal, we were able to successfully decrypt encrypted messages that are difficult to access from the outside due to complex algorithms and confirm the contents. In addition, the database structure of the two messenger apps was analyzed in detail and the information was organized into a folder structure and file format that could be used at any time. It is expected that more accurate and detailed digital forensic analysis will be possible in the future by applying more advanced technology and methodology based on the analyzed information. It is expected that this research will help increase understanding of secure messengers such as Signal and Telegram, which will open up possibilities for use in various aspects such as personal information protection and crime prevention.

• Korean Journal of Culture and Social Issue
• /
• v.8 no.1
• /
• pp.165-187
• /
• 2002

This study examines Korean representation of the biotechnology and psychological factors which can influence lay people's perception and attitude about biotechnology. Korean college students(N=433) and lay adults(N=90) whom had college education participated in the study. Participants of the study 1 were asked to list words which comes to mind when associate with the biotechnology in broad sense, and several specific applications in health, medicines, agriculture and research. Participants of the study 2 were asked to list possible benefits and costs of biotechnology and their specific applications. In study 3, Participants responded the questionnaires about perceptions and attitudes of biotechnology. Korean people associated the biotechnology with its costs or risks and benefits. Korean college students mainly got the informations of the biotechnology from TV, newspapers, or internet. They trusted the scientist group and NGO group on their judgements about the assessment of risk and benefit of the biotechnology. College students showed the positive attitude with the applications in medicines and negative attitude with the applications in agriculture and public using of individual's genetic information. The radicalism, sensitivity in behavioral activation system, and trust/cynicism were to be found as a significant influencing factor for interest/knowledge and behavioral intention in related with biotechnology. Finally, more extensive knowledge of biotechnology did not lead to greater acceptance of it.

• Information Systems Review
• /
• v.24 no.1
• /
• pp.105-123
• /
• 2022

Thanks to the remarkable success of Artificial Intelligence (A.I.) techniques, a new possibility for its application on the real-world problem has begun. One of the prominent applications is the bankruptcy prediction model as it is often used as a basic knowledge base for credit scoring models in the financial industry. As a result, there has been extensive research on how to improve the prediction accuracy of the model. However, despite its impressive performance, it is difficult to implement machine learning (ML)-based models due to its intrinsic trait of obscurity, especially when the field requires or values an explanation about the result obtained by the model. The financial domain is one of the areas where explanation matters to stakeholders such as domain experts and customers. In this paper, we propose a novel approach to incorporate financial domain knowledge into local rule generation to provide explanations for the bankruptcy prediction model at instance level. The result shows the proposed method successfully selects and classifies the extracted rules based on the feasibility and information they convey to the users.

• Knowledge Management Research
• /
• v.25 no.2
• /
• pp.115-134
• /
• 2024

In an age where consumers can easily search and pass on their opinions of products and purchasing decisions through the internet, Electronic-word-of-mouth(Ewom) plays an important role in decision making of other potential customers. In this study, we empirically analyze the impact EWOM on consumer purchase decisions, when contradictory Ewom is presented from varying sources of information, such as experts and general consumers. First, we find that when there is a consensus among different information sources there exists a positive relationship between Ewom sentiment and purchase intent, confirming the results of previous literature. However, when expert opinion and consumer opinion do not match we find that consumer opinion is more impactful on purchasing decisions compared to the expert opinion, regardless of product types. The findings of this study add insight to the current literature by examining the effect of contradictory Ewom on purchase decisions, and also to industry marketers by presenting a more efficient strategy in promoting positive Ewom for different product types.

• Journal of Advanced Navigation Technology
• /
• v.27 no.6
• /
• pp.835-840
• /
• 2023

RFID(Radio Frequency Identification) systems are attracting attention as a key component of Internet of Things technology due to the cost and energy efficiency of application services. In order to use RFID technology in the IoT application service field, it is necessary to be able to store and manage various information for a long period of time as well as simple recognition between the reader and tag of the RFID system. And in order to read and write information to tags, a performance improvement technology that is strong and reliable in poor wireless channels is needed. In particular, in the UHF(Ultra High Frequency) RFID system, since multiple tags communicate passively in a crowded environment, it is essential to improve the recognition rate and transmission speed of individual tags. In this paper, Middleton's Class A impulsive noise model was selected to analyze the performance of the RFID system in an impulsive noise environment, and FM0 encoding and Miller encoding were applied to the tag to analyze the error rate performance of the RFID system. As a result of analyzing the performance of the RFID system in Middleton's Class A impulsive noise channel, it was found that the larger the Gaussian noise to impulsive noise power ratio and the impulsive noise index, the more similar the characteristics to the Gaussian noise channel.