• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.5
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• pp.394-405
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• 2019

The purpose of this study is to clarify the concept of the frail elderly and to obtain theoretical evidence. The research method was conducted using the basic principles for conceptual analysis of Walker and Avant(2005). As a Result of a review of the literature about how to utilize the concept of a frail elderly, frail elderly might be in the intermediate state of health and disease. They can be defined as physically vulnerable in the sarcopenia, inflammation, insulin resistance, and preceding advanced disease, lead to hospitalization, falls, disability, and death. The attributes were physiological, psychological, and socio-environmental and economic factors, so they had multidimensional factors. They were required the assist daily living of another person. Also, their attributes had decreased the amount of recovery time and degree, and exhaustion. The attributes of frail elderly consisted of these facts: dynamic process, multidimensional factors, dependency, vulnerability. The frail elderly was a dynamic process that involves the possibility of change to health and disease, and include physical, mental, cognitive, and social environmental factors. In addition, the frail elderly was difficulty in daily life, physical vulnerability and difficulty in adaption. In conclusion, frail elderly as defined by the results of this study will contribute to the foundation of health care systems, including community visiting nursing to understand the level of frail elderly and systemic management to do not go into long term care.

• The Korean Journal of Archival Studies
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• no.39
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• pp.45-72
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• 2014

In the recent years, Gov.2.0, which strengthens not only a claim for freedom of information but also sharing public information, became a new paradigm of government operations. In line with the paradigm the Korean government promotes the Gov.3.0 policy. This study exams the freedom of information system, which expends its roles and responsibilities for enhancing the usage of public information in the Gov.3.0 era. Furthermore, it analyzes the system's usability from the perspective of users. The freedom of information system is the fundamental portal for all the public information's disclosure and usage. Without providing the solution for problems of the system, the Korean government's Gov3.0 policy cannot succeed. Also, Park Geun-hye Government's Gov.3.0 initiatives which consists of tasks, such as reinforcing freedom of information, immediate releasing original documents, and expending public access to information, should be done through the freedom of information system. The importance of the system is increasingly heavy. It is not only the simple online contact point for public information, but also a public sphere for sharing public raw data and for implementing the Gov.3.0 vision. However, the current system still does not slove it problems. This study analyzes the system's problems in terms of usability and sustainability. As a result, it provides three alternative strategies for the freedom of information system, including 'personnel and financial support expansion', 'strengthening user-friendly operating' and 'establishing long-term strategies for system improvement.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.3
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• pp.141-148
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• 2021

This paper presents a method to detect zebra-crossing using deep learning which combines SegNet and ResNet. For the blind, a safe crossing system is important to know exactly where the zebra-crossings are. Zebra-crossing detection by deep learning can be a good solution to this problem and robotic vision-based assistive technologies sprung up over the past few years, which focused on specific scene objects using monocular detectors. These traditional methods have achieved significant results with relatively long processing times, and enhanced the zebra-crossing perception to a large extent. However, running all detectors jointly incurs a long latency and becomes computationally prohibitive on wearable embedded systems. In this paper, we propose a model for fast and stable segmentation of zebra-crossing from captured images. The model is improved based on a combination of SegNet and ResNet and consists of three steps. First, the input image is subsampled to extract image features and the convolutional neural network of ResNet is modified to make it the new encoder. Second, through the SegNet original up-sampling network, the abstract features are restored to the original image size. Finally, the method classifies all pixels and calculates the accuracy of each pixel. The experimental results prove the efficiency of the modified semantic segmentation algorithm with a relatively high computing speed.

• Knowledge Management Research
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• v.22 no.4
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• pp.27-40
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• 2021

Click-Through Rate (CTR) prediction is a key function that determines the ranking of candidate items in the recommendation system and recommends high-ranking items to reduce customer information overload and achieve profit maximization through sales promotion. The fields of natural language processing and image classification are achieving remarkable growth through the use of deep neural networks. Recently, a transformer model based on an attention mechanism, differentiated from the mainstream models in the fields of natural language processing and image classification, has been proposed to achieve state-of-the-art in this field. In this study, we present a method for improving the performance of a transformer model for CTR prediction. In order to analyze the effect of discrete and categorical CTR data characteristics different from natural language and image data on performance, experiments on embedding regularization and transformer normalization are performed. According to the experimental results, it was confirmed that the prediction performance of the transformer was significantly improved when the L2 generalization was applied in the embedding process for CTR data input processing and when batch normalization was applied instead of layer normalization, which is the default regularization method, to the transformer model.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.263-278
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• 2022

With the recent advent of IoT technology, automatic pet feeders are being distributed so that owners can feed their companion animals while they are out. However, due to behaviors of pets, the method of measuring weight, which is important in automatic feeding, can be easily damaged and broken when using the scale. The 3D camera method has disadvantages due to its cost, and the 2D camera method has relatively poor accuracy when compared to 3D camera method. Hence, the purpose of this study is to propose a deep learning approach that can accurately estimate weight while simply using a 2D camera. For this, various convolutional neural networks were used, and among them, the ResNet101-based model showed the best performance: an average absolute error of 3.06 grams and an average absolute ratio error of 3.40%, which could be used commercially in terms of technical and financial viability. The result of this study can be useful for the practitioners to predict the weight of a standardized object such as feed only through an easy 2D image.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.1
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• pp.51-58
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• 2022

Drivable area detection, one of the main functions of advanced driver assistance systems, means detecting an area where a vehicle can safely drive. The drivable area detection is closely related to the safety of the driver and it requires high accuracy with real-time operation. To satisfy these conditions, V-disparity-based method is widely used to detect a drivable area by calculating the road disparity value in each row of an image. However, the V-disparity-based method can falsely detect a non-road area as a road when the disparity value is not accurate or the disparity value of the object is equal to the disparity value of the road. In a road environment including vegetation, such as a highway and a country road, the vegetation area may be falsely detected as the drivable area because the disparity characteristics of the vegetation are similar to those of the road. Therefore, this paper proposes a drivable area detection method and hardware architecture with a high accuracy in road environments including vegetation areas by reducing the number of false detections caused by V-disparity characteristic. When 289 images provided by KITTI road dataset are used to evaluate the road detection performance of the proposed method, it shows an accuracy of 90.12% and a recall of 97.96%. In addition, when the proposed hardware architecture is implemented on the FPGA platform, it uses 8925 slice registers and 7066 slice LUTs.

• Journal of the Korea Society of Computer and Information
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• v.27 no.5
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• pp.1-9
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• 2022

Localization systems can be used with various circumstances like measuring population movement and rescue technology, even in security technology (like infiltration detection system). Vision sensors such as camera often used for localization is susceptible with light and temperature, and can cause invasion of privacy. In this paper, we used ultra-wideband radar technology (which is not limited by aforementioned problems) and machine learning techniques to measure the number and location of occupants in other indoor spaces behind the wall. We used four different algorithms and compared their results, including extremely randomized tree for four different situations; detect the number of occupants in a classroom, split the classroom into 28 locations and check the position of occupant, select one out of the 28 locations, divide it into 16 fine-grained locations, and check the position of occupant, and checking the positions of two occupants (existing in different locations). Overall, four algorithms showed good results and we verified that detecting the number and location of occupants are possible with high accuracy using machine learning. Also we have considered the possibility of service expansion using the oneM2M standard platform and expect to develop more service and products if this technology is used in various fields.

• Journal of the Institute of Convergence Signal Processing
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• v.22 no.4
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• pp.179-184
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• 2021

Along with the development of the fourth industry, the public sector has increasingly paid more attention to search using drones and real-time monitoring, for various goals. The drones are used and researched to complete a variety of searching and monitoring missions, including search for missing persons, security, coastal patrol and monitoring, speed enforcement, highway and urban traffic monitoring, fire and wildfire monitoring, monitoring of illegal fishing in reservoirs and protest rally monitoring. Police stations, fire departments and military authorities, however, concentrate on the hardware part, so there are little research on efficient communication systems for the real-time monitoring of data collected from high-performance resolution and infrared thermal imagining cameras, and analysis programs suitable for special missions. In order to increase the efficiency of drones with the searching mission, this paper, therefore, attempts to propose an image analysis technique to increase the precision of search by producing image data suitable for searching missions, based on images obtained from drones and provide the foundation for improving relevant policies and establishing proper platforms, based on actual field cases and experiments.

• Journal of Appropriate Technology
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• v.7 no.2
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• pp.196-205
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• 2021

As industrial safety increases, various industrial accident prevention technologies using smart factory technology are being studied. However, small and medium enterprises (SMEs), which account for the majority of industrial accidents, are having difficulties in preventing industrial accidents by applying these smart factory technologies due to practical problems. In this study, customized monitoring and warning systems for each type of industrial accident were developed and applied to the actual field. Through this, we demonstrated industrial accident prevention technology through appropriate smart factory technology used by SMEs. A customized monitoring system using vision, current, temperature, and gas sensors was established for the four major disaster types: worker body access, short circuit and overcurrent, fire and burns due to high temperature, and emission of hazardous gas. In addition, a notification method suitable for each work environment was applied so that the monitored risk factors could be recognized quickly, and real-time data transmission and display enabled workers and managers to understand the disaster risk effectively. Through the application and demonstration of these appropriate smart factory technologies, the spread of these industrial safety technologies is to be discussed.

• Journal of Appropriate Technology
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• v.7 no.1
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• pp.115-124
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• 2021

Due to the successful accomplishments of the first-stage base center project, a road-map for the second-stage, global base center 2030 project has recently been proposed. The vision of the base center is to build a technology centered, cooperation based platform for a sustainable global community. The global base center 2030 project is based on three core strategies as well as three key strategies. The main goal of the core strategy is to establish an interdisciplinary smart platform, as well as a global tech-coordination facility to implement sustainable, inclusive, and innovative science and technology based ODA projects. To achieve such goals, the global center will focus on developing a global living lab, interdisciplinary smart linkage systems, and a global operating platform. The main goals for the key strategies are to solve issues at the base centers while establishing an international relationship through sustainable technology. To achieve such goals, key projects are centered in establishing a ICT package, and a global living lab based on smart interconnected system. With this, a global inter-connected business platform will also be established to support technical and operational issues.