• Journal of Auto-vehicle Safety Association
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• v.13 no.4
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• pp.14-19
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• 2021

This paper presents LiDAR static obstacle map based vehicle dynamic state estimation algorithm for urban autonomous driving. In an autonomous driving, state estimation of host vehicle is important for accurate prediction of ego motion and perceived object. Therefore, in a situation in which noise exists in the control input of the vehicle, state estimation using sensor such as LiDAR and vision is required. However, it is difficult to obtain a measurement for the vehicle state because the recognition sensor of autonomous vehicle perceives including a dynamic object. The proposed algorithm consists of two parts. First, a Bayesian rule-based static obstacle map is constructed using continuous LiDAR point cloud input. Second, vehicle odometry during the time interval is calculated by matching the static obstacle map using Normal Distribution Transformation (NDT) method. And the velocity and yaw rate of vehicle are estimated based on the Extended Kalman Filter (EKF) using vehicle odometry as measurement. The proposed algorithm is implemented in the Linux Robot Operating System (ROS) environment, and is verified with data obtained from actual driving on urban roads. The test results show a more robust and accurate dynamic state estimation result when there is a bias in the chassis IMU sensor.

• Journal of Korean Clinical Nursing Research
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• v.27 no.3
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• pp.233-244
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• 2021

Purpose: The purpose of this study was to investigate the actual utilization of clinical practice guidelines developed by Hospital Nurses Association. Methods: The subjects were 70 nurses who were in charge of guideline distributions in 70 advanced general hospital and general hospitals with 500 beds or more nationwide. Data were collected between June and August, 2020 by mail (return rate: 88.6%). Data were analyzed using descriptive statistics, t-test, and ANOVA with SPSS/WIN 24.0. Results: Among the clinical practice guidelines developed by Hospital Nurses Association, 72.9~90.1% were placed with book and electronic file in nursing department and 24.3~35.8% were placed with book and electronic file in each nursing unit at hospital. The average number of utilized clinical practice guidelines were 3.96±3.88, and average score of guideline utilization was score 2.85±0.79 which means 'use sometimes'. Conclusion: To improve the distribution and utilization of the clinical practice guidelines, it is necessary to enhance the recognition of values of evidence based nursing practice targeting head of nursing department and to stimulate the distribution and utilization of the clinical practice guidelines using diverse education programs for staff nurses.

• Journal of the Society of Disaster Information
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• v.18 no.1
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• pp.154-162
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• 2022

Purpose: The government intends to come up with measures to revitalize safety management at construction sites to shift safety management at construction sites from human capabilities to system-oriented management systems using advanced technologies AIoT and Mobile technologies. Method: The construction site safety management monitoring system using AIoT and Mobile technology conducted an experiment on the effectiveness of the construction site by applying three algorithms: virtual fence, fire monitoring, and recognition of not wearing a safety helmet. Result: The number of workers in the experiment was 215 and 7.61 virtual fence intrusion was 3.5% compared to the number of subjects and 0.16 fire detection were 0.07% compared to the subjects, and the average monthly rate of not wearing a safety helmet was 8.79, 4.05% compared to the subjects. Conclusion: It was found that the construction site safety management monitoring system using AIoT and Mobile technology has a valid effect on the construction site.

• The Journal of the Korea Contents Association
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• v.22 no.10
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• pp.535-545
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• 2022

The purpose of this study is to classify the various problems in surface road according to their severity and to propose a priority decision making process for road policy makers. For this purpose, the road problems reported by Cheok-cheok app were classified, and the EPDO was adopted and calculated as an index of their severity. To test applicability of the proposed process, some images of road problems reported by the app were classified and annotated, and the Deep Learning was used for machine learning of the curated images, and then the other images of road problems were used for verification. The detecting success rate of the road problems with high severity such as road kills, obstacles in a lane, road surface cracks was over 90%, which shows the applicability of the proposed process. It is expected that the proposed process will make the app possible to be used in the filed to make a priority decision making by classifying the level of severity of the reported road problems automatically.

• Journal of the Korea Convergence Society
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• v.13 no.5
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• pp.559-565
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• 2022

As South Korea has become an aged society, the dementia rate has also been increasing. With the introduction of both de-institutionalization and community care in 2017, the importance of community-based care has increased. Perspectives on dementia have also been shifting from the medical model and social model to the person-centered model. This paper suggests ways to improve dementia service through analyzing the dementia knowledge of care-givers at ADS. The paper includes an FGI with the care-givers who have worked for more than 2 years. Based on such FGI, the paper draws sub-themes from 7 areas. Among the suggestions for improvement are: community-based service with daily routine practice, improving awareness of dementia for co-living, and person-centered service based on individuality and diverseness.

• Informatization Policy
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• v.29 no.4
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• pp.43-66
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• 2022

With the development of artificial intelligence technology, competition for artificial intelligence technology patents around the world is intensifying. During the period 2000 ~ 2021, artificial intelligence technology patent applications at the US Patent and Trademark Office have been steadily increasing, and the growth rate has been steeper since the 2010s. As a result of analyzing Korea's artificial intelligence technology competitiveness through patent indices, it is evaluated that patent activity, impact, and marketability are superior in areas such as auditory intelligence and visual intelligence. However, compared to other countries, overall Korea's artificial intelligence technology patents are good in terms of activity and marketability, but somewhat inferior in technological impact. While noise canceling and voice recognition have recently decreased as topics for artificial intelligence, growth is expected in areas such as model learning optimization, smart sensors, and autonomous driving. In the case of Korea, efforts are required as there is a slight lack of patent applications in areas such as fraud detection/security and medical vision learning.

• Journal of Software Assessment and Valuation
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• v.17 no.2
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• pp.161-172
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• 2021

Neuromorphic architecture is drawing attention as a next-generation computing that supports artificial intelligence technology with low energy. However, FPGA embedded boards based on Neuromorphic architecturehave limited resources due to size and power. In this paper, we compared and evaluated the image reduction method using the interpolation method that rescales the size without considering the feature points and the DCT (Discrete Cosine Transform) method that preserves the feature points as much as possible based on energy. The scaled images were compared and analyzed for accuracy through CNN (Convolutional Neural Networks) in a PC environment and in the Nengo framework of an FPGA embedded board.. As a result of the experiment, DCT based classification showed about 1.9% higher performance than that of interpolation representation in both CNN and FPGA nengo environments. Based on the experimental results, when the DCT method is used in a limited resource environment such as an embedded board, a lot of resources are allocated to the expression of neurons used for classification, and the recognition rate is expected to increase.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.899-904
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• 2022

As the world becomes an aging society due to a decrease in the birth rate and an increase in life expectancy, a system for health management of the elderly population is needed. Among them, various studies on occupancy and activity types are being conducted for smart home care services for indoor health management. In this paper, we propose a random forest model that classifies activity type as well as occupancy status through indoor temperature and humidity, CO2, fine dust values and UWB radar positioning for smart home care service. The experiment measures indoor environment and occupant positioning data at 2-second intervals using three sensors that measure indoor temperature and humidity, CO2, and fine dust and two UWB radars. The measured data is divided into 80% training set data and 20% test set data after correcting outliers and missing values, and the random forest model is applied to evaluate the list of important variables, accuracy, sensitivity, and specificity.

• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.641-649
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• 2021

Fuel oil consumption (FOC) must be minimized to determine the economic route of a ship; hence, the ship power must be predicted prior to route planning. For this purpose, a numerical method using test results of a model has been widely used. However, predicting ship power using this method is challenging owing to the uncertainty of the model test. An onboard test should be conducted to solve this problem; however, it requires considerable resources and time. Therefore, in this study, a deep feed-forward neural network (DFN) is used to predict ship power using deep learning methods that involve data pattern recognition. To use data in the DFN, the input data and a label (output of prediction) should be configured. In this study, the input data are configured using ocean environmental data (wave height, wave period, wave direction, wind speed, wind direction, and sea surface temperature) and the ship's operational data (draft, speed, and heading). The ship power is selected as the label. In addition, various treatments have been used to improve the prediction accuracy. First, ocean environmental data related to wind and waves are preprocessed using values relative to the ship's velocity. Second, the structure of the DFN is changed based on the characteristics of the input data. Third, the prediction accuracy is analyzed using a combination comprising five hyperparameters (number of hidden layers, number of hidden nodes, learning rate, dropout, and gradient optimizer). Finally, k-means clustering is performed to analyze the effect of the sea state and ship operational status by categorizing it into several models. The performances of various prediction models are compared and analyzed using the DFN in this study.

• Journal of the Korean Society of Food Culture
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• v.37 no.4
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• pp.335-344
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• 2022

The study was aimed to investigate the operational meal costs by kindergarten size in Seoul and to analyze recognition for optimal meal prices. A survey (31.6% recovery rate) was conducted on all kindergartens (779 kindergartens) in Seoul on April 2021 using descriptive analysis, t-test, and dispersion method. A price sensitivity measurement (psm) method was used to determine optimal meal prices. Result showed an average food cost for kindergartens of 2,647 won, an average labor cost of 605 won, an average operating cost of 146 won, and the total meal cost of 3,506 won. Total meal cost decreased with increasing kindergarten size (p<0.001). On the other hand, kindergartens with more students decreased the ratio of food cost to total meal cost, and operating cost and labor costs (p<0.001) increased. The optimal price of kindergarten operators' meal cost (OPP) was KRW 3,673. Furthermore, the analysis showed the sensitivity of operators' meal costs to kindergarten size was insignificant.