• Journal of Biosystems Engineering
• /
• v.19 no.1
• /
• pp.22-32
• /
• 1994

The combine harvester is considered as an important but complicated and costly machine. The appropriate size of combine has to be developed to use efficiently in Korea. But the combine is such a complicated machine that a complete design model to develop a new type is impossible without understanding the relationship between each factor. The combine capacity is generally limited by the cleaning shoe performance. So a design model for a cleaning shoe has to be developed first for the complete combine design. The objective of this research was to develop a cleaning model of head-feeding combine to predict grain separation from chaff and broken straw on a sieve. A developed physically based model can explain the situation which can happen during separation process. A test apparatus based on the field going machine was developed. The test materials were paddy rice and barley. The data obtained were analyzed by the hand and the video camera. The developed model was verified as an adequate model through the test with $R^2$ of 0.934 and 0.837. The model can be used to evaluate design and operation alternatives of combine and also applied to the automatic control of separation unit of combine with a loss monitering sensors.

• Korean Journal of Adult Nursing
• /
• v.19 no.4
• /
• pp.547-555
• /
• 2007

Purpose: The purpose of this study was to test a hypothetical model of health-related quality of life in patients with heart failure. The hypothetical model was derived from the Wilson and Cleary's model, the Rector's model, and published research findings. Methods: Data from 103 patients with heart failure were analyzed to determine the best multivariate health-related quality of life model given variables derived from the prior studies. The statistics programs SPSS 12.0 and LISREL 8.7 program were used for descriptive statistics and covariance structure analysis respectively. Results: The overall fitness of the path final model was good(GFI=.97, AGFI=.95, NNFI=1.06, NFI=.96, p=.96). Symptoms were directly affected by gender. HYHA Class was directly affected by only gender. Physical functioning limitation was directly affected by exercise. Health perception was directly affected by economics, symptom, and physical functioning limitation. Depression was directly affected by exercise and health perception. Heath-related quality of life was directly affected by physical functioning limitation and depression, indirectly affected by gender, economics, exercise, symptoms, NYHA Class, and health perception. This path analysis model explained 51% of health-related quality of life in patients with heart failure. Conclusion: To improve of health-related quality of life with heart failure patients, it is necessary to make nursing interventions for physical functioning and depression.

• Journal of Institute of Control, Robotics and Systems
• /
• v.18 no.10
• /
• pp.940-946
• /
• 2012

This paper proposes a method to predict maximum traction for unmanned robots on rough terrain in order to improve traversability. For a traction prediction, we use a friction-slip model based on modified Brixius model derived empirically in terramechanics which is a function of mobility number $B_n$ and slip ratio S. A friction-slip model includes characteristics of various rough terrains where robots are operated such as soil, sandy soil and grass-covered soil. Using a friction-slip model, we build a prediction model for terrain parameters on which we can know maximum static friction and optimal slip with respect to mobility number $B_n$. In this paper, Mobility number $B_n$ is estimated by modified Willoughby Sinkage model which is a function of sinkage z and slip ratio S. Therefore, if sinkage z and slip ratio are measured once by sensors such as a laser sensor and a velocity sensor, then mobility number $B_n$ is estimated and maximum traction is predicted through a prediction model for terrain parameters. Estimation results for maximum traction are shown on simulation using MATLAB. Prediction Performance for maximum traction of various terrains is evaluated as high accuracy by analyzing estimation errors.

• Journal of Information Technology Services
• /
• v.5 no.1
• /
• pp.111-128
• /
• 2006

It was on the rise importantly to provide the efficient management process of the organization for dealing with the change about information and business management quickly and consistently. It was suggested with the architectural model on information technology to provide it in theoretically. The Federal Government and budget organization of the USA used it on actual business and the terms of EA (Enterprise Architecture) and are raising the efficiency of management. NCA (National Computerization Agency) of Korea published the book - "The Research about establishing ITA (Information Technology Architecture) and appling the standards". After being applied the model on MOGAHA (Ministry of Government Administration and Home Affairs) and MIC(Minisstry of Information and Communication), the concrete case was made. MOMAF (Ministry of Maritime Affairs and Fisheries) drove the leading model. The report ascertained the basic contents of ITA and researched the case of USA, MOGAHA, MIC, and tried to analyze the contents of appling maritime and fisheries area. The report contained the definition of purpose through analyzing environment and establishing the vision and the principles based on them. The report also contained the contents of architecture based on the standard of NCA - "The Government Standard Meta Model version 2.0" - and researched the MOMAF's Reference model using Government Reference model. The report established the investment architecture and the process of information technology asset management. It ascertained the characteristic of maritime & fisheries area and the subject of developing the MOMAF's ITA sustainably.

• Tribology and Lubricants
• /
• v.39 no.3
• /
• pp.118-122
• /
• 2023

In our study, we develop a finite element model based on Archard's wear law to predict the cumulative wear and the evolution of the tool profile in friction stir welding (FSW) applications. Our model considers the rotational and translational behaviors of the tool, providing a comprehensive description of the wear process. We validate the accuracy of our model by comparing it against experimental results, examining both the predicted cumulative wear and the resulting changes to the tool profile caused by wear. We perform a detailed comparison between the predictions of the model and experimental data by manipulating non-dimensional coefficients comprising model parameters, such as element sizes and time increments. This comparison facilitates the identification of a specific non-dimensional coefficient condition that best replicates the experimentally observed cumulative wear. We also directly compare the worn tool profiles predicted by the model using this specific non-dimensional coefficient condition with the profiles obtained from wear experiments. Through this process, we identify the model settings that yield a tool wear profile closely aligning with the experimental results. Our research demonstrates that carefully selecting non-dimensional coefficients can significantly enhance the predictive accuracy of finite element models for tool wear in FSW processes. The results from our study hold potential implications for enhancing tool longevity and welding quality in industrial applications.

• Nuclear Engineering and Technology
• /
• v.55 no.5
• /
• pp.1924-1934
• /
• 2023

In this study, the probabilistic fatigue life model for Ni-base alloys was developed based on the Weibull distribution using statistical analysis of fatigue data reported in NUREG/CR-6909 and the new fatigue data of Alloy 52M/152 and 82/182. The developed Weibull model can consider right-censored data (i.e., non-failed data) and quantify the improved safety (or reliability) based on the level of failure probability. The overall margin in the current fatigue design limit model (ASME design curve + NUREG/CR-6909 F_en model) is similar to that of the Weibull model with a cumulative failure probability of approximately 2.5%. The margin in the current fatigue design limit model demonstrated inconsistencies for the Ni-base alloy weld data, whereas the Weibull model showed a consistent margin. Therefore, the Weibull model can systematically mitigate the excessive safety margin.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.42 no.2
• /
• pp.444-452
• /
• 2017

Recently, a demand of Internet-of-things (IoT) rises dramatically and an interest in Low Power Wide Area (LPWA) grows larger accordingly. In this paper, performance in LoRa which is included in LPWA standard is analyzed. Particularly, after measuring Received Signal Strength Indication (RSSI) of received signal on Line-of-sight (LoS) and Non-line-of-sight (NLoS) environment and it is compared with RSSI which theoretical path loss model is applied to. Among many path loss models, the simulation for theoretical RSSI use Log-distance, Two-ray model and Okumura-Hata model that is based on the test database. Consequently, the result of Okumura-Hata model is the most similar with the measured RSSI. When a network based on LoRa is built, this result can used to decide optimal node arrangement.

• Journal of Korea Water Resources Association
• /
• v.54 no.3
• /
• pp.157-166
• /
• 2021

Forecasting dam inflow based on high reliability is required for efficient dam operation. In this study, deep learning technique, which is one of the data-driven methods and has been used in many fields of research, was manipulated to predict the dam inflow. The Long Short-Term Memory deep learning with Sequence-to-Sequence model (LSTM-s2s), which provides high performance in predicting time-series data, was applied for forecasting inflow of Soyang River dam. Various statistical metrics or evaluation indicators, including correlation coefficient (CC), Nash-Sutcliffe efficiency coefficient (NSE), percent bias (PBIAS), and error in peak value (PE), were used to evaluate the predictive performance of the model. The result of this study presented that the LSTM-s2s model showed high accuracy in the prediction of dam inflow and also provided good performance for runoff event based runoff prediction. It was found that the deep learning based approach could be used for efficient dam operation for water resource management during wet and dry seasons.

• Smart Media Journal
• /
• v.12 no.2
• /
• pp.66-75
• /
• 2023

Recently, the number of cloud web applications is increasing owing to the accelerated migration of enterprises and public sector information systems to the cloud. Traditional network attacks on cloud web applications are characterized by Denial of Service (DoS) attacks, which consume network resources with a large number of packets. However, HTTP DoS attacks, which consume application resources, are also increasing recently; as such, developing security technologies to prevent them is necessary. In particular, since low-bandwidth HTTP DoS attacks do not consume network resources, they are difficult to identify using traditional security solutions that monitor network metrics. In this paper, we propose a new detection model for detecting HTTP DoS attacks on cloud web applications by collecting the application metrics of web servers and learning them using machine learning. We collected 18 types of application metrics from an Apache web server and used five machine learning and two deep learning models to train the collected data. Further, we confirmed the superiority of the application metrics-based machine learning model by collecting and training 6 additional network metrics and comparing their performance with the proposed models. Among HTTP DoS attacks, we injected the RUDY and HULK attacks, which are low- and high-bandwidth attacks, respectively. As a result of detecting these two attacks using the proposed model, we found out that the F1 scores of the application metrics-based machine learning model were about 0.3 and 0.1 higher than that of the network metrics-based model, respectively.

• Journal of Physiology & Pathology in Korean Medicine
• /
• v.20 no.2
• /
• pp.279-291
• /
• 2006

Cheong-Gang Medical Chart is 60 years worth of diagnosis records kept by Oriental Medicine Doctor Kim Young Hoon [金永勳, 號 晴崗 1882-1974], who held practice in Seoul's Jong-ro from 1915 till 1974. Kim Young Hoon's eldest son, Kim Ki Su (金琦洙) donated the medical records exceeding a thousand volumes to KyungHee University, and researches are being made presently. The author of Cheong-Gang Medical Chart, Kim Young Hoon, was a medical scholar who studied the essence of the traditional medicine of his time. He was handed down the quintessence of traditional medicine by keeping in touch with the prominent oriental doctors in Seoul at that time, and he constantly applied it to his practice and made records of it. Consequently, his diagnosis charts contain a whole new form of prescriptions, treatment skills, and processes of clinical application that have never been seen before in the texts of Korean Medicine. The writer has written a paper on the present condition of Cheong-Gang Medical Chart, which was published in the Journal of Korean Oriental Medicine in 2004. This manuscript reports the results of the test studies made to develop an efficient database model as a prior step to organizing the medical records into a data bank.