• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.39 no.11
• /
• pp.1123-1129
• /
• 2015

A pair of two-way valves typically is used in automotive washing machines, where the water flow direction is frequently reversed and highly pressurized clean water is sprayed to remove the oil and dirt remaining on machined engine and transmission blocks. Although this valve system has been widely used because of its competitive price, its application is sometimes restricted by surging effects, such as pressure ripples occurring in rapid changes in water flow caused by inaccurate valve control. As an alternative, one three-way reversing valve can replace the valve system because it provides rapid and accurate changes to the water flow direction without any precise control device. However, a cavitation effect occurs because of the complicated bottom plug shape of the valve. In this study, the cavitation index and percent of cavitation (POC) were introduced to numerically evaluate fluid flows via computational fluid dynamics (CFD) analysis. To reduce the cavitation effect generated by the bottom plug, the optimal shape design was carried out through a parametric study, in which a simple computer-aided engineering (CAE) model was applied to avoid time-consuming CFD analysis and difficulties in achieving convergence. The optimal shape design process using full factorial design of experiments (DOEs) and an artificial neural network meta-model yielded the optimal waist and tail length of the bottom plug with a POC value of less than 30%, which meets the requirement of no cavitation occurrence. The optimal waist length, tail length and POC value were found to 6.42 mm, 6.96 mm and 27%, respectively.

• Journal of Welding and Joining
• /
• v.32 no.6
• /
• pp.8-13
• /
• 2014

One of the main challenges of the automatic arc welding process which has been widely used in various constructions such as steel structures, bridges, autos, motorcycles, construction machinery, ships, offshore structures, pressure vessels, and pipelines is to create specific welding knowledge and techniques with high quality and productivity of the production-based industry. Commercially available automated arc welding systems use simple control techniques that focus on linear system models with a small subset of the larger set of welding parameters, thereby limiting the number of applications that can be automated. However, the correlations of welding parameters and bead geometry as welding quality have mostly been linked by a trial and error method to adjust the welding parameters. In addition, the systematic correlation between these parameters have not been identified yet. To solve such problems, a new or modified models to determine the welding parameters for tandem GMA (Gas Metal Arc) welding process is required. In this study, A new predictive model called STACO model, has been proposed. Based on the experimental results, STACO model was developed with the help of a standard statistical package program, MINITAB software and MATLAB software. Cross-comparative analysis has been applied to verify the reliability of the developed model.

• Journal of Broadcast Engineering
• /
• v.10 no.1 s.26
• /
• pp.83-102
• /
• 2005

The problem of video scheduling is analyzed in the framework of divisible load scheduling. A divisible load can be divided into any number of fractions (parts) and can be processed/computed independently on the processors in a distributed computing system/network, as there are no precedence relationships. In the video scheduling, a frame can be split into any number of fractions (tiles) and can be processed independently on the processors in the network, and then the results are collected to recompose the single processed frame. The divisible load arrives at one of the processors in the network (root processor) and the results of the computation are collected and stored in the same processor. In this problem communication delay plays an important role. Communication delay is the time to send/distribute the load fractions to other processors in the network. and the time to collect the results of computation from other processors by the root processors. The objective in this scheduling problem is that of obtaining the load fractions assigned to each processor in the network such that the processing time of the entire load is a minimum. We derive closed-form expression for the processing time by taking Into consideration the communication delay in the load distribution process and the communication delay In the result collection process. Using this closed-form expression, we also obtain the optimal number of processors that are required to solve this scheduling problem. This scheduling problem is formulated as a linear pro-gramming problem and its solution using neural network is also presented. Numerical examples are presented for ease of understanding.

• Journal of Advanced Navigation Technology
• /
• v.21 no.2
• /
• pp.206-211
• /
• 2017

Recently computing devices by connecting to a network, grid computing, the next generation of digital neural networks that provide maximum service will connect all of the computer such as a PC or server, PDA into one giant network makes the virtual machine. Therefore, we propose the grid computing implementation model to be applied to meteorological business field as follows. First, grid computing will be used for tasks such as the development of numerical models below the mid-scale or test operations, and the final backup of the weather supercomputer. Second, the resources that will constitute grid computing are limited to business PCs and Linux servers operated by the central government considering operational efficiency. Third, the network is restricted to the LAN section, which suggests the implementation of high performance computing.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
• /
• v.8 no.2
• /
• pp.217-231
• /
• 1997

This paper first reviewed the current neurological theories concerning the etiology of ADHD and secondly, examined results of studies that applied neuropsychological assessment methods in the examination of ADHD children both here in Korea and abroad. ADHD children were found to exhibit characteristic responses indicating deficits in vigilance, sustained attention, distractibility, allocation and regulation of attention in many assessments of attention, in addition to deficits in executive functioning, working and associative memory. Such neuropsychological assessment results suggest that in addition to dysfunction in the frontal lobe and the reticular activation system, dysfunction may exist in other neural pathways involving many areas of the brain. However, because a substantial number of neuropsychological assessment tools being employed in Korea for ADHD children had been developed abroad, a Korean standardization project involving ADHD and normal control children, in addition to other child psychiatric population pools must be conducted in order to obtain appropriate age norms and test validity, and in order to make possible a more accurate and precise comparison and interpretation in the assessment of ADHD children.

• Journal of Korean Society of Water and Wastewater
• /
• v.25 no.4
• /
• pp.581-589
• /
• 2011

Generally, treated water or raw water is transported into storage reservoirs which are receiving facilities of local governments from multi-regional water supply systems. A water supply control and operation center is operated not only to manage the water facilities more economically and efficiently but also to mitigate the shortage of water resources due to the increase in water consumption. To achieve the goal, important information such as the flow-rate in the systems, water levels of storage reservoirs or tanks, and pump-operation schedule should be considered based on the resonable water demand forecasting. However, it is difficult to acquire the pattern of water demand used in local government, since the operating information is not shared between multi-regional and local water systems. The pattern of water demand is irregular and unpredictable. Also, additional changes such as an abrupt accident and frequent changes of electric power rates could occur. Consequently, it is not easy to forecast accurate water demands. Therefore, it is necessary to introduce a short-term water demands forecasting and to develop an application of the forecasting models. In this study, the forecasting simulator for water demand is developed based on mathematical and neural network methods as linear and non-linear models to implement the optimal water demands forecasting. It is shown that MLP(Multi-Layered Perceptron) and ANFIS(Adaptive Neuro-Fuzzy Inference System) can be applied to obtain better forecasting results in multi-regional water supply systems with a large scale and local water supply systems with small or medium scale than conventional methods, respectively.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.12 no.3
• /
• pp.202-209
• /
• 2002

This paper deals with simplification of classification rules for data mining and rule bases for control systems. Datamining that extracts useful information from such a large amount of data is one of important issues. There are various ways in classification methodologies for data mining such as the decision trees and neural networks, but the result should be explicit and understandable and the classification rules be short and clear. The rough sets theory is an effective technique in extracting knowledge from incomplete and inconsistent data and provides a good solution for classification and approximation by using various attributes effectively This paper investigates granularity of knowledge for reasoning of uncertain concopts by using rough set approximations and uses a hierarchical classification structure that is more effective technique for classification by applying core to upper level. The proposed classification methodology makes analysis of an information system eary and generates minimal classification rules.

• Parasites, Hosts and Diseases
• /
• v.58 no.4
• /
• pp.461-466
• /
• 2020

Toxoplasma gondii is an obligate intracellular protozoan parasite that can invade various organs in the host body, including the central nervous system. Chronic intracranial T. gondii is known to be associated with neuroprotection against neurodegenerative diseases through interaction with host brain cells in various ways. The present study investigated the neuroprotective effects of chronic T. gondii infection in mice with cerebral ischemia experimentally produced by middle cerebral artery occlusion (MCAO) surgery. The neurobehavioral effects of cerebral ischemia were assessed by measurement of Garcia score and Rotarod behavior tests. The volume of brain ischemia was measured by triphenyltetrazolium chloride staining. The expression levels of related genes and proteins were determined. After cerebral ischemia, corrected infarction volume was significantly reduced in T. gondii infected mice, and their neurobehavioral function was significantly better than that of the uninfection control group. Chronic T. gondii infection induced the expression of hypoxia-inducible factor 1-alpha (HIF-1α) in the brain before MCAO. T. gondii infection also increased the expression of vascular endothelial growth factor after the cerebral ischemia. It is suggested that chronic intracerebral infection of T. gondii may be a potential preconditioning strategy to reduce neural deficits associated with cerebral ischemia and induce brain ischemic tolerance through the regulation of HIF-1α expression.

• Safety and Health at Work
• /
• v.2 no.4
• /
• pp.336-347
• /
• 2011

Objectives: In Korea, an average of 258 workers claim compensation for their noise-induced hearing loss (NIHL) on an annual basis. Indeed, hearing disorder ranks first in the number of diagnoses made by occupational medical check-ups. Against this backdrop, this study analyzed the impact of 19 types of noise-generating machines and equipment on the sound pressure levels in workplaces and NIHL occurrence based on a 2009 national survey on work environments. Methods: Through this analysis, a series of statistical models were built to determine posterior probabilities for each worksite with an aim to present risk ratings for noise levels at work. Results: It was found that air compressors and grinding machines came in first and second, respectively in the number of installed noise-generating machines and equipment. However, there was no direct relationship between workplace noise and NIHL among workers since noise-control equipment and protective gear had been in place. By building a logistic regression model and neural network, statistical models were set to identify the influence of the noise-generating machines and equipment on workplace noise levels and NIHL occurrence. Conclusion: This study offered NIHL prevention measures which are fit for the worksites in each risk grade.

• Korean Journal of Agricultural Science
• /
• v.46 no.2
• /
• pp.381-393
• /
• 2019

A smart farm is a system that combines information and communication technology (ICT), internet of things (IoT), and agricultural technology that enable a farm to operate with minimal labor and to automatically control of a greenhouse environment. Machine learning based on recently data-driven techniques has emerged with big data technologies and high-performance computing to create opportunities to quantify data intensive processes in agricultural operational environments. This paper presents research on the application of machine learning technology to diagnose the growth status of crops and predicting the harvest time of strawberries in a greenhouse according to image processing techniques. To classify the growth stages of the strawberries, we used object inference and detection with machine learning model based on deep learning neural networks and TensorFlow. The classification accuracy was compared based on the training data volume and training epoch. As a result, it was able to classify with an accuracy of over 90% with 200 training images and 8,000 training steps. The detection and classification of the strawberry maturities could be identified with an accuracy of over 90% at the mature and over mature stages of the strawberries. Concurrently, the experimental results are promising, and they show that this approach can be applied to develop a machine learning model for predicting the strawberry harvesting time and can be used to provide key decision support information to both farmers and policy makers about optimal harvest times and harvest planning.