• International Journal of Control, Automation, and Systems
• /
• v.6 no.3
• /
• pp.453-459
• /
• 2008

The neural network is currently being used throughout numerous control system fields. However, it is not easy to obtain an input-output pattern when the neural network is used for the system of a single feedback controller and it is difficult to obtain satisfactory performance with when the load changes rapidly or disturbance is applied. To resolve these problems, this paper proposes a new mode to implement a neural network controller by installing a real object for control and an algorithm for this, which can replace the existing method of implementing a neural network controller by utilizing activation function at the output node. The real plant object for controlling of this mode implements a simple neural network controller replacing the activation function and provides the error back propagation path to calculate the error at the output node. As the controller is designed using a simple structure neural network, the input-output pattern problem is solved naturally and real-time learning becomes possible through the general error back propagation algorithm. The new algorithm applied neural network controller gives excellent performance for initial and tracking response and shows a robust performance for rapid load change and disturbance, in which the permissible error surpasses the range border. The effect of the proposed control algorithm was verified in a test that controlled the speed of a motor equipped with a high speed computing capable DSP on which the proposed algorithm was loaded.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 2002.11a
• /
• pp.596-602
• /
• 2002

An easy elevator for learning originated is opened to compare the existed learning equipment, and it had a high studying efficient that the sequence control circuit can opens and closes with the wire. The structure of equipment to be controlled from the first floor to the fifth floors is demonstrated a constructive apparatus by a lamp atc to express the function of the open-close of the door according to the cage moving with a mechanical actuation of the forward-reverse breaker and the motor of load and a mechanical actuation of hand-operation control components of push-button S/W and L/S and relay etc. These components let connects each other in order to control of the elevator function with the auto program and the designed sequence control circuit. Consequent1y the process of these functions of 1~5steps could operates the cage with an auto program of the elevator and the sequence control circuit. The sequence control circuit is controlled by the step of forward and reverse to follow as that the sensor function of the L/S1~L/S5 let posit with the control switchs of S/W1~S/W5 of PLC testing panel and switchs of S/W1~S/W5 installed on the transparent acryl plate of the frame. In here, improved apparatus is a hand-auto operation combined learning equipment to study the principle and a technique of the originated sequence control circuit and the auto program of PLC.

• Journal of the Korean Society of Physical Medicine
• /
• v.4 no.2
• /
• pp.93-100
• /
• 2009

Purpose：The purpose of this study was to examine the effects of self-controlled knowledge of result (KR) versus the yoked KR on learning of knee joint proprioception. Methods：Forty volunteer subjects (20 men and 20 women) were randomly assigned to each four groups: 1) self-controlled KR in open kinematic chain, 2) yoked KR in open kinematic chain, 3) self controlled KR in close kinematic chain, and 4) yoked KR in close kinematic chain. The difference between the angle of position and reproduction angle was determined as a proprioception error and measured using an angle reproduction test. The subjects in self-controlled groups were provided with feedback whenever they requested it, whereas the subjects in yoked groups were not provided with feedback. The data were analyzed using a one-way ANOVA. Results：The proprioception errors in close kinematic chain groups decreased significantly compared with those in close kinematic chain groups(p<.05). The proprioception errors in the self-controlled group decreased significantly compared with those in yoked groups during acquisition and retention test(p<.05). Conclusion：Self-controlled knowledge of result during open kinematic chain movement is considered to be a good method on motor learning.

• Korean Journal of Childcare and Education
• /
• v.17 no.5
• /
• pp.83-103
• /
• 2021

Objective: This study aimed to develop a longitudinal predictive model that identifies first-grade children who are at risk for ADHD and to investigate the factors that predict the probability of belonging to the at-risk group for ADHD by using machine learning. Methods: The data of 1,445 first-grade children from the 1st, 3rd, 6th, 7th, and 8th waves of the Korean Children's Panel were analyzed. The output factors were the at-risk and non-risk group for ADHD divided by the CBCL DSM-ADHD scale. Prenatal as well as developmental factors during infancy and early childhood were used as input factors. Results: The model that best classifies the at-risk and the non-risk group for ADHD was the LASSO model. The input factors which increased the probability of being in the at-risk group for ADHD were temperament of negative emotionality, communication abilities, gross motor skills, social competences, and academic readiness. Conclusion/Implications: The outcomes indicate that children who showed specific risk indicators during infancy and early childhood are likely to be classified as being at risk for ADHD when entering elementary schools. The results may enable parents and clinicians to identify children with ADHD early by observing early signs and thus provide interventions as early as possible.

• Safety and Health at Work
• /
• v.10 no.1
• /
• pp.67-74
• /
• 2019

Background: Few studies have examined the effects of a forward rotating shift pattern on police employee performance and well-being. This study sought to compare sleep duration, cognitive performance, and vigilance at the start and end of each shift within a three-shift, forward rotating shift pattern, common in United Kingdom police forces. Methods: Twenty-three police employee participants were recruited from North Yorkshire Police (mean age, 43 years). The participants were all working the same, 10-day, forward rotating shift pattern. No other exclusion criteria were stipulated. Sleep data were gathered using both actigraphy and self-reported methods; cognitive performance and vigilance were assessed using a customized test battery, comprising five tests: motor praxis task, visual object learning task, NBACK, digital symbol substitution task, and psychomotor vigilance test. Statistical comparisons were conducted, taking into account the shift type, shift number, and the start and end of each shift worked. Results: Sleep duration was found to be significantly reduced after night shifts. Results showed a significant main effect of shift type in the visual object learning task and NBACK task and also a significant main effect of start/end in the digital symbol substitution task, along with a number of significant interactions. Conclusion: The results of the tests indicated that learning and practice effects may have an effect on results of some of the tests. However, it is also possible that due to the fast rotating nature of the shift pattern, participants did not adjust to any particular shift; hence, their performance in the cognitive and vigilance tests did not suffer significantly as a result of this particular shift pattern.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.1
• /
• pp.36-43
• /
• 2021

In this research, the accuracy of YOLO v3 algorithm in object detection during AGV (Automated Guided Vehicle) operation was investigated. First of all, AGV with 2D LiDAR and stereo camera was prepared. AGV was driven along the route scanned with SLAM (Simultaneous Localization and Mapping) using 2D LiDAR while front objects were detected through stereo camera. In order to evaluate the accuracy of YOLO v3 algorithm, recall, AP (Average Precision), and mAP (mean Average Precision) of the algorithm were measured with a degree of machine learning. Experimental results show that mAP, precision, and recall are improved by 10%, 6.8%, and 16.4%, respectively, when YOLO v3 is fitted with 4000 training dataset and 500 testing dataset which were collected through online search and is trained additionally with 1200 dataset collected from the stereo camera on AGV.

• Journal of IKEEE
• /
• v.26 no.3
• /
• pp.488-493
• /
• 2022

In manufacturing sites, bearing fault in eletrically driven motors cause the entire system to shut down. Stopping the operation of this environment causes huge losses in time and money. The reason of this bearing defects can be various factors such as wear due to continuous contact of rotating elements, excessive load addition, and operating environment. In this paper, a motor driving environment is created which is similar to the domestic manufacturing sites. In addition, based on the established environment, we propose a dataset for bearing fault detection by collecting changes in vibration characteristics that vary depending on normal and defective conditions. The sensor used to collect the vibration characteristics is Microphone G.R.A.S. 40PH-10. We used various machine learning models to build a prototype bearing fault detection system trained on the proposed dataset. As the result, based on the deep neural network model, it shows high accuracy performance of 92.3% in the time domain and 98.3% in the frequency domain.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
• /
• v.9 no.2
• /
• pp.165-173
• /
• 1998

Objectives：This study was conducted to compare the cognitive characteristics and visual-motor coordination ability of children with Asperger’s disorder and with those of children with PDD NOS. Methods：27 children(13 in AS group and 14 in PDD NOS group) were individually assessed using the K-WISC and BGT, and the results of those tests were analyzed. Results：The mean FSIQ of the AS group was significantly higher than that of the PDD NOS group. There was also a large discrepancy between VIQ and PIQ in the PDD NOS, while there was not significant discrepancy in the AS. The AS was distinguished from PDD NOS group by significantly higher scores in Vocabulary and Comprehension subscales and lower score in Block design. Also, when compared with the PDD NOS, the AS showed more difficulties in visual-motor coordination. Conclusion：The AS showed relatively good verbal and learning ability, while the PDD NOS relatively superior ability in visuospatial function and visual-motor coordination. The findings indicated that the K-WISC and BGT might be useful assessment tool to differentiate the AS from PDD NOS.

• The Journal of the Acoustical Society of Korea
• /
• v.30 no.8
• /
• pp.446-460
• /
• 2011

The use of induction motors has been recently increasing with automation in aeronautical and automotive industries, and it playes a significant role. This has motivated that many researchers have studied on developing fault detection and classification systems of an induction motor in order to minimize economical damage caused by its fault. With this reason, this paper proposed feature vector extraction methods based on STE (short-time energy)+SVD (singular value decomposition) and DCT (discrete cosine transform)+SVD techniques to early detect and diagnose faults of induction motors, and classified faults of an induction motor into different types of them by using extracted features as inputs of BPNN (back propagation neural network) and multi-layer SVM (support vector machine). When BPNN and multi-lay SVM are used as classifiers for fault classification, there are many settings that affect classification performance: the number of input layers, the number of hidden layers and learning algorithms for BPNN, and standard deviation values of Gaussian radial basis function for multi-layer SVM. Therefore, this paper quantitatively simulated to find appropriate settings for those classifiers yielding higher classification performance than others.

• Proceedings of the KIEE Conference
• /
• 2006.10d
• /
• pp.47-49
• /
• 2006

This paper is proposed an efficiency optimization control algorithm for a synchronous reluctance motor which minimizes the copper and iron losses. The design of the speed controller based on adaptive learning mechanism-fuzzy neural networks(ALM-FNN) controller that is implemented using adaptive, fuzzy control and neural networks. The control performance of the hybrid artificial intelligent controller is evaluated by analysis for various operating conditions. Analysis results are presented to show the validity of the proposed algorithm.