• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.830-835
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• 1993

The errors in machining process by CNC machining center are due to many elements, such as the delay of the servo drivers, friction and the gain mismatch between x-axis and y-axis motors and so on. We made a counter circuit to measure the output of motor encoders for the motion error analysis of a CNC machining center, and have measured the errors experimentally when the CNC performs a circular interpolation. We have also used an iterative learning method to reduce the radius errors and stick motion errors generated by the CNC machining center performing a circular interpolation. The proposed learning scheme worked well and the circle obtained has smaller error.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.745-750
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• 2008

Uncertain dynamic parameters and joint flexibility have been problem to control robot manipulator precisely. Hence, even if the controller tracks the desired trajectory well with the feedback of the motor encoders, it is hard to achieve the desired behavior at the end-effector. In this paper, robot trajectory is taught by a general heuristic iterative learning control (ILC) algorithm in order to reduce tracking error of the tool center point (TCP) and the results of tracking with 6 DOF industrial robot manipulator are presented. The performance is verified based on ISO 9283.

• ETRI Journal
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• v.45 no.1
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• pp.93-104
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• 2023

Coreference resolution is a task in discourse analysis that links several headwords used in any document object. We suggest pointer networks-based coreference resolution for Korean using multi-task learning (MTL) with an attention mechanism for a hierarchical structure. As Korean is a head-final language, the head can easily be found. Our model learns the distribution by referring to the same entity position and utilizes a pointer network to conduct coreference resolution depending on the input headword. As the input is a document, the input sequence is very long. Thus, the core idea is to learn the word- and sentence-level distributions in parallel with MTL, while using a shared representation to address the long sequence problem. The suggested technique is used to generate word representations for Korean based on contextual information using pre-trained language models for Korean. In the same experimental conditions, our model performed roughly 1.8% better on CoNLL F1 than previous research without hierarchical structure.

• Journal of Life Science
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• v.19 no.7
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• pp.917-922
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• 2009

The effect of adding docosahexaenoic acid into an n-3 fatty acid adequate diet on the improvement of learning related brain function was investigated. On the second day after conception, Sprague Dawley strain dams were subjected to a diet containing either n-3 fatty acid adequate (Adq, 3.4% linolenic acid) or n-3 fatty acid adequate+docosahexaenoic acid (Adq+DHA, 3.31%linolenic acid plus 9.65% DHA). After weaning, male pups were fed on the same diet of their respective dams until adulthood. Motor activity and Morris water maze tests were measured at 10 weeks. In the motor activity test, there were no statistically significant differences in moving time and moving distance between the Adq and Adq+DHA diet groups. The n-3 fatty acid adequate with DHA (Adq+DHA) group tended to show a shorter escape latency, swimming time and swimming distance compared to the n-3 fatty acid adequate group (Adq), but the differences were not statistically significant. There was no difference in resting time, but the Adq+DHA group showed a higher swimming speed compared to the Adq group. In memory retention trials, the numbers of crossing of the platform position (region A), in which the hidden platform was placed, were significantly greater than those of other regions for both Adq and Adq+DHA groups. Based on these results, adding DHA into the n-3 fatty acid adequate diet from gestation to adulthood tended to induce better spatial learning performance in Sprague Dawley rats as assessed by the Morris water maze test, although the difference was not significant.

• Journal of the Korean Society of Systems Engineering
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• v.15 no.2
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• pp.72-78
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• 2019

Pathology is the motor that drives healthcare to understand diseases. The way pathologists diagnose diseases, which involves manual observation of images under a microscope has been used for the last 150 years, it's time to change. This paper is specifically based on tumor detection using deep learning techniques. Pathologist examine the specimen slides from the specific portion of body (e-g liver, breast, prostate region) and then examine it under the microscope to identify the effected cells among all the normal cells. This process is time consuming and not sufficiently accurate. So, there is a need of a system that can detect tumor automatically in less time. Solution to this problem is computational pathology: an approach to examine tissue data obtained through whole slide imaging using modern image analysis algorithms and to analyze clinically relevant information from these data. Artificial Intelligence models like machine learning and deep learning are used at the molecular levels to generate diagnostic inferences and predictions; and presents this clinically actionable knowledge to pathologist through dynamic and integrated reports. Which enables physicians, laboratory personnel, and other health care system to make the best possible medical decisions. I will discuss the techniques for the automated tumor detection system within the new discipline of computational pathology, which will be useful for the future practice of pathology and, more broadly, medical practice in general.

• Journal of the Korea Safety Management & Science
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• v.23 no.4
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• pp.93-104
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• 2021

In this paper, we propose a method for diagnosing overload and working load of collaborative robots through performance analysis of machine learning algorithms. To this end, an experiment was conducted to perform pick & place operation while changing the payload weight of a cooperative robot with a payload capacity of 10 kg. In this experiment, motor torque, position, and speed data generated from the robot controller were collected, and as a result of t-test and f-test, different characteristics were found for each weight based on a payload of 10 kg. In addition, to predict overload and working load from the collected data, machine learning algorithms such as Neural Network, Decision Tree, Random Forest, and Gradient Boosting models were used for experiments. As a result of the experiment, the neural network with more than 99.6% of explanatory power showed the best performance in prediction and classification. The practical contribution of the proposed study is that it suggests a method to collect data required for analysis from the robot without attaching additional sensors to the collaborative robot and the usefulness of a machine learning algorithm for diagnosing robot overload and working load.

• Physical Therapy Korea
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• v.12 no.3
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• pp.56-66
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• 2005

The purposes of this study were to test that complex motor training enhance motor function significantly, to test change in cerebellum, and to test the synaptic plasticity into the immunohistochemistry response of synaptophysin. Using an animal model of fetal alcohol syndrome - which equates peak blood alcohol concentrations across developmental period - the effects of alcohol on body weight during periods were examined. The effect of complex motor training on motor function and synaptic plasticity of rat exposed alcohol on postnatal days 4 through 10 were studied. Newborn rats were assigned to one of two groups: (1) normal group (NG), via artificial rearing to milk formula and (2) alcohol groups (AG), via 4.5 g/kg/day of ethanol in a milk solution. After completion of the treatments, the pups were fostered back to lactating dams, where they were raised in standard cages (two-and three animals per cage) until they were postnatal 48 days. Rats from alcohol group of postnatal treatment then spent 10 days in one of two groups: Alcohol-experimental group was had got complex motor training (learning traverse a set of 6 elevated obstacles) for 4 weeks. The alcohol-control group was not trained. Before consider replacing with "the experiment/study", (avoid using "got" in writing) the rats were examined during four behavioral tests and their body weights were measured, then their coronal sections were processed in rabbit polyclonal antibody synaptophysin. The synaptophysin expression in the cerebellar cortex was investigated using a light microscope. The results of this study were as follows: 1. The alcohol groups contained significantly higher alcohol concentrations than the normal group. 2. The alcohol groups had significantly lower body weights than the normal group. 3. In alcohol groups performed significantly lower than the normal group on the motor behavioral test. 4. In alcohol-control group showed significantly decreased immunohistochemistric response of the synaptophysin in the cerebellar cortex compared to the nomal group. These results suggest that improved motor function induced by complex motor training after postnatal exposure is associated with dynamically altered expression of synaptophysin in cerebellar cortex and that is related with synaptic plasticity. Also, these data can potentially serve as a model for therapeutic intervention.

• The Transactions of the Korean Institute of Power Electronics
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• v.11 no.1
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• pp.29-37
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• 2006

Precise control of interior permanent magnet synchronous motor(IPMSM) over wide speed range is an engineering challenge. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using learning mechanism-fuzzy neural network(LM-FNN) and ANN(artificial neural network) control. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility md numerical processing capability. Also, this paper proposes speed control of IPMSM using LM-FNN and estimation of speed using artificial neural network controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. 'The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. Analysis results to verify the effectiveness of the new hybrid intelligent control proposed in this paper.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.1
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• pp.123-133
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• 1998

In this paper, an algorithm of fault detection and diagnosis during operation of induction motors under the condition of various loads and rates is investigated. For this purpose, the spectrum pattern of input currents is used in monitoring the state of induction motors, and by clustering the spectrum pattern of input currents, the newly occurrence of spectrum patterns caused by faults are detected. For the diagnosis of the fault detected, a fuzzy fault tree is designed, and the fuzzy relation equation representing the relation between an induction motor fault and each fault type, is solved. The solution of the fuzzy relation equation shows the possibility of occurence of each fault. The results obtained are summarized as follows : (1) Using clustering algorithm by unsupervised learning, an on-line fault detection method unaffected by the characteristics of loads and rates is implemented, and the degree of dependency for experts during fault detection is reduced. (2) With the fuzzy fault tree, the fault diagnosis process become systematic and expandable to the whole system, and the diagnosis for sub-systems can be made as an object-oriented module.

• Journal of Internet Computing and Services
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• v.22 no.3
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• pp.67-73
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• 2021

In this paper, in order to prevent the fall of the drone, a study was conducted to collect vibration data from the motor connected to the propeller of the drone, and to predict the abnormal vibration of the drone using recurrent neural network (RNN) and long short term memory (LSTM). In order to collect the vibration data of the drone, a vibration sensor is attached to the motor connected to the propeller of the drone to collect vibration data on normal, bar damage, rotor damage, and shaft deflection, and abnormal vibration data are collected through LSTM and RNN. The root mean square error (RMSE) value of the vibration prediction result were compared and analyzed. As a result of the comparative simulation, it was confirmed that both the predicted result through RNN and LSTM predicted the abnormal vibration pattern very accurately. However, the vibration predicted by the LSTM was found to be 15.4% lower on average than the vibration predicted by the RNN.