• The Journal of Asian Finance, Economics and Business
• /
• v.8 no.4
• /
• pp.1021-1032
• /
• 2021

This research identifies the main factors of the psychological climate that directly affect the performance of banking employees in Vietnam. Besides, this research also takes into consideration the differences in gender, age, educational level, and income on working performance. A survey was obtained from 207 employees working at joint-stock commercial banks and the analysis was handled with SPSS 20 software supports. The result shows that the measurement scales meet the requirements of validity and reliability. Regression analysis demonstrates that there are four factors directly affecting the working performance: friendliness, personal development and learning opportunities, straight and open communication, and the support from the senior management. These four factors have created a healthy psychological climate in the banks, where employees will feel comfortable and happy to improve work performance. Furthermore, this research has found that the higher the income, the more efficiently employees will work. The results of this research contribute to the measurement scale of working environment factors. At the same time, this research also proposes some recommendations for organizational managers to build a reasonable working environment that can inspire a sense of mental comfort for employees to work at their full capacity and to achieve the highest performance.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.21 no.1
• /
• pp.291-299
• /
• 1996

A new identification method using a higher order multilayer neural network is proposed for identifying a complex dynamic system such as a robotic manipulator. The input torque data for learning of the neural identifier are generated for producing effective output trajectories by a minimization process of a specific performance index function which indicates the difference between the reference points and the present joint positions and their velocities of the robotic manipulator. Computer simulation results show that the proposed identification method is very effective for identifying the systems with complex dynamics and large moment of inertia.

• Journal of the Korean Society of Manufacturing Technology Engineers
• /
• v.8 no.2
• /
• pp.108-115
• /
• 1999

This paper proposes the robot inverse calibration method using a neural networks. A highorder networks called Pi-Sigma networks has been used. The Pi-Sigma networks uses linear summing units in the hidden layer and product unit in output layer. The inverse calibration model which compensates the difference of joint variables only between measuring value and analytic value about the desired pose(position orientation) of a robot is proposed. The compensated values are determined by using the weights obtained from the learning process of the neural networks previously. To prove the reasonableness the SCARA type direct drive robot(4-DOF) and anthropomorphic robot(6-DOF) are simulated. It shows that the proposed calibration method can reduce the errors of the joint variables from $\pm$3 to $\pm$0.1.

• Journal of Korean Institute of Industrial Engineers
• /
• v.25 no.3
• /
• pp.382-392
• /
• 1999

Industrial robots have increased in both the number and applications in today's material handling systems. However, traditional approaches to robot controling have had limited success in complicated environment, especially for real time applications. One of the main reasons for this is that most traditional methods use a set of kinematic equations to figure out the physical environment of the robot. In this paper, a neural network model to solve robot manipulator's inverse kinematics problem is suggested. It is composed of two Self-Organizing Feature Maps by which the workspace of robot environment and the joint space of robot manipulator is inter-linked to enable the learning of the inverse kinematic relationship between workspace and joint space. The proposed model has been simulated with two robot manipulators, one, consisting of 2 links in 2-dimensional workspace and the other, consisting of 3 links in 2-dimensional workspace, and the performance has been tested by accuracy of the manipulator's positioning and the response time.

• Journal of the Korean Society for Precision Engineering
• /
• v.16 no.7
• /
• pp.66-72
• /
• 1999

Robot calibration is very important to improve the accuracy of robot manipulators. However, the calibration procedure is very time consuming and laborious work for users. In this paper, we propose a method of relative error compensation to make the calibration procedure easier. The method is completed by a Pi-Sigma network architecture which has sufficient capability to approximate the relative relationship between the accuracy compensations and robot configurations while maintaining an efficient network learning ability. By experiment of 4-DOF SCARA robot, KIRO-3, it is shown that both the error of joint angles and the positioning error of end effector are drop to 15$\%$. These results are similar to those of other calibration methods, but the number of measurement is remarkably decreased by the suggested compensation method.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 1999.10a
• /
• pp.131-136
• /
• 1999

This paper proposes the robot inverse calibration method using a neural networks. A highorder networks called Pi-Sigma networks has been used. The Pi-Sigma networks uses linear summing units in the hidden layer and product unit in output layer. The inverse calibration model which compensates the difference of joint variables only between measuring value and analytic value about the desired pose(position, orientation) of a robot is proposed. The compensated values are determined by using the weights obtained from the learning process of the neural networks previously. To prove the reasonableness, the SCARA type direct drive robot(4-DOF) and anthropomorphic robot(6-DOF) are simulated. It shows that the proposed calibration method can reduce the errors of the joint variables from $\pm$2$^{\circ}$to $\pm$ 0.1$^{\circ}$.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 1999.05a
• /
• pp.199-204
• /
• 1999

The robot inverse calibration method using a neural networks is proposed in this paper. A high-order networks has been used in this study. The Pi-Sigma networks uses linear summing units in the hidden layer and product unit in output layer. The inverse calibration model which compensates the difference of joint variables only between measuring value and analytic value about the desired pose(position orientation) of a robot is proposed. The compensated values are determined by using the weights obtained from the learning process of the neural networks previously. To prove the reasonableness, the selected compliance automatic robot arm type direct drive robot and anthropomorphic robot are simulated. It shows that the proposed calibration method can reduce the errors of the joint variables from ${\pm}$0.15$^{\circ}$to ${\pm}$0.12$^{\circ}$.

• Korean Journal of Radiology
• /
• v.22 no.4
• /
• pp.612-623
• /
• 2021

Objective: To evaluate the diagnostic performance of a deep learning algorithm for the automated detection of developmental dysplasia of the hip (DDH) on anteroposterior (AP) radiographs. Materials and Methods: Of 2601 hip AP radiographs, 5076 cropped unilateral hip joint images were used to construct a dataset that was further divided into training (80%), validation (10%), or test sets (10%). Three radiologists were asked to label the hip images as normal or DDH. To investigate the diagnostic performance of the deep learning algorithm, we calculated the receiver operating characteristics (ROC), precision-recall curve (PRC) plots, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) and compared them with the performance of radiologists with different levels of experience. Results: The area under the ROC plot generated by the deep learning algorithm and radiologists was 0.988 and 0.988-0.919, respectively. The area under the PRC plot generated by the deep learning algorithm and radiologists was 0.973 and 0.618-0.958, respectively. The sensitivity, specificity, PPV, and NPV of the proposed deep learning algorithm were 98.0, 98.1, 84.5, and 99.8%, respectively. There was no significant difference in the diagnosis of DDH by the algorithm and the radiologist with experience in pediatric radiology (p = 0.180). However, the proposed model showed higher sensitivity, specificity, and PPV, compared to the radiologist without experience in pediatric radiology (p < 0.001). Conclusion: The proposed deep learning algorithm provided an accurate diagnosis of DDH on hip radiographs, which was comparable to the diagnosis by an experienced radiologist.

• KIPS Transactions on Software and Data Engineering
• /
• v.8 no.12
• /
• pp.491-498
• /
• 2019

Digital twin is a technology that virtualizes physical objects of the real world on a computer. It is used by collecting sensor data through IoT, and using the collected data to connect physical objects and virtual objects in both directions. It has an advantage of minimizing risk by tuning an operation of virtual model through simulation and responding to varying environment by exploiting experiments in advance. Recently, artificial intelligence and machine learning technologies have been attracting attention, so that tendency to virtualize a behavior of physical objects, observe virtual models, and apply various scenarios is increasing. In particular, recognition of each robot's motion is needed to build digital twin for co-robot which is a heart of industry 4.0 factory automation. Compared with modeling based research for recognizing motion of co-robot, there are few attempts to predict motion based on sensor data. Therefore, in this paper, an experimental environment for collecting current and inertia data in co-robot to detect the motion of the robot is built, and a motion prediction model based on the collected sensor data is proposed. The proposed method classifies the co-robot's motion commands into 9 types based on joint position and uses current and inertial sensor values to predict them by accumulated learning. The data used for accumulating learning is the sensor values that are collected when the co-robot operates with margin in input parameters of the motion commands. Through this, the model is constructed to predict not only the nine movements along the same path but also the movements along the similar path. As a result of learning using SVM, the accuracy, precision, and recall factors of the model were evaluated as 97% on average.

• Journal of The Korean Association For Science Education
• /
• v.38 no.2
• /
• pp.147-160
• /
• 2018

The purpose of this study is to construct a Teachers' Learning Community (TLC) with three teachers from the same school and to develop a joint teaching plan for students through the TLC. The conclusions from this study are as follows: The TLC with the same grade helps teachers to implement STEAM classes, where teachers overcame difficulties of STEAM lessons and successfully implemented them. Teachers in this study expressed difficulties of STEAM lessons including lack of time, difficulties of STEAM lesson implementation, and difficulties of developing a good STEAM lesson. Teachers worked together to develop a common teaching plan, to overcome the burden of teaching, and to plan better lessons through discussions and cross-checking. In addition, teachers newly discovered difficulties of lesson implementation as they watch each other teaching using a joint lesson plan. Teachers will conduct a better lesson as they improve these difficulties, where a better lesson means having students reach learning goals and learn from the lesson. Teachers in TLC felt that their lesson improved and they themselves growing through a series of courses of watching and learning each other's lessons.