• Journal of Industrial Convergence
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• v.20 no.10
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• pp.137-146
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• 2022

The use of RPA (Robotic Process Automation) has been recently reviewed in various industries, but it seems that it is not being applied to companies faster than ever expected. In this study, three perceived risk factors affecting the acceptance conflict and acceptance intention of RPA technology were proposed and the effects of RPA on acceptance conflict and acceptance intention were investigated using RPA experienced people, gender and ICT industries as control variables. For the research, online survey was conducted targeting office workers and analyzed the results by using SPSS 22.0 and AMOS 22.0. As a result, it was found that among the three perceived risk factors, concern about introduction failure, employment insecurity, and execution errors, employment insecurity and execution errors did not affect the acceptance conflict and acceptance intention of RPA. This research shows that concerns over the introduction failure affected the acceptance conflict and acceptance intention. In addition, the acceptance conflict was judged as a factor of the mediation effect of the acceptance intention. From the perspective of companies that want to apply RPA, the theoretical and practical implications of business management are meaningful in that they can identify and respond to particularly important factors among perceived risks.

• The Journal of Korean Institute for Practical Engineering Education
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• v.3 no.1
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• pp.143-148
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• 2011

In this study, the damage to the bridge structure such as the crack and water leakage was assessed due to the increase of the vehicle load and traffic on the roads. In order to make this into the database, as a part of the automation system development for the bridge maintenance, the students themselves designed and developed their own inspection robotic system based on the idea of robots currently being developed overseas. Its field testing was conducted and its applicability assessed. During the design and fabrication, its connection to the details of the unit course taken in the undergraduate level was focused. In terms of new product development, the field application was possible due to the support of the academic-industrial cooperation firms. Furthermore, through the survey of the students, the improvements in the practical skills of the students who participated in this development process was affirmed.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.392-399
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• 2023

This paper proposes a deep learning model to determine the region and depth of cabbage cores for robotic automation of the cabbage core removal process during the kimchi manufacturing process. In addition, rather than predicting the depth of the measured cabbage, a model was presented that simultaneously detects and classifies the area by converting it into a discrete class. For deep learning model learning and verification, RGB images of the harvested cabbage 522 were obtained. The core region and depth labeling and data augmentation techniques from the acquired images was processed. MAP, IoU, acuity, sensitivity, specificity, and F1-score were selected to evaluate the performance of the proposed YOLO-v4 deep learning model-based cabbage core area detection and classification model. As a result, the mAP and IoU values were 0.97 and 0.91, respectively, and the acuity and F1-score values were 96.2% and 95.5% for depth classification, respectively. Through the results of this study, it was confirmed that the depth information of cabbage can be classified, and that it can be used in the development of a robot-automation system for the cabbage core removal process in the future.

• Journal of Biosystems Engineering
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• v.28 no.6
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• pp.517-524
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• 2003

There have been worldwide research and development efforts to automate various processes of bio-production and those efforts will be expanded with priority given to tasks which require high intensive labor or produce high value-added product and tasks under hostile environment. In the field of bio-production capabilities of the versatility and robustness of automated system have been major bottlenecks along with economical efficiency. This paper introduces a new concept of automation based on tole-operation, which can provide solutions to overcome inherent difficulties in automating bio-production processes. Operator(farmer), computer, and automatic machinery share their roles utilizing their maximum merits to accomplish given tasks successfully. Among processes of greenhouse watermelon cultivation tasks such as pruning, watering, pesticide application, and harvest with loading were chosen based on the required labor intensiveness and functional similarities to realize the proposed concept. The developed system was composed of 5 major hardware modules such as wireless remote monitoring and task control module, wireless remote image acquisition and data transmission module, gantry system equipped with 4 d.o.f. Cartesian type robotic manipulator, exchangeable modular type end-effectors, and guided watermelon loading and storage module. The system was operated through the graphic user interface using touch screen monitor and wireless data communication among operator, computer, and machine. The proposed system showed practical and feasible way of automation in the field of volatile bio-production process.

• Journal of the Institute of Convergence Signal Processing
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• v.15 no.3
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• pp.104-111
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• 2014

In this paper, we deal with robotic automation for assembling car brake modules. A car brake module is comprises of a torque member, two brake pads, and two pad liners. In the assembly process, brake pads and pad liners are needed to be inserted in a torque member. If we use a typical robotic hand for the assembly, task time takes too long. So, we propose two methods. The first method is to use an end effector that has five grippers capable of gripping five assembly parts. In the first method we attached the implemented end effector to a conventional 6 degrees of freedom industrial manipulator and performed the bake module assembly task. Experimental results show that the task time is remarkably reduced. The brake module assembly task needs the robot to change its orientation frequently, so, in the second method, we added one degree of freedom to the end effector that is used in the first method. By attaching it to a conventional 6 degrees of freedom industrial manipulator, we composed a 7 degrees of freedom redundant manipulator. A redundant manipulator has the advantage of flexible manipulation so the robot can change its orientation easily and can perform assembly task very fast. Experimental results show that the second method dramatically reduce whole task time for brake module assembly.

• International Journal of Control, Automation, and Systems
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• v.1 no.1
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• pp.142-148
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• 2003

Reinforcement learning is considered as an important tool for robotic learning in unknown/uncertain environments. In this paper, we propose an evaluation function expressed in a vector form to realize multi-dimensional reinforcement learning. The novel feature of the proposed method is that learning one behavior induces parallel learning of other behaviors though the objectives of each behavior are different. In brief, all behaviors watch other behaviors from a critical point of view. Therefore, in the proposed method, there is cross-criticism and parallel learning that make the multi-dimensional learning process more efficient. By ap-plying the proposed learning method, we carried out multi-dimensional evaluation (reward) and multi-dimensional learning simultaneously in one trial. A special neural network (Q-net), in which the weights and the output are represented by vectors, is proposed to realize a critic net-work for Q-learning. The proposed learning method is applied for behavior planning of mobile robots.

• Journal of Information Technology Services
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• v.20 no.4
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• pp.113-128
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• 2021

In the rapidly developing 4th industrial revolution. RPA is increasing in use at home and abroad due to its advantages of simplifying workflow and providing flexibility and scalability at the same time. Thus, this paper conducted an empirical study on companies using RPA to determine which factors affect the intention to use the services provided by RPA systems. As system characteristics, exogenous variables were selected as information quality, system quality, and service quality of the information system success model. The endogenous variables were selected as the system acceptance factors for the performance and effort expectancy of the integrated technology acceptance model, and the perceived economic values and functional values were additionally selected. For the purpose of this study, a structured questionnaire was used for empirical analysis and the proposed hypothesis was verified through the path analysis of structural equations. As a result of the study, there was no significant relationship between service quality and effort expectancy, between service quality and economic value, and it was verified that the relationship between other factors was positively significant.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.9
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• pp.776-787
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• 2002

Automation of welding process in shipyards is ultimately necessary, since the welding site is spatially enclosed by floors and girders, and therefore welding operators are exposed to hostile working conditions. To solve this problem, a welding mobile robot that can navigate autonomously within the enclosure has been developed. To achieve the welding task in the closed space, the robotic welding system needs a sensor system for the working environment recognition and the weld seam tracking, and a specially designed environment recognition strategy. In this paper, a three-dimensional laser vision system is developed based on the optical triangulation technology in order to provide robots with 3D work environmental map. Using this sensor system, a spatial filter based on neural network technology is designed for extracting the center of laser stripe, and evaluated in various situations. An environment modeling algorithm structure is proposed and tested, which is composed of the laser scanning module for 3D voxel modeling and the plane reconstruction module for mobile robot localization. Finally, an environmental recognition strategy for welding mobile robot is developed in order to recognize the work environments efficiently. The design of the sensor system, the algorithm for sensing the partially structured environment with plane segments, and the recognition strategy and tactics for sensing the work environment are described and discussed with a series of experiments in detail.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.5
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• pp.230-238
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• 2023

Small and medium-sized food manufacturing enterprises are largely reliant on manual labor, from inputting raw materials to palletizing the final product. Recently, there has been a trend toward smartness and digitization through the implementation of robotics and sensor data technology. In this study, we examined the effectiveness of improvement through 3D simulation on two repetitive work processes within a food manufacturing company. These processes involve workers whose speed cannot match the capacity of the applied equipment. Two manual processes were selected: the weighing and packing process performed by workers after skewer assembly, and the manual batch process of counting randomly delivered frozen foods, packing (both internal and external), and palletizing. The production volume, utilization rate, and number of workers were chosen as verification indicators. As a result of the simulation for improving the 3D process, production increased by 13.5% and 56.8% compared to the existing process, respectively. This was particularly evident in the process of applying palletizing robots. In both processes, as the utilization rate and number of input workers decreased, robots could replace tasks with high worker fatigue, thereby reducing work overload. This study demonstrates the potential to visually compare the process flow improvement using 3D simulations and confirms the possibility of pre-validation for improvement.

• Journal of the Korean Geotechnical Society
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• v.37 no.11
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• pp.119-129
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• 2021

The process of constructing the DB in the current geotechnical information DB system needs a lot of human and time resource consumption. In addition, it causes accuracy problems frequently because the current input method is a person viewing the PDF and directly inputting the results. Therefore, this study proposes building an automatic digital DB using AI (artificial intelligence) of boring logs. In order to automatically construct DB for various boring log formats without exception, the boring log forms were classified using the deep learning model ResNet 34 for a total of 6 boring log forms. As a result, the overall accuracy was 99.7, and the ROC_AUC score was 1.0, which separated the boring log forms with very high performance. After that, the text in the PDF is automatically read using the robotic processing automation technique fine-tuned for each form. Furthermore, the general information, strata information, and standard penetration test information were extracted, separated, and saved in the same format provided by the geotechnical information DB system. Finally, the information in the boring log was automatically converted into a DB at a speed of 140 pages per second.