• Journal of the Korean Society of Manufacturing Process Engineers
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• v.21 no.4
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• pp.60-69
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• 2022

To keep pace with Industry 4.0, it is imperative for companies to redesign their working environments by adopting robotic automation systems. Automation lines are facilitating the latest cutting-edge technologies, such as 3D vision and industrial robots, to outdo competitors by reducing costs. Considering the nature of the manufacturing industry, a time-saving workflow and smooth linkwork between processes is vital. At Dellics, without any additional new installation in the automation lines, only a few improvements to the working process could raise productivity. Three requirements are the development of gripping technology by utilizing a 3D vision system for the recognition of the material shape and location, research on lighting projectors to target long distances and high illumination, and testing of algorithms/software to improve measurement accuracy and identify products. With some of the functional requisites mentioned above, improved robotic automation systems should provide an improved working environment to maximize overall production efficiency. In this article, the ways in which such a system can become the groundwork for establishing an unmanned working infrastructure are discussed.

• Journal of the Korea Safety Management & Science
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• v.24 no.4
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• pp.85-93
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• 2022

Today's customer service providers, who have the greatest impact on customer satisfaction, are experiencing severe stress and job burnout due to various causes. Unlike general companies, the corporation has a relatively high level of dissatisfaction with customer service since there is a large conflict between the provision of kindness and the reasonable handling of civil complaints according to laws and regulations. In order to analyze the environment of the NPS' customer service providers, 5.583 branch employees working at the National Pension Service and 407 call center employees were surveyed online using the questionnaire function of the Enterprise resource planning system. The contents of the survey consisted of a survey on customer-facing employees, the level of awareness of customer-facing workers protection measures, and opinions on improvement and supplementation related to customer-facing workers protection measures. As a result of the survey, 72.8% of the total respondents experienced grievance complaints, and the proportion of call center employees was even higher at 89.0%. In addition, both the branch and the call center had the largest share of complaints about obstruction of business, unreasonable demands, abusive language, and verbal abuse. More than 40% of call center employees in their 20s and 30s experienced the highest frequency of complaints 13 or more times a year. The most difficult thing in the process of responding to complaints was that both branch offices and call centers had insufficient psychological recovery time, lack of space, and lack of help from colleagues and superiors. Based on the survey analysis, it is suggested to establish a countermeasure through case analysis rather than the right to suspend work for civil complaints that cannot be handled, such as customized manuals and action strategies for the age group with high grievance complaints.

• Journal of the Korea Institute of Military Science and Technology
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• v.25 no.6
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• pp.637-647
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• 2022

Since the concept of Manned-UnManned Teaming(MUM-T) and Unmanned Aircraft System(UAS) can efficiently respond to rapidly changing battle space, many studies are being conducted as key components of the mosaic warfare environment. In this paper, we propose a rule-based AI engagement model based on Basic Fighter Maneuver(BFM) capable of Within-Visual-Range(WVR) air-to-air combat and a simulation environment in which human pilots can participate. In order to develop a rule-based AI engagement model that can pilot a fighter with a 6-DOF dynamics model, tactical manuals and human pilot experience were configured as knowledge specifications and modeled as a behavior tree structure. Based on this, we improved the shortcomings of existing air combat models. The proposed model not only showed a 100 % winning rate in engagement with human pilots, but also visualized decision-making processes such as tactical situations and maneuvering behaviors in real time. We expect that the results of this research will serve as a basis for development of various AI-based engagement models and simulators for human pilot training and embedded software test platform for fighter.

• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.61-68
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• 2022

In this paper, we propose a system to predict the GPS trajectory of a pedestrian based on a deep learning model. Pedestrian trajectory prediction is a study that can prevent pedestrian danger and collision situations through notifications, and has an impact on business such as various marketing. In addition, it can be used not only for pedestrians but also for path prediction of unmanned transportation, which is receiving a lot of spotlight. Among various trajectory prediction methods, this paper is a study of trajectory prediction using GPS data. It is a deep learning model-based study that predicts the next route by learning the GPS trajectory of pedestrians, which is time series data. In this paper, we presented a data set construction method that allows the deep learning model to learn the GPS route of pedestrians, and proposes a trajectory prediction deep learning model that does not have large restrictions on the prediction range. The parameters suitable for the trajectory prediction deep learning model of this study are presented, and the model's test performance are presented.

• Journal of Aerospace System Engineering
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• v.16 no.6
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• pp.114-122
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• 2022

This paper explores three-dimensional unsteady computational fluid dynamic (CFD) analyses with an overset grid technique to analyse the wake effect created by a rotating propeller on a hand-launched unmanned aerial vehicle (UAV). Additionally, the influence of actual aileron deflection on the equilibrium condition of the rolling moment is examined in various hand-launched take-off conditions. The results of this study demonstrate the importance of initial aileron deflection in increasing the initial rolling stability during the hand-launched take-off process. Furthermore, an aerodynamic database is constructed to rapidly predict the aileron set values required for different take-off speeds and angle-of-attacks.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.27 no.1
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• pp.62-68
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• 2023

An aerial relay system utilizing an unmanned aerial vehicle(UAV) or drone is addressed for event-driven operations such as temporary communication services for disaster affected area, military and first responder support. UAV relay system (URS) targets to provide a reliable communication service to a remote user equipment or an operator, therefore, a fast UAV placement to guarantee a minimum quality of service(QoS) is important when an operation is requested. Researches on UAV utilization in communication systems mostly target to derive the optimal position of UAV to maximize the performance, however, fast deployment of UAV is much more important than optimal placement under emergency situations. To this end, this paper derives the feasible area for UAV placement, investigates the effect of performance requirements on that area, and suggests UAV placement to certainly guarantee the performance requirements. Simulation results demonstrate that the feasible area derived in this paper matches that obtained by an exhaustive search.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.3 no.2
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• pp.67-72
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• 2022

Ecological research relies on the interpretation of large amounts of visual data obtained from extensive wildlife surveys, but such large-scale image interpretation is costly and time-consuming. Using an artificial intelligence (AI) machine learning model, especially convolution neural networks (CNN), it is possible to streamline these manual tasks on image information and to protect wildlife and record and predict behavior. Ecological research using deep-learning-based object recognition technology includes various research purposes such as identifying, detecting, and identifying species of wild animals, and identification of the location of poachers in real-time. These advances in the application of AI technology can enable efficient management of endangered wildlife, animal detection in various environments, and real-time analysis of image information collected by unmanned aerial vehicles. Furthermore, the need for school education and social use on biodiversity and environmental issues using AI is raised. School education and citizen science related to ecological activities using AI technology can enhance environmental awareness, and strengthen more knowledge and problem-solving skills in science and research processes. Under these prospects, in this paper, we compare the results of our early 2013 study, which automatically identified African cichlid fish species using photographic data of them, with the results of reanalysis by CNN deep learning method. By using PyTorch and PyTorch Lightning frameworks, we achieve an accuracy of 82.54% and an F1-score of 0.77 with minimal programming and data preprocessing effort. This is a significant improvement over the previous our machine learning methods, which required heavy feature engineering costs and had 78% accuracy.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.4
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• pp.165-170
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• 2022

By reducing time spent outside, a shopping system was developed for middle-aged and elderly people who mainly use neighborhood marts and neighborhood mart managers. The main functions of this app are direct shopping and online shopping, and it was developed using QR code using Zxing library on Android and Kakao Map using Kakao API. In addition, it provides information such as payment statistics and bulletin board posts that members need through recycler view and graphs in an easy-to-read manner. Through this system, members can efficiently manage by reducing fatigue when using the mart through direct purchase using QR code and delivery through map, and reducing manpower wastage as a mart manager. Also, as a mart manager, more consumers will be able to sell more items.

• Nuclear Engineering and Technology
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• v.54 no.3
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• pp.1037-1048
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• 2022

A radioactive isotope identification algorithm is a prerequisite for a low-resolution scintillation detector applied to an unmanned radiation monitoring system. In this paper, a sparse representation with dictionary learning approach is proposed and applied to plastic gamma-ray spectra. Label-consistent K-SVD was used to learn a discriminative dictionary for the spectra corresponding to a mixture of four isotopes (¹³³Ba, ²²Na, ¹³⁷Cs, and ⁶⁰Co). A Monte Carlo simulation was employed to produce the simulated data as learning samples. Experimental measurement was conducted to obtain practical spectra. After determining the hyper parameters, two dictionaries tailored to the learning samples were tested by varying with the source position and the measurement time. They achieved average accuracies of 97.6% and 98.0% for all testing spectra. The average accuracy of each dictionary was above 96% for spectra measured over 2 s. They also showed acceptable performance when the spectra were artificially shifted. Thus, the proposed method could be useful for identifying radioisotopes in gamma-ray spectra from a plastic scintillation detector even when a dictionary is adapted to only simulated data. Furthermore, owing to the outstanding properties of sparse representation, the proposed approach can easily be built into an insitu monitoring system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.2
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• pp.207-213
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• 2022

Recently, with the improvement of the performance of IoT technology and AI, automation and unmanned work are progressing in a wide range of fields, and interest in image processing, which is the basis of automation such as object recognition and object classification, is increasing. Image noise removal is an important process used as a preprocessing step in an image processing system, and various studies have been conducted. However, in most cases, it is difficult to preserve detailed information due to the smoothing effect in high-frequency components such as edges. In this paper, we propose an algorithm to restore damaged images in AWGN(additive white Gaussian noise) using fuzzy weights based on Gaussian distribution. The proposed algorithm switched the filtering process by comparing the filtering mask and the noise estimate with each other, and reconstructed the image by calculating the fuzzy weights according to the low-frequency and high-frequency components of the image.