• Knowledge Management Research
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• v.19 no.1
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• pp.133-146
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• 2018

This study develops an Education framework for users who need public open data for workplace etiquette education in a timely manner by mobile application. It facilitates utilizing efficiently Workplace etiquette contents that scattered in various platforms such as blogs, Youtube and web-sites run by private education agencies. Furthermore, it makes Public open data for workplace etiquette through gathering 'metadata', which is a comprehensive source of workplace etiquette. Accordingly, framework changes recognition about necessity of workplace etiquette education positively and suggests method that can promote effective workplace etiquette education. If the system in the study can provide public open data of workplace etiquette education, many young job applicants and workers will have a proper perception on it and sound workplace etiquette culture will be settled in the companies. Public data has been rising as a vital national strategic asset these days. Hopefully the public data will pave a way to discover the blue ocean in the market and open up a new type of businesses.

• The Journal of Korea Robotics Society
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• v.16 no.3
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• pp.270-275
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• 2021

Most of the initial forms of cooperative robots were intended to repeat simple tasks in a given space. So, they showed no significant difference from industrial robots. However, research for improving worker's productivity and supplementing human's limited working hours is expanding. Also, there have been active attempts to use it as a service robot by applying AI technology. In line with these social changes, we produced a mobile manipulator that can improve the worker's efficiency and completely replace one person. First, we combined cooperative robot with mobile robot. Second, we applied speech recognition technology and deep learning based object detection. Finally, we integrated all the systems by ROS (robot operating system). This system can communicate with workers by voice and drive autonomously and perform the Pick & Place task.

• KDI Journal of Economic Policy
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• v.44 no.2
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• pp.29-44
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• 2022

This study compares the wage equation in Korea to those in other countries, focusing on the wage returns to adult education and training (AET) participation. It is found that the wage compensation structure in Korea is associated mainly with job characteristics such as tenure and workplace size rather than with worker characteristics such as AET participation and cognitive abilities. It is also found that Korea's AET participation is skewed toward non-job-related AET, relative to the situations in other countries. These findings imply that the link between a worker's productivity and wage should be strengthened in order to incentivize workers to invest in AET relevant to the labor market.

• Korean Journal of Artificial Intelligence
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• v.9 no.1
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• pp.1-7
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• 2021

In this paper, We focused the issue of creating a socially problematic nurse schedule. The nurse schedule should be prepared in consideration of three shifts, appropriate placement of experienced workers, the fairness of work assignment, and legal work standards. Because of the complex structure of the nurse schedule, which must reflect various requirements, in most hospitals, the nurse in charge writes it by hand with a lot of time and effort. This study attempted to automatically create an optimized nurse schedule based on legal labor standards and fairness. We developed an I/O Q-Learning algorithm-based model based on Python and Web Application for automatic nurse schedule. The model was trained to converge to 100 by creating an Fairness Indicator Score(FIS) that considers Labor Standards Act, Work equity, Work preference. Manual nurse schedules and this model are compared with FIS. This model showed a higher work equity index of 13.31 points, work preference index of 1.52 points, and FIS of 16.38 points. This study was able to automatically generate nurse schedule based on reinforcement Learning. In addition, as a result of creating the nurse schedule of E hospital using this model, it was possible to reduce the time required from 88 hours to 3 hours. If additional supplementation of FIS and reinforcement Learning techniques such as DQN, CNN, Monte Carlo Simulation and AlphaZero additionally utilize a more an optimized model can be developed.

• Journal of the Korea Society of Computer and Information
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• v.28 no.5
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• pp.17-28
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• 2023

Potholes that occur on paved roads can have fatal consequences for vehicles traveling at high speeds and may even lead to fatalities. While manual detection of potholes using human labor is commonly used to prevent pothole-related accidents, it is economically and temporally inefficient due to the exposure of workers on the road and the difficulty in predicting potholes in certain categories. Therefore, completely preventing potholes is nearly impossible, and even preventing their formation is limited due to the influence of ground conditions closely related to road environments. Additionally, labeling work guided by experts is required for dataset construction. Thus, in this paper, we utilized the Mean Teacher technique, one of the semi-supervised learning-based knowledge distillation methods, to achieve robust performance in pothole image classification even with limited labeled data. We demonstrated this using performance metrics and GradCAM, showing that when using semi-supervised learning, 15 pre-trained CNN models achieved an average accuracy of 90.41%, with a minimum of 2% and a maximum of 9% performance difference compared to supervised learning.

• Journal of the Korea Society of Computer and Information
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• v.23 no.2
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• pp.63-70
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• 2018

Safety management agent manages the risk behavior of the worker with the naked eye, but there is a real difficulty for one the agent to manage all the workers. In this paper, IoT device is attached to a harness safety belt that a worker wears to solve this problem, and behavior data is upload to the cloud in real time. We analyze the upload data through the deep learning and analyze the risk behavior of the worker. When the analysis result is judged to be dangerous behavior, we designed and implemented a system that informs the manager through monitoring application. In order to confirm that the risk behavior analysis through the deep learning is normally performed, the data values of 4 behaviors (walking, running, standing and sitting) were collected from IMU sensor for 60 minutes and learned through Tensorflow, Inception model. In order to verify the accuracy of the proposed system, we conducted inference experiments five times for each of the four behaviors, and confirmed the accuracy of the inference result to be 96.0%.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.203-208
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• 2022

The occurrence of Type 2 Diabetes Mellitus (T2DM) is hoarding globally. All kinds of Diabetes Mellitus is controlled to disrupt over 415 million grownups worldwide. It was the seventh prime cause of demise widespread with a measured 1.6 million deaths right prompted by diabetes during 2016. Over 90% of diabetes cases are T2DM, with the utmost persons having at smallest one other chronic condition in UK. In valuation of contemporary applications of Big Data (BD) to Diabetes Medicare by sighted its upcoming abilities, it is compulsory to transmit out a bottomless revision over foremost theoretical literatures. The long-term growth in medicine and, in explicit, in the field of "Diabetology", is powerfully encroached to a sequence of differences and inventions. The medical and healthcare data from varied bases like analysis and treatment tactics which assistances healthcare workers to guess the actual perceptions about the development of Diabetes Medicare measures accessible by them. Apache Spark extracts "Resilient Distributed Dataset (RDD)", a vital data structure distributed finished a cluster on machines. Machine Learning (ML) deals a note-worthy method for building elegant and automatic algorithms. ML library involving of communal ML algorithms like Support Vector Classification and Random Forest are investigated in this projected work by using Jupiter Notebook - Python code, where significant quantity of result (Accuracy) is carried out by the models.

• The Journal of Korea Robotics Society
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• v.17 no.4
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• pp.455-462
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• 2022

Since electric connectors such as power connectors have a small assembly tolerance and have a complex shape, the assembly process is performed manually by workers. Especially, it is difficult to overcome the assembly error, and the assembly takes a long time due to the error correction process, which makes it difficult to automate the assembly task. To deal with this problem, a reinforcement learning-based assembly strategy using contact states was proposed to quickly perform the assembly process in an unstructured environment. This method learns to generate a search trajectory to quickly find a hole based on the contact state obtained from the force/torque data. It can also learn the stiffness needed to avoid excessive contact forces during assembly. To verify this proposed method, power connector assembly process was performed 200 times, and it was shown to have an assembly success rate of 100% in a translation error within ±4 mm and a rotation error within ±3.5°. Furthermore, it was verified that the assembly time was about 2.3 sec, including the search time of about 1 sec, which is faster than the previous methods.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.91-94
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• 2023

According to most experts and health workers, a living creature's body heat is little understood and crucial in the identification of disorders. Doctors in ancient medicine used wet mud or slurry clay to heal patients. When either of these progressed throughout the body, the area that dried up first was called the infected part. Today, thermal cameras that generate images with electromagnetic frequencies can be used to accomplish this. Thermography can detect swelling and clot areas that predict cancer without the need for harmful radiation and irritational touch. It has a significant benefit in medical testing because it can be utilized before any observable symptoms appear. In this work, machine learning (ML) is defined as statistical approaches that enable software systems to learn from data without having to be explicitly coded. By taking note of these heat scans of breasts and pinpointing suspected places where a doctor needs to conduct additional investigation, ML can assist in this endeavor. Thermal imaging is a more cost-effective alternative to other approaches that require specialized equipment, allowing machines to deliver a more convenient and effective approach to doctors.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.4
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• pp.1-8
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• 2020

In this study, we have investigated potential application of 3D model-based learning material in training of new workers for production of electrical systems in the ship-building. Most of new trainees such as new employees or workers on job-transition have lots of difficulty when given the first task due to their poor ability to understand related engineering drawings. This is mainly caused by the fact that drawings are loaded with too many ambiguous symbols to precisely describe the characteristics of components required for installation of electrical equipments and cables. As one way to improve the ability of new employees to read drawings, we have suggested a new training method based on 3D models of major components required for production of the electrical systems in the ship.