• Journal of the Institute of Convergence Signal Processing
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• v.21 no.1
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• pp.1-6
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• 2020

To make an automated system for a salt field, it is necessary to measure the salinity and water level of the evaporation site. In this paper, a method to simultaneously measure the salinity and water level by measuring the buoyancy forces of two fixed buoyancy bodies is proposed. The proposed measurement method measures the buoyancy of the main part and reference part when the measuring device is immersed in the salty water, and simultaneously measures the salinity and water level through the sum and difference of the two buoyancy forces. Since there is no mechanical movement in the measurement of buoyancy, measurement errors and maintenance needs can be reduced in the mudy environment of salt field. By applying the proposed method, we developed a system that can simultaneously measure salinity and water level remotely at the evaporation site of a salt field. Through a measurement experiment using a reference salty water having various levels of salinity, the results of a salinity error of 0% and a water level error of 2mm were obtained, and the effectiveness of the proposed salinity and water level measuring device was verified. When an automated system is constructed using the developed salinity and water level measuring device, labor reduction, work environment improvement, and productivity improvement are expected.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.2
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• pp.406-413
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• 2022

The subsea pipeline pre-commissioning stage consists of the following processes: Flooding, Venting, Hydrotesting, Dewatering, Drying, and N2 Purging. Among these processes, drying and nitrogen purging processes are stipulated to reduce and maintain the relative humidity below dew point to prevent the generation of hydrate and the risk of gas explosion in the pipeline during operation. The purpose of this study is to develop an analysis method for the air drying and nitrogen purging process during pre-commissioning of the subsea pipeline, and to evaluate the applicability of the analysis method through comparison with on-site measurement results. An analysis method using Computational Fluid Dynamics (CFD) was introduced and applied as a method for evaluating the relative humidity inside a subsea pipeline, and it was confirmed that analysis results were in good agreement with the on-site measurement results for the air drying and nitrogen purging process of the offshore pipeline. If the developed air drying and nitrogen purging analysis method are used as pre-engineering tools for pre-commissioning of subsea pipelines in the future, it is expected to have a significant impact on the improvement of work productivity.

• Tunnel and Underground Space
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• v.32 no.5
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• pp.298-311
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• 2022

Some problems, such as aging workers, a decreased population due to a low birth rate, and shortage of skilled workers, are rising in construction sites. Therefore research for smart construction technology that can be improved for productivity, safety, and quality has been recently developed with government support by replacing traditional construction technology with advanced digital technology. In particular, the motor grader that mainly performs road surface flattening is a construction machine that requires the application of automation technology for repetitive construction. It is predicted that the construction period will be shortened if the construction automation technology such as trajectory tracking, automation work, and remote control technology is applied. In this study, we introduce the hardware and software architecture of the smart motor grader to apply unmanned and automation technology and then analyze the traditional earthwork method of the motor grader. We suggested the application plans for the path pattern and blade control method of the smart motor grader based on this. In addition, we verified the performance of waypoint-based path-following depending on scenarios and the blade control's performance through tests.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.189-196
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• 2023

As the manufacturing paradigm shifts, various collaborative robots are creating new markets. Demand for collaborative robots is increasing in all industries for the purpose of easy operation, productivity improvement, and replacement of manpower who do simple tasks compared to existing industrial robots. However, accidents frequently occur during work caused by collaborative robots in industrial sites, threatening the safety of workers. In order to construct an industrial site through robots in a human-centered environment, the safety of workers must be guaranteed, and there is a need to develop a collaborative robot guard system that provides reliable communication without the possibility of dispatch. It is necessary to double prevent accidents that occur within the working radius of cobots and reduce the risk of safety accidents through sensors and computer vision. We build a system based on OPC UA, an international protocol for communication with various industrial equipment, and propose a collaborative robot guard system through image analysis using ultrasonic sensors and CNN (Convolution Neural Network). The proposed system evaluates the possibility of robot control in an unsafe situation for a worker.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1249-1249
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• 2022

The facade, an exterior material of a building, is one of the crucial factors that determine its morphological identity and its functional levels, such as energy performance, earthquake and fire resistance. However, regardless of the type of exterior materials, huge property and human casualties are continuing due to frequent exterior materials dropout accidents. The quality of the building envelope depends on the detailed design and is closely related to the back frames that support the exterior material. Detailed design means the creation of a shop drawing, which is the stage of developing the basic design to a level where construction is possible by specifying the exact necessary details. However, due to chronic problems in the construction industry, such as reducing working hours and the lack of design personnel, detailed design is not being appropriately implemented. Considering these characteristics, it is necessary to develop the detailed design process of exterior materials and works based on the domain-expert knowledge of the construction industry using artificial intelligence (AI). Therefore, this study aims to establish a detailed design automation algorithm for AI-based condition-responsive exterior wall panels and their back frames. The scope of the study is limited to "detailed design" performed based on the working drawings during the exterior work process and "stone panels" among exterior materials. First, working-level data on stone works is collected to analyze the existing detailed design process. After that, design parameters are derived by analyzing factors that affect the design of the building's exterior wall and back frames, such as structure, floor height, wind load, lift limit, and transportation elements. The relational expression between the derived parameters is derived, and it is algorithmized to implement a rule-based AI design. These algorithms can be applied to detailed designs based on 3D BIM to automatically calculate quantity and unit price. The next goal is to derive the iterative elements that occur in the process and implement a robotic process automation (RPA)-based system to link the entire "Detailed design-Quality calculation-Order process." This study is significant because it expands the design automation research, which has been rather limited to basic and implemented design, to the detailed design area at the beginning of the construction execution and increases the productivity by using AI. In addition, it can help fundamentally improve the working environment of the construction industry through the development of direct and applicable technologies to practice.

• Journal of the Korea Society of Computer and Information
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• v.14 no.10
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• pp.55-61
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• 2009

This paper describes a point of production(POP) system which collects and manages real-time shop floor machining information in a wafer dicing process. The system are composed of POP terminal, line controller and network. In the configuration of the system, LAN and RS485 network are used for connection with the upper management system and down stratum respectively. As a bridge between POP terminal and server, a line controller is used. The real-time information which is the base of production management are collected from information resources such as machine, product and worker. The collected information are used for the calculation of optimal cutting condition. The collection of the information includes cutting speed, spout of pure water, accumulated count of cut in process for blade and wafer defect. In order to manage machining information in wafer dicing process, production planning information is delivered to the shop floor, and production result information is collected from the shop floor, delivered to the server and used for managing production plan. From the result of the system application, production progress status, work and non-working hour analysis for each machine, and wafer defect analysis are available, and they are used for quality and productivity improvements in wafer dicing process. A case study is implemented to evaluate the performance of the system.

• 한국노년학
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• v.29 no.3
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• pp.953-970
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• 2009

The successful aging paradigm suggests that the elderly keep their social ties and actively participate in various activities in later life. Many Korean studies on aging, however, have overlooked the importance of various social activities and relationships beyond kinship. This article aims to investigate the perceived social activities among Korean elders to comprehend the subjectivity of successful aging. 613 respondents, over 50 years old, are selected from a national survey data which was collected by face-to-face interview based on the structured questionnaire. The respondents are classified into pre old (50-64), young old (65-74), old old (over 75). Major findings are as follows. The social activities are grouped into two sub-categories: 'relational activity' and 'productive activity'. The activity types are different by the age cohorts. Various activities are considered in terms of one's needs or motivations, and properly chosen based on the subjective evaluation during life course. Both demographic variables such as sex, area and social context variables such as marital status, living-together show influence on the activity type choices. Plans for later life and work ethics are important as well. Policies regarding social participation emphasizing productivity should positively consider relational motives, as perceived by the Korean elderly.

• Journal of Service Research and Studies
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• v.13 no.4
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• pp.206-223
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• 2023

The introduction of Smart technologies such as Artificial Intelligence(AI) systems are have a powerful impact in a variety of industry fields. Some experts predict that smart technology will completely change people's daily life and work styles, causing technological innovation, productivity improvement, and discovery and emergence of new fields. On the one hand, this vision cannot ignore negative views and concerns. Despite many social debates about employment, such as job loss and rising unemployment, there have not been many studies based on employee experience that provide a fundamental solution to the conflict between AI and employment. Therefore, this study finds out the effects and related factors of AI concierge robots for hotel employees, focusing on the hotel industry, and how employees' perceptions of AI concierge robots affect user resistance and turnover intention. This study, conducted a questionnaire survey of 322 hotel employees who had experience working with AI concierge robots in China, and used SPSS and SmartPLS statistical analysis programs to draw conclusions. We found that hotel employees' perceptions of AI concierge robots were significantly related to user resistance and turnover intention, and this association was related to employee self-efficacy, perceived organizational support, quality of AI services and new tasks. In addition, it was found that the quality of AI concierge robots directly or indirectly had the greatest influence on user resistance and turnover intention. The findings of this study provide theoretical implications for academia and practical implications for industry practitioners.

• Korean Chemical Engineering Research
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• v.61 no.4
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• pp.542-549
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• 2023

Since the growing interest in surrogate modeling, there has been continuous research aimed at simulating nonlinear chemical processes using data-driven machine learning. However, the opaque nature of machine learning models, which limits their interpretability, poses a challenge for their practical application in industry. Therefore, this study aims to analyze chemical processes using Explainable Artificial Intelligence (XAI), a concept that improves interpretability while ensuring model accuracy. While conventional sensitivity analysis of chemical processes has been limited to calculating and ranking the sensitivity indices of variables, we propose a methodology that utilizes XAI to not only perform global and local sensitivity analysis, but also examine the interactions among variables to gain physical insights from the data. For the ammonia synthesis process, which is the target process of the case study, we set the temperature of the preheater leading to the first reactor and the split ratio of the cold shot to the three reactors as process variables. By integrating Matlab and Aspen Plus, we obtained data on ammonia production and the maximum temperatures of the three reactors while systematically varying the process variables. We then trained tree-based models and performed sensitivity analysis using the SHAP technique, one of the XAI methods, on the most accurate model. The global sensitivity analysis showed that the preheater temperature had the greatest effect, and the local sensitivity analysis provided insights for defining the ranges of process variables to improve productivity and prevent overheating. By constructing alternative models for chemical processes and using XAI for sensitivity analysis, this work contributes to providing both quantitative and qualitative feedback for process optimization.

• Information Systems Review
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• v.25 no.2
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• pp.181-203
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• 2023

With the fierce competition of companies for the attraction of outstanding individuals, job satisfaction of employees has been of importance. In this circumstance, many companies try to invest in job satisfaction improvement by finding employees' everyday experiences and difficulties. However, due to a lack of understanding of the employee experience, their investments are not paying off. This study examined the relationship between employee experience and job satisfaction using employee reviews and company ratings from Glassdoor, one of the largest employee communities worldwide. We use text mining techniques such as K-means clustering and LDA topic-based sentiment analysis to extract key experience factors by job level, and DistilBERT sentiment analysis to measure the sentiment score of each employee experience factor. The drawn employee experience factors and each sentiment score were analyzed quantitatively, and thereby relations between each employee experience factor and job satisfaction were analyzed. As a result, this study found that there is a significant difference between the workplace experiences of managers and general employees. In addition, employee experiences that affect job satisfaction also differed between positions, such as customer relationship and autonomy, which did not affect the satisfaction of managers. This study used text mining and quantitative modeling method based on theory of work adjustment so as to find and verify main factors of employee experience, and thus expanded research literature. In addition, the results of this study are applicable to the personnel management strategy for improving employees' job satisfaction, and are expected to improve corporate productivity ultimately.