• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.3
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• pp.77-85
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• 2024

In this dissertation, a we designed and implemented a patrol robot that integrates a thermal imaging camera, speed dome camera, PTZ camera, radar, lidar sensor, and smartphone. This robot has the ability to monitor and respond efficiently even in complex environments, and is especially designed to demonstrate high performance even at night or in low visibility conditions. An orbital movement system was selected for the robot's mobility, and a smartphone-based control system was developed for real-time data processing and decision-making. The combination of various sensors allows the robot to comprehensively perceive the environment and quickly detect hazards. Thermal imaging cameras are used for night surveillance, speed domes and PTZ cameras are used for wide-area monitoring, and radar and LIDAR are used for obstacle detection and avoidance. The smartphone-based control system provides a user-friendly interface. The proposed robot system can be used in various fields such as security, surveillance, and disaster response. Future research should include improving the robot's autonomous patrol algorithm, developing a multi-robot collaboration system, and long-term testing in a real environment. This study is expected to contribute to the development of the field of intelligent surveillance robots.

• The Journal of the Acoustical Society of Korea
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• v.43 no.1
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• pp.39-48
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• 2024

A car horn serves a crucial safety role as a means of communication between drivers and a part that alerts pedestrians in advance. While previous studies have utilized finite element method and electric circuit model to simulate and analyze characteristics of the car horns, there remains a lack of research on design methods of a trumpet horn. This paper presents a design approach that predicts the operating frequency based on the acoustic reactance at the throat of the horn, once the vibrating part is determined. We deal with a horn combining both an exponential horn and a waveguide in the acoustic section, and confirm that the acoustic reactance at the horn throat measured by impedance tube experiment agrees well compared with the numerical result obtained using the finite element method. The resonance frequency of the car horn is predicted using the COMSOL Multiphysics finite element numerical analysis model, and the proposed design method is validated by measuring the operating frequency of the designed horn in a sound pressure experiment. As a result, the resonance measured in a semi-anechoic chamber environment by applying a DC voltage of 12 [V] excluding the holder occurs accurately within a few [Hz] of the design operating frequency. This paper discuss the design method of a trumpet horn from the perspective of the horn's acoustic reactance, and is expected to be useful for designing horn systems.

• Advanced Industrial SCIence
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• v.3 no.1
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• pp.23-29
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• 2024

In this scholarly investigation, the focus is placed on the transformative potential of edge computing in enhancing Intelligent Transportation Systems (ITS) for the facilitation of autonomous driving. The intrinsic capability of edge computing to process voluminous datasets locally and in a real-time manner is identified as paramount in meeting the exigent requirements of autonomous vehicles, encompassing expedited decision-making processes and the bolstering of safety protocols. This inquiry delves into the synergy between edge computing and extant ITS infrastructures, elucidating the manner in which localized data processing can substantially diminish latency, thereby augmenting the responsiveness of autonomous vehicles. Further, the study scrutinizes the deployment of edge servers, an array of sensors, and Vehicle-to-Everything (V2X) communication technologies, positing these elements as constituents of a robust framework designed to support instantaneous traffic management, collision avoidance mechanisms, and the dynamic optimization of vehicular routes. Moreover, this research addresses the principal challenges encountered in the incorporation of edge computing within ITS, including issues related to security, the integration of data, and the scalability of systems. It proffers insights into viable solutions and delineates directions for future scholarly inquiry.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.403-409
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• 2024

In this paper, the guidelines for the design of an Artificial Intelligence(AI) based Integrated Process Safety Management(PSM) system to enhance workplace safety using data from process safety reports submitted by hazardous and risky facility operators in accordance with the Occupational Safety and Health Act is proposed. The system composed of the proposed guidelines is to be implemented separately by individual facility operators and specialized process safety management agencies for single or multiple workplaces. It is structured with key components and stages, including data collection and preprocessing, expansion and segmentation, labeling, and the construction of training datasets. It enables the collection of process operation data and change approval data from various processes, allowing potential fault prediction and maintenance planning through the analysis of all data generated in workplace operations, thereby supporting decision-making during process operation. Moreover, it offers utility and effectiveness in time and cost savings, detection and prediction of various risk factors, including human errors, and continuous model improvement through the use of accurate and reliable training data and specialized datasets. Through this approach, it becomes possible to enhance workplace safety and prevent accidents.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.891-899
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• 2024

The Fourth Industrial Revolution is centered on a personalized demand fulfillment economy and is all about transformation and flexible processing that can deliver what customers want in real time across space and time. This paper implements the construction and operation of a packaging platform that can instantly procure the required packaging products based on real-time orders and evaluates its performance. The components of customer satisfaction are flexible and dependent on the situation which requires efficient management of enterprise operational processes based on an e-SCM platform. An OMS optimized for these conditions plays an important role in maximizing and differentiating the efficiency of a company's operations and improving its cost advantage. OMS is a system of mass customization that provides efficient MOT(Moment of Truth) logistics services to meet the eco-friendly issues of many individual customers and achieve optimized logistics operation goals to enhance repurchase intentions and sustainable business. OMS precisely analyzes the collected data to support information and decision-making related to efficiency, productivity, cost and provide accurate reports. It uses data visualization tools to express data visually and suggests directions for improvement of the operational process through statistics and prediction analysis.

• Journal of the Korean Society of Radiology
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• v.83 no.1
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• pp.138-148
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• 2022

Purpose This study evaluated the rates and annual trends of pediatric CT scans in South Korea using a nationwide population-based database. Materials and Methods Data regarding pediatric CT scan usage between 2012 and 2017 were retrieved from the health insurance review and assessment service. Data on the age, sex, diagnosis, and the anatomical area of involved patients were also extracted. Results A total of 576376 CT examinations were performed among 58527528 children aged below 18 years (9.8 scans/1000 children), and the number of CT examinations per 1000 children was noted to have increased by 23.2% from 9.0 in 2012 to 11.0 in 2017. Specifically, the number of CT examinations increased by 32.9% for the 6-12 years of age group (7.4/1000 to 9.8/1000) and by 34.0% for the 13-18 years of age group (11.4/1000 to 15.3/1000). Moreover, majority of the CT scans were limited to the head (39.1%), followed by the extremities (32.5%) and the abdomen (13.7%). Notably, the number of extremity CT scans increased by 83.6% (2.3/1000 to 4.2/1000), and its proportion as compared to other scans increased from 25.3% to 37.7%. Conclusion CT scans in the pediatric population increased continuously from 2012 to 2017 at an annual rate of 4.4%. Therefore, physicians should balance the benefits of CT with its potential harms from associated radiation exposure in pediatric patients.

• Korean Chemical Engineering Research
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• v.62 no.3
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• pp.214-224
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• 2024

Valves play an essential role in a chemical plant such as regulating fluid flow and pressure. Therefore, optimal selection of the valve size and type is essential task. Valve size and type have been selected based on theoretical formulas about calculating valve sizing coefficient (Cv). However, this approach has limitations such as requiring expert knowledge and consuming substantial time and costs. Herein, this study developed a model for predicting valve sizes and types using machine learning. We developed models using four algorithms: ANN, Random Forest, XGBoost, and Catboost and model performances were evaluated using NRMSE & R² score for size prediction and F1 score for type prediction. Additionally, a case study was conducted to explore the impact of phases on valve selection, using four datasets: total fluids, liquids, gases, and steam. As a result of the study, for valve size prediction, total fluid, liquid, and gas dataset demonstrated the best performance with Catboost (Based on R², total: 0.99216, liquid: 0.98602, gas: 0.99300. Based on NRMSE, total: 0.04072, liquid: 0.04886, gas: 0.03619) and steam dataset showed the best performance with RandomForest (R²: 0.99028, NRMSE: 0.03493). For valve type prediction, Catboost outperformed all datasets with the highest F1 scores (total: 0.95766, liquids: 0.96264, gases: 0.95770, steam: 1.0000). In Engineering Procurement Construction industry, the proposed fluid-specific machine learning-based model is expected to guide the selection of suitable valves based on given process conditions and facilitate faster decision-making.

• Journal of Venture Innovation
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• v.7 no.3
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• pp.85-100
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• 2024

ESG management refers to corporate management that takes into account environmental, social, and governance factors, while digital transformation goes beyond the mere automation or digitization of existing tasks to drive an innovative change in the essence of work and the way value is created. Therefore, digital transformation can help companies achieve ESG goals and implement sustainable business practices, establishing a complementary relationship between digital transformation and ESG management for corporate sustainability and growth. This relationship maximizes the synergy of integrating digital transformation with ESG management, enabling companies to utilize resources efficiently and prevent redundant investments, ultimately enhancing sustainable management performance. In this study, we propose the simultaneous promotion of business process reengineering (BPR), in which both digital transformation and ESG management are integrated. This is because the collection, analysis, and decision-making processes related to various data for promoting ESG management must be organically integrated with digital transformation technologies. Therefore, we analyzed each ESG management objective presented in the K-ESG guidelines and identified the corresponding digital transformation technologies through expert interviews and a review of prior research. The K-ESG guidelines serve as a useful ESG diagnostic system that enables companies to identify improvement tasks and manage performance based on goals through self-assessment of ESG levels. By developing a model based on the K-ESG guidelines for the integrated promotion of digital transformation and ESG management, companies can simultaneously improve ESG performance and drive digital innovation, reducing redundant investments and trial-and-error while utilizing diverse resources efficiently. This study provides practical and academic implications by developing a concrete and actionable new research model for researchers and businesses.

• Journal of Internet Computing and Services
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• v.25 no.3
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• pp.83-92
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• 2024

Recently, the importance of HR analytics for data-based decision-making in establishing and operating an effective human resource management system for companies is increasing. In addition, there is growing interest in the effect of employees' perceptions of organizational justice on positive organizational behavior. Therefore, in this study, among the various factors affecting teamwork behavior, we analyzed the impact on teamwork behavior such as perception of organizational justice and organizational culture. Organizational justice has a significant impact on the formation of members' attitudes, but its meaning may vary depending on the organizational context. In this study, we divided organizational justice into four types (procedural, distributive, interpersonal, and informational fairness) and confirmed their impact on teamwork behavior. In addition, organizational culture was divided into hierarchy culture and innovation culture, and how to regulate these relationships was examined. To analyze these relationships, individual-level data collected from 657 people at domestic companies were used for analysis. According to the analysis results, in a hierarchical culture, procedural justice and information justice had a positive influence on teamwork behavior through the mediating process of job satisfaction, and in an innovative culture, interpersonal justice and information justice had a positive influence on teamwork behavior through job satisfaction. It was confirmed to have a (+) effect. These research results provide implications for people management by indicating that, although organizational justice is important to members and organizations, it may be perceived differently and have different meanings depending on the organizational context. Through the use of the information presented in this study, we will provide value that can effectively and efficiently implement a company's human resource management system.

• Journal of Advanced Navigation Technology
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• v.28 no.4
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• pp.408-419
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• 2024

Currently, the airport is expected to improve flight punctuality and operational efficiency after establishing A-CDM, which provides a foundation for mutual cooperation based on an information sharing system among stakeholders. An important element of the A-CDM is to share time information generated by each stakeholder from the arrival of an aircraft to ground operations and departure of the aircraft, thereby supporting timely arrival and departure of aircraft and improving the efficiency and punctuality of airport operations. In the UAM system, a vertiport that plays a role similar to an airport also needs to establish a system to share time information generated by each stakeholder for efficient operation of limited resources. In this regard, a method is needed to identify time information that needs to be shared by each stakeholder and apply technology to share it. In this paper, we propose an application method for system technology that classifies and mutually shares time information generated by stakeholders related to Vertiport operation according to data characteristics.