• Korean Journal of Artificial Intelligence
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• v.8 no.2
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• pp.23-28
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• 2020

Purpose: Today, we often hear complaints from customers: poor quality, poor service, expensive prices, etc. Customer complaints are an indication that the company's products and services do not meet customer requirements, which in turn causes customer complaints. An important content of corporate marketing practice is how to use the opportunity of handling customer complaints to win the trust of customers and gain a competitive advantage. According to the concept of marketing, the way for an enterprise to obtain profits is to continuously meet the needs of customers. However, with increasingly fierce market competition and the overall formation of a buyer's market, providing high-quality products and high-efficiency and high-level services have become the eternal theme of enterprises. Therefore, meeting the actual needs of customers and effectively handling customer complaints are issues that we must take seriously. Research design, data, and methodology: This article mainly analyzes the causes of customer complaints, proposes relevant solutions for different types of complaints, builds a customer complaint management system, improves the efficiency and ability of handling complaints, and provides more references and basis for enterprises to solve customer complaints. Conclusions: To further improve the quality of enterprise products and service standards, to help enterprises increase customer loyalty and satisfaction, and to enable enterprises to gain advantages in the increasingly competitive global market.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.04a
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• pp.133-134
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• 2022

This study proposed vision artificial intelligence-based automated supervision technology for external insulation and finishing system, and basic research was conducted for it. The automated supervision technology proposed in this study consists of the object detection model (YOLOv5) and the part that derives necessary information based on the object detection result and then determines whether the external insulation-related adhesion regulations are complied with. As a result of a test, the judgement accuracy of the proposed model showed about 70%. The results of this study are expected to contribute to securing the external insulation quality and further contributing to the realization of energy-saving eco-friendly buildings. As further research, it is necessary to develop a technology that can improve the accuracy of the object detection model by supplementing the number of data for model training and determine additional related regulations such as the adhesive area ratio.

• Clean Technology
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• v.29 no.2
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• pp.145-150
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• 2023

Tele-metering systems have been useful tools for managing domestic wastewater treatment plants (WWTP) over the last decade. They mostly generate water quality data for discharged water to ensure that it complies with mandatory regulations and they may be able to produce every operation parameter and additional measurements in the near future. A sub-big data group, comprised of about 150,000 data points from four domestic WWTPs, was ready to be classified and also analyzed to optimize the WWTP process. We used the Statistical Product and Service Solutions (SPSS) 25 package in order to statistically treat the data with linear regression and correlation analysis. The major independent variables for analysis were water temperature, sludge recycle rate, electricity used, and water quality of the influent while the dependent variables representing the water quality of the effluent included the total nitrogen, which is the most emphasized index for discharged flow in plants. The water temperature and consumed electricity showed a strong correlation with the total nitrogen but the other indices' mutual correlations with other variables were found to be fuzzy due to the large errors involved. In addition, a multilayer perceptron analysis method was applied to TMS data along with root mean square error (RMSE) analysis. This study showed that the RMSE in the SS, T-N, and TOC predictions were in the range of 10% to 20%.

• Health Policy and Management
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• v.34 no.1
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• pp.4-16
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• 2024

Smart hospitals involve the use of recent ICT (information and communications technology) technologies to improve healthcare access, efficiency, and effectiveness. Standardization in smart hospital technologies is crucial for interoperability, scalability, policy formulation, quality control, and maintenance. This study reviewed relevant international standards for smart hospitals and the organizations that develop them. Specific attention was paid to robotics in smart hospitals and the potential for standardization in this area. The study used online resources and existing standards to analyze technologies, standards, and practices in smart hospitals. Key technologies of smart hospitals were identified. Relevant standards from ISO (International Organization for Standardization) and IEC (International Electrotechnical Commission) were mapped to each core technology. Korea's leadership in smart hospital technology were highlighted. Approaches for standardizing smart hospitals were proposed. Finally, potential new international standard items for robotics in smart hospitals were identified and categorized by function: sampling, remote operation, delivery, disinfection, and movement tracking/contact tracing. Standardization in smart hospital technologies is crucial for ensuring interoperability, scalability, ethical use of artificial intelligence, and quality control. Implementing international standards in smart hospitals is expected to benefit individuals, healthcare institutions, nations, and industry by improving healthcare access, quality, and competitiveness.

• International journal of advanced smart convergence
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• v.13 no.3
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• pp.89-100
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• 2024

Integrating advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), and big data is transforming elderly care services, particularly in nursing homes. This study explores the impact of these technologies on the quality of care in nursing homes in Tongling City, China. Using a mixed-methods approach, data were collected from 298 elderly residents across 12 nursing homes through detailed surveys and interviews. The findings indicate that smart platforms and intelligent terminals significantly enhance service quality, with institutional conditions and social participation emerging as the most influential factors. Although the study's regional focus may limit the generalizability of the findings, it introduces novel applications of AI in dietary management and IoT in personalized environmental monitoring, which contribute original insights to the broader field of smart elderly care. These results underscore the transformative potential of advanced technologies in improving elderly care and offer a model that can be adapted to similar contexts globally. Future research should focus on longitudinal studies to assess the long-term impact of these technologies and explore their applicability in diverse cultural and regional settings.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1480-1493
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• 2018

Recently, computational intelligence has received a lot of attention from researchers due to its potential applications to artificial intelligence. In computer science, computational intelligence refers to a machine's ability to learn how to compete various tasks, such as making observations or carrying out experiments. We adopted a computational intelligence solution to monitoring residual resources in cloud computing environments. The proposed residual resource monitoring scheme periodically monitors the cloud-based host machines, so that the post migration performance of a virtual machine is as consistent with the pre-migration performance as possible. To this end, we use a novel similarity measure to find the best target host to migrate a virtual machine to. The design of the proposed residual resource monitoring scheme helps maintain the quality of service and service level agreement during the migration. We carried out a number of experimental evaluations to demonstrate the effectiveness of the proposed residual resource monitoring scheme. Our results show that the proposed scheme intelligently measures the similarities between virtual machines in cloud computing environments without causing performance degradation, whilst preserving the quality of service and service level agreement.

• Journal of Korean Society for Quality Management
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• v.51 no.1
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• pp.55-65
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• 2023

Purpose: The purpose of this study is to propose an optimization process to improve product yield in the process using process data. Recently, research for low-cost and high-efficiency production in the manufacturing process using machine learning or deep learning has continued. Therefore, this study derives major variables that affect product defects in the manufacturing process using eXplainable Artificial Intelligence(XAI) method. After that, the optimal range of the variables is presented to propose a methodology for improving product yield. Methods: This study is conducted using the injection molding machine AI dataset released on the Korea AI Manufacturing Platform(KAMP) organized by KAIST. Using the XAI-based SHAP method, major variables affecting product defects are extracted from each process data. XGBoost and LightGBM were used as learning algorithms, 5-6 variables are extracted as the main process variables for the injection process. Subsequently, the optimal control range of each process variable is presented using the ICE method. Finally, the product yield improvement methodology of this study is proposed through a validation process using Test Data. Results: The results of this study are as follows. In the injection process data, it was confirmed that XGBoost had an improvement defect rate of 0.21% and LightGBM had an improvement defect rate of 0.29%, which were improved by 0.79%p and 0.71%p, respectively, compared to the existing defect rate of 1.00%. Conclusion: This study is a case study. A research methodology was proposed in the injection process, and it was confirmed that the product yield was improved through verification.

• Journal of the Korean Society of Radiology
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• v.83 no.6
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• pp.1229-1239
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• 2022

Recently, artificial intelligence (AI) technology has shown potential clinical utility in a wide range of MRI fields. In particular, AI models for improving the efficiency of the image acquisition process and the quality of reconstructed images are being actively developed by the MR research community. AI is expected to further reduce acquisition times in various MRI protocols used in clinical practice when compared to current parallel imaging techniques. Additionally, AI can help with tasks such as planning, parameter optimization, artifact reduction, and quality assessment. Furthermore, AI is being actively applied to automate MR image analysis such as image registration, segmentation, and object detection. For this reason, it is important to consider the effects of protocols or devices in MR image analysis. In this review article, we briefly introduced issues related to AI application of MR image acquisition and reconstruction.

• The Mathematical Education
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• v.63 no.2
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• pp.369-385
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• 2024

With the advancement of digital technology, a variety of digital materials are being utilized in education. For their appropriate use of digital resources, teachers need to be able to evaluate the quality of digital resource and determine the suitability for teaching. This study explored how preservice teachers evaluate TocToc-Math, an Artificial Intelligence (AI)-based math support system. Based on an evaluation framework developed through prior research, preservice teachers evaluated TocToc-Math with evidence-based criteria, including content quality, pedagogy, technology use, and mathematics curriculum alignment. The findings shows that preservice teachers positively evaluated TocToc-Math overall. The evaluation tendencies of preservice teachers were classified into three groups, and the specific characteristics of each factor differed depending on the group. Based on the research results, we suggest implications for improving preservice teachers' evaluation abilities regarding the use of digital technology and AI in mathematics education.

• Journal of Korean Neurosurgical Society
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• v.67 no.5
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• pp.493-509
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• 2024

In neurointensive care units (NICUs), particularly in cases involving traumatic brain injury (TBI), swift and accurate decision-making is critical because of rapidly changing patient conditions and the risk of secondary brain injury. The use of artificial intelligence (AI) in NICU can enhance clinical decision support and provide valuable assistance in these complex scenarios. This article aims to provide a comprehensive review of the current status and future prospects of AI utilization in the NICU, along with the challenges that must be overcome to realize this. Presently, the primary application of AI in NICU is outcome prediction through the analysis of preadmission and high-resolution data during admission. Recent applications include augmented neuromonitoring via signal quality control and real-time event prediction. In addition, AI can integrate data gathered from various measures and support minimally invasive neuromonitoring to increase patient safety. However, despite the recent surge in AI adoption within the NICU, the majority of AI applications have been limited to simple classification tasks, thus leaving the true potential of AI largely untapped. Emerging AI technologies, such as generalist medical AI and digital twins, harbor immense potential for enhancing advanced neurocritical care through broader AI applications. If challenges such as acquiring high-quality data and ethical issues are overcome, these new AI technologies can be clinically utilized in the actual NICU environment. Emphasizing the need for continuous research and development to maximize the potential of AI in the NICU, we anticipate that this will further enhance the efficiency and accuracy of TBI treatment within the NICU.