• Asia Marketing Journal
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• v.7 no.3
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• pp.121-141
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• 2005

Most of the people in marketing area know that database marketing has been one of the most powerful marketing tools and thus database marketing industry grows bigger and bigger. For both effective database marketing and database marketing industry, personal data are the very essential resources. Unfortunately, in Korea, both database marketing and database marketing industry stays far behind compared to other countries because it is practically very hard to legally trade personal data for database marketing purpose. Instead Korea has a illegal spam problem which might be a natural consequency of strong restriction on personal data in the situation of huge demand for personal data. KT SODiS can be called the frontier of Korea's database marketing industry since it is the first legal business in this area. In the first 5 months, SODiS obtained 2 millions of legal customer consents which can be the strong base to help database marketing activities of other companies. This case shows marketing strategies of KT SODiS to establish infrastructure for Korea's database marketing industry and suggests some future tasks to further develop the industry.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.80-80
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• 2022

Estimation of heading date of paddy rice is laborious and time consuming. Therefore, automatic estimation of heading date of paddy rice is highly essential. In this experiment, deep learning classification models were used to classify two difference categories of rice (vegetative and reproductive stage) based on the panicle initiation of paddy field. Specifically, the dataset includes 444 slanted view images belonging to two categories and was then expanded to include 1,497 images via IMGAUG data augmentation technique. We adopt two transfer learning strategies: (First, used transferring model weights already trained on ImageNet to six classification network models: VGGNet, ResNet, DenseNet, InceptionV3, Xception and MobileNet, Second, fine-tuned some layers of the network according to our dataset). After training the CNN model, we used several evaluation metrics commonly used for classification tasks, including Accuracy, Precision, Recall, and F1-score. In addition, GradCAM was used to generate visual explanations for each image patch. Experimental results showed that the InceptionV3 is the best performing model in terms of the accuracy, average recall, precision, and F1-score. The fine-tuned InceptionV3 model achieved an overall classification accuracy of 0.95 with a high F1-score of 0.95. Our CNN model also represented the change of rice heading date under different date of transplanting. This study demonstrated that image based deep learning model can reliably be used as an automatic monitoring system to detect the heading date of rice crops using CCTV camera.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.33 no.4
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• pp.419-426
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• 2023

Objectives: This study aims to develop an Occupational Safety and Health (OSH) guide for the safe cleaning of contaminated machinery, equipment, and parts used in the electronics manufacturing process. Methods: A literature review, field investigations, and discussions were conducted. An initial draft of an OSH guide was developed and reviewed by experts with significant experience in maintenance work in the electronics manufacturing process in order to refine the guide. Results: Workers involved in cleaning processes with chemicals, solvents, and abrasive blasting can face exposure to a wide range of chemicals, abrasives, and noise. Identifying potential risks associated with each cleaning technique was an essential first step toward enhancing safety measures. The OSH guide comprises approximately eleven to twelve sections spanning 20-25 pages. It includes engineering and administrative protocols systematically organized to address the necessary actions before, during, and after cleaning tasks, depending on the technique. It is recommended that airline respirator masks be used in conjunction with an air purification system to ensure adherence to air quality standard "D" for atmosphere level. The use of an oil-free air compressor is advised, preferably a stationary model that does not rely on fuel sources like diesel. Conclusions: This OSH guide is designed to protect workers involved in maintenance activity in the electronics industry and aligns with global standards, such as those from the International Organization for Standardization (ISO) and Semiconductor Equipment and Material International, ensuring a higher level of safety and compliance.

• Journal of the Korean Applied Science and Technology
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• v.39 no.6
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• pp.769-782
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• 2022

The purpose is van Manen's interpretation of verbal violence experienced by nursing students during their growing up period in order to use it as basic data to improve the verbal communication essential for solving nursing problems and performing tasks with guardians and peers. For this, 10 students enrolled in the nursing department of A University in Gyeonggi-do were selected and data were collected through in-depth interviews. Data analysis conducted an existential inquiry process to focus on the essence of experience. Five thematic statements in this study were as follows: 'Beginning with a trivial conversation', 'Getting confused mind', 'Being an opportunity to reflect on myself', 'Changing the frame of my thought' and 'Making a mature me'. As a result, it confirmed the necessity of strengthening language usage and personal competency that respect the other party. Therefore, it is suggested that follow-up studies on empathy or self-positive effects are needed for effective communication techniques.

• Korea Trade Review
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• v.48 no.4
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• pp.263-283
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• 2023

Companies must strengthen core competencies by concentrating resources to secure a competitive edge and operate efficient processes from a company-wide perspective. To this end, it is seeking to concentrate its capabilities and reduce costs by pooling non-essential tasks or facilities that require a lot of time and capital at a strategic level. Therefore, logistics companies should actively utilize logistics coorperate system in order to maximize the use of logistics resources according to the limitations of human resources, physical resources, and time. This study is an empirical analysis of the strategy of logistics companies and the impact of logistics coorperate on corporate performance, and a survey and analysis was conducted on domestic logistics companies. The results of the empirical analysis showed that the cost·relationship·information-oriented strategy of logistics has a positive(+) effect on the financial·operation·strategic performance indicators of companies through logistics coorperate. The results derived from this paper will be used as an important determining factor in establishing a logistics strategy and logistics coorperate to improve the performance of logistics companies and logistics service companies.

• Education of Primary School Mathematics
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• v.26 no.4
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• pp.317-332
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• 2023

Mathematical modeling activities hold the potential for diverse applications, involving the transformation of real-life situations into mathematical models to facilitate problem-solving. In order to assess the cognitive, affective, and social dimensions of students' engagement in mathematical modeling activities, this study conducted sessions with ten groups of fifth-grade elementary school students. The ensuing processes and outcomes were thoroughly analyzed. As a result, each group effectively applied mathematical concepts and principles in creating mathematical models and gathering essential information to address real-world tasks. This led to notable shifts in interest, enhanced mathematical proficiency, and altered attitudes towards mathematics, all while promoting increased collaboration and communication among group members. Based on these analytical findings, the study offers valuable pedagogical insights and practical guidance for effectively implementing mathematical modeling activities.

• Computers and Concrete
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• v.33 no.5
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• pp.535-544
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• 2024

In the rapidly advancing landscape of computer vision (CV) technology, there is a burgeoning interest in its integration with the construction industry. Camera calibration is the process of deriving intrinsic and extrinsic parameters that affect when the coordinates of the 3D real world are projected onto the 2D plane, where the intrinsic parameters are internal factors of the camera, and extrinsic parameters are external factors such as the position and rotation of the camera. Camera pose estimation or extrinsic calibration, which estimates extrinsic parameters, is essential information for CV application at construction since it can be used for indoor navigation of construction robots and field monitoring by restoring depth information. Traditionally, camera pose estimation methods for cameras relied on target objects such as markers or patterns. However, these methods, which are marker- or pattern-based, are often time-consuming due to the requirement of installing a target object for estimation. As a solution to this challenge, this study introduces a novel framework that facilitates camera pose estimation using standardized materials found commonly in construction sites, such as concrete forms. The proposed framework obtains 3D real-world coordinates by referring to construction materials with certain specifications, extracts the 2D coordinates of the corresponding image plane through keypoint detection, and derives the camera's coordinate through the perspective-n-point (PnP) method which derives the extrinsic parameters by matching 3D and 2D coordinate pairs. This framework presents a substantial advancement as it streamlines the extrinsic calibration process, thereby potentially enhancing the efficiency of CV technology application and data collection at construction sites. This approach holds promise for expediting and optimizing various construction-related tasks by automating and simplifying the calibration procedure.

• Smart Media Journal
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• v.13 no.4
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• pp.48-56
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• 2024

In the livestock industry, detecting environmental outliers and predicting data are crucial tasks. Outliers in livestock environment data, typically gathered through time-series methods, can signal rapid changes in the environment and potential unexpected epidemics. Prompt detection and response to these outliers are essential to minimize stress in livestock and reduce economic losses for farmers by early detection of epidemic conditions. This study employs two methods to experiment and compare performances in setting thresholds that define outliers in livestock environment data outlier detection. The first method is an outlier detection using Mean Squared Error (MSE), and the second is an outlier detection using a Dynamic Threshold, which analyzes variability against the average value of previous data to identify outliers. The MSE-based method demonstrated a 94.98% accuracy rate, while the Dynamic Threshold method, which uses standard deviation, showed superior performance with 99.66% accuracy.

• International Journal of Computer Science & Network Security
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• v.24 no.8
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• pp.14-20
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• 2024

Traffic sign recognition is an essential feature of intelligent transportation systems and Advanced Driver Assistance Systems (ADAS), which are necessary for improving road safety and advancing the development of autonomous cars. This research investigates the incorporation of the YOLOv9 model into traffic sign recognition systems, utilizing its sophisticated functionalities such as Programmable Gradient Information (PGI) and Generalized Efficient Layer Aggregation Network (GELAN) to tackle enduring difficulties in object detection. We employed a publically accessible dataset obtained from Roboflow, which consisted of 3130 images classified into five distinct categories: speed_40, speed_60, stop, green, and red. The dataset was separated into training (68%), validation (21%), and testing (12%) subsets in a methodical manner to ensure a thorough examination. Our comprehensive trials have shown that YOLOv9 obtains a mean Average Precision (mAP@0.5) of 0.959, suggesting exceptional precision and recall for the majority of traffic sign classes. However, there is still potential for improvement specifically in the red traffic sign class. An analysis was conducted on the distribution of instances among different traffic sign categories and the differences in size within the dataset. This analysis aimed to guarantee that the model would perform well in real-world circumstances. The findings validate that YOLOv9 substantially improves the precision and dependability of traffic sign identification, establishing it as a dependable option for implementation in intelligent transportation systems and ADAS. The incorporation of YOLOv9 in real-world traffic sign recognition and classification tasks demonstrates its promise in making roadways safer and more efficient.

• Journal of Practical Engineering Education
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• v.16 no.4
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• pp.513-520
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• 2024

This study investigates the changes in teachers' roles as the impact of AI on school education expands. Traditionally, teachers have been responsible for core aspects of classroom instruction, curriculum development, assessment, and feedback. AI can automate these processes, particularly enhancing efficiency through personalized learning. AI also supports complex classroom management tasks such as student tracking, behavior detection, and group activity analysis using integrated camera and microphone systems. However, AI struggles to automate aspects of counseling and interpersonal communication, which are crucial in student life guidance. While direct conversational replacement by AI is challenging, AI can assist teachers by providing data-driven insights and pre-conversation resources. Key competencies required for teachers in the AI era include expertise in advanced instructional methods, dataset analysis, personalized learning facilitation, student and parent counseling, and AI digital literacy. Teachers should collaborate with AI to emphasize creativity, adjust personalized learning paths based on AI-generated datasets, and focus on areas less amenable to AI automation, such as individualized learning and counseling. Essential skills include AI digital literacy and proficiency in understanding and managing student data.