• Journal of the Korean Institute of Navigation
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• v.22 no.2
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• pp.13-22
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• 1998

Shipbome Automatic Identification System (AIS) will be an important manne equipment used for identification, surveillance and communication in the 21st century, which is currently being researched in developed countries. A technical scheme of AlS is proposed in this paper. The main component of the AlS is a broadcast transponder, and the core technology is a VHF radio data link with high capacity, named STDMA (Self-organized Time Division Multiple Access). The ships installed the AlS, which will automatically and periodically broadcast their positions and identities in the marine VHF channels, can be displayed on a screen of an ECDIS on board or in VTS centers. The AlS is able to support not only broadcast service but also point-to-point communication service. This paper presents the configuration, operation principle and functionality of the AlS as well as the scenario of STDMA. In addition, the standardization work of AlS in IMO is introduced in this pauer, too.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.107-114
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• 2023

This study described a method of designing and implementing an artificial intelligence-based learning model for predicting the usage of building members. Artificial intelligence (AI) is widely used in various fields thanks to the development of technology, but in the field of building information management (BIM), the case of utilizing AI technology is very low due to the specificity of the data in the field and the difficulty of collecting big data. Therefore, AI problems for BIM were discovered, and a new preprocessing technique was devised to solve the specificity of data in the field. An artificial intelligence model was implemented based on the designed preprocessing technique, and it was confirmed that the accuracy of predicting the construction component usage of the implemented artificial intelligence model is at a level that can be used in the actual industry.

• Economic and Environmental Geology
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• v.34 no.5
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• pp.437-446
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• 2001

Zoned epidotes formed by the propylitic alteration of the Bobae sericite deposit in western Pusan show complex compositional zoning patterns, such as multiple growth zoning, oscillatory zoning, patchy zoning and irregular zoning. The complex zoned epidote, in general, shows AI-rich cores and Fe-rich rims. Pistacite component (Ps) in the epidote ranges from 18.5 to 74.3 mot.%. Remnant textures in multiple growth zoning indicate that the earlier zone was partially resorbed prior to growth of later one. Multiple growth zoning and oscillatory zoning suggest that hydrothermal system underwent rapid changes and fluctuations in fluid chemistry, redox condition, or temperature.

• Journal of Wellbeing Management and Applied Psychology
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• v.6 no.2
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• pp.27-31
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• 2023

Purpose: Establishment of a real-time monitoring system for odor control in traditional markets in Gangwon-do and a system for linking prevention facilities. Research design, data and methodology: Build server and system logic based on data through real-time monitoring device (sensor-based). A temporary data generation program for deep learning is developed to develop a model for odor data. Results: A REST API was developed for using the model prediction service, and a test was performed to find an algorithm with high prediction probability and parameter values optimized for learning. In the deep learning algorithm for AI modeling development, Pandas was used for data analysis and processing, and TensorFlow V2 (keras) was used as the deep learning library. The activation function was swish, the performance of the model was optimized for Adam, the performance was measured with MSE, the model method was Functional API, and the model storage format was Sequential API (LSTM)/HDF5. Conclusions: The developed system has the potential to effectively monitor and manage odors in traditional markets. By utilizing real-time data, the system can provide timely alerts and facilitate preventive measures to control and mitigate odors. The AI modeling component enhances the system's predictive capabilities, allowing for proactive odor management.

• Proceedings of the KSME Conference
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• 2001.11a
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• pp.623-628
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• 2001

The styling of passenger car wheels and their effect on vehicle appearance has increased in importance in recent years. The wheel designer has been given the task of insuring that a wheel design meets its engineering objectives without affecting the styling theme. The wheel and tire system is considered as a vehicle component whose dynamic modal information of the tire/wheel system are employed in the modal synthesis model of the vehicle. The Vibration characteristics of a passenger car wheel play an important role to judge a ride comfortability and quality for a passenger car. In this paper, the vibration characteristics of a AI-alloy and steel wheel for passenger car are studied. Natural frequency, damping and mode shape are determined experimentally by frequency response function method. Results show that wheel material property, size and design are parameter for shift of natural frequency and damping.

• Journal of Engineering Education Research
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• v.23 no.3
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• pp.41-48
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• 2020

In light of the 4th industrial revolution, this research identifies critical thinking education as the key component of cultivating a new pool of integrative talents. It seeks to find ways to incorporate artificial intelligence, one of the biggest upcoming innovations, into critical thinking education. This paper aims to propose an education model that raises awareness on related issues of AI and set a healthy direction for its development through debates on topics raised by the film, Ex Machina, which depicts the dangerous implications of AI technology.

• Journal of Information Processing Systems
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• v.17 no.2
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• pp.227-241
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• 2021

Object detection techniques based on deep learning such as YOLO have high detection performance and precision in a single channel video stream. In order to expand to multiple channel object detection in real-time, however, high-performance hardware is required. In this paper, we propose a novel back-end server framework, a real-time AI vision platform (RAVIP), which can extend the object detection function from single channel to simultaneous multi-channels, which can work well even in low-end server hardware. RAVIP assembles appropriate component modules from the RODEM (real-time object detection module) Base to create per-channel instances for each channel, enabling efficient parallelization of object detection instances on limited hardware resources through continuous monitoring with respect to resource utilization. Through practical experiments, RAVIP shows that it is possible to optimize CPU, GPU, and memory utilization while performing object detection service in a multi-channel situation. In addition, it has been proven that RAVIP can provide object detection services with 25 FPS for all 16 channels at the same time.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.597-602
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• 2011

A robust asset management methodology is essential for effective decision-making of maintenance, repair and rehabilitation of a bridge network. It can be achieved by a computer-based bridge management system (BMS). Successful BMS development requires a reliable bridge deterioration model, which is the most crucial component in a BMS, and an optimal management philosophy. The maintenance optimization methodology proposed in this paper is developed for a small bridge network with limited structural condition rating records. . The methodology is organized in three major components: (1) bridge health index (BHI); (2) maintenance and budget optimization; and (3) reliable Artificial Intelligence (AI) based bridge deterioration model. The outcomes of the paper will help to identify BMS implementation problems and to provide appropriate solutions for managing small bridge networks.

• Smart Structures and Systems
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• v.31 no.4
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• pp.335-349
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• 2023

Bridges constantly undergo deterioration and damage, the most common ones being concrete damage and exposed rebar. Periodic inspection of bridges to identify damages can aid in their quick remediation. Likewise, identifying components can provide context for damage assessment and help gauge a bridge's state of interaction with its surroundings. Current inspection techniques rely on manual site visits, which can be time-consuming and costly. More recently, robotic inspection assisted by autonomous data analytics based on Computer Vision (CV) and Artificial Intelligence (AI) has been viewed as a suitable alternative to manual inspection because of its efficiency and accuracy. To aid research in this avenue, this study performs a comparative assessment of different architectures, loss functions, and ensembling strategies for the autonomous segmentation of bridge components and damages. The experiments lead to several interesting discoveries. Nested Reg-UNet architecture is found to outperform five other state-of-the-art architectures in both damage and component segmentation tasks. The architecture is built by combining a Nested UNet style dense configuration with a pretrained RegNet encoder. In terms of the mean Intersection over Union (mIoU) metric, the Nested Reg-UNet architecture provides an improvement of 2.86% on the damage segmentation task and 1.66% on the component segmentation task compared to the state-of-the-art UNet architecture. Furthermore, it is demonstrated that incorporating the Lovasz-Softmax loss function to counter class imbalance can boost performance by 3.44% in the component segmentation task over the most employed alternative, weighted Cross Entropy (wCE). Finally, weighted softmax ensembling is found to be quite effective when used synchronously with the Nested Reg-UNet architecture by providing mIoU improvement of 0.74% in the component segmentation task and 1.14% in the damage segmentation task over a single-architecture baseline. Overall, the best mIoU of 92.50% for the component segmentation task and 84.19% for the damage segmentation task validate the feasibility of these techniques for autonomous bridge component and damage segmentation using RGB images.

• Korean Journal of Weed Science
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• v.9 no.2
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• pp.103-107
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• 1989

Five rice cultivars were treated by four dose of butachlor at transplanting seedling stage. Visual injury rate by butachlor was lowest in Zhy-Lian-Ai-dun-Nam and highest in Samseungbyeo, but reduction of yield was higher in Zhy-Lian-Ai-Yun-Nam and Hangangchalbyeo than in Samseungbyeo and Cheungcheungbyeo. Reduction of panicle number by butachlor was most effective factor to yield reduction.