• Corrosion Science and Technology
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• v.23 no.3
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• pp.228-234
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• 2024

Zinc wiping is a phenomenon used to control zinc-coating thickness on steel substrate during hot dip galvanizing by equipment called air knife. Uniformity of zinc coating weight in length and width profile along with surface quality are most critical quality parameters of galvanized steel. Deviation from tolerance level of coating thickness causes issues like overcoating (excess consumption of costly zinc) or undercoating leading to rejections due to non-compliance of customer requirement. Main contributor of deviation from target coating weight is dynamic change in air knives equipment setup when thickness, width, and type of substrate changes. Additionally, cold coating measurement gauge measure coating weight after solidification but are installed down the line from air knife resulting in delayed feedback. This study presents a coating weight control model (Galvantage) predicting critical air knife parameters air pressure, knife distance from strip and line speed for coating control. A reverse engineering approach is adopted to design a predictive, prescriptive, and descriptive model recommending air knife setups that estimate air knife distance and expected coating weight in real time. Implementation of this model eliminates feedback lag experienced due to location of coating gauge and achieving setup without trial-error by operator.

• Safety and Health at Work
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• v.15 no.3
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• pp.373-378
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• 2024

Introduction: Following the Republic of Korea electric power industry site-specific safety management system, this paper proposes a novel safety autonomous platform (SAP) architecture that can automatically and precisely manage on-site safety through ensemble artificial intelligence (AI) models. The ensemble AI model was generated from video information and worker's biometric information as learning data and the estimation results of this model are based on standard operating procedures of the workplace and safety rules. Methods: The ensemble AI model is designed and implemented by the Hadoop ecosystem with Kafka/NiFi, Spark/Hive, HUE, and ELK (Elasticsearch, Logstash, Kibana). Results: The functional evaluation shows that the main function of this SAP architecture was operated successfully. Discussion: The proposed model is confirmed to work well with safety mobility gateways to provide some safety applications.

• Journal of Advanced Navigation Technology
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• v.27 no.2
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• pp.235-246
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• 2023

In the rapid technological development of artificial intelligence (AI), electric vehicles, and robots based the fourth industrial revolution, semiconductors determine the core performance, and semiconductor competitiveness is directly related to national competitiveness. However, the Korean semiconductor industry has continuously weakened its competitiveness in the system semiconductor field, excluding memory semiconductors, so in this study, a new smart contract basedblockchain business model to engage the global market, which is the most urgent need for the growth of Korean fabless system semiconductor industry in recession. F-SBM (Fabless-Smart contract based Blockchain Model) proposed. In this study, through the new F-SBM, it was verified how to engage new customers for fabless firms through smart contract based consortium blockchain regarding technology, economy, and reliability items of fabless. This model has great significance in improving the high entry barriers to engaging new customers for the long-cherished desire of the Korean fabless system semiconductor industry and deriving new growth solutions.

• Smart Media Journal
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• v.13 no.1
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• pp.9-17
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• 2024

This study aims to develop an integrated agricultural distribution network management system to improve the quality, profit, and decision-making efficiency of agricultural products. We adopt two key techniques: crop maturity detection based on the YOLOX target detection algorithm and market price prediction based on the Prophet model. By training the target detection model, it was possible to accurately identify crops of various maturity stages, thereby optimizing the shipment timing. At the same time, by collecting historical market price data and predicting prices using the Prophet model, we provided reliable price trend information to shipping decision makers. According to the results of the study, it was found that the performance of the model considering the holiday factor was significantly superior to that of the model that did not, proving that the effect of the holiday on the price was strong. The system provides strong tools and decision support to farmers and agricultural distribution managers, helping them make smart decisions during various seasons and holidays. In addition, it is possible to optimize the distribution network of agricultural products and improve the quality and profit of agricultural products.

• Korean Journal of Applied Biomechanics
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• v.32 no.1
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• pp.1-8
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• 2022

Objective: This study proposes a methodology to analyze important variables that have a significant impact on the putting direction prediction using a machine learning-based putting direction prediction model trained with IMU sensor data. Method: Putting data were collected using an IMU sensor measuring 12 variables from 6 adult males in their 20s at K University who had no golf experience. The data was preprocessed so that it could be applied to machine learning, and a model was built using five machine learning algorithms. Finally, by comparing the performance of the built models, the model with the highest performance was selected as the proposed model, and then 12 variables of the IMU sensor were applied one by one to analyze important variables affecting the learning performance. Results: As a result of comparing the performance of five machine learning algorithms (K-NN, Naive Bayes, Decision Tree, Random Forest, and Light GBM), the prediction accuracy of the Light GBM-based prediction model was higher than that of other algorithms. Using the Light GBM algorithm, which had excellent performance, an experiment was performed to rank the importance of variables that affect the direction prediction of the model. Conclusion: Among the five machine learning algorithms, the algorithm that best predicts the putting direction was the Light GBM algorithm. When the model predicted the putting direction, the variable that had the greatest influence was the left-right inclination (Roll).

• Journal of the military operations research society of Korea
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• v.23 no.2
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• pp.45-71
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• 1997

There has been considerable research recently on uncertainty handling in the fields of artificial intelligence and knowledge-based system. Various numerical and non-numerical methods have been proposed for representing and propagating uncertainty in knowledge-based system. The Bayesian method, the Dempster-Shafer's Evidence Theory, the Certainty Factor model and the Fuzzy Set Theory are most frequently appeared in the knowledge-based system. Each of these four methods views uncertainty from a different perspective and propagates it differently. There is no single method which can handle uncertainty properly in all kinds of knowledge-based systems' domain. Therefore a knowledge-based system will work more effectively when the uncertainty handling method in the system fits to the system's environment. This paper proposed a framework for selecting proper uncertainty handling methods in knowledge-based system with respect to characteristics of problem domain and cognitive styles of experts. A schema with strategic/operational and unstructured/structured classification is employed to differenciate domain. And a schema with systematic/intuitive and preceptive/receptive classification is employed to differenciate experts' cognitive style. The characteristics of uncertainty handling methods are compared with characteristics of problem domains and cognitive styles respectively. Then a proper uncertainty handling method is proposed for each category.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.6
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• pp.239-248
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• 2018

Many new technologies are studied with the arrival of the 4th industrial revolution. In particular, emotional intelligence is one of the popular issues. Researchers are focused on emotional analysis studies for music services, based on artificial intelligence and pattern recognition. However, they do not consider how we recommend proper music according to the specific emotion of the user. This is the practical issue for music-related IoT applications. Thus, in this paper, we propose an probability-based music emotion classification technique that makes it possible to classify music with high precision based on the range of emotion, when developing music related services. For user emotion recognition, one of the popular emotional model, Russell model, is referenced. For the features of music, the average amplitude, peak-average, the number of wavelength, average wavelength, and beats per minute were extracted. Multiple regressions were derived using regression analysis based on the collected data, and probability-based emotion classification was carried out. In our 2 different experiments, the emotion matching rate shows 70.94% and 86.21% by the proposed technique, and 66.83% and 76.85% by the survey participants. From the experiment, the proposed technique generates improved results for music classification.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1031-1042
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• 2023

In this study, a deep learning model was developed to predict the yield of cabbage and radish, one of the five major supply and demand management vegetables, using satellite images of Landsat 8. To predict the yield of cabbage and radish in Gangwon-do from 2015 to 2020, satellite images from June to September, the growing period of cabbage and radish, were used. Normalized difference vegetation index, enhanced vegetation index, lead area index, and land surface temperature were employed in this study as input data for the yield model. Crop yields can be effectively predicted using satellite images because satellites collect continuous spatiotemporal data on the global environment. Based on the model developed previous study, a model designed for input data was proposed in this study. Using time series satellite images, convolutional neural network, a deep learning model, was used to predict crop yield. Landsat 8 provides images every 16 days, but it is difficult to acquire images especially in summer due to the influence of weather such as clouds. As a result, yield prediction was conducted by splitting June to July into one part and August to September into two. Yield prediction was performed using a machine learning approach and reference models , and modeling performance was compared. The model's performance and early predictability were assessed using year-by-year cross-validation and early prediction. The findings of this study could be applied as basic studies to predict the yield of field crops in Korea.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1993.10a
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• pp.550-555
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• 1993

The aim of this paper is to present the development of visual seam tracking system equipped with visual range finder. The visual range finder, which consists of a CCD camera and a diode laser system with line generating optics, developed to recognize the types of weld joints and detect the location of weld joints. In practical applications, however, images of the weld joints are often degraded due to spatters, are flares, surface specularity, and welding smoke. To overcome the problem, this paper proposes a syntactic approach which is a class of artificial intelligence techniques. In the approach, the type of weld joint is inferred based upon the production rules which are linguiques grammars consisting of a set of line and junction primitives of laser strip image projected on weld joint. The production rules eliminate several noisy primitives to create new primitives through the merging process of primitives. After the recognition of weld joint, arc welding is started and the location of weld joints is repeatedly detected using a spring model-based template matching in which the template model is a by-product of the recognition process of weld joint. To show the effectiveness of the proposed approach a series of experiments-identification and robotic tracking-are conducted for four different types of weld joints.

• Journal of Information Technology Services
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• v.20 no.1
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• pp.81-92
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• 2021

As the degree of economic development of society increases, the maintenance issues on the existing social overhead capital becomes essential. Accordingly, the adaptation of the concept of Level of service in highway maintenance is indispensable. It is also crucial to manage and perform the service level such as road assets to provide universal services to users. In this regards, the purpose of this study is to improve the maintenance service rating model and to focus on the assessment items and weights among the improvements. Particularly, in determining weights, an Analytic Hierarchy Process (AHP) is performed based on the survey response results. After then, this study conducts unsupervised neural network models such as Self-Organizing Map (SOM) and Davies-Bouldin (DB) Index to divide proper sub-groups and determine priorities. This paper identifies similar cases by grouping the results of the responses based on the similarity of the survey responses. This can effectively support decision making in general situations where many evaluation factors need to be considered at once, resulting in reasonable policy decisions. It is the process of using advanced technology to find optimized management methods for maintenance.