• International Journal of Internet, Broadcasting and Communication
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• v.15 no.1
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• pp.157-163
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• 2023

Recently, as businesses and data types become more complex and diverse, efficient data analysis using machine learning is required. However, since communication in the cloud environment is greatly affected by network latency, data analysis is not smooth if information delay occurs. In this paper, SPT (Safe Proper Time) was applied to the cluster-based machine learning data analysis agent proposed in previous studies to solve this delay problem. SPT is a method of remotely and directly accessing memory to a cluster that processes data between layers, effectively improving data transfer speed and ensuring timeliness and reliability of data transfer.

• Journal of Auto-vehicle Safety Association
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• v.10 no.1
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• pp.38-44
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• 2018

This paper presents an analysis on driving safety in lane change situation based on road driving data. Autonomous driving is a global trend in vehicle industry. LKAS technologies are already applied in commercial vehicle and researches about lane change maneuver have been actively studied. In autonomous vehicle, not only safety control issue but also imitating human driving maneuver is important. Driving data analysis in lane change situation has been usually dealt with ego vehicle information such as longitudinal acceleration, yaw rate, and steering angle. For this reason, developing safety index according to surrounding vehicle information based on human driving data is needed. In this research, driving data is collected from perception module using LIDAR, radar and RT-GPS sensors. By analyzing human driving pattern in lane change maneuver, safety index that considers both ego vehicle and surrounding vehicle state by using relative velocity and longitudinal clearance has been designed.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2001.10a
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• pp.50-54
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• 2001

Information systems development entails planning, analysis, design and construction phases. The analysis phase identifying user requirements is the most important of these phases. Since unidentified defects in the early phase causes increased work and costs as development proceeds, the quality of analysis results affects the quality of the resultant system. Major tasks in the analysis phase are data modeling and process modeling. Research on building a knowledge-based system for data modeling have been conducted much, however, not sufficiently for process modeling. As a system environment with high user interaction increases, research on process modeling methods and knowledge- based systems considering such environment are required. In this research, a process modeling framework for information systems with high user interaction is suggested and a knowledge-based system for supporting the suggested framework is implemented. A proposed model consists of the following tasks: event analysis, process analysis, and event/process interaction analysis. Event analysis identifies business events and their responses. Process analysis break down the processes of an enterprise into progressively increasing details. Decomposition begins at the function level and ends when the elementary process level is reached. Event/process interaction analysis verifies the results of process analysis and event analysis. A knowledge-based system for supporting a proposed process modeling framework is implemented in a web-based environment.

• KIPS Transactions on Computer and Communication Systems
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• v.3 no.10
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• pp.383-392
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• 2014

The research for coatings is one of the most popular and active research in the polymer industry. For the coatings, electronics industry, medical and optical fields are growing more important. In particular, the trend is the increasing of the technical requirements for the performance and accuracy of the coatings by the development of automotive and electronic parts. In addition, the industry has a need of more intelligent and automated system in the industry is increasing by introduction of the IoT and big data analysis based on the environmental information and the context information. In this paper, we propose an optimization model for the design of experiments based coating formulation data objects using the Internet technologies and big data analytics. In this paper, the coating formulation was calculated based on the best data analysis is based on the experimental design, modify the operator with respect to the error caused based on the coating formulation used in the actual production site data and the corrected result data. Further optimization model to correct the reference value by leveraging big data analysis and Internet of things technology only existing coating formulation is applied as the reference data using a manufacturing environment and context information retrieval in color and quality, the most important factor in maintaining and was derived. Based on data obtained from an experiment and analysis is improving the accuracy of the combination data and making it possible to give a LOT shorter working hours per data. Also the data shortens the production time due to the reduction in the delivery time per treatment and It can contribute to cost reduction or the like defect rate reduced. Further, it is possible to obtain a standard data in the manufacturing process for the various models.

• Journal of the Ergonomics Society of Korea
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• v.16 no.1
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• pp.119-126
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• 1997

Most of human factors experiments in nuclear industry domain produe lots of experimental data, thus much time is reauired to analyze the data. DAEXESS was developed to reduce resource demands necessary for the analysis work through systematic data analysis requirements and automated data processing based on computer technology. Physilolgical data, human behavior recording data, system log data and verbal protocl can be collected, synthesized and easily analyzed with with respect to time domain in DAEXESS so that analyser is able to look into inte- grated information on operating context. DAEXESS assists analyser to carry out qualitative and quantitative data analysis easily.

• Journal of the Korea Society of Computer and Information
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• v.28 no.1
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• pp.189-199
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• 2023

This study aimed to identify the development direction of Arduino-based boards relating to artificial intelligence based on social awareness identified using big data analytical methods. For the purpose, big data were extracted through the Textom website, focusing on keywords that included 'Arduino + artificial intelligence' and 'Arduino + AI', and these data were refined and analyzed using the Textom website and the UNICET program. In this study, big data analyses, including frequency analysis, TF-IDF analysis, Degree Centrality analysis, N-gram analysis, and CONCOR analysis, were performed. The analyses' results confirmed that keywords relating to education and coding education, keywords relating to making and experience based on Arduino, and keywords relating to programs were the main keywords used in Arduino- and artificial intelligence-related Internet documents, and clusters were formed based on these keywords confirmed. The social awareness of Arduino and artificial intelligence was evaluated, and the direction of board development was identified based on this social awareness. This study is meaningful in that it identified various factors of board development based on the general public's social awareness, which was evaluated using a big data analysis method. This study may serve as a point of reference for future researchers or developers wishing to understand user needs using big data analysis methods.

• Journal of Information Technology Services
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• v.21 no.2
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• pp.97-107
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• 2022

Recently, interest in smart factories is increasing. Investments to improve intelligence/automation are also being made continuously in manufacturing plants. Facility automation based on sensor data collection is now essential. In addition, we are operating our factories based on data generated in all areas of production, including production management, facility operation, and quality management, and an integrated standard information system. When producing LCD polarizer products, it is most important to link trace information between data generated by individual production processes. All systems involved in production must ensure that there is no data loss and data integrity is ensured. The large-capacity data collected from individual systems is composed of key values linked to each other. A real-time quality analysis processing system based on connected integrated system data is required. In this study, large-capacity data collection, storage, integration and loss prevention methods were presented for optimization of LCD polarizer production. The identification Risk model of inspection products can be added, and the applicable product model is designed to be continuously expanded. A quality inspection and analysis system that maximizes the yield rate was designed by using the final inspection image of the product using big data technology. In the case of products that are predefined as analysable products, it is designed to be verified with the big data knn analysis model, and individual analysis results are continuously applied to the actual production site to operate in a virtuous cycle structure. Production Optimization was performed by applying it to the currently produced LCD polarizer production line.

• Journal of Korea Multimedia Society
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• v.12 no.6
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• pp.777-784
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• 2009

Recently, the importance of geoCRM (geographic Customer Relationship Management) systems are growing rapidly. So, result of the recognition that their applications extend well beyond the traditional CRM systems with the advent of ubiquitous environment and generalized location based services. A majority of traditional CRM systems are either incapable of managing spatial data or are not user-friendly when doing so. On the other hand, the geoCRM systems can be built as providing the geographic-based functions about CRM, including spatial and market analyses and the visualization of customer data, etc. However, it lacks the specific model and implementation of the geoCRM systems, being caused by the incomprehension of needs, the absence of related standards and the difficulties of development, and so on. In this paper, we develop a new spatial analysis supporting system that to enhance productivity through the convenient use and management of spatial data. The functionality provided by our system includes a set of analysis functions based on data mining techniques which allow a user to affect powerful transformation on spatial data. Particularly, both spatial data and non-spatial attributes can be efficiently handled as an object through our OODBMS.

• International journal of advanced smart convergence
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• v.2 no.1
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• pp.36-41
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• 2013

Decision-making is to extract information that can be executed in the future, it refers to the process of discovering a new data model that is induced in the data. In other words, it is to find out the information to peel off to find the vein to catch the relationship between the hidden patterns in data. The information found here, is a process of finding the relationship between the useful patterns by applying modeling techniques and sophisticated statistical analysis of the data. It is called data mining which is a key technology for marketing database. Therefore, research for cluster analysis of the current is performed actively, which is capable of extracting information on the basis of the large data set without a clear criterion. The EM and K-means methods are used a lot in particular, how the result values of evaluating are come out in experiments, which are depending on the size of the data by the type of distance-based and probability-based data analysis.

• International journal of advanced smart convergence
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• v.12 no.3
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• pp.104-108
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• 2023

This paper deals with research on innovative systems using Python-based artificial intelligence technology in the field of plant growth monitoring. The importance of monitoring and analyzing the health status and growth environment of plants in real time contributes to improving the efficiency and quality of crop production. This paper proposes a method of processing and analyzing plant image data using computer vision and deep learning technologies. The system was implemented using Python language and the main deep learning framework, TensorFlow, PyTorch. A camera system that monitors plants in real time acquires image data and provides it as input to a deep neural network model. This model was used to determine the growth state of plants, the presence of pests, and nutritional status. The proposed system provides users with information on plant state changes in real time by providing monitoring results in the form of visual or notification. In addition, it is also used to predict future growth conditions or anomalies by building data analysis and prediction models based on the collected data. This paper is about the design and implementation of Python-based plant growth monitoring systems, data processing and analysis methods, and is expected to contribute to important research areas for improving plant production efficiency and reducing resource consumption.