• Journal of the Korean earth science society
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• v.45 no.2
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• pp.111-120
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• 2024

Cloud coverage is a key factor in determining whether to proceed with observations. In the past, human judgment played an important role in weather evaluation for observations. However, the development of remote and robotic observation has diminished the role of human judgment. Moreover, it is not easy to evaluate weather conditions automatically because of the diverse cloud shapes and their rapid movement. In this paper, we present the development of a cloud monitoring program by applying a machine learning-based Python module "cloudynight" on all-sky camera images obtained at Miryang Arirang Astronomical Observatory (MAAO). The machine learning model was built by training 39,996 subregions divided from 1,212 images with altitude/azimuth angles and extracting 16 feature spaces. For our training model, the F1-score from the validation samples was 0.97, indicating good performance in identifying clouds in the all-sky image. As a result, this program calculates "Cloudiness" as the ratio of the number of total subregions to the number of subregions predicted to be covered by clouds. In the robotic observation, we set a policy that allows the telescope system to halt the observation when the "Cloudiness" exceeds 0.6 during the last 30 minutes. Following this policy, we found that there were no improper halts in the telescope system due to incorrect program decisions. We expect that robotic observation with the 0.7 m telescope at MAAO can be successfully operated using the cloud monitoring program.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.6
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• pp.3769-3777
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• 2015

Overseas Expansion is essential to expand domestic geospatial industries in a state of saturation. But current overseas expansion method has be limited to expand global market. Inter-industries convergence strategies may be most resonable alternative to expand global market through raising expansion possibility to developing countries with ODA funds and to developed countries with converging global competitive industries. This research analyzed the industry to develop a suitable way fusion between these industries. As a result, easiness of convergence, confidentiality of information, complementarity of poor infrastructure, responsiveness of various demands and sustainability of system are needed to successful convergence on multiple industries. This convergence framework is consists of geospatial convergence common framework based on cloud computing, inter-industries convergence model and institutional supporting system for overseas expansion.

• Tunnel and Underground Space
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• v.19 no.1
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• pp.1-10
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• 2009

Site characterization for structural stability in rock masses mainly involves the collection of joint property data, and in the current practice, much of this data is collected by hand directly at exposed slopes and outcrops. There are many issues with the collection of this data in the field, including issues of safety, slope access, field time, lack of data quantity, reusability of data and human bias. It is shown that information on joint orientation, spacing and roughness in rock masses, can be automatically extracted from LIDAR (light detection and ranging) point floods using the currently available Split-FX point cloud processing software, thereby reducing processing time, safety and human bias issues.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3960-3975
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• 2022

To analyze and compare the most influencing factors on cloud computing adoption (CCA) in the healthcare organization, a systematic review and meta-analyses of studies was performed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and Cochrane collaboration recommendations. A search of PubMed, ScienceDirect, Springer, Wiley Online, and Taylor & Francis Online digital libraries (From inception to January 19, 2022) was performed. A total of 17 studies met the defined studies' inclusion and exclusion criteria. Statistical significance difference favoring most influencing factors on CCA were (MD 0.76, 95% CI -1.48 - 3.01, p <0.00001, I² = 90%), (MD 1.40, 95% CI -4.76 - 7.55, p < 0.00007, I² = 97%) (MD 0.17, 95% CI -2.69 - 3.03, p<0.00001, I² = 96%) for technology vs. organizational, technology vs. environmental and business vs. human factors, respectively. Organizational and environmental factors had greater impacts on CCA compared with technological factors. Moreover, business factors were more influential than the human factors.

• Journal of IKEEE
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• v.27 no.4
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• pp.439-449
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• 2023

In an era marked by the increasing use of drones and the growing demand for indoor surveillance, the development of a robust application for detecting and tracking both drones and humans within indoor spaces becomes imperative. This study presents an innovative application that uses FMCW radar to detect human and drone motions from the cloud point. At the outset, the DBSCAN (Density-based Spatial Clustering of Applications with Noise) algorithm is utilized to categorize cloud points into distinct groups, each representing the objects present in the tracking area. Notably, this algorithm demonstrates remarkable efficiency, particularly in clustering drone point clouds, achieving an impressive accuracy of up to 92.8%. Subsequently, the clusters are discerned and classified into either humans or drones by employing a deep learning model. A trio of models, including Deep Neural Network (DNN), Residual Network (ResNet), and Long Short-Term Memory (LSTM), are applied, and the outcomes reveal that the ResNet model achieves the highest accuracy. It attains an impressive 98.62% accuracy for identifying drone clusters and a noteworthy 96.75% accuracy for human clusters.

• IEIE Transactions on Smart Processing and Computing
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• v.2 no.6
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• pp.367-372
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• 2013

Cloud computing technology has been developing at an increasing expansion rate. Today most of firms are using this technology, making improving the quality of service one of the most important issues. To achieve this, the system must operate efficiently with less idle time and without deteriorating the customer satisfaction. This paper focuses on enhancing the efficiency of a conventional Genetic Algorithm (GA) for task scheduling in cloud computing using Fuzzy Logic (FL). This study collected a group of task schedules and assessed the quality of each task schedule with the user expectation. The work iterates the best scheduling order genetic operations to make the optimal task schedule. General GA takes considerable time to find the correct scheduling order when all the fitness function parameters are the same. GA is an intuitive approach for solving problems because it covers all possible aspects of the problem. When this approach is combined with fuzzy logic (FL), it behaves like a human brain as a problem solver from an existing database (Memory). The present scheme compares GA with and without FL. Using FL, the proposed system at a 100, 400 and 1000 sample size*5 gave 70%, 57% and 47% better improvement in the task time compared to GA.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.673-676
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• 2017

Accompanying the Internet of Things (IoT) is a demand of advanced applications and services utilizing the potential of the IoT environment. Monitoring the environment for a provision of context-aware services to the human beings is one of the new trends in our future life. The IoTivity Cloud is one of the most notable open-source platform bringing an opportunity to collect, analyze, and interpret a huge amount of data available in the IoT environment. Based on the IoTivity Cloud, we aim to develop a novel platform for comprehensive monitoring of a future network, which facilitates on-demand data collection to enable the network behavior prediction and the quality of user experience maintenance. In consideration of performance evaluation of the monitoring platform, this paper presents results of a preliminary test on the data acquisition/supply process in the IoTivity Cloud.

• Electronics and Telecommunications Trends
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• v.34 no.1
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• pp.1-12
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• 2019

The role of Internet of Things (IoT) has been evolving from connectivity to intelligent and autonomous functions. The increase in the number of connected things and the volume of data has revealed the limit of cloud-based intelligent IoT. Meanwhile, the development of microprocessors for the IoT has enabled their intelligent decision making and reactions without the intervention of the cloud; this phase is referred to as the "autonomous IoT era." However, intelligence is not the only function of the IoT. When a cyber physical system (CPS) is running on the cloud, the real-time synchronization between the real and virtual worlds cannot be guaranteed. If a CPS is running on the IoT, both the worlds can be synchronized closely enough for a zero- time gap, i.e., achieving the goals of autonomous IoT. ETRI implements intelligence into the role of IoT and collaborates their decision making and reactions without the intervention of humans. Then, we focus on the development of a new IoT computing paradigm that enables human-like discussions.

• Journal of Navigation and Port Research
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• v.46 no.5
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• pp.458-470
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• 2022

As the 4th industrial revolution progresses, the application of several cutting-edge technologies such as the Internet of Things, big data, and mixed reality (MR) in relation to autonomous ships is being considered in the maritime logistics field. The aim of this study was to apply the concept of a digital twin model based on Human Machine Interaction (HMI) including a digital twin model and the role of an operator to a ship. The role of the digital twin is divided into information provision, support, decision, and implementation. The role of the operator is divided into operation, decision-making, supervision, and standby. The system constituting the ship was investigated. The digital twin system that could be applied to the ship was also investigated. The cloud-based digital twin system architecture that could apply investigated applications was divided into ship data collection (part 1), cloud system (part 2), analysis system/ application (part 3), and MR/mobile system (part 4). A Mixed Reality device HoloLens was used as an HMI equipment to perform a simulation test of a digital twin system of an 8 m battery-based electric propulsion ship.

• Convergence Security Journal
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• v.23 no.1
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• pp.79-87
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• 2023

Precision Medicine, which utilizes personal health information, genetic information, clinical information, etc., is growing as the next-generation medical industry. In Korea, medical institutions and information communication companies have coll aborated to provide cloud-based Precision Medicine Hospital Information Systems (P-HIS) to about 90 primary medical ins titutions over the past five years, and plan to continue promoting and expanding it to primary and secondary medical insti tutions for the next four years. Precision medicine is directly related to human health and life, making information protecti on and healthcare information protection very important. Therefore, this paper analyzes the preliminary research on inform ation protection models that can be utilized in cloud-based Precision Medicine Hospital Information Systems and ultimately proposes research on ways to improve information protection in P-HIS.