• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.10
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• pp.3275-3298
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• 2022

Cloud-based architectures for precision agriculture are domain-specific controlled and require remote access to process and analyze the collected data over third-party cloud computing platforms. Due to the dynamic changes in agricultural parameters and restrictions in terms of accessing cloud platforms, developing a locally controlled and real-time configured architecture is crucial for efficient water irrigation and farmers management in agricultural fields. Thus, we present a new implementation of an independent sensor-enabled architecture using variety of wireless-based sensors to capture soil moisture level, amount of supplied water, and compute the reference evapotranspiration (ETo). Both parameters of soil moisture content and ETo values was then used to manage the amount of irrigated water in a small-scale agriculture field for 356 days. We collected around 34,200 experimental data samples to evaluate the performance of the architecture under different agriculture parameters and conditions, which have significant influence on realizing real-time monitoring of agricultural fields. In a proof of concept, we provide empirical results that show that our architecture performs favorably against the cloud-based architecture, as evaluated on collected experimental data through different statistical performance models. Experimental results demonstrate that the architecture has potential practical application in a many of farming activities, including water irrigation management and agricultural condition control.

• 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 Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.204-206
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• 2022

Digital twin, which is evaluated as the core of smart factory advancement, is a technology that implements a digital replica in the virtual world with the same properties and functions of assets in the real world. Since the smart factory to which digital twin is applied can support services such as real-time production process monitoring, production process simulation, and predictive maintenance of facilities, it is expected to contribute to reducing production costs and improving productivity. AAS (Asset Administration Shell) is an essential technology for implementing digital twin and supports a method to digitally represent physical assets in real world. In this paper, we design AAS for manufacturing data gathering to be used in real-time CNC (Computer Numerical Control) monitoring system in operation by considering manufacturing facility in smart factory as assets.

• Journal of Korean Society for Atmospheric Environment
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• v.34 no.3
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• pp.393-405
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• 2018

Volatile organic compounds (VOCs) are representative air pollutants due to their detrimental effects on human health and their role in formation of secondary organic aerosols. Assessments and monitoring programs of VOCs using periodic grab sampling like Tedlar bags, canisters, and sorbent traps provide limited information, often with delay times of days or weeks. Selected ion flow tube mass spectrometry (SIFT-MS) is an emerging analytical technique for the real-time quantification of VOCs in air. It relies on chemical ionization of the VOCs molecules in air introduced into helium carrier gas using $H_3O^+$, $NO^+$, and $O_2{^+}$ precursor ions. Real-time monitoring method of 60 VOCs in the ambient air was developed using TO-15 standard gas mixture. Calibration curves, method detection limit, and quantitation reproducibility of the target compounds were tested. Dynamic dilution system was used to dilute standard gas from 0.174 ppbv to 100 ppbv, where calibration curves showed good linearity with $r^2$> 0.95 in all target analytes. Limit of detection (LOD) all compounds were sub ppbv, and some halogenated compounds showed pptv levels. Seven consecutive analyses of target compounds showed good repeatability with relative standard deviation of less than 10%. One day monitoring of VOCs in ambient air was conducted in Geoje. Average concentration of target VOCs in Geoje were relatively lower than other regions, among which formaldehyde showed the highest concentration ($15.4{\pm}5.78ppbv$). SIFT-MS provided good temporal resolution data (1 data per 3.2 minute), which can be used for identifying ephemeral short-term event. It is expected that SIFT-MS will be a versatile monitoring platform for VOCs in ambient air.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.12
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• pp.546-554
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• 2019

Typically, closed-circuit television (CCTV) monitoring is mainly used for post-processes (i.e. to provide evidence after an incident has occurred), but by using a streaming video feed, machine-based learning, and advanced image recognition techniques, current technology can be extended to respond to crimes or reports of missing persons in real time. The multi-CCTV cooperation technique developed in this study is a program model that delivers similarity information about a suspect (or moving object) extracted via CCTV at one location and sent to a monitoring agent to track the selected suspect or object when he, she, or it moves out of range to another CCTV camera. To improve the operating efficiency of local government CCTV control centers, we describe here the partial automation of a CCTV control system that currently relies upon monitoring by human agents. We envisage an integrated crime prevention service, which incorporates the cooperative CCTV network suggested in this study and that can easily be experienced by citizens in ways such as determining a precise individual location in real time and providing a crime prevention service linked to smartphones and/or crime prevention/safety information.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2006.06a
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• pp.65-66
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• 2006

As industrial technology recently, a field of marine engineering is demanding integration and manless system. In this study, tank monitoring system is developed, which is able to watch real-time the marine tank for measuring a level, pressure, temperature. The system will provide efficiently measuring data for operator.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2009.06a
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• pp.1059-1060
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• 2009

• 연구논문집
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• s.24
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• pp.129-140
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• 1994

Design and implementation of monitoring and control system using dual-microprocessor node is presented for real time process. The proposed system is implemented with 2 of the single chip microprocessors in tightly coupled mode and results in speed up of $s_p=1.74.$ Under the assumption that the nodes are interconnected in multidrop. the overall system performance such as average throughout-delay characteristics and effective throughput are analyzed using M/G/1 gueueing model, and results show that the proposed node can be used to medium sized distributed monitoring and control system.

• Journal of KIISE:Computing Practices and Letters
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• v.10 no.3
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• pp.221-230
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• 2004

In general, remote monitoring systems monitor the status of distributed hosts and/or applications in real-time for diverse managerial purposes. However, most of the extant systems have a few undesirable problems. First of all, they are platform-dependent and are not resilient to network and/or host failures. Moreover, they normally focus on the resource usage trends in monitored hosts, rather than on the status change of the applications running on them. We strongly believe that the latter has more direct and profound effect on the resource usage patterns on each host. In this paper, we present the design and implementation of the Remote Applications Monitoring System (RAMS) that enables us to effectively manage distributed applications through a real-time monitoring of their respective resource usages. The RAMS is a centralized system that consists of many distributed agents and a single centralized manager. An agent on each host is in charge of collecting and reporting the status of local applications. The manager handles agent registration and provides a central access point to the selection and monitoring of distributed applications. The salient features of the system include robustness and portability The adoption of JINI greatly facilitates an automatic recovery from partial network failure and host failure.

• The Journal of the Korea Contents Association
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• v.11 no.4
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• pp.101-111
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• 2011

Due to recent development in wireless communication technologies and mobile information devices, the services on ubiquitous computing technology without time and place restriction have been spotlighted. Moreover, the interest of the Wireless Sensor Networks (WSNs) and context-awareness technologies have largely been escalating and their technologies utilization is active in the various applications such as healthcare, farm management and so on. However, the direct adaption of the existing context-awareness technique to the WSN technology has several drawbacks as follows. First, such systems waste precious energy of sensor nodes, due to unnecessary data transmissions. Secondly, since the existing work was designed to support only specific applications, it is required to implement a new context-awareness application for a specific purpose. Therefore, we, in this paper, propose a new real-time monitoring system based on context-awareness in WSNs. Our system not only enhances energy efficiency by reducing data transmissions by doing context-awareness on a sensor node, but also is scalable in terms of supporting new context-awareness functionalities through modularization.