• Journal of Information Technology Applications and Management
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• v.23 no.3
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• pp.61-69
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• 2016

Growing information threats have threatened organization to lose information security controls in these days. Many organizations have accepted the various information security management systems does mention necessity of a continuous evaluation process for the executions of information security management in a theoretical aspect. This study suggests a continuous evaluation process for information security management reflecting the real execution of managers and employees in organizations.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.33 no.3
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• pp.375-383
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• 2023

Objectives: Coal-fired power plants feature diverse working conditions, including multi-layered employment structures and irregular work cycles due to outsourcing and non-standardized tasks. The current uniform occupational environment measurement systems have limitations in accurately assessing and evaluating these varied conditions. This study aims to propose alternative measurement and assessment strategies to supplement existing methods. Methods: Major domestic coal-fired power plants were selected as the study targets. To prepare for the study and establish strategies, work processes were identified and existing occupational environment measurement results were compared and analyzed. The study proceeded by employing three strategies: specific exposure groups (SEGs) measurement, continuous monitoring, and supplementary measurements, which were then compared and discussed. Results: Previous exposure index evaluations (5,268 cases) indicated that crystalline silica, a type of respirable particulate matter, had detection limits below the threshold (non-detectable) in 82.6% (4,349 cases) of instances. Exposures below 10% of the exposure limit were observed at a very low concentration of 96.1%. Similar exposure group measurements yielded results where detection limits were below the threshold in 38.2% of cases, and exposures below 10% of the limit were observed in 70.6%. Continuous monitoring indicated detection limits below the threshold in 12.6% of cases, and exposures below 10% of the limit were observed in 75.6%. Instances requiring active workplace management accounted for more than 30% of cases, with SEGs at 11.8% (four cases), showing a higher proportion compared to 3.0% (four cases) in continuous monitoring. For coal dust, exposures below 10% of the limit were highest in legal measurements at 90.2% (113 cases), followed by 74.0% (91 cases) in continuous monitoring, and 47.0% (16 cases) in SEGs. Instances exceeding 30% were most prevalent in SEGs at 14.7% (five cases), followed by legal measurements at 5.0% (eight cases), and continuous monitoring at 2.4% (three cases). When examining exposure levels through arithmetic means, crystalline silica was found to be 104.7% higher in SEGs at 0.0088 mg/m³ compared to 0.0043 mg/m³ in continuous monitoring. Coal dust measurements were highest in SEGs at 0.1247 mg/m³, followed by 0.1224 mg/m³ in legal measurements, and 0.0935 mg/m³ in continuous monitoring. Conclusions: Strategies involving SEGs measurement and continuous monitoring can enhance measurement reliability in environments with irregular work processes and frequent fluctuations in working conditions, as observed in coal-fired power plants. These strategies reduce the likelihood of omitting or underestimating processes and enhance measurement accuracy. In particular, a significant reduction in detection limits below the threshold for crystalline silica was observed. Supplementary measurements can identify worker exposure characteristics, uncover potential risks in blind spots of management, and provide a complementary method for legal measurements.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.18-18
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• 1996

This paper reviews some of the most prominent and promising areas of chemical process control both in relations to batch and continuous processes. These areas include the modeling, optimization, control and monitoring of chemical processes and entire plants. Most of these areas explicitly utilize a model of the process. For this purpose the types of models used are examined in some detail. These types of models are categorized in knowledge-driven and datadriven classes. In the areas of modeling and optimization, attention is paid to batch reactors using the Tendency Modeling approach. These Tendency models consist of data- and knowledge-driven components and are often called Gray or Hybrid models. In the case of continuous processes, emphasis is placed in the closed-loop identification of a state space model and their use in Model Predictive Control nonlinear processes, such as the Fluidized Catalytic Cracking process. The effective monitoring of multivariate process is examined through the use of statistical charts obtained by the use of Principal Component Analysis (PMC). Static and dynamic charts account for the cross and auto-correlation of the substantial number of variables measured on-line. Centralized and de-centralized chart also aim in isolating the source of process disturbances so that they can be eliminated. Even though significant progress has been made during the last decade, the challenges for the next ten years are substantial. Present progress is strongly influenced by the economical benefits industry is deriving from the use of these advanced techniques. Future progress will be further catalyzed from the harmonious collaboration of University and Industrial researchers.

• Coupled systems mechanics
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• v.3 no.1
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• pp.27-52
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• 2014

For landfill monitoring and aftercare, long-term prognoses of emission and deformation behaviour are required. Landfills may be considered as heterogeneous porous soil-like structures, in which flow and transport processes of gases and liquids interact with local material degradation and mechanical deformation of the solid skeleton. Therefore, in the framework of continuous porous media mechanics a model is developed that permits the investigation of coupled mechanical, hydraulical and biochemical processes in municipal solid waste landfills.

• Ocean Science Journal
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• v.40 no.1
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• pp.25-44
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• 2005

Long-term, continuous, and real-time ocean monitoring has been undertaken in order to evaluate various oceanographic phenomena and processes in the East/Japan Sea. Recent technical advances combined with our concerted efforts have allowed us to establish a real-time monitoring system and to accumulate considerable knowledge on what has been taking place in water properties, current systems, and circulation in the East Sea. We have obtained information on volume transport across the Korea Strait through cable voltage measurements and continuous temperature and salinity profile data from ARGO floats placed throughout entire East Sea since 1997. These ARGO float data have been utilized to estimate deep current, inertial kinetic energy, and changes in water mass, especially in the northern East Sea. We have also developed the East Sea Real-time Ocean Buoy (ESROB) in coastal regions and made continual improvements till it has evolved into the most up-to-date and effective monitoring system as a result of remarkable technical progress in data communication systems. Atmospheric and oceanic measurements by ESROB have contributed to the recognition of coastal wind variability, current fluctuations, and internal waves near and off the eastern coast of Korea. Long-tenn current meter moorings have been in operation since 1996 between Ulleungdo and Dokdo to monitor the interbasin deep water exchanges between the Japanese and Ulleung Basins. In addition, remotely sensed satellite data could facilitate the investigation of atmospheric and oceanic surface conditions such as sea surface temperature (SST), sea surface height, near-surface winds, oceanic color, surface roughness, and so on. These satellite data revealed surface frontal structures with a fairly good spatial resolution, seasonal cycle of SST, atmospheric wind forcing, geostrophic current anomalies, and biogeochemical processes associated with physical forcing and processes. Since the East Sea has been recognized as a natural laboratory for global oceanic changes and a clue to abrupt climate change, we aim at constructing a 4-D continuous real-time monitoring system, over a decade at least, using the most advanced techniques to understand a variety of oceanic processes in the East Sea.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.4 no.1
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• pp.93-100
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• 2000

This paper deals with some fundamental matters pertaining to estimation of critical quantities associated with continuous processes which are frequently related to the quality rating of the product. Specifically, it examines bounds on estimation and bounds on the estimation error variance. It draws on recent results from the theory of mathematical inequalities and their applications.

• Smart Structures and Systems
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• v.12 no.2
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• pp.209-233
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• 2013

With an annual growth rate of about 30%, wind energy systems, such as wind turbines, represent one of the fastest growing renewable energy technologies. Continuous structural health monitoring of wind turbines can help improving structural reliability and facilitating optimal decisions with respect to maintenance and operation at minimum associated life-cycle costs. This paper presents an integrated monitoring system that is designed to support structural assessment and life-cycle management of wind turbines. The monitoring system systematically integrates a wide variety of hardware and software modules, including sensors and computer systems for automated data acquisition, data analysis and data archival, a multiagent-based system for self-diagnosis of sensor malfunctions, a model updating and damage detection framework for structural assessment, and a management module for monitoring the structural condition and the operational efficiency of the wind turbine. The monitoring system has been installed on a 500 kW wind turbine located in Germany. Since its initial deployment in 2009, the system automatically collects and processes structural, environmental, and operational wind turbine data. The results demonstrate the potential of the proposed approach not only to ensure continuous safety of the structures, but also to enable cost-efficient maintenance and operation of wind turbines.

• Proceedings of the IEEK Conference
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• 2003.07d
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• pp.1315-1318
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• 2003

Wastewater treatment processes are usually located in the outskirts of cities. But these processes should be dealt with continuous maintenance by expert operators. Therefore, in this paper, unmaned and automated control system is designed for the SBR(Sequencing Batch Reactor) plant. This plant is constructed in Gimhae city. Networks and wireless modules are employed for the data transmission. A local controller is in the SBR plant as a client and a monitoring system is located in the other place as a server. Remote control and monitoring system are constructed at the laboratory of ours. Measured data from plant sensors are translated to the remote site using communication modules, and then the data could be displayed and analyzed by means of remote monitoring and control systems.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.3
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• pp.50-60
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• 1998

This paper is concerned with the design of two residual control charts for real-time monitoring of the continuous flow processes. Two different control charts are designed under the situation that observations are correlated each other. Kalman-Filter based model estimation is employed when the process model is known. A black-box approach, based on Back-Propagation Neural Network, is also applied for the design of control chart when there is no prior information of process model. Performance of the designed control charts and traditional control charts is evaluated. Average run length(ARL) is adopted as a criterion for comparison. Experimental results show that the designed control chart using the Neural Network's modeling has shorter ARL than that of the other control charts when process mean is shifted. This means that the designed control chart detects the out-of-control state of the process faster than the others. The designed control chart using the Kalman-Filter based model estimation also has better performance than traditional control chart when process is out-of-control state.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1243-1244
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• 2022

In recent years, the growing interest in off-site construction has led to factories scaling up their manufacturing and production processes in the construction sector. Consequently, continuous large-scale site monitoring in low-variability environments, such as prefabricated components production plants (precast concrete production), has gained increasing importance. Although many studies on computer vision-based site monitoring have been conducted, challenges for deploying this technology for large-scale field applications still remain. One of the issues is collecting and transmitting vast amounts of video data. Continuous site monitoring systems are based on real-time video data collection and analysis, which requires excessive computational resources and network traffic. In addition, it is difficult to integrate various object information with different sizes and scales into a single scene. Various sizes and types of objects (e.g., workers, heavy equipment, and materials) exist in a plant production environment, and these objects should be detected simultaneously for effective site monitoring. However, with the existing object detection algorithms, it is difficult to simultaneously detect objects with significant differences in size because collecting and training massive amounts of object image data with various scales is necessary. This study thus developed a large-scale site monitoring system using edge computing and a small-object detection system to solve these problems. Edge computing is a distributed information technology architecture wherein the image or video data is processed near the originating source, not on a centralized server or cloud. By inferring information from the AI computing module equipped with CCTVs and communicating only the processed information with the server, it is possible to reduce excessive network traffic. Small-object detection is an innovative method to detect different-sized objects by cropping the raw image and setting the appropriate number of rows and columns for image splitting based on the target object size. This enables the detection of small objects from cropped and magnified images. The detected small objects can then be expressed in the original image. In the inference process, this study used the YOLO-v5 algorithm, known for its fast processing speed and widely used for real-time object detection. This method could effectively detect large and even small objects that were difficult to detect with the existing object detection algorithms. When the large-scale site monitoring system was tested, it performed well in detecting small objects, such as workers in a large-scale view of construction sites, which were inaccurately detected by the existing algorithms. Our next goal is to incorporate various safety monitoring and risk analysis algorithms into this system, such as collision risk estimation, based on the time-to-collision concept, enabling the optimization of safety routes by accumulating workers' paths and inferring the risky areas based on workers' trajectory patterns. Through such developments, this continuous large-scale site monitoring system can guide a construction plant's safety management system more effectively.