• Korean Journal of Hazardous Materials
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• v.2 no.1
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• pp.31-37
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• 2014

Since the unprecedented hydrogen fluoride leak accident in 2012, there has been growing demand for customized technical information for rapid response and chemical accident management agencies including the Ministry of Environment, the National Emergency Management Agency, and the National Police Agency need more information on chemicals and accident management. In this regard, this study aims to provide reliable technical data and guidelines to initial response agencies, similar to accident management technical reports of the US and Canada. In this study, we conducted a questionnaire survey and interviews on initial response agencies like fire stations, police stations, and local governments to identify new information items for appropriate initial response and improvements of current guidelines. We also collected and reviewed the Canada's TIPS, US EPA's hydrogen fluoride documents, domestic and foreign literature on applicability tests of control chemicals, and interview data, and then produced items to be listed in the technical guidelines. In addition, to establish database of on-site technical information, we carried out applicability tests for accident control data including ① emergency shut down devide, safety guard, shut down valve, ground connection, dyke, transfer pipe, scrubber, and sensor; ② literature and field survey on distribution type and transportation/storage characteristics (container identification, valve, ground connection, etc.); ③ classification and identification of storage/transportation facilities and emergency management methodslike leak prevention, chemicals control, and cutoff or bypass of rain drainage; ④ domestic/foreign analysis methods and environmental standards including portable detection methods, test standards, and exposure limits; and ⑤ comparison/evaluation of neutralization efficiency of control chemicals on toxic substances.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.87-95
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• 2020

Generally, occupational safety and particularly construction safety is an intricate phenomenon. Industry professionals have devoted vital attention to enforcing Occupational Safety and Health (OHS) from the last three decades to enhance safety management in construction. Despite the efforts of the safety professionals and government agencies, current safety management still relies on manual inspections which are infrequent, time-consuming and prone to error. Extensive research has been carried out to deal with high fatality rates confronting by the construction industry. Sensor systems, visualization-based technologies, and tracking techniques have been deployed by researchers in the last decade. Recently in the construction industry, computer vision has attracted significant attention worldwide. However, the literature revealed the narrow scope of the computer vision technology for safety management, hence, broad scope research for safety monitoring is desired to attain a complete automatic job site monitoring. With this regard, the development of a broader scope computer vision-based risk recognition system for correlation detection between the construction entities is inevitable. For this purpose, a detailed analysis has been conducted and related rules which depict the correlations (positive and negative) between the construction entities were extracted. Deep learning supported Mask R-CNN algorithm is applied to train the model. As proof of concept, a prototype is developed based on real scenarios. The proposed approach is expected to enhance the effectiveness of safety inspection and reduce the encountered burden on safety managers. It is anticipated that this approach may enable a reduction in injuries and fatalities by implementing the exact relevant safety rules and will contribute to enhance the overall safety management and monitoring performance.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.12
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• pp.357-362
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• 2023

Bridges are important transportation infrastructure, but they are subject to damage and cracking due to various environmental factors and constant traffic loads, which accelerate their aging. With many bridges now older than their original construction, there is a need for systems to ensure safety and diagnose deterioration. Bridges are already utilizing structural health monitoring (SHM) technology to monitor the condition of bridges in real time or periodically. Along with this technology, the development of intelligent bridge monitoring technology utilizing artificial intelligence and Internet of Things technology is underway. In this paper, we study an edge system technique for predicting bridge safety using fast Fourier transform and dimensionality reduction algorithm for maintenance of aging bridges. In particular, unlike previous studies, we investigate whether it is possible to form a dataset using sensor data collected from actual bridges and check the safety of bridges.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.46-63
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• 2024

One of the biggest dangers in the globe is water contamination. Water is a necessity for human survival. In most cities, the digging of borewells is restricted. In some cities, the borewell is allowed for only drinking water. Hence, the scarcity of drinking water is a vital issue for industries and villas. Most of the water sources in and around the cities are also polluted, and it will cause significant health issues. Real-time quality observation is necessary to guarantee a secure supply of drinking water. We offer a model of a low-cost system of monitoring real-time water quality using IoT to address this issue. The potential for supporting the real world has expanded with the introduction of IoT and other sensors. Multiple sensors make up the suggested system, which is utilized to identify the physical and chemical features of the water. Various sensors can measure the parameters such as temperature, pH, and turbidity. The core controller can process the values measured by sensors. An Arduino model is implemented in the core controller. The sensor data is forwarded to the cloud database using a WI-FI setup. The observed data will be transferred and stored in a cloud-based database for further processing. It wasn't easy to analyze the water quality every time. Hence, an Optimized Neural Network-based automation system identifies water quality from remote locations. The performance of the feed-forward neural network classifier is further enhanced with a hybrid GA- PSO algorithm. The optimized neural network outperforms water quality prediction applications and yields 91% accuracy. The accuracy of the developed model is increased by 20% because of optimizing network parameters compared to the traditional feed-forward neural network. Significant improvement in precision and recall is also evidenced in the proposed work.

• Structural Engineering and Mechanics
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• v.89 no.6
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• pp.589-599
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• 2024

This study presents the usability of the high-rate single-frequency Precise Point Positioning (SF-PPP) technique based on 20 Hz Global Positioning Systems (GPS)-only observations in detecting dynamic motions. SF-PPP solutions were obtained from post-mission and real-time GNSS corrections. These include the International GNSS Service (IGS)-Final, IGS real-time (RT), real-time MADOCA (Multi-GNSS Advanced Demonstration tool for Orbit and Clock Analysis), and real-time products from the Australian/New Zealand satellite-based augmentation systems (SBAS, known as SouthPAN). SF-PPP results were compared with LVDT (Linear Variable Differential Transformer) sensor and single-frequency relative positioning (SF-RP) solutions. The findings show that the SF-PPP technique successfully detects the harmonic motions, and the real-time products-based PPP solutions were as accurate as the final post-mission products. In the frequency domain, all GNSS-based methods evaluated in this contribution correctly detect the dominant frequency of short-term harmonic oscillations, while the differences in the amplitude values corresponding to the peak frequency do not exceed 1.1 mm. However, evaluations in the time domain show that SF-PPP needs high-pass filtering to detect accurate displacement since SF-PPP solutions include trends and low-frequency fluctuations, mainly due to atmospheric effects. Findings obtained in the time domain indicate that final, real-time, and MADOCA-based PPP results capture short-term dynamic behaviors with an accuracy ranging from 3.4 mm to 8.5 mm, and SBAS-based PPP solutions have several times higher RMSE values compared to other methods. However, after high-pass filtering, the accuracies obtained from PPP methods decreased to a few mm. The outcomes demonstrate the potential of the high-rate SF-PPP method to reliably monitor structural and earthquake-induced ground motions and vibration frequencies of structures.

• Journal of Environmental Health Sciences
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• v.34 no.5
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• pp.374-381
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• 2008

In this study, the factors affecting biological N and P removal using SND (simultaneous nitrification and denitrification) process were investigated and evaluated to examine the possibility of treating N and P through SND with NADH by surveying N and P traces in an aeration tank. Variations of $NH_4^+$-N+$NO_3^-$-N concentration were used to estimate the degree of SND in each point (P2, P3, P4, P5) of the aeration tank and these variations showed that denitrification efficiency in P2 (front zone), nitrification and denitrification efficiencies in P4 (middle zone) were 67%, 86% and 39%, respectively. When $PO_4^{-3}$-P concentration was analyzed in each point of the aeration tank, it was shown that $PO_4^{-3}$-P concentration coming into P2 was 1.25 mg/L, which increased to 2.22 mg/L by P release in P2 zone and then decreased to 0.74 mg/L by P uptake in P4. Consequently, we were able to estimate which high P removal efficiency observed in this study was caused by biological phosphorus removal. To determine the operating factors affecting effluent T-N, we analyzed the correlation among FN/M ratio, C/N ratio, Temp., SRT etc and these results showed that the correlation among FN/M ratio, C/N ratio and Temp was not high. However, the relationship of SRT and other parameters (effluent $NH_4^+$-N and effluent BOD) and the short SRT could have an affect on effluent $NH_4^+$-N and so effluent BOD could be increased. Thus, SRT operation should be controlled over 10 days. The results for analyzing the correlation between SRT and influent $NO_3^-$-N in order to investigate the operating factors affecting effluent T-P showed that T-P or $PO_4^{-3}$-P was not highly correlation with SRT, whereas $PO_4^{-3}$-P concentration increased along with increasing $NO_3^-$-N concentration into P2. Based on these results, we concluded, using regression analysis (R2=0.97), that effluent $PO_4^{-3}$-P concentration depends on $NO_3^-$-N concentration into P2.

• Journal of the Korean Society for Nondestructive Testing
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• v.31 no.4
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• pp.351-359
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• 2011

In a structure, damage can occur at several scales from micro-cracking to corrosion or loose bolts. This makes the identification of damage difficult with one mode of sensing. Hence, a multi-mode actuated sensing system is proposed based on a self-sensing circuit using a piezoelectric sensor. In the self sensing-based multi-mode actuated sensing, one mode provides a wide frequency-band structural response from the self-sensed impedance measurement and the other mode provides a specific frequency-induced structural wavelet response from the self-sensed guided wave measurement. In this study, an experimental study on the pipeline system is carried out to verify the effectiveness and the robustness of the proposed structural health monitoring approach. Different types of structural damage are artificially inflicted on the pipeline system. To classify the multiple types of structural damage, a supervised learning-based statistical pattern recognition is implemented by composing a two-dimensional space using the damage indices extracted from the impedance and guided wave features. For more systematic damage classification, several control parameters to determine an optimal decision boundary for the supervised learning-based pattern recognition are optimized. Finally, further research issues will be discussed for real-world implementation of the proposed approach.

• Journal of Life Science
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• v.30 no.10
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• pp.835-843
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• 2020

Obesity, the world's leading metabolic disease, is a serious health problem in both industrialized and developing countries. Natural substances are of great interest in preventative medicine, especially in the field of metabolic syndromes-from insulin resistance to obesity and diabetes. In the present study, we investigated the effect of A. pullulans SM-2001 Extract (Polycan^®) on the adipocyte differentiation of 3T3-L1 preadipocytes and the anti-obesity effect of 3T3-L1 adipocytes. Although β-glucan has been found to have health benefits in the regulation of the immune system and blood cholesterol levels, its role in obesity has not been fully investigated. Polycan^® suppressed lipid accumulation and glycerol-3-phosphate dehydrogenase (GPDH) activity without affecting cell viability in 3T3-L1 preadipocytes and adipocytes. Polycan^® also inhibited cellular lipid accumulation through down-regulation of transcription factors, such as PPARγ and C/EBPα, and induced dose-dependent phosphorylation of AMP-activated protein kinase (AMPK)-a cellular energy sensor-while the total AMPK protein content remained unchanged. Taken together, this shows that the activation of AMPK by Polycan^® in adipocytes plays a critical role in Polycan^®-induced inhibition of adipocyte differentiation. Our results show that Polycan^® has an anti-obesity action in vitro, suggesting a potential novel preventative agent for obesity and other metabolic diseases.

• Journal of Digital Convergence
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• v.13 no.4
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• pp.227-233
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• 2015

The recent health care is growing rapidly want to receive offers users a variety of medical services, can be exploited easily exposed to a third party information on the role of the patient's hospital staff (doctors, nurses, pharmacists, etc.) depending on the patient clearly may have to be classified. In this paper, in order to ensure safe use by third parties in the health care environment, classify the attributes of patient information and patient privacy protection technique using hierarchical multi-property rights proposed to classify information according to the role of patient hospital officials The. Hospital patients and to prevent the proposed method is represented by a mathematical model, the information (the data consumer, time, sensor, an object, duty, and the delegation circumstances, and so on) the privacy attribute of a patient from being exploited illegally patient information from a third party the prevention of the leakage of the privacy information of the patient in synchronization with the attribute information between the parties.

• Smart Structures and Systems
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• v.14 no.2
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• pp.209-224
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• 2014

With more and more built long-term structural health monitoring (SHM) systems, it has been considered to apply monitored data to learn the reliability of bridges. In this paper, based on a long-term SHM system, especially in which the sensors were embedded from the beginning of the construction of the bridge, a method to calculate the localized reliability around an embedded sensor is recommended and implemented. In the reliability analysis, the probability distribution of loading can be the statistics of stress transferred from the monitored strain which covered the effects of both the live and dead loads directly, and it means that the mean value and deviation of loads are fully derived from the monitored data. The probability distribution of resistance may be the statistics of strength of the material of the bridge accordingly. With five years' monitored strains, the localized reliabilities around the monitoring sensors of a bridge were computed by the method. Further, the monitored stresses are classified into two time segments in one year period to count the loading probability distribution according to the local climate conditions, which helps us to learn the reliability in different time segments and their evolvement trends. The results show that reliabilities and their evolvement trends in different parts of the bridge are different though they are all reliable yet. The method recommended in this paper is feasible to learn the localized reliabilities revealed from monitored data of a long-term SHM system of bridges, which would help bridge engineers and managers to decide a bridge inspection or maintenance strategy.