• Journal of the Korean Society of Environmental Restoration Technology
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• v.25 no.2
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• pp.55-67
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• 2022

Land use is a critical factor that affects the hydrological characteristics of watersheds, thereby determining the biological condition of streams. This study analyzes the effects of land uses in the watersheds on biological indicators of streams across the Han River basin using a linear model (LM) and generalized additive model (GAM). LULC and biological monitoring data of streams were obtained from the Korean Ministry of Environment. The proportions of urban, agricultural, and forest areas in the watersheds were regressed to the three biological indicators, including diatom, benthic macroinvertebrate, and fish of streams. The estimated LM and GAM models for the biological indicators were then compared, using regression determination R² and AIC values. The results revealed that GAM models performed better than the LM models in explaining the variances of biological indicators of streams, indicating the non-linear relationships between biological indicators and land uses in watersheds. Also, the results suggested that the indicator of macroinvertebrates was the most sensitive indicator to land uses in watersheds. Although non-linear relationships between watershed land uses and biological indicators of streams could vary among biological indicators, it was consistent that streams' biological integrity significantly deteriorated by a relatively low percentage of urban areas. Meanwhile, biological indicators of streams were negatively affected by the relatively high percentage of agricultural areas. The results of this study can be integrated into effective quantitative criteria for the watershed management and land use plans to enhance the biological integrity of streams. In specific, land uses management plans in watersheds may need more close attention to urban land use changes than agricultural land uses to sustain the biological integrity of streams.

• Journal of Korean Society of Steel Construction
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• v.10 no.3 s.36
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• pp.369-381
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• 1998

This paper is intended to show some new approaches and concepts for the development of system model and prototype software for computer-aided Monitoring and Assessment(M&A) of grand bridges. The system model that incorporates optimal strategies for M&A of grand bridges is suggested. Reliability models are developed and a reliability-based capacity rating method is established for the evaluation of the bridge safety and reliability based on the real responses measured from a series of field load tests. Based on the proposed models, an integrated prototype software is then developed for computer-aided M&A of grand bridges under the environment of the graphic user interface, which is successfully applied to the M&A of an existing cable-stayed bridge. It may be stated that the system model and prototype software developed in this paper can be utilized and implemented in the development of the computer-aided M&A system for cable-stayed and suspension bridges.

• Membrane Journal
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• v.21 no.2
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• pp.163-170
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• 2011

Membrane fouling control in water treatment may be the main obstacle for wider implementation and lower cost. A novel fluorescent spectroscope sensor device for membrane fouling integrity monitoring has been developed and evaluated in this study. PSf membranes for water treatment has been fabricated with three types of organic fluorescent materials, OB, FP, KCB. The fluorescent signal from membrane surface was analyzed throughout the filtration process. It was found that the fluorescent signal due to the membrane fouling decreased and the developed device is reliable for membrane fouling monitoring.

• Journal of Advanced Navigation Technology
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• v.18 no.4
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• pp.278-287
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• 2014

ADS-B is a newly developed air surveillance technology to address the consistently increasing air traffic needs. ADS-B attracts attention for replacing or complementing a conventional radar since ADS-B can provide more accurate position information with a shorter interval when compared to the radar. However, as ADS-B uses wireless data links on exchanging information between an aircraft and a ground ADS-B system, and uses protocols without integrity support on exchanging information within the ground ADS-B system, a system which can monitor the operational status of an ADS-B system is essential. In this work, we design and implement an ADS-B monitoring system capable of displaying air traffic situation that can show the comprehensive air traffic situation while monitoring the operational status of the ADS-B system. The implemented ADS-B monitoring system has been verified with a configured ADS-B ground system by displaying ADS-B surveillance data, radar surveillance data, and flight information after receiving live surveillance data of in flight aircrafts, and virtual flight information data.

• Journal of Communications and Networks
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• v.17 no.5
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• pp.453-462
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• 2015

Body area networks (BANs) have emerged as an enabling technique for e-healthcare systems, which can be used to continuously and remotely monitor patients' health. In BANs, the data of a patient's vital body functions and movements can be collected by small wearable or implantable sensors and sent using shortrange wireless communication techniques. Due to the shared wireless medium between the sensors in BANs, it may be possible to have malicious attacks on e-healthcare systems. The security and privacy issues of BANs are becoming more and more important. To provide secure and correct association of a group of sensors with a patient and satisfy the requirements of data confidentiality and integrity in BANs, we propose a novel enhanced secure sensor association and key management protocol based on elliptic curve cryptography and hash chains. The authentication procedure and group key generation are very simple and efficient. Therefore, our protocol can be easily implemented in the power and resource constrained sensor nodes in BANs. From a comparison of results, furthermore, we can conclude that the proposed protocol dramatically reduces the computation and communication cost for the authentication and key derivation compared with previous protocols. We believe that our protocol is attractive in the application of BANs.

• Journal of Welding and Joining
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• v.12 no.1
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• pp.59-72
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• 1994

This paper presents an on-line quality monitoring and control method to obtain a uniform weld quality in gas metal arc welding (GMAW) processes. The geometrical parameters of the weld pool such as the top bead width and the penetration depth plus half back width are utilized to assess the integrity of the weld quality. Since a good quality weld is characterized by a relatively high depth-to-width ratio in its dimensions, the second geometrical parameter is regulated to a desired one. The monitoring variables are the surface temperatures measured at various points on the top surface of the weldment which are strongly related to the formation of the weld pool The relationship between the measured temperatures and the weld pool size is implemented on the multilayer perceptrons which are powerful for realization of complex mapping characteristics through training by samples. For on-line quality monitoring and control, it is prerequisite to estimate the weld pool sizes in the region of transient states. For this purpose, the time history of the surface temperatures is used as the input to the neural estimator. The control purpose is to obtain a uniform weld quality. In this research, the weld pool size is directly regulated to a desired one. The proposed controller is composed of a neural pool size estimator, a neural feedforward controller and a conventional feedback controller. The pool size estimator predicts the weld pool size under growing. The feedforward controller compensates for the nonlinear characteristics of the welding process. A series of simulation studies shows that the proposed control method improves the overall system response in the presence of changes in torch travel speed during GMA welding and guarantees the uniform weld quality.

• Journal of Radiation Protection and Research
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• v.34 no.4
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• pp.195-200
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• 2009

effluents to the environment. The activity of carbon-14, one of the radioactive effluents, in the environment is already high level and its effect on radiation exposure to the public and the environment is insignificant; thus, NPPs did not perform the carbon-14 monitoring in effluents in the past. By the way, effluents of noble gas and particulate radioactive materials originated from nuclear fuels has been continuously reduced due to both the advancement of manufacturing and integrity technology for nuclear fuels and the improvement of operation methods of NPPs. Futhermore, the portion of dose assessment by tritium and carbon-14 to the public has been relatively increased because the lower limit of detection for low-energy beta sources, such as tritium and carbon-14, is low due to the advancement of radiation detection technology. In this paper, the technical background for carbon-14 monitoring in nuclear facilities was investigated using United States technical reports and papers. This paper also reviews whether carbon-14 monitoring is necessary or not based on the investigated documents.

• Smart Structures and Systems
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• v.30 no.6
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• pp.687-701
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• 2022

Inevitable response loss under complex operational conditions significantly affects the integrity and quality of measured data, leading the structural health monitoring (SHM) ineffective. To remedy the impact of data loss, a common way is to transfer the recorded response of available measure point to where the data loss occurred by establishing the response mapping from measured data. However, the current research has yet addressed the structural condition changes afterward and response mapping learning from a small sample. So, this paper proposes a novel data driven structural response reconstruction method based on a sophisticated designed generating adversarial network (UAGAN). Advanced deep learning techniques including U-shaped dense blocks, self-attention and a customized loss function are specialized and embedded in UAGAN to improve the universal and representative features extraction and generalized responses mapping establishment. In numerical validation, UAGAN efficiently and accurately captures the distinguished features of structural response from only 40 training samples of the intact structure. Besides, the established response mapping is universal, which effectively reconstructs responses of the structure suffered up to 10% random stiffness reduction or structural damage. In the experimental validation, UAGAN is trained with ambient response and applied to reconstruct response measured under earthquake. The reconstruction losses of response in the time and frequency domains reached 16% and 17%, that is better than the previous research, demonstrating the leading performance of the sophisticated designed network. In addition, the identified modal parameters from reconstructed and the corresponding true responses are highly consistent indicates that the proposed UAGAN is very potential to be applied to practical civil engineering.

• Smart Structures and Systems
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• v.29 no.1
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• pp.77-91
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• 2022

Various monitoring systems have been implemented in civil infrastructure to ensure structural safety and integrity. In long-term monitoring, these systems generate a large amount of data, where anomalies are not unusual and can pose unique challenges for structural health monitoring applications, such as system identification and damage detection. Therefore, developing efficient techniques is quite essential to recognize the anomalies in monitoring data. In this study, several machine learning techniques are explored and implemented to detect and classify various types of data anomalies. A field dataset, which consists of one month long acceleration data obtained from a long-span cable-stayed bridge in China, is employed to examine the machine learning techniques for automated data anomaly detection. These techniques include the statistic-based pattern recognition network, spectrogram-based convolutional neural network, image-based time history convolutional neural network, image-based time-frequency hybrid convolution neural network (GoogLeNet), and proposed ensemble neural network model. The ensemble model deliberately combines different machine learning models to enhance anomaly classification performance. The results show that all these techniques can successfully detect and classify six types of data anomalies (i.e., missing, minor, outlier, square, trend, drift). Moreover, both image-based time history convolutional neural network and GoogLeNet are further investigated for the capability of autonomous online anomaly classification and found to effectively classify anomalies with decent performance. As seen in comparison with accuracy, the proposed ensemble neural network model outperforms the other three machine learning techniques. This study also evaluates the proposed ensemble neural network model to a blind test dataset. As found in the results, this ensemble model is effective for data anomaly detection and applicable for the signal characteristics changing over time.

• Smart Structures and Systems
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• v.32 no.3
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• pp.153-166
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• 2023

Health monitoring of civil infrastructures, in particular, old bridges that are still in service, has become more than necessary, given the risk that a possible degradation or failure of these infrastructures can induce on the safety of users in addition to the resulting commercial and economic impact. Bridge integrity assessment has attracted significant research efforts over the past forty years with the aim of developing new damage identification methods applicable to real structures. The bridge of Ouled Mimoun (Tlemcen, Algeria) is one of the oldest railway structure in the country. It was built in 1889. This bridge, which is too low with respect to the level of the road, has suffered multiple shocks from various machines that caused considerable damage to its central part. The present work aims to analyze the stability of this bridge by identifying damages and evaluating the damage rate in different parts of the structure on the basis of a finite element model. The applied method is based on an inverse analysis of the normal stress responses that were calculated from the corresponding recorded strains, during the passage of a real train, by means of a set of strain gauges placed on certain elements of the bridge. The results obtained from the inverse analysis made it possible to successfully locate areas that were really damaged and to estimate the damage rate. These results were also used to detect an excessive rigidity in certain elements due to the presence of plates, which were neglected in the numerical reference model. In the case of the continuous bridge monitoring, this developed method will be a very powerful tool as a smart health monitoring system, allowing engineers to take in time decisions in the event of bridge damage.