• 한국환경복원기술학회지
• /
• 제25권2호
• /
• pp.55-67
• /
• 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.

• 한국강구조학회 논문집
• /
• 제10권3호통권36호
• /
• pp.369-381
• /
• 1998

본 연구에서는 대형교량의 전산화 모니터링 및 분석평가(Monitoring and Assessment: M&A)를 위한 시스템모형과 소프트웨어의 개발에 대한 새로운 접근방법과 개념을 제안하였다. 제안된 시스템모형은 대형교량의 M&A를 위한 최적의 방법을 반영한 모형이다. 이를 위하여 교량의 확률적 분석평가를 위한 신뢰성 모형이 개발되었고, 일련의 현장재하시험으로부터 계측된 실응답에 기초한 교량의 안전성과 신뢰성의 평가를 위해 신뢰성에 기초한 내하력 평가방법을 확립하였다. 또한 제안된 모델에 기초하여 대형교량의 전산화 M&A를 위한 시범소프트웨어(prototype software)를 개발하여 실제 사장교에 적용하였다. 본 연구에서 개발된 시스템모형 및 시범소프트웨어는 향후 사장교나 현수교와 같은 장대교량의 전산화 유지관리 시스템의 개발에 활용될 수 있다.

• 멤브레인
• /
• 제21권2호
• /
• pp.163-170
• /
• 2011

수처리 분리막 공정에서 막 오염 제어 기술은 현장 적용 기술 및 경제성 확보에 있어 매우 중요하다. 본 연구에서는 형광 나노 입자 및 형광 분광 분석법을 도입함으로써 수처리 분리막 공정에서 막 오염 정도를 실시간으로 측정 모니터링 할 수 있는 센싱 기술을 개발하고자 하였다. 막 오염 정도를 모니터링 할 수 있는 분리막 제조를 위해 세 종류의 형광물질 OB, FP, KCB를 담지한 다공성 polysulfone (PSf) 비대칭 막을 제조하였다. 형광 분광 분석 시스템을 이용하여 분리막 표면에서의 오염 정도를 실시간으로 측정한 결과, 형광 물질을 첨가한 막은 막 오염이 진행됨에 따라 형광 신호가 크게 감소함을 보여 막 표면 오염층의 모니터링 분석이 가능함을 확인하였다.

• 한국항행학회논문지
• /
• 제18권4호
• /
• pp.278-287
• /
• 2014

ADS-B는 지속적으로 증가하는 항공 수요에 대응하기 위해 새롭게 개발된 항공 감시 기술로 레이더에 비해 높은 정확도와 빠른 갱신 주기를 제공하여 레이더를 대체하거나 보완할 수 있을 것으로 주목받고 있다. 하지만 지상 시스템과 항공기 간의 정보 교환에 무선 데이터링크를 사용하며 지상 시스템 내부의 정보 교환에 무결성이 보장되지 않는 프로토콜을 사용하기 때문에 ADS-B 시스템의 운용 상태를 감시할 수 있는 시스템이 반드시 필요하다. 본 논문에서는 ADS-B 시스템의 운용 상태 감시 기능과 더불어 ADS-B 감시 자료, 레이더 감시 자료, 비행 정보를 활용하여 항공 교통 상황을 종합적으로 현시할 수 있는 항공 교통 상황 종합 현시 기능을 갖는 ADS-B 모니터링 시스템을 설계하고 구현하였다. 구현한 시스템은 직접 구성한 ADS-B 지상 시스템과 연결하여 비행중인 항공기의 ADS-B 감시 자료와 레이더 감시 자료, 가상의 비행 정보를 수신한 뒤 현시하는 방법을 통하여 검증하였다.

• Journal of Communications and Networks
• /
• 제17권5호
• /
• pp.453-462
• /
• 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
• /
• 제12권1호
• /
• pp.59-72
• /
• 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
• /
• 제34권4호
• /
• pp.195-200
• /
• 2009

원전에서는 많은 종류의 방사성물질이 생성되어 일부는 환경으로 방사성유출물로서 배출되고 있다. 이러한 방사성유출물 중에서 탄소 동위원소인 Carbon-14는 자연에서 이미 높은 준위의 백그라운드를 형성하고 있기 때문에, 원전에서 Carbon-14가 배출되더라도 환경이나 일반인의 피폭방사선량에 미치는 영향이 미미하여 과거에는 배출감시와 환경감시를 수행하지 않았다. 그런데, 핵연료 제조기술 발달과 운전방법 개선으로 핵연료로부터 불활성기체와 입자방사성물질의 방출이 계속 감소하고 있다. 또한 방사선계측기술의 향상에 따라 삼중수소와 Carbon-14 같은 저준위 베타방사능 핵종의 검출준위가 낮아져, 이들 핵종이 일반인 선량평가에서 미치는 비율이 상대적으로 높아지고 있다. 본 논문은 원자력시설에서 발생하는 Carbon-14에 대해 미국의 기술보고서와 논문 등을 검토하여 배출관리와 환경 영향평가에 대한 방사선감시의 기술적 배경을 조사하였다. 이를 바탕으로 Carbon-14 방사성핵종의 배출감시 방안에 대한 타당성을 제시하고자 하였다.

• Smart Structures and Systems
• /
• 제30권6호
• /
• pp.687-701
• /
• 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
• /
• 제29권1호
• /
• pp.77-91
• /
• 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
• /
• 제32권3호
• /
• pp.153-166
• /
• 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.