• Journal of Information Processing Systems
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• 제14권1호
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• pp.162-175
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• 2018

Currently, electricity consumption and feedback mechanisms are being widely researched in Internet of Things (IoT) areas to realise power consumption monitoring and management through the remote control of appliances. This paper aims to develop a smart electricity utilisation IoT platform with a deep belief network for electricity utilisation feature modelling. In the end node of electricity utilisation, a smart monitoring and control module is developed for automatically operating air conditioners with a gateway, which connects and controls the appliances through an embedded ZigBee solution. To collect electricity consumption data, a programmable smart IoT gateway is developed to connect an IoT cloud server of smart electricity utilisation via the Internet and report the operational parameters and working states. The cloud platform manages the behaviour planning functions of the energy-saving strategies based on the power consumption features analysed by a deep belief network algorithm, which enables the automatic classification of the electricity utilisation situation. Besides increasing the user's comfort and improving the user's experience, the established feature models provide reliable information and effective control suggestions for power reduction by refining the air conditioner operation habits of each house. In addition, several data visualisation technologies are utilised to present the power consumption datasets intuitively.

• Smart Structures and Systems
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• 제30권6호
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• pp.661-671
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• 2022

Stayed cables are the key components for transmitting loads in cable-stayed bridges. Therefore, it is very important to evaluate the cable force condition to ensure bridge safety. An online condition assessment and anomaly localization method is proposed for cables based on the spatiotemporal correlation of grouped cable forces. First, an anomaly sensitive feature index is obtained based on the distribution characteristics of grouped cable forces. Second, an adaptive anomaly detection method based on the k-nearest neighbor rule is used to perform dissimilarity measurements on the extracted feature index, and such a method can effectively remove the interference of environment factors and vehicle loads on online condition assessment of the grouped cable forces. Furthermore, an online anomaly isolation and localization method for stay cables is established, and the complete decomposition contributions method is used to decompose the feature matrix of the grouped cable forces and build an anomaly isolation index. Finally, case studies were carried out to validate the proposed method using an in-service cable-stayed bridge equipped with a structural health monitoring system. The results show that the proposed approach is sensitive to the abnormal distribution of grouped cable forces and is robust to the influence of interference factors. In addition, the proposed approach can also localize the cables with abnormal cable forces online, which can be successfully applied to the field monitoring of cables for cable-stayed bridges.

• 한국정보전자통신기술학회논문지
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• 제10권5호
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• pp.424-434
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• 2017

산업 무선 센서 네트워크에서는 많은 응용들이 복합적인 서비스 품질 지원을 요구한다. 본 논문에서는 산업 무선 센서 네트워크에서 실시간 서비스의 신뢰성을 향상시키기 위하여 기회적 실시간 모니터링 데이터 전달 프로토콜을 제안한다. 전송 실패를 복구하기 위해 가장 많이 알려진 재전송 기법들은 실시간 요구사항에 위배되는 추가적 딜레이를 발생시키기 때문에 실시간 데이터 전달에 적절치 않다. 제안 프로토콜은 무선 네트워크의 브로드캐스팅 특성과 시간적 기회 제공 방법을 사용한다. 브로드캐스팅 특성을 통해서 라디오 반경 내 모든 이웃에게 전달하고, 시간적 기회 제공 방법으로 중계 우선순위를 정하여 모든 노드들이 중계 기회를 얻을 수 있게 한다. 제안 방안은 최대한 많은 노드가 라우팅에 참여하여 실시간 데이터 전송 확률을 높인다. 시뮬레이션을 통하여 제안 방안이 실시간 데이터 전송과 신뢰성 측면에서 우월함을 보인다.

• 한국방재학회 논문집
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• 제5권2호
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• pp.37-44
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• 2005

본 연구의 대상유역인 동의대 시험유역은 공간적으로 주위가 산의 능선으로 둘러싸여 유역내의 유출은 거의 대부분 단일 유출구로만 유출이 이루어지며, 부산지방의 도시유역의 특징인 경사지형의 특성을 잘 반영하고 있다. 유역기초자료 및 기상관측장비(EMS)와 자동수위관측장비(AWS)를 통해 수집된 각종 수문자료들과 유역상세자료들을 조사하여 ILLUDAS 모형과 SWMM 모형, HEC-HMS 모형의 기본입력자료로 사용하여 시험유역 유출특성을 검토하고, HEC-HMS 모형에 대한 검정 및 검증을 통해 시험유역 저류지설계에 사용한다. HEC-HMS 모형에 소하천 설계기준인 30년 설계강우를 설정하고 불투수율의 변화양상에 따라 설계홍수량을 산정하고, 유출누가곡선상에서 저류지의 용량을 결정하였다. 시험유역의 최종 유출부 상류에 $54,000m^3$의 가상 저류지를 설계하였고, 저류지 설계 후 유출양상을 검토해본 결과 유출의 첨두량이 감소함을 확인할 수 있었다. 이는 저류지설계로 도시지역의 유출이 감소됨으로서 도시홍수방재에 있어서 적용성이 있음을 확인할 수 있었다.

• Smart Structures and Systems
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• 제31권4호
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• pp.421-436
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• 2023

This paper presents a vibration displacement measurement and damage identification method for a space truss structure from its vibration videos. Features from Accelerated Segment Test (FAST) algorithm is combined with adaptive threshold strategy to detect the feature points of high quality within the Region of Interest (ROI), around each node of the truss structure. Then these points are tracked by Kanade-Lucas-Tomasi (KLT) algorithm along the video frame sequences to obtain the vibration displacement time histories. For some cases with the image plane not parallel to the truss structural plane, the scale factors cannot be applied directly. Therefore, these videos are processed with homography transformation. After scale factor adaptation, tracking results are expressed in physical units and compared with ground truth data. The main operational frequencies and the corresponding mode shapes are identified by using Subspace Stochastic Identification (SSI) from the obtained vibration displacement responses and compared with ground truth data. Structural damages are quantified by elemental stiffness reductions. A Bayesian inference-based objective function is constructed based on natural frequencies to identify the damage by model updating. The Success-History based Adaptive Differential Evolution with Linear Population Size Reduction (L-SHADE) is applied to minimise the objective function by tuning the damage parameter of each element. The locations and severities of damage in each case are then identified. The accuracy and effectiveness are verified by comparison of the identified results with the ground truth data.

• 한국유체기계학회 논문집
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• 제7권2호
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• pp.41-49
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• 2004

Condition monitoring is very important in turbo machinery because single failure could cause critical damages to its plant. So, automatic fault recognition has been one of the main research topics in condition monitoring area. We have used a relatively new fault recognition method, Hidden Markov Model(HMM), for mechanical system. It has been widely used in speech recognition, however, its application to fault recognition of mechanical signal has been very limited despite its good potential. In this paper, discrete HMM(DHMM) was used to recognize the faults of rotor system to study its fault recognition ability. We set up a rotor kit under unbalance and oil whirl conditions and sampled vibration signals of two failure conditions. DHMMS of each failure condition were trained using sampled signals. Next, we changed the setup and the rotating speed of the rotor kit. We sampled vibration signals and each DHMM was applied to these sampled data. It was found that DHMMs trained by data of one rotating speed have shown good fault recognition ability in spite of lack of training data, but DHMMs trained by data of four different rotating speeds have shown better robustness.

• 제어로봇시스템학회논문지
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• 제15권4호
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• pp.406-412
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• 2009

ECG is used on purpose to keep good health or monitor cardiac function of aged person as well as on purpose to diagnose the disease of heart patients. The ambulatory ECG monitoring system under guarantee of safety and accuracy is very efficient to prevent the progress of heart disease and sudden death. These systems can detect the temporary change of ECG that is very significant to diagnose heart disease such as myocardial ischemia, arrhyamia and cardiac infarction. In this paper, we describe the ECG signal analysis algorithm and measurement device for ECG monitoring. The authors designed a small-size portable ECG device that consisted of instrumentation amplifier, micro-controller, filter and RF module. The device measures ECG with four electrodes on the body and detects QRS complex and ST level change in realtime. Also it transmits the measured signals to the personal computer. The developed software for ECG analysis in personal computer has the function to detect the feature points and ST level changes.

• Smart Structures and Systems
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• 제17권3호
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• pp.445-470
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• 2016

In this study, the Bayesian probabilistic framework is investigated for modal identification and modal identifiability based on the field measurements provided in the structural health monitoring benchmark problem of an instrumented cable-stayed bridge named Ting Kau Bridge (TKB). The comprehensive structural health monitoring system on the cable-stayed TKB has been operated for more than ten years and it is recognized as one of the best test-beds with readily available field measurements. The benchmark problem of the cable-stayed bridge is established to stimulate investigations on modal identifiability and the present paper addresses this benchmark problem from the Bayesian prospective. In contrast to deterministic approaches, an appealing feature of the Bayesian approach is that not only the optimal values of the modal parameters can be obtained but also the associated estimation uncertainty can be quantified in the form of probability distribution. The uncertainty quantification provides necessary information to evaluate the reliability of parametric identification results as well as modal identifiability. Herein, the Bayesian spectral density approach is conducted for output-only modal identification and the Bayesian model class selection approach is used to evaluate the significance of different modes in modal identification. Detailed analysis on the modal identification and modal identifiability based on the measurements of the bridge will be presented. Moreover, the advantages and potentials of Bayesian probabilistic framework on structural health monitoring will be discussed.

• Korean Chemical Engineering Research
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• 제48권4호
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• pp.475-482
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• 2010

화상분석(image analysis)을 이용하여 제품의 외관(外觀) 품질을 정량적으로 추정할 수 있는 소프트 센서를 설계하고, 이를 제품의 품질 모니터링에 적용하는 연구를 수행하였다. 여기에 사용된 방법론은 크게 다음의 세 단계로 구성되어 있다: (1) 웨이블릿 변환(wavelet transform)을 이용한 화상으로부터의 질감(texture) 특징 추출, (2) 추출된 질감특징의 부공간 투영(projection on subspace)을 통한 제품 외관의 추정, 그리고 (3) 질감특징의 잠재변수(latent variables) 즉, 외관의 수치적 추정치를 목적에 맞게 사용. 이 방법에서는 제품의 외관을 서로 다른 불연속적인 부류로의 분류 보다는, 연속적인 외관 변화를 일관적이고 정량적으로 추정하는데 초점을 두고자 한다. 이 방법은 인조대리석 외관의 수치적 추정과 품질 모니터링 적용사례를 통해 설명되었다.

• Smart Structures and Systems
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• 제6권3호
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• pp.335-347
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• 2010

Recent advances in hardware and instrumentation technology have allowed the possibility of deploying very large sensor arrays on structures. Exploiting the huge amount of data that can result in order to perform vibration-based structural health monitoring (SHM) is not a trivial task and requires research into a number of specific problems. In terms of pressing problems of interest, this paper discusses: the design and optimisation of appropriate sensor networks, efficient data reduction techniques, efficient and automated feature extraction methods, reliable methods to deal with environmental and operational variability, efficient training of machine learning techniques and multi-scale approaches for dealing with very local damage. The paper is a result of the ESF-S3T Eurocores project "Smart Sensing For Structural Health Monitoring" (S3HM) in which a consortium of academic partners from across Europe are attempting to address issues in the design of automated vibration-based SHM systems for structures.