scholarly journals Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects

2022 ◽  
Vol 157 ◽  
pp. 102555
Author(s):  
Hao Fu ◽  
William H.K. Lam ◽  
Hu Shao ◽  
Lina Kattan ◽  
Mostafa Salari
Keyword(s):  
Sensor Locations

Optimal Sensor Placement Algorithm for Structural Damage Identification

2020 ◽  
Vol 14 (1) ◽  
pp. 69-81
Author(s):  
C.H. Li ◽  
Q.W. Yang
Keyword(s):  
Structural Damage ◽  
Damage Identification ◽  
Sensor Placement ◽  
Optimization Procedure ◽  
Frame Structure ◽  
Truss Structures ◽  
Optimal Sensor Placement ◽  
Structural Damage Identification ◽  
Placement Algorithm ◽  
Sensor Locations

Background: Structural damage identification is a very important subject in the field of civil, mechanical and aerospace engineering according to recent patents. Optimal sensor placement is one of the key problems to be solved in structural damage identification. Methods: This paper presents a simple and convenient algorithm for optimizing sensor locations for structural damage identification. Unlike other algorithms found in the published papers, the optimization procedure of sensor placement is divided into two stages. The first stage is to determine the key parts in the whole structure by their contribution to the global flexibility perturbation. The second stage is to place sensors on the nodes associated with those key parts for monitoring possible damage more efficiently. With the sensor locations determined by the proposed optimization process, structural damage can be readily identified by using the incomplete modes yielded from these optimized sensor measurements. In addition, an Improved Ridge Estimate (IRE) technique is proposed in this study to effectively resist the data errors due to modal truncation and measurement noise. Two truss structures and a frame structure are used as examples to demonstrate the feasibility and efficiency of the presented algorithm. Results: From the numerical results, structural damages can be successfully detected by the proposed method using the partial modes yielded by the optimal measurement with 5% noise level. Conclusion: It has been shown that the proposed method is simple to implement and effective for structural damage identification.

Health monitoring sensor placement optimization based on initial sensor layout using improved partheno-genetic algorithm

2020 ◽  
pp. 136943322094719
Author(s):  
Xianrong Qin ◽  
Pengming Zhan ◽  
Chuanqiang Yu ◽  
Qing Zhang ◽  
Yuantao Sun
Keyword(s):  
Genetic Algorithm ◽  
Health Monitoring ◽  
Large Scale ◽  
Complex Structure ◽  
Sensor Placement ◽  
Placement Problem ◽  
Optimal Sensor Placement ◽  
Placement Optimization ◽  
Sensor Placement Optimization ◽  
Sensor Locations

Optimal sensor placement is an important component of a reliability structural health monitoring system for a large-scale complex structure. However, the current research mainly focuses on optimizing sensor placement problem for structures without any initial sensor layout. In some cases, the experienced engineers will first determine the key position of whole structure must place sensors, that is, initial sensor layout. Moreover, current genetic algorithm or partheno-genetic algorithm will change the position of the initial sensor locations in the iterative process, so it is unadaptable for optimal sensor placement problem based on initial sensor layout. In this article, an optimal sensor placement method based on initial sensor layout using improved partheno-genetic algorithm is proposed. First, some improved genetic operations of partheno-genetic algorithm for sensor placement optimization with initial sensor layout are presented, such as segmented swap, reverse and insert operator to avoid the change of initial sensor locations. Then, the objective function for optimal sensor placement problem is presented based on modal assurance criterion, modal energy criterion, and sensor placement cost. At last, the effectiveness and reliability of the proposed method are validated by a numerical example of a quayside container crane. Furthermore, the sensor placement result with the proposed method is better than that with effective independence method without initial sensor layout and the traditional partheno-genetic algorithm.

Determination of key sensor locations for non-point pollutant sources management in sewer network

2013 ◽  
Vol 30 (1) ◽  
pp. 20-26 ◽  
Author(s):  
OnYu Kang ◽  
SeungChul Lee ◽  
Kailas Wasewar ◽  
MinJeong Kim ◽  
Hongbin Liu ◽  
...  
Keyword(s):  
Sewer Network ◽  
Pollutant Sources ◽  
Sensor Locations

scholarly journals Linear stochastic estimation of the coherent structures in internal combustion engine flow

2019 ◽  
Vol 21 (9) ◽  
pp. 1738-1749 ◽  
Author(s):  
Daniel Butcher ◽  
Adrian Spencer
Keyword(s):  
Internal Combustion Engine ◽  
Combustion Engine ◽  
Internal Combustion ◽  
Stochastic Estimation ◽  
Linear Stochastic Estimation ◽  
Reference Velocity ◽  
Current Cycle ◽  
Vortex Centre ◽  
Point Velocity ◽  
Sensor Locations

A methodology for estimating the in-cylinder flow of an internal combustion engine from a number of point velocity measurements (sensors) is presented. Particle image velocimetry is used to provide reference velocity fields for the linear stochastic estimation technique to investigate the number of point measurements required to provide a representative estimation of the flow field. A systematic iterative approach is taken, with sensor locations randomly generated in each iteration to negate sensor location effects. It was found that an overall velocity distribution accuracy of at least 75% may be achieved with 7 sensors and 95% with 35 sensors, with the potential for fewer if sensor locations are optimised. The accuracy of vortex centre location predictions is typically within 2–3 mm, suggesting that the presented technique could characterise individual cycle flow fields by indicating vortex locations, swirl magnitude or tumble, for example. With this information on the current cycle, a control system may be enabled to activate in-cycle adjustment of injection and/or ignition timing, for example, to minimise emissions.

Evaluation of four sensor locations for physical activity assessment

2016 ◽  
Vol 53 ◽  
pp. 103-109 ◽  
Author(s):  
Mark C. Schall ◽  
Nathan B. Fethke ◽  
Howard Chen
Keyword(s):  
Physical Activity ◽  
Physical Activity Assessment ◽  
Activity Assessment ◽  
Sensor Locations

scholarly journals Deep Learning in Gait Parameter Prediction for OA and TKA Patients Wearing IMU Sensors

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5553
Author(s):  
Mohsen Sharifi Renani ◽  
Casey A. Myers ◽  
Rohola Zandie ◽  
Mohammad H. Mahoor ◽  
Bradley S. Davidson ◽  
...  
Keyword(s):  
Inertial Measurement Unit ◽  
Measurement Unit ◽  
Gait Patterns ◽  
Movement Data ◽  
Gait Parameters ◽  
Activity Types ◽  
Spatial Parameters ◽  
Total Knee ◽  
Insight Into ◽  
Sensor Locations

Quantitative assessments of patient movement quality in osteoarthritis (OA), specifically spatiotemporal gait parameters (STGPs), can provide in-depth insight into gait patterns, activity types, and changes in mobility after total knee arthroplasty (TKA). A study was conducted to benchmark the ability of multiple deep neural network (DNN) architectures to predict 12 STGPs from inertial measurement unit (IMU) data and to identify an optimal sensor combination, which has yet to be studied for OA and TKA subjects. DNNs were trained using movement data from 29 subjects, walking at slow, normal, and fast paces and evaluated with cross-fold validation over the subjects. Optimal sensor locations were determined by comparing prediction accuracy with 15 IMU configurations (pelvis, thigh, shank, and feet). Percent error across the 12 STGPs ranged from 2.1% (stride time) to 73.7% (toe-out angle) and overall was more accurate in temporal parameters than spatial parameters. The most and least accurate sensor combinations were feet-thighs and singular pelvis, respectively. DNNs showed promising results in predicting STGPs for OA and TKA subjects based on signals from IMU sensors and overcomes the dependency on sensor locations that can hinder the design of patient monitoring systems for clinical application.

scholarly journals SHMnet: Condition assessment of bolted connection with beyond human-level performance

2019 ◽  
Vol 19 (4) ◽  
pp. 1188-1201 ◽  
Author(s):  
Tong Zhang ◽  
Suryakanta Biswal ◽  
Ying Wang
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Condition ◽  
Steel Frame ◽  
Mode Shapes ◽  
Repeated Impact ◽  
Traditional Methods ◽  
Bolted Connection ◽  
Structural Health ◽  
Sensor Locations

Deep learning algorithms are transforming a variety of research areas with accuracy levels that the traditional methods cannot compete with. Recently, increasingly more research efforts have been put into the structural health monitoring domain. In this work, we propose a new deep convolutional neural network, namely SHMnet, for a challenging structural condition identification case, that is, steel frame with bolted connection damage. We perform systematic studies on the optimisation of network architecture and the preparation of the training data. In the laboratory, repeated impact hammer tests are conducted on a steel frame with different bolted connection damage scenarios, as small as one bolt loosened. The time-domain monitoring data from a single accelerometer are used for training. We conduct parametric studies on different layer numbers, different sensor locations, the quantity of the training datasets and noise levels. The results show that the proposed SHMnet is effective and reliable with at least four independent training datasets and by avoiding vibration node points as sensor locations. Under up to 60% additive Gaussian noise, the average identification accuracy is over 98%. In comparison, the traditional methods based on the identified modal parameters inevitably fail due to the unnoticeable changes of identified natural frequencies and mode shapes. The results provide confidence in using the developed method as an effective structural condition identification framework. It has the potential to transform the structural health monitoring practice. The code and relevant information can be found at https://github.com/capepoint/SHMnet .

Implementation and evaluation of a laser scan localization scheme for sensor nodes

Sensor Review ◽  
2020 ◽  
Vol 40 (1) ◽  
pp. 97-106
Author(s):  
Shugong Wei
Keyword(s):  
Theoretical Analysis ◽  
Laser Beam ◽  
Design Methodology ◽  
Sensor Nodes ◽  
Content Type ◽  
Localization Accuracy ◽  
Localization System ◽  
Localization Scheme ◽  
Experimental Apparatus ◽  
Sensor Locations

Purpose In this paper, an experimental apparatus was designed and subsequent theoretical analysis and simulations were conducted on the effectiveness and advantages of a novel laser beam scan localization (BLS) system. Design/methodology/approach The system used a moving location assistant (LA) with a laser beam, through which the deployed area was scanned. The laser beam sent identity documents (IDs) to unknown nodes to obtain the sensor locations. Findings The results showed that the system yielded significant benefits compared with other localization methods, and a high localization accuracy could be achieved without the aid of expensive hardware on the sensor nodes. Furthermore, four positioning mode features in this localization system were realized and compared. Originality/value In this paper, an experimental apparatus was designed and subsequent theoretical analysis and simulations were conducted on the effectiveness and advantages of a novel laser BLS system. The system used a moving LA with a laser beam, through which the deployed area was scanned. The laser beam sent IDs to unknown nodes to obtain the sensor locations.

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