Optimal Sensor and Actuator Configuration for Structural Identification

2001 ◽  
Author(s):  
C. Papadimitriou ◽  
K. Christodoulou ◽  
M. Pavlidou ◽  
S. A. Karamanos
Keyword(s):  
Information Entropy ◽  
Structural Model ◽  
Model Updating ◽  
Cost Effective ◽  
Entropy Measure ◽  
Parameter Estimates ◽  
Discrete Optimization Problem ◽  
Damage Scenarios ◽  
The One ◽  
Theoretical Developments

Abstract A methodology is presented for designing cost-effective optimal sensor and actuator configurations useful for structural model updating and health monitoring purposes. The optimal sensor and actuator configuration is selected such that the resulting measured data are most informative about the condition of the structure. This selection is based on an information entropy measure of the uncertainty in the model parameter estimates obtained using a statistical system identification methodology. The optimal sensor and actuator configuration is selected as the one that minimizes the information entropy. A discrete optimization problem arises which is solved efficiently using genetic algorithms. This study also addresses important issues related to robustness of the optimal sensor and actuator configuration to unavoidable uncertainties in the structural model, as well as issues related to the optimal sensor and actuator configurations designed to monitor multiple damage scenarios. The theoretical developments are illustrated by designing the optimal configuration for a 40-DOF two-dimensional truss structure subjected to an impulse hammer excitation.

Optimal Sensor Placement Methodology for Identification with Unmeasured Excitation

2001 ◽  
Vol 123 (4) ◽  
pp. 677-686 ◽  
Author(s):  
Ka-Veng Yuen ◽  
Lambros S. Katafygiotis ◽  
Costas Papadimitriou ◽  
Neil C. Mickleborough
Keyword(s):  
Structural Model ◽  
Model Updating ◽  
Cost Effective ◽  
Ambient Vibration ◽  
Entropy Measure ◽  
Parameter Estimates ◽  
Earthquake Excitation ◽  
Practical Applications ◽  
Ambient Vibration Tests ◽  
Sensor Configuration

A methodology is presented for designing cost-effective optimal sensor configurations for structural model updating and health monitoring purposes. The optimal sensor configuration is selected such that the resulting measured data are most informative about the condition of the structure. This selection is based on an information entropy measure of the uncertainty in the model parameter estimates obtained using a statistical system identification method. The methodology is developed for the uncertain excitation case encountered in practical applications for which data are to be taken either from ambient vibration tests or from other uncertain excitations such as earthquake and wind. Important issues related to robustness of the optimal sensor configuration to uncertainties in the structural model are addressed. The theoretical developments are illustrated by designing the optimal configuration for a simple 8-DOF chain-like model of a structure subjected to an unmeasured base excitation and a 40-DOF truss model subjected to wind/earthquake excitation.

scholarly journals Estimating Simultaneous Equation Models through an Entropy-Based Incremental Variational Bayes Learning Algorithm

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 384
Author(s):  
Rocío Hernández-Sanjaime ◽  
Martín González ◽  
Antonio Peñalver ◽  
Jose J. López-Espín
Keyword(s):  
Statistical Theory ◽  
Learning Algorithm ◽  
Real Life ◽  
Simulated Data ◽  
Simultaneous Equation ◽  
Variational Bayes ◽  
Parameter Estimates ◽  
Step Method ◽  
The One ◽  
Simultaneous Equation Models

The presence of unaccounted heterogeneity in simultaneous equation models (SEMs) is frequently problematic in many real-life applications. Under the usual assumption of homogeneity, the model can be seriously misspecified, and it can potentially induce an important bias in the parameter estimates. This paper focuses on SEMs in which data are heterogeneous and tend to form clustering structures in the endogenous-variable dataset. Because the identification of different clusters is not straightforward, a two-step strategy that first forms groups among the endogenous observations and then uses the standard simultaneous equation scheme is provided. Methodologically, the proposed approach is based on a variational Bayes learning algorithm and does not need to be executed for varying numbers of groups in order to identify the one that adequately fits the data. We describe the statistical theory, evaluate the performance of the suggested algorithm by using simulated data, and apply the two-step method to a macroeconomic problem.

scholarly journals Sprouting of Sorghum (Sorghum bicolor [L.] Moench): Effect of Drying Treatment on Protein and Starch Features

Foods ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 407 ◽  
Author(s):  
Mia Marchini ◽  
Alessandra Marti ◽  
Claudia Folli ◽  
Barbara Prandi ◽  
Tommaso Ganino ◽  
...  
Keyword(s):  
Food Production ◽  
Cost Effective ◽  
High Exposure ◽  
Effective Technology ◽  
Drying Treatment ◽  
Higher Temperature ◽  
Lower Temperature ◽  
The One ◽  
Drying Conditions ◽  
Sorghum Bicolor L

The nutritional and physicochemical properties of sorghum proteins and starch make the use of this cereal for food production challenging. Sprouting is a cost-effective technology to improve the nutritional and functional profile of grains. Two drying treatments were used after sorghum sprouting to investigate whether the drying phase could improve the protein and starch functionalities. Results showed that the drying treatment at lower temperature/longer time (40 °C for 12 h) extended the enzymatic activity that started during sprouting compared to the one performed at higher temperature/shorter time (50 °C for 6 h). An increased protein hydrolysis and water- and oil-holding capacity were found in the flour obtained by the former treatment. Higher protein matrix hydrolysis caused high exposure of starch to enzymes, thus increasing its digestibility, while worsening the technological functionality. Overall, modulating drying conditions could represent a further way, in addition to sprouting, to improve sorghum flour’s nutritional profile.

scholarly journals IIMLP: integrated information-entropy-based method for LncRNA prediction

2021 ◽  
Vol 22 (S3) ◽  
Author(s):  
Junyi Li ◽  
Huinian Li ◽  
Xiao Ye ◽  
Li Zhang ◽  
Qingzhe Xu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Dna Sequences ◽  
Information Entropy ◽  
Area Under The Curve ◽  
Prediction Method ◽  
Machine Learning Algorithms ◽  
Reading Frame ◽  
Non Coding Rna ◽  
The One ◽  
Long Non Coding Rna

Abstract Background The prediction of long non-coding RNA (lncRNA) has attracted great attention from researchers, as more and more evidence indicate that various complex human diseases are closely related to lncRNAs. In the era of bio-med big data, in addition to the prediction of lncRNAs by biological experimental methods, many computational methods based on machine learning have been proposed to make better use of the sequence resources of lncRNAs. Results We developed the lncRNA prediction method by integrating information-entropy-based features and machine learning algorithms. We calculate generalized topological entropy and generate 6 novel features for lncRNA sequences. By employing these 6 features and other features such as open reading frame, we apply supporting vector machine, XGBoost and random forest algorithms to distinguish human lncRNAs. We compare our method with the one which has more K-mer features and results show that our method has higher area under the curve up to 99.7905%. Conclusions We develop an accurate and efficient method which has novel information entropy features to analyze and classify lncRNAs. Our method is also extendable for research on the other functional elements in DNA sequences.

scholarly journals Factors Affecting the Lut Desert Tourism in Iran: Developing an Interpretive-Structural Model

2021 ◽  
Vol 13 (13) ◽  
pp. 7245
Author(s):  
Beniamino Murgante ◽  
Mohammad Eskandari Sani ◽  
Sara Pishgahi ◽  
Moslem Zarghamfard ◽  
Fatemeh Kahaki
Keyword(s):  
Sampling Method ◽  
Structural Model ◽  
Tourism Development ◽  
Cost Effective ◽  
Structural Modeling ◽  
Interpretive Structural Modeling ◽  
Factors Affecting ◽  
Purposive Sampling ◽  
Interpretive Structural Model ◽  
Lut Desert

The Lut desert is one of the largest and most attractive deserts in Iran. The value of desert tourism remains unclear for Iran’s economy and has only recently been taken into consideration by the authorities, although its true national and international value remains unclear. This study was aimed at investigating the factors that influence tourism development in the Lut desert. Data collected through the purposive sampling method was analyzed using Interpretive Structural Modeling and the MICMAC Analysis. According to the results, cost-effective travel expenses, security, and safety provided in the desert, together with appropriate media advertising and illustration of the Lut desert (branding) are the leading factors that influence tourism in the Lut desert in Iran. This paper highlighted the importance of desert tourism, especially in this region.

The Four-States Model of Memory Retrieval Experiences

2007 ◽  
Vol 215 (1) ◽  
pp. 61-71 ◽  
Author(s):  
Edgar Erdfelder ◽  
Lutz Cüpper ◽  
Tina-Sarah Auer ◽  
Monika Undorf
Keyword(s):  
Signal Detection ◽  
Memory Retrieval ◽  
Measurement Model ◽  
Standard Errors ◽  
Parameter Estimates ◽  
Unequal Variances ◽  
One Dimensional ◽  
Detection Model ◽  
Memory States ◽  
The One

Abstract. A memory measurement model is presented that accounts for judgments of remembering, knowing, and guessing in old-new recognition tasks by assuming four disjoint latent memory states: recollection, familiarity, uncertainty, and rejection. This four-states model can be applied to both Tulving's (1985) remember-know procedure (RK version) and Gardiner and coworker's ( Gardiner, Java, & Richardson-Klavehn, 1996 ; Gardiner, Richardson-Klavehn, & Ramponi, 1997 ) remember-know-guess procedure (RKG version). It is shown that the RK version of the model fits remember-know data approximately as well as the one-dimensional signal detection model does. In contrast, the RKG version of the four-states model outperforms the corresponding detection model even if unequal variances for old and new items are allowed for.We show empirically that the two versions of the four-statesmodelmeasure the same state probabilities. However, the RKG version, requiring remember-know-guess judgments, provides parameter estimates with smaller standard errors and is therefore recommended for routine use.

Case Studies Highlighting Rapid Repair Methods of Pressurised Pipelines Damaged by Anchors

2018 ◽  
Author(s):  
Dale Millward
Keyword(s):  
Case Studies ◽  
Health And Safety ◽  
Cost Effective ◽  
Pipeline System ◽  
Subsea Pipeline ◽  
Damage Scenarios ◽  
Fail Safe ◽  
Subsea Pipelines ◽  
Marine Vessels ◽  
Pipeline Design

Effective pipeline design and regular maintenance can assist in prolonging the lifespan of subsea pipelines, however the presence of marine vessels can significantly increase the risk of pipeline damage from anchor hazards. As noted in the Health and Safety Executive – Guideline for Pipeline Operators on Pipeline Anchor Hazards 2009. “Anchor hazards can pose a significant threat to pipeline integrity. The consequences of damage to a pipeline could include loss of life, injury, fire, explosion, loss of buoyancy around a vessel and major pollution”. This paper will describe state of the art pipeline isolation tooling that enables safe modification of pressurised subsea pipelines. Double Block and Bleed (DBB) isolation tools have been utilised to greatly reduce downtime, increase safety and maximise unplanned maintenance, providing cost-effective solutions to the end user. High integrity isolation methods, in compliance with international subsea system intervention and isolation guidelines (IMCA D 044 / IMCA D 006), that enable piggable and unpiggable pipeline systems to be isolated before any breaking of containment, will also be explained. This paper will discuss subsea pipeline damage scenarios and repair options available to ensure a safe isolation of the pipeline and contents in the event of an incident DNV GL type approved isolation technology enables the installation of a fail-safe, DBB isolation in the event of a midline defect. The paper will conclude with case studies highlighting challenging subsea pipeline repair scenarios successfully executed, without depressurising the entire pipeline system, and in some cases without shutting down or interrupting production.

Sign in / Sign up

Export Citation Format

Share Document