scholarly journals Separating damage from environmental effects affecting civil structures for near real-time damage detection

2017 ◽  
Vol 17 (4) ◽  
pp. 850-868 ◽  
Author(s):  
William Soo Lon Wah ◽  
Yung-Tsang Chen ◽  
Gethin Wyn Roberts ◽  
Ahmed Elamin
Keyword(s):  
Damage Detection ◽  
Real Time ◽  
Environmental Effects ◽  
Environmental Conditions ◽  
Principal Component ◽  
Detection Methods ◽  
Real Time Monitoring ◽  
Civil Structures ◽  
Data Set ◽  
Wide Range

Analyzing changes in vibration properties (e.g. natural frequencies) of structures as a result of damage has been heavily used by researchers for damage detection of civil structures. These changes, however, are not only caused by damage of the structural components, but they are also affected by the varying environmental conditions the structures are faced with, such as the temperature change, which limits the use of most damage detection methods presented in the literature that did not account for these effects. In this article, a damage detection method capable of distinguishing between the effects of damage and of the changing environmental conditions affecting damage sensitivity features is proposed. This method eliminates the need to form the baseline of the undamaged structure using damage sensitivity features obtained from a wide range of environmental conditions, as conventionally has been done, and utilizes features from two extreme and opposite environmental conditions as baselines. To allow near real-time monitoring, subsequent measurements are added one at a time to the baseline to create new data sets. Principal component analysis is then introduced for processing each data set so that patterns can be extracted and damage can be distinguished from environmental effects. The proposed method is tested using a two-dimensional truss structure and validated using measurements from the Z24 Bridge which was monitored for nearly a year, with damage scenarios applied to it near the end of the monitoring period. The results demonstrate the robustness of the proposed method for damage detection under changing environmental conditions. The method also works despite the nonlinear effects produced by environmental conditions on damage sensitivity features. Moreover, since each measurement is allowed to be analyzed one at a time, near real-time monitoring is possible. Damage progression can also be given from the method which makes it advantageous for damage evolution monitoring.

scholarly journals A Process Analytical Concept for In-Line FTIR Monitoring of Polysiloxane Formation

Polymers ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 2473
Author(s):  
Julia C. Steinbach ◽  
Markus Schneider ◽  
Otto Hauler ◽  
Günter Lorenz ◽  
Karsten Rebner ◽  
...  
Keyword(s):  
Process Control ◽  
Real Time ◽  
Time Course ◽  
Morphological Changes ◽  
Univariate Analysis ◽  
Principal Component ◽  
Synthesis Process ◽  
Real Time Monitoring ◽  
Wide Range ◽  
Batch Modelling

The chemical synthesis of polysiloxanes from monomeric starting materials involves a series of hydrolysis, condensation and modification reactions with complex monomeric and oligomeric reaction mixtures. Real-time monitoring and precise process control of the synthesis process is of great importance to ensure reproducible intermediates and products and can readily be performed by optical spectroscopy. In chemical reactions involving rapid and simultaneous functional group transformations and complex reaction mixtures, however, the spectroscopic signals are often ambiguous due to overlapping bands, shifting peaks and changing baselines. The univariate analysis of individual absorbance signals is hence often only of limited use. In contrast, batch modelling based on the multivariate analysis of the time course of principal components (PCs) derived from the reaction spectra provides a more efficient tool for real-time monitoring. In batch modelling, not only single absorbance bands are used but information over a broad range of wavelengths is extracted from the evolving spectral fingerprints and used for analysis. Thereby, process control can be based on numerous chemical and morphological changes taking place during synthesis. “Bad” (or abnormal) batches can quickly be distinguished from “normal” ones by comparing the respective reaction trajectories in real time. In this work, FTIR spectroscopy was combined with multivariate data analysis for the in-line process characterization and batch modelling of polysiloxane formation. The synthesis was conducted under different starting conditions using various reactant concentrations. The complex spectral information was evaluated using chemometrics (principal component analysis, PCA). Specific spectral features at different stages of the reaction were assigned to the corresponding reaction steps. Reaction trajectories were derived based on batch modelling using a wide range of wavelengths. Subsequently, complexity was reduced again to the most relevant absorbance signals in order to derive a concept for a low-cost process spectroscopic set-up which could be used for real-time process monitoring and reaction control.

scholarly journals Implementation of a Piezo-diagnostics Approach for Damage Detection Based on PCA in a Linux-Based Embedded Platform

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3730 ◽  
Author(s):  
Jhonatan Camacho ◽  
Andrés Quintero ◽  
Magda Ruiz ◽  
Rodolfo Villamizar ◽  
Luis Mujica
Keyword(s):  
Statistical Model ◽  
Damage Detection ◽  
Structural Integrity ◽  
Statistical Processing ◽  
Principal Component ◽  
Detection Methods ◽  
Active System ◽  
Pipe Surface ◽  
Wave Response ◽  
Piezoelectric Devices

The implementation of damage-detection methods for continuously assessing structural integrity entails systems with attractive features such as storage capabilities, memory capacity, computational complexity and time-consuming processing. In this sense, embedded hardware platforms are a promising technology for developing integrated solutions in Structural Health Monitoring. In this paper, design, test, and specifications for a standalone inspection prototype are presented, which take advantage of piezo-diagnostics principle, statistical processing via Principal Component Analysis (PCA) and embedded systems. The equipment corresponds to a piezoelectric active system with the capability to detect defects in structures, by using a PCA-based algorithm embedded in the Odroid-U3 ARM Linux platform. The operation of the equipment consists of applying, at one side of the structure, wide guided waves by means of piezoelectric devices operated in actuation mode and to record the wave response in another side of the structure by using the same kind of piezoelectric devices operated in sensor mode. Based on the nominal response of the guide wave (no damages), represented by means of a PCA statistical model, the system can detect damages between the actuated/sensed points through squared prediction error (Q-statistical index). The system performance was evaluated in a pipe test bench where two kinds of damages were studied: first, a mass is added to the pipe surface, and then leaks are provoked to the pipe structure by means of a drill tool. The experiments were conducted on two lab structures: (i) a meter carbon-steel pipe section and (ii) a pipe loop structure. The wave response was recorded between the instrumented points for two conditions: (i) The pipe in nominal conditions, where several repetitions will be applied to build the nominal statistical model and (ii) when damage is caused to the pipe (mass adding or leak). Damage conditions were graphically recognized through the Q-statistic chart. Thus, the feasibility to implement an automated real-time diagnostic system is demonstrated with minimum processing resources and hardware flexibility.

scholarly journals Direct Analysis in Real Time by Mass Spectrometric Technique for Determining the Variation in Metabolite Profiles ofCinnamomum tamalaNees and Eberm Genotypes

2012 ◽  
Vol 2012 ◽  
pp. 1-6 ◽  
Author(s):  
Vineeta Singh ◽  
Atul Kumar Gupta ◽  
S. P. Singh ◽  
Anil Kumar
Keyword(s):  
Real Time ◽  
Principal Component ◽  
Direct Analysis ◽  
Ion Source ◽  
Diabetic Patients ◽  
Phytochemical Analysis ◽  
Active Components ◽  
Cinnamomum Tamala ◽  
Wide Range ◽  
Metabolite Profiles

Cinnamomum tamalaNees & Eberm. is an important traditional medicinal plant, mentioned in various ancient literatures such as Ayurveda. Several of its medicinal properties have recently been proved. To characterize diversity in terms of metabolite profiles ofCinnamomum tamalaNees and Eberm genotypes, a newly emerging mass spectral ionization technique direct time in real time (DART) is very helpful. The DART ion source has been used to analyze an extremely wide range of phytochemicals present in leaves ofCinnamomum tamala. Ten genotypes were assessed for the presence of different phytochemicals. Phytochemical analysis showed the presence of mainly terpenes and phenols. These constituents vary in the different genotypes ofCinnamomum tamala. Principal component analysis has also been employed to analyze the DART data of theseCinnamomumgenotypes. The result shows that the genotype ofCinnamomum tamalacould be differentiated using DART MS data. The active components present inCinnamomum tamalamay be contributing significantly to high amount of antioxidant property of leaves and, in turn, conditional effects for diabetic patients.

scholarly journals Real-time gaze estimation via pupil center tracking

2018 ◽  
Vol 9 (1) ◽  
pp. 6-18 ◽  
Author(s):  
Dario Cazzato ◽  
Fabio Dominio ◽  
Roberto Manduchi ◽  
Silvia M. Castro
Keyword(s):  
Real Time ◽  
Learning Algorithm ◽  
Natural Environments ◽  
Gaze Estimation ◽  
Head Pose ◽  
Data Set ◽  
Gaze Tracking ◽  
Illumination Changes ◽  
Wide Range ◽  
Estimation System

Abstract Automatic gaze estimation not based on commercial and expensive eye tracking hardware solutions can enable several applications in the fields of human computer interaction (HCI) and human behavior analysis. It is therefore not surprising that several related techniques and methods have been investigated in recent years. However, very few camera-based systems proposed in the literature are both real-time and robust. In this work, we propose a real-time user-calibration-free gaze estimation system that does not need person-dependent calibration, can deal with illumination changes and head pose variations, and can work with a wide range of distances from the camera. Our solution is based on a 3-D appearance-based method that processes the images from a built-in laptop camera. Real-time performance is obtained by combining head pose information with geometrical eye features to train a machine learning algorithm. Our method has been validated on a data set of images of users in natural environments, and shows promising results. The possibility of a real-time implementation, combined with the good quality of gaze tracking, make this system suitable for various HCI applications.

scholarly journals TreeNet–The Biological Drought and Growth Indicator Network

2021 ◽  
Vol 4 ◽  
Author(s):  
Roman Zweifel ◽  
Sophia Etzold ◽  
David Basler ◽  
Reinhard Bischoff ◽  
Sabine Braun ◽  
...  
Keyword(s):  
Data Processing ◽  
Real Time ◽  
Environmental Conditions ◽  
Forest Growth ◽  
Water Dynamics ◽  
Automated System ◽  
Indicator Network ◽  
High Temporal Resolution ◽  
Stem Size ◽  
Wide Range

The TreeNet research and monitoring network has been continuously collecting data from point dendrometers and air and soil microclimate using an automated system since 2011. The goal of TreeNet is to generate high temporal resolution datasets of tree growth and tree water dynamics for research and to provide near real-time indicators of forest growth performance and drought stress to a wide audience. This paper explains the key working steps from the installation of sensors in the field to data acquisition, data transmission, data processing, and online visualization. Moreover, we discuss the underlying premises to convert dynamic stem size changes into relevant biological information. Every 10 min, the stem radii of about 420 trees from 13 species at 61 sites in Switzerland are measured electronically with micrometer precision, in parallel with the environmental conditions above and below ground. The data are automatically transmitted, processed and stored on a central server. Automated data processing (R-based functions) includes screening of outliers, interpolation of data gaps, and extraction of radial stem growth and water deficit for each tree. These long-term data are used for scientific investigations as well as to calculate and display daily indicators of growth trends and drought levels in Switzerland based on historical and current data. The current collection of over 100 million data points forms the basis for identifying dynamics of tree-, site- and species-specific processes along environmental gradients. TreeNet is one of the few forest networks capable of tracking the diurnal and seasonal cycles of tree physiology in near real-time, covering a wide range of temperate forest species and their respective environmental conditions.

scholarly journals Evaluation of Unsupervised Change Detection Methods Applied to Landslide Inventory Mapping Using ASTER Imagery

2018 ◽  
Vol 10 (12) ◽  
pp. 1987 ◽  
Author(s):  
Rocío Ramos-Bernal ◽  
René Vázquez-Jiménez ◽  
Raúl Romero-Calcerrada ◽  
Patricia Arrogante-Funes ◽  
Carlos Novillo
Keyword(s):  
Change Detection ◽  
Vegetation Index ◽  
Thermal Emission ◽  
Principal Component ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Data Types ◽  
Statistical Parameters ◽  
Wide Range ◽  
The World

Natural hazards include a wide range of high-impact phenomena that affect socioeconomic and natural systems. Landslides are a natural hazard whose destructive power has caused a significant number of victims and substantial damage around the world. Remote sensing provides many data types and techniques that can be applied to monitor their effects through landslides inventory maps. Three unsupervised change detection methods were applied to the Advanced Spaceborne Thermal Emission and Reflection Radiometer (Aster)-derived images from an area prone to landslides in the south of Mexico. Linear Regression (LR), Chi-Square Transformation, and Change Vector Analysis were applied to the principal component and the Normalized Difference Vegetation Index (NDVI) data to obtain the difference image of change. The thresholding was performed on the change histogram using two approaches: the statistical parameters and the secant method. According to previous works, a slope mask was used to classify the pixels as landslide/No-landslide; a cloud mask was used to eliminate false positives; and finally, those landslides less than 450 m2 (two Aster pixels) were discriminated. To assess the landslide detection accuracy, 617 polygons (35,017 pixels) were sampled, classified as real landslide/No-landslide, and defined as ground-truth according to the interpretation of color aerial photo slides to obtain omission/commission errors and Kappa coefficient of agreement. The results showed that the LR using NDVI data performs the best results in landslide detection. Change detection is a suitable technique that can be applied for the landslides mapping and we think that it can be replicated in other parts of the world with results similar to those obtained in the present work.

Structural Health Monitoring Using Synchrosqueezed Wavelet Transform on IASC-ASCE Benchmark Phase I

2020 ◽  
Vol 20 (12) ◽  
pp. 2050138
Author(s):  
Wilson D. Sanchez ◽  
Jose V. de Brito ◽  
Suzana M. Avila
Keyword(s):  
Wavelet Transform ◽  
Dynamic Response ◽  
Phase I ◽  
Damage Detection ◽  
Health Monitoring ◽  
Natural Frequencies ◽  
Detection Methods ◽  
State Of Health ◽  
Civil Structures ◽  
Synchrosqueezed Wavelet Transform

Civil structures suffer deterioration either for years of service, deficiency due to environmental factors or damages caused by factors such as earthquakes, winds, impact loads, and cyclical loads. When a structure ages, it is necessary to know its state of health and make a decision of maintenance or replacement. When a structure such as a bridge or building is subjected to destructive environmental forces, determining its state of health becomes a priority since its recovery is urgently required to function normally. Structural Health Monitoring (SHM) is a technology that aims to prevent the collapse of structures and loss of human life through early diagnosis of the health status of a structure. There are a large number of damage detection methods that can be classified into (1) non-destructive testing methods, (2) dynamic characteristics-based damage detection methods, (3) dynamic response-based, (4) multi-scale damage detection method and (5) damage detection methods with consideration of uncertainties. In this work, it is implemented synchrosqueezed wavelet transform (SWT), which can be classified as a methods based on the dynamic response. To validate the robustness of the method it is identified first, the natural frequencies of the Benchmark Phase I without damage, which consists of a steel structure of 4-story [Formula: see text] bay 3D steel frame structure subjected to ambient vibrations. Subsequently, some damage patterns are validated according to IASC-ASCE SHM Task Group. The results obtained in the identification of natural frequencies are compared with those reported in literature. SWT was efficient, presenting a minimum error of 0.12[Formula: see text] and a maximum of 3.06[Formula: see text] in the identification of natural frequencies about the AISCE-ASCE group model. SWT overcomes some other damage detection methods, which are deficient in the identification of closely spaced frequencies, commonly present in many civil structures due to symmetric geometry or similar physical properties in different directions.

scholarly journals A lectin-coupled porous silicon-based biosensor: label-free optical detection of bacteria in a real-time mode

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Mona Yaghoubi ◽  
Fereshteh Rahimi ◽  
Babak Negahdari ◽  
Ali Hossein Rezayan ◽  
Azizollah Shafiekhani
Keyword(s):  
Fourier Transform ◽  
Porous Silicon ◽  
Real Time ◽  
Limit Of Detection ◽  
Principal Component ◽  
The Other ◽  
Detection Methods ◽  
Carbohydrate Binding ◽  
Label Free ◽  
E Coli

Abstract Accuracy and speed of detection, along with technical and instrumental simplicity, are indispensable for the bacterial detection methods. Porous silicon (PSi) has unique optical and chemical properties which makes it a good candidate for biosensing applications. On the other hand, lectins have specific carbohydrate-binding properties and are inexpensive compared to popular antibodies. We propose a lectin-conjugated PSi-based biosensor for label-free and real-time detection of Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus) by reflectometric interference Fourier transform spectroscopy (RIFTS). We modified meso-PSiO2 (10–40 nm pore diameter) with three lectins of ConA (Concanavalin A), WGA (Wheat Germ Agglutinin), and UEA (Ulex europaeus agglutinin) with various carbohydrate specificities, as bioreceptor. The results showed that ConA and WGA have the highest binding affinity for E. coli and S. aureus respectively and hence can effectively detect them. This was confirmed by 6.8% and 7.8% decrease in peak amplitude of fast Fourier transform (FFT) spectra (at 105 cells mL−1 concentration). A limit of detection (LOD) of about 103 cells mL−1 and a linear response range of 103 to 105 cells mL−1 were observed for both ConA-E. coli and WGA-S. aureus interaction platforms that are comparable to the other reports in the literature. Dissimilar response patterns among lectins can be attributed to the different bacterial cell wall structures. Further assessments were carried out by applying the biosensor for the detection of Klebsiella aerogenes and Bacillus subtilis bacteria. The overall obtained results reinforced the conjecture that the WGA and ConA have a stronger interaction with Gram-positive and Gram-negative bacteria, respectively. Therefore, it seems that specific lectins can be suggested for bacterial Gram-typing or even serotyping. These observations were confirmed by the principal component analysis (PCA) model.

scholarly journals Alkalmazott mesterséges intelligencia felhasználási területei és biztonsági kérdései – Mesterséges intelligencia a gyakorlatban

2020 ◽  
Vol 1 (1) ◽  
pp. 35-42
Author(s):  
Péter Ekler ◽  
Dániel Pásztor
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Anomaly Detection ◽  
Real Time ◽  
Learning Algorithm ◽  
Sensor Data ◽  
Test Field ◽  
Real Time System ◽  
Data Set ◽  
Wide Range

Összefoglalás. A mesterséges intelligencia az elmúlt években hatalmas fejlődésen ment keresztül, melynek köszönhetően ma már rengeteg különböző szakterületen megtalálható valamilyen formában, rengeteg kutatás szerves részévé vált. Ez leginkább az egyre inkább fejlődő tanulóalgoritmusoknak, illetve a Big Data környezetnek köszönhető, mely óriási mennyiségű tanítóadatot képes szolgáltatni. A cikk célja, hogy összefoglalja a technológia jelenlegi állapotát. Ismertetésre kerül a mesterséges intelligencia történelme, az alkalmazási területek egy nagyobb része, melyek központi eleme a mesterséges intelligencia. Ezek mellett rámutat a mesterséges intelligencia különböző biztonsági réseire, illetve a kiberbiztonság területén való felhasználhatóságra. A cikk a jelenlegi mesterséges intelligencia alkalmazások egy szeletét mutatja be, melyek jól illusztrálják a széles felhasználási területet. Summary. In the past years artificial intelligence has seen several improvements, which drove its usage to grow in various different areas and became the focus of many researches. This can be attributed to improvements made in the learning algorithms and Big Data techniques, which can provide tremendous amount of training. The goal of this paper is to summarize the current state of artificial intelligence. We present its history, introduce the terminology used, and show technological areas using artificial intelligence as a core part of their applications. The paper also introduces the security concerns related to artificial intelligence solutions but also highlights how the technology can be used to enhance security in different applications. Finally, we present future opportunities and possible improvements. The paper shows some general artificial intelligence applications that demonstrate the wide range usage of the technology. Many applications are built around artificial intelligence technologies and there are many services that a developer can use to achieve intelligent behavior. The foundation of different approaches is a well-designed learning algorithm, while the key to every learning algorithm is the quality of the data set that is used during the learning phase. There are applications that focus on image processing like face detection or other gesture detection to identify a person. Other solutions compare signatures while others are for object or plate number detection (for example the automatic parking system of an office building). Artificial intelligence and accurate data handling can be also used for anomaly detection in a real time system. For example, there are ongoing researches for anomaly detection at the ZalaZone autonomous car test field based on the collected sensor data. There are also more general applications like user profiling and automatic content recommendation by using behavior analysis techniques. However, the artificial intelligence technology also has security risks needed to be eliminated before applying an application publicly. One concern is the generation of fake contents. These must be detected with other algorithms that focus on small but noticeable differences. It is also essential to protect the data which is used by the learning algorithm and protect the logic flow of the solution. Network security can help to protect these applications. Artificial intelligence can also help strengthen the security of a solution as it is able to detect network anomalies and signs of a security issue. Therefore, the technology is widely used in IT security to prevent different type of attacks. As different BigData technologies, computational power, and storage capacity increase over time, there is space for improved artificial intelligence solution that can learn from large and real time data sets. The advancements in sensors can also help to give more precise data for different solutions. Finally, advanced natural language processing can help with communication between humans and computer based solutions.

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