Densely distributed and real-time scour hole monitoring using piezoelectric rod sensors

This study aims to validate a piezoelectric driven-rod scour monitoring system that can sense changes in scour depth along the entire rod at its instrumented location. The proposed sensor is a polymeric slender rod with a thin strip of polyvinylidene fluoride that runs through its midline. Extraction of the fundamental frequency allows the direct calculation of the exposed length (or scour depth) of the slender rod undergoing fluid flow excitation. First, laboratory validation in dry conditions is presented. Second, hydrodynamic testing of the sensor system in a soil-bed flume is discussed. Each rod was installed using a three-dimensional-printed footing designed for ease of installation and stabilization during testing. The sensors were installed in a layout designed to capture symmetric scour conditions around a scaled pier. In order to analyze the system out of steady-state conditions, water velocity was increased in stages during testing to induce different degrees of scour. As ambient water flow excited the portion of the exposed rods, the embedded piezoelectric element outputted a time-varying voltage signal. Different methods were then employed to extract the fundamental frequency of each rod, and the results were compared. Further testing was also performed to characterize the relationship between frequency outputs and flow velocity, which were previously thought to be independent. In general, the proposed driven-rod scour monitoring system successfully captured changing frequencies under varied flow conditions

Simulink/Modelsim Co-Simulation and FPGA Realization of Speed Control IC for PMSM Drive

AbstractPMSM (Permanent Magnetic Synchronous Motor) has been increasingly used in many high performance application due to its advantages of high power density, high power factor and efficiency. The design and implementation of a fuzzy-control based speed control IC for PMSM from Simulink/Modelsim co-simulation to FPGA (Field Programmable Gate Array) realization is presented in this paper. Firstly, a SVPWM scheme, vector control method and fuzzy controller are derived and applied in the speed control IC of PMSM drive. Secondly, the Very-High-Speed IC Hardware Description Language (VHDL) is adopted to describe the behavior of the aforementioned control algorithms. To evaluate the effectiveness and correctness of the proposed speed control IC, a co-simulation work performed by Matlab/Simulink and Modelsim is firstly conducted. Then, an experimental system by FPGA chip, Nios processor and motor driving board is set up to further validate the performance of the proposed speed control IC. Finally, the results in simulation and in experiment will be compared and discussed

Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition)

In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. For example, a key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process versus those that measure fl ux through the autophagy pathway (i.e., the complete process including the amount and rate of cargo sequestered and degraded). In particular, a block in macroautophagy that results in autophagosome accumulation must be differentiated from stimuli that increase autophagic activity, defi ned as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (inmost higher eukaryotes and some protists such as Dictyostelium ) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the fi eld understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. It is worth emphasizing here that lysosomal digestion is a stage of autophagy and evaluating its competence is a crucial part of the evaluation of autophagic flux, or complete autophagy. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. Along these lines, because of the potential for pleiotropic effects due to blocking autophagy through genetic manipulation it is imperative to delete or knock down more than one autophagy-related gene. In addition, some individual Atg proteins, or groups of proteins, are involved in other cellular pathways so not all Atg proteins can be used as a specific marker for an autophagic process. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular autophagy assays, we hope to encourage technical innovation in the field

On-line Damage Identification of Structural System Based on Adaptive Extended Kalman Filter

結構健康度檢測（Structural Health Monitoring）在土木工程領域裡頭一直非常受到重視，為了實現這個理念，系統識別（System Identification）與傷害檢測（Damage Detection）變成近十或十五年裡最重要的技術。在一個強大的動力事件中，結構系統也許遭受了某種程度的損害，而此結構系統的損壞將會反應在參數數值的變動上，且被包含在振動反應的量測中。因此系統識別的技術，尤其是即時識別的技術（On-line Identification Technique）近年來大量的被採用。 在即時識別的技術上，時間域的分析（Time Domain Analysis）以迭代運算（iterative computation）的形式被應用，例如卡氏過濾理論（Kalman Filter Technique）。然而此種迭代運算非常地倚賴過去的資料且無法識別系統參數瞬間的改變，為了克服這個缺點，適應性卡氏過濾理論（Adaptive Kalman Filter）使用了適應性尋跡的技術（Adaptive Tracking Technique）而被提出。 在這篇論文中將提出一個適應性尋跡的技術，此技術運用卡氏過濾理論為基礎，並著眼於誤差共變數矩陣（error covariance matrix）的發展。首先，每一個時間點的殘餘誤差（residual error）將被算出，而適應性矩陣（adaptation matrix）根據殘餘誤差產生，最後透過推薦的誤差指數（error index）使得系統參數可以即時的被識別。此方法能夠在嚴重的動力事件中識別系統參數瞬間的改變，此外也可以應用於識別非線性系統之恢復力（restoring force）的背脊線（backbone curve）。為了驗證這方法，結構系統的反應將以數值模擬與實驗量測兩種方法產生，而用以識別結構系統參數。最後，識別結果會有所比較與討論。Structural health monitoring (SHM) received considerable attention in civil engineering. To realize this, system identification and damage detection becomes the most important technique in the last ten or fifteen years. During a severe dynamic event, the structural system may suffer certain degree of damage. The damage of the structural system will be reflected by the variations of parametric value, and contained in the response measurements. Therefore, system identification techniques, especially on-line identification techniques, are commonly used recently. For the on-line identification techniques, time domain analysis has been applied with iterative computation, such as Kalman filter technique. However, the iterative analyses rely highly on the past data, and can not detect the abrupt change of system parameters. To overcome this drawback, adaptive Kalman filter are proposed using adaptive tracking techniques. In this thesis, an adaptive tracking technique based on the Kalman filter will be proposed. This proposed method is focus on the development of error covariance matrix. Firstly, the residual error of each time step is calculated, and then the adaptation matrix is generated in accordance with the residual error. Through the proposed error index the on-line adaptive tracking of system parameter can be identified. The proposed method is capable of tracking the abrupt change of parameters from a severe dynamic event. Moreover, it is also applied to identity the backbone curve of the inelastic restoring force of the nonlinear system. To verify the adaptive tracking technique, the responses of the structural system will be simulated numerically and measured experimentally, then, used to identify the parameters in structural system. Finally, the identification results are compared and discussed.Acknowledgement I Abstract (In Chinese) III Abstract (In English) V Contents VII List of Tables XI List of Figures XIII Chapter 1 Introduction 1 1.1 Structural Health monitoring and System Identification 1 1.2 On-line Identification Technique 3 1.3 Literature Review 4 1.4 Objective and Scope 7 Chapter 2 Theory of Kalman Filter 9 2.1 Ordinary Kalman Filter 10 2.1.1 The Filtering problem 11 2.1.2 State-pace Model 11 2.1.3 Predicted Estimate 13 2.1.4 Updated Estimate 14 2.1.5 Kalman Gain Computation 16 2.1.6 Kalman Filter Algorithm 17 2.1.7 Alternative Form of Kalman Filter 19 2.2 Extended Kalman Filter 22 2.2.1 Linearization 23 2.2.2 Extended Kalman Filter Algorithm 25 2.3 Adaptive Kalman Filter 27 2.3.1 Fading Kalman Filter Algorithm 28 2.3.2 Adaptive Kalman Filter Algorithm 31 2.4 Identification Structure with Abrupt Change of Stiffness 35 2.5 Identification Structure with Stiffness Degradation 37 Chapter 3 Numerical Study 39 3.1 Structural Identification Algorithm 41 3.2 Identification with Abrupt Change of Stiffness 44 3.2.1 SDOF Structural System with Different Noise Levels 46 3.2.2 SDOF Structural System with Different Stiffness Reductions 48 3.2.3 SDOF Structural System with Different Damaged Times 51 3.2.4 MDOF Structural System using Error Index 53 3.3 Identification with Stiffness and Strength Degradations 57 3.2.1 SDOF Structural System with Stiffness Degradations 59 3.2.2 SDOF Structural System with Strength Degradations 61 3.4 Summary 65 Chapter 4 Experimental Study 67 4.1 Steel Structure using Shaking Table Test Data 69 4.1.1 The Installations of Experiment 70 4.1.2 The Measured Accelerations and Identified Results 71 4.2 Concrete Structure using Shaking Table Test Data 75 4.2.1 The Installations of Experiment 75 4.2.2 The Measured Accelerations and Identified Results of Pure Frame 77 4.2.3 The Measured Accelerations and Identified Results of Brick Wall 79 4.3 Summary 82 Chapter 5 Conclusions and Future Work 85 5.1 Conclusions 85 5.2 Future Works 88 References 91 Appendix A Notation 189 Appendix B List of Cases 19

Practical Implementation of Recursive Subspace Identification on Seismically Excited Structures with Fixed Window

As one of the most catastrophic natural disasters worldwide, earthquakes and their effect on structures are very important to structural health monitoring (SHM), particularly for the ones living around the Pacific ring of fire. In this regard, SHM techniques with real-time or online processing can be used to identify states of structures, track modal parameters, provide a warning message about damage, and help post-earthquake reconnaissance and rehabilitation. For instance, a recursive formulation based on subspace identification (SI) has been demonstrated that it is capable to track system changes. In this study, a recursive subspace identification (RSI) algorithm with a fixed window is proposed to investigate the time-varying dynamic characteristics under seismic excitations. Subsequently, some suggestion is described and discussed for practical implementation. For verifying the proposed algorithm, different datasets from full-scale experiments are applied to examine its applicability. In other words, the practicability of implementing RSI in real-time or online has been developed and examined in this paper

Practical Implementation of Recursive Subspace Identification on Seismically Excited Structures with Fixed Window

As one of the most catastrophic natural disasters worldwide, earthquakes and their effect on structures are very important to structural health monitoring (SHM), particularly for the ones living around the Pacific ring of fire. In this regard, SHM techniques with real-time or online processing can be used to identify states of structures, track modal parameters, provide a warning message about damage, and help post-earthquake reconnaissance and rehabilitation. For instance, a recursive formulation based on subspace identification (SI) has been demonstrated that it is capable to track system changes. In this study, a recursive subspace identification (RSI) algorithm with a fixed window is proposed to investigate the time-varying dynamic characteristics under seismic excitations. Subsequently, some suggestion is described and discussed for practical implementation. For verifying the proposed algorithm, different datasets from full-scale experiments are applied to examine its applicability. In other words, the practicability of implementing RSI in real-time or online has been developed and examined in this paper

SHM data anomaly classification using machine learning strategies: a comparative study

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