Optimization of Rapid State Estimation in Structures Subjected to High-Rate Boundary Change

2020 ◽  
Author(s):  
James Scheppegrell ◽  
Adriane G. Moura ◽  
Jacob Dodson ◽  
Austin Downey
Keyword(s):  
Frequency Response ◽  
State Estimation ◽  
Real Time ◽  
Performance Metrics ◽  
Numerical Models ◽  
Vertical Axis ◽  
High Rate ◽  
Position Measurement ◽  
Data Set ◽  
Variable Boundary

Abstract Many structures are subjected to varying forces, moving boundaries, and other dynamic conditions. Whether part of a vehicle, building, or active energy mitigation device, data on such changes can represent useful knowledge, but also presents challenges in its collection and analysis. In systems where changes occur rapidly, assessment of the system’s state within a useful time span is required to enable an appropriate response before the system’s state changes further. Rapid state estimation is especially important but poses unique difficulties. In determining the state of a structural system subjected to high-rate dynamic changes, measuring the frequency response is one method that can be used to draw inferences, provided the system is adequately understood and defined. The work presented here is the result of an investigation into methods to determine the frequency response, and thus state, of a structure subjected to high-rate boundary changes in real-time. In order to facilitate development, the Air Force Research Laboratory created the DROPBEAR, a testbed with an oscillating beam subjected to a continuously variable boundary condition. One end of the beam is held by a stationary fixed support, while a pinned support is able to move along the beam’s length. The free end of the beam structure is instrumented with acceleration, velocity, and position sensors measuring the beam’s vertical axis. Direct position measurement of the pin location is also taken to provide a reference for comparison with numerical models. This work presents a numerical investigation into methods for extracting the frequency response of a structure in real-time. An FFT based method with a rolling window is used to track the frequency of a data set generated to represent the range of the DROPBEAR, and is run with multiple window lengths. The frequency precision and latency of the FFT method is analyzed in each configuration. A specialized frequency extraction technique, Delayed Comparison Error Minimization, is implemented with parameters optimized for the frequency range of interest. The performance metrics of latency and precision are analyzed and compared to the baseline rolling FFT method results, and applicability is discussed.

Performance and Near-Wake Measurements for a Vertical Axis Turbine at Moderate Reynolds Number

2013 ◽  
Author(s):  
Peter Bachant ◽  
Martin Wosnik
Keyword(s):  
Reynolds Number ◽  
Numerical Models ◽  
Vertical Axis ◽  
Speed Ratio ◽  
Reynolds Stresses ◽  
Near Wake ◽  
Mean Velocity ◽  
Data Set ◽  
Moderate Reynolds Number ◽  
Vertical Axis Turbine

Experiments were performed with a 1 m diameter, 1 m tall three-bladed vertical axis turbine in a towing tank. Rotor power and drag (or thrust) were measured at a tow speed of 1 m/s, corresponding to a turbine diameter Reynolds number ReD = 106 and an approximate blade chord Reynolds number Rec = λU∞c/ν ∼ 105. Mechanical exergy efficiency estimates were computed from power and drag measurements using an actuator disk approach. Characteristics of the turbine’s near-wake were measured at one turbine diameter downstream. Variation of all three mean and fluctuating velocity components in the vertical and cross-stream directions were measured at peak turbine power output via acoustic Doppler velocimeter. The effect of tip speed ratio on near-wake mean velocity was observed at the turbine center line. Transverse profiles of mean velocity, fluctuating velocity, and Reynolds stresses were also measured at the turbine’s quarter height for two tip speed ratios of interest. Results are compared and contrasted with previous lower Reynolds number studies, and will provide a detailed data set for validation of numerical models.

Methodology for Real-time State Estimation at Unobserved Locations for Structures Experiencing High-rate Dynamics

2019 ◽  
Author(s):  
AUSTIN DOWNEY ◽  
JONATHAN HONG ◽  
BRYAN JOYCE ◽  
JACOB DODSON ◽  
CHAO HU ◽  
...  
Keyword(s):  
State Estimation ◽  
Real Time ◽  
High Rate

Integrated Quantitative Analysis of the Phosphoproteome and Transcriptome in Tamoxifen-resistant Breast Cancer*

2020 ◽  
Author(s):  
Lungwani Muungo
Keyword(s):  
Breast Cancer ◽  
High Rate ◽  
System Level ◽  
Clinical Samples ◽  
Transcriptome Data ◽  
Data Set ◽  
Reporter Assays ◽  
Treated Breast ◽  
Mcf 7 ◽  
Resistant Cells

Quantitative phosphoproteome and transcriptome analysisof ligand-stimulated MCF-7 human breast cancer cells wasperformed to understand the mechanisms of tamoxifen resistanceat a system level. Phosphoproteome data revealed thatWT cells were more enriched with phospho-proteins thantamoxifen-resistant cells after stimulation with ligands.Surprisingly, decreased phosphorylation after ligand perturbationwas more common than increased phosphorylation.In particular, 17?-estradiol induced down-regulation inWT cells at a very high rate. 17?-Estradiol and the ErbBligand heregulin induced almost equal numbers of up-regulatedphospho-proteins in WT cells. Pathway and motifactivity analyses using transcriptome data additionallysuggested that deregulated activation of GSK3? (glycogensynthasekinase 3?) and MAPK1/3 signaling might be associatedwith altered activation of cAMP-responsive elementbindingprotein and AP-1 transcription factors intamoxifen-resistant cells, and this hypothesis was validatedby reporter assays. An examination of clinical samples revealedthat inhibitory phosphorylation of GSK3? at serine 9was significantly lower in tamoxifen-treated breast cancerpatients that eventually had relapses, implying that activationof GSK3? may be associated with the tamoxifen-resistantphenotype. Thus, the combined phosphoproteomeand transcriptome data set analyses revealed distinct signal

scholarly journals An efficient technique for CT scan images classification of COVID-19

2020 ◽  
pp. 1-14
Author(s):  
Esraa Hassan ◽  
Noha A. Hikal ◽  
Samir Elmuogy
Keyword(s):  
Neural Network ◽  
Diagnostic Tool ◽  
Deep Neural Network ◽  
Data Augmentation ◽  
Performance Metrics ◽  
Classification Model ◽  
Data Set ◽  
Training Models ◽  
The Earth ◽  
Diagnostic Time

Nowadays, Coronavirus (COVID-19) considered one of the most critical pandemics in the earth. This is due its ability to spread rapidly between humans as well as animals. COVID_19 expected to outbreak around the world, around 70 % of the earth population might infected with COVID-19 in the incoming years. Therefore, an accurate and efficient diagnostic tool is highly required, which the main objective of our study. Manual classification was mainly used to detect different diseases, but it took too much time in addition to the probability of human errors. Automatic image classification reduces doctors diagnostic time, which could save human’s life. We propose an automatic classification architecture based on deep neural network called Worried Deep Neural Network (WDNN) model with transfer learning. Comparative analysis reveals that the proposed WDNN model outperforms by using three pre-training models: InceptionV3, ResNet50, and VGG19 in terms of various performance metrics. Due to the shortage of COVID-19 data set, data augmentation was used to increase the number of images in the positive class, then normalization used to make all images have the same size. Experimentation is done on COVID-19 dataset collected from different cases with total 2623 where (1573 training,524 validation,524 test). Our proposed model achieved 99,046, 98,684, 99,119, 98,90 In terms of Accuracy, precision, Recall, F-score, respectively. The results are compared with both the traditional machine learning methods and those using Convolutional Neural Networks (CNNs). The results demonstrate the ability of our classification model to use as an alternative of the current diagnostic tool.

scholarly journals Microarchitectural Characterization on a Mobile Workload

2021 ◽  
Vol 11 (3) ◽  
pp. 1225
Author(s):  
Woohyong Lee ◽  
Jiyoung Lee ◽  
Bo Kyung Park ◽  
R. Young Chul Kim
Keyword(s):  
Performance Monitoring ◽  
Performance Metrics ◽  
Performance Comparison ◽  
Instruction Level Parallelism ◽  
Data Set ◽  
Performance Events ◽  
Hardware Performance Counters ◽  
On Chip ◽  
The Comparative Study ◽  
Level Parallelism

Geekbench is one of the most referenced cross-platform benchmarks in the mobile world. Most of its workloads are synthetic but some of them aim to simulate real-world behavior. In the mobile world, its microarchitectural behavior has been reported rarely since the hardware profiling features are limited to the public. As a popular mobile performance workload, it is hard to find Geekbench’s microarchitecture characteristics in mobile devices. In this paper, a thorough experimental study of Geekbench performance characterization is reported with detailed performance metrics. This study also identifies mobile system on chip (SoC) microarchitecture impacts, such as the cache subsystem, instruction-level parallelism, and branch performance. After the study, we could understand the bottleneck of workloads, especially in the cache sub-system. This means that the change of data set size directly impacts performance score significantly in some systems and will ruin the fairness of the CPU benchmark. In the experiment, Samsung’s Exynos9820-based platform was used as the tested device with Android Native Development Kit (NDK) built binaries. The Exynos9820 is a superscalar processor capable of dual issuing some instructions. To help performance analysis, we enable the capability to collect performance events with performance monitoring unit (PMU) registers. The PMU is a set of hardware performance counters which are built into microprocessors to store the counts of hardware-related activities. Throughout the experiment, functional and microarchitectural performance profiles were fully studied. This paper describes the details of the mobile performance studies above. In our experiment, the ARM DS5 tool was used for collecting runtime PMU profiles including OS-level performance data. After the comparative study is completed, users will understand more about the mobile architecture behavior, and this will help to evaluate which benchmark is preferable for fair performance comparison.

scholarly journals An automatically generated high-resolution earthquake catalogue for the 2016–2017 Central Italy seismic sequence, including P and S phase arrival times

2020 ◽  
Author(s):  
D Spallarossa ◽  
M Cattaneo ◽  
D Scafidi ◽  
M Michele ◽  
L Chiaraluce ◽  
...  
Keyword(s):  
Real Time ◽  
Central Italy ◽  
Earthquake Catalogue ◽  
Hazard Evaluation ◽  
Depth Range ◽  
Data Set ◽  
Epicentral Area ◽  
Earthquake Parameters ◽  
Waveform Data ◽  
Arrival Times

Summary The 2016–17 central Italy earthquake sequence began with the first mainshock near the town of Amatrice on August 24 (MW 6.0), and was followed by two subsequent large events near Visso on October 26 (MW 5.9) and Norcia on October 30 (MW 6.5), plus a cluster of 4 events with MW > 5.0 within few hours on January 18, 2017. The affected area had been monitored before the sequence started by the permanent Italian National Seismic Network (RSNC), and was enhanced during the sequence by temporary stations deployed by the National Institute of Geophysics and Volcanology and the British Geological Survey. By the middle of September, there was a dense network of 155 stations, with a mean separation in the epicentral area of 6–10 km, comparable to the most likely earthquake depth range in the region. This network configuration was kept stable for an entire year, producing 2.5 TB of continuous waveform recordings. Here we describe how this data was used to develop a large and comprehensive earthquake catalogue using the Complete Automatic Seismic Processor (CASP) procedure. This procedure detected more than 450,000 events in the year following the first mainshock, and determined their phase arrival times through an advanced picker engine (RSNI-Picker2), producing a set of about 7 million P- and 10 million S-wave arrival times. These were then used to locate the events using a non-linear location (NLL) algorithm, a 1D velocity model calibrated for the area, and station corrections and then to compute their local magnitudes (ML). The procedure was validated by comparison of the derived data for phase picks and earthquake parameters with a handpicked reference catalogue (hereinafter referred to as ‘RefCat’). The automated procedure takes less than 12 hours on an Intel Core-i7 workstation to analyse the primary waveform data and to detect and locate 3000 events on the most seismically active day of the sequence. This proves the concept that the CASP algorithm can provide effectively real-time data for input into daily operational earthquake forecasts, The results show that there have been significant improvements compared to RefCat obtained in the same period using manual phase picks. The number of detected and located events is higher (from 84,401 to 450,000), the magnitude of completeness is lower (from ML 1.4 to 0.6), and also the number of phase picks is greater with an average number of 72 picked arrival for a ML = 1.4 compared with 30 phases for RefCat using manual phase picking. These propagate into formal uncertainties of ± 0.9km in epicentral location and ± 1.5km in depth for the enhanced catalogue for the vast majority of the events. Together, these provide a significant improvement in the resolution of fine structures such as local planar structures and clusters, in particular the identification of shallow events occurring in parts of the crust previously thought to be inactive. The lower completeness magnitude provides a rich data set for development and testing of analysis techniques of seismic sequences evolution, including real-time, operational monitoring of b-value, time-dependent hazard evaluation and aftershock forecasting.

MobileNet-Yolo Based wildlife detection model: A case study in yunnan tongbiguan nature reserve, China

2021 ◽  
pp. 1-11
Author(s):  
Tingting Zhao ◽  
Xiaoli Yi ◽  
Zhiyong Zeng ◽  
Tao Feng
Keyword(s):  
Real Time ◽  
Nature Reserve ◽  
Complex Model ◽  
Training Data ◽  
Mean Average Precision ◽  
Detection Accuracy ◽  
High Definition ◽  
Average Precision ◽  
Data Set ◽  
Real Time Detection

YTNR (Yunnan Tongbiguan Nature Reserve) is located in the westernmost part of China’s tropical regions and is the only area in China with the tropical biota of the Irrawaddy River system. The reserve has abundant tropical flora and fauna resources. In order to realize the real-time detection of wild animals in this area, this paper proposes an improved YOLO (You only look once) network. The original YOLO model can achieve higher detection accuracy, but due to the complex model structure, it cannot achieve a faster detection speed on the CPU detection platform. Therefore, the lightweight network MobileNet is introduced to replace the backbone feature extraction network in YOLO, which realizes real-time detection on the CPU platform. In response to the difficulty in collecting wild animal image data, the research team deployed 50 high-definition cameras in the study area and conducted continuous observations for more than 1,000 hours. In the end, this research uses 1410 images of wildlife collected in the field and 1577 wildlife images from the internet to construct a research data set combined with the manual annotation of domain experts. At the same time, transfer learning is introduced to solve the problem of insufficient training data and the network is difficult to fit. The experimental results show that our model trained on a training set containing 2419 animal images has a mean average precision of 93.6% and an FPS (Frame Per Second) of 3.8 under the CPU. Compared with YOLO, the mean average precision is increased by 7.7%, and the FPS value is increased by 3.

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