Intelligent electromagnetic metasurface camera: system design and experimental results

Electromagnetic (EM) sensing is uniquely positioned among nondestructive examination options, which enables us to see clearly targets, even when they visually invisible, and thus has found many valuable applications in science, engineering and military. However, it is suffering from increasingly critical challenges from energy consumption, cost, efficiency, portability, etc., with the rapidly growing demands for the high-quality sensing with three-dimensional high-frame-rate schemes. To address these difficulties, we propose the concept of intelligent EM metasurface camera by the synergetic exploitation of inexpensive programmable metasurfaces with modern machine learning techniques, and establish a Bayesian inference framework for it. Such EM camera introduces the intelligence over the entire sensing chain of data acquisition and processing, and exhibits good performance in terms of the image quality and efficiency, even when it is deployed in highly noisy environment. Selected experimental results in real-world settings are provided to demonstrate that the developed EM metasurface camera enables us to see clearly human behaviors behind a 60 cm-thickness reinforced concrete wall with the frame rate in order of tens of Hz. We expect that the presented strategy could have considerable impacts on sensing and beyond, and open up a promising route toward smart community and beyond.

Inservice Inspection of Extended Dry Storage of Spent Nuclear Fuel, Part II: NDE/Sensor Technology Development and Codification

This paper describes development and demonstration of nondestructive examination (NDE) technologies to support periodic examinations of interim dry storage system (DSS) canisters for spent nuclear fuel in the USA to verify continued safe operation and that the canister confinement is intact and performing its intended safety function. Specifically, this work relates to NDE technology development for “canister” based DSS systems, which form the majority population of DSSs in the USA for interim storage of spent nuclear fuel. Consideration of potential degradation of the welded stainless-steel canister in these systems is required for continued usage in the period of extended operation (PEO) beyond the initial license or certified term. Physical access to the canister surface is constrained due to narrow annulus spaces between the canister and the overpack, tortuous entry pathways, and high temperatures and radiation doses that can be damaging to materials and electronics related to inspections. Several activities to demonstrate NDE technologies for the inspections of different DSS systems are summarized.

STUDY ON IN-SERVICE INSPECTION PROGRAM AND METHOD FOR FUEL HANDLING SYSTEM OF RDE

One of the main programs that should be established on the designing process of the fuel handling system is to establish an in-service inspection program for maintaining the integrity of the system, structure and component during service lifetime. The most important role of in-service inspection is the nondestructive examination techniques. The objective of this study is to propose a preliminary program for examining the integrity of the fuel handling system during operation and determining the best method to confirm the defects. The proposed programs are described as follows, defining the operating environment of the fuel handling system, identifying the material characteristics during operation which indicates to promote the defects, and selecting the appropriate method of non-destructive examination and analysis technique for such kind of defects. The proposed in-service inspection program is expected to give significant additional value to the fuel handling system design of RDE.

AE-RTISNet: Aeronautics Engine Radiographic Testing Inspection System Net with an Improved Fast Region-Based Convolutional Neural Network Framework

To ensure safety in aircraft flying, we aimed to use deep learning methods of nondestructive examination with multiple defect detection paradigms for X-ray image detection. The use of the fast region-based convolutional neural network (Fast R-CNN)-driven model was to augment and improve the existing automated non-destructive testing (NDT) diagnosis. Within the context of X-ray screening, limited numbers and insufficient types of X-ray aeronautics engine defect data samples can, thus, pose another problem in the performance accuracy of training models tackling multiple detections. To overcome this issue, we employed a deep learning paradigm of transfer learning tackling both single and multiple detection. Overall, the achieved results obtained more than 90% accuracy based on the aeronautics engine radiographic testing inspection system net (AE-RTISNet) retrained with eight types of defect detection. Caffe structure software was used to perform network tracking detection over multiple Fast R-CNNs. We determined that the AE-RTISNet provided the best results compared with the more traditional multiple Fast R-CNN approaches, which were simple to translate to C++ code and installed in the Jetson™ TX2 embedded computer. With the use of the lightning memory-mapped database (LMDB) format, all input images were 640 × 480 pixels. The results achieved a 0.9 mean average precision (mAP) on eight types of material defect classifier problems and required approximately 100 microseconds.

AE-RTISNet: Aeronautics Engine Radiographic Testing Inspection System Net with Improved Fast Region-based Convolutional Neural Networks Framework

To ensure the safety in aircraft flying, we aim use of the deep learning methods of nondestructive examination with multiple defect detection paradigms for X-ray image detection posed. The use of the Fast Region-based Convolutional Neural Networks (Fast R-CNN) driven model seeks to augment and improve existing automated Non-Destructive Testing (NDT) diagnosis. Within the context of X-ray screening, limited numbers insufficient types of X-ray aeronautics engine defect data samples can thus pose another problem in training model tackling multiple detections perform accuracy. To overcome this issue, we employ a deep learning paradigm of transfer learning tackling both single and multiple detection. Overall the achieve result get more then 90% accuracy based on the AE-RTISNet retrained with 8 types of defect detection. Caffe structure software to make networks tracking detection over multiples Fast R-CNN. We consider the AE-RTISNet provide best results to the more traditional multiple Fast R-CNN approaches simpler translate to C++ code and installed in the Jetson™ TX2 embedded computer. With the use of LMDB format, all images using input images of size 640 × 480 pixel. The results scope achieves 0.9 mean average precision (mAP) on 8 types of material defect classifiers problem and requires approximately 100 microseconds.