查看更多>>摘要：This work carries out both theoretical and experimental studies on three dimensional surface measurement of objects in underwater environments based on the use of the projection of continuous-wave laser-beam-based monochromatic structured light. The mathematical analysis and description of the generation and propagation of sinusoidal fringe patterns in air and underwater environments are presented, which provides the physical basis for the proposed method of measuring the underwater objects. In the experimental investigation we completed the in-door measurements of both static and dynamic objects located in a tank filled with real turbid seawater or lake water. Experimental results have indicated that the proposed approach is capable of yielding high-accuracy reconstructions for underwater 3D imaging of both static and dynamic objects, which shows the possibility of building a portable instrument based on the optical design and measuring technique presented in this work for carrying out high-accuracy in-situ underwater measurement.
查看更多>>摘要：In fitting concentric geometric objects to digitized data, two approaches are commonly used in practice, the geometric approach and the algebraic approach. The former is iterative, and it requires a good initial guess. This paper focuses on the latter that is based on minimizing the algebraic distances when a constraint is imposed on parametric space yielding non-iterative methods. Each method depends on the constraint imposed on the parametric space and can be solved using the generalized eigenvalue problem. In this paper, we review the two existing methods developed. After establishing a general mathematical framework to solve this problem, the statistical properties of the methods have been established. Our analysis allows us to develop three estimators that outperform the existing ones. Moreover, the superiority of our methods and their practical performances are assessed by a series of numerical experiments on both synthetic and real data.
查看更多>>摘要：This paper proposed a split-core magnetoelectric current sensor consisting of a symmetric Terfenol-D/PZT/ Terfenol-D composite, three magnetic core, a pair of permanent magnets and a packaging shell. The TerfenolD/PZT/Terfenol-D element and three magnetic cores are connected in series to form a closed magnetic ring. In practical application, the sensor can be opened and closed during installation. For detecting the 50 Hz current, the detection sensitivity reaches 52.79 mV/A with R2 = 0.9988 in range of 10A to 1000A. In order to realize the wireless and self-powered measurement of current, this paper proposed a wireless current measurement system combining the split-core current sensor, a RMS detection module, a 4G-RTU module, a current transformer energy harvester and a monitoring platform. Within the allowable error range, the proposed measurement system can realize the wireless measurement of 50 Hz current. These results show that the proposed split-core magnetoelectric current sensor is expected to provide a new idea about current on-line monitoring for Internet of Things in Power Systems, which is of great significance and application.
查看更多>>摘要：Accurate identification of coal-gangue have great significance for separation of coal-gangue. The traditional coal gangue identification method has the disadvantages of low accuracy and slow speed. Therefore, an intelligent classification method of coal-gangue based on multispectral imaging technology and target detection is proposed in this paper. According to the model structure of YOLOv5, add scSE module in CSPDarknet and CSP module. The improved YOLOv5 is referred to as YOLOv5.1. To begin with, the multispectral data of coal-gangue are collected, and the collected coal-gangue images are screened. Beside, three bands with high recognition rate and correlation are selected from 25 bands to form pseudo-RGB images. Otherwise, the RGB image of coal-gangue was detected by theYOLOv5.1. By detecting the separated single band, the recognition rate and correlation of band 6, 10 and 12 are higher. The experimental results show that the average accuracy of detecting coal-gangue in the test set reaches 98.34 %, and the detection time is about 3.62 s by using the model of YOLOv5.1 to train the RGB image of coal-gangue. This method can not only accurately identify coal-gangue, but also obtain the relative position of coal-gangue, which can be effectively used for coal-gangue identification.
查看更多>>摘要：Synchronous machine is one of the critical power generation parts in the power system. Its stable operation ensures people's normal economic activities. Winding is an essential component of a synchronous machine, and the winding fault is a common fault type. The reliable and efficient fault diagnosis of synchronous machine winding is of great significance to ensure the stability of the power system. Therefore, this paper proposes an anomaly detection method of synchronous machine winding fault based on isolation forest (IF) and impulse frequency response analysis (IFRA). Firstly, the basic principle of the anomaly detection method is introduced, and mathematical-statistical indicators of IFRA signatures used are then explained. Besides, the experimental verification is carried out on a 5 kW synchronous machine, and the performance of the anomaly detection method for winding fault is compared with other conventional methods. The experimental results show that the proposed method is feasible and effective, and the generalization ability of the data is strong. The comparative experimental results show that the proposed method is superior to the existing conventional supervised learning method. It has a shorter calculation time and higher accuracy, with stronger robustness, which is more suitable for the actual data structure.
查看更多>>摘要：Geodetic monitoring of a tall structure means employing various methods of measurement and data processing for assessing its verticality. Terrestrial laser scanning (TLS) is a method of collecting lots of quality spatial data in a short time period. It enables detailed surface 3D parametric modelling. Advantages over the traditional cross-sectioning approach include direct obtaining of the structure axis inclination, analysing structure deformations relative to the modelled surface, and so forth. The subject of research was 52-metretall cylinder-shaped bell tower of St. Anthony of Padua Church in Belgrade, Serbia, which has been inclined since its completion in 1962. An original algorithm was developed for estimating parameters of a cylinder approximating the tower surface and their precision. The horizontal displacement of the cylinder top base centre ranging from 1.174 m to 1.196 m over the five-year period of geodetic monitoring of the tower indicates the clear existence of the structure tilting trend.
Segovia Ramirez, IsaacParra Chaparro, Jesus RafaelGarcia Marquez, Fausto Pedro
12页
查看更多>>摘要：Photovoltaic solar energy is a fast-growing renewable energy that needs reliable condition monitoring systems to ensure the productivity of solar plants. Unmanned aerial vehicles are widely implanted to reduce maintenance costs in photovoltaic plants, leading suitable information for fault detection and diagnosis. This paper presents a novel condition monitoring system for photovoltaic panels composed by a radiometric sensor embedded in an unmanned aerial vehicle. A new contribution based on the field of view analysis with infrared sensors is proposed according to the measurement conditions, and the increment of unmanned aerial vehicle positioning reliability by a real-time kinetic navigation system. Several scenarios are designed for testing the reliability and suitability of the system. The results show significant improvements of the proposed approach compared with the reference based on GPS navigation system, higher than 65% for 83% of the cases, minimizing the errors and increasing the accuracy of the inspection, avoiding the analysis of areas outside the panels. The application of real time kinematic ensures a suitable field of view definition in 97% of the cases, while inaccurate results are detected with GPS in more than 20% of the experiments.
查看更多>>摘要：The rub-impact is a familiar failure and its timely detection is vital to maintain the security of whole system. The change of dynamic behavior caused by rub-impact fault can be reflected by the fast-oscillating instantaneous frequency (IF) of the vibration signal. The recently developed adaptive chirp mode decomposition (ACMD) has exhibited satisfactory capability in extracting fast-oscillating IF. However, the ACMD is easily affected by the noise, which greatly limits its application scopes. To overcome this issue, a novel method named sparsity-assisted adaptive chirp mode decomposition (S-ACMD) is developed, which incorporates sparsity-assisted IF update scheme that fully exploits the sparse prior of fast-oscillating IF caused by rub-impact fault. Meanwhile, the parameter selection method for getting optimal results is described. The efficacy of the proposed S-ACMD is confirmed by the vibration signals from dynamic simulation and rotor test rig, and the results show that it has strong anti-noise ability and can accurately extract rub-impact fault related fast-oscillating IF even in noisy cases.
Hilal, Anwer MustafaAl-Wesabi, Fahd N.Althobaiti, Maha M.Al Duhayyim, Mesfer...
10页
查看更多>>摘要：Advanced Hyperspectral Imaging (HIS) systems generate massive volumes of datasets that can provide significant details, when appropriately mined. However, analysis and the interpretation of such huge volume of data is a challenging task to accomplish. Therefore, Deep Learning (DL) methods are highly helpful in solving conventional image processing tasks and it also offers new stimulating issues in spatial-spectral domain. Since effective ground feature extraction from HSI is a challenging research domain, the current research article designs an Intelligent DL-based Hyperspectral Signal Classification (IDL-HSSC) scheme for complex measurement systems. The aim of the proposed IDL-HSSC technique is to classify the HSI under appropriate class labels to understand the ground features. Besides, IDL-HSSC technique involves the design of Tree Growth Algorithm (TGA) with SqueezeNet model for the extraction of feature vectors, where TGA is employed to select the hyperparameters. Moreover, Biogeography-Based Optimization (BBO) with Cascaded Forward Neural Network (CFNN) is also employed as a classifier to categorize the images under appropriate class labels. Both TGA and BBO algorithms are designed for the optimization of parameters used in SqueezeNet and CFNN techniques which in turn helps in accomplishing the maximum classification outcomes. In order to ensure the proficient performance of the proposed IDL-HSSC technique, a wide range of experiments was conducted on diverse benchmark datasets. The experimental outcomes established the supreme performance of the proposed IDL-HSSC technique over recent state-of-the-art methods.
查看更多>>摘要：Information entropy is a fundamental property of measurement data and can be decomposed into functional and relative uncertainty components. The purpose of this paper is to mathematically isolate functional entropy and to demonstrate its application to model data. The equation for functional entropy is developed and its utility is demonstrated using hypothetical data that describes different processes that contribute to the same effect. Functional entropy assigns an effectiveness value to these processes in the fundamental units of information, which makes it a useful tool for comparing different data sets and their associated processes.
NSTL
Elsevier