Acquisition Equipment Research Articles

Three-Dimensional-Scanning of Pipe Inner Walls Based on Line Laser.

In this study, an innovative laser 3D-scanning technology is proposed to scan pipe inner walls in order to solve the problems of the exorbitant expenses and operational complexities of the current equipment for the 3D data acquisition of the pipe inner wall, and the difficulty of both the efficiency and accuracy of traditional light stripe-center extraction methods. The core of this technology is the monocular-structured light 3D scanner, the image processing strategy based on tracking speckles, and the improved gray barycenter method. The experimental results demonstrate a 52% reduction in the average standard error of the improved gray barycenter method when compared to the traditional gray barycenter method, along with an 83% decrease in the operation time when compared to the Steger method. In addition, the size data of the inner wall of the pipe obtained using this technology is accurate, and the average deviation of the inner diameter and length of the pipe is less than 0.13 mm and 0.41 mm, respectively. In general, it not only reduces the cost, but also ensures high efficiency and high precision, providing a new and efficient method for the 3D data acquisition of the inner wall of the pipe.

A multi-stage LSTM federated forecasting method for multi-loads under multi-time scales

For multiple integrated energy systems with similar energy using behavior, the federated learning mechanism can build a higher precision multivariate load prediction model for each integrated energy system. However, the data of different loads in these systems often have different time scales, limited by the characteristics and frequency of data acquisition equipment. Systems often set different hyperparameters with each other to ensure prediction accuracy, which makes it difficult to use federated learning for co-training. In this paper, a multi-stage Long Short-Term Memory (LSTM) federated model based on interpolation method is proposed for multi-node multivariate load forecasting on multi-time scales. In the first stage, the load data of larger time scales are interpolated to unify all the load data to the same time scale. In the second stage, each node (i.e., each integrated energy system) uses its own data to carry out multivariate load training and establish its multivariate load forecasting model. In the third stage, based on the federated learning mechanism and fine-tuning strategy, the LSTM federated prediction of multiple loads is established by fusion training between nodes. Experimental results show that the proposed method can obtain higher precision multi-source load prediction results for multiple integrated energy systems.

Extraction of Corn Plant Phenotypic Parameters with Keypoint Detection and Stereo Images

Corn is a global crop that requires the breeding of superior varieties. A crucial aspect of the breeding process is the accurate extraction of phenotypic parameters from corn plants. The existing challenges in phenotypic parameter extraction include low precision, excessive manual involvement, prolonged processing time, and equipment complexity. This study addresses these challenges by opting for binocular cameras as the data acquisition equipment. The proposed stereo corn phenotype extraction algorithm (SCPE) leverages binocular images for phenotypic parameter extraction. The SCPE consists of two modules: the YOLOv7-SlimPose model and the phenotypic parameter extraction module. The YOLOv7-SlimPose model was developed by optimizing the neck component, refining the loss function, and pruning the model based on YOLOv7-Pose. This model can better detect bounding boxes and keypoints with fewer parameters. The phenotypic parameter extraction module can construct the skeleton of the corn plant and extract phenotypic parameters based on the coordinates of the keypoints detected. The results showed the effectiveness of the approach, with the YOLOv7-SlimPose model achieving a keypoint mean average precision (mAP) of 96.8% with 65.1 million parameters and a speed of 0.09 s/item. The phenotypic parameter extraction module processed one corn plant in approximately 0.2 s, resulting in a total time cost of 0.38 s for the entire SCPE algorithm to construct the skeleton and extract the phenotypic parameters. The SCPE algorithm is economical and effective for extracting phenotypic parameters from corn plants, and the skeleton of corn plants can be constructed to evaluate the growth of corn as a reference. This proposal can also serve as a valuable reference for similar functions in other crops such as sorghum, rice, and wheat.

Learning-Based Vehicle State Estimation Using Gaussian Process Regression Combined with Extended Kalman Filter

In order to address the challenge of accurate vehicle state estimation, especially in highly nonlinear and complex operating conditions, this paper proposes a learning-based method for vehicle state estimation. To achieve this, a novel theoretical framework is constructed that combines the model-based Extended Kalman Filter (EKF) with Gaussian process regression (GPR). By establishing a dynamic model of the vehicle and describing the errors using machine learning techniques, the sources of state estimation errors are analyzed. Furthermore, by considering the model uncertainty, an error prediction model is developed through GPR learning from real-world vehicle data. Combined with EKF, the proposed method enables high-precision online estimation of vehicle state. To validate the proposed method, tests are performed on a real vehicle test platform equipped with high-precision sensors and data acquisition equipment, under two different operating conditions: emergency and normal. The results are compared with those obtained using EKF and State Observer, which demonstrates a more accurate and adaptable vehicle state estimation, and provides a method for quantitatively describing the confidence interval of vehicle state estimation result.

Simulation of e-learning in vocal network teaching experience system based on intelligent Internet of things technology

With the rapid development of smart Internet of Things technology, education is also starting to use this technology to improve the learning experience. As an art subject, vocal music teaching has many limitations in the traditional face-to-face teaching methods, and e-learning provides new possibilities for vocal music network teaching. We analyze the problems and challenges existing in the traditional face-to-face mode of vocal music teaching, and then based on the intelligent Internet of Things technology, we design a vocal music network teaching experience system. The system combines sound acquisition equipment, intelligent audio processing algorithm, virtual classroom and other technologies to realize the simulation experience of online vocal music teaching. We have developed an intelligent audio processing algorithm for analyzing and processing students’ singing sounds. This algorithm can detect problems in tone, timbre, rhythm, and more, and provide real-time feedback and advice. In this way, students can understand the shortcomings of their own singing skills, and make timely adjustments and improvements. This study shows that e-learning based on intelligent Internet of Things technology has important application value in vocal music network teaching. By simulating the classroom environment and providing real-time feedback, students can obtain a better learning experience and improve their vocal skills.

Determine the location of acoustic emission source on wood surface by using a wavelet-energy attenuation method

ABSTRACT To determine the location of wood damage and fracture, an acoustic emission source localization method based on wavelet-energy attenuation law was proposed. High-speed acquisition equipment and LabVIEW software were used to design a three-channel AE signal acquisition system. The wavelet transform is used to decompose the original signals into five frequency levels, and the second, third, and fourth level were selected for reconstruction of the acoustic emission signal. Finally, the reconstructed signals was used to determine the location of the acoustic emission source in accordance with the energy attenuation law. The results indicated that when the distance between the three sensors were 100 and 200 mm, the average location error obtained using the original signals in accordance with the energy attenuation law was 12.6 and 18.4 mm, respectively. However, the average location error obtained using the reconstructed signals in accordance with the energy attenuation law was 2.2 and 6.9 mm, respectively. These results reveal that the proposed method can achieve high-precision localization of acoustic emission source. This research enrich and expand the content of AE technology and wooden building research, provide new ideas and methods for non-destructive testing of similar structures, and promote research progress in related fields..

L-PBF AM process failures causal chain: an FMEA-based monitoring approach for process control

ABSTRACT The laser powder bed fusion (L-PBF) additive manufacturing process offers many advantages. However, its industrial integration still needs to be improved due to the certification constraints of the produced parts. Thus, this paper proposes a phenomenological causal chain of process failures to ensure the control of the L-PBF process and further certify the quality of the parts produced. Consequences of unsuitable thermal regimes are analysed using a failure mode and effects analysis (FMEA) approach, allowing a more global understanding of all the causal chain-related data. The proposed approach is based on standard acquisition equipment available on industrial machines and proves relevant for identifying process failures. Experimental validation of the proposed monitoring method is implemented on a use case using dedicated software. Finally, it is shown that the method is relevant in this confrontation and compatible with the time scale of the process for future NC integration and real-time monitoring.

Towards achieving geo‐indistinguishability for 3D GPS location: A 3D Laplace mechanism approach

SummaryAs the scope of human exploration continues to expand from land to space and the oceans, location‐based data analysis and services are facing unprecedented opportunities and challenges. The wide application of various services based on spatial location in the fields of medical, transportation, financial, social and so forth not only provides great convenience but also exposes the disadvantages of location privacy leakage. Most present location privacy protection solutions are only relevant to 2D locations. However, with the widespread use of positioning‐enabled aircraft, sensing devices and acquisition equipment, there is an urgent need to achieve privacy protection for 3D spatial location data. Therefore, this article proposes a 3D geo‐indistinguishability perturbation method for 3D GPS Locations. The suggested approach calculates GPS coordinate perturbation accurately using the 3D Laplace mechanism, allowing for point‐to‐point perturbation while protecting the privacy of 3D spatial GPS coordinates. Experimental examination of real spatial location datasets demonstrates that the proposed 3D spatial location perturbation method outperforms other existing methods in terms of location service quality loss, data availability, and operational efficiency with the same privacy budget.

Three-Dimensional Reconstruction Method of Rail Corrugation Based on the Vision Detection

In order to realize the visual identification of the three-dimensional geometric characteristics of rail corrugation, the three-dimensional reconstruction method of rail corrugation based on vision detection is developed in the present paper. The acquisition equipment is designed to acquire the images of rail corrugation and corresponding depth information. Then, the image preprocessing method is proposed to achieve the automatic segmentation of rail corrugation images. The fitting equation between the image and depth of rail corrugation is obtained, which allows the three-dimensional reconstruction of rail corrugation. Furthermore, based on the finite element model of the wheel-rail system and the wear calculation model, the practicality and effectiveness of the three-dimensional reconstruction method are verified. Results show that the depth-grayscale correlation equation is well fitted with the coefficient of determination of 0.94 and the root mean square error of 0.02. The three-dimensional reconstruction method has an error of 2.63% at the maximum wear depth. When the three-dimensional reconstruction method is applied to the wear prediction of rail corrugation, the wear prediction of rail corrugation with the three-dimensional reconstruction is approximately the same as the actual wear distribution, which has an error of 5.02% at the maximum wear depth. The aforementioned results demonstrate the effectiveness of the three-dimensional reconstruction method of rail corrugation based on the vision detection.

Decoding Typical Flight States Based on Neural Signals from the Midbrain Motor Nuclei of Pigeons.

Exploring the neural encoding mechanism and decoding of motion state switching during flight can advance our knowledge of avian behavior control and contribute to the development of avian robots. However, limited acquisition equipment and neural signal quality have posed challenges, thus we understand little about the neural mechanisms of avian flight. We used chronically implanted micro-electrode arrays to record the local field potentials (LFPs) in the formation reticularis medialis mesencephali (FRM) of pigeons during various motion states in their natural outdoor flight. Subsequently, coherence-based functional connectivity networks under different bands were constructed and the topological features were extracted. Finally, we used a support vector machine model to decode different flight states. Our findings indicate that the gamma band (80-150 Hz) in the FRM exhibits significant power for identifying different states in pigeons. Specifically, the avian brain transmitted flight related information more efficiently during the accelerated take-off or decelerated landing states, compared with the uniform flight and baseline states. Finally, we achieved a best average accuracy of 0.86 using the connectivity features in the 80-150 Hz band and 0.89 using the fused features for state decoding. Our results open up possibilities for further research into the neural mechanism of avian flight and contribute to the understanding of flight behavior control in birds.

Denoising Diffusion Probabilistic Model with Adversarial Learning for Remote Sensing Super-Resolution

Single Image Super-Resolution (SISR) for image enhancement enables the generation of high spatial resolution in Remote Sensing (RS) images without incurring additional costs. This approach offers a practical solution to obtain high-resolution RS images, addressing challenges posed by the expense of acquisition equipment and unpredictable weather conditions. To address the over-smoothing of the previous SISR models, the diffusion model has been incorporated into RS SISR to generate Super-Resolution (SR) images with enhanced textural details. In this paper, we propose a Diffusion model with Adversarial Learning Strategy (DiffALS) to refine the generative capability of the diffusion model. DiffALS integrates an additional Noise Discriminator (ND) into the training process, employing an adversarial learning strategy on the data distribution learning. This ND guides noise prediction by considering the general correspondence between the noisy image in each step, thereby enhancing the diversity of generated data and the detailed texture prediction of the diffusion model. Furthermore, considering that the diffusion model may exhibit suboptimal performance on traditional pixel-level metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM), we showcase the effectiveness of DiffALS through downstream semantic segmentation applications. Extensive experiments demonstrate that the proposed model achieves remarkable accuracy and notable visual enhancements. Compared to other state-of-the-art methods, our model establishes an improvement of 189 for Fréchet Inception Distance (FID) and 0.002 for Learned Perceptual Image Patch Similarity (LPIPS) in a SR dataset, namely Alsat, and achieves improvements of 0.4%, 0.3%, and 0.2% for F1 score, MIoU, and Accuracy, respectively, in a segmentation dataset, namely Vaihingen.

A Collaborative Platform for Advancing Automatic Interpretation in ECG Signals.

Numerous papers report the efficiency of the automatic interpretation capabilities of commercial algorithms. Unfortunately, these algorithms are proprietary, and academia has no means of directly contributing to these results. In fact, nothing at the same stage of development exists in academia. Despite the extensive research in ECG signal processing, from signal conditioning to expert systems, a cohesive single application for clinical use is not ready yet. This is due to a serious lack of coordination in the academic efforts, which involve not only algorithms for signal processing, but also the signal acquisition equipment itself. For instance, the different sampling rates and the different noise levels frequently found in the available signal databases can cause severe incompatibility problems when the integration of different algorithms is desired. Therefore, this work aims to solve this incompatibility problem by providing the academic community with a diagnostic-grade electrocardiograph. The intention is to create a new standardized ECG signals database in order to address the automatic interpretation problem and create an electrocardiography system that can fully assist clinical practitioners, as the proprietary systems do. Achieving this objective is expected through an open and coordinated collaboration platform for which a webpage has already been created.

A Self-Attention CycleGAN for Cross-Domain Semi-Supervised Contactless Palmprint Recognition

Nowadays, there is a growing concern about contactless palmprint recognition because of its high-recognition rate, efficiency, and convenience. With the development of image acquisition equipment, it is an often case that the palmprint images for identification and for registration are captured by different devices. At the same time, a large amount of well-labeled palmprint images are difficult to collect. Therefore, the performance of most existing contactless palmprint recognition methods will be poor in real-life applications. To address these issues, we proposed a self-attention CycleGAN for cross-domain semi-supervised palmprint recognition. Based on CycleGAN, the styles of contactless palmprint images in source domain and target domain can be swapped. Specifically, the spatial features are captured through self-attention modules by modeling long-range dependencies. In addition, an extra source domain classifier is trained with the labeled source domain images to give the unlabeled images in target domain a pseudo-label, by which images in target domain are efficiently utilized. The experiment results showed that our method achieved competitive performance.

Research on impact resistance of new suspended elastic safety net system

In order to reduce the direct injuries caused by falling from height, we propose a new type of suspended elastic safety net system suitable for pylon assembly construction. Analyze the correlation between damping and toughness of the protection system, establish the falling object motion control method, and construct the mechanism model of the protection system accordingly. Full-scale impact tests are carried out using test pylons on transmission line projects. Test data is collected using high-speed cameras and dynamic data acquisition equipment to analyze the key impact dynamic response characteristics of the test model. The experimental results show that the system has three working stages. In the first stage, the elastic safety net is transformed from an initial equilibrium state, where it is subjected only to its gravity, to a taut state. In the second stage, as the elongation of the system continues to increase in the direction of impact, the internal forces transmitted by the net to the elastic rod and the supporting rope gradually increase until the energy dissipating devices connected to the elastic rod are activated. In the third stage, additional elongation of the system is provided by activating the support springs in the bearing supports. Based on the failure modes observed in the tests, the key factors leading to the breakdown of the system are investigated. A new numerical simulation method is developed which is not only capable of predicting the load-bearing capacity of an elastic safety net system under the impact of a falling object, but also accurately simulates the main operating characteristics, such as buckling of the elastic rods, activation of the energy-absorbing devices, and deformation of the net ropes. The numerical simulation model was evaluated using the experimental results, and the numerical response of the system was in good agreement with the experimental data.

Breast ultrasound image despeckling using multi-filtering DFrFT and adaptive fast BM3D

Improving the quality of breast ultrasound images is of great significance for clinical diagnosis which can greatly boost the diagnostic accuracy of ultrasonography. However, due to the influence of ultrasound imaging principles and acquisition equipment, the collected ultrasound images naturally contain a large amount of speckle noise, which leads to a decrease in image quality and affects clinical diagnosis. To overcome this problem, we propose an improved denoising algorithm combining multi-filter DFrFT (Discrete Fractional Fourier Transform) and the adaptive fast BM3D (Block Matching and 3D collaborative filtering) method. Firstly, we provide the multi-filtering DFrFT method for preprocessing the original breast ultrasound image so as to remove some speckle noise early in fractional transformation domain. Based on the fractional frequency spectrum characteristics of breast ultrasound images, three types of filters are designed correspondingly in low, medium, and high frequency domains. And by integrating filtered images, the enhanced images are obtained which not only remove some speckle noise in background but also preserve the details of breast lesions. Secondly, for further enhancing the image quality on the basis of multi-filter DFrFT, we propose the adaptive fast BM3D method by introducing the DBSCAN-based super pixel segmentation to block matching process, which utilizes super pixel segmentation labels to provide a reference on how similar it is between target block and retrieval blocks. It reduces the number of blocks to be retrieved and make the matched blocks with more similar features. At last, the local noise parameter estimation is also adopted in the hard threshold filtering process of traditional BM3D algorithm to achieve local adaptive filtering and further improving the denoising effect. The synthetic data and real breast ultrasound data examples show that this combined method can improve the speckle suppression level and keep the fidelity of structure effectively without increasing time cost.

A 3D urban scene reconstruction enhancement approach based on adaptive viewpoint selection of panoramic videos

AbstractThe widely used unmanned aerial vehicle oblique photogrammetry often suffers from information loss in complex urban environments, leading to geometric and textural defects in the resulting models. In this study, a close‐range panoramic optimal viewpoint selection assisted 3D urban scene reconstruction enhancement method is proposed for areas prone to defects. We first introduce the ground panoramic data acquisition equipment and strategy, which are different from those of the single‐lens supplementary photography method. Data acquisition is accomplished through a single and continuous surround‐style collection approach. The full coverage of the panoramic video in the space–time dimension enables the acquisition of texture details without considering camera station planning. Then, a panoramic multiview image generation approach is proposed. Adaptive viewpoint selection is achieved using unbiased sampling points from the rough scene model, and viewpoint optimisation is adopted to ensure sufficient image overlap and intersection effects, thus improving the scene reconstructability. Finally, the 3D model is generated by photogrammetric processing of the panoramic multiview images, resulting in an enhanced modelling effect. To validate the proposed method, we conducted experiments using real data from Qingdao, China. Both the qualitative and quantitative results demonstrate a significant improvement in the quality of geometric and textural reconstruction. The tie‐point reprojection errors are less than 1 pixel, and the registration accuracy with the model from oblique photogrammetry is comparable to that of optimised‐view photography. By eliminating the need for on‐site camera station planning or manual flight operations and effectively minimising the redundancy of panoramic videos, our approach significantly reduces the photography and computation costs associated with reconstruction enhancement. Thus, it presents a feasible technical solution for the generation of urban 3D fine models.

An Interpretable Aerodynamic Identification Model for Hypersonic Wind Tunnels

Aerodynamic identification accuracy is one of the key factors determining the success or failure of hypersonic aircraft development. However, the inertial force (mid-frequency) generated by the shock flow and the instrument noise (high-frequency) introduced by the acquisition equipment seriously affect the identification accuracy. To address this challenge, first, a convolutional neural network is introduced to filter out high-frequency noise, and the influence of kernel size on feature extraction ability is discussed. Second, a dense block with adaptive EMD, which filters out inertial force component, alleviates the dependence of the model on the number of samples, and gives a distinct physical meaning to the output of each layer, is proposed. Based on the above research, an aerodynamic identification model based on a large convolutional kernel and dense block (AI-LSK&DB) is proposed. Wind tunnel experimental results show that the identification accuracy, robustness, and stability of AI-LSK&DB are significantly improved compared with those of frequency domain models and deep learning models.

An Optimization Design of System Layout Based on Machine Vision

Machine vision is based on computer, through the comprehensive utilization of a series of advanced theoretical knowledge such as image acquisition equipment, digital processing technology and related algorithms. It can quickly and accurately extract the required features from the input data and describe them, which plays an important role in modern industrial production. Machine vision can intuitively express the image information that people are exposed to in a certain way, and process and process it accurately and quickly. This paper aims to use machine vision to quickly obtain spatial position, size and other data, combined with artificial intelligence genetic algorithm, particle swarm algorithm, intelligent plane optimization. This paper mainly uses the experimental simulation method and the comparison method to study the optimization method of the system layout, puts forward two improvement schemes of the storage system layout, and makes a comparative analysis of their space loss parameters. The experimental results show that in terms of total space loss, the value of improved scheme 1 is 0.741, which is higher than that of improved scheme 2.

Cyber Security Based Application-Specific Integrated Circuit for Epileptic Seizure Prediction Using Convolutional Neural Network

In the event of an epileptic attack, the Field-Programmable Gate Array (FPGA)-accelerated Convolutional Neural Network (CNN) model is paired with Electroencephalogram (EEG) acquisition equipment to produce a reliable production system that can be used in clinical medical diagnosis. Additionally, this study includes cybersecurity to protect both the epileptic patient’s data and the prediction system. Epilepsy is a frequent neurological disorder that manifests as recurrent seizures, a sign that indicates rapid intervention is necessary to minimize adverse events and improve patient health. The study provides a new real-time design for predicting epileptic seizures based on the Application-Specific Integrated Circuit (ASIC)-based Very Large-Scale Integration (VLSI) architecture. As a first step, EEG data from epilepsy patients were captured and pre-processed. Afterwards, faults and artefacts in the data were removed. Additionally, data was divided into short-time windows and then classified as either ictal, pre-seizure, or interictal. The CNN model was adapted for EEG signal analysis and then trained with categorized data. This technique is more effective and efficient for predicting epileptic seizures accurately, which is advantageous for patient monitoring and treatment. Additionally, cybersecurity measures were implemented to secure patient data and the prediction system.

Design and research on multi-sensory comfort data acquiring of tight sportswear in motion

This study aims to achieve intelligent monitoring of clothing comfort in motion, providing data and technical assistance for the research of comfort in motion and an efficient design foundation for sportswear comfort optimization. In view of the present situation of smart wear used in clothing comfort research, this paper primarily presents the construction of an acquisition system in motion, including temperature, humidity, and pressure data acquisition device, data pre-processing, data storage, and so on, which was called the ATHPD system for short. With the help of this acquisition system, tights in motion may now be tested for pressure, heat, and humidity for the first time. It can also transmit data wirelessly. Meanwhile, in order to verify the effectiveness of the acquisition system, by comparing the measurement data of the acquisition system (ATHPD system) with the measurement data of existing acquisition equipment (AMI3037 pressure measurement system, DS1923 button temperature and humidity recorder). The findings demonstrate that there is no significant difference between the data collected by the two methods, which proves the reliability of the acquisition system in this paper. The system guarantees the same quantity of temperature, humidity, and pressure data to be gathered at the same time and the consistency of the corresponding time points and also provides relevant data support for the evaluation and prediction model construction of human comfort in motion.