Intelligent Measurement System Research Articles

Pose Estimation of a Cobot Implemented on a Small AI-Powered Computing System and a Stereo Camera for Precision Evaluation

The precision of robotic manipulators in the industrial or medical field is very important, especially when it comes to repetitive or exhaustive tasks. Geometric deformations are the most common in this field. For this reason, new robotic vision techniques have been proposed, including 3D methods that made it possible to determine the geometric distances between the parts of a robotic manipulator. The aim of this work is to measure the angular position of a robotic arm with six degrees of freedom. For this purpose, a stereo camera and a convolutional neural network algorithm are used to reduce the degradation of precision caused by geometric errors. This method is not intended to replace encoders, but to enhance accuracy by compensating for degradation through an intelligent visual measurement system. The camera is tested and the accuracy is about one millimeter. The implementation of this method leads to better results than traditional and simple neural network methods.

Design and Application of Driving Resistance Test Device for Aircraft Tire and Soil Pavement

In view of the lack of soil bins for studying the surface interaction between aircraft wheels and soil, this study designed an indoor test bench for aircraft wheels and soil, including a soil container, loading vehicle, and intelligent measurement and control system, to test key parameters such as tire speed and wheel frictional resistance. The test system is capable of achieving speed regulation ranging from 0 to 30 km/h. The vertical load adjustment range with an adjustment interval of 10 kg spans from 90 to 140 kg. The soil type, compaction degree, and other conditions can be modified as per requirements to vary multiple test conditions, thereby enabling us to explore their influence on the driving resistance of the wheels. Moreover, the test data can be collected and processed in real time. A performance test of a wheel–soil table was carried out. The results show that the wheel–soil table test system is stable and reliable and can determine the relationship between the tire and soil, and the structural design of the test system meets the use requirements. In addition, it achieves the target test speed, data acquisition frequency, and stability. In terms of functionality and operational difficulty, the data acquisition of the entire test process is automated, and the test system achieves better informationization than previous methods. The overall operation of the wheel–soil platform is stable and powerful; thus, the model test platform design goal is achieved, and the testing requirements are met.

Coil Formation and Biomimetic Performance Characterization of Twisted Coiled Polymer Artificial Muscles

A biological muscle's force is nonlinearly constrained by its current state (force, length, and speed) and state history. To investigate if artificial muscles can mimic (i.e. biomimetic) the complete mechanical state spectrum of biological muscles, this study uses a novel method to characterize twisted coiled polymer actuators (TCPAs) mechanically. Thus, comprehensive and reproducible test procedures are established to verify artificial muscle biomimetics regarding stress, strain, and strain rate combinations intrinsic to biological muscle. A rheometer performs novel high‐precision mechanical characterization methods to comprehensively verify biomimetic performance. Sample twist level, torque, length, force, and temperature are controlled and measured during twist‐induced coiling, heatsetting/annealing, and mechanical testing. TCPAs are formed from linear low‐density polyethylene monofilament. Linear low‐density polyethylene (LLDPE) TCPAs generate larger stresses than biological muscle through the entire spectrum of strains—contracting more than 40%, exerting more than 0.3 MPa at rest length, and withstanding tension of 8 MPa without damage. Thus, the LLDPE TCPAs attain biological muscle performance statically, but additional tests are required to assess this dynamically. The mechanical performance of LLDPE TCPAs enables biomimetic actuation with an intelligent control and measurement system. Their high‐throughput textile manufacturability positions them for advanced biomechatronic applications—including prosthetics and exoskeletons.

Deep learning for the detection and classification of adhesion defects in antique plaster layers

This paper aims is to show an automated intelligent measurement system for the detection of adhesion defects between architectural antique plaster layers. The method emulates the traditional conservators’ procedure based on acoustical perturbations, auscultation, detection and classification. The system makes use of a hardware device, known in literature as PICUS, for the generation and acquisition of acoustic signals, while the processing of the acquired signals is handled by a deep learning (DL) architecture designed ad hoc. After a brief description of the PICUS system and the acoustic data acquisition procedure, the whole architecture of the DL system is carefully described. The proposed method has been validated by a significant case study. The system shows an accuracy of up to 82% (± 2%) in multi-class classification and up to 99% (± 1%) in binary classification. In particular, the obtained results suggest a satisfactory precision in the detection of areas where stabilization is necessary.

Practical Application of Intelligent Vision Measurement System Based on Deep Learning

To comprehensively assess the true visual function of clinical dry eye patients and the comprehensive impact of blinking characteristics on functional vision of the human eye, an intelligent vision measurement system has been designed and developed to detect and analyze blinks from the side. The system employs deep learning keypoint recognition technology to analyze eyelid features from a lateral perspective. It presents the data of identified key points for the upper and lower eyelids in a line chart format and annotates the trough of each blink. By setting benchmark values, the system automatically calculates the proportion of complete and incomplete blinks in the tested individuals. The results indicate that the system is stable in performance and accurate in measurement, successfully achieving the anticipated design objectives. It thereby provides reliable technical support for future clinical applications.

Robust Intelligent Monitoring and Measurement System toward Downhole Dynamic Liquid Level.

Dynamic liquid level monitoring and measurement in oil wells is essential in ensuring the safe and efficient operation of oil extraction machinery and formulating rational extraction policies that enhance the productivity of oilfields. This paper presents an intelligent infrasound-based measurement method for oil wells' dynamic liquid levels; it is designed to address the challenges of conventional measurement methods, including high costs, low precision, low robustness and inadequate real-time performance. Firstly, a novel noise reduction algorithm is introduced to effectively mitigate both periodic and stochastic noise, thereby significantly improving the accuracy of dynamic liquid level detection. Additionally, leveraging the PyQT framework, a software platform for real-time dynamic liquid level monitoring is engineered, capable of generating liquid level profiles, computing the sound velocity and liquid depth and visualizing the monitoring data. To bolster the data storage and analytical capabilities, the system incorporates an around-the-clock unattended monitoring approach, utilizing Internet of Things (IoT) technology to facilitate the transmission of the collected dynamic liquid level data and computed results to the oilfield's central data repository via LoRa and 4G communication modules. Field trials on dynamic liquid level monitoring and measurement in oil wells demonstrate a measurement range of 600 m to 3000 m, with consistent and reliable results, fulfilling the requirements for oil well dynamic liquid level monitoring and measurement. This innovative system offers a new perspective and methodology for the computation and surveillance of dynamic liquid level depths.

Correlation-driven multi-level learning for anomaly detection on multiple energy sources

Advanced metering infrastructure (AMI) has been widely used as an intelligent energy consumption measurement system. Electric power was the representative energy source collected by AMI; most existing studies to detect abnormal energy consumption have focused on a single energy source, i.e., power. Recently, other energy sources such as water, gas, and heating have also been actively collected. As a result, it is necessary to develop a unified methodology for anomaly detection across multiple energy sources; however, research efforts have rarely been made to tackle this issue. In this study, we propose a new correlation-driven multi-level learning model for anomaly detection on multiple energy sources. The distinguishing property of the model incorporates multiple energy sources in multi-levels based on the strength of the correlations between them. Our model is scalable to integrate arbitrary new energy sources, with further performance improvement, considering both correlated and non-correlated sources. Through extensive experiments on real-world datasets consisting of three to five energy sources, we demonstrate that the proposed model clearly outperforms the existing multimodal learning and recent time-series anomaly detection models and makes further performance improvement as more energy sources are integrated, showing the scalability of the proposed model.

The Current Situation, Characteristics and Prospects of the Research on Tennis Theme Literature in China from the Perspective of Artificial Intelligence

With the rapid development of artificial intelligence technology, the function of artificial intelligence to help the high-quality development of sports is increasingly visible, and the continuous, comprehensive and deep integration of artificial intelligence and sports will be an inevitable trend. This paper summarized and analyzed the characteristics of the tennis research theme literature published in the core journals of China from 2012 to 2022 by using the methods of literature and logical induction. The results show that, in the past 10 years, there are problems such as insufficient use of artificial intelligence in technical and tactical statistical analysis, insufficient use in tennis professionalization research, lack of artificial intelligence perspectives in tennis tournament research, and shortage of artificial intelligence use in tennis psychology research. In the future, scientific research on tennis in China should rely on scientific and technological means such as 5G technology, big data technology, computer vision and intelligent wearable devices. etc, cross integrate multidisciplinary knowledge, accurately locate the physical fitness weaknesses of Chinese tennis players, focus on building an intelligent measurement system for tennis players, and scientifically develop a big data platform for tennis skills and tactics; And guided by practical problems, we should focus on exploring intelligent wearable devices suitable for tennis sports programs, and integrating computer vision technology with intelligent wearable devices to achieve complementary advantages and enhance the accuracy and credibility of data collection. And use natural science methods to conduct quantitative analysis to provide scientific solutions for the development of tennis.

DESIGN OF AN INTELLIGENT INFORMATION MEASUREMENT SYSTEM FOR PHOTOVOLTAIC DC MICROGRIDS

The integration of photovoltaic (PV) systems into direct current (DC) microgrids has garnered significant attention as a promising solution for sustainable energy distribution. In this article, the development of intelligent information measurement systems holds the utmost importance to optimize system performance, ensure stability, and facilitate efficient energy management. The aim of the research. This article presents the design of the intelligent information measurement system for PV-DC microgrids to achieve advanced functions such as power prediction, performance monitoring, fault diagnosis, maximum power extraction etc. by performing electrical and non-electrical measurements. These systems enhance data processing capabilities, enabling predictive analytics and adaptive control strategies. The research highlights the synergistic effects of electrical and non-electrical measurements in designing information measurement systems. By leveraging real-time electrical measurements for system control and non-electrical measurements for forecasting energy availability and assessing environmental impact, intelligent decision-making and adaptive control strategies are facilitated, resulting in efficient energy management. Conclusion. An intelligent information measurement system is designed to perform advanced functions to increase the efficiency of these systems. The research also provides a holistic understanding of the importance of measurements in developing intelligent information measurement systems for PV-DC microgrids. The study contributes to advancing microgrid technology and promoting sustainable and decentralised energy solutions. Keywords: Information measurement system, DC-DC converters, DC microgrid, measurements in microgrids.

Construction of intelligent energy measurement system suitable for energy block chain technology

Abstract With the increasing scale and complexity of power systems, it is gradually difficult for the traditional central control system to meet the control and management needs of the new power systems. In this paper, a control strategy based on multi-terminal information fusion is proposed to fuse and integrate the information from different control nodes to form a global new power system control strategy. A hierarchical cluster-based control node organization is also proposed to organize the control nodes according to function and hierarchy to realize the cooperative control and maximum power point tracking of the new power system. Finally, the effectiveness of the effect of the cooperative control strategy is verified by modeling and simulating the cooperative control network of the new power system. The simulation results show that the charge frequency fluctuation range is stable in [46.38Hz, 55.15Hz] for all three scenarios, which is only 0.02Hz away from the lower limit of the set allowable frequency fluctuation range [46.35Hz, 56.38Hz], indicating that the cooperative control strategy stabilizes the charge frequency fluctuation within the effective range. Therefore, this paper achieves further exploration of the optimization and improvement of the new power system cooperative control network, which achieves more efficient, stable, and reliable power system control and management.

Data Recognition for Multi-Source Heterogeneous Experimental Detection in Cloud Edge Collaboratives

This article proposes a multisource heterogeneous experimental data recognition method based on CRNN and DBNet in a cloud-edge collaborative environment in an attempt to address the issues of low efficiency and a high error rate that come with traditional manual data detection and recognition. Firstly, a recognition architecture for experimental detection data of intelligent measurement systems is designed based on a cloud-edge collaborative environment to improve the efficiency of data processing. Then, the improved DBNet network is used in the edge computing center to detect the text, and the correction module is used to correct the deviation of the detected text to ensure the standardization of the text. Finally, in the central cloud, the end-to-end indefinite length character recognition (CRNN) algorithm is used to analyze and identify the text order, rules, and other information of the image after the correction is completed, extract the test detection data, and convert the detection data into row data according to the two-dimensional table structure, and conduct structured storage and management through the relational database. An experimental analysis of the proposed method is conducted based on the deep learning framework, and results show that its accuracy rate and recall rate are close to 96% and 94%, respectively, with an average accuracy of 95.09%. This fully demonstrates the proposed method is effective and, therefore, applicable to power equipment experimental detection data recognition.

Deep learning–based intelligent measurement methods and system for CMM

Extensive manual intervention and management are typically required when using coordinate measuring machines (CMMs) for inspections in production lines leading to low efficiency. This study presents a deep learning–based intelligent measurement method and system for measuring typical features (including holes, cylinders, balls, steps, and slots) of common components to improve inspection efficiency. This method combines vision sensors and a trigger probe. The You Only Look Once algorithm was employed to learn and achieve intelligent detection of features. An image-matching algorithm based on image inverse perspective transformation was designed, and the ant colony algorithm was implemented to optimize the measurement sequence. Then, an automatic approach for feature measurement path planning was designed. The presented system was tested using CMM, and a component with multiple typical features was measured. Results show that this method and system can be efficaciously implemented for intelligent measurement of typical features.

Application of deep neural network and gamma radiation to monitor the transport of petroleum by-products through polyducts

To continuously monitor information about the transport of fluids by sequential batches in polyduct, found in the petrochemical industry, it is necessary to manage the mixing zone – transmix – that occurs when two fluids are being transported. This scenario demonstrates the need to estimate the interface region and the purity of the fluids in this region to improve the management of the pipeline and, thus, reduce associated costs. This study presents a measurement system based on the dual-modality gamma densitometry technique in combination with a deep neural network with seven hidden layers to predict the purity level of four different fluids (Gasoline, Glycerol, Kerosene and Oil Fuel) in the transmix. The detection geometry is composed of a137Cs radioactive source (emitting gamma rays of 661.657 keV) and two NaI(Tl) scintillator detectors to record the transmitted and scattered photons. The study was performed by computer simulations using the MCNP6 code, and the information recorded in the detectors was used as input data for training and evaluating the deep neural network. The proposed intelligent measurement system is able to predict the purity level of fluids with errors with mean squared error values below 1.4 and mean absolute percentage error values below 5.73% for all analyzed data.

Intelligent information and measurement system for measuring the parameters of oxide coatings in the process of microarc oxidation

Intelligent information and measurement system for measuring the parameters of oxide coatings in the process of microarc oxidation

Optimization of the fixed-point representation of measurement data for intelligent measurement systems

The development of intelligent measurement systems by implementing AI (artificial intelligence) algorithms on edge sensor nodes is very topical. Due to very limited hardware of sensor nodes, the choice of the optimal digital format for representation of measurement data and parameters of AI algorithms becomes an important issue. The floating-point format is impractical to implement on sensor nodes due to great complexity, while the fixed-point format has significantly less complexity, representing an ideal solution for intelligent sensor nodes. This paper is dealing with performance analysis and optimization of the fixed-point format for measurement data modeled by the Gaussian distribution. The performance analysis is performed in a generalized way, having wide applicability. Furthermore, the paper proposes a rule for optimization of the fixed-point format, providing a high and almost constant level of quality of the fixed-point representation in a very wide variance range of measurement data. Results are confirmed by simulations.

Learning model for battery lifetime prediction of LoRa sensors in additive manufacturing

Today, an innovative leap for wireless sensor networks, leading to the realization of novel and intelligent industrial measurement systems, is represented by the requirements arising from the Industry 4.0 and Industrial Internet of Things (IIoT) paradigms. In fact, unprecedented challenges to measurement capabilities are being faced, with the ever-increasing need to collect reliable yet accurate data from mobile, battery-powered nodes over potentially large areas. Therefore, optimizing energy consumption and predicting battery life are key issues that need to be accurately addressed in such IoT-based measurement systems. This is the case for the additive manufacturing application considered in this work, where smart battery-powered sensors embedded in manufactured artifacts need to reliably transmit their measured data to better control production and final use, despite being physically inaccessible. A Low Power Wide Area Network (LPWAN), and in particular LoRaWAN (Long Range WAN), represents a promising solution to ensure sensor connectivity in the aforementioned scenario, being optimized to minimize energy consumption while guaranteeing long-range operation and low- cost deployment. In the presented application, LoRa equipped sensors are embedded in artifacts to monitor a set of meaningful parameters throughout their lifetime. In this context, once the sensors are embedded, they are inaccessible, and their only power source is the originally installed battery. Therefore, in this paper, the battery lifetime prediction and estimation problems are thoroughly investigated. For this purpose, an innovative model based on an Artificial Neural Network (ANN) is proposed, developed starting from the discharge curve of lithium-thionyl chloride batteries used in the additive manufacturing application. The results of experimental campaigns carried out on real sensors were compared with those of the model and used to tune it appropriately. The results obtained are encouraging and pave the way for interesting future developments.

SmaEPho–Smart Photometry in Education 4.0

Digitization offers new perspectives for educational research to identify the effects of visualizations regarding cognitive processing. In addition, new types of data can be generated, expanding the possibilities for visualizing cognitive processes and understanding human learning. Digital twins are already used in Industry 4.0, as an additional visualization to a real object, for data mining and data analysis for process optimization. The increasing integration of digital twins in the industrial sector requires the formulation of corresponding educational goals to ensure high-quality and future-oriented education. Therefore, future generations must be introduced to technologies from industry during their education. In this paper, an intelligent photometric measurement system called SmaEPho with a digital twin for science, technology, engineering, and mathematics (STEM) learning is presented. In addition to its function as a photometric measurement device, an intelligent sensor technology allows for data generation on the user’s usage behavior. The digital twin reflects and visualizes these data in real-time. This enables a variety of new didactic and methodological approaches in teaching. A first study evaluating the hardware and tracking components of SmaEPho shows that the deviation accuracy of the measurement system is sufficient for experimental applications in schools. Another study with n=52 students confirmed the excellent usability of the SmaEPho hardware platform. These research results lay the foundation for a variety of future research questions on data analysis and machine learning algorithms with the aim of increasing the quality of education. The use of intelligent digital twins as an element of digitization in educational contexts offers the extended possibility of identifying cognitive processing steps using this technology.

A Digital Twin-Based Intelligent Robotic Measurement System for Freeform Surface Parts

This paper proposed a novel framework of a digital twin-based robotic measurement system for freeform surface parts, which can improve the efficiency, autonomy, and intelligence of the robotic measurement system. This framework provides core services for robotic measurement tasks including visibility computation, collision detection, sensor simulation, path planning, and 3D inspection. These services improve the adaptability and the simulation capability of digital twin-based robot measurement system for freeform surface parts. Based on the proposed framework, an effective viewpoint planning method combining viewpoint sampling strategy and nest-best-view planner (VS-NBV) for freeform surface parts is proposed and verified. And case studies demonstrated the advantages of the digital twin-based robotic measurement system in terms of viewpoint planning, measurement path planning, object reconstruction, and 3D inspection.

Design and Implementation of an Intelligent Power Metering and Measurement System based on IoT Technology

Intelligent power metering and measurement systems are important for expanding the category and quantity of power metering. This paper proposes a design and implementation method of an intelligent power metering and measurement system based on IoT technology. Composed of the perception layer, the network layer, the data layer, the analysis layer, and the application layer, the overall design scheme can not only realize the intelligence, automation, and data visualization of power metering and measurement but also improves the standardization of laboratory management through a standardized metering and measurement system.

Research on the promotion mechanism and path algorithm optimization of endogenous development ability intelligent measurement using AF method and logistic regression——From the perspective of farmers' income structure in Tibet related areas on the plateau

Farmers' income is an important factor restricting the endogenous development capacity of farmers in the post-poverty alleviation era, and few existing studies have been located in Tibet-related areas of the plateau to explore the endogenous development capacity of farmers from the perspective of farmers' income structure. Based on the data related to the endogenous development capacity of poverty-alleviated farmers in Tibet-related areas of the Plateau of Sichuan Province obtained in 2021, an intelligent measurement system for the endogenous development ability of farmers is constructed and measured using the AF method, and the relationship between the income structure of farmers and their endogenous development capacity is analyzed by logistic regression. The results show that operating income, property income and wage income have obvious role in promoting endogenous development capacity, and there is a certain negative correlation between transfer income and farmers' endogenous development capacity; and relevant suggestions for achieving sustainable development and optimizing the endogenous development capacity of farmers in Tibet-related areas of the plateau in the post-poverty alleviation period are proposed.