Intelligent Measurement Research Articles

ASSESSMENT OF TOOL NOSE WEAR USING SCANNED IMAGES OF CUTTING INSERTS

The surface quality of final product in machining is governed by many intimately related factors such as tool conditions and machining parameters. Amongst these factors, tool wear is essentially one of the most prominent influences on dimensional accuracy, surface roughness and tool life. Since the measurement of tool wear in manufacturing is still done manually, automated and intelligent measurement of wear are gaining more interest in the perspective of reducing human interference and hence, the accurate assessment of tool condition. This research work proposes a fast and reliable image processing method in measuring the nose wear of cutting inserts. Two image digitization methods were used, which are flatbed scanner (CanoScan5600F) and 3-D metrology system (Alicona InfiniteFocus). A sub-pixel edge detection algorithm was developed in the segmentation of nose area to improve the measurement accuracy. Scanning of tool nose was conducted before and after the machining for the measurement of wear area. Results show that about 5% to 6% of average absolute deviations were obtained from the measurement of nose wear area (Ap)and nose flank wear (VBc(max)) using images from InfiniteFocus and scanner.

Research on the limit prevention system for near electric operations in power transmission and transformation engineering based on pulse radiation

Abstract To solve the problems of high risk and low efficiency in manual control of near electric operation construction, this paper proposes a pulse ray based near electric operation anti limit system for power transmission and transformation engineering. The software and hardware of the system are designed according to the functional requirements of the system. By using a pulse laser module to detect whether there is object intrusion in the near electric work area, the on-site construction safety control distance is obtained by setting the voltage level. When there is object intrusion, both the on-site and system backend will sound an alarm simultaneously. Through on-site testing, the accuracy of object intrusion warning is 100%, successfully solving the problem of the lack of intelligent control measures in near electric operations, achieving autonomous control, precise detection, and real-time warning of near electric operations.

Intelligent measurement of fundamental frequency of complex periodic signals based on maximum periodicity

Intelligent measurement of fundamental frequency of complex periodic signals based on maximum periodicity

An automatic rebar spacing measuring method based on the YOLOv8-GB model

In engineering construction projects, rebar spacing measurement requires significant manual labor with low efficiency. This paper proposes a new intelligent rebar spacing measurement method based on the YOLOv8-GB model to save the workforce and improve efficiency. This method collects images of rebars to be measured using a binocular camera, utilizes the proposed YOLOv8-GB model to extract rebars from the scene, and achieves spacing measurement. The system is deployed on the NVIDIA Jetson TX2 NX for on-site portable measurement and can run in real-time at 24 frames per second. Experimental results show that the improved YOLOv8-GB network, compared with the YOLOv8n network, increased Recall, Precision, mAP@0.5, and mAP50-95 by 0.6 %, 5.5 %, 2.3 %, and 7.6 %, respectively. The measurement system built with YOLOv8-GB achieved an average absolute error of ± 1.7 mm, ±2.1 mm, and ± 2.7 mm for rebar spacing measurements on three different ground textures, with average relative errors of 0.85 %, 0.93 %, and 1.32 %, meeting engineering requirements. Compared to the measurement system built with YOLOv8n, the average absolute error decreased by 37.0 %, 8.0 %, and 25.0 % under the three different ground textures, while the average relative error decreased by 36.1 %, 8.8 %, and 23.7 %, respectively.

Experimental study on the influence of subgrade moisture content on compaction quality control indexes

ABSTRACT Intelligent compaction (IC) technology, which is based on the relationships between the subgrade stiffness properties and intelligent compaction measurement values (ICMVs), has been widely used in the compaction quality control (QC) of subgrade construction. The influence of moisture content (MC) on the stiffness properties is significant and well known but is often ignored in the current practice of IC. In this study, the relationship between the vibration compaction value (VCV) and subgrade reaction modulus (K 30) considering the influence of moisture content is investigated. The results show that the influence of MC on quality control indexes gradually becomes significant, especially in the later stage of compaction. Furthermore, K 30 is more sensitive than the VCV to moisture content, which means that the influence of MC is easy to ignore when taking the VCV as the ICMV. Finally, the specified VCV should be obtained when the MC is on both sides of the optimum moisture content (OMC) to ensure the accuracy of the QC of compaction.

Speech Intelligibility Outcomes Associated With Treatment for Acquired Apraxia of Speech: Magnitude of Change and Stability of Measurement.

The purpose of this investigation was to examine single-word speech intelligibility outcomes following sound production treatment in a group of 22 speakers with chronic acquired apraxia of speech (AOS) and aphasia. Also, the stability of repeated posttreatment intelligibility measures was examined for two scoring methods. The Assessment of Intelligibility of Dysarthric Speech was administered twice to each participant at pretreatment and twice at 8 weeks posttreatment. The test-retest reliability of the pretreatment samples was evaluated in a prior study; repeated samples were found to be stable over sampling times. For the current study, the three expert listeners who had rated the pretreatment samples scored the posttreatment samples using transcription and multiple-choice scoring formats. An additional expert listener, blinded to sampling time, scored pre- and posttreatment samples. The posttreatment samples were found to be stable over sampling times for the group. Posttreatment intelligibility scores were statistically significantly higher than the pretreatment scores for both scoring methods (i.e., increases of 9%-10%). Scores derived from the two scoring methods were strongly, positively correlated, with multiple-choice scores being significantly higher than transcription. The scoring methods did not differ significantly in the amount of change found from pre- to posttreatment. There were no statistically significant correlations between AOS severity and changes in pre- to posttreatment intelligibility scores. Performance for most participants was similar to group performance. Stability of posttreatment intelligibility samples supports use of intelligibility scores as outcome measures. This group of speakers demonstrated statistically significant increases in single-word speech intelligibility following sound production treatment. https://doi.org/10.23641/asha.26972425.

Predicting the intelligibility of Mandarin Chinese with manipulated and intact tonal information for normal-hearing listeners.

Objective indices for predicting speech intelligibility offer a quick and convenient alternative to behavioral measures of speech intelligibility. However, most such indices are designed for a specific language, such as English, and they do not take adequate account of tonal information in speech when applied to languages like Mandarin Chinese (hereafter called Mandarin) for which the patterns of fundamental frequency (F0) variation play an important role in distinguishing speech sounds with similar phonetic content. To address this, two experiments with normal-hearing listeners were conducted examining: (1) The impact of manipulations of tonal information on the intelligibility of Mandarin sentences presented in speech-shaped noise (SSN) at several signal-to-noise ratios (SNRs); (2) The intelligibility of Mandarin sentences with intact tonal information presented in SSN, pink noise, and babble at several SNRs. The outcomes were not correctly predicted by the Hearing Aid Speech Perception Index (HASPI-V1). A new intelligibility metric was developed that used one acoustic feature from HASPI-V1 plus Hilbert time envelope and temporal fine structure information from multiple frequency bands. For the new metric, the Pearson correlation between obtained and predicted intelligibility was 0.923 and the root mean square error was 0.119. The new metric provides a potential tool for evaluating Mandarin intelligibility.

Storm Classification and Dynamic Targeting for a Smart Ice Cloud Sensing Satellite

Smart Ice Cloud Sensing (SMICES) is a small-sat concept in which a radar intelligently targets ice storms based on information collected by a look-ahead radiometer. Often, space observations are performed by continuously collecting data from an instrument aimed at nadir (e.g., directly below the space platform). However, if the platform has the ability to assess science utility of features that will be overflown, an intelligent measurement scheme can improve science return. In the case of SMICES, power constraints and the rarity of storms mean that with blind nadir targeting SMICES would collect a limited amount of ice-storm radar data. The work proposed acquires measurements to maximize acquired high-interest storms while concurrently collecting a background sampling of all features. This is accomplished through two steps: storm classification and dynamic targeting. For classification, we describe a multistep use of machine learning and digital twin of Earth’s atmosphere to create a classifier. We discuss an autonomous data labeling pipeline used to train five different models to identify storms in tropical and nontropical regions and assess the results and impact of expected noise. For dynamic targeting, six algorithms, ranging from “blind” to more selective, are described and evaluated on their improvement over “blind” targeting.

تقييم المخاطرة المعزز بالذكاء الصناعي في مشاريع الأمن المادي دراسة تطبيقية لتحسين إدارة المشاريع الأمنية

The study aims to investigate how AI methods can be used to improve risk assessment in the field of physical security. A quantitative approach was applied in this study, where data was collected from 160 individuals working in this sector, and their responses were processed and evaluated using modern statistical analysis techniques. The results showed that AI-enhanced risk assessment plays a crucial role in reducing human errors, as AI contributes to eliminating biases and shortcomings caused by human factors, which enhances the accuracy of predictions and the effectiveness of decisions taken in the context of risk management. The results revealed that AI increases the response of security teams to risks, by providing a comprehensive and in-depth analysis that enables them to take quick and intelligent measures based on actual facts, which enhances the efficiency of security operations. It has also been proven that the application of AI contributes to the development of security sustainability, as it enables continuous analysis and adjustment of security methods to suit changing threats. The study emphasizes the need to integrate AI into risk management strategies, which enhances the ability of organizations to successfully solve security problems and enhances long-term sustainability. The results highlight the need to enhance training and development for workers in this sector to ensure the best use of contemporary technology, which contributes to increasing the effectiveness and sustainability of physical security.

Quantifying the impact of ASR-based instruction: What does the iSpraak platform learner data show?

Computer-assisted Pronunciation Training (CAPT) tools have become increasingly dependent on Automatic Speech Recognition (ASR) technology to provide automated corrective pronunciation feedback to learners. The extent to which ASR-based tools measurably improve second language (L2) pronunciation is of great interest to language educators globally, and Computer-assisted Language Learning (CALL) researchers. Studies to date have largely been conducted by research practitioners with small-to-medium sized samples at single institutions. The findings and conclusions drawn from such small-scale data collection might be significantly bolstered by analysing the vast stores of learner data from large CAPT platforms. This study is informed by a sizable eight-year dataset from iSpraak, an open-source pronunciation tool designed to model and evaluate L2 speech. Quantitative analysis of anonymised learner interactions with this application reveals significant gains in intelligibility measures across multiple languages. Results also suggest that the extent of ASR’s ability to improve learner pronunciation may be L2 dependent.

Fully automated measurement of noise, signal-to-noise ratio, and contrast-to-noise ratio on chest CT images: feasibility and efficiency.

Rapid and accurate measurement of computed tomography (CT) image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) is a clinical challenge. To explore the feasibility of intelligent measurement of chest CT image noise, SNR, and CNR. A total of 300 chest CT scans were included in the study, which was divided into research dataset, internal test dataset, and external test dataset. Based on the research dataset, automatically segment and measure the average CT values and standard deviation (SD) of CT values for background air and lung field under different thresholds to obtain noise, SNR, and CNR results. Using the results of manual measurements as the reference standard, we determine the optimal threshold with the highest consistency. Using internal and external test datasets, validate the consistency of automated measurements of noise, SNR, and CNR at the optimal CT threshold with reference standards. With background air set at -900 HU and lung field at -800 HU as thresholds, the automated measurements of noise, SNR, and CNR demonstrate the highest consistency with the reference standards. At the optimal threshold, the noise, SNR, and CNR measured automatically on both the internal (intraclass correlation coefficient [ICC] = 0.85-0.96) and external (ICC = 0.75-0.85) test datasets exhibit high consistency with their respective reference standards. The method we explored can intelligently measure the noise, SNR, and CNR of chest CT images, exhibits high consistency with radiologists, and offers a novel tool for image quality evaluation and analysis.

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.

Smart Temperature Monitoring System for Machining Process Using Internet of Things

Outer surface integrity must be maintained throughout the machining process during the production of metal parts, which need the ability to monitor temperatures in real time. Changes in temperature during the machining process lead to changes in the behavior of the product after work is completed.To achieve quality and productivity criteria, the metal parts manufacturing process requires a continuous and precise temperature measuring and monitoring system by using IOT. An intelligent temperature measurement can be used during machining processes on conventional, non-conventional, and computer numerical control (CNC) machines. This paper presents a performance analysis of a temperature measuring system for machining process applications via Arduino IDE. In this proposal, a temperature monitoring system based on Arduino is used. The system would utilize an: MLX90614 infrared thermometer sensor” to measure the temperature of the workpiece during machining processes. The methodology's aim is to use the smart manufacturing model to measure and monitor the arising temperatures throughout the machining process in order to build a new tool path rapidly and efficiently. The placement of a non-contact infrared “MLX90614” temperature sensor allowed measuring of the average cutting temperature as well as the maximum cutting temperature. The data acquisition process was carried out using the “Xampp” Control Panel software, which communicated with the ESP32 “microcontroller” board using the Arduino IDE program. Experiments were carried out, so that the temperature of the cutting, which was measured, was recorded, and the results of the experiments were analyzed. Index Terms: Smart Manufacturing, Internet of Things, MLX90614 Infrared Temperature Sensor, Machining Parameters.

Intelligent and Secure Cloud–Edge Collaborative Industrial Information Encryption Strategy Based on Credibility Assessment

As industries develop and informatization accelerates, enterprise collaboration is increasing. However, current architectures face malicious attacks, data tampering, privacy issues, and security and efficiency problems in information exchange and enterprise credibility. Additionally, the complexity of cyber threats requires integrating intelligent security measures to proactively defend against sophisticated attacks. To address these challenges, this paper introduces an intelligent and secure cloud–edge collaborative industrial information encryption strategy based on credibility assessment. The proposed strategy incorporates adaptive encryption specifically designed for cloud–edge and edge–edge architectures and utilizes attribute encryption to control access to user-downloaded data, ensuring secure information exchange. A mechanism for assessing enterprise credibility over a defined period helps maintain a trusted collaborative environment, crucial for identifying and mitigating risks from potentially malicious or unreliable entities. Furthermore, integrating intelligent threat detection and response systems enhances overall security by continuously monitoring and analyzing network traffic for anomalies. Experimental analysis evaluates the security of communication paths and examines how enterprise integrity influences collaboration outcomes. Simulation results show that this approach enhances enterprise integrity, reduces losses caused by harmful actors, and promotes efficient collaboration without compromising security. This intelligent and secure strategy not only safeguards sensitive data but also ensures the resilience and trustworthiness of the collaborative network.

Pipeline impact force observation-based intelligent measurement method for liquid flow

This paper proposes an innovative measurement method that uses the impact force generated when the liquid flows through the pipe as an observation indicator, and successfully establishes a non-linear mapping relationship between the impact force sequence and the weight of the flowing liquid by training and learning the collected impact force sequence through the CLCD (CNN-LSTM-CNN-Double) network architecture. In response to the challenges such as the prevalent interference factors and inconsistent flow time lengths in the collected data, this paper introduces a new weight ratio algorithm, WRP (Weight-Ratio-Process), which effectively improves the robustness and accuracy of data processing. The experimental results show that the effective detection rate of the method reaches 90 % when the weighing error is set to ±5g on the constructed fluid impact force test platform. When the error range is relaxed to ±15g, the effective detection rate is increased to 98 %. This achievement demonstrates the broad application potential and practical value of the method in the field of fluid transport measurement.

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.

Intelligent analysis and measurement of semicircular canal spatial attitude.

The primary aim of this investigation was to devise an intelligent approach for interpreting and measuring the spatial orientation of semicircular canals based on cranial MRI. The ultimate objective is to employ this intelligent method to construct a precise mathematical model that accurately represents the spatial orientation of the semicircular canals. Using a dataset of 115 cranial MRI scans, this study employed the nnDetection deep learning algorithm to perform automated segmentation of the semicircular canals and the eyeballs (left and right). The center points of each semicircular canal were organized into an ordered structure using point characteristic analysis. Subsequently, a point-by-point plane fit was performed along these centerlines, and the normal vector of the semicircular canals was computed using the singular value decomposition method and calibrated to a standard spatial coordinate system whose transverse planes were the top of the common crus and the bottom of the eyeballs. The nnDetection target recognition segmentation algorithm achieved Dice values of 0.9585 and 0.9663. The direction angles of the unit normal vectors for the left anterior, lateral, and posterior semicircular canal planes were [80.19°, 124.32°, 36.08°], [169.88°, 100.04°, 91.32°], and [79.33°, 130.63°, 137.4°], respectively. For the right side, the angles were [79.03°, 125.41°, 142.42°], [171.45°, 98.53°, 89.43°], and [80.12°, 132.42°, 44.11°], respectively. This study successfully achieved real-time automated understanding and measurement of the spatial orientation of semicircular canals, providing a solid foundation for personalized diagnosis and treatment optimization of vestibular diseases. It also establishes essential tools and a theoretical basis for future research into vestibular function and related diseases.

Effects and mechanisms of intelligent electricity system on urban carbon reduction

This paper provides evidence on the effectiveness of China's intelligent electricity system in reducing carbon dioxide emissions in the electricity sector at the city and plant levels. The actual application level of intelligence technology in cities was quantified through text analysis of newspaper reports, demonstrating its effectiveness in reducing urban carbon dioxide emissions. Additionally, this paper explores four potential pathways through which intelligent electricity system measures impact the power industry: the adjustment effect, technological progress effect, and carbon transfer effect of energy supply structure, as well as the electricity substitution effect on the demand side. The findings suggest that combining information technology and intelligent technology in the electricity sector could enhance carbon reduction efforts.

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.

Integrating Intelligent Sensors for Safe UAV Distribution: Design and Evaluation of Ranging System

In our increasingly electrified society, electricity has become indispensable to both production and daily life. However, high-load electric energy transmission presents inherent safety risks. To ensure the secure transportation of electric energy, live work on distribution networks is essential. However, traditional live work techniques require a high level of dependence on worker experience, which frequently leads to inaccurate safety distances and degrades worker security. This study proposes the integration of Unmanned Aerial Vehicles (UAVs) with intelligent robot sensing technology to enhance safety and efficiency in live work. By accurately measuring safety distances, UAVs offer a promising solution to mitigate risks associated with high-voltage circuits. Comparative analysis between traditional and intelligent live work safety distance measurements reveals the two live works under the high voltage circuit were 92% and 96%, respectively, and the accuracy of the two live work safe distance measurements under the low voltage circuit was 84% and 99%, respectively. Results demonstrate that UAVs equipped with intelligent sensing technology achieve superior accuracy in safe ranging for live work, thereby ensuring stable energy transmission and safeguarding the lives of workers in distribution networks. Doi: 10.28991/HIJ-2024-05-03-04 Full Text: PDF