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The present study aimed to display the gamma energy spectrum as a 2D digital image. This process was performed via LabVIEW software. A code was developed for this purpose. It was tested on the gamma energy spectra of Cs-137 and Bi-207 radiation sources. The process of image acquisition of the energy spectrum was performed in two ways; horizontal and vertical. In addition to grayscale image acquisition, the colored image acquisition was also performed using color palettes defined by DICOM standard via Vision Assistant Express. [ABSTRACT FROM AUTHOR]

Copyright of Mechanical & Electronic Control Engineering is the property of Omniscient Pte. Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper describes the control system for a dispersion interferometer based on a CO2 laser developed by the Budker Institute of Nuclear Physics (Novosibirsk, Russia) for the Globus-M2 tokamak (Ioffe Institute, St. Petersburg, Russia). The basis of this system is the programmable controller ADAM-5510E/TCP with a set of analog and digital input/output modules and specially developed software that allows monitoring and control of the operation and parameters of interferometer elements. During the year of the dispersion interferometer operation as a standard diagnostic at the Globus-M2 tokamak, the control system showed good levels of reliability and sustainability. [ABSTRACT FROM AUTHOR]

Simple Summary: The backfat thickness of sows reflects their nutrient reserve levels. Controlling the backfat thickness within an appropriate range at different production stages can improve the production performance of sows. However, in large-scale pig farms, due to the cumbersome process of measuring backfat thickness and the large number of sows, it is often not possible to obtain or visually measure the backfat thickness during production. Often, a small difference in feed intake will lead to a large difference in production performance, such that sow production performance cannot be fully optimized. In this study, the relationships between backfat thickness and parameters related to hip area, hip width, hip height, and hip radius are discussed, and a sow backfat thickness estimation model is established. Finally, a sow backfat thickness estimation system is built using the LabVIEW 2023 Q1 (64-bit) software development platform. After inputting the sow buttock image, the system can automatically preprocess the image and extract the parameters required to quickly estimate the backfat thickness of the sow, thereby enhancing the automation of the sow farm. Controlling the backfat thickness of sows within an appropriate range during different production stages helps to increase the number of pigs weaned per sow per year and ultimately enhances the economic benefit to the pig farm. To obtain the backfat thickness of sows automatically, a backfat thickness estimation method based on machine vision is proposed. First, the backfat thickness values and 3D images of the buttocks of 154 Landrace–Yorkshire crossbred sows were obtained using a veterinary ultrasound backfat meter and Azure Kinect DK camera. After preprocessing the 3D images utilizing Python 3.9.16 software, 10 external morphological parameters reflecting the area, width, height, and contour radius of the sow's buttocks were extracted. The relationships between backfat thickness and external morphological parameters were analyzed in a randomly selected group of 100 sows. A significant positive correlation was observed between backfat thickness and buttock morphological parameters, with the Pearson coefficient for the fitted ellipse area achieving values up to 0.90. A backfat thickness estimation model was developed based on selected buttock feature parameters. The model's generalization performance was evaluated using 54 additional sows that were not involved in the model development. The coefficient of determination (R2) between the estimated and actual backfat thicknesses was 0.8923, with a mean absolute error (MAE) of 1.23 mm and a mean absolute percentage error (MAPE) of 5.73%. These metrics indicate that the model can meet production requirements, and the proposed technique offers improved estimation accuracy compared to existing methods. Ultimately, a backfat thickness automatic estimation system was developed using LabVIEW 2023 Q1 (64-bit) software. This research helps to address the cumbersome process of measuring sow backfat thickness and promotes the automation of sow farms. [ABSTRACT FROM AUTHOR]

Milling parts made from glass fiber-reinforced polymer (GFRP) composite materials are recommended to achieve the geometric shapes and dimensional tolerances required for large parts manufactured using the spray lay-up technique. The quality of the surfaces machined by milling is significantly influenced by the temperature generated in the cutting zone. This study aims to develop an Artificial Neural Network (ANN) model to predict the temperature generated when milling GFRP. The ANN model for temperature prediction was created using a virtual instrument developed in the graphical programming language LabVIEW. Predicting temperature is crucial because excessive heat during milling can lead to several issues, such as tool wear and thermal degradation in the polymer matrix. The temperature in the tool–workpiece contact surface during the milling process was measured using a thermography technique with a ThermaCAM SC 640 camera (provided by FLIR Systems AB, Danderyd, Sweden), and the data were analyzed using the ThermaCAM Researcher Professional 2.8 SR-2 software. Experimental research shows that the cutting speed has a much more significant effect on the temperature in the cutting zone compared to axial depth of cut and feed speed. The maximum temperature of 85.19 °C was measured in the tool–workpiece contact zone during machining at a cutting speed of 75.39 m/min, a feed rate of 250 mm/min, and an axial depth of cut of 12 mm. This temperature rise occurred due to the larger contact area and heightened friction resulting from the abrasive characteristics of the reinforcement material. [ABSTRACT FROM AUTHOR]

Copyright of Railway Construction Technology is the property of Railway Construction Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Nowadays, city logistics issues are getting more and more attention. Within this, our City Logistics Research Group is currently focusing on the urban concentrated sets of delivery locations. MS Excel- and AnyLogic-based simulation models were developed in the last years to analyze them, but still, problems were encountered above a certain model size in both cases. For this reason, we started looking for a new tool that is flexible, fast, and available for everyone. The use of Python Programming Language was selected, with the primary goal of eliminating the previous technical problems and obtaining detailed information about the examined processes. In this paper, we are going to present the problems of the previous models, the development, operation, and main advantages of the new, Python-based DES model, the design and parameterization of the pilot models, the results of the tests, and the next steps of the research. [ABSTRACT FROM AUTHOR]

Abstract In this article, power quality disturbances, such as voltage and current unbalances, series and shunt faults, are monitored in three-phase synchronous machines. Because the imbalances affect machine efficiency and service life, it is essential to figure out whether the three-phase voltages/currents are balanced or unbalanced. An integrated detection algorithm is proposed to detect the unbalanced voltages/currents and various abnormal conditions precisely and quickly, which is based on the coherence estimator. Most existing detection methods require more than one cycle time to determine the contingency conditions, while the proposed technique detects these situations in a fast and discrete protection system. The suggested scheme acquires instantaneous measurements of the three-phase voltages and currents taken at the synchronous machine terminals in order to calculate fifteen coherence coefficients that are utilized to detect and assess any unbalance or trouble accurately and swiftly. A new proposal for imbalance and disturbance indicators based on the coherence estimators is developed in this study. Multiple test cases have been conducted to validate the proposed algorithm capabilities using a real power system, which includes a motor-generator set, a three-phase load and measurement transformers. The experimental results have been revealed that the relay reliability and accuracy percentages have been found to be 98.28% and 98.52%, respectively. The quantitative findings of the imbalance and disturbance assessment are recorded.

The objective of this paper is to realize an angular position control system with a direct current motor. For this purpose, an Arduino microcontroller board is used to control the LS-25GA370 DC motor. The L298N dual H-bridge motor driver is used to modulate the PWM signal and control the travel direction. PID (proportional, integral and derivative) control algorithms are used to generate the PWM output. To implement a PID controller, the tuning parameters of the controller, namely proportional gain, integral gain and derivative gain must be carefully determined. Since the system is nonlinear, the PID controller is the best choice to accomplish this task. The sensor used was a Hall type encoder. To test the system, a LabVIEW interface was used, which communicates with the Arduino Uno platform through the serial port. This technical solution, using PID controller, can be applied for robotic arm position control system and many other industrial applications.

As the market share of electric vehicles continues to rise, consumer demands for comfort within the vehicle interior have also increased. The noise generated by electric seats during operation has become one of the primary sources of in-cabin noise. However, the offline detection methods for electric seat noise severely limit production capacity. To address this issue, this paper presents an online quality inspection system for automotive electric seats, developed using LabVIEW. This system is capable of simultaneously detecting both the noise and electrical functions of electric seats, thereby resolving problems associated with multiple detection processes and low integration levels that affect production efficiency on the assembly line. The system employs NI boards (9250 + 9182) to collect noise data, while communication between LabVIEW and the Programmable Logic Controller (PLC) allows for programmed control of the seat motor to gather motor current. Additionally, a supervisory computer was developed to process the collected data, which includes generating frequency and time-domain graphs, conducting data analysis and evaluation, and performing database queries. By being co-located with the production line, the system features a highly integrated hardware and software design that facilitates the online synchronous detection of noise performance and electrical functions in automotive electric seats, effectively streamlining the detection process and enhancing overall integration. Practical verification results indicate that the system improves the production line cycle time by 34.84%, enabling rapid and accurate identification of non-conforming items in the seat motor, with a detection time of less than 86 s, thereby meeting the quality inspection needs for automotive electric seats. [ABSTRACT FROM AUTHOR]

In this article, power quality disturbances, such as voltage and current unbalances, series and shunt faults, are monitored in three-phase synchronous machines. Because the imbalances affect machine efficiency and service life, it is essential to figure out whether the three-phase voltages/currents are balanced or unbalanced. An integrated detection algorithm is proposed to detect the unbalanced voltages/currents and various abnormal conditions precisely and quickly, which is based on the coherence estimator. Most existing detection methods require more than one cycle time to determine the contingency conditions, while the proposed technique detects these situations in a fast and discrete protection system. The suggested scheme acquires instantaneous measurements of the three-phase voltages and currents taken at the synchronous machine terminals in order to calculate fifteen coherence coefficients that are utilized to detect and assess any unbalance or trouble accurately and swiftly. A new proposal for imbalance and disturbance indicators based on the coherence estimators is developed in this study. Multiple test cases have been conducted to validate the proposed algorithm capabilities using a real power system, which includes a motor-generator set, a three-phase load and measurement transformers. The experimental results have been revealed that the relay reliability and accuracy percentages have been found to be 98.28% and 98.52%, respectively. The quantitative findings of the imbalance and disturbance assessment are recorded. [ABSTRACT FROM AUTHOR]

The proposed method deals with the study and analysis of EEG signal using LabVIEW Biomedical toolkit effectively. The objective of this paper is to identify brain disorders, using LabVIEW. An analysis of cerebral disorders was conducted using LabVIEW. A technique is suggested that uses LabVIEW Biomedical for analyzing EEG signals. Devices considered in this study include signal acquisition, data interfaces such as myDAQ, amplification, and feature extraction capabilities. Signal conditioning is adopted with an undertaking amplifier (LM741), followed by an instrumentation amplifier (AD8232). DAQ cards are ADC cards. A biomedical toolkit is used to extract the features of the EEG signal using LabVIEW. Based on the extracted features, a comparison is conducted between the patient's code defaults and the number of arrhythmic features found. Following this, specialists can monitor their patients' EEG signals remotely and dynamically in real-time via a web page without installing any software from a telemedicine application. This system will bridge the gap between the patient's deteriorating health condition and healthcare entities. [ABSTRACT FROM AUTHOR]

Precision machining technology has received significant attention from researchers and engineers. With the increasing complexity of product designs and continuous advancements in high-tech industries, the precision requirements for manufacturing are constantly escalating. For researchers who are new to precision machining, conducting experiments directly on commercial equipment is resource-intensive and does not accommodate diverse working scenarios. Therefore, designing a generalized precision machining experimental test platform is particularly important. This paper presents a practical plan to construct such a platform, integrating key components such as a gantry-type Cartesian coordinate robot, a 2D rotary table, a 2D precision slide stage, a galvanometer, and a telecentric lens. The platform serves as a test environment for verifying the feasibility of various precision machining control algorithms. It not only demonstrates the desired stability and scalability but also offers a user-friendly operational interface via the LabVIEW front panel. This facilitates simple and efficient experimental operations, providing an effective and reliable environment for testing precision machining control algorithms. [ABSTRACT FROM AUTHOR]

India is experiencing rapid expansion in renewable energy resources as a result of economic challenges posed by rising oil prices and natural concerns. Since the early 2000s, solar photovoltaic (PV) adoption has accelerated exponentially. By 2015, global installed capacity will exceed 222GW. By 2050, it is anticipated to reach 4500GW. Solar photovoltaic panels on polycrystalline roofs have an efficiency of 22.7%. The cancer tropics travel through eight states in India, where summer temperatures range between 40 and 50 degrees Celsius, reducing the efficiency of solar panels. As a result, efficient Maximum Power Point Training (MPPT) controllers are required to close this gap. The experimental test was conducted in Ghaziabad at latitude 28.6692 (N) and longitude 77.4538 (W) (E). The solar photovoltaic simulator was created with LabVIEW software. The effect of temperature, irradiance, series resistance, shunt resistance, and ideal factor fluctuations on the performance of a solar panel has been investigated. The simulated findings are validated against an experimental setup to ensure the accuracy and reliability of the results using NI myRIO and LabVIEW. [ABSTRACT FROM AUTHOR]

Conventional baseband processing techniques for communication must be adapted to the specificities of terahertz channels. Currently, there is limited research on channel coding applicable to terahertz bands, particularly in terms of performing real-time tests that differentiate them from offline processing. This paper presents a test platform for a 300 GHz wireless communication system based on the PXIe platform. The experiment utilizes a high-performance, low-complexity serial scheduling approach with the Minimum Sum-Log Likelihood Ratio Belief Propagation (MS-LLR-BP) soft decision decoding algorithm to test point-to-point real-time line-of-sight communication for low-density parity-check (LDPC) code communication systems. The results indicate that the implementation of LDPC coding in QPSK terahertz wireless transmission systems results in a performance enhancement of approximately 25–55% in bit error rate (BER), demonstrating the excellent performance of LDPC codes against terahertz channel fading. This highlights their potential in future ultra-high-speed communication systems. [ABSTRACT FROM AUTHOR]

Due to the lack of real-time planning for fire escape routes in large buildings, the current route planning methods fail to adequately consider factors related to the fire situation. This study introduces a real-time fire monitoring and dynamic path planning system based on an improved ant colony algorithm, comprising a hierarchical arrangement of upper and lower computing units. The lower unit employs an array of sensors to collect environmental data in real time, which is subsequently transmitted to an upper-level computer equipped with LabVIEW. Following a comprehensive data analysis, pertinent visualizations are presented. Capitalizing on the acquired fire situational awareness, a propagation model for fire spreading is developed. An enhanced ant colony algorithm is then deployed to calculate and plan escape routes by introducing a fire spread model to enhance the accuracy of escape route planning and incorporating the A* algorithm to improve the convergence speed of the ant colony algorithm. In response to potential anomalies in sensor data under elevated temperature conditions, a correction model for data integrity is proposed. The real-time depiction of escape routes is facilitated through the integration of LabVIEW2018 and MATLAB2023a, ensuring the dependability and safety of the path planning process. Empirical results demonstrate the system's capability to perform real-time fire surveillance coupled with efficient escape route planning. When benchmarked against the traditional ant colony algorithm, the refined version exhibits expedited convergence, augmented real-time performance, and effectuates an average reduction of 17.1% in the length of the escape trajectory. Such advancements contribute significantly to enhancing evacuation efficiency and minimizing potential casualties. [ABSTRACT FROM AUTHOR]

To investigate the functionality and performance of a prototype microjet engine, we constructed a versatile test stand tailored to the specifications of a 400 N prototype. This test stand facilitated a comprehensive study by enabling real-time recording of 45 essential parameters for analysis, encompassing temperatures, pressures, speed, fuel flow, thrust, vibration, and various other monitored metrics. All parameters and control elements were seamlessly integrated via a data acquisition and control system, utilizing a compactDAQ (Data Acquisition) system from National Instruments and a custom Virtual Instrument programmed with graphical language. The test stand offers both manual and automated operation modes, with the flexibility for hybrid operation. For instance, following the idle regime, manual control using a potentiometer can seamlessly transition from automated control via a proportional control (P control) mechanism. Before the experimental campaign, rigorous verification and validation tests were conducted to ensure the reliability and accuracy of the setup. The experimental campaign comprised a series of manual tests focusing on the fuel system and automated tests covering starting, idle, working, and stopping regimes. This structured approach allowed for a comprehensive evaluation across different operational scenarios, providing insights into the engine's behavior and performance under varying conditions. [ABSTRACT FROM AUTHOR]

This article studies the transformation and assembly process of the Volkswagen (VW) Crafter from conventional to hybrid vehicle of the department of vehicles engineering, University of Debrecen, and uses a computer-aided simulation (CAS) to design the vehicle based on the real measurement data (hardware-in-the-loop, HIL method) obtained from an online CAN bus data measurement platform using MATLAB/Simulink/Simscape and LabVIEW software. The conventional vehicle powered by a 6-speed manual transmission and a 4-stroke, 2.0 Turbocharged Direct Injection Common Rail (TDI CR) Diesel engine and the transformed hybrid electrified powertrain are designed to compare performance. A novel methodology is introduced using Netcan plus 110 devices for the CAN bus analysis of the vehicle's hybrid version. The acquired raw CAN data is analyzed using LabVIEW and decoded with the help of the database (DBC) file into physical values. A classical proportional integral derivative (PID) controller is utilized in the hybrid powertrain system to manage the vehicle consumption and CO2 emissions. However, the intricate nonlinearities and other external environments could make its performance unsatisfactory. This study develops the energy management strategies (EMSs) on the basis of enhanced proportional integral derivative-based genetic algorithm (GA-PID), and compares with proportional integral-based particle swarm optimization (PSO-PI) and fractional order proportional integral derivative (FOPID) controllers, regulating the vehicle speed, allocating optimal torque and speed to the motor and engine and reducing the fuel and energy consumption and the CO2 emissions. The integral time absolute error (ITAE) is proposed as a fitness function for the optimization. The GA-PID demonstrates superior performance, achieving energy efficiency of 90%, extending the battery pack range from 128.75 km to 185.3281 km and reducing the emissions to 74.79 gCO2/km. It outperforms the PSO-PI and FOPID strategies by consuming less battery and motor energy and achieving higher system efficiency. [ABSTRACT FROM AUTHOR]

In today's technology-driven era, the demand for engineers with strong programming skills continues to rise across diverse industries. LabVIEW software stands out as a key tool in engineering, supporting applications ranging from test and measurement systems to automation and control across different sectors, like manufacturing, aerospace, and automotive. Proficiency in LabVIEW therefore enables engineers to work on a wide range of projects and applications. This article presents two distinct pedagogical approaches to teaching LabVIEW programming at the university level. The introductory course is taught using traditional direct teaching methods, with an emphasis on face-to-face teaching and classroom lectures. In contrast, the advanced course uses a flipped classroom model, where students work independently on preparatory material before participating in laboratory exercises. The latter course concludes with a large project, which the student must complete within four hours. The article provides details on the teaching methods and content of the individual courses, as well as an explanation of the assessment process for the final project. The analysis of the final project results confirms that the students have acquired the knowledge necessary to create functional LabVIEW programs with the appropriate programming style. [ABSTRACT FROM AUTHOR]

The magnetoelectric (ME) sensor is a new and promising type of magnetic field sensor with ultrahigh sensitivity. However, there are few reports on the research of real‐time measurement system which can promote its practical application. Herein, a novel real‐time measuring approach for weak AC magnetic fields at relatively low frequency is proposed using Metglas/PZT‐5B ME sensors. The system mainly consists of an oscilloscope, a signal generator, and a program developed with LabVIEW programming. Real‐time measurement of relatively low‐frequency magnetic fields has been achieved by using frequency upconversion methods, simultaneously displaying the frequency and magnitude of the magnetic field. As a result, the real‐time measurement system is able to detect a weak AC magnetic field as low as 0.1 nT@1 Hz, which is promising to push the ME sensor to practical application. [ABSTRACT FROM AUTHOR]

Robots are increasingly present in everyday life, replacing human involvement in various domains. In situations involving danger or life-threatening conditions, it is safer to deploy robots instead of humans. However, there are still numerous applications where human intervention remains indispensable. The strategy to control a robot can be developed based on intelligent adaptive programmed algorithms or by harnessing the physiological signals of the robot operator, such as body movements, brain EEG, and muscle EMG which is a more intuitive approach. This study focuses on creating a control platform for a 3-finger gripper, utilizing Electromyography (EMG) signals derived from the operator’s forearm muscles. The developed platform consisted of a Robotiq three-finger gripper, a Delsys Trigno wireless EMG, as well as an NI CompactRIO data acquisition platform. The control process was developed using NI LabVIEW software, which extracts, processes, and analyzes the EMG signals, which are subsequently transformed into control signals to operate the robotic gripper in real-time. The system operates by transmitting the EMG signals from the operator's forearm muscles to the robotic gripper once they surpass a user-defined threshold. To evaluate the system's performance, a comprehensive set of regressive tests was conducted on the forearm muscles of three different operators based on four distinct case scenarios. Despite of the gripper’s structural design weakness to perform pinching, however, the results demonstrated an impressive average success rate of 95% for tasks involving the opening and closing of the gripper to perform grasping. This success rate was consistent across scenarios that included alterations to the scissor configuration of the gripper. [ABSTRACT FROM AUTHOR]

Depression is one of the mental disorders with a significant impact on individuals' quality of life and productivity. The diagnostic process for depression, which typically relies on subjective assessment, often encounters challenges of uncertainty and variability in symptoms. This study aims to develop a fuzzy model for predicting depression levels based on five primary symptom variables: worthlessness, concentration, suicidal ideation, sleep disturbance, and hopelessness. The model is implemented on two platforms, Python and LabVIEW, to evaluate the accuracy and consistency of prediction results between these platforms. The analysis process begins with data preprocessing, input variable fuzzification, inference using 243 fuzzy rules, and defuzzification to generate a crisp output value classified into four depression levels: No Depression, Mild, Moderate, and Severe. The study results indicate a very small error margin between the two platforms, with error values below 0.01 in each trial. These findings suggest that both Python and LabVIEW can produce nearly identical and consistent predictions. This conclusion supports the effectiveness of fuzzy logic in addressing uncertainty in clinical data, especially for cases of depression with varying symptoms. Nonetheless, there are limitations related to the subjectivity in selecting membership functions and rules, as well as limitations in the number of variables used. Therefore, this study recommends expanding the developed fuzzy model with additional variables or integrating it with machine learning approaches to improve prediction accuracy. These findings are expected to serve as a foundation for the development of fuzzy-based systems in future mental health diagnostics.

The design of a smart water monitoring and control system in urban areas plays a pivotal role in providing efficient distribution mechanisms to reduce leakage, especially in regions facing water scarcity and limited resources. The convergence of the Internet of Things (IoT) and blockchain technology to improve the system's performance, enhance its security, and provide a decentralized and tamper-proof environment presents an excellent opportunity to evolve the system further and form a state-of-the-art Water 4.0 ecosystem. The proposed Blockchain of Things (BCoT) water system is introduced as a pilot to explore its potential in delivering and managing Water 4.0 applications. An Ethereum platform formed the heart of the BCoT system, while a Raspberry Pi 4 acted as a node to the blockchain that collected data from various sensors and microcontrollers via MQTT programmed by Node-Red. LabVIEW software also provided supervisory control and data acquisition (SCADA). The BCoT system was tested, and its functionality was verified, showing good promise to take smart water systems to a new level of innovation that may resolve the many challenges faced by countries with limited water resources and address the challenges of the 21st-century "Water 4.0" ecosystem. [ABSTRACT FROM AUTHOR]

Visible and near-infrared (VIS-NIR) spectral technology is fast, accurate, and non-destructive, which meets the requirements of the separation of coal and gangue. However, the performance of the classification models established by the spectra collected according to the usual single-location collection methods was unsatisfactory. Simulating the production conditions of coal gangue sorting, this paper proposed a multilocation spectral feature fusion identification method for coal and gangue to improve the identification accuracy and developed an identification system. In the multilocation spectral collection method, the spectra of the sample were collected at three locations under the probe, which obtained richer information. In the classification models built based on different feature fusion methods, the one-dimensional convolutional neural network model with feature layer fusion had the strongest classification ability, with an accuracy of 97.61%, an improvement of 8.84% over the best single-location model. The regions of interest for model decision-making were visualized using the Gradient-weighted Class Activation Mapping (Grad-CAM) method to explore the reasons why the multilocation feature fusion method was more advanced. Furthermore, a coal gangue identification system was developed using LabVIEW, which can achieve fast and accurate recognition with an accuracy of 93.15% and an expenditure time of 1.241 s. [ABSTRACT FROM AUTHOR]

A novel detection method, known as RGB‐tracking, was utilized to quantify the relative color changes in reactants/products during the dyeing of a solution of indigo as a model compound. The RGB‐tracking system consists of a digital camera, a light source, a data acquisition device, a LabVIEW program, and its built‐in function (Machine Vision). For the reduction of indigo, two types of reagents were employed: sodium dithionite (Na2S2O4) for the chemical redox process and yeast (Saccharomyces cerevisiae) for the biological fermentation process. As a result, in the case of chemical redox during the reduction of the indigo solution, the progress of the reaction can be described by a color change, that is, the curves of R‐ and G‐pixels, respectively. When the curve for the G‐pixels began to increase, this indicated that leuco‐indigo is being formed. A dramatic increase in the R‐curve indicates that the amount of leuco‐indigo has exceeded than that of indigo itself and that the reaction will gradually reach equilibrium. In contrast to this, additional complex behavior was observed in the case of biological fermentation. When the RGB‐tracking curves for the R‐ and G‐pixels reach their maximum values, this indicates that the concentration of leuco‐indigo was significantly higher than that of indigo itself. The flattening of the RGB‐tracking curves indicates that the reaction has reached equilibrium. [ABSTRACT FROM AUTHOR]

This research proposes an application of the image processing device for the detection of knitted fabrics. The machine rolls up knitted fabrics and then the fabrics moves through a detection area, where the camera is connected to the image processing device. Next, an image captured after a defect exists is sent to be processed and analyzed by the NI myRIO device. An area pixel is computed based on Hue, Saturation, and Lightness (HSL) system and interpreted for defect selection. This procedure is performed on region of interest (ROI) to verify the defect. Three defects, examined in this research, consist of small holes, loose threads, and crook knitting needles. These knitted fabric defects are classified from the proposed weighted voting classifier, the results were found that the defects of three knitted fabrics can be accurately predicted. The proposed method can be available to the textile industry. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Nowadays the detection of fabric defects is an active research topic to detect and resolve the difficulties faced in processing fabric in printing and knitting in textile industries. The traditional approach of visual screening of human fabrics is exceedingly time consuming and it is not reliable as it is much susceptible to human errors. There are two major issues in defect inspection like defect identification and classification in fabric. Automatic identification of defects is quite important in the current scenario. For enhancing the quality of the fabric, this paper proposes a Texture Defect Detection (TDD) algorithm. This TDD algorithm utilizes pre-processing for the extraction of luminance plane and Discrete wavelet frame decomposition for dividing the image into several subbands with same resolution as input image. Statistical features are extracted using Gray Level Co-occurrence Matrix and these features are applied to Support Vector Machine for classifying the defective images. This improves the quality of texture segmentation and classification of visible defects. The experimental setup is done with the fabric conveyor and three high resolution industrial cameras acA4600-7gc for covering the entire width of the fabric while running. This TDD algorithm is developed under LabVIEW platform. Textile Texture Database (TILDA) multi-class dataset is used for testing the proposed algorithm. This algorithm is tested for 4 different classes of fabric defects including 2800 defective and 400 non defective fabric images. The success rate of detection of fabric defect is 96.56% with the images from the database. The validation results with real time fabric images show 97% of accuracy in the detection of defects in fabric images. [ABSTRACT FROM AUTHOR]

In the present paper, an affordable innovative physical experimental equipment consisting of an upper computer, an ultrasonic sensor module, and an Arduino microcontroller has been designed. The relationship between the position of the slider fixed on two springs and time is measured by using the ultrasonic sensor module. A system for slider motion data and image acquisition is constructed by using the LabVIEW interface of Arduino UNO R3. The purpose of this experiment is to demonstrate and interpret the propagation of waves represented by harmonic motion. The spring oscillator system including a slider and two springs is measured and recorded, and the motion can be realized using curve fitting to the wave equation in Sigmaplot. The vibration periods obtained from experimental measurements and curve fitting of the wave equation are 1.130 s and 1.165 s, respectively. The experimental data are in good agreement with the theoretical model. The experimental measurement results show that the maximum kinetic energy is 0.0792 J, the maximum potential energy is 0.0795 J, and the total energy at the position of half the amplitude is 0.0791 J. The results verify the mechanical energy conservation of spring oscillator system in a short time. This self-made instrument has improved the visualization and the automation level of the corresponding experiments. [ABSTRACT FROM AUTHOR]

In the process of metal wire and additive manufacturing, due to changes in temperature, humidity, current, voltage, and other parameters, as well as the failure of machinery and equipment, a failure may occur in the manufacturing process that seriously affects the current situation of production efficiency and product quality. Based on the demand for monitoring of the key impact parameters of additive manufacturing, this paper develops a parameter monitoring and prediction system for the additive manufacturing feeding process to provide a basis for future fault diagnosis. The fault diagnosis and prediction system for metal wire supply and additive manufacturing utilizes STM 32 as its core, enabling the capture and transmission of temperature, humidity, current, and voltage data. The upper computer system, designed on the LabVIEW 2019 virtual instrument platform, incorporates an LSTM neural network model and facilitates a connection between LabVIEW and MATLAB 2019 to achieve the prediction function. The monitoring and prediction system established in this study is intended to provide basic research assistance in the field of fault diagnosis. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In light of the heavy load applied by traditional air spring test equipment and its complex structural system design, a lever-type torque loading air spring test system is designed. It adopts the principle of Chinese scales to apply the load on the air spring in the form of proportional amplification, which can apply a simulated load on the air spring of 500–800 kg, using the vertical sliding shaft as the transverse limit to make the air spring elongate and compress by 280 mm in the longitudinal direction. The measurement and control system of the test equipment is then developed based on the LabVIEW platform, and the required sensors are selected and installed. The system can achieve real-time data acquisition of the air pressure, load, height and other parameters of the air spring and air spring charging and discharging control. Following the debugging of the function of the test equipment, the function of each subsystem is normal and able to meet the requirements of air spring characteristic and pressure tightness tests. For small spaces, such as laboratories, by reducing the installation of hydraulic and other oil source systems, avoiding the use of large mass blocks to simulate the loading of air spring loads and optimizing the complex installation and debugging process, this miniaturized design for air spring test equipment has benefits for practical applications. [ABSTRACT FROM AUTHOR]

Copyright of Smart Rail Transit is the property of Smart Rail Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Utilizing an embedded system is crucial for exploring various fields of study, such as ground wave propagation, which interests earth and civil engineering scientists/students. The constructed system includes a data acquisition (DAQ) module and vibration sensors, which can be employed to study wave propagation. It is particularly beneficial for students lacking access to necessary instruments. The sensors utilized are MEMS accelerometers. Developed using LabVIEW programming software, a graphical interface controls the DAQ and visualizes the wave signals. The primary parameter of the DAQ is the sampling frequency, which is adjustable by the user from 1 kHz to 25 kHz. Consequently, when testing with distances ranging from 1 to 10 m between the sensor and the source, a sampling rate exceeding 10 kHz represents ground acceleration reliably. Another aspect addressed in subsequent studies using this system involves analyzing the results to characterize ground wave attributes. [ABSTRACT FROM AUTHOR]

Copyright of Dicle University Journal of Engineering / Dicle Üniversitesi Mühendislik Dergisi is the property of Dicle Universitesi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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