Search

Fused deposition modeling (FDM), a method of additive manufacturing (AM), comprises the extrusion of materials via a nozzle and the subsequent combining of the layers to create 3D-printed objects. FDM is a widely used method for 3D-printing objects since it is affordable, effective, and easy to use. Some defects such as poor infill, elephant foot, layer shift, and poor surface finish arise in the FDM components at the printing stage due to variations in printing parameters such as printing speed, change in nozzle, or bed temperature. Proper fault classification is required to identify the cause of faulty products. In this work, the multi-sensory data are gathered using different sensors such as vibration, current, temperature, and sound sensors. The data acquisition is performed by using the National Instrumentation (NI) Data Acquisition System (DAQ) which provides the synchronous multi-sensory data for the model training. To induce the faults, the data are captured under different conditions such as variations in printing speed, temperate, and jerk during the printing. The collected data are used to train the machine learning (ML) and deep learning (DL) classification models to classify the variation in printing parameters. The ML models such as k-nearest neighbor (KNN), decision tree (DT), extra trees (ET), and random forest (RF) with convolutional neural network (CNN) as a DL model are used to classify the variable operation printing parameters. Out of the available models, in ML models, the RF classifier shows a classification accuracy of around 91% whereas, in the DL model, the CNN model shows good classification performance with accuracy ranging from 92 to 94% under variable operating conditions. [ABSTRACT FROM AUTHOR]

The present work intends to characterise the combination of two netted software defined radios (SDR) and different radio frequency (RF) front‐ends installed in them, together with a two‐stratum time dissemination and synchronisation system. A modified implementation of the Network Time Protocol is used as coarse event synchronisation for either SDR back‐end. The White rabbit light embedded node implementation of the commonly known white rabbit synchronisation system (IEEE 1588 PTP‐2019) or arbitrary wave form generators are used as fine time dissemination system for either SDR. The resulting netted transceiver system is intended to compose a demonstrator for proof‐of‐concept experiments. The findings presented in this article show that the chosen combination of hardware and software is suitable for radar applications operating within L‐, S‐ and C‐bands. A channel coherency throughout the network with a relative modified Allan deviation of less than 80 fs for averaging intervals of 1 s, a phase noise better than −118 dBc/Hz at 10 Hz frequency offset and a fractional frequency lower than ±2.5⋅10−13 $\pm 2.5\cdot 1{0}^{-13}$ was measured. Within a single transceiver node, a fractional frequency lower than ±2⋅10−13 $\pm 2\cdot 1{0}^{-13}$ and phase noise of −124 dBc/Hz at 10 Hz frequency offset were measured as well. Multistatic radar systems exploit wide spatial diversity to enhance target detection and tracking, albeit with increased complexity when compared to a monostatic configuration. To exploit these benefits, all participating transceiver nodes within the netted radar need to synchronise to a common time base t0 $\left({t}_{0}\right)$. The achieved synchronisation level increases the accuracy with which the chain of timed events at the transmitter and at the receiver side occur, intending to maximise the signal‐to‐noise ratio available at the receiver. The Universal software radio peripheral model X310 with two different daughterboard models as RF front‐end was used as SDR on either radar node. A network combining data and synchronisation purposes allowed the radar nodes under test to operate synchronously. The two‐stratum synchronisation system used glass fibres between 2 m and 5 km of length or coaxial cables with 2 m in length for network traffic, time and frequency dissemination purposes. The multiple RF front‐ends were stimulated by means of arbitrary waveform generators with calibrated traceability. Up‐chirp and sinusoid waveforms were used as stimuli for measuring the offset of either channel with regards of t0 ${t}_{0}$ to ultimately estimate the achievable coherency limits of the system under test. Both analogue and digital evaluation methods were considered. [ABSTRACT FROM AUTHOR]

As the important component of rocket engines, small pipeline systems have the characteristics of being lightweight and having structural complexity and obvious randomness in vibration effects. Mastering the dynamic characteristics of the pipeline systems is a key technical approach to improving the overall lifespan of engines. This study used a noncontact sound pressure sensor for dynamic parameter collection, reducing the experimental errors caused by the accelerometer's weight and signal line interference, etc., factors. The dynamic characteristic experimental and simulation study was conducted on several types of combined configuration small pipeline systems, including plane pipelines, space pipelines, support plates, and clamps. The errors of the top six modal results were all less than 20%, with a minimum error of 0.5%, proving the correctness and effectiveness of the dynamic characteristic experimental and simulation methods. The modified modeling and FEA methods were used to study the structural optimization of pipeline systems, which can provide engine pipeline systems reasonable guidance for natural frequency modulation and parameter sensitivity results analysis. [ABSTRACT FROM AUTHOR]

Wood plate bark removal processing is critical for ensuring the quality of wood processing and its products. To address the issue of lack of datasets available for the application of deep learning methods to this field, and to fill the research gap of deep learning methods in the application field of wood plate bark removal equipment, a benchmark for wood plate segmentation in bark removal processing is proposed in this study. Firstly, a costumed image acquisition device is designed and assembled on bark removal equipment to capture wood plate images in real industrial settings. After data filtering, enhancement, annotation, recording, and partitioning, a benchmark dataset named the WPS-dataset containing 4863 images was constructed. The WPS-dataset is evaluated by training six typical semantic segmentation models. The experimental results show that the models effectively learn and understand the WPS-dataset characteristics during training, resulting in high performance and accuracy in wood plate segmentation tasks. The WPS-dataset can lay a solid foundation for future research in bark removal processing and contribute to advancements in this field. [ABSTRACT FROM AUTHOR]

This study delves into the challenge of efficiently digitalising wind turbine maintenance data, traditionally hindered by non‐standardised formats necessitating manual, expert intervention. Highlighting the discrepancies in past reliability studies based on different key performance indicators (KPIs), the paper underscores the importance of consistent standards, like RDS‐PP, for maintenance data categorisation. Leveraging on established digitalisation workflows, we investigate the efficacy of text classifiers in automating the categorisation process against conventional manual labelling. Results indicate that while classifiers exhibit high performance for specific datasets, their general applicability across diverse wind farms is limited at the present stage. Furthermore, differences in failure rate KPIs derived from manual versus classifier‐processed data reveal uncertainties in both methods. The study suggests that enhanced clarity in maintenance reporting and refined designation systems can lead to more accurate KPIs. [ABSTRACT FROM AUTHOR]

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]

Human Activity Recognition (HAR) is a highly promising research area meant to automatically identify and interpret human behaviour using data received from sensors in various contexts. The potential uses of HAR are many, among them health care, sports coaching or monitoring the elderly or disabled. Nonetheless, there are numerous hurdles to be circumvented for HAR's precision and usefulness to be improved. One of the challenges is that there is no uniformity in data collection and annotation making it difficult to compare findings among different studies. Furthermore, more comprehensive datasets are necessary so as to include a wider range of human activities in different contexts while complex activities, which consist of multiple sub‐activities, are still a challenge for recognition systems. Researchers have proposed new frontiers such as multi‐modal sensor data fusion and deep learning approaches for enhancing HAR accuracy while addressing these issues. Also, we are seeing more non‐traditional applications such as robotics and virtual reality/augmented world going forward with their use cases of HAR. This article offers an extensive review on the recent advances in HAR and highlights the major challenges facing this field as well as future opportunities for further researches. [ABSTRACT FROM AUTHOR]

This paper measures the accelerator, brake pedal, clutch, transmission device and steering wheel under different driving conditions in real-time and accurately on the simulation experiment platform. The goal is to conduct human-machine-road system interaction in a virtual simulation of human-vehicle-road systems. Fitting test data establishes the mathematical model of traffic control parameters. In terms of hardware, the distributed architecture of upper and lower computers is utilized. The system communicates point-to-point with the host computer through the RS-232 serial port. The system adopts multi-thread technology and serial communication technology. The simulation system of driving operation is designed with the visual central controller. The system takes 89S52 as the core. The slave program is written in C language. Then, the system establishes a multi-target coordinated obstacle avoidance method based on a multi-sensor information vehicle cooperation collision avoidance method. The multi-vehicle cooperation obstacle avoidance problem is transformed into an optimal control problem under multiple constraints. Simulation analysis shows that the velocity and displacement obtained by the multi-robot collaborative collision avoidance method are in good agreement with the measured values. Compared with the time series algorithm, the output accuracy of the proposed collaborative collision avoidance algorithm is significantly reduced, and the changes in velocity and displacement in the time domain are more stable. [ABSTRACT FROM AUTHOR]

Abstract The present work intends to characterise the combination of two netted software defined radios (SDR) and different radio frequency (RF) front‐ends installed in them, together with a two‐stratum time dissemination and synchronisation system. A modified implementation of the Network Time Protocol is used as coarse event synchronisation for either SDR back‐end. The White rabbit light embedded node implementation of the commonly known white rabbit synchronisation system (IEEE 1588 PTP‐2019) or arbitrary wave form generators are used as fine time dissemination system for either SDR. The resulting netted transceiver system is intended to compose a demonstrator for proof‐of‐concept experiments. The findings presented in this article show that the chosen combination of hardware and software is suitable for radar applications operating within L‐, S‐ and C‐bands. A channel coherency throughout the network with a relative modified Allan deviation of less than 80 fs for averaging intervals of 1 s, a phase noise better than −118 dBc/Hz at 10 Hz frequency offset and a fractional frequency lower than ±2.5⋅10−13 was measured. Within a single transceiver node, a fractional frequency lower than ±2⋅10−13 and phase noise of −124 dBc/Hz at 10 Hz frequency offset were measured as well. Multistatic radar systems exploit wide spatial diversity to enhance target detection and tracking, albeit with increased complexity when compared to a monostatic configuration. To exploit these benefits, all participating transceiver nodes within the netted radar need to synchronise to a common time base t0. The achieved synchronisation level increases the accuracy with which the chain of timed events at the transmitter and at the receiver side occur, intending to maximise the signal‐to‐noise ratio available at the receiver. The Universal software radio peripheral model X310 with two different daughterboard models as RF front‐end was used as SDR on either radar node. A network combining data and synchronisation purposes allowed the radar nodes under test to operate synchronously. The two‐stratum synchronisation system used glass fibres between 2 m and 5 km of length or coaxial cables with 2 m in length for network traffic, time and frequency dissemination purposes. The multiple RF front‐ends were stimulated by means of arbitrary waveform generators with calibrated traceability. Up‐chirp and sinusoid waveforms were used as stimuli for measuring the offset of either channel with regards of t0 to ultimately estimate the achievable coherency limits of the system under test. Both analogue and digital evaluation methods were considered.

ObjectiveThis study aims to simulate the actual forces applied to drill rods during the borehole drilling for gas drainage from underground coal mines to satisfy the requirements of drill rods’ fatigue tests. MethodsThis study designed a dynamic loading system for full-size drill rods in coal mines. This system consists of a drill rod loading platform, a hydraulic pump unit with power recovery, and a measurement and control system, allowing for the composite loading of dynamic torque, axial force, and radial displacement on drill rods of different specifications. In this system, torque is loaded to drill rods using the hydraulic power recovery method. Specifically, two identical hydraulic motors, acting as loads mutually, are mechanically connected to the tested drill rod. The active motor drives the drill rod and the loading motor to rotate, while the loading motor returns the output flow to the active motor, thus achieving torque loading and power recovery. The energy loss of the closed-loop system is compensated by two hydraulic compensating pumps. The loading of axial force and radial displacement is controlled based on the electro-hydraulic proportion. Based on various variables collected by sensors, the programmable logic controller (PLC) performs closed-loop adjustment of the force and displacement of the loading cylinder to achieve precise control. During tests, various operating parameters and power recovery efficiency are acquired using the measurement and control system and then displayed in the upper computer in real time. The loading capacity of the dynamic loading system was verified using a round drill rod measuring ø127 mm×3 000 mm. Results and ConclusionThe results indicate that the torque and rotational speed of the dynamic loading system can be adjusted by changing the flow or pressure of hydraulic compensating pumps, the displacement relationship between the active and loading motors, and the gearbox gear, thereby meeting various operation conditions of the tests. In the case of the same displacement between the active and loading motors, the power recovery efficiency can reach the theoretical maximum (77%). Under the operating condition of a low rotational speed combined with a high torque, the measured torque loaded to the drill rod reached the maximum (35080 N∙m), corresponding to a rotational speed of 28 r/min, motor power of 50.7 kW, and power recovery efficiency of 66.2%. This suggests an effective reduction of the installed power of the dynamic loading system. Overall, the proposed dynamic loading system for drill rods in coal mines overcomes the limitations of existing devices for fatigue tests, such as insufficient loading capacity and high energy consumption, providing strong equipment support for drill rod tests and being of great application value.

Abstract The Internet of Things (IoT) consist of a network of interconnected nodes constantly communicating, exchanging, and transferring data over various network protocols. Intrusion detection systems using deep learning are a common method used for providing security in IoT. However, traditional deep learning IDS systems do not accurately classify the attack and also require high computation time. Thus, to solve this issue, herein, we propose an advance Intrusion detection framework using Self-Attention Progressive Generative Adversarial Network (SAPGAN) framework for detecting security threats in IoT networks. In our proposed framework, at first, the IoT data are gathered. Then, the data are fed to pre-processing. In pre-processing, it restored the missing value using Local least squares. Then the preprocessing output is fed to feature selection. At feature selection, the optimum features are compiled using a modified War Strategy Optimization Algorithm (WSOA). Based upon the optimum features, the intruders were categorized into two categories named Anomaly and Normal using the proposed framework. Numerous attacks are assembled, including camera-based flood, DDoS, RTSP brute force, etc. We have compared our proposed framework using state of the art model and efficiency of 23.19%, 27.55%, and 18.35% higher accuracy and 14.46%, 26.76%, and 13.65% lower computational time compared to traditional models.

Abstract Portable data acquisition devices, such as the National Instrument (NI) myDAQ, allow users to analyze electronic circuits without advanced measurement systems, making them invaluable in educational settings. Combined with NI Laboratory Virtual Instrument Engineering Workbench (LabVIEW) on a personal computer, the NI myDAQ facilitates practical experiments and analyses anytime, anywhere. However, these devices can malfunction due to misuse or operation beyond their design limitations. This research presents the design, operation, and implementation of an energy-efficient test bench for the control and measurement functionalities of the NI myDAQ, aimed at detecting device failures through a specialized hardware and software setup using the loopback method. The test bench efficiently verifies all input and output ports, providing fast and accurate fault detection, and generates detailed performance reports to aid in long-term monitoring. By automating fault detection and reducing manual intervention, this setup ensures reliable operation, making it an ideal, resource-efficient solution for educational environments where practical, portable systems are essential for hands-on learning.

Abstract This study delves into the challenge of efficiently digitalising wind turbine maintenance data, traditionally hindered by non‐standardised formats necessitating manual, expert intervention. Highlighting the discrepancies in past reliability studies based on different key performance indicators (KPIs), the paper underscores the importance of consistent standards, like RDS‐PP, for maintenance data categorisation. Leveraging on established digitalisation workflows, we investigate the efficacy of text classifiers in automating the categorisation process against conventional manual labelling. Results indicate that while classifiers exhibit high performance for specific datasets, their general applicability across diverse wind farms is limited at the present stage. Furthermore, differences in failure rate KPIs derived from manual versus classifier‐processed data reveal uncertainties in both methods. The study suggests that enhanced clarity in maintenance reporting and refined designation systems can lead to more accurate KPIs.

Fused deposition modeling (FDM), a method of additive manufacturing (AM), comprises the extrusion of materials via a nozzle and the subsequent combining of the layers to create 3D-printed objects. FDM is a widely used method for 3D-printing objects since it is affordable, effective, and easy to use. Some defects such as poor infill, elephant foot, layer shift, and poor surface finish arise in the FDM components at the printing stage due to variations in printing parameters such as printing speed, change in nozzle, or bed temperature. Proper fault classification is required to identify the cause of faulty products. In this work, the multi-sensory data are gathered using different sensors such as vibration, current, temperature, and sound sensors. The data acquisition is performed by using the National Instrumentation (NI) Data Acquisition System (DAQ) which provides the synchronous multi-sensory data for the model training. To induce the faults, the data are captured under different conditions such as variations in printing speed, temperate, and jerk during the printing. The collected data are used to train the machine learning (ML) and deep learning (DL) classification models to classify the variation in printing parameters. The ML models such as k-nearest neighbor (KNN), decision tree (DT), extra trees (ET), and random forest (RF) with convolutional neural network (CNN) as a DL model are used to classify the variable operation printing parameters. Out of the available models, in ML models, the RF classifier shows a classification accuracy of around 91% whereas, in the DL model, the CNN model shows good classification performance with accuracy ranging from 92 to 94% under variable operating conditions.

Abstract The Internet of Things (IoT) is the recent technology intended to facilitate the daily life of humans by providing the power to connect, control and automate objects in the physical world. In this logic, the IoT helps to improve our way of producing and working in various areas (e.g. agriculture, industry, healthcare, transportation etc). Basically, an IoT network comprises physical devices, equipped with sensors and transmitters, that are interconnected with each other and/or connected to the Internet. Its main objective is to gather and transmit data to a storage system such as a server or cloud to enable processing and analysis, ultimately facilitating rapid decision‐making or enhancements to the user experience. In the realm of Connected Objects, an effective IoT data collection system plays a vital role by providing several benefits, such as real‐time data monitoring, enhanced decision‐making, increased operational efficiency etc. However, because of the resource limitations linked to connected objects, such as low memory and battery, or even single‐use devices etc. IoT data collecting presents several challenges including scalability, security, interoperability, flexibility etc. for both researchers and companies. The authors categorise current IoT data collection techniques and perform a comparative evaluation of these methods based on the topics analysed and elaborated by the authors. In addition, a comprehensive analysis of recent advances in IoT data collection is provided, highlighting different data types and sources, transmission protocols from connected sensors to a storage platform (server or cloud), the IoT data collection framework, and principles for streamlining the collection process. Finally, the most important research questions and future prospects for the effective collection of IoT data are summarised.

This paper presents a system and model for data acquisition and augmentation in OLED panel defect detection to improve detection efficiency. It addresses the challenges of data scarcity, data acquisition difficulties, and classification of different defect types. The proposed system acquires a hypothetical base dataset and employs an image generation model for data augmentation. While image generation models have been instrumental in overcoming data scarcity, time and cost constraints in various fields, they still pose limitations in generating images with regular patterns and detecting defects within such data. Even when datasets are available, the precise definition and classification of different defect types becomes imperative. In this paper, we investigate the feasibility of using an image generation model to generate pattern images for OLED panel defect detection and apply it for data augmentation. In addition, we introduce an OLED panel defect data acquisition system, improve the limitations of data augmentation, and address the challenges of defect detection data augmentation using image generation models.

We analyze a remote sensing system in the Internet of things, where uncoordinated nodes send status updates to a common receiver to achieve information freshness, quantified through age of information. We consider a finite horizon scheduling over a random multiple access channel, where colliding messages are lost. We show that nodes must adopt a further randomization to deviate from identical schedules and escape collision deadlocks. Moreover, we discuss the impact of feedback availability if, due to, e.g., energy expenditure, it decreases the number of transmission opportunities.

Surface quality, machining efficiency, and tool life are all significantly impacted by tool wear in metal cutting machining. Research priorities and areas of focus in tool wear are shifting as intelligent machining becomes the norm. Unfortunately, there are currently no acknowledged most effective ways for analyzing tool based on the energy signals specially in the machining of titanium and its alloys. In the present work, the titanium machining was performed under different lubrication conditions such as dry, minimum quantity lubrication (MQL), liquid nitrogen and hybrid, etc. Then, the spectrograms are used to transform the acquired energy data into time–frequency features. Starting with a set of randomly generated hyper parameters (HPs), the long short-term memory (LSTM) model is fine-tuned using sine cosine algorithm (SCA) with loss serving as the fitness function. The confusion matrix provides additional validation of the 98.08% classification accuracy. Additional evaluations of the suggested method's superiority include its specificity, sensitivity, F1-score, and area under the curve (AUC). [ABSTRACT FROM AUTHOR]

Real and virtual instruments for data acquisition and signal processing are widely used in industry, entertainment and education. In this paper I compared different hardware and software solutions in instrumentation, and I analyzed these technics in a particular case: generation and processing of audio signals using additive and FM synthesis. The comparative study reveals the advantages and the disadvantages of the real and virtual instrumentation especially in academic education. [ABSTRACT FROM AUTHOR]

The notion of IoT, also known as the Internet of Things, has been there for many years, but recent technical progress has elevated its prominence. The increasing need for creative ideas, approaches, and technology is driven by the fast evolution of our world today. The construction sector has enthusiastically adopted the IoT as an advanced technology to develop intelligent structures, enhance resource efficiency, and manage construction expenses. One of the main benefits of incorporating IoT into construction operations is the notable advantage of being able to gather and analyse real-time data. The use of IoT technologies in the construction industry can greatly augment productivity and efficiency. This review article examines the IoT and its applications in different fields of the civil engineering and construction industry. It investigates the advantages of IoT in this sector and explores how it is implemented. [ABSTRACT FROM AUTHOR]

Customer data acquisition is an important task in data-driven business analytics. Recently, there has been a growing interest in the effective use of an organization's internal customer data, also known as first-party data. This work studies the acquisition of new data for business analytics based on first-party data resource. We address issues related to both acquisition cost and data quality. To reduce acquisition cost, we consider using auction-based methods, such as the generalized second price (GSP) auction, for acquiring data with differential prices for different customers. We find that the GSP-based data acquisition method incurs a lower cost and/or achieves a higher response rate than fixed price methods. To maximize data quality, we propose novel optimization models for different data acquisition methods and data quality measures. The proposed models maximize the quality of the acquired data while satisfying budget constraints. We derive and discuss the solutions to the optimization models analytically and provide managerial insights from the solutions. The proposed approach is effective in increasing customer responses, reducing selection bias, and enabling more accurate estimation and prediction for business analytics. The results of the experimental evaluation demonstrate the advantage of the proposed approach over existing data acquisition methods. History: Accepted by Ram Ramesh, Area Editor for Data Science and Machine Learning. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0037) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2022.0037). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

The Simultaneous Localization and Mapping (SLAM) scanner is an easy and portable Light Detection and Ranging (LiDAR) data acquisition device. Its main output is a 3D point cloud covering the scanned scene. Regarding the importance of accuracy in the survey domain, this paper aims to assess the accuracy of two SLAM scanners: the NavVis VLX and the BLK2GO scanner. This assessment is conducted for both outdoor and indoor environments. In this context, two types of reference data were used: the total station (TS) and the static scanner Z+F Imager 5016. To carry out the assessment, four comparisons were tested: cloud-to-cloud, cloud-to-mesh, mesh-to-mesh, and edge detection board assessment. However, the results of the assessments confirmed that the accuracy of indoor SLAM scanner measurements (5 mm) was greater than that of outdoor ones (between 10 mm and 60 mm). Moreover, the comparison of cloud-to-cloud provided the best accuracy regarding direct accuracy measurement without manipulations. Finally, based on the high accuracy, scanning speed, flexibility, and the accuracy differences between tested cases, it was confirmed that SLAM scanners are effective tools for data acquisition. [ABSTRACT FROM AUTHOR]

Considering the disadvantages of traditional railway operation and maintenance manual inspection, including low efficiency, high cost, and detection blind spots, to solve the problems faced by drones in data acquisition, data processing and application, this paper innovatively proposed a route planning algorithm with a safe buffer zone. The algorithm adopted a partition operation strategy, and used two methods of orthography and side-view shooting to effectively improve the operation efficiency and the security of data acquisition. In addition, based on the data of UAV inspection aerial photography project, this paper formed a typical disease sample library in railway operation and maintenance environment through relevant processing, and used YOLOv7 target detection algorithm to realize the rapid automatic detection of three typical diseases of color steel tile, dustproof net and plastic film. Combined with human-computer interaction, the intelligent extraction and rapid filing of external environmental hazards were realized, and a set of intelligent monitoring technology scheme of railway operation and maintenance environment based on UAV platform was formed. The results show that, the effective combination of UAV technology, target detection technology and computer technology can realize rapid automatic hidden danger identification, which can improve the efficiency of railway operation and maintenance inspection by 150%, and provide important technical support for railway safety management. [ABSTRACT FROM AUTHOR]

In the present digital environment, a data-driven organizational culture has become a vital emerging driver of organizational growth. This data-driven culture has assumed an advanced shape due to adoption of artificial intelligence (AI) integrated business analytics tools in the organization. Data-driven culture in the organization could considerably impact product innovation strategy as well as organizational process alteration. In this context, the aim of this study is to investigate how an organization's data-driven culture impacts process performance and product innovation that led to enhanced organizational overall performance and higher business value. Methodologically, supported by relevant extant literature and inputs from the resource-based view and dynamic capability theories (organizational context), a conceptual model and a set of hypotheses are initially developed. These are subsequently statistically validated through a survey involving 513 usable responses from employees of different organizations using business analytics tools embedded with AI capability. The findings demonstrate that an organizational data-driven culture has considerable moderating impact on product innovation and process improvement, which ultimately enhance business value through improved organizational overall performance. [ABSTRACT FROM AUTHOR]

Power quality measurements are essential to monitor, analyse and control the operation of smart grids within power systems. This work aims to develop and calibrate a PQ network analyser. As the penetration of non-linear loads connected to power systems is increasing every day, it is essential to measure power quality. In this sense, a power quality (PQ) analyser is based on the high-speed sampling of electrical signals in single-phase and three-phase electrical installations, which are available in real time for analysis using wireless Wi-Fi (Wireless-Fidelity) networks. The PQAE (Power Quality Analyser Embedded) power quality analyser has met the calibration standards for Class A devices from IEC 61000-4-30, IEC 61000-4-7 and IEC 62586-2. In this paper, a complete guide to the tests included in this standard has been provided. The Fast Fourier Transform (FFT) obtains the harmonic components from the measured signals and the window functions used reduce spectral leakage. The window size depends on the fundamental frequency of, intensity of and changes in the signal. Harmonic measurements from the 2nd to 50th harmonics for each phase of the voltage and each phase and neutral of the current have been performed, using the Fast Fourier transform algorithm with various window functions and their comparisons. PQAE is developed on an open-source platform that allows you to adapt its programming to the measurement needs of the users. [ABSTRACT FROM AUTHOR]

This paper presents an energy-efficient and high-accuracy sampling synchronization approach for real-time synchronous data acquisition in wireless sensor networks (saWSNs). A proprietary protocol based on time-division multiple access (TDMA) and deep energy-efficient coding in sensor firmware is proposed. A real saWSN model based on 2.4 GHz nRF52832 system-on-chip (SoC) sensors was designed and experimentally tested. The obtained results confirmed significant improvements in data synchronization accuracy (even by several times) and power consumption (even by a hundred times) compared to other recently reported studies. The results demonstrated a sampling synchronization accuracy of 0.8 μs and ultra-low power consumption of 15 μW per 1 kb/s throughput for data. The protocol was well designed, stable, and importantly, lightweight. The complexity and computational performance of the proposed scheme were small. The CPU load for the proposed solution was <2% for a sampling event handler below 200 Hz. Furthermore, the transmission reliability was high with a packet error rate (PER) not exceeding 0.18% for TXPWR ≥ −4 dBm and 0.03% for TXPWR ≥ 3 dBm. The efficiency of the proposed protocol was compared with other solutions presented in the manuscript. While the number of new proposals is large, the technical advantage of our solution is significant. [ABSTRACT FROM AUTHOR]

A four‐degree‐of‐freedom upper limb exoskeleton rehabilitation robot system with a gravity compensation device is constructed. The objective is to address the rehabilitation training needs of patients with upper limb motor dysfunction. A BP neural network adaptive control method based on particle swarm optimization is proposed. First, the degrees of freedom of the human body are analyzed, and a Lagrange method is employed to construct a dynamic model. Second, a particle swarm optimization back propagation neural network adaptive control algorithm based on particle swarm optimization is presented. Subsequently, the range of motion of the upper limbs is analyzed with reference to muscle anatomy and a three‐dimensional motion capture system. And the robot structure design is analyzed in detail. Finally, simulation experiments were conducted, and the results demonstrated that the proposed method exhibited high effectiveness and accuracy. [ABSTRACT FROM AUTHOR]

In a drone system with dual data links, this article presents a redundant data processing algorithm that can minimize flight control instability without increasing the weight and price of the aircraft by software processing of duplicate received messages. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is the recent technology intended to facilitate the daily life of humans by providing the power to connect, control and automate objects in the physical world. In this logic, the IoT helps to improve our way of producing and working in various areas (e.g. agriculture, industry, healthcare, transportation etc). Basically, an IoT network comprises physical devices, equipped with sensors and transmitters, that are interconnected with each other and/or connected to the Internet. Its main objective is to gather and transmit data to a storage system such as a server or cloud to enable processing and analysis, ultimately facilitating rapid decision‐making or enhancements to the user experience. In the realm of Connected Objects, an effective IoT data collection system plays a vital role by providing several benefits, such as real‐time data monitoring, enhanced decision‐making, increased operational efficiency etc. However, because of the resource limitations linked to connected objects, such as low memory and battery, or even single‐use devices etc. IoT data collecting presents several challenges including scalability, security, interoperability, flexibility etc. for both researchers and companies. The authors categorise current IoT data collection techniques and perform a comparative evaluation of these methods based on the topics analysed and elaborated by the authors. In addition, a comprehensive analysis of recent advances in IoT data collection is provided, highlighting different data types and sources, transmission protocols from connected sensors to a storage platform (server or cloud), the IoT data collection framework, and principles for streamlining the collection process. Finally, the most important research questions and future prospects for the effective collection of IoT data are summarised. [ABSTRACT FROM AUTHOR]

Sufficient survey data are required to describe the stochastic behaviors of live loads. However, due to manual and on-site operation required by traditional survey methods, traditional surveys face challenges like occupant resistance, high costs, and long implementation periods. This study proposes a new survey method to access live load data online and automatically. Required samples are acquired from multi-source, open-access and dynamically updated data on the Internet. The change intervals, geometrical dimensions and object quantities are obtained from transaction information, building attributes and virtual reality models on real estate websites, respectively. The object weights are collected from commodity information on e-commerce websites. The integration of the aforementioned data allows for the extraction of necessary statistics to describe a live load process. The proposed method is applied to a live load survey in China, covering 20040 m2, with around 90000 samples acquired for object weights and load changes. The survey results reveal that about 70%–80% of the amplitude statistics are attributable to 1/6 of the total object types. [ABSTRACT FROM AUTHOR]

Ion mobility spectrometers (IMS) are used in a wide variety of applications, including trace gas detection in safety and security applications, but also in more analytical applications, e.g., in medicine or food quality monitoring. Consequently, IMS are often coupled with other separation techniques and laboratory equipment, requiring synchronization between the external equipment and the IMS electronics. In addition, IMS and the associated electronics are becoming increasingly complex due to ongoing instrumental developments. In this work, we present an open source data acquisition hardware tailored to the requirements of advanced IMS, but also applicable to other applications. The data acquisition hardware provides trigger pulses for synchronized operation of the IMS ion gate or external devices. In addition, the data acquisition hardware allows for parallel digitalization using two isolated 16-bit analog-to-digital converters (ADC) with up to 250 kilosamples per second. The galvanically isolated trigger input ensures a synchronized start of the IMS measurements, particularly when connecting external instrumentation such as a gas chromatograph. Furthermore, due to the isolated ADCs, the hardware allows great flexibility in defining the ground potential of the instrument setup.

Abstract Spread spectrum technology has been widely employed for positioning and communicating with autonomous underwater vehicles (AUVs), but conventional spread spectrum sequences lack confidentiality and reliability in UWA channel. Considering the limitations of conventional sequences and the characteristics of underwater acoustic (UWA) channel, a composite chaotic orthogonal sequence (CCOS) based on the UWA channel is proposed. The confidentiality of the CCOS is superior to that of the m‐sequence, while the autocorrelation performance of the CCOS is superior to that of the orthogonal sequence. Moreover, the CCOS can compensate for the imbalance of logistic chaotic sequence when assigned certain initial values. Acquisition is a crucial component of accessing the spread spectrum signal; therefore, the acquisition performance indicates the applicability of the CCOS. The source–target model is established to simulate communication with an underwater moving target. To simulate the acquisition process, a parallel algorithm based on fast Fourier transform is adopted, and the entire simulation process is completed based on the BELLHOP ray acoustic model. Through data processing, the Doppler shift error is less than half of the frequency‐search element. Furthermore, the acquisition probabilities of the CCOS with different numbers of bits are over 90%, which demonstrates the reliability of the CCOS.

Aim: Exploration and developing mechanisms of advanced data acquisition necessary for training an artificial intelligence model capable of effectively detecting areas with increased susceptibility to fire situations. The study focuses on utilizing data from satellite missions and ground-based sensors, which provide both high-resolution imagery and precise data on temperature, humidity, and other environmental factors. By analysing these diverse data sources, the research aims to create a comprehensive and efficient model capable of early detection of potential fire hazards, which is crucial for prevention for fire-prone situations. Project and methods: It centres on a project that aims to enhance fire detection and management through the integration of artificial intelligence with data acquired from satellite systems and internet of things devices. The methodologies employed in this project involve a combination of advanced data acquisition, machine learning techniques, and the synthesis of diverse environmental data to train artificial intelligence models that can predict and detect fire incidents more effectively. Results: Significant advancements in fire detection and management have been demonstrated through the integration of artificial intelligence (AI) with satellite data and IoT: 1. Enhanced monitoring capabilities the use of satellite data systems enabled real-time monitoring of thermal anomalies and vegetation health, crucial for early detection and effective monitoring of wildfires. This real-time capability allowed for quicker responses and more informed decision-making in firefighting efforts. 2. Effective integration of data sources: the integration of satellite and surface data proved to be effective in enhancing the predictive capabilities of the fire management systems. This comprehensive approach allowed for a better understanding of fire dynamics and contributed to more accurate and timely predictions. Conclusions: It could be emphasize the significant benefits and future potential of integrating artificial intelligence with satellite and internet of things data for improving fire detection and management. The integration of satellite imagery and internet of things sensor data is essential for enhancing the predictive accuracy of artificial intelligence systems. This integration allows for a comprehensive assessment of fire risks, providing actionable intelligence that is critical for prevention for fire-prone situations. These conclusions underscore the transformative potential of artificial intelligence in enhancing fire management systems. Keywords: data acquisition, artificial intelligence, IoT, satellite data systems, fire management systems

The Intelligent Nursing Assistant Robot (INA) emerged as an innovative solution with COVID straining the healthcare systems and over 180 million cases globally by April 2021. INA protects overworked and exposed staff by remotely monitoring patients in quarantine. This robot boasts features like a thermal camera for precise temperature readings and a high-definition camera for visual checks. Facial recognition aids in patient identification, while its mobile control via an Android app and Pixy camera with Arduino connection offers flexibility. Built with durable Cagebot materials, INA provides a safe and versatile tool for overwhelmed healthcare professionals during this critical time.

Measure while drilling (MWD) refers to the acquisition of real-time data associated with the drilling process, including information related to the geological characteristics encountered in hard-rock mining. The availability of large quantities of low-cost MWD data from blast holes compared to expensive and sparsely collected orebody knowledge (OBK) data from exploration drill holes make the former more desirable for characterizing pre-excavation subsurface conditions. Machine learning (ML) plays a critical role in the real-time or near-real-time analysis of MWD data to enable timely enhancement of OBK for operational purposes. Applications can be categorized into three areas, focused on the mechanical properties of the rock mass, the lithology of the rock, as well as, related to that, the estimation of the geochemical species in the rock mass. From a review of the open literature, the following can be concluded: (i) The most important MWD metrics are the rate of penetration (rop), torque (tor), weight on bit (wob), bit air pressure (bap), and drill rotation speed (rpm). (ii) Multilayer perceptron analysis has mostly been used, followed by Gaussian processes and other methods, mainly to identify rock types. (iii) Recent advances in deep learning methods designed to deal with unstructured data, such as borehole images and vibrational signals, have not yet been fully exploited, although this is an emerging trend. (iv) Significant recent developments in explainable artificial intelligence could also be used to better advantage in understanding the association between MWD metrics and the mechanical and geochemical structure and properties of drilled rock.

The standardized workflow of computer-aided static guided implant surgery includes preoperative examination, data acquisition, guide design, guide fabrication and surgery. Errors may occur at each step, leading to irreversible cumulative effects and thus impacting the accuracy of implant placement. However, clinicians tend to focus on factors causing errors in surgical operations, ignoring the possibility of irreversible errors in nonstandard guided surgery. Based on the clinical practice of domestic experts and research progress at home and abroad, this paper summarizes the sources of errors in guided implant surgery from the perspectives of preoperative inspection, data collection, guide designing and manufacturing and describes strategies to resolve errors so as to gain expert consensus. Consensus recommendation: 1. Preoperative considerations: the appropriate implant guide type should be selected according to the patient's oral condition before surgery, and a retaining screw-assisted support guide should be selected if necessary. 2. Data acquisition should be standardized as much as possible, including beam CT and extraoral scanning. CBCT performed with the patient’s head fixed and with a small field of view is recommended. For patients with metal prostheses inside the mouth, a registration marker guide should be used, and the ambient temperature and light of the external oral scanner should be reasonably controlled. 3. Optimization of computer-aided design: it is recommended to select a handle-guided planting system and a closed metal sleeve and to register images by overlapping markers. Properly designing the retaining screws, extending the support structure of the guide plate and increasing the length of the guide section are methods to feasibly reduce the incidence of surgical errors. 4. Improving computer-aided production: it is also crucial to set the best printing parameters according to different printing technologies and to choose the most appropriate postprocessing procedures.

[Introduction] Plasma disruption poses a significant threat to the tokamak nuclear device during its running and can cause damage to the device. Such damage can be reduced by adopting the disruption mitigation system, which has an action time highly dependent on the real-time running plasma disruption prediction system for predicting the plasma disruption moment. The deep-learning-based neural network has been used to train plasma disruption prediction models, and the real-time running of the deep-learning-based disruption prediction models requires a huge amount of real-time data from multiple diagnostics. [Method] The article proposed a design scheme for a real-time data acquisition system. The real-time data acquisition and transmission system was designed based on the modular structure and divided into the multiple channels acquisition module, ADC converting control and data reading module, data grouping and packing module and data transmission network module. The data transmission network module was developed on the hardware UDP network stack running on the FPGA at a speed of 10 G. This hardware UDP network stack featured a deterministic data transmission process, enabling a very low transmission latency of the system. [Result] The real-time data acquisition system has a sampling rate reaching 2 MSa/s, a data throughput rate exceeding 9.3 Gb/s, and a data transmission latency of less than 10 μs. [Conclusion] This data acquisition system facilitates the fast transmission of diagnostic data streams to disruption prediction models. The high sampling rates enable the system to perform real-time transmission of one-dimensional diagnostics such as radiation and electron temperature, improving the temporal resolution of data. The high data throughput rate can increase the transmission volume of diagnostic data, and the low data transmission latency can reduce the time required for disruption prediction models to obtain diagnostics data.

The demonstration of compliance of rolling stock against disturbance limits for railway signaling, and in particular track circuits, is subject to a large deal of variability, caused by the diverse values of the electrical parameters of the railway line and resulting transfer functions, as well as the operating conditions of the rolling stock during tests. Instrumental uncertainty is evaluated with a type B approach and shown to be much less than the experimental variability. Repeated test runs in acceleration, coasting, cruising, and braking conditions are considered, deriving both max-hold (spread) and sample (or experimental) standard deviation curves compared to the respective mean values (type A approach to the evaluation of uncertainty, as defined in of the Guide to the Uncertainty in Measurement. The major source of variability affecting a significant portion of the spectrum is caused by the superposed oscillations of the onboard LC filter, for which different choices of the transformation window duration are discussed. The test runs and the acquired data covered, overall, 1 day of tests along about 300 km of the Italian 3 kV DC railway network. [ABSTRACT FROM AUTHOR]

The article discusses the importance of digital images as research data in social sciences and humanities, highlighting the gap in understanding interactions with image data compared to textual materials. The ImAccess project at Tampere University aims to investigate the use of images as research data, focusing on historical photograph archives and contemporary image data from social media platforms. The project explores user practices, barriers, desires for image collections, and the development of tools for visual image searching. The findings emphasize the need for support, collaborations, and metadata enhancement to improve the accessibility and use of image data in research. [Extracted from the article]

Compressive sensing (CS) is recognized for its adeptness at compressing signals, making it a pivotal technology in the context of sensor data acquisition. With the proliferation of image data in Internet of Things (IoT) systems, CS is expected to reduce the transmission cost of signals captured by various sensor devices. However, the quality of CS-reconstructed signals inevitably degrades as the sampling rate decreases, which poses a challenge in terms of the inference accuracy in downstream computer vision (CV) tasks. This limitation imposes an obstacle to the real-world application of existing CS techniques, especially for reducing transmission costs in sensor-rich environments. In response to this challenge, this paper contributes a CV-oriented adaptive CS framework based on saliency detection to the field of sensing technology that enables sensor systems to intelligently prioritize and transmit the most relevant data. Unlike existing CS techniques, the proposal prioritizes the accuracy of reconstructed images for CV purposes, not only for visual quality. The primary objective of this proposal is to enhance the preservation of information critical for CV tasks while optimizing the utilization of sensor data. This work conducts experiments on various realistic scenario datasets collected by real sensor devices. Experimental results demonstrate superior performance compared to existing CS sampling techniques across the STL10, Intel, and Imagenette datasets for classification and KITTI for object detection. Compared with the baseline uniform sampling technique, the average classification accuracy shows a maximum improvement of 26.23%, 11.69%, and 18.25%, respectively, at specific sampling rates. In addition, even at very low sampling rates, the proposal is demonstrated to be robust in terms of classification and detection as compared to state-of-the-art CS techniques. This ensures essential information for CV tasks is retained, improving the efficacy of sensor-based data acquisition systems. [ABSTRACT FROM AUTHOR]