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Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation 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 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]

Copyright of HMD: Praxis der Wirtschaftsinformatik is the property of Springer Nature 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.)

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]

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]

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]

This study designs an automatic monitoring and alarm system for integrated meteorological observation based on the cloud platform to improve the automatic monitoring ability for integrated meteorological observation and the operation and maintenance management level of meteorological departments. A meteorological data collector was used to collect comprehensive meteorological observation data, transmit the collected comprehensive meteorological observation data to the main control processing module, preprocess the comprehensive meteorological observation data, and finally transmit the pre-processed comprehensive meteorological observation data to the cloud service module through the data transmission module. In the cloud service module, cloud computing and an improved K-means algorithm were used to analyze the comprehensive meteorological observation data, mine abnormal comprehensive meteorological observation data, and send alarm information to the application module. Users can view the comprehensive meteorological observation data in the cloud server on the client in real-time, thus realizing automatic monitoring and alarm for comprehensive meteorological observation. Results show that the average transmission rate of various meteorological comprehensive observation data transmitted by the system is as high as 99.48%, thereby achieving clustering of abnormal meteorological comprehensive observation data. Upon detecting abnormal meteorological data, the system sends alarm information to the mobile phone client to complete the monitoring of abnormal meteorological information. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The last few decades have emerged as a remarkable era for exploring and employing electromyography (EMG) signals and their attributes in various applications such as clinical assessment and rehabilitation engineering. An EMG signal-based system encapsulates different domains of signal acquisition and processing, statistical analysis, and control systems in a single framework. This survey attempts to highlight and distinguish the time- and frequency-based signal processing according to the applications of EMG signals. When EMG signals are used for clinical assessment, time–frequency analysis involves transforming the signals in different domains and extracting useful physiological information. On the other hand, the concept of time and frequency deals with extracting time, frequency, or time–frequency-based features when EMG signals are used for pattern recognition-based control applications such as robotics and augmented reality. It is often very difficult and confusing to distinguish and establish a clear understanding between these domains reported in various literature. Hence, this study first presents different signal acquisition systems and pre-processing techniques, followed by comprehending the concepts in time, frequency, and time–frequency-based approaches based on the applications. Next, the review of various post-processing techniques, different feature extraction routines, and a survey of different classifiers used in the pattern recognition step is done. The work concludes with a study of innovative applications of EMG signals reported in recent years, provides an overview of EMG signal-based limb prosthetics, and suggests a few futuristic research ideas. [ABSTRACT FROM AUTHOR]

The analysis workflow of Prompt gamma activation analysis (PGAA) at the Budapest Neutron Centre's PGAA and NIPS-NORMA facilities, at the MLZ FRM II PGAA station, and other centers worldwide relied on the use of the Hypermet-PC gamma spectrometry software and the ProSpeRo concentration calculation Excel macro. The sustained interest of our user community amid the reduced availability of multiple large-scale neutron sources worldwide called for more efficient utilization of the operational PGAA facilities. The present paper addresses both measurement and data evaluation bottlenecks of the analysis procedure to achieve higher productivity and superior spectroscopic performance. [ABSTRACT FROM AUTHOR]

The acquisition and analysis of high-quality sensor data constitute an essential requirement in shaping the development of fully autonomous driving systems. This process is indispensable for enhancing road safety and ensuring the effectiveness of the technological advancements in the automotive industry. This study introduces the Interaction of Autonomous and Manually Controlled Vehicles (IAMCV) dataset, a novel and extensive dataset focused on intervehicle interactions. The dataset, enriched with a sophisticated array of sensors such as lidar, cameras, inertial measurement unit/Global Positioning System, and vehicle bus data acquisition, provides a comprehensive representation of real-world driving scenarios that include roundabouts, intersections, country roads, and highways, recorded across diverse locations in Germany. Furthermore, the study shows the versatility of the IAMCV dataset through several proof-of-concept use cases. First, an unsupervised trajectory clustering algorithm illustrates the dataset’s capability in categorizing vehicle movements without the need for labeled training data. Second, we compare an online camera calibration method with the Robot Operating System-based standard, using images captured in the dataset. Finally, a preliminary test employing the YOLOv8 object-detection model is conducted, augmented by reflections on the transferability of object detection across various lidar resolutions. These use cases underscore the practical utility of the collected dataset, emphasizing its potential to advance research and innovation in the area of intelligent vehicles. [ABSTRACT FROM AUTHOR]

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.

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.

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.

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

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.

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.