Data Acquisition Cycle Research Articles

A Zero-Shot Learning Approach for Blockage Detection and Identification Based on the Stacking Ensemble Model.

A data-driven approach to defect identification requires many labeled samples for model training. Yet new defects tend to appear during data acquisition cycles, which can lead to a lack of labeled samples of these new defects. Aiming at solving this problem, we proposed a zero-shot pipeline blockage detection and identification method based on stacking ensemble learning. The experimental signals were first decomposed using variational modal decomposition (VMD), and then, the information entropy was calculated for each intrinsic modal function (IMF) component to construct the feature sets. Second, the attribute matrix was established according to the attribute descriptions of the defect categories, and the stacking ensemble attribute learner was used for the attribute learning of defect features. Finally, defect identification was accomplished by comparing the similarity within the attribute matrices. The experimental results show that target defects can be identified even without targeted training samples. The model showed better classification performance on the six sets of experimental data, and the average recognition accuracy of the model for unknown defect categories reached 72.5%.

A stable diffusion enhanced YOLOV5 model for metal stamped part defect detection based on improved network structure

Machine-vision-based defect detection for large metal stamping is a fundamental requirement for improving product quality and inspection speed. However, the performance of machine vision in detecting defects is limited by the large variety of stamped products, significant dimensional differences, and long data-acquisition cycles. To overcome these problems, metal stamping detection has been achieved using the improved YOLOV5 model, which features a slim neck and a multiheaded self-attentive mechanism for detecting wrinkles, holes, and cracks in metal stamping. An image-processing module was used to reduce the impact of metal reflections and improve image quality. Stable diffusion improvement was used to augment the dataset to overcome the small dataset size problem and enhance its generalisation capability. In addition, a BotNet structure with a self-attentive mechanism was introduced into the YOLOV5 model backbone to improve the image feature extraction capability. We then optimised the prediction head structure of YOLOV5 to improve the detection speed and accuracy. Ablation experiments were performed to analyse and verify the effectiveness of each module. The results of the ablation experiments show that the mAP of our proposed stable diffusion improvement data enhancement method, and the YOLO-Bot-VOV algorithm, for metal stamped part defect detection reached 98.2 %, and the parameters were reduced by 0.432 million.

Developing an ethical framework-guided instrument for assessing bias in EHR-based Big Data studies: a research protocol

IntroductionThe emergence of Big Data health research has exponentially advanced the fields of medicine and public health but has also faced many ethical challenges. One of most worrying but still...

Improved Label-Free Quantification of Intact Proteoforms Using Field Asymmetric Ion Mobility Spectrometry.

The high-throughput quantification of intact proteoforms using a label-free approach is typically performed on proteins in the 0-30 kDa mass range extracted from whole cell or tissue lysates. Unfortunately, even when high-resolution separation of proteoforms is achieved by either high-performance liquid chromatography or capillary electrophoresis, the number of proteoforms that can be identified and quantified is inevitably limited by the inherent sample complexity. Here, we benchmark label-free quantification of proteoforms of Escherichia coli by applying gas-phase fractionation (GPF) via field asymmetric ion mobility spectrometry (FAIMS). Recent advances in Orbitrap instrumentation have enabled the acquisition of high-quality intact and fragmentation mass spectra without the need for averaging time-domain transients prior to Fourier transform. The resulting speed improvements allowed for the application of multiple FAIMS compensation voltages in the same liquid chromatography-tandem mass spectrometry experiment without increasing the overall data acquisition cycle. As a result, the application of FAIMS to label-free quantification based on intact mass spectra substantially increases the number of both identified and quantified proteoforms without penalizing quantification accuracy in comparison to traditional label-free experiments that do not adopt GPF.

A Distributed Big Data Analytics Architecture for Vehicle Sensor Data.

The unceasingly increasing needs for data acquisition, storage and analysis in transportation systems have led to the adoption of new technologies and methods in order to provide efficient and reliable solutions. Both highways and vehicles, nowadays, host a vast variety of sensors collecting different types of highly fluctuating data such as speed, acceleration, direction, and so on. From the vast volume and variety of these data emerges the need for the employment of big data techniques and analytics in the context of state-of-the-art intelligent transportation systems (ITS). Moreover, the scalability needs of fleet and traffic management systems point to the direction of designing and deploying distributed architecture solutions that can be expanded in order to avoid technological and/or technical entrapments. Based on the needs and gaps detected in the literature as well as the available technologies for data gathering, storage and analysis for ITS, the aim of this study is to provide a distributed architecture platform to address these deficiencies. The architectural design of the system proposed, engages big data frameworks and tools (e.g., NoSQL Mongo DB, Apache Hadoop, etc.) as well as analytics tools (e.g., Apache Spark). The main contribution of this study is the introduction of a holistic platform that can be used for the needs of the ITS domain offering continuous collection, storage and data analysis capabilities. To achieve that, different modules of state-of-the-art methods and tools were utilized and combined in a unified platform that supports the entire cycle of data acquisition, storage and analysis in a single point. This leads to a complete solution for ITS applications which lifts the limitations imposed in legacy and current systems by the vast amounts of rapidly changing data, while offering a reliable system for acquisition, storage as well as timely analysis and reporting capabilities of these data.

Optimizing the Data Acquisition Cycle Time of a Sensor MAC in Industrial WSNs

To reduce the damage of people and properties in industry fields, the response time of a MAC protocol should be kept as small as possible. Among the solutions suggested by various MAC protocols, shortening the data acquisition cycle time (DACT) is considered as the most effective way of reducing the response time. However, a small DACT is often fails to provide the sensor nodes with a sufficient time duration to transmit their data packets in TDMA-based MAC protocols while it surely increases the degree of contention among the sensor nodes in CSMA-based MAC protocols. In summary, a WSN with a small DACT usually fails to attain the satisfactory level of data transmission efficiency in WSNs. On the contrary, a WSN with a large DACT incurs an excess delay in data packet transmission. Therefore, we propose an analytical study for calculating the expected size of time slots so that an optimized DACT can be found. Our optimized DACT is precisely suited to the level-order data transmission that greatly improves the data transmission efficiency in industrial WSNs. In simulation experiments, it is shown that the proposed approach attains a higher packet deliver ratio than that of other competing approaches. Keywords— data acquisition cycle time, data aggregation, packet delivery ratio, probability distribution function, sharable time slot.

New trends in the ultrasonic imaging of concrete structures by means of 3D-FMC technology

The novel technologies in ultrasound generation and data processing offer new opportunities for three-dimensional imaging of concrete structures. Especially the Full-Matrix-Capture (FMC) technique with its real-time imaging capability can be successfully utilized for quality assurance in the construction industry. Although, the available testing instruments on the market implement the principle of linear transducer array with its two-dimensional reconstruction of B-Scan images based on the Synthetic Aperture Focusing Technique (SAFT) principle. The Dry-Point-Contact (DPC) transducers utilized in commercially available instruments for concrete testing with their matrix-like layout offer a direct opportunity to implement a three-dimensional Full-Matrix-Capture (FMC) data acquisition cycle. The obtained data can be in real-time processed by 3D-TFM reconstruction and visualized and evaluated three-dimensionally. In the present contribution both state-of-the-art and novel true-3D imaging approaches and data assessment are considered on real concrete inspection objects and the advantages of enhanced data processing with respect to improved information content and easiness of result, interpretation is discussed.

A Multicast-based MAC Protocol for Improving Data Transmission Efficiency in Industrial WSNs.

In industrial WSNs, attaining the desired level of data transmission efficiency is highly challenging not only for a frequently changing network topology but also for a short data acquisition cycle time. A short data acquisition time often restrains a sensor MAC protocol to incorporate an effective data packet salvaging technique. Therefore, we propose a data transmission technique in which a sending node multicasts data packets to all candidate parents including its primary parent. As a result, the necessity of data retransmission attempts is totally avoided, which greatly improves the data transmission efficiency in industrial WSNs. By simulation experiments, we have shown that the proposed multicast-based MAC protocol surpasses all the competing MAC protocols regarding the successful data packet delivery and power saving with a significant margin

Development and Implementation of Remote Duty Cycle Data Acquisition and Analysis

In the competitive automotive industry, launch of a new vehicle has become a norm to stay ahead, also the vehicle manufacturers are competing in terms of increased warranty on the new launch. Hence it is imperative for rigorous validation of new vehicle in very short period and requires to map customer usage pattern in least possible time. This work is based on an extension of internet of things (IOT), which provides a tool of capturing vehicle duty cycle by using combination of analog and digital sensors with appropriate ADC. The system is enabled with algorithm/ coded to log result, whenever measured physical parameter goes above/below predetermined level. The work involves implementation of an auto start and auto shutdown of the system based on vehicle ignition. Thus, this paper presents a system that is capable of continuous, real time recording and edge computing (auto post processing) physical quantity and ensures complete elimination of human interface, thereby enabling Remote Data Acquisition, Analysis and Reporting system (Proposed system). The proposed system is fit and forget and cost-effective solution, it can be fitted in any number of vehicles to acquire data for large number of kilometers to map system level usage pattern. Against the conventional method with limited kilometers of data to map customer pattern. The present work is an implementation of Remote Data Acquisition, Analysis and Reporting system in measurement of required vehicle parameters of temperature and humidity in field working conditions.

Smart Wireless Climate Sensor Node for Indoor Comfort Quality Monitoring Application

The indoor environment climate should be controlled by continuously maintaining the temperature and relative humidity to achieve thermal comfort. A monitoring system of both parameters is the first step to improving indoor comfort quality. This paper presents a smart wireless climate sensor node for indoor temperature and humidity monitoring with a powering strategy and design approach for autonomous operation. The data logging results are sent to the cloud using Internet of Things protocol for thermal comfort monitoring and analysis. The monitoring and analysis results are useful to monitor and control the indoor thermal comfort condition for room occupants. A sensor node was designed that includes a low-power mode and compact size features. It consists of a built-in AVR-based microcontroller, a temperature and humidity sensor, and a wireless module with a supercapacitor as the power storage. A low-power algorithm and Internet of Things system were implemented to reduce the total energy consumption as low as possible during operation while improving the thermal comfort quality. This developed sensor node has a small error for temperature, and relative humidity sensed values resulting from calibration. At the same time, it also consumes low power for one cycle of data acquisition. The device was integrated with an Internet of Things monitoring system to monitor indoor thermal comfort in the field experiment. The experiment results showed that the indoor temperature and relative humidity were measured and recorded in the range of 25–30 °C and 30–40%, respectively. This prototype is a preliminary design to achieve an autonomous sensor node with a low-power energy consumption goal. Thus, with this feature, the developed sensor node has potential to couple with a micro energy harvester module toward a fully autonomous active node in further development.

Rapid Discrimination of Citrus reticulata 'Chachi' by Electrospray Ionization-Ion Mobility-High-Resolution Mass Spectrometry.

A common idea is that some dishonest businessmen often disguise Citrus reticulata Blanco varieties as Citrus reticulata ‘Chachi’, which places consumers at risk of economic losses. In this work, we combined high-resolution ion mobility (U-shaped mobility analyzer) with high-resolution mass spectrometry to rapidly distinguish Citrus reticulata ‘Chachi’ from other Citrus species. The samples were analyzed directly through simple extraction and the analytes were separated in one second. It only took about 1 min to perform a cycle of sample analysis and data acquisition. The results showed that polymethoxylated flavones and their isomers were separated easily by the ion mobility analyzer and preliminarily identified according to the accurate mass. Moreover, the collision cross-section values of all analytes, which could be used as auxiliary parameters to characterize and identify the compounds in the samples, were measured. Twenty-four samples were grouped as two clusters by multivariate analysis, which meant that Citrus reticulata ‘Chachi’ could be effectively differentiated. It was confirmed that the developed method had the potential to rapidly separate polymethoxylated flavones and distinguish between Citrus reticulata ‘Chachi’ and other Citrus reticulata Blanco varieties.

Low-Cost Wireless Sensing System for Precision Agriculture Applications in Orchards

Wireless sensor networks (WSNs) can be reliable tools in agricultural management. In this work, a low cost, low power consumption, and simple wireless sensing system dedicated for agricultural environments is presented. The system is applicable to small to medium sized fields, located anywhere with cellular network coverage, even in isolated rural areas. The novelty of the developed system lies in the fact that it uses a dummy device as Coordinator which through simple but advanced programming can receive, process, and send data packets from all End-nodes to the cloud via a 4G cellular network. Furthermore, it is energy independent, using solar energy harvesting panels, making it feasible to operate in remote, isolated fields. A star topology was followed for the sake of simplification, low energy demands and increased network reliability. The developed system was tested and evaluated in laboratory and real field environment with satisfactory operation in terms of independence, and operational reliability concerning packet losses, communication range (>250 m covering fields up to 36 ha), energy autonomy, and uninterrupted operation. The network can support up to seven nodes in a 30 min data acquisition cycle. These results confirmed the potential of this system to serve as a viable option for monitoring environmental, soil, and crop parameters.

Fast estimation method of volumes of landslide deposit by the 3D reconstruction of smartphone images

The fast estimation of volumes of landslide deposit has great significance for emergency rescue decision-making, such as deposit filling and excavation and road dredging. The existing methods for the volumes of landslide deposit estimation have the disadvantages of a long data acquisition cycle, poor timeliness and high cost. Therefore, this paper researches a fast estimation method of volumes of landslide deposit based on the 3D reconstruction of smartphone images. First, the improved algorithms of speeded up robust features (SURF) and oriented fast and rotated brief (ORB) algorithms are combined to realize the fast matching of landslide feature points. Second, the 3D scene sparse reconstruction of landslide deposit is realized through boundary constraints. Third, taking the steepest gradient into account, the interpolation of the bottom of the landslide deposit and the fast estimation of the volumes are carried out. Finally, a prototype system is developed, and a case study is performed. The experimental results show that the estimation results of the proposed method are between those of the commercial software ContextCapture, Metashape and Pix4Dmapper, which are commonly used in the field of 3D reconstruction, but the estimation speed is 1–2 times faster than that of the commercial software mentioned above. The proposed method can realize the low-cost and fast estimation of landslide deposit volumes and provide technical support for landslide emergency rescue.

Mutually orthogonal Golay complementary sequences in the simultaneous synthetic aperture method for medical ultrasound diagnostics. An experimental study

Complementary Golay coded sequences (CGCS) have several advantages over conventional short pulse transmitted signals. Specifically, CGCS allow the signal-to-noise ratio (SNR) to be increased. Moreover, due to matched filtering and compression, echoes resembling the short pulse waveform with substantially higher amplitude can be obtained. However, CGCS require two subsequent transmissions to obtain a single compressed signal. This decreases the data acquisition rate and the frame rate of ultrasound imaging by two-fold. To alleviate this problem, mutually orthogonal Golay complementary sequences (MOGCS) can be used. MOGCS allow the simultaneous transmission of two CGCS pairs to be implemented, yielding the acoustic data for two image frames in one data acquisition cycle.The main objective of this work was an experimental study of the most crucial parameters of the received acoustic signals, e.g. the signal-to-noise ratio (SNR), the side-lobes level (SLL) of the signal and the axial resolution, obtained from simultaneous transmission of two pairs of CGCS comprising a MOGCS set to demonstrate their feasibility of being used in ultrasonography. For this purpose, a simultaneous synthetic transmit aperture method (SSTA) was proposed. The SSTA is based on MOGCS transmission and simultaneous reconstruction of two image frames from a single data acquisition cycle. This doubles the image reconstruction rate in comparison with conventional CGCS signals.In this paper, the ultrasound data from a perfect reflector, commercial phantoms and in vivo measurements were analysed. Two 16-bit long CGCS pairs comprising the MOGCS set were programmed and transmitted using the Verasonics Vantage™ research ultrasound system equipped with a Philips ATL L7-4 linear array ultrasound probe. It was shown that the signal parameters and overall quality of reconstructed B-mode images did not deteriorate when using the MOGCS in comparison to the conventional CGCS and short pulse signals explored so far.

Interoperable medical data: The missing link for understanding COVID-19.

Being able to link clinical outcomes to SARS‐CoV‐2 virus strains is a critical component of understanding COVID‐19. Here, we discuss how current processes hamper sustainable data collection to enable meaningful analysis and insights. Following the ‘Fast Healthcare Interoperable Resource’ (FHIR) implementation guide, we introduce an ontology‐based standard questionnaire to overcome these shortcomings and describe patient 'journeys' in coordination with the World Health Organization's recommendations. We identify steps in the clinical health data acquisition cycle and workflows that likely have the biggest impact in the data‐driven understanding of this virus. Specifically, we recommend detailed symptoms and medical history using the FHIR standards. We have taken the first steps towards this by making patient status mandatory in GISAID (‘Global Initiative on Sharing All Influenza Data’), immediately resulting in a measurable increase in the fraction of cases with useful patient information. The main remaining limitation is the lack of controlled vocabulary or a medical ontology.

Process Monitoring in Heavy Duty Drilling Rigs—Data Acquisition System and Cycle Identification Algorithms

The monitoring of drilling processes is a well-known topic in the mining industry. It is widely used for rock mass characterization, bit wear monitoring and drilling process assessment. However on-board monitoring systems used for this purpose are installed only on a limited number of machines, and breakdowns are possible. There is a need for a data acquisition system that can be used on different drilling rigs and for an automatic data analysis procedure. In this paper, we focused on the automatic detection of drilling cycles, presenting a simple yet reliable system to be universally installed on drilling rigs. The proposed solution covers hardware and software. It is based on the measurement of electric current and acoustic signals. The signal processing methods include threshold-based segmentation, a short-time envelope spectrum and a spectrum for the representation of results. The results of the research have been verified on a real drilling rig within the testing site of its manufacturer by comparing the results with the data of the on-board monitoring system installed on the machine. Novel aspects of our approach include the detection of the pre-boring stage, which has an intermediate amplitude that masks the real drilling cycles, and the use of the percussion instantaneous frequency, which is estimated by acoustic recordings.

Frequency-Diverse Computational Automotive Radar Technique for Debris Detection

Frequency-diversity is a computational imaging technique that can offer all-electronic imaging systems by leveraging spatio-temporally incoherent radiation patterns as an enabling technology. This approach exhibits a significant contrast to conventional imaging modalities, such as synthetic aperture radars (SAR) and phased arrays in that the raster scanning requirement of a scene to be imaged (mechanical or electronic) can be broken and replaced by a quasi-random interrogation of the scene. This aspect of frequency-diverse computational imaging systems significantly simplifies the physical hardware requirements of conventional radars. Despite this advantage, the application of the frequency-diversity technique has been mostly limited to static imaging scenarios, where the position of the scene to be imaged remains fixed over the data acquisition cycle. This limitation hinders the frequency-diverse computational radars from being deployed for applications where the scene dynamics may vary over the data acquisition cycle, such as in automotive radars. In this paper, we demonstrate that by modifying the sensing matrix to account for the movement of the radar platform, frequency-diverse computational imaging radars can be successfully used in debris detection on roads. We show that operating within the frequency band of 77–81 GHz, the presented dynamic frequency-diverse radar technique can produce high fidelity point spread function (PSF) patterns eliminating the distortions caused by the motion of the radar. We also prove that the PSF patterns of the radar are in excellent agreement with theoretical diffraction limited resolution limits.

A two-channel slotted sense multiple access protocol for timely and reliable data transmission in industrial wireless sensor networks

In designing a real-time MAC protocol for wireless sensor networks, it is desirable to shorten the data acquisition cycle time to satisfy more applications with a tighter time constraint in data collection. To achieve this, one often tries to improve throughput by employing a slot reuse technique. Meanwhile, the slotted sense multiple access protocol that uses a sharable slot was proven to be advantageous for timely and reliable data delivery. However, since the nodes at lower tree levels process more packets or bigger packets with data aggregation, they need a bigger sharable slot. Thus, it is not easy to employ a slot reuse technique due to the size variation of sharable slots. To tackle this problem, we try to reduce the size of an aggregated packet by limiting the number of packets for aggregation using two channels. If every node is allowed to send a single packet during data acquisition cycle time, an equal-sized sharable slot can be allocated to every tree level. It is shown that the proposed approach can reduce data acquisition cycle time significantly while maintaining good reliability in data transmission compared with slotted sense multiple access and also reduce energy consumption greatly compared with another multi-channel MAC protocol.

Hydraulic hybrid passenger vehicle: Fuel savings possibilities

This work proposes to model a small series hydraulic hybrid vehicle (SHHV) for the power demand of a real driving cycle. The real driving cycle was measured with a low-cost Bluetooth Scanner connected to the On-Board Diagnostic (OBD) port of the vehicle and applied to a computational model. The hydraulic power to drive the modeled SHHV was supplied by a hydraulic pump and/or by an accumulator. Optimization tools were used to size the hydraulic components. Furthermore, the optimization tools were also used to calculate the values of the control variables for activating the pump, accumulator and the hydraulic motor, in order to have the minimum fuel consumption by the vehicle’s combustion engine. The model implemented had its fuel consumption reduced by 37% in relation to the passenger car utilized for the driving cycle data acquisition.

A Study of Situation Awareness-based Resource Management Scheme in Cloud Environment

In view of the defects of the current cloud resource management, which results in low utilisation of resources. This paper proposes a new method for the cloud environment, which is based on situation awareness. Meanwhile it is particularly important to collect historical load data of resources in the process of situation awareness. In order to improve the quality of data acquisition and reduce the burden of the data collection system, a new adaptive data acquisition algorithm is also designed. The proposed scheme comprehensively evaluates the current situation of the use of resources using FAHP (fuzzy analytic hierarchy process). Meanwhile, the cycle of data acquisition is adjusted in real-time, according to the result of resource evaluation. Then, in order to improve the utilisation of resources and achieve its efficient use, the improved BP algorithm is used to forecast the resource requirement of the next time which can provide the basis for rational allocation.