Data Acquisition Problems Research Articles

Energy Potential Mapping: Open Data in Support of Urban Transition Planning

Cities play a key role in driving the transition to sustainable energy. Urban areas represent between 60% and 80% of global energy consumption and are a significant source of CO2 emissions, making energy management at the urban scale an important area of research. Urban energy systems have a strong influence on the environment, economy, social dimensions and urban spatial planning. Energy consumption affects the urban microclimate, urban comfort, human health, and conversely, urban physical, economic and social characteristics affect the energy urban profile. In order to improve the quality of energy strategies, policies, and plans, local authorities need decision support tools, like energy potential mapping, which have risen significance in the last decades. Energy data are crucial for those tools. They can increase the quality and effectiveness of energy planning but also support the integration between energy and spatial planning. Energy data can also stimulate citizen engagement as well as encourage sustainable behaviours and CO2 emission reduction. This paper aims to increase the practice of data-aware planning, through the study of problems in energy data acquisition and processing observed in European projects focused on developing energy mapping tools. The problems observed attend to two main areas: technical and socio-economic issues. Those were derived from a comparison of energy mapping tools, and the work conducted for the PLANHEAT development. The scope of the research is to understand the main recurring issues in energy data acquisition and processing, in order to overcome the barriers in data availability. Increasing awareness of the relevance of energy data can foster the use of energy mapping tools, increasing the quality of energy policies and planning.

Fine-grained vehicle type detection and recognition based on dense attention network

Intelligent transportation is an indispensable part of the smart city and the primary development direction of the future transportation systems. Vehicle detection and recognition, which is one of the most important aspects of intelligent transportation, plays a very important role in various areas of our daily life; one such important area is criminal investigation. In the fine-grained vehicle type detection and recognition, several difficult issues such as problems in data acquisition and tagging, dramatic variance in the data of different vehicle types, and challenges in identifying vehicles of the same brand with highly similar appearances remain unsolved. For the problems of data acquisition and tagging, this paper presents a strategy for automatic data acquisition and tagging based on object detection that can label the vehicle images efficiently while rapidly acquiring all types of fine-grained models. Considering the problem of data imbalance in the training process, this paper proposes a Faster-RCNN based data equalizing strategy (Faster-BRCNN), thereby improving the performance of object detection. In view of the severe information attenuation caused by the feature information transfer obstruct between layers in the traditional deep learning network, the lack of mutual dependency of these features, and the inability of the network to focus on the important region and characteristics, we propose an intensive dense attention network (DA-Net). Through its intensive connection and attention unit, we enhance the model's detection ability. The proposed method achieves mAP of 94.5% and 95.8% in the Stanford Cars and FZU Cars datasets, respectively, thereby verifying its effectiveness.

Research and development of monitoring system and data monitoring system and data acquisition of CNC machine tool in intelligent manufacturing

Intelligent manufacturing as the development direction of the new generation manufacturing system has become a hot research topic. Computer numerical control (CNC) machine tools are the core manufacturing equipment in discrete manufacturing enterprises, collecting and monitoring the data is an important part of intelligent manufacturing workshops. It has a great significance to improve the production efficiency of enterprises and eliminate information islands. The purpose of this article is to solve the problems of data acquisition and monitoring of CNC machine tools in the manufacturing workshop of enterprises. This article uses FOCAS data acquisition method to research and develop the data acquisition and monitoring system of CNC machine tools in intelligent manufacturing workshop. The research results show that the equipment information model based on MTConnect protocol and FOCAS can solve the data acquisition and storage functions of CNC machine tools well. Using the object-oriented Petri net model, it can solve various uncertain factors in numerical control (NC) machining tasks and realize the monitoring function of CNC machining tasks in the workshop. Based on the NC program analysis, the calculation method of machining time in the NC program can determine the preventive maintenance cycle of the machine based on the machine fault information. Based on VS2013 development environment, Qt application framework and SQL Server 2012 database, the numerical control machine tool data acquisition and monitoring prototype system was developed, and the system was verified in the workshop to prove the effectiveness of the system.

Detection of a thin layer by seismic reflection with different geophones at a site in Liangzhu, Southeastern China

Geophone properties are important factors for high-resolution seismic exploration and greatly influence the quality of data. In this study, we will address the problems of ultra-shallow seismic data acquisition and processing. The purpose of our study is to evaluate the applicability of using different seismic geophones and processing techniques to detect a thin soil layer that is approximately 20 cm in thickness at a depth of approximately 2 m in the Liangzhu archaeological site in southeastern China. We collected seismic data using two types of geophones: normal geophones with different natural frequencies inserted into the ground and towable cable geophones contacting the earth’s surface. We considered combining the frequency advantages of these two different types of geophones to broaden the effective bandwidth of the acquired data. To achieve this, we designed a partial frequency match filtering method for data processing, which helps to improve the resolution of the reflected seismic data. The stacking profile results show that match filtering is effective in detecting a thin layer compared to drilling samples and GPR data the velocity of the thin bed is 350ms while from seismic section interval velocity of the contact between clayey soils and rammed sandy soils about 600 ms.

A Research Study for the Design of a Portable and Configurable Ground Test System for the A400M Aircraft

This paper presents a study of the most suitable ground test equipment for the Airbus A400M aircraft aimed at optimizing a design that combines portability, flexibility, and configurability. The study is based on current trends in the configuration of the different parts that are involved in the process of designing Aircraft Interface Modules (AIMs). Researches have been conducted facing the real problems of data acquisition and control in the assembly, maintenance, and repair processes of aircraft. In this sense, the number of signals used simultaneously in historical tests has been determined to be a parameter that plays an important role in the analysis. This work is within the framework of a project that our working group of the Department of Electronic Technology has carried out together with the company Airbus Defense and Space (Airbus DS). Thus, all the analyses and researches for the different configurations of the test equipment have been validated for the company. Some state-of-the-art systems have been considered in the process of designing the structure of the test system while analyzing the main advantages and drawbacks. Every hardware or software component was identified and justified according to its importance with respect to the use case. A comparison of how every approach meets the requirements was taken into account to define a methodology for designing a portable and configurable ground testing system. Thus, the conclusions of our study are being used by Airbus DS for the design of a prototype for the next generation of AIMs in A400M.

Super-resolution algorithm applied to images acquired at millimeter wave frequency in single pixel and computational ghost imaging configurations

ABSTRACTThe lack of low cost, high-resolution two-dimensional imaging detectors and the expensiveness of the measurements make millimeter wave imaging challenging. Single detector configurations are promising to solve data acquisition problem in millimeter wave imaging. In this study, millimeter wave imaging in transmission mode is done with single pixel imaging and computational ghost imaging configurations. In order to reduce the number of measurements in each configuration, Compressed Sensing architecture is used. To increase the image resolution, software-based super-resolution is applied. To the best of our knowledge, this is first study comparing the computational ghost imaging with single pixel imaging, besides software-based super-resolution method is applied to the real world millimeter wave images. It is seen that super-resolution increases the quality in both configurations while computational ghost imaging has better quality compared to single pixel imaging in millimeter waves.

Multiple Line-Outage Detection in Power System With Load Stochastic Perturbations

In this brief, we consider multiple line-outage detection problem based on dynamic model of the power system subjects to load stochastic perturbations. A novel detection algorithm is proposed using data obtained from phase measurement units (PMUs). In order to reduce the computation burden, the algorithm is further improved by means of introducing the original topology of power system. Moreover, we propose a predictive algorithm to solve the sampling interval problem of data acquisition. Finally, simulation results are given to demonstrate the applicability of the proposed algorithm.

Big Data Acquisition Under Failures in FiWi Enhanced Smart Grid

With the increasing of monitoring devices and advanced measurement infrastructures, smart grids (SGs) collect large amounts of data every moment, which gives rise to the SG big data. In order to fulfill diverse communication requirements of various energy-related data in SG, it is obviously impractical to rely on a single communication technology, and a hybrid communication architecture of low-latency fiber optic and cost-effective wireless technologies could be a promising solution. Note that, low-latency data acquisition under failures is of particular importance to SG reliability, considering that SGs are vulnerable to various failures. Toward this end, we provide in this paper a hybrid SG communication architecture integrating fiber optic and WiFi-based mesh networks, i.e., fiber-wireless (FiWi) enhanced SG, and study the problem of data acquisition under failures in FiWi enhanced SG. The problem is first formulated as a constrained optimization problem, and then three algorithms are proposed as our solutions, i.e., an optimal enumeration routing algorithm (OERA), a greedy approximation routing algorithm (GARA), and a heuristic greedy routing algorithm (HGRA). Numerical results reveal that both GARA and HGRA can achieve near-optimal solutions to the problem of data acquisition under failures, and have higher computational efficiency compared to our benchmark, i.e., OERA.

A Vision-Based Technique for in-Flight Measurement of Helicopter Blade Motion

The measurement of helicopter main rotor blade angles during flight is a key capability to implement advanced applications, such as strategies for the reduction of emitted noise and to develop innovative flight control laws. The approach proposed in this work for the real-time estimation of blade angles is based on a stereoscopic system mounted on the top of the main rotor and pointing to an optical target placed on the blade root. An advanced image-processing algorithm was developed to match the target features in the left and right camera images, which was required for the 3D reconstruction of the target based on a triangulation method. This algorithm was customized for the target used in this specific application, in order to implement a procedure that is both reliable in blob matching and characterized by a very low computation effort. This allowed the system to speed up the triangulation procedure aimed at obtaining the 3-D coordinates of the features, in view of real-time applications, even with very compact processing units that can be accommodated on the main rotor head. An inverse problem for the 3-D rotation of the target was solved using the Singular Value Decomposition technique, thus improving the robustness of the measurement. The stereoscopic system was developed in order to be integrated on board an AW139 helicopter main rotor hub, equipped with a synchronous lighting device and a pre-processing unit. The latter enabled the system to automatically extract the minimum set of information to be transferred, by means of a slipring, to the processing unit hosted in the fuselage. For the full assessment of the reliability and accuracy of the integrated system, harsh dynamic and accuracy trials were conducted on laboratory test benches. The harsh dynamic tests demonstrated that the system can work continuously in realistic conditions without any structural or data acquisition problems. The accuracy tests, based on a robot test rig simulating the motion of the blade, demonstrated the capability of the system and the accuracy of the measurement technique developed. The discrepancy between the reference blade angles and the estimated ones was found to be less than 0.360 for all the realistic blade angle combinations tested.

An Application of Integrated 3D Technologies for Replicas in Cultural Heritage

In recent decades, 3D acquisition by laser scanning or digital photogrammetry has become one of the standard methods of documenting cultural heritage, because it permits one to analyze the shape, geometry, and location of any artefact without necessarily coming into contact with it. The recording of three-dimensional metrical data of an asset allows one to preserve and monitor, but also to understand and explain the history and cultural heritage shared. In essence, it constitutes a digital archive of the state of an artefact, which can be used for various purposes, be remodeled, or kept safely stored. With the introduction of 3D printing, digital data can once again take on material form and become physical objects from the corresponding mathematical models in a relatively short time and often at low cost. This possibility has led to a different consideration of the concept of virtual data, no longer necessarily linked to simple visual fruition. The importance of creating high-resolution physical copies has been reassessed in light of different types of events that increasingly threaten the protection of cultural heritage. The aim of this research is to analyze the critical issues in the production process of the replicas, focusing on potential problems in data acquisition and processing and on the accuracy of the resulting 3D printing. The metric precision of the printed model with 3D technology are fundamental for everything concerning geomatics and must be related to the same characteristics of the digital model obtained through the survey analysis.

Neural Network with Extended Input for Estimating Electricity Consumption Using Background-based Data Generation

Non-intrusive load monitoring (NILM) can infer the electricity consumption of individual target appliances by only collecting and analyzing the aggregate power data at the single power entrance point. This detailed information obtained is of great significance to smart grid operation and energy saving. For solving the problems of feature loss and training data acquisition in current deep neural network-based NILM methods, this paper presents an extended input neural network method using background-based data generation for estimating electricity consumption. First, the extended input method is proposed, which ensures a complete working cycle to be fed into the neural network each time. Then, a synthetic method based on the power use background is used to generate abundant and desirable training data. Finally, the proposed methods are validated through comparison tests.

Research on TD-LTE Technology Applied to the Monitoring and Control System for the Photovoltaic Power Station

According to the development status of photovoltaic power station, this paper expatiates the requirements of the monitoring and control system and presents the method of TD-LTE technology applied to the system. This paper analyses the architecture of the monitoring and control system, and the technical characteristics of TD-LTE network, which can effectively solve the photovoltaic power station data acquisition, video surveillance, mobile maintenance and other business communications problems, and provide an effective broadband wireless private network communications solutions. The whole system is flexible, reliable, and will promote the efficiency of operation and maintenance of large-scale photovoltaic power station.

UAV-based multispectral remote sensing for precision agriculture: A comparison between different cameras

Unmanned aerial vehicle (UAV)-based multispectral remote sensing has shown great potential for precision agriculture. However, there are many problems in data acquisition, processing and application, which have stunted its development. In this study, a narrowband Mini-MCA6 multispectral camera and a sunshine-sensor-equipped broadband Sequoia multispectral camera were mounted on a multirotor micro-UAV. They were used to simultaneously collect multispectral imagery and soil–plant analysis development (SPAD) values of maize at multiple sampling points in the field, in addition to the spectral reflectances of six standard diffuse reflectance panels with different reflectance values (4.5%, 20%, 30%, 40%, 60% and 65%). The accuracies of the reflectance and vegetation indices (VIs) derived from the imagery were compared, and the effectiveness and accuracy of the SPAD prediction from the normalized difference vegetation index (NDVI) and red-edge NDVI (reNDVI) under different nitrogen treatments were examined at the plot level. The results show that the narrowband Mini-MCA6 camera could produce more accurate reflectance values than the broadband Sequoia camera, but only if the appropriate calibration method (the nonlinear subband empirical line method) was adopted, especially in visible (blue, green and red) bands. However, the accuracy of the VIs was not completely dependent on the accuracy of the reflectance, i.e., the NDVI from Mini-MCA6 was slightly better than that from Sequoia, whereas Sequoia produced more accurate reNDVI than did Mini-MCA6. At the plot level, reNDVI performed better than NDVI in SPAD prediction regardless of which camera was employed. Moreover, the reNDVI had relatively low sensitivity to the vegetation coverage and was insignificantly affected by environmental factors (e.g., exposed sandy soil). This study indicates that UAV multispectral remote sensing technology is instructive for precision agriculture, but more effort is needed regarding calibration methods for vegetation, postprocessing techniques and robust quantitative studies.

Application of Ifsar Technology in Topographic Mapping: JUPEM’s Experience

Abstract. The application of Interferometric Synthetic Aperture Radar (IFSAR) in topographic mapping has increased during the past decades. This is due to the advantages that IFSAR technology offers in solving data acquisition problems in tropical regions. Unlike aerial photography, radar technology offers wave penetration through cloud cover, fog and haze. As a consequence, images can be made free of any natural phenomenon defects. In Malaysia, Department of Survey and Mapping Malaysia (JUPEM) has been utilizing the IFSAR products since 2009 to update topographic maps at 1 : 50,000 map scales. Orthorectified radar imagery (ORI), Digital Surface Models (DSM) and Digital Terrain Models (DTM) procured under the project have been further processed before the products are ingested into a revamped mapping workflow consisting of stereo and mono digitizing processes. The paper will highlight the experience of Department of Survey and Mapping Malaysia (DSMM)/ JUPEM in using such technology in order to speed up mapping production.

Tracking wakefulness as it fades: Micro-measures of alertness

A major problem in psychology and physiology experiments is drowsiness: around a third of participants show decreased wakefulness despite being instructed to stay alert. In some non-visual experiments participants keep their eyes closed throughout the task, thus promoting the occurrence of such periods of varying alertness. These wakefulness changes contribute to systematic noise in data and measures of interest. To account for this omnipresent problem in data acquisition we defined criteria and code to allow researchers to detect and control for varying alertness in electroencephalography (EEG) experiments under eyes-closed settings. We first revise a visual-scoring method developed for detection and characterization of the sleep-onset process, and adapt the same for detection of alertness levels. Furthermore, we show the major issues preventing the practical use of this method, and overcome these issues by developing an automated method (micro-measures algorithm) based on frequency and sleep graphoelements, which are capable of detecting micro variations in alertness. The validity of the micro-measures algorithm was verified by training and testing using a dataset where participants are known to fall asleep. In addition, we tested generalisability by independent validation on another dataset. The methods developed constitute a unique tool to assess micro variations in levels of alertness and control trial-by-trial retrospectively or prospectively in every experiment performed with EEG in cognitive neuroscience under eyes-closed settings.

E-sports analysis data acquisition algorithm based on convolutional neural network

At present, e-sports has become one of the most important industries. How to analyze e-sports data has become an urgent problem to be solved. Currently, some hot competition items do not provide data interfaces, so that the training set required for data analysis cannot be directly and accurately acquired. Data can only be obtained by watching video games in person. This method is obviously inefficient and the accuracy cannot be guaranteed. This paper proposes a data acquisition algorithm based on convolutional neural network algorithm. It also introduces transfer learning, improves the sample training method and data acquisition method, and finally solves the problem of data acquisition. According to the test, this algorithm achieves about 91% accuracy.

Diagnosis and Early Warning of Wind Turbine Faults Based on Cluster Analysis Theory and Modified ANFIS

The construction of large-scale wind farms results in a dramatic increase of wind turbine (WT) faults. The failure mode is also becoming increasingly complex. This study proposes a new model for early warning and diagnosis of WT faults to solve the problem of Supervisory Control And Data Acquisition (SCADA) systems, given that the traditional threshold method cannot provide timely warning. First, the characteristic quantity of fault early warning and diagnosis analyzed by clustering analysis can obtain in advance abnormal data in the normal threshold range by considering the effects of wind speed. Based on domain knowledge, Adaptive Neuro-fuzzy Inference System (ANFIS) is then modified to establish the fault early warning and diagnosis model. This approach improves the accuracy of the model under the condition of absent and sparse training data. Case analysis shows that the effect of the early warning and diagnosis model in this study is better than that of the traditional threshold method.

自行高炮双CAN总线采集系统设计与实现

在某自行高炮作战试验鉴定过程中，针对需要实时记录车辆内部两路总线数据的问题，设计了一种基于双CAN总线的自动测试系统。该系统采用飞思卡尔MC9S12XEP100MAL单片机作为处理芯片，通过外接GPS授时芯片提高了数据测量精度，提出了主核与辅核XGATE数据共享方法以及双缓冲区周期交换策略，实现了双CAN口数据的不间断采集。应用表明，该系统能够适用于该自行高炮武器系统总线数据的实时采集、存储及传输任务，解决了作战试验鉴定过程中数据采集的问题。 Test system based on dual CAN bus is designed in order to solve the problem of real-time recording of two bus data in the vehicle during the battle test of a self propelled antiaircraft gun. The system uses Freescale Carle MC9S12XEP100MAL chip as the processing chip, and improves the data measurement accuracy through the external GPS timing chip. The method of data sharing between main core and auxiliary kernel XGATE and double buffer cycle switching strategy are proposed, and the uninterrupted data acquisition of double CAN port is realized. The application shows that the system can be applied to the real-time acquisition, storage and transmission of the bus data of the self-propelled antiaircraft gun weapon system. It is easy to use and solves the problem of data acquisition in the process of operational test identification.

Noisy Compressive Sampling Based on Block-Sparse Tensors: Performance Limits and Beamforming Techniques

Compressive sampling (CS) is an emerging research area for the acquisition of sparse signals at a rate lower than the Shannon sampling rate. Recently, CS has been extended to the challenging problem of multidimensional data acquisition. In this context, block-sparse core tensors have been introduced as the natural multidimensional extension of block-sparse vectors. The $(M_1,\ldots,M_Q)$ block sparsity for a tensor assumes that $Q$ support sets, characterized by $M_q$ indices corresponding to the nonzero entries, fully describe the sparsity pattern of the considered tensor. In the context of CS with Gaussian measurement matrices, the Cramer–Rao bound (CRB) on the estimation accuracy of a Bernoulli-distributed block-sparse core tensor is derived. This prior assumes that each entry of the core tensor has a given probability to be nonzero, leading to random supports of truncated Binomial-distributed cardinalities. Based on the limit form of the Poisson distribution, an approximated CRB expression is given for large dictionaries and a highly block-sparse core tensor. Using the property that the mode unfolding matrices of a block-sparse tensor follow the multiple-measurement vectors (MMV) model with a joint sparsity pattern, a fast and accurate estimation scheme, called Beamformed mOde based Sparse Estimator (BOSE), is proposed in the second part of this paper. The main contribution of the BOSE is to “map” the MMV model onto the single MV model, thanks to beamforming techniques. Finally, the proposed performance bounds and the BOSE are applied in the context of CS to $\text{1}\text{)}$ nonbandlimited multidimensional signals with separable sampling kernels and $\text{2)}$ for multipath channels in a multiple-input multiple-output wireless communication scheme.

A Wide-Area Backup Protection Algorithm Based on Distance Protection Fitting Factor

In view of the time delay and potential information loss problem in wide-area data acquisition, a wide-area backup protection algorithm based on the distance protection fitting factor is proposed in this paper. First, the contribution of the distance protection operating information to fault identification is defined as the distance protection contribution degree. Second, the protection fitness function and protection fitness expectation function are constructed using the distance protection contribution degree as the weight. And then, the distance protection fitting factor is gained by calculating the division of the protection fitness function by the protection fitness expectation function. Thus, the fault line can be identified. Finally, simulation tests on the IEEE 10-machine 39-bus system and Guizhou Duyun real-time digital simulator verify that the proposed method is not affected by the time delay in wide-area data acquisition and is able to identify the fault line correctly even when a large amount of distance protection operating information is lost or in error. Meanwhile, the uncertainty problem in traditional weight setting due to reliance on subjective experience is avoided. Besides, being small in computation amount, the proposed method is easy to implement in engineering practice.