Data Acquisition Errors Research Articles

Continuous imputation of missing values in time series via Wasserstein generative adversarial imputation networks and variational auto-encoders model

The occurrence of missing values in time series is a common phenomenon attributed to equipment malfunction during data acquisition and transmission errors. However, most imputation methods overlook the continuous imputation of missing values, significantly losing local information resources. In this sense, we propose a new Wasserstein Generative Adversarial Imputation Network coupled with a Variational Autoencoder model (WGAIN-VAE). WGAIN-VAE utilizes a VAE as the model’s generator, leveraging the VAE’s data reconstruction capability to explore and capture latent data evolution features beyond missing positions. Simultaneously, it introduces a signaling information matrix to exit the initial model training and optimizes the neural network structure of the discriminator along with the loss function calculation strategy. Our empirical evidence indicates that the proposed model supplanted classical methods based on evaluation metrics frequently used to measure the accuracy of forecasting or adjustment models in time series.

Full operating parameter recording as an essential component of the reproducibility of laser-tissue interaction and treatments.

The number and diversity of published peer-reviewed studies in the discipline of laser dentistry have grown considerably during the past 10 years. Within primary research, the development of protocols to guide and formulate clinical practice demands precision and ease of reproducibility. Errors in data acquisition and management may become amplified as the applied randomized clinical trials (RCTs) forge new levels of clinical diversity and predictability in the use of laser photonic energy in both ablative (surgical) and sub-ablative (photobiomodulation (PBM) or photodynamic therapy (PDT)) applications. A comprehensive range of empirical and computational operating parameters must be included in published studies to facilitate the uniformity of powerand time-related values of laser irradiation. Choosing the correct "tissue irradiation parameters" is difficult and depends on the pathology and symptoms, the surface area to be treated, laser wavelength, the thermal relaxation time of each targeted tissue, and controlling penetration depth of the light into tissues. Therefore, to allow the reproducibility of the results, it is recommended that authors mention with the greatest care and clarity the irradiation parameters used in their study. This paper outlines the concerns felt regarding the general shortfalls and proposes a minimum range of laser operating parameters that should be represented in future peer-reviewed publications.

Clinical Quality Control of MRI Total Kidney Volume Measurements in Autosomal Dominant Polycystic Kidney Disease.

Total kidney volume measured on MRI is an important biomarker for assessing the progression of autosomal dominant polycystic kidney disease and response to treatment. However, we have noticed that there can be substantial differences in the kidney volume measurements obtained from the various pulse sequences commonly included in an MRI exam. Here we examine kidney volume measurement variability among five commonly acquired MRI pulse sequences in abdominal MRI exams in 105 patients with ADPKD. Right and left kidney volumes were independently measured by three expert observers using model-assisted segmentation for axial T2, coronal T2, axial single-shot fast spin echo (SSFP), coronal SSFP, and axial 3D T1 images obtained on a single MRI from ADPKD patients. Outlier measurements were analyzed for data acquisition errors. Most of the outlier values (88%) were due to breathing during scanning causing slice misregistration with gaps or duplication of imaging slices (n = 35), slice misregistration from using multiple breath holds during acquisition (n = 25), composing of two overlapping acquisitions (n = 17), or kidneys not entirely within the field of view (n = 4). After excluding outlier measurements, the coefficient of variation among the five measurements decreased from 4.6% pre to 3.2%. Compared to the average of all sequences without errors, TKV measured on axial and coronal T2 weighted imaging were 1.2% and 1.8% greater, axial SSFP was 0.4% greater, coronal SSFP was 1.7% lower and axial T1 was 1.5% lower than the mean, indicating intrinsic measurement biases related to the different MRI contrast mechanisms. In conclusion, MRI data acquisition errors are common but can be identified using outlier analysis and excluded to improve organ volume measurement consistency. Bias toward larger volume measurements on T2 sequences and smaller volumes on axial T1 sequences can also be mitigated by averaging data from all error-free sequences acquired.

A graph policy network approach for Volt-Var Control in power distribution systems

Volt-var control (VVC) is the problem of operating power distribution systems within healthy regimes by controlling actuators in power systems. Existing works have mostly adopted the conventional routine of representing the power systems (a graph with tree topology) as vectors to train deep reinforcement learning (RL) policies. We propose a framework that combines RL with graph neural networks and study the benefits and limitations of graph-based policy in the VVC setting. Our results show that graph-based policies converge to the same rewards asymptotically, however at a slower rate when compared to vector representation counterpart. We conduct further analysis on the impact of both observations and actions: On the observation end, we examine the robustness of graph-based policies on two typical data acquisition errors in power systems, namely sensor communication failure and measurement misalignment. Furthermore, we study the robustness of graph-based policies to erroneous topological information in the graph representation. Our results reveal that graph-based policies are significantly more robust than policies with conventional dense network representations. On the action end, we show that actuators have various impacts on the system, thus using a graph representation induced by the physical power systems topology may not be the optimal choice. In the end, we conduct a case study to demonstrate that the choice of readout function architecture and graph augmentation can further improve training performance and robustness.

Study on dip error compensation model of laser displacement sensor and its application in measurement of spiral curved surface profile

Featuring high resolution, excellent stability, great rapidity, and small size, the laser displacement sensor (LDS) has been extensively utilized in multiple domains. However, its accuracy is severely constrained by data acquisition (DAQ) errors. To solve this problem, we concentrate on the problem that DAQ errors seriously deteriorate the LDS measuring accuracy. Based on laser triangulation, we analyze the impact of laser beams and convergent spot centroid on the system measuring accuracy and establish mathematical models for quantitatively calculating LDS measured surface dip errors and charge-coupled device (CCD) dip errors. Further, these models can be used to compensate for the data collected by the LDS in real-time. Finally, experimental results show that after these two compensation models are used to correct measuring results, the accuracy of LDS-based DAQ is greatly improved, the measurement error is well controlled within 0.008 mm, and the LDS measuring accuracy on spiral curved surfaces is effectively boosted.

Research on Civil Engineering Software for Engineering Computation

Learning how to compute and simulate civil engineering processes is the necessary task for civil engineers, instructors, and learners. In general, with the continuous improvement of computer technology, the computing software promotes the reform of engineering industry. In general, the software can handle complex models with ease and accuracy. Therefore compared to traditional manual calculations, computerized software can avoid errors in data acquisition and calculation. Therefore, many problems in civil engineering have been improved. China is also strongly supporting and promoting the modern computing software usages into education, which further encourages BIM-based computing. In this research we propose to analyze civil engineering computing software implementation based on the experiment of BIM Glodon software. We use the Glodon GCL 2013 for empirical experiment for the engineering drawings and related quantities of components of civil engineering computing. This article presents the BIM Glodon Software makes advanced specific operations for civil engineering computing such as construction drawing, steel metering, and computing. The article reviews the related studies that are considered as valuable resources to software research and development, shows many scholars have also conducted in-depth research in computing development. According to our research result, we recommend that the development companies need to focus on upgrading their software to be more consumer friendly, cost effective.

Fuzzy Clustering Analysis of Power Incomplete Data based on Improved IVAEGAN Model

The scale of data generated by the complex and huge power system during operation is also very large. With the data acquisition of various information systems, it is easy to form the situation of incomplete power data information, which cannot guarantee the efficiency and quality of work, and reduce the security and reliability of the entire power grid. When incomplete data and incomplete data sets are caused by data storage failure or data acquisition errors, fuzzy clustering of data will face great difficulties. The fuzzy clustering of incomplete data of the power equipment is divided into the processing of incomplete data and the clustering analysis of "recovered" complete data. This paper proposes an IVAEGAN-IFCM interval fuzzy clustering algorithm, which uses interval data sets to fill in the incomplete data, and then completes the clustering of interval data. At the same time, the whole numerical data set is transformed into a complete interval data set. The final clustering result is obtained by interval fuzzy mean clustering analysis of the whole interval data set. Finally, the algorithm proposed in this paper and other machine learning training data sets is made for experimental analysis. The experimental results show that the algorithm proposed in this paper can complete incomplete data sets with high precision clustering. Compared with other contrast methods, it shows higher clustering accuracy. Compared with the numerical clustering algorithm, the clustering accuracy is improved by more than 4.3%, and it has better robustness. It also shows better generalization on the artificial data sets and other complex data sets. It is helpful to improve the technical level of the existing power grid and has important theoretical research value and engineering practice significance.

Experimental Investigation of Wing Accuracy Quantification using Point Cloud and Surface Deviation

A rapidly advancing lean production industry demands quick manufacturing solutions with greater precision and accuracy. This paper proposes a framework for the accurate quantification of a die-casted wing using laser scanning and reverse engineering technique. In this technique, the wing upper and lower surfaces are scanned using a Coordinate Measuring Machine (CMM). This scanned data is then imported into CAD software to generate the surface using Free Form Reverse Engineering (FFRE). The model fitness test patronizes the curve fitting used for the surface generation. The generated surface and the original 3D CAD model are investigated using deviation analysis for inaccuracies originating due to manufacturing and data acquisition. The wing is further analyzed by the point data to 3D CAD model deviation analysis. The methodology adopted significantly minimizes the data acquisition and data processing error allowing deviation to be solely traced back to the manufacturing technique.

An Automated CAD System of CT Chest Images for COVID-19 Based on Genetic Algorithm and K-Nearest Neighbor Classifier

The detection of COVID-19 from computed tomography (CT) scans suffered from inaccuracies due to its difficulty in data acquisition and radiologist errors. Therefore, a fully automated computer-aided detection (CAD) system is proposed to detect coronavirus versus non-coronavirus images. In this paper, a total of 200 images for coronavirus and non-coronavirus are employed based on 90% for training images and 10% testing images. The proposed system comprised five stages for organizing the virus prevalence. In the first stage, the images are preprocessed by thresholding-based lung segmentation. Afterward, the feature extraction technique was performed on segmented images, while the genetic algorithm performed on sixty-four extracted features to adopt the superior features. In the final stage, the K-nearest neighbor (KNN) and decision tree are applied for COVID-19 classification. The findings of this paper confirmed that the KNN classifier with K=3 is accomplished for COVID-19 detection with high accuracy of 100% on CT images. However, the decision tree for COVID-19 classification is achieved 95% accuracy. This system is used to facilitate the radiologist’s role in the prediction of COVID-19 images. This system will prove to be valuable to the research community working on automation of COVID-19 images prediction.

Different Approaches for Missing Data Handling in Fuzzy Clustering: A Review

Introduction: Incomplete data sets containing some missing attributes is a prevailing problem in many research areas. The reasons for the lack of missing attributes may be several; human error in tabulating/recording the data, machine failure, errors in data acquisition or refusal of a patient/customer to answer few questions in a questionnaire or survey. Further, clustering of such data sets becomes a challenge. Objective: In this paper, we presented a critical review of various methodologies proposed for handling missing data in clustering. The focus of this paper is the comparison of various imputation techniques based FCM clustering and the four clustering strategies proposed by Hathway and Bezdek. Methods: In this paper, we imputed the missing values in incomplete datasets by various imputation/ non-imputation techniques to complete the data set and then conventional fuzzy clustering algorithm is applied to get the clustering results. Results: Experiments on various synthetic data sets and real data sets from UCI repository are carried out. To evaluate the performance of the various imputation/ non-imputation based FCM clustering algorithm, several performance criteria and statistical tests are considered. Experimental results on various data sets show that the linear interpolation based FCM clustering performs significantly better than other imputation as well as non-imputation techniques. Conclusion: It is concluded that the clustering algorithm is data specific, no clustering technique can give good results on all data sets. It depends upon both the data type and the percentage of missing attributes in the dataset. Through this study, we have shown that the linear interpolation based FCM clustering algorithm can be used effectively for clustering of incomplete data set.

Are geometric morphometric analyses replicable? Evaluating landmark measurement error and its impact on extant and fossil Microtus classification.

Geometric morphometric analyses are frequently employed to quantify biological shape and shape variation. Despite the popularity of this technique, quantification of measurement error in geometric morphometric datasets and its impact on statistical results is seldom assessed in the literature. Here, we evaluate error on 2D landmark coordinate configurations of the lower first molar of five North American Microtus (vole) species. We acquired data from the same specimens several times to quantify error from four data acquisition sources: specimen presentation, imaging devices, interobserver variation, and intraobserver variation. We then evaluated the impact of those errors on linear discriminant analysis‐based classifications of the five species using recent specimens of known species affinity and fossil specimens of unknown species affinity. Results indicate that data acquisition error can be substantial, sometimes explaining >30% of the total variation among datasets. Comparisons of datasets digitized by different individuals exhibit the greatest discrepancies in landmark precision, and comparison of datasets photographed from different presentation angles yields the greatest discrepancies in species classification results. All error sources impact statistical classification to some extent. For example, no two landmark dataset replicates exhibit the same predicted group memberships of recent or fossil specimens. Our findings emphasize the need to mitigate error as much as possible during geometric morphometric data collection. Though the impact of measurement error on statistical fidelity is likely analysis‐specific, we recommend that all geometric morphometric studies standardize specimen imaging equipment, specimen presentations (if analyses are 2D), and landmark digitizers to reduce error and subsequent analytical misinterpretations.

Multi-Channel and Simultaneous Data Acquisition System for Magnetic Tensor Detection

This paper reports the design of a multi-channel and simultaneous data acquisition system(MSDAS). It can collect 12-channel magnetic induction component signals with magnetic tensor information rapidly and simultaneously. The STM32F103RCT6 and a 16-bit AD7606 are used as the MSDAQ’s microcontroller and the analog-to-digital(A/D) converter, respectively. Moreover, FATFS file system and TCP/IP protocol are integrated such that the MSDAS is capable of local storage and network simultaneous data transmission in real time. Experiments show that MSDAS can achieve data acquisition error ≤0.5% with the simultaneous sampling rate up to 500Hz. It is verified that the measurement results of magnetic tensor are basically consistent with the theory.

Research on security monitoring system for wind-solar complementary power generation based on internet of things

When traditional system is used to monitor wind-solar complementary power generation, there are problems such as large errors in temperature and wind speed acquired and high power consumption of nodes when the sensor transmits signals. Therefore, a security monitoring system for wind-solar complementary power generation based on internet of things (IoT) is designed. The system hardware is composed of I/O control panel, sensor, GPRS and data processing board. The system software mainly acquires data and processes the acquisition interrupt program. On this basis, the security monitoring of wind-solar complementary power generation is finally realised. The analysis of simulation experiments shows that compared with the traditional system, the data acquisition error of the proposed system is small, which is in good agreement with the actual value. Moreover, the power consumption of nodes is only 15.8 mw when the sensor transmits the signal, indicating that the system has strong reliability and practical application value.

Global Iterative Geometric Calibration of a Linear Optical Satellite Based on Sparse GCPs

Independent methods for geometric calibration (GC) have become an important research direction in the field of optical satellite technology. The main purpose of this research is to eliminate dependence on ground calibration sites using relative constraints between images. Based on a systematic analysis of these relative constraints, we found that it was difficult, if not impossible, to completely eliminate ground constraints, although the number of ground control points (GCPs) required can be greatly reduced. To achieve practical GC with high accuracy and low cost, we proposed a new method to compensate for systematic errors in linear optical satellite data acquisition using only the relative constraints between two overlapped images, namely, the corresponding elevation constraints and sparse GCPs. We first demonstrated the feasibility of GC with relative constraints and established an optimized GC model suitable for these relative constraints. We then presented a global iterative method to eliminate inaccuracies in internal calibration caused by the different distributions of GCPs within two images. The nadir (NAD) linear camera on board the Zi-Yuan 3 (ZY-3) satellite was used to evaluate the feasibility of the presented GC method; the results indicated that the present method effectively compensated for systematic errors. Thus, this article demonstrated the feasibility of GC without calibration sites.

IPRDataCol - software for field labelling and data storage

Abstract The software IPRDataCol was developed in 2017 at the Instituto Agronomico do Parana (IAPAR) by a research group (Nucleo1) studying information technology applied to agricultural research. Due to the software architecture, whether the label-generating nor the data capture module use any native database, i.e, the program can be used directly from portable storage devices with flash memory. It is designed as a support tool to meet the needs of different research projects, regarding optimization in the identification of experimental plots in the field or laboratory/greenhouse, with subsequent logging of yield data measured by electronic scales, and storage in digital files. The software can be downloaded free of charge from the internet (http://nucleo1.org), without registration. The use of this program will contribute to minimize errors in data acquisition since the typing phase is not necessary for yield data measured by electronic scales.

Investigation into the Development and Testing of a Simplex Capacitance Sensor for Rotor Tip Clearance Measurement in Turbo Machinery

The rotor tip clearance is a vital parameter to characterize turbomachinery performance. It is an essential measurement during engine development and testing. The efficiency of the major modules like fan, compressor and turbine depends on the optimum running clearances. The capacitive sensor is preferred for tip clearance measurements because of various inherent advantages. They are fabricated through complex processes for turbomachinery application and are expensive. These sensors are exposed to harsh engine environments and are likely to exhibit degraded performance. Insulation failure at sensing head, connector contamination and micro-cracks in the MI (Mineral Insulated) cable leads to noise in the signal output. Frequent mechanical failures are also seen due to work hardening of MI cables, lead connector detachments and failure of micro fins at interconnects. Fault fixing of these is tedious and very expensive. This paper presents the concerted effort in developing a simple capacitance sensor addressing potential sensor failure issues. The design features include simple construction, ease of sensor assembly, component realization and troubleshooting. The design also addresses all safety requirements. The sensor has a large sensing area (7.6 mm diameter) producing very stable, high-level signal. The developed sensor produces output that varies from 1 VP-P to 11 VP-P for blade-tip gaps between 3 mm to 0.4 mm respectively. Blade pass simulation studies across the sensor, display almost flat response at minimum clearance. This feature results in very minimal data acquisition errors. The sensor along with RC (Resistance-Capacitance) conditioning electronics was successfully used for clearance measurement on fan stage of gas turbine engine. Test results are in line with the predictions. Extensive tip clearance measurements on the engine validate the design. The current work provides an insight into the development of a simplex capacitance sensor for tip clearance measurement.

Recent Developments in Tele-Ultrasonography.

A long-standing trend that will continue to grow in healthcare is providing high quality services for all the patient, no matter the distance and no matter the place. One approach currently being used to increase population access to healthcare services is telemedicine. This narrative review presents one branch of e-health, in particular the use of teleultrasonography (TUS) in clinical practice, the challenges and barriers encountered. Current advances in ultrasound technology, including the growth of portable and small ultrasound devices have increased the range of applications of TUS, from traumatic patients in emergency medicine, maternal ultrasound and even for monitoring and screening for chronic illnesses. Even though some barriers are still looking for a solution, like standardized training and protocols, errors in data acquisition, the lack of trained professionals to operate in remote areas, TUS has the potential to redesign future health care systems.

A method for improved 4D-computed tomography data acquisition

In four-dimensional time-dependent computed tomography (4D-CT) of the lungs, irregularities in breathing movements can cause errors in data acquisition, or even data loss. We present a method based on sending a synthetic, regular breathing signal to the CT instead of the real signal, which ensures 4D-CT data sets without data loss. Subsequent correction of the signal based on the real breathing curve enables an accurate reconstruction of the size and movement of the target volume. This makes it possible to plan radiation treatment based on the obtained data. The method was tested with dynamic thorax phantom measurements using synthetic and real breathing patterns.

A state estimation approach for fault location in transmission lines considering data acquisition errors and non-synchronized records

Abstract The performance of algorithms for fault location is directly related to the accuracy of its input data. Thus, factors such as errors in the line parameters, failures in synchronization of oscillographic records and errors in measurements of voltage and current can significantly influence the accuracy of algorithms that use bad data to indicate the faults location. This paper presents a methodology for fault location based on the theory of state estimation in order to determine the location of faults more accurately by considering realistic systematic errors that may be present in measurements of voltage and current. The methodology developed is innovative because, besides calculating the most likely fault distance obtained from measurement errors, the variance associated with the distance found is also determined, using the errors theory. The obtained results are relevant to show that the proposed estimation approach works even adopting realistic variances. Moreover, the fault location brings performance gains compared to a traditional algorithm, available in the literature.

Motion tracking and gait feature estimation for recognising Parkinson's disease using MS Kinect.

BackgroundAnalysis of gait features provides important information during the treatment of neurological disorders, including Parkinson’s disease. It is also used to observe the effects of medication and rehabilitation. The methodology presented in this paper enables the detection of selected gait attributes by Microsoft (MS) Kinect image and depth sensors to track movements in three-dimensional space.Methods The experimental part of the paper is devoted to the study of three sets of individuals: 18 patients with Parkinson’s disease, 18 healthy aged-matched individuals, and 15 students. The methodological part of the paper includes the use of digital signal-processing methods for rejecting gross data-acquisition errors, segmenting video frames, and extracting gait features. The proposed algorithm describes methods for estimating the leg length, normalised average stride length (SL), and gait velocity (GV) of the individuals in the given sets using MS Kinect data.ResultsThe main objective of this work involves the recognition of selected gait disorders in both the clinical and everyday settings. The results obtained include an evaluation of leg lengths, with a mean difference of 0.004 m in the complete set of 51 individuals studied, and of the gait features of patients with Parkinson’s disease (SL: 0.38 m, GV: 0.61 m/s) and an age-matched reference set (SL: 0.54 m, GV: 0.81 m/s). Combining both features allowed for the use of neural networks to classify and evaluate the selectivity, specificity, and accuracy. The achieved accuracy was 97.2 %, which suggests the potential use of MS Kinect image and depth sensors for these applications.ConclusionsDiscussion points include the possibility of using the MS Kinect sensors as inexpensive replacements for complex multi-camera systems and treadmill walking in gait-feature detection for the recognition of selected gait disorders.