Unreliable Measurement Research Articles

Improving the Reliability of Online Bushing Monitoring

This paper discusses the common issue of bushing failures in power transformers and presents methods for preventing such failures through online monitoring. The paper presents a method based on the comparison of bushing leakage currents from different transformers connected to the same busbars to overcome problems with unreliable measurement results due to grid voltage imbalance or frequent transformer outages.

152-LB: Implementing a Pilot Clinic for Steroid-Induced Hyperglycemia in Graft vs. Host Disease

Introduction: Malignant hematologic disorders are often treated with hematopoietic stem cell transplantation. This can be complicated by graft versus host disease (GVHD). Treatment is high dose corticosteroids, which often leads to steroid induced hyperglycemia (SIH). Uncontrolled hyperglycemia is associated with increased morbidity and mortality. Despite evidence favoring glycemic control in this population, there are no formal protocols or clinics in place to achieve this. Methods: The University of Kansas Blood and Marrow Transplant Program collaborated with the Department of Endocrinology to establish a pilot clinic for patients with GVHD who have SIH. Over 12 months, patients were referred to a physician assistant specializing in diabetes. They were seen at 1-4 week intervals based on acuity and glycemic control. Data was collected on blood glucose, hemoglobin A1c, fructosamine, steroid doses, insulin doses, hospitalizations, infections, and death. Results: 64 patients were referred to this clinic. 24 were excluded due to lack of hyperglycemia or declined being seen. Median time to index visit from referral was 20.5 days (IQR 8.25-29). Median A1c at index visit was 5.9 (IQR 5.4-7.2) and at 3 months was 5.3 (IQR 4.6-6.3). Median highest glucose within the first 3 months was 230 mg/dL (IQR 183-362) and at 3-6 months was 161 mg/dL (IQR 119.8-247, p-value = 0.0025). Median lowest glucose at 3 months was 86 mg/dL (IQR 78.5-103) and at 6 months was 87 mg/dL (IQR 75-115.8). There were 20 patients who were hospitalized and 9 patient deaths. Conclusions: Managing SIH is complex and requires a multidisciplinary approach. Patients who were seen had reduced episodes of hyperglycemia without increased episodes of hypoglycemia. A1c is an unreliable measurement in this patient population due to high cell turnover and frequent transfusions. Future studies using continuous glucose monitors could be investigated to assess the efficacy and feasibility of this pilot clinic. Disclosure D. Pham: None. K. Grdinovac: None. J. Leal: None. L. Rodriguez: None. A. Salva: None. W. Wesson: None. J. McGuirk: None.

Flight data validation of an icing accretion estimation scheme using super-twisting observers

This paper develops a generalised multivariable super-twisting observer for a class of nonlinear systems in which the unmeasured variables linked to the known state dependent matrix function appear multiplicatively. A sufficient condition is given to guarantee that the reconstruction errors associated with the unmeasurable variables converge to zero in finite time. This approach is then used to address the aircraft icing accretion estimation problem despite unreliable sensor measurement. The efficacy of the approach has been evaluated via real flight data recorded under natural icing conditions. Results show that the observer has the capability to estimate the change of the drag coefficient induced by icing accretion and to reconstruct the unreliable pitch rate sensor measurement simultaneously.

Improved Predictions of Dissolved Organic Carbon–Water Partitioning and Unveiling the Chemodiversity of Dissolved Organic Matter

Dissolved organic matter (DOM) is a complex mixture of organic compounds with chemical heterogeneity in natural soils, sediments, and waters. Predicting the dissolved organic carbon (DOC)–water partition coefficients (KDOC) of organic pollutants to DOMs from various sources is important for assessing their fate and bioavailability. However, the accuracy of the recently developed polyparameter linear free energy relationship (pp-LFER) KDOC models is generally restricted by the small data set, inclusion of experimental data from unreliable measurement methods, or undefined selection standards. The aim of this study was to establish pp-LFER models for predicting the KDOC of nonionic organic chemicals to a variety of sources of DOMs and to get an understanding of DOM chemodiversity. A reliable and expanded experimental KDOC data set was compiled. Improved pp-LFER models were developed and assessed for All DOM (DOM from all sources), Aldrich humic acid (HA), Roth HA, soil porewater DOM, sediment porewater DOM, natural aquatic DOM, natural terrestrial DOM, natural DOM, and commercial DOM. The models developed in this study were reasonably robust and accurate with a root mean square error (RMSE) of 0.538 for the All DOM model and RMSE from 0.196 to 0.860 for the specific DOMs. Also, the models generally performed better than previously published ones. Moreover, the system parameters of the models well described the chemical variabilities between soil porewater DOM and sediment porewater DOM, between natural aquatic DOM and natural terrestrial DOM, and between natural DOM and commercial DOM regarding polarizability, dipolarity, H-bond-accepting and -donating ability, and cavity formation energy. This study provides effective tools to assess the tendencies of organic chemicals to DOMs and improves our understanding of the chemical heterogeneity of DOMs from different sources.

Adversarial Attack Mitigation Strategy for Machine Learning-Based Network Attack Detection Model in Power System

The network attack detection model based on machine learning (ML) has received extensive attention and research in PMU measurement data protection of power systems. However, well-trained ML-based detection models are vulnerable to adversarial attacks. By adding meticulously designed perturbations to the original data, the attacker can significantly decrease the accuracy and reliability of the model, causing the control center to receive unreliable PMU measurement data. This paper takes the network attack detection model in the power system as a case study to analyze the vulnerability of the ML-based detection model under adversarial attacks. And then, a mitigation strategy for adversarial attacks based on causal theory is proposed, which can enhance the robustness of the detection model under different adversarial attack scenarios. Unlike adversarial training, this mitigation strategy does not require adversarial samples to train models, saving computing resources. Furthermore, the strategy only needs a small amount of detection model information and can be migrated to various models. Simulation experiments on the IEEE node systems verify the threat of adversarial attacks against different ML-based detection models and the effectiveness of the proposed mitigation strategy.

Calibration of cognitive tests to address the reliability paradox for decision-conflict tasks

Standard, well-established cognitive tasks that produce reliable effects in group comparisons also lead to unreliable measurement when assessing individual differences. This reliability paradox has been demonstrated in decision-conflict tasks such as the Simon, Flanker, and Stroop tasks, which measure various aspects of cognitive control. We aim to address this paradox by implementing carefully calibrated versions of the standard tests with an additional manipulation to encourage processing of conflicting information, as well as combinations of standard tasks. Over five experiments, we show that a Flanker task and a combined Simon and Stroop task with the additional manipulation produced reliable estimates of individual differences in under 100 trials per task, which improves on the reliability seen in benchmark Flanker, Simon, and Stroop data. We make these tasks freely available and discuss both theoretical and applied implications regarding how the cognitive testing of individual differences is carried out.

Fiber Orientation Reconstruction from SEM Images of Fiber-Reinforced Composites

The orientation of fibers in composites reinforced with short fibers can provide insight into the microstructure of the material and considerably affect its macroscopic characteristics. However, the present standard techniques for detecting fiber orientation and length based on microscopic image processing have faults in practical applications, including high effort, low efficiency, and unreliable measurement results. In this study, a method for measuring fiber orientation based on 3D reconstructions of scanning electron microscope (SEM) images is provided. The geodesic active contour (GAC) model is applied to segment the fibers in the SEM images. Matching the fiber contours with the scale-invariant feature transformation (SIFT) algorithm successfully extracts 3D orientation information from the fiber contours. The unit vector of the fiber axis is fitted from the extracted point cloud using the ordinary least squares (OLS) method. With a maximum deviation of 3.83° and an average deviation of 1.50°, the measurement findings of this method are substantially comparable to those of the image-measuring instrument. This paper offers a quantitative approach to studying the microstructure of short fiber-reinforced composites, thereby furnishing objective evidence to support the development and research of such materials.

Liver stiffness is associated with excess mortality in the general population driven by heart failure: The Rotterdam Study.

Elevated liver stiffness reflects hepatic fibrosis but can also be secondary to venous congestion. We aimed to study the association between liver stiffness and mortality in the general population, stratified for heart failure and/or coronary heart disease (CHD). We analysed individuals enrolled in the ongoing prospective population-based Rotterdam Study who attended a visit between 2009-2014 that included liver stiffness measurement. Exclusion criteria for the primary analysis were incomplete data on heart failure, unreliable liver stiffness, alcohol abuse and viral hepatitis, leaving 4.153 participants (aged 67.5± 8.4 years, 44.2% male) for analysis with a median follow-up of 6.0 (interquartile range: 5.1-7.0) years. Secondary analysis included participants with viral hepatitis, alcohol abuse and/or unreliable measurement. The association between liver stiffness and mortality was assessed using Cox regression. Associations between heart failure, CHD, and echocardiographic characteristics and liver stiffness were quantified with linear regression. Liver stiffness ≥8.0kPa was associated with mortality (aHR: 1.37, 95%CI: 1.00-1.89). However, this was driven by participants with heart failure (aHR: 2.48, 95%CI: 1.15-5.35), since high liver stiffness was not associated with mortality in participants without heart failure and/or CHD (aHR: 1.07, 95%CI: 0.70-1.64). Results were consistent when individuals with viral hepatitis, alcohol abuse or unreliable liver stiffness measurement were not excluded. Several cardiovascular characteristics were significantly associated with higher liver stiffness, e.g. heart failure, moderate/poor diastolic dysfunction, and right atrium diameter > 4.5cm. In our cohort of community-dwelling elderly, high liver stiffness was associated with excess mortality, primarily explained by participants with heart failure. Moreover, heart failure and its indicators were associated with increased liver stiffness.

Thyroid Cancer Persistence in Patients with Unreliable Thyroglobulin Measurement: Circulating microRNA as Candidate Alternative Biomarkers.

We aimed to evaluate the role of circulating miRNAs as a biomarker of the persistence of papillary thyroid cancer (PTC) in patients with an "uninformative" thyroglobulin (Tg) measurement. We prospectively enrolled 49 consecutive PTC patients with Tg-positive antibodies (TgAb) who had undergone a (near)-total thyroidectomy and 131I therapy (RIT). The serum thyroid stimulating hormone (TSH), Tg, and TgAb levels were measured before and at 6 and 12 months after RIT, respectively. The serum miRNA (221, 222, 375, 155, and 146b) levels were measured simultaneously. The response to the initial therapy was assessed according to the 2015 ATA criteria. A decrease in 50% or more of serum miRNA over time was observed in 41/49 PTC patients, who showed an excellent response (ER), but six and two patients were classified to have an indeterminate/incomplete biochemical or incomplete structural response to initial therapy. Serum miRNA kinetics emerge as a promising biomarker for the early detection of a persistent disease in PTC patients with uninformative Tg results.

Automatic quality control of telemetric rain gauge data providing quantitative quality information (RainGaugeQC)

Abstract. The RainGaugeQC scheme described in this paper is intended for real-time quality control of telemetric rain gauge data. It consists of several checks: detection of exceedance of the natural limit and climate-based threshold as well as checking of the conformity of rain gauge and radar observations, the consistency of time series from heated and unheated sensors, and the spatial consistency of adjacent gauges. The proposed approach is focused on assessing the reliability of individual rain gauge observations. A quantitative indicator of reliability, called the quality index (QI), describes the quality of each measurement as a number in the range from 0.0 (completely unreliable measurement) to 1.0 (perfect measurement). The QI of a measurement which fails any check is lowered, and only a measurement very likely to be erroneous is replaced with a “no data” value. The performance of this scheme has been evaluated by analysing the spatial distribution of the precipitation field and comparing it with precipitation observations and estimates provided by other techniques. The effectiveness of the RainGaugeQC scheme was also analysed in terms of the statistics of QI reduction. The quality information provided is very useful in further applications of rain gauge data. The scheme is used operationally by the Polish national meteorological and hydrological service (Institute of Meteorology and Water Management – National Research Institute).

Research on a hydrate saturation prediction method based on an analysis of factors that influence the prediction accuracy of sea area hydrate saturation

It has been predicted that China has about 80 billion tons of oil equivalent of natural gas hydrate resources in its sea. China has conducted at least six drilling and two trial exploitations in the South China Sea, achieving good results. However, these achievements do not necessarily mean that the research area can support commercial exploitation. Only when the natural gas hydrate resources in the research area reach a certain scale will it be valuable for commercial exploitation. A reliable quantitative prediction method is necessary to clarify the scale of gas hydrate in the research area. However, the classical Wood method’s application to the prediction of suspended hydrate saturation in the Shenhu maritime area of China results in a large prediction error; the analysis shows that an unreliable measurement of reservoir parameters is the main reason for the large prediction error. In order to clarify the influence of reservoir parameters, this paper—by analyzing the measurement sources of the parameters of reservoir parameters, firstly indicates that the inaccurate measurement of three reservoir parameters—matrix composition, porosity, and density—is the main cause of prediction error. Then, using the design of different reservoir parameter measurement schemes to conduct comparative analysis, this paper points out that unreliable porosity and density measurement may lead to large prediction errors, while unreliable measurements of matrix composition have a relatively small impact on prediction accuracy. Further analysis shows that the absolute value of the prediction error caused by uncertainty in synthetic reservoir parameter measurement is sometimes larger than the sum of the absolute prediction error caused by the single parameter measurement uncertainty. In addressing the problem of large prediction errors caused by the inaccurate measurement of reservoir parameters, this paper proposes a hydrate saturation prediction method based on non-hydrate correction—called the “Wood-Bao method”. Simulation and actual data studies show that the prediction effect of this method is superior to that of the Wood method.

Systolic blood pressure measurements are unreliable for the management of acute spontaneous intracerebral hemorrhage.

Whether systolic blood pressure (SBP) is reliable in acute spontaneous intracerebral (sICH) by assessing agreement between simultaneous BP measurements obtained from cuff non-invasive blood pressure (NIBP) and radial arterial invasive blood pressure (AIBP) devices. Among 766 prospectively screened sICH subjects, 303 (39.5%) had NIBP and AIBP measurements. During the first 24 h, 2157 simultaneous paired measurement readings were abstracted. Paired NIBP/AIBP measurements were included in a Bland-Altman technique with 95% agreement limits and coefficients from regression analysis derived from a bootstrap procedure. Variance for SBP was 66.1 mmHg, which was larger than the 44.3 mg Hg for diastolic blood pressure (DBP) or the 46.1 mmHg for mean arterial pressure (MAP). Pairwise comparison of mean biases showed a significant difference between SBP when compared to DBP (p < 0.0001) or MAP (p < 0.0001). The mean bias between DBP and MAP was not different (p = 0.68). Regression-based Bland Altman analysis found significant bias (slope -0.16, 95% CI -0.23, -0.09, p < 0.05) over the range of mean SBP. Bias over the range of mean DBP or MAP was not significant. We concluded that SBP is an unreliable blood pressure measurement in patients with sICH.

A4LidarTag: Depth-Based Fiducial Marker for Extrinsic Calibration of Solid-State Lidar and Camera

Visual-based simultaneous localization and mapping (SLAM) systems perform weakly in object tracking and map reconstruction due to the unreliable depth measurement originating from image-only data. Light Detection and Ranging (LiDAR) can be coupled to overcome the drawback of uncertain depth estimation. The prerequisite for performing data fusion is to align visual-Lidar sensors to a specific coordinate system with extrinsic pose by calibrating. The conventional extrinsic calibration frameworks either rely on markers in artificial large-size calibration boards or uncontrollable natural scenes (Fig. 2 ), limiting stability and convenience. In this paper, we have designed a novel marker pattern, A4LidarTag, composed of circular holes. The difference in depth measurement is used to encode location information. Based on A4LidarTag, the automatic extrinsic calibration framework between solid-state Lidar (SSL) and the camera is developed. The proposed framework can be implemented in close range (within 1 m) and on an A4-size calibration board. The average reprojection error resulting from Lidar point clouds projection is about 0.12pixels. Experiments show excellent efficiency and versatility in both indoor and outdoor scenes.

A "Goldilocks zone" for mind-wandering reports? A secondary data analysis of how few thought probes are enough for reliable and valid measurement.

Mind-wandering assessment relies heavily on the thought probe technique as a reliable and valid method to assess momentary task-unrelated thought (TUT), but there is little guidance available to help researchers decide how many probes to include within a task. Too few probes may lead to unreliable measurement, but too many probes might artificially disrupt normal thought flow and produce reactive effects. Is there a "Goldilocks zone" for how few thought probes can be used to reliably and validly assess individual differences in mind-wandering propensity? We address this question by reanalyzing two published datasets (Study 1, n = 541; Study 2, ns ≈ 260 per condition) in which thought probes were presented in multiple tasks. Our primary analyses randomly sampled probes in increments of two for each subject in each task. A series of confirmatory factor analyses for each probe "bin" size tested whether the latent correlations between TUT rate and theoretically relevant constructs like working memory capacity, attention-control ability, disorganized schizotypy, and retrospective self-reported mind wandering changed as more probes assessed the TUT rate. TUT rates were remarkably similar across increasing probe-bin sizes and zero-order correlations within and between tasks stabilized at 8-10 probes; moreover, TUT-rate correlations with other latent variables stabilized at about 8 thought probes. Our provisional recommendation (with caveats) is that researchers may use as few as 8 thought probes in prototypical cognitive tasks to gain reliable and valid information about individual differences in TUT rate.

Two-dimensional shear wave elastography for assessing liver fibrosis in patients with chronic liver disease: a prospective cohort study.

Background/AimsThe objective of this study was to determine whether the newly developed two-dimensional shear wave elastography (2D-SWE, RS85, Samsung-shearwave imaging) was more valid and reliable than transient elastography (TE) for predicting the stage of liver fibrosis.MethodsThe study prospectively enrolled a total of 116 patients with chronic liver disease who underwent 2D-SWE, TE, laboratory testing, and liver biopsy on the same day from two tertiary care hospitals. One patient with unreliable measurement was excluded. The measurement of 2D-SWE was considered acceptable when a homogenous color pattern in a region of interest of at least 10 mm was detected at 10 different sites. Diagnostic performance was calculated using area under the receiver operating characteristic curve (AUROC).ResultsLiver fibrosis stages included F0 (18%), F1 (19%), F2 (24%), F3 (22%), and F4 (17%). Interclass correlation coefficient for inter-observer agreement in 2D-SWE was 0.994 (95% confidence interval [CI], 0.988 to 0.997). Overall, the results of 2D-SWE and stages of histological fibrosis were significantly correlated (r = 0.601, p < 0.001). For The 2D-SWE showed good diagnostic ability (AUROC, 0.851; 95% CI, 0.773 to 0.911) comparable to TE (AUROC, 0.859; 95% CI, 0.781 to 0.916) for the diagnosis of significant fibrosis (≥ F2), and the cut-off value was 5.8 kPa. AUROC and optimal cut-off of 2D-SWE for the diagnosis of liver cirrhosis were 0.889 (95% CI, 0.817 to 0.940) and 9.6 kPa, respectively. TE showed similar diagnostic performance in distinguishing cirrhosis (AUROC, 0.938; 95% CI, 0.877 to 0.974; p = 0.08).Conclusions2D-SWE is comparable to TE in diagnosing significant fibrosis and liver cirrhosis with high reliability.

Does discrimination mediate the association between education and later‐life cognitive outcomes in a cohort of Black Americans? An example of parametric g‐estimation with latent variables in the STAR study

AbstractBackgroundIdentifying mediators between education and later‐life cognition is an important step in developing prevention measures. We evaluate everyday and major lifetime discrimination as potential mediators of the impact of education on later‐life cognition. To address bias that might occur due to either time‐varying confounding or unreliable measurement of discrimination, we use a novel parametric g‐estimation approach.MethodParticipants in the Study of Healthy Aging in African Americans (STAR, n = 764, ages 52‐90) self‐reported education (high school [HS] degree, general educational development [GED], or less [ref]; bachelor’s degree; or graduate degree). Validated measures of everyday discrimination (9‐items assessing frequency with 6‐point Likert responses) and major lifetime discrimination (9‐items, dichotomous yes‐no), were conceptualized as latent variables (Table 1). The observed cognitive outcome was the average of the verbal episodic memory, semantic memory, and executive function Spanish and English Neuropsychological Assessment Scales (SENAS). We used linear regression to estimate the total effect of education on later‐life cognition and parametric g‐estimation with latent discrimination measures to estimate the controlled direct effect (CDE) of education on cognition if the discrimination measures were set to their sample averages. Models were adjusted for measurement error in the discrimination measures using Fuller’s Method. Standard errors were bootstrapped.ResultMost participants were female (68.32%) and the average age was 69.0 (Table 2). Both bachelor’s (b=0.41[0.21,0.60]) and graduate (b=0.43[0.22,0.64]) degrees were associated with better cognition compared to HS/GED or less (Table 3). More education was associated with higher levels of major lifetime discrimination. The CDEs of a bachelor’s and graduate degree, if everyone had the average level of everyday and major life discrimination, were 0.32[0.06,0.61] and 0.55[0.24,0.82], respectively (Table 3 and as a function of different fixed averages in Figure 2).ConclusionThis study provides a novel demonstration of parametric g‐estimation methods that correct for measurement error with latent variables and allow for possible feedback between education and early and late‐life confounders. Accounting for exposure to discrimination slightly increased the magnitude of the association between graduate education and cognition in this older sample of Black American adults, because education was associated with greater exposure to major life discrimination.

A Novel Thyroglobulin Immunoassay Using the Specimen-Pretreatment Process Improves the Accuracy of Thyroglobulin Measurements in Anti-Thyroglobulin Positive Specimens.

Recently, second-generation thyroglobulin (Tg) sandwich immunoassays have been used in clinical laboratories to measure the serum Tg levels, which is a tumor marker used to monitor postoperative patients with differentiated thyroid cancers. However, these immunoassays are often subject to Tg autoantibody (TgAb) interference. TgAb interference is inevitable for almost all Tg immunoassays, resulting in unreliable Tg measurement values of TgAb-positive samples. To address TgAb interference, we have developed a novel immunoassay based on a fully automated chemiluminescent enzyme immunoassay system using the effective specimen-pretreatment process to inactivate TgAb in blood and evaluated its assay performance. The developed assay was traceable to BCR457 IRMM reference material with a limit of quantification of 0.03 ng/mL. The pretreatment process inactivated almost all TgAb in specimens and allowed accurate Tg measurements in TgAb-positive samples in which TgAb interference was observed using the immunoassays. Size-exclusion chromatography analysis of immunoreactive Tg molecule in a TgAb-positive serum verified disruption of the Tg-TgAb immune complex by the pretreatment process. Good correlation of Tg values in TgAb-negative specimens was observed between the new Tg immunoassay and the second-generation sandwich immunoassays. However, there were numerous discrepant samples on bias plots between the new Tg immunoassay and the second-generation sandwich immunoassays for TgAb-positive specimens. This study indicates the new Tg immunoassay with the specimen-pretreatment process is both robust and free from interference by TgAb. Thus, this novel assay is superior to second-generation sandwich immunoassays and gives accurate Tg concentrations even for TgAb-positive cases.

Online MR evaluation of inter- and intra-fraction uterus motions and bladder volume changes during cervical cancer external beam radiotherapy

Purpose/objective(s)The purpose of the study was to assess the uterus motions and bladder volume changes of fractional movements in cervical sites throughout the external beam radiotherapy (EBRT) treatment.Materials/methodsA prospective online MR imaging tracking study was conducted in EBRT 43 patients with at least 4 scans during each treatment (before: ultrasound scan, MRI scan, CBCT scan, after: MRI scan) were included. In order to improve the treatment repeatability, each patient was instructed to empty the bladder and drink 500 ml water 1 h before CT simulation and each treatment. If the ultrasound scan result reached the CT simulation volume of bladder, the treatment began. Bladder was outlined on the T2 weighted axial sequence and CBCT image by the two observers to avoid the influence of contouring. The data of bladder volume and scanning time were accurately recorded. The bladder volumes, filling rates and uterus motion were retrospectively analyzed by MIM software.ResultsInter-fraction variation of the bladder volume was significant (p < 0.0001). Intra-fraction mean increase of the bladder volume was modest (30 cc) but significant (p < 0.001). Both inter- and intra-fraction of the uterus motion were significant. The average time between the pre-and post-fraction MRI scans was 27.82 ± 7.12 min (range 10–55 min) for IMRT plans and 24.14 ± 5.86 min (range7-38 min) for VMAT plan. Average bladder filling rate was 3.43 ml/min. The bladder filling rate did not change significantly with the course of treatment, but the bladder was more intolerant.ConclusionThis is the most detailed assessment of intra-fraction and inter-fraction motion during EBRT for cervical cancer. Finally, this study will inform appropriate treatment margins for online adaptive radiotherapy. We suggest that at least one image scan is needed before the EBRT. The portable US scanner provides a quick but unreliable measurement of the bladder volume. There is a significant statistical difference between the results of ultrasonic scanning and that of image scanning.

Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Deep Learning and Soft Sensing

The Remaining useful life (RUL) prediction is of great concern for the reliability and safety of lithium-ion batteries in electric vehicles (EVs), but the prediction precision is still unsatisfactory due to the unreliable measurement and fluctuation of data. Aiming to solve these issues, an adaptive sliding window-based gated recurrent unit neural network (GRU NN) is constructed in this paper to achieve the precise RUL prediction of LIBs with the soft sensing method. To evaluate the battery degradation performance, an indirect health indicator (HI), i.e., the constant current duration (CCD), is firstly extracted from charge voltage data, providing a reliable soft measurement of battery capacity. Then, a GRU NN with an adaptive sliding window is designed to learn the long-term dependencies and simultaneously fit the local regenerations and fluctuations. Employing the inherent memory units and gate mechanism of a GRU, the designed model can learn the long-term dependencies of HIs to the utmost with low computation cost. Furthermore, since the length of the sliding window updates timely according to the variation of HIs, the model can also capture the local tendency of HIs and address the influence of local regeneration. The effectiveness and advantages of the integrated prediction methodology are validated via experiments and comparison, and a more precise RUL prediction result is provided as well.

Standartization of Forensic Activities and Certification of Expert Laboratories as a Factor for the Development of Forensic Examination

The author has carried out the analysis of the perspectives for certification of domestic forensic laboratories for the compliance with international standards, which would contribute to the creation of an effective legal mechanism for the transition to a new level of development of forensic technologies. The main factors of administrative and legal regulation in the field of standardization have been studied. In accordance with the requirements of DSTU ISO / IEC 17025 the principles of organization and implementation of the quality management system (QMS) into forensic laboratories have been determined.&#x0D; It has been established that the execution of the DSTU ISO / IEC 17025 requirements needs significant resources and financial costs from the laboratory. The author has provided clarification to the main stages of preparation for certification, which include measures on developing management documentation (Guidelines, Regulations on forensic laboratory, Accreditation Area), on inventory of existing equipment, metrological support of laboratory activities, preparation and submission of applications to the accreditation agency. It has been noted that certified forensic laboratories should use certified expert methods within their activities that can ensure reliable results during the research. Methodologies should be tested in other reputable organizations or validated directly in the laboratory. Considerable attention has been paid to the quality of standard samples, reagents and materials. The emphasis has been placed on the need to participate in proficiency testing programs based on the results of interlaboratory comparative tests as an effective way to monitor the activities of the forensic laboratory.&#x0D; The author has substantiated the conclusion that the standardization processes of forensic activity and certification of forensic laboratories are aimed at increasing the level of metrological work to protect citizens and the national economy of Ukraine from the consequences of unreliable measurement results.