Instrumental Errors Research Articles

Pengembangan Kurikulum Diklat Teknis Pengelolaan Barang Milik Daerah Berbasis Kompetensi Pegawai

The low competency of employees in the management of regional property in the West Java Provincial Government is a challenge for the West Java Province's Human Resource Development Agency (BPSDM) in developing apparatus competencies through education and training. The research aims to find out how much influence the policy instruments (policy instrument) and policy implementation (policy implementation) have in developing employee competencies through the technical training curriculum for regional property management. This study uses descriptive analysis with a qualitative approach. The data was explored in the form of the implementation of the technical training curriculum for the management of regional property held at the Human Resources Development Agency (BPSDM) of West Java Province in 2018. Data and information collection methods were carried out through observation, interviews, and document review. The results of the study show that there were errors in policy instruments (policy instruments) and policy implementation in determining the technical training curriculum for the management of regional property. Policy instruments through the establishment of curriculum (learning objectives, training courses, media and learning methods) and policy implementation through the implementation of curriculum (teaching and learning process and assessment) have not been oriented to competency requirements in accordance with training competency/employee position standards. competency of education and training graduates is not optimal.Keywords: Development of competency-based, profesional, competency-based curriculum

Single-replicate longitudinal data analysis in the presence of multiple instrumental measurement errors

In this paper, a novel method as a combination of the expectation–maximization (EM) algorithm and Variogram is proposed to decompose the longitudinal measurement errors in the absence of replications and the presence of multiple instrumental measurement errors. In the proposed method, multiple measurements are considered where the units are observed by several distinct instruments (gauges). The approach decouples the observed variance of the measurement model into the process and measurement system variances. In addition, it decomposes the variance of multiple instruments into the process and instrument variances. In the end, the proposed model is validated and tested based on simulated longitudinal data as well as a real case study related to the Framingham Heart study, measuring systolic blood pressure by multiple instruments. In addition, the robustness of the proposed method to the missing values, a common problem in longitudinal data, is demonstrated.

Solar irradiance measurement instrumentation and power solar generation forecasting based on Artificial Neural Networks (ANN): A review of five years research trend.

The increased demand for solar renewable energy sources has created recent interest in the economic and technical issues related to the integration of Photovoltaic (PV) into the grid. Solar photovoltaic power generation forecasting is a crucial aspect of ensuring optimum grid control and power solar plant design. Accurate forecasting provides significant information to grid operators and power system designers in generating an optimal solar photovoltaic plant and to manage the power of demand and supply. This paper presents an extensive review on the implementation of Artificial Neural Networks (ANN) on solar power generation forecasting. The instrument used to measure the solar irradiance is analysed and discussed, specifically on studies that were published from February 1st, 2014 to February 1st, 2019. The selected papers were obtained from five major databases, namely, Direct Science, IEEE Xplore, Google Scholar, MDPI, and Scopus. The results of the review demonstrate the increased application of ANN on solar power generation forecasting. The hybrid system of ANN produces accurate results compared to individual models. The review also revealed that improvement forecasting accuracy can be achieved through proper handling and calibration of the solar irradiance instrument. This finding indicates that improvements in solar forecasting accuracy can be increased by reducing instrument errors that measure the weather parameter.

Improving the measurement accuracy of magnetometer algorithmic correction of its instrumental errors

In many applications of the magnetic method it is advantageous to have information about the geomagnetic field strength and its gradients. The direct measurement of gradients does not depend on geomagnetic variations or normal field values. Special equipment is required for this purpose. The results of the development and construction of a three-component magnetometer-gradiometer are presented. The device is designed to measure on the earth's surface the absolute values of the three components of the geomagnetic field vector and the corresponding three components of the gradient. Installation of additional measuring sensors in the device – accelerometers, allows you to calculate the orientation of these vectors in space. The device of a magnetometer-gradiometer is described, its functional scheme and operating principle are presented. A set of instrumental errors that occur in the manufacture of three-axis systems of ferrosondes and accelerometers for measuring the components of the geomagnetic field strength and determining the orientation of the device is considered. The paper presents a method for determining instrumental errors and algorithmic correction of information signals coming from measuring sensors to significantly improve the accuracy of measurements. Examples of field tests of the device are given. The presented magnetometer-gradiometer can be used for accurate localization of previously identified ore bodies and determining the details of their structure.

Dynamics of Onboard Gravity Gradiometers

The paper addresses the dynamics of on-board gravity gradiometers for measuring the second derivatives of the geopotential. Mathematical models of accelerometer and dumbbell versions of the devices are developed, and their instrumental errors are analyzed.

Application of PCA Analysis and QR Decomposition to Address RFM's Ill-Posedness

Rational function model (<small>RFM</small>) is the most widely used sensor model in the remote sensing community. However, it suffers from ill-posedness, challenging its feasibility. This problem, i.e., ill-posedness, is mainly caused due to highly correlated coefficients of the <small>RFM</small>, which magnifies any small perturbations of observations, such as noise and instrumental error. This paper outlines a novel two-step method, called principal component analysis (<small>PCA</small>)-<small>RFM</small>, based on the integration of <small>PCA</small> and QR decomposition. In the first step, the <small>PCA</small>-<small>RFM</small> reduces the observational perturbations from the design matrix using the <small>PCA</small>. In the next step, the <small>RFM</small>'s coefficients are estimated using a <small>QR</small> decomposition with column pivoting and least square method. According to the results, the <small>PCA</small>-<small>RFM</small> is less sensitive than its rivals to the changes of the ground control point (<small>GCPs</small>) distribution. Geometrically speaking, in addition, <small>PCA</small>-<small>RFM</small> is more accurate than recently established methods even in the presence of the small number of <small>GCPs</small>.

Improve the Model Stability of Dam’s Displacement Prediction Using a Numerical-Statistical Combined Model

In most studies of dam's displacement prediction based on monitoring data, emphasis was given on improving the prediction accuracy, while the model stability was merely considered. This study proposed a numerical-statistical combined model which aims to improve the model stability. The displacement was modelled within three modules: recoverable displacement (i.e., displacement induced by the external load including the water pressure and temperature), non-recoverable displacement (i.e., displacement due to the inherent variations of the materials such as the creep and fatigue of the concrete), and measurement errors (i.e., instrument error and human error). To reduce the random errors and increase the model stability, we used the numerical simulation to constrain the coefficients of explanatory variables for the recoverable displacement. The non-recoverable displacement was estimated by empirical equations, and the measurement errors were given by Gaussian distributions. The randomness of coefficients in the model among all monitoring points are constrained further by random coefficient model. We adopted the root mean square error (RMSE) at varying time and the change ratio of the coefficients (CRC) to evaluate the model stability. Results indicated that the proposed model not only has better prediction accuracy but also has better model stability compared with the statistical model and coordinates-included statistical model proposed in previous studies.

Supplementary pressure comparison from (0.7 to 7) MPa in gas media at gauge mode

This report gives the results of a comparison of pressure standards of three National Metrology institutes in the range of 0.7 MPa to 7 MPa. This comparison was piloted by UME and was carried out from February 2017 to March 2019. This work is a part of the GULFMET project and is registered as a supplementary comparison GULFMET.M.P-S1. The transfer standard used was a digital pressure gauge, model 745-1K, serial number 114944, manufactured by Paroscientific Inc, with a resolution of 1 hPa. The reference values have been determined from the weighted mean of the deviations which is a measurement error of the test instrument reported by the participants for each specified pressure.Main textTo reach the main text of this paper, click on Final Report. Note that this text is that which appears in Appendix B of the BIPM key comparison database kcdb.bipm.org/.The final report has been peer-reviewed and approved for publication by the CCM, according to the provisions of the CIPM Mutual Recognition Arrangement (CIPM MRA).

Interval Probabilistic Energy Flow Calculation of CCHP Campus Microgrid Considering Interval Uncertainties of Distribution Parameters

Due to the absence of historical data and the errors of measurement instruments, there may be uncertainties in the distribution parameters of the random variables describing the uncertain fluctuations of node power including renewable energy station output and load power in the combined cooling heating and power (CCHP) campus microgrid. In this paper, intervals are used to describe the uncertainties of distribution parameters of the random variables, and an interval probabilistic energy flow (IPEF) calculation model of the CCHP campus microgrid is established. Introducing the interval arithmetic (IA) into the cumulant method, an IA-based IPEF algorithm is proposed to obtain the analytical expressions of probability density function or cumulative distribution function intervals of the state variables. Moreover, affine arithmetic (AA) is introduced to address the interval extension problem in the calculation, and an AA&IA-based IPEF algorithm is proposed. By constructing the correlation transformation matrixes, the correlation among different node power is considered in the IPEF calculation. A case study on a CCHP campus microgrid demonstrates that the results of the AA&IA-based IPEF algorithm are more accurate than those of the IA-based IPEF algorithm by using the results of the double-layer Monte Carlo method as a reference. Moreover, the proposed algorithms are more efficient than the double-layer Monte Carlo method.

Study on energy efficiency evaluation and influencing factors of ground source thermal energy management system operation

The paper combines the testing work of ground source heat pump systems, discusses the installation, use methods and test errors of various testing instruments, combines the field test experience, gives the main points of ground source heat pump energy efficiency testing, and according to the problems encountered during the test. The faults with high frequency are analyzed, and the methods of fault diagnosis and troubleshooting are given. This paper uses the ground-source heat pump experimental platform to set up multiple sets of experimental schemes to study the impact of factors such as pipe diameter, single and double U, and flow rate on system energy efficiency. At the same time, it combines engineering cases to ?large flow small temperature difference?, ?imbalance? phenomenon is analyzed, and feasible solutions and suggestions are given. By analyzing the energy efficiency and benefits of actual engineering of multiple ground source heat pump systems, the paper concludes that ground source heat pump systems have a wider application prospect in a certain area of Hubei.

Mathematical model of functioning of multisensor converter of binary mechanical signals in electric on the basis of fiber-optic digital-to-analog converter

The design and operation principle of a multisensor converter of binary mechanical signals into electrical signals consisting of a fiber-optic digital-analog converter, a photo amplifier and a voltage-to-code converter with double integration are considered. A generalized mathematical model of multisensor converter functioning has been developed, which combines particular mathematical models of fiber-optic digital-to-analog converter, photo-amplifier and voltage-to-code converter with double integration. The mathematical model of functioning of the multisensor converter in the form of analytical expressions for definition of the output electric code depending on values of bit digits of the input mechanical code taking into account a complex of constructive, circuit and power parameters of the converter is developed. The analytical description of the signal in the frequency converter in code, using which an algorithm is developed for numerical analysis of mathematical models of the functioning of the devices under study, providing the maximum permissible values of the instrumental errors of manufacture of the transducer elements in which is implemented the complete accuracy of the device. The presented results can be used for the development of multisensor converters of binary displacements of control systems, control and monitoring of energy-saturated objects, for which high noise immunity, electrical neutrality, low chemical activity and information security are of paramount importance.

Mode diagnostics of pipeline systems of the energetics

The paper proposes a method for diagnosing gradual failures in pipeline power systems, based on tracking the dynamics of flow regime parameters. The method also makes it possible to promptly adjust the coefficients of a mathematical model of the system objects. Conclusions are made based on the analysis of the entire set of measurements, which are considered random variables due to measurement errors. Conclusions are made based on the analysis of the entire set of measurements, which are considered random variables due to instrumental errors. Examples of a gas pumping unit and a complex looped gas pipeline system are given. Calculations are performed using standard software.

Consideration Factors in Application of Thermocouple Sensors for RCS Temperature Instrumentation

Because of harsh radiated environmental conditions, it is necessary to use thermocouples (TCs) in the temperature instrumentation channels of a reactor coolant system (RCS) in an integrated pressurized water reactor vessel. Conventionally, resistance temperature detectors (RTDs) have been adopted for RCS temperature measurement.Therefore, we have conducted an analysis and review of instrumentation error factors in the measurement circuits of RTD and TC sensors to specify the influence on measurement accuracy for application of TCs instead of RTDs for RCS temperature instrumentation.From the review and analysis results, it is anticipated that a measurement accuracy deterioration would be an issue and that a drift range should be investigated for the anticipated operational temperature conditions.

A Study Interfrequency Onboard Delays of Signals Navigation Satellites GLONASS

The problem of interfrequency signal delays estimation of GLONASS system navigation satellite is considered Such delays in the radio-frequency massage of navigation satellites are the source of the instrumental error of pseudorange measurements. The estimation method of interfrequency signal delays is proposed, based on the calculation of first differences of pseudorange measurements on navigation satellites, the relative position of which differs by no more than 1°. The experiment on estimation of inter-frequency delays signal on the basis of the developed method was conducted. The experiment is based on the use of calibrated navigation receiver to separate errors caused by signal delays in the receiver and navigation satellite. Also, to minimize the effects of the Earth's ionosphere layer navigation signal delay, only those measurements taken at a low Total Electronic Concentration TEC are included in the processing. Pseudorange measurements using GLONASS and GPS navigation satellites signals were used as the main source data. The results of the estimation for all GLONASS orbital group satellites were compared with the form values provided in the navigation massage. The results of experimental research have been analyzed and the values of standard deviation of GLONASS orbital group delays have been obtained. To confirm the adequacy of the results was checked for internal convergence of the results and determined the error of the developed method for assessing the interfrequency delays of navigation spacecraft.

A new high-precision sphere-fitting method with small segment angles

Measuring a spherical surface with small segment angle (e.g. 1°) precisely is a big challenge in scientific research and engineering applications. To fit sphere surfaces with small segment angles with high precision, a five-parameter constrained nonlinear least square fitting (CNLSF) method is presented in this work, where an Adam optimization algorithm with a prominent anti-interference ability is employed. We discuss various factors which influence the final uncertainty evaluation of the sphere’s radius. A Monte Carlo simulation is conducted by superimposing Gaussian noise on the ideal spherical data to simulate practical instrument errors, then the relationship between the fitting bias of the sphere’s radius and noise the level, the number of sampling points and the segment angle is investigated by comparing the performance of the linear least square fitting algorithm, the nonlinear least square fitting algorithm and the CNLSF algorithm. Finally, the simulated and experimental results show that the CNLSF method performs more precisely and robustly than the other algorithms, even when the corresponding segment angle approaches 1°, which illustrates that it is a promising and practical method in the real measurement world.

Calibration of the Thermal Model of an Inertial Measurement Unit with Three Angular Rate Sensors

The paper considers the techniques developed for calibration of an inertial measurement unit (IMU) containing three angular rate sensors (ARS). Calibration is performed on a two-axis calibration turntable with a thermal chamber. For instrumental errors of ARS measurements, a priori parametric model is introduced, which includes biases, scale factor errors, small angles of nonorthogonality of sensitivity axes, and coefficients of temperature dependence for all these measurement errors. The problem of determining parameters of the parametric model is reduced to the problem of optimal estimation of the linear dynamic system state vector. In this context, the measurement vector is the vector of a small rotation between the model orientation of the IMU (calculated from the ARS readings) and the model orientation of the turntable faceplate (calculated from the turntable measurements). The approach proposed for formalization of the calibration problem is a modification of the method for calibration of an IMU as a whole inseparable unit, using a single-axis turntable with the horizontal axis of rotation, previously developed in the Laboratory of Control and Navigation of Lomonosov Moscow State University. The calibration experiment consists in a sequence of horizontal rotations about each of the ARS sensitivity axes. The results of numerical simulation and covariance analysis are considered.

CHLOROPHYLL-A CONCENTRATION RETRIEVAL USING CONVOLUTIONAL NEURAL NETWORKS IN LAGUNA LAKE, PHILIPPINES

Abstract. Two existing chlorophyll-a (chl-a) concentration retrieval procedures, which are analytical and empirical, are hindered by the complexity in radiative transfer equation (RTE) and in statistical analyses, respectively. Another promising model in this direction is the use of artificial neural networks (ANN). Mostly, a pixel-to-pixel with one-layer ANN model is used; where in fact that the satellite instrumental errors and man-made objects in water bodies might affect the retrieval and should be taken into account. In this study, the mask-based neural structure, called convolutional neural networks (CNN) model containing both the target and neighborhood pixels, is proposed to reduce the influence of the aforementioned premises. The proposed model is an end-to-end multiple-layer model which integrates band expansion, feature extraction, and chl-a estimation into the structure, leading to an optimal chl-a concentration retrieval. In addition to that, a two-stage training is also proposed to solve the problem of insufficient in-situ samples which happens in most of the time. In the first stage, the proposed model is trained by using the chl-a concentration derived from the water product, provided by satellite agency, and is refined with the in-situ samples in the second stage. Eight Sentinel-3 images from different acquisition time and coincide in-situ measurements over Laguna Lake waters of Philippines were utilized to conduct the model training and testing. Based on quantitative accuracy assessment, the proposed method outperformed the existing dual- and triple- bands combinations in chl-a concentration retrieval.

Deep Learning Predictive Models for Cognitive Radio System

Cognitive Radio (CR) was introduced to improve the utilization of Radio Frequencies (RF) that remain under-utilized by the primary users (licensee). The main idea behind CR is to allow un-licensed (secondary) users to occupy vacancies in licensed bands. However, CR mandates the secondary user to vacate the frequency band within a specified time after the primary user attempts to use the frequency band. CR does not expect the primary users to share their frequency usage schedules and hence the secondary users have to scan and predict the vacancy. The advantage for the secondary users is that they do not pay for utilization of band, if they are conformal to the CR specifications. CR is the next generation of smart communication systems. CR requires continuous monitoring of the intended RF band in the intended geographical area. This information may be used to predict spectral vacancies (white spaces). Certain bands, e.g. Analog TV bands, will have pre declared utilization schedules but in general, spectrum utilization is a random process and hence prediction can be difficult. However, Deep Learning (DL) techniques can improve the accuracy of prediction. Deep Learning techniques require large and clean data sets to work correctly. Such data sets are also necessary to compare achievable accuracy of prediction algorithms. Towards this end, we have created data sets that can be used for simulation, training and testing of CR over GSM band (890-960MHz). A typical file with two hour of observations will have about 1.2 million samples. More than 1000 sets of data samples have been captured from urban and rural areas in India. All the data sets have been cleaned to avoid instrument errors and statistical outliers. In this paper we have used these standardized data sets to perform a comparative analysis of three DL methods for CR, viz. Auto-encoder (AE), Long Short-Term Memory (LSTM) and Multi Layer Perceptron (MLP). Results of the comparison are discussed.

Reflection on modern methods: five myths about measurement error in epidemiological research.

Epidemiologists are often confronted with datasets to analyse which contain measurement error due to, for instance, mistaken data entries, inaccurate recordings and measurement instrument or procedural errors. If the effect of measurement error is misjudged, the data analyses are hampered and the validity of the study’s inferences may be affected. In this paper, we describe five myths that contribute to misjudgments about measurement error, regarding expected structure, impact and solutions to mitigate the problems resulting from mismeasurements. The aim is to clarify these measurement error misconceptions. We show that the influence of measurement error in an epidemiological data analysis can play out in ways that go beyond simple heuristics, such as heuristics about whether or not to expect attenuation of the effect estimates. Whereas we encourage epidemiologists to deliberate about the structure and potential impact of measurement error in their analyses, we also recommend exercising restraint when making claims about the magnitude or even direction of effect of measurement error if not accompanied by statistical measurement error corrections or quantitative bias analysis. Suggestions for alleviating the problems or investigating the structure and magnitude of measurement error are given.

THE ESTIMATION OF TEACHING PROFESSION EDUCATION (TPE) PARTICIPANTS’ ABILITY IN INDONESIA: AN ASSESSMENT MODEL WITH PARTIAL SCORING

The ability of TPE participants in arranging lesson plans is essential to be mastered as an indicator of pedagogical competence which should be possessed before conducting a teaching process in the class. This research aimed to analyze the estimation of TPE participants’ ability in composing a lesson plan which uses the assessment with partial scoring. This research used quantitative descriptive explorative approach. Data were collected through document study of lesson plan made by TPE participants assessed by lecturers in a workshop. In the sample were 236 respondents selected by using purposive sampling technique. The data analysis was performed to estimate participants’ skill using Item Response Theory through Partial Credit Model (PCM). The estimation result produced the interpretation of items with 5 categories of scoring 0, 1, 2, 3, 4 and has 4 thresholds such as the threshold of category 0 to category 1, category 1 to category 2, category 2 to category 3, and from category 3 to category 4. Besides that the information function value of the instrument and Standard Error of Measurement based on the analysis obtained items in the instrument which have higher information function value compared to the mistakes of the measurement or the estimation of measurement which has relatively small errors, so it can be stated that learning model is using accurate partial scoring to estimate the ability of TPE participants in composing lesson plans. Keywords: teaching profession, partial scoring, item response model, partial credit model.