THE APPLICATION OF THE SEMIPARAMETRIC GSTAR MODEL IN DETERMINING GAMMA-RAY LOG DATA ON SOIL LAYERS

This research examines the semiparametric Generalized Space-Time Autoregressive (GSTAR) spacetime modeling and determines its spatial weight. In general, the spatial weights used are uniform, binary weights, and based on the distance, the result is a fixed weight. The GSTAR model is a stochastic model that takes into account its random variables. Thus, it is necessary to study the random spatial weights. This study introduced a new method to estimate the observed value of the GSTAR model semiparametric with a uniform kernel. The data involved the Gamma Ray (GR) log data on four coal drill holes. The semiparametric GSTAR modeling aimed to predict the amount of log GR in the unobserved soil layer based on the observation data information on the layer above it and its surrounding location. The results revealed that semiparametric GSTAR modeling could predict the presence of coal seams and their thickness of drill holes. The results also highlight the validity test on the out-sample data that the error in each borehole results in a small error. In addition, the error tends to approach the actual observed value at a depth of 1 meter down.

Generalized Convexity Properties and Shape-Based Approximation in Networks Reliability

Some properties of generalized convexity for sets and functions are identified in case of the reliability polynomials of two dual minimal networks. A method of approximating the reliability polynomials of two dual minimal network is developed based on their mutual complementarity properties. The approximating objects are from the class of quadratic spline functions, constructed based on both interpolation conditions and shape knowledge. It is proved that the approximant objects preserve both the high-order convexity and some extremum properties of the exact reliability polynomials. It leads to pointing out the area of the network where the maximum number of paths is achieved. Numerical examples and simulations show the performance of the algorithm, both in terms of low complexity, small error and shape preserving. Possibilities of increasing the accuracy of approximation are discussed.

Effects of fallow tillage on winter wheat yield and predictions under different precipitation types

In northern China, precipitation that is primarily concentrated during the fallow period is insufficient for the growth stage, creates a moisture shortage, and leads to low, unstable yields. Yield prediction in the early growth stages significantly informs field management decisions for winter wheat (Triticum aestivum L.). A 10-year field experiment carried out in the Loess Plateau area tested how three tillage practices (deep ploughing (DP), subsoiling (SS), and no tillage (NT)) influenced cultivation and yield across different fallow periods. The experiment used the random forest (RF) algorithm to construct a prediction model of yields and yield components. Our results revealed that tillage during the fallow period was more effective than NT in improving yield in dryland wheat. Under drought condition, DP during the fallow period achieved a higher yield than SS, especially in drought years; DP was 16% higher than SS. RF was deemed fit for yield prediction across different precipitation years. An RF model was developed using meteorological factors for fixed variables and soil water storage after tillage during a fallow period for a control variable. Small error values existed in the prediction yield, spike number, and grains number per spike. Additionally, the relative error of crop yield under fallow tillage (5.24%) was smaller than that of NT (6.49%). The prediction error of relative meteorological yield was minimum and optimal, indicating that the model is suitable to explain the influence of meteorological factors on yield.

Research and Design on the Coordinate Control System of Once-through Boiler Power Unit

Taking the 1000 MW ultra supercritical unit as an object of study, The nonlinear model is builded through mechanism analysis, which is linearized based on small error, the multi-variable decoupling controller is designed to achieve full decoupling of the input and output variables, and finally the three-input-three-output coordinated control system (CCS) is established. The control system is verified by coal quality and the specific enthalpy of feed water disturbance when load changing, the simulation result shows that the improved CCS has good control performance.

PERAMALAN JUMLAH PENUMPANG PESAWAT BANDARA I GUSTI NGURAH RAI MENGGUNAKAN EXPONENTIAL SMOOTHING DAN RUEY-CHYN TSAUR

Bali province is a tourist destination island with good transportation. Airplane is the most used transportation to go to Bali. Convenience of the airline passengers are the most important thing for I Gusti Ngurah Rai Airport Authorithy. An exact forecast method is needed to predict the numbers of passenger in the future. There are two types of forecasting methods; triple exponential smoothing and Fuzzy Time Series Ruey-Chyn Tsaur, however based on the research Fuzzy Time Series Ruey-Chyn Tsaur is better than triple exponential smoothing due to a small error MAPE (Mean Absolute Percentage Error) of 2,4% and plot is close to actual data.

Equivalent Resilient Modulus Inversion and Calculation of Different Asphalt Pavement Structures

Based on the modulus inversion theory and the equivalent principle of deflection basin, by analyzing the deflection basin data of each structure layer measured by the FWD, the obtained equivalent resilient moduli of different structural layers in three different structures (a semirigid type Asphalt pavement and two inverted asphalt pavements) were compared. At the same time, the calculated equivalent resilient modulus of the top surface of the structural layer based on the inversion method was used to modify the existing theory formula. The results show that, with the inversion method and the theoretical calculation method, the calculated equivalent resilient modulus of the top surface of the cushion layer has a small error, but the theoretical calculation method overestimates the equivalent resilient modulus of the top surface of the cement stabilized crushed stone layer and the top surface of the graded crushed stone transition layer, especially for the inverted asphalt pavement; by contrast, the corresponding result of the inversion method is closer to the value in actual engineering. While determining the equivalent resilient modulus of the cushion layer, the influence of the thickness of the cement stabilized crushed stone layer needs to be considered, and the inverted asphalt pavement structure should adopt a thicker asphalt layer to reduce the modulus deviation; at the same time, the more the structural layers and the larger the difference in the interlayer modulus ratio, the larger the deviation of equivalent resilient modulus of the top surface of the base layer; for the inverted asphalt pavement and semirigid asphalt pavement, the correction coefficients of the calculation formula of the equivalent resilient modulus of the top surface of cement stabilized gravel layer are 0.35∼0.55 and 0.65∼0.75, respectively. The inversion method can be used to determine the equivalent resilient modulus of each structural layer of the inverted asphalt pavement and semirigid asphalt pavement, and its results can provide a basis for the design of the structure reconstruction of asphalt pavement.

Multiparty Reach and Frequency Histogram: Private, Secure, and Practical

Consider the setting where multiple parties each hold a multiset of users and the task is to estimate the reach (i.e., the number of distinct users appearing across all parties) and the frequency histogram (i.e., fraction of users appearing a given number of times across all parties). In this work we introduce a new sketch for this task, based on an exponentially distributed counting Bloom filter. We combine this sketch with a communication-efficient multi-party protocol to solve the task in the multi-worker setting. Our protocol exhibits both differential privacy and security guarantees in the honest-but-curious model and in the presence of large subsets of colluding workers; furthermore, its reach and frequency histogram estimates have a provably small error. Finally, we show the practicality of the protocol by evaluating it on internet-scale audiences.

Investigation rectifier circuits based on the mathematical models in the two-dimensional space

The mathematical model for the rectifier circuit using semiconductor diodes is setup in this paper. The properties of the rectifier circuit presented by the ordinary differential equation containing a control parameter K. When K is large enough, the studied equation gives a trajectory approximating to a trajectory of the rectifier circuit above. The theorem about the approximation of these solutions with arbitrary small error (this error can be controlled by increasing K). The usefulness of this model is illustrated via concrete example. This study can to get more profound results in further and investigate an optimal process for an assembly line of rectifiers in electrical engineering.

Evaluating Precision of Annular Pressure Buildup (APB) Estimation Using Machine-Learning Tools

Summary Annular pressure buildup (APB) is caused by heating of the trapped drilling fluids (during production), which may lead to burst/collapse of the casing or axial ballooning, especially in subsea high-pressure/high-temperature wells. The objective of this paper is to apply machine-learning (ML) tools to increase precision of the APB estimation, and thereby improve the fluid and casing design for APB mitigation in a given well. The APB estimation methods in literature involve theoretical and computational tools that accommodate two separate effects: volumetric expansion [pressure/volume/temperature (PVT) response] of the annulus drilling fluids and circumferential expansion (and corresponding mechanical equilibrium) of the well casings. In the present work, ML algorithms were used to accurately model “fluid density = f(T, P)” based on the experimental PVT data of a given fluid at a range of (T, P) conditions. Sensitivity analysis was performed to demonstrate improvement in precision of APB estimation (for different subsea well scenarios using different fluids) using the ML-basedmodels. This study demonstrates that, in several subsea scenarios, a relatively small error in the experimental fluid PVT data can lead to significant variation in APB estimation. The ML-based models for “density = f(T, P)” for the fluids ensure that the cumulative error during the modeling process is minimized. The use of certain ML-based density models was shown to improve the precision of APB estimation by several hundred psi. This advantage of the ML-based density models could be used to improve the safety factors for APB mitigation, and accordingly, the work may be used to better handle the APB issue in the subsea high-pressure/high-temperature wells.

Correction of Systematic Error in Global Temperature Analysis Related to Aging Effects

The white paint or white plastic of the housings of weather stations ages which leads to increased absorption of solar radiation and to increased temperature measurements. This alone would be a small error. However, the homogenization algorithms used by many different organizations repeatedly add this small value each time a weather station is renovated, renewed or replaced, which results in a substantial systematic error. This error occurs, because steps in the temperature data series are corrected as if they were permanent, but this is not always the case, particularly not in case of weather station aging and renewal.An in-depth analysis of the weather station data sets (homogenized and non-homogenized) confirmed the presence of the systematic error, proved the existence of statistically significant aging effects and allowed to quantify the size of the aging effects.I have quantified the effect of the aging effects on the climate curves by adding the aging functions to the temperature data points in the intervals between homogenizations. This corrected data base is analyzed using the GISTEMP tool.Here I show a reduction of the temperature change between the decades 1880-1890 and 2010-2020 from 1.43°C to 0.83°C CI(95%) [0.46°C; 1.19°C].