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Approximate Computing (AC) have widely adopted in designing large-scale logic circuits. In particular, approximate adder and multiplier circuits have been considerably targeted in the realm of image processing due to their high energy-saving while preserving proper level of computing accuracy. Nevertheless, intensive research and development are maintained in the means of seeking more matured designs that effectively prioritize design overheads over error resilience. In this paper, low error-distance approximate 4:2 compressor circuits are proposed to construct high-speed approximate adders. The developed compressors realize high logic computing and incur competitive area and power consumption, and therefore, they were leveraged to configure an approximate 8×8 multiplier designs. To achieve a favorable trade-off between computational accuracy and hardware resource usage, we develop a simulation framework that evaluates the accuracy of the proposed multiplier designs at the gate-level (measuring error distance) and at the application-level (evaluating SNR and SSIM) of an image. The framework truncates specific propagated carry bits, i.e., least significant bits (LSBs), to realize profitable area- and power-saving. Furthermore, two main high-speed multiplier designs are proposed herein, namely High Computing Performance Approximate Multiplier (HCP-AMUL) and HCP Low Error Approximate Multiplier (HCPLE-AMUL). Matlab R2022b along with VS Code are used for running simulations and accuracy evaluation, while Vivado 2018.2 is utilized for HDL reconfigurable logic design and implementation and evaluation of area, power, and speed, configured on an FPGA Xilinx Nexys 4 Artix-7 (XC7A100T1CSG324) trainer board. The experimental results demonstrate the efficacy of the developed multipliers as the developed HCPLE-AMUL delivers 54.26%, 11.72%, and 449.85 of speedup, power saving, and Power-Delay-AreaError-Product (PDAEP) improvement, respectively. On the other hand, the presented HCP-AMUL realizes an improved saving of area and power at the expense of an acceptable lowering of computation accuracy. It achieves 9.66%, 505.40, and 53.73% of power saving, PDAEP, and speedup respectively, Thus, the proposed compressor and multiplier circuits potentially can be promising approximate computing modules for image processing applications to provide improved trade-off between computation accuracy and logic utilization complexity. [ABSTRACT FROM AUTHOR]

The Global Ecosystem Dynamics Investigation (GEDI) system provides essential data for estimating forest canopy height on a global scale. However, factors such as complex topography and dense canopy can significantly reduce the accuracy of GEDI canopy height estimations. We selected the South Taihang region of Henan Province, China, as our study area and proposed an optimization framework to improve GEDI canopy height estimation accuracy. This framework includes correcting geolocation errors in GEDI footprints, screening and analyzing features that affect estimation errors, and combining two regression models with feature selection methods. Our findings reveal a geolocation error of 4 to 6 m in GEDI footprints at the orbital scale, along with an overestimation of GEDI canopy height in the South Taihang region. Relative height (RH), waveform characteristics, topographic features, and canopy cover significantly influenced the estimation error. Some studies have suggested that GEDI canopy height estimates for areas with high canopy cover lead to underestimation, However, our study found that accuracy increased with higher canopy cover in complex terrain and dense vegetation. The model's performance improved significantly after incorporating the canopy cover parameter into the optimization model. Overall, the R2 of the best-optimized model was improved from 0.06 to 0.61, the RMSE was decreased from 8.73 m to 2.23 m, and the rRMSE decreased from 65% to 17%, resulting in an accuracy improvement of 74.45%. In general, this study reveals the factors affecting the accuracy of GEDI canopy height estimation in areas with complex terrain and dense vegetation cover, on the premise of minimizing GEDI geolocation errors. Employing the proposed optimization framework significantly enhanced the accuracy of GEDI canopy height estimates. This study also highlighted the crucial role of canopy cover in improving the precision of GEDI canopy height estimation, providing an effective approach for forest monitoring in such regions and vegetation conditions. Future studies should further improve the classification of tree species and expand the diversity of sample tree species to test the accuracy of canopy height estimated by GEDI in different forest structures, consider the distortion of optical remote sensing images caused by rugged terrain, and further mine the information in GEDI waveforms so as to enhance the applicability of the optimization framework in more diverse forest environments. [ABSTRACT FROM AUTHOR]

Significance: Many researchers have proposed various non-invasive glucose monitoring (NIGM) approaches using wearable or portable devices. However, due to the limited capacity of detectors for such compact devices and the movement of the body during measurement, the precision of the acquired data frequently diminishes, which can cause problems during actual use in daily life. In addition, intensive smoothing is often used in post-processing to mitigate the effects of erroneous values. However, this requires a considerable amount of data and results in a delay in the response to the actual blood glucose level (BGL). Aim: Instead of just applying data smoothing in the post-process of the data acquisition, we propose an active low-quality data screening method in the pre-process. In the proposal phase of the screening process, we employ an analytical approach to examine and formulate factors that might affect the BGL estimation accuracy. Approach: A signal quality index inspired by the standard deviation concept is introduced to detect visually apparent noise on signals. Furthermore, the total estimation error in the metabolic index (MI) is calculated based on potential perturbations defined by the signal-to-noise ratio (SNR) and the uncertainty due to discrete sampling. Thereafter, the acquired data were screened by these quality indices. Results: By applying the proposed data screening process to the data obtained from a commercially available smartwatch device in the pre-process, the estimation accuracy of the MI-based BGL was improved significantly. Conclusions: Adopting the proposed screen process improves BGL estimation accuracy in the smartwatch-based prototype. Applying the proposed screen process will facilitate the integration of wearable and continuous BGL monitoring into size-and SNR-limited devices such as smartwatches and smart rings. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The range–Doppler (R–D) model is extensively employed for the geometric processing of synthetic aperture radar (SAR) images. Refining the sensor motion state and imaging parameters is the most common method for achieving high-precision geometric processing using the R–D model, comprising a process that involves numerous parameters and complex computations. In order to reduce the specialization and complexity of parameter optimization in the classic R–D model, we introduced a novel approach called ICRD (image compensation-based range–Doppler) to improve the positioning accuracy of the R–D model, implementing a low-order polynomial to compensate for the original imaging errors without altering the initial positioning parameters. We also designed low-order polynomial compensation models with different parameters. The models were evaluated on various SAR images from different platforms and bands, including spaceborne TerraSAR-X and Gaofen3-C images, manned airborne SAR-X images, and unmanned aerial vehicle-mounted miniSAR-Ku images. Furthermore, image positioning experiments involving the use of different polynomial compensation models and various numbers and distributions of ground control points (GCPs) were conducted. The experimental results demonstrate that geometric processing accuracy comparable to that of the classical rigorous positioning method can be achieved, even when applying only an affine transformation model to the images. Compared to classical refinement models, however, the proposed image-compensated R–D model is much simpler and easy to implement. Thus, this study provides a convenient, robust, and widely applicable method for the geometric-positioning processing of SAR images, offering a potential approach for the joint-positioning processing of multi-source SAR images. [ABSTRACT FROM AUTHOR]

Various error factors influence the inversion of forest canopy height using GEDI full-waveform LiDAR data, and the interaction of these factors impacts the accuracy of forest canopy height estimation. From an error perspective, there is still a lack of methods to fully correct the impact of various error factors on the retrieval of forest canopy height from GEDI. From the modeling perspective, establishing clear coupling models between various environments, collection parameters, and GEDI forest canopy height errors is challenging. Understanding the comprehensive impact of various environments and collection parameters on the accuracy of GEDI data is crucial for extracting high-quality and precise forest canopy heights. First, we quantitatively assessed the accuracy of GEDI L2A data in forest canopy height inversion and conducted an error analysis. A GEDI forest canopy height error correction model has been developed, taking into account both forest density and terrain effects. This study elucidated the influence of forest density and terrain on the error in forest canopy height estimation, ultimately leading to an improvement in the accuracy of forest canopy height inversion. In light of the identified error patterns, quality control criteria for GEDI footprints are formulated, and a correction model for GEDI forest canopy height is established to achieve high-precision inversion. We selected 19 forest areas located in the United States with high-accuracy Digital Terrain Models (DTMs) and Canopy Height Models (CHMs) to analyze the error factors of GEDI forest canopy heights and assess the proposed accuracy improvement for GEDI forest canopy heights. The findings reveal a decrease in the corrected RMSE value of forest canopy height from 5.60 m to 4.19 m, indicating a 25.18% improvement in accuracy. [ABSTRACT FROM AUTHOR]

Coupling solar-induced chlorophyll fluorescence (SIF) with gross primary productivity (GPP) for ecological function integration research presents numerous uncertainties, especially in ecologically fragile and climate-sensitive arid regions. Therefore, evaluating the suitability of SIF data for estimating GPP and the feasibility of improving its accuracy in the northern region of Xinjiang is of profound significance for revealing the spatial distribution patterns of GPP and the strong coupling relationship between GPP and SIF in arid regions, achieving the goal of "carbon neutrality" in arid regions. This study is based on multisource SIF satellite data and GPP observation data from sites in three typical ecosystems (cultivated and farmlands, pasture grasslands, and desert vegetation). Two precision improvement methods (canopy and linear) are used to couple multiple indicators to determine the suitability of multisource SIF data for GPP estimation and the operability of accuracy improvement methods in arid regions reveal the spatial characteristics of SIF (GPP). The results indicate the following. (1) The interannual variation of GPP shows an inverted "U" shape, with peaks values in June and July. The cultivated and farmland areas have the highest peak value among the sites (0.35 gC/m2/month). (2) The overall suitability ranking of multisource SIF satellite products for GPP estimation in arid regions is RTSIF > CSIF > SIF_OCO2_005 > GOSIF. RTSIF shows better suitability in the pasture grassland and cultivated and farmland areas (R2 values of 0.85 and 0.84, respectively). (3) The canopy method is suitable for areas with a high leaf area proportion (R2 improvement range: 0.05–0.06), while the linear method is applicable across different surface types (R2 improvement range: 0.01–0.13). However, the improvement effect of the linear method is relatively weaker in areas with high vegetation cover. (4) Combining land use data, the overall improvement of SIF (GPP) is approximately 0.11%, and the peak values of its are mainly distributed in the northern and southern slopes of the Tianshan Mountains, while the low values are primarily found in the Gurbantunggut Desert. The annual mean value of SIF (GPP) is about 0.13 mW/m2/nm/sr. This paper elucidates the applicability of SIF for GPP estimation and the feasibility of improving its accuracy, laying the theoretical foundation for the spatiotemporal coupling study of GPP and SIF in an arid region, and providing practical evidence for achieving carbon neutrality goals. [ABSTRACT FROM AUTHOR]

The accuracy of digital elevation models (DEMs) is crucial for practical applications in complex terrain. In this study, various correction models were employed to evaluate and correct the 90 m SRTM3 DEM and TanDEM-X DEM data in the Qinghai-Tibet Plateau region. A simple and universal three-dimensional data co-registration method was utilized for horizontal alignment. The study demonstrated a significant reduction in horizontal discrepancies between datasets after data co-registration. By comparing the performance of five different correction methods, it was found that the RF model exhibited excellent accuracy and robustness, making it particularly suitable for applications that require high precision. The XGB and BPNN models offer a good balance between accuracy and time efficiency, making them suitable for research scenarios that are less sensitive to precision requirements. In contrast, the MLR and IDW methods performed poorly and are not recommended for high-precision applications. Furthermore, the corrected DEMs showed improved resistance to the influence of high slopes, enhancing their applicability in complex terrain areas. This study provides an important reference for the correction of high-quality DEMs in the Qinghai-Tibet Plateau region and offers valuable insights for DEM research in similar areas.

Purpose: Histotripsy is a nonionizing, noninvasive, and nonthermal focal tumor therapy. Cone‐beam computed tomography (CBCT) guidance was developed for targeting tumors not visible on ultrasound. This approach assumes cavitation is formed at the geometrical focal point of the therapy transducer. In practice, the exact location might vary slightly between transducers. In this study, we present a phantom with an embedded target to evaluate CBCT‐guided histotripsy accuracy and assess the completeness of treatments. Methods: Spherical (2.8 cm) targets with alternating layers of agar and radiopaque barium were embedded in larger phantoms with similar layers. The layer geometry was designed so that targets were visible on pre‐treatment CBCT scans. The actual histotripsy treatment zone was visualized via the mixing of adjacent barium and agar layers in post‐treatment CBCT images. CBCT‐guided histotripsy treatments of the targets were performed in six phantoms. Offsets between planned and actual treatment zones were measured and used for calibration refinement. To measure targeting accuracy after calibration refinement, six additional phantoms were treated. In a separate investigation, two groups (N = 3) of phantoms were treated to assess visualization of incomplete treatments ("undertreatment" group: 2 cm treatment within 2.8 cm tumor, "mistarget" group: 2.8 cm treatment intentionally shifted laterally). Treatment zones were segmented (3D Slicer 5.0.3), and the centroid distance between the prescribed target and actual treatment zones was quantified. Results: In the calibration refinement group, a 2 mm offset in the direction of ultrasound propagation (Z) was measured. After calibration refinement, the centroid‐to‐centroid distance between prescribed and actual treatment volumes was 0.5 ± 0.2 mm. Average difference between the prescribed and measured treatment sizes in the incomplete treatment groups was 0.5 ± 0.7 mm. In the mistarget group, the distance between prescribed and measured shifts was 0.2 ± 0.1 mm. Conclusion: The proposed prototype phantom allowed for accurate measurement of treatment size and location, and the CBCT visible target provided a simple way to detect misalignments for preliminary quality assurance of CBCT‐guided histotripsy. [ABSTRACT FROM AUTHOR]

In UWB-based indoor positioning, it is important to observe the ranging performance of the UWB module to prevent positioning errors. Ranging is the initial process in computing positioning. This research aims to observe the ranging accuracy and precision of the ESP32 UWB Pro with a Display module and analyze its performance in indoor positioning using TDoA and Trilateration. The ranging method was held using the SS-TWR which is the basic ranging used generally in UWB. ESP32 Pro is a module consisting of ESP32 and OLED display which is integrated with Decawave DW 1000. Analysis of 6750 ranging error data is carried out to determine the appropriate method to increase accuracy. The convergence of error ranges that occur leads to the use of regression as an error mitigation method for Decawave on the ESP32 UWB Pro with Display module. Increasing the accuracy of ranging regression can reduce the error from MAE of 79.98cm to only 5.05cm. It's applied to positioning to obtain the accuracy and precision performance of the TDoA and Trilateration positioning. The resulting MAE values are 7.47cm for X and 10.49cm for Y in TDoA Positioning. Meanwhile, in Trilateration, the MAE was 8.15cm for X and 8.47cm for Y. Our findings indicate that an increase in ranging accuracy with regression had an impact on positioning accuracy. However, the spread of error positioning shows that it's still weak in precision. [ABSTRACT FROM AUTHOR]

Industrial robots are complex systems, as they require the integration of several sub-assemblies to perform accurate operations. Moreover, they may experience remarkable dynamic actions due to high kinematic requirements, which are necessary to obtain reduced cycle times. The dynamic design of industrial robots can therefore be demanding, since the single structural component can induce an impact both in the design phase (development strategy and computational time) and at the machine level (global stiffness and natural frequencies). To this end, the present paper proposes first a topology optimization procedure based on the Equivalent Static Loads (ESL) method that integrates flexible multibody simulation outputs. The same procedure also foresees an intermediate static reduction to reduce and to precisely define the application points of the ESL. Secondly, an optimization procedure based on the Quasi-Static Loads (QSL) method integrating flexible multibody simulation outputs is proposed as well. The objective is to carry out a comparison between the two methods and consequently evaluate the benefits and drawbacks of the two. In the end, practical guidelines regarding the selection and application of the two methods are also provided to the reader. [ABSTRACT FROM AUTHOR]

Classifying unbalanced datasets is a challenging task, and traditional Support Vector Machine (SVM) algorithms often struggle to achieve accurate results. In this paper, we propose an Ant Colony Optimization based Support Vector Machine (SVM-ACO) algorithm that outperforms SVM on unbalanced datasets. Traditional SVM algorithms tend to be biased towards the majority class, leading to suboptimal performance for the minority class. However, SVM-ACO addresses this issue by incorporating the ant colony optimization approach, which assigns different weights to samples based on their class membership, giving higher importance to the minority class. This enables SVM-ACO to achieve a more balanced classification performance and improved accuracy. We compare the accuracy achieved by SVM-ACO and SVM on various imbalanced datasets from the literature. The experimental results support the effectiveness of SVM-ACO in addressing the challenges of unbalanced datasets. By incorporating ant colony optimization, SVM-ACO achieves improved accuracy and provides a promising alternative for handling unbalanced classification problems. [ABSTRACT FROM AUTHOR]

The range–Doppler (R–D) model is extensively employed for the geometric processing of synthetic aperture radar (SAR) images. Refining the sensor motion state and imaging parameters is the most common method for achieving high-precision geometric processing using the R–D model, comprising a process that involves numerous parameters and complex computations. In order to reduce the specialization and complexity of parameter optimization in the classic R–D model, we introduced a novel approach called ICRD (image compensation-based range–Doppler) to improve the positioning accuracy of the R–D model, implementing a low-order polynomial to compensate for the original imaging errors without altering the initial positioning parameters. We also designed low-order polynomial compensation models with different parameters. The models were evaluated on various SAR images from different platforms and bands, including spaceborne TerraSAR-X and Gaofen3-C images, manned airborne SAR-X images, and unmanned aerial vehicle-mounted miniSAR-Ku images. Furthermore, image positioning experiments involving the use of different polynomial compensation models and various numbers and distributions of ground control points (GCPs) were conducted. The experimental results demonstrate that geometric processing accuracy comparable to that of the classical rigorous positioning method can be achieved, even when applying only an affine transformation model to the images. Compared to classical refinement models, however, the proposed image-compensated R–D model is much simpler and easy to implement. Thus, this study provides a convenient, robust, and widely applicable method for the geometric-positioning processing of SAR images, offering a potential approach for the joint-positioning processing of multi-source SAR images.

Coupling solar-induced chlorophyll fluorescence (SIF) with gross primary productivity (GPP) for ecological function integration research presents numerous uncertainties, especially in ecologically fragile and climate-sensitive arid regions. Therefore, evaluating the suitability of SIF data for estimating GPP and the feasibility of improving its accuracy in the northern region of Xinjiang is of profound significance for revealing the spatial distribution patterns of GPP and the strong coupling relationship between GPP and SIF in arid regions, achieving the goal of “carbon neutrality” in arid regions. This study is based on multisource SIF satellite data and GPP observation data from sites in three typical ecosystems (cultivated and farmlands, pasture grasslands, and desert vegetation). Two precision improvement methods (canopy and linear) are used to couple multiple indicators to determine the suitability of multisource SIF data for GPP estimation and the operability of accuracy improvement methods in arid regions reveal the spatial characteristics of SIF (GPP). The results indicate the following. (1) The interannual variation of GPP shows an inverted “U” shape, with peaks values in June and July. The cultivated and farmland areas have the highest peak value among the sites (0.35 gC/m2/month). (2) The overall suitability ranking of multisource SIF satellite products for GPP estimation in arid regions is RTSIF > CSIF > SIF_OCO2_005 > GOSIF. RTSIF shows better suitability in the pasture grassland and cultivated and farmland areas (R2 values of 0.85 and 0.84, respectively). (3) The canopy method is suitable for areas with a high leaf area proportion (R2 improvement range: 0.05–0.06), while the linear method is applicable across different surface types (R2 improvement range: 0.01–0.13). However, the improvement effect of the linear method is relatively weaker in areas with high vegetation cover. (4) Combining land use data, the overall improvement of SIF (GPP) is approximately 0.11%, and the peak values of its are mainly distributed in the northern and southern slopes of the Tianshan Mountains, while the low values are primarily found in the Gurbantunggut Desert. The annual mean value of SIF (GPP) is about 0.13 mW/m2/nm/sr. This paper elucidates the applicability of SIF for GPP estimation and the feasibility of improving its accuracy, laying the theoretical foundation for the spatiotemporal coupling study of GPP and SIF in an arid region, and providing practical evidence for achieving carbon neutrality goals.

Multi-source data-fusion approaches have been developed for estimating regional precipitation. However, studies considering the specific upper limits of the improved gridded rainfall data for different fusion approaches are limited. Here, the potential ranges of accuracy improvement for satellite and reanalysis rainfall products were addressed using various machine learning fusion approaches, including multivariate linear regression (MLR), feedforward neural network (FNN), random forest (RF), and long short-term memory (LSTM), over the Chinese mainland. All four fusion methods reduce errors in the original precipitation products. The upper limits of accuracy improvement in terms of correlation coefficient (CC) and root mean square error (RMSE) were 30.65 and 15.27%, respectively. M-RF showed the best average CC (0.828) and RMSE (4.62 mm/day) in the four seasons. LSTM performed the best under light rainfall events, whereas MLR and RF exhibited better performance under moderate and heavy rainfall events, respectively. Overall, these results serve as a basis for the fusion approach and technique selection, based on the comprehensive validation in different climate zones, altitudes, and seasons over the Chinese mainland. HIGHLIGHTS Machine learning fusion approaches were used for precipitation product estimation.; Four different models were used: MLR, feedforward neural network, RF, and LSTM.; All four fusion methods reduced errors in the original precipitation products.; LSTM showed the best performance for light rainfall events.; MLR and RF performed better for moderate and heavy rainfall events.;

The object of research is the process of temperature measurement with a platinum thermistor. We have conducted studies on the cubic transformation function of the thermistor when using redundancy that yielded the equation of redundant measurements of the desired temperature. Owing to this, it became possible to directly apply the resulting equation without additional measures to linearize the function of the thermistor transformation. In addition, the obtained value of the desired temperature does not depend on the values of the parameters of the cubic transformation function and their deviations from the nominal values. Experimental studies have proven that the value of the normalized temperature T0 has a greater influence on the result of redundant measurements and the value of the normalized temperature ΔT on the entire range of measured temperatures Tx is almost unaffected. The best accuracy results (value of relative error δ=0.02 %) were obtained at T0 values lower than –60 °C. When the error of reproduction of normalized temperatures increased from ±0.02 °C to ±0.1 °C, the best accuracy results (value of relative error δ=0.06 %) were obtained at values of normalized temperature T0 below –130 °C. Analysis of results of the absolute error ΔT revealed that with an error of reproduction of normalized temperatures of ±0.02 °C and at T0=–180 °C, its value does not exceed 0.02 °C, that is, it is within the error of reproduction of normalized temperatures. This allows us to state that it is recommended to use sources of standardized temperatures of high accuracy during measurement control. Thus, there are reasons to assert the prospect for redundant measurements when directly measuring temperature with a thermistor with a cubic transformation function with high accuracy. [ABSTRACT FROM AUTHOR]

Smart Grids (SG) encompass the utilization of large-scale data, advanced communication infrastructure, and enhanced efficiency in the management of electricity demand, distribution, and productivity through the application of machine learning techniques. The utilization of machine learning facilitates the creation and implementation of proactive and automated decision-making methods for smart grids. In this paper, we provide an experimental study to understand the power demands of consumers (domestic and commercial) in SGs. The power demand source is considered a smart plug reading dataset. This dataset is large dataset and consists of more than 850 user plug readings. From the dataset, we have extracted two different user data. Additionally, their hourly, daily, weekly, and monthly power demand is analysed individually. Next, these power demand patterns are utilized as a time series problem and the data is transformed into 5 neighbour problems to predict the next hour, day, week, and month power demand. To learn from the transformed data, Artificial Neural Network (ANN) and Linear Regression (LR) ML algorithms are used. According to the conducted experiments, we found that ANN provides more accurate prediction than LR Additionally, we observe that the prediction of hourly demand is more accurate than the prediction of daily, weekly, and monthly demand. Additionally, the prediction of each kind of pattern needs an individually refined model for performing with better accuracy. [ABSTRACT FROM AUTHOR]

We propose a flexible 3D reconstruction method based on the RGB-D data stream. Compared to previous methods using pure voxels or pure points as representations, our works propose a new representation combining voxels and points to improve the reconstruction accuracy. A key insight is that points can store additional depth data that are not sampled by regular voxels. Thus, by integrating points and voxels, the 3D reconstruction process can be accelerated due to higher data utilization. Furthermore, depth information stored in points is used to refine the noisy depth image through a depth image refinement method, consequently improving the reconstructed shape quality. Extensive comparative experiments are performed including different representations (pure voxels/points) and various methods (fusion-based/learning-based and online/offline) to illustrate the effectiveness of our work. Experimental results demonstrate that our method can achieve real-time performance, effectively avoid artifacts, and reach state-of-the-art accuracy levels. More importantly, we provide a novel idea to balance the conflict between memory overhead and reconstruction accuracy. [ABSTRACT FROM AUTHOR]

Accurate measurement of solar radiation is essential for understanding climate patterns, assessing solar energy potential, and predicting weather conditions. Over the years, solar radiation measuring instruments such as pyranometers and solarimeters have been used to achieve this objective. However, the high cost of these standard instruments makes the technology less accessible, especially to students and academic researchers in low-to-middle-income countries. A low-cost prototype solarimeter has been developed that operates using a mini solar PV panel and a microcontroller. During testing, it was found that as temperature increased the instrument had significant accuracy deviations. As such, this study seeks to optimise the performance of the prototype solarimeter using Artificial Neural Networks (ANNs), a powerful data-driven machine learning tool for optimisation. Solar radiation data was simultaneously collected using the prototype solarimeter and a standard solarimeter. Corresponding ambient temperature was also recorded for each solar radiation measurement. The data was used to train the ANN model to learn data patterns and to predict accurate solar radiation in spite of the ambient temperature. Results of the study revealed that temperature has a negative correlation (–0.7381) with accuracy, such that an increase in temperature reduces the accuracy of the prototype solarimeter. Increased temperature caused an accuracy deviation of about 27.16 %. The ANN model successfully predicts accurate solar radiation measurement with an R-squared of 0.974, RMSE of 49.24 W/m2, and an accuracy of 86.32 % which represents a 13.39 % improvement in the performance of the prototype solarimeter. This study's novelty stands in its attempt to use machine learning to address the temperature sensitivity of a PV-based solar irradiance instrument. The results revealed here exposes the potency of deep learning models for optimising engineering systems.

A large number of new energy units represented by wind power and photovoltaics are connected to the grid, making accurate calculation and verification of short-circuit current difficult. Given the phase mapping and the influence of electrical components (components between new energy units and grid) from the unit port to the grid-connected point, such as box transformer, collecting line, main transformer and transmission line are not considered when constructing the short-circuit current mapping model of new energy grid-connected point in the latest researches, an improved engineering grid short-circuit current calculation method that takes into account the phase characteristics of new energy sources and the influence of components between new energy units and grid is proposed. Firstly, combined with the national operation regulations, a supplementary theoretical modeling of the port voltage to short-circuit current phase mapping of new energy units is proposed. Secondly, the model is processed by engineering, and an iterative calculation method is proposed to extrapolate the obtained mapping back to the point of coupling. Then, the engineered short-circuit current amplitude-phase mapping model is applied to the existing local iterative calculation to obtain an improved calculation method. Finally, the new energy grid-connected model is built in the PSCAD simulation software to verify the accuracy of amplitude-phase mapping theory of new energy unit port voltage to short-circuit current and the effectiveness of iterative calculation theory of extra polating the unit port mapping model back to the point of coupling. On this basis, the mapping model is further verified by experiments in IEEE 39-node system. The results show that the proposed improved calculation method can improve the calculation accuracy of short-circuit current to a certain extent.

The recent developments in the Global Navigation Satellite Systems (GNSS) and the advent of Intelligent Transportation Systems (ITS) have accelerated the need for accurate, reliable, and robust land vehicle positioning. Current low-cost, single-frequency GNSS receivers are universally available and have already been employed in a variety of urban mobility applications. However, low-cost GNSS receivers provide low (5-15 m) accuracy that deteriorates rapidly in deep urban and harsh environments imposing a significant impact on the effectiveness and the reliability of critical ITS services. In this paper, a novel vehicle ranking and selection methodology for cooperative positioning (CP) is developed aiming at capitalizing on low-cost GNSS receivers’ potential and maximizing their benefits in safety-critical vehicular applications. The proposed method is based on the Multi-attribute Decision-Making (MADM) theory and provides a prioritization of the neighboring vehicles in the vicinity of a target vehicle using criteria related to position accuracy and reliability. By selecting the optimal neighbor vehicle for CP, the low-cost receiver of the target vehicle enhances its location-awareness, and hence, its absolute/relative positioning accuracy. The proposed optimal vehicle selection process was inspired by the Cooperative-Differential GNSS (C-DGNSS) technique. An additional contribution of the proposed methodology is the expansion of the “moving base station” concept for use in ITS. Various MADM algorithms are considered and simulated employing real experimental data from multiple, low-cost GNSS receivers. The optimal MADM algorithm proposed is TOPSIS because the derived rankings offer maximum stability and similarity with Average Correlation Index (ACI) = 0.78, thus satisfying the requirements for critical applications.

One of the key goals of geodesy is to study the fine structure of the Earth's gravity field and construct a high-resolution gravity field model (GFM). Aiming at the current insufficient resolution problem of the EIGEN_6C4 model, the refined ultra-high degree models EIGEN_3660 and EIGEN_5480 are determined with a spectral expansion approach in this study, which is to augment EIGEN_6C4 model using topographic potential models (TPMs). A comparative spectral evaluation for EIGEN_6C4, EIGEN_3660, and EIGEN_5480 models indicates that the gravity field spectral powers of EIGEN_3660 and EIGEN_5480 models outperform the EIGEN_6C4 model after degree 2000. The augmented models EIGEN_3660 and EIGEN_5480 are verified using the deflection of the vertical (DOV) of China and Colorado, gravity data from Australia and mainland America, and GNSS/leveling in China. The validation results indicate that the accuracy of EIGEN_3660 and EIGEN_5480 models in determining height anomaly, DOV, and gravity anomaly outperform the EIGEN_6C4 model, and the EIGEN_5480 model has optimal accuracy. The accuracy of EIGEN_5480 model in determining south–north component and east–west component of the DOV in China has been improved by about 21.1% and 23.1% compared to the EIGEN_6C4 model, respectively. In the mountainous Colorado, the accuracy of EIGEN_5480 model in determining south–north component and east–west component of the DOV has been improved by about 28.2% and 35.2% compared to EIGEN_6C4 model, respectively. In addition, gravity value comparison results in Australia and mainland America indicate that the accuracy of the EIGEN_5480 model for deriving gravity anomalies is improved by 16.5% and 11.3% compared to the EIGEN_6C4 model, respectively. [ABSTRACT FROM AUTHOR]

The technology development, combined with chip size reductions, has significantly increased power density. Furthermore, the power limitation in portable devices has increased the importance of power consumption considerably. Approximate computing as an emerging paradigm leverages the error resiliency of applications to decrease power consumption. In this paper, we propose four approximate 4:2 compressors. We utilize the gate diffusion input to achieve significant area reduction in the proposed compressors. Afterward, we employ these compressors in the structure of Dadda multipliers. Compared to the state-of-the-art works, the proposed compressors result in, on average, 45% and 42% higher accuracy and lower area, respectively. Circuit simulations were carried out at a 32-nm carbon nanotube field-effect transistor technology node by HSPICE. The comparisons between our proposed compressors and exact compressor indicate that there has been an average improvement of 55%, 60% and 46% in the number of transistors, power consumption, and delay, respectively. In addition, the proposed multipliers were used in three image processing applications, including image multiplication, sharpening, and smoothing. The results show up to 46% and 70% improvement in the mean structure similarity index metric and peak signal-to-noise ratio compared to the state-of-the-art. [ABSTRACT FROM AUTHOR]

Copyright of Coal Geology & Exploration is the property of Xian Research Institute of China Coal Research Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper describes a method for increasing the accuracy and precision of temperature measurements of a liquid based on the central limit theorem. A thermometer immersed in a liquid exhibits a response with determined accuracy and precision. This measurement is integrated with an instrumentation and control system that imposes the behavioral conditions of the central limit theorem (CLT). The oversampling method exhibited an increasing measurement resolution. Through periodic sampling of large groups, an increase in the accuracy and formula of the increase in precision is developed. A measurement group sequencing algorithm and experimental system were developed to obtain the results of this system. Hundreds of thousands of experimental results are obtained and seem to demonstrate the proposed idea's validity. [ABSTRACT FROM AUTHOR]

This study explored the role of written languaging (WL) in response to automated written corrective feedback (AWCF) in L2 accuracy improvement in English classrooms at a university in China. A total of 254 freshmen enrolled in intermediate composition classes participated, and they wrote 4 essays and received AWCF. A half of them engaged in WL after receiving correction, while the other half did not. To measure whether WL could contribute to L2 accuracy improvement, error correction tests were conducted three times. AWCF targeted all types of errors, but the study focused on investigating six language features (nouns, conjunctions, subject-verb agreement, sentence constituents, articles, and prepositions) known as the most frequent errors intermediate students make. The results proved that WL had a positive effect on accuracy improvement overall. However, when individual language features were considered separately, the effects were not the same. The inherent complexity of the features along with L1 influence appeared to affect the effects of WL for accuracy improvement. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Engineering Technology is the property of Editorial Department of Electric Power Engineering Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Nowadays, disposable injection pumps are widely used in hospitals and home care settings to provide various therapies such as chemotherapy, antimicrobial, analgesic, and anesthetic treatments, as well as for postoperative pain control and chronic pain control. Since the accuracy of the injection is very important in infusion pumps based on the flow rate, it is therefore important to reduce the error in this device. In this study, the basic design principles of these pumps and the design problems of the sample appearance available in the market were investigated. Since one of the vital problems of this type of pump is their inaccuracy, because they are unable to inject a certain amount of drug for a certain period of time, so one of the main objectives of this study is to improve the accuracy of the injection. Also, as this device is available to the patient for a long time at the time of injection, ease of use is one of the design goals. Finally, this paper ends with a design and prototype which is better in the shape of the device and a big improvement in the accuracy.

This paper proposed an innovative method, which adopts training dataset characteristics, to improve the accuracy and interpretability of multi-scenario building energy consumption prediction. It adopts the pattern recognition, time-spectrograms, and quantitative training data properties (discrete level, daily cycle intensity, and feature similarity), and investigates and analyzes the data from both time and frequency domain perspectives. The prediction predictability and interpretability are enhanced by combining correlation analysis with the input features. The results show that the energy consumptions for both air conditioning (AC) system and the whole building are significantly impacted by the opening rate of AC terminal equipment. The proposed method can achieve a reduction of 68% for the coefficient-of-variation of root mean square error of the total energy consumption and a decrease of 67% for the AC energy consumption. Training data for AC and total energy consumptions show that the discrete level and feature similarity affect prediction performance, with correlation values ranging from 0.4 to 0.6. Moreover, there is a negative correlation (−0.37) between the daily cycle's intensity and the AC energy use, indicating that prediction performance improves with increasing daily cycle intensity. For building energy consumption prediction, the three indicators, namely discreteness, daily cycle intensity, and feature similarity of training data, can be used to effectively analyze the prediction effect of the target task. [ABSTRACT FROM AUTHOR]

The object of research is multiple measurements. The research aims to improve the accuracy of multiple measurements with a non-linear and unstable sensor transformation function. It is proved that the redundant measurement equation ensures the independence of the measurement result from the parameters of the transformation function and their deviations from the nominal values. It was found that the result of redundant measurements is affected by the reproduction errors of normalized temperatures T1 and T2. It is shown that the best accuracy results are obtained with a reproduction error of normalized temperature T2 within ±1.0 % and temperature T1 within ±0.1 %. This makes it possible to reduce the accuracy requirements for the source of reproduction of normalized temperature Т2. The possibility of processing the results of multiple measurements by two approaches is presented. Computer modeling using the first approach found that with a reproduction error of normalized temperature T2 within ±0.5 %, the relative measurement error is 0.003 %. When modeling the second approach, the relative error is 0.05 %. It was also found that with an increase in the reproduction error of normalized temperature T2 to ±1.0 %, the value of the relative error is 0.04 %. Due to this, when applying the second approach, it becomes possible to choose a non-high-precision source of reproduction of normalized temperature T2. In addition, the sensitivity of the second approach to the digit range of measuring devices was found, which leads to the dependence of the measurement result on their accuracy. There are reasons to assert the possibility of increasing the accuracy of multiple measurements by processing the results of intermediate measurements according to redundant measurement equations using two approaches. [ABSTRACT FROM AUTHOR]

As short-term load forecasting is essential for the day-to-day operation planning of power systems, we built an ensemble learning model to perform such forecasting for Thai data. The proposed model uses voting regression (VR), producing forecasts with weighted averages of forecasts from five individual models: three parametric multiple linear regressors and two non-parametric machine-learning models. The regressors are linear regression models with gradient-descent (LR), ordinary least-squares (OLS) estimators, and generalized least-squares auto-regression (GLSAR) models. In contrast, the machine-learning models are decision trees (DT) and random forests (RF). To select the best model variables and hyper-parameters, we used cross-validation (CV) performance instead of the test data performance, which yielded overly good test performance. We compared various validation schemes and found that the Blocked-CV scheme gives the validation error closest to the test error. Using Blocked-CV, the test results show that the VR model outperforms all its individual predictors. [ABSTRACT FROM AUTHOR]

Jung Kyung Hong,1,2,* Taeyoung Lee,3,* Roben Deocampo Delos Reyes,4 Joonki Hong,3,4 Hai Hong Tran,4 Dongheon Lee,4 Jinhwan Jung,4 In-Young Yoon1,2 1Department of Psychiatry, Seoul National University Bundang Hospital, Seongnam, Korea; 2Seoul National University College of Medicine, Seoul, Korea; 3Korea Advanced Institute of Science and Technology, Daejeon, Korea; 4Asleep Inc., Seoul, Korea*These authors contributed equally to this workCorrespondence: In-Young YoonDepartment of Psychiatry, Seoul National University Bundang Hospital, 82, Gumi-ro 173beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, 463-707, KoreaTel +82-31-787-7433Fax +82-31-787-4058Email iyoon@snu.ac.krJinhwan JungR&D Division, Asleep Inc, Asleep, 15, Teheran-ro 82-gil, Gangnam-gu, Seoul, KoreaTel +82-10-6228-7137Email insomnia@asleep.aiStudy Objectives: Automated sleep stage scoring is not yet vigorously used in practice because of the black-box nature and the risk of wrong predictions. The objective of this study was to introduce a confidence-based framework to detect the possibly wrong predictions that would inform clinicians about which epochs would require a manual review and investigate the potential to improve accuracy for automated sleep stage scoring.Methods: We used 702 polysomnography studies from a local clinical dataset (SNUBH dataset) and 2804 from an open dataset (SHHS dataset) for experiments. We adapted the state-of-the-art TinySleepNet architecture to train the classifier and modified the ConfidNet architecture to train an auxiliary confidence model. For the confidence model, we developed a novel method, Dropout Correct Rate (DCR), and the performance of it was compared with other existing methods.Results: Confidence estimates (0.754) reflected accuracy (0.758) well in general. The best performance for differentiating correct and wrong predictions was shown when using the DCR method (AUROC: 0.812) compared to the existing approaches which largely failed to detect wrong predictions. By reviewing only 20% of epochs that received the lowest confidence values, the overall accuracy of sleep stage scoring was improved from 76% to 87%. For patients with reduced accuracy (ie, individuals with obesity or severe sleep apnea), the possible improvement range after applying confidence estimation was even greater.Conclusion: To the best of our knowledge, this is the first study applying confidence estimation on automated sleep stage scoring. Reliable confidence estimates by the DCR method help screen out most of the wrong predictions, which would increase the reliability and interpretability of automated sleep stage scoring.Keywords: confidence estimation, deep learning, electroencephalography, polysomnography, sleep stages, accuracy improvement

Several important problems in Mechanics can be efficiently solved using Raviart–Thomas mixed finite element methods. Whenever the domain of interest has a curved boundary the methods of this family for N-simplexes are the natural choice. But in this case the question arises on the best way to prescribe normal flux conditions across the boundary, if any. It is generally acknowledged that the normal component of the flux variable should preferably not take up corresponding prescribed values at nodes shifted to the boundary of the approximating polytope in the underlying normal direction. This is because an accuracy downgrade is to be expected, as shown in Bertrand and Starke (2016). In that work an order-preserving technique was studied, based on a parametric version of these elements with curved simplexes. In this work an alternative with straight-edged triangles for two-dimensional problems is examined. The key feature of this approach is a Petrov–Galerkin formulation, in which the test-flux space is a little different from the shape-flux space. Based on previous author’s experience with this technique, as applied to Lagrange finite elements, it would lead to an overall accuracy improvement here as well. The experimentation reported hereafter provides examples and counterexamples confirming or not such an expectation, depending on the unknown field of the mixed problem at hand.

Compared with traditional CNC machine tools, industrial robots have higher flexibility and lower cost in the machining field. However, the relatively low stiffness of the robot makes it difficult to meet the accuracy requirements. In this paper, a robot machining posture optimization method is proposed to improve the performance. First, a deformation index considering the robot stiffness compensation matrix K C is proposed to evaluate the stiffness performance of the robot machining trajectory. Then, by minimizing the deformation index under consideration of kinematic constraints, a robot posture optimization model is established. The discrete Dijkstra optimization method is proposed to solve the global optimal solution to the model. Finally, the effectiveness of the robot deformation index and posture optimization method has been verified by a series of simulations and experiments in the Motoman ES165D robot. [ABSTRACT FROM AUTHOR]

One of the key goals of geodesy is to study the fine structure of the Earth’s gravity field and construct a high-resolution gravity field model (GFM). Aiming at the current insufficient resolution problem of the EIGEN_6C4 model, the refined ultra-high degree models EIGEN_3660 and EIGEN_5480 are determined with a spectral expansion approach in this study, which is to augment EIGEN_6C4 model using topographic potential models (TPMs). A comparative spectral evaluation for EIGEN_6C4, EIGEN_3660, and EIGEN_5480 models indicates that the gravity field spectral powers of EIGEN_3660 and EIGEN_5480 models outperform the EIGEN_6C4 model after degree 2000. The augmented models EIGEN_3660 and EIGEN_5480 are verified using the deflection of the vertical (DOV) of China and Colorado, gravity data from Australia and mainland America, and GNSS/leveling in China. The validation results indicate that the accuracy of EIGEN_3660 and EIGEN_5480 models in determining height anomaly, DOV, and gravity anomaly outperform the EIGEN_6C4 model, and the EIGEN_5480 model has optimal accuracy. The accuracy of EIGEN_5480 model in determining south–north component and east–west component of the DOV in China has been improved by about 21.1% and 23.1% compared to the EIGEN_6C4 model, respectively. In the mountainous Colorado, the accuracy of EIGEN_5480 model in determining south–north component and east–west component of the DOV has been improved by about 28.2% and 35.2% compared to EIGEN_6C4 model, respectively. In addition, gravity value comparison results in Australia and mainland America indicate that the accuracy of the EIGEN_5480 model for deriving gravity anomalies is improved by 16.5% and 11.3% compared to the EIGEN_6C4 model, respectively.

Early diagnosis of breast cancer plays a crucial role in treating the patient. Nowadays, data mining algorithms can provide intelligent methods in the health and treatment system that accurately detect breast cancer. The purpose of this study is breast cancer detection using ensemble classifier based on WBC and WDBC prepared databasesa. Our proposed model in the WBC database (reducing features by cfs+ optimizing samples using Resample+ ensemble classifier using data mining algorithms (kstar + random forest + Naïve Bayes and Bayes network)) has the best detection accuracy ( 100%), implementation time (0 seconds) and without any errors and on the WDBC database (reducing features by cfs+ optimizing samples using Resample+ ensemble classifier using data mining algorithms (IBK algorithm+ Naïve Bayes, Bayes network and kstar)) has an accuracy of 99/29, the implementation time is 0 seconds, and the mean absolute error is 0/007. The results of this study show that according to the ensemble classifier methods using data mining algorithms on the prepared database, new systems can be designed to help physicians that facilitate treatment processes. [ABSTRACT FROM AUTHOR]

The present article describes a method for enhanced accuracy in machining free-form surfaces produced on CNC milling machines. In this method, surface patch machining programs are generated based on their nominal CAD model. After the pretreatment, coordinate control measurements are carried out. The obtained results of the measurements contain information on the values and distribution of observed machining deviations. These data, after appropriate processing, are used to build a corrected CAD model of the surface produced. This model, made using reverse engineering techniques, compensates for the observed machining deviations. After regeneration of machining programs, the object processing and control measurements are repeated. As a result of the conducted procedure, the accuracy of the manufacture of the surface object is increased. This article also proposes the introduction of a simple procedure for the filtration of measurement data. Its purpose is to minimise the effect of random phenomena on the final machining error correction. The final part of the article presents the effects of the proposed method of increasing the accuracy of manufacturing on 'raw' and filtered measurement data. In both cases, a significant improvement in the accuracy of the machining process was achieved, with better final results obtained from the filtered measurement data. The method proposed in the article has been verified for three-axis machining with a ball-end cutter. [ABSTRACT FROM AUTHOR]