Effect Of Error Propagation Research Articles

Quartile Regression and Ensemble Models for Extreme Events of Multi-Time Step-Ahead Monthly Reservoir Inflow Forecasting

Amidst changing climatic conditions, accurately predicting reservoir inflows in an extreme event is challenging and inevitable for reservoir management. This study proposed an innovative strategy under such circumstances through rigorous experimentation and investigations using 18 years of monthly data collected from the Huai Nam Sai reservoir in the southern region of Thailand. The study employed a two-step approach: (1) isolating extreme and normal events using quantile regression (QR) at the 75th, 80th, and 90th quantiles and (2) comparing the forecasting performance of individual machine learning models and their combinations, including Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM), and Multiple Linear Regression (MLR). Forecasting accuracy was assessed at four lead times—3, 6, 9, and 12 months—using ten-fold cross-validation, resulting in 16 model configurations for each forecast period. The results show that combining quantile regression (QR) to distinguish between extreme and normal events with hybrid models significantly improves the accuracy of monthly reservoir inflow forecasting, except for the 9-month lead time, where the XG model continues to deliver the best performance. The top-performing models, based on normalized scores for 3-, 6-, 9-, and 12-month-ahead forecasts, are XG-MLR-75, RF-XG-80, XG-75, and XG-RF-75, respectively. Another crucial finding of this research is the uneven decline in prediction accuracy as lead time increases. Notably, the model performed best at t + 9, followed by t + 3, t + 12, and t + 6, respectively. This pattern is influenced by model characteristics, error propagation, temporal variability, data dynamics, and seasonal effects. Improving the accuracy and efficiency of hybrid model forecasting can greatly enhance hydrological operational planning and management.

A Survey on the Advanced Encryption Standard (AES): A Pillar of Modern Cryptography

As the digital landscape expands and our reliance on secure data transmission grows, the adoption of encryption technologies has become paramount. Over time, we have witnessed a progression from rudimentary ciphering methods, like substitution ciphers, to the sophisticated algorithms of today, epitomized by the Advanced Encryption Standard (AES). AES’s ascendancy to the forefront of encryption can be attributed to its unparalleled security features, surpassing its predecessors such as the Data Encryption Standard (DES). AES boasts robust security measures, rendering it virtually impervious to conventional cryptographic attacks. Its resilience against decryption is underscored by its intricate encryption process, which involves intricate operations such as byte substitution, row shifting, column mixing and round key addition. Conversely, decryption reverses these steps, ensuring the confidentiality of encrypted data remains intact. Despite the emergence of various cryptographic attacks, none have posed a significant threat to a properly implemented Full-AES algorithm. Most attacks target incomplete implementations, highlighting the importance of proper implementation practices in maximizing AES’s security benefits. Beyond its security prowess, AES stands out for its efficiency, sustainability and simplicity. Despite its widespread adoption and robustness against cryptanalytic attacks, AES exhibits an intriguing phenomenon known as error propagation. Through theoretical analysis and empirical investigations, we elucidate the mechanisms by which errors propagate within AES, shedding light on the vulnerabilities they introduce. Additionally, we explore the implications of error propagation in practical scenarios, including its impact on cryptographic protocols, error correction mechanisms and overall system reliability. Our findings underscore the importance of comprehensively understanding and mitigating the effects of error propagation in AES implementations, offering insights into enhancing the resilience of cryptographic systems against unforeseen adversities.

WITHDRAWN: Digital twin-driven machining process for multi-process irregular-shaped parts manufacturing

With the advancement of high-end manufacturing industries, certain critical parts exhibit peculiar shapes and characteristics of multi-process manufacturing, which intensify milling difficulties and give rise to error propagation effects during the machining process. This necessitates machining systems to be adaptive to different working conditions and capable of real-time error analysis and optimization. Digital twin technology can create virtual replicas of physical entities to observe, analyze, and optimize the machining process. Therefore, this paper proposes a framework for digital twin-driven machining of multi-process irregular-shaped parts, consisting of preparation, processing, and optimization stages. In the preparation stage, a mapping relationship between the workpiece and machine tool motion coordinates is established to create a twin simulation environment consistent with actual operational parameters. In the processing stage, the digital thread dynamically updates the digital twin workpiece and machine tool status in real-time, analyzing the machining errors for each process to assess the finished quality. In the optimization stage, a cloud-based machining knowledge graph is constructed to enhance the precision of machining optimization decisions under different error sources and causes in the digital twin system. Finally, a case study of a typical multi-process irregular-shaped part machining task validates the reliability and superiority of the digital twin-driven method. The case study demonstrates that in a total of 260 machining processes, the digital twin-driven method improves machining accuracy by 40%, saves 34.9% of machining time, and alleviates 70% of machining downtime compared to existing machining methods.

A novel iterative detection method based on a lattice reduction-aided algorithm for MIMO OFDM systems

The lattice reduction-aided algorithm has received broad attention from researchers since it operates as a maximum likelihood receiver with better system performance for multiple-input multiple-output orthogonal frequency division multiplexing systems and contains a full diversity. A novel iterative detection algorithm canceling parallel iterations that employ the lattice reduction-aided approach is proposed. Soft information is exchanged through the detector itself. Its iteration occurs inside the detector, which reduces much of the exchange cost between the multiple-input multiple-output orthogonal frequency division multiplexing detector and the turbo decoder. Since the parallel interference cancellation algorithm is constrained by the accuracy of the initial value of the detection, it is easy to form error propagation after several iterations. Due to the lattice reduction-aided algorithm, its performance is approximated with the maximum likelihood algorithm. Therefore, the lattice reduction-aided algorithm is introduced into the parallel interference cancellation algorithm to make its detection algorithm more accurate and overcome the effect of error propagation in the manuscript. Simulation results indicate that the proposed algorithm leads to an improvement of 0.8–2 dB when the bit error rate is set to 10–4 when compared to other algorithms.

A critical evaluation of SEMM-based joint identification procedure to reduce the error propagation effects

Mechanical joints play a critical role in many engineering structures, but identifying their characteristics can be challenging, particularly when the joint interface (coupling DoFs) cannot be directly measured. In these cases, an existing iterative procedure based on the use of the System Equivalent Model Mixing (SEMM) expansion technique and substructure decoupling can be used to identify the joint properties by taking measurement on other accessible DoFs (internal DoFs). Despite the potential of this procedure, it is prone to large error propagation. In addition, in the SEMM expansion a weighted pseudo-inverse operation is needed to ensure the convergence of the iterative procedure when both coupling and internal DoFs (extended interface) are involved in the decoupling.This paper focuses on the detection of the error sources in the process and on the definition of some strategies to limit error propagation. The use of internal DoFs only (pseudo-interface) in the decoupling is proposed. This avoids the use of coupling DoFs affected by the expansion error. Furthermore, two strategies are proposed to improve the conditioning of the procedure when using the extended interface. Both strategies are based on the Truncated Singular Values Decomposition (TSVD). It is shown that the weights clearly indicate the number of singular values to be retained in the matrices to be inverted.The proposed improvements are validated on a laboratory benchmark. Measurements on the benchmark are performed to validate the strategies with experimental data. In addition, the Monte Carlo method is applied using noise-polluted numerical data to evaluate the potential of the proposed strategies to mitigate the error propagation in the SEMM-based joint identification procedure.

A Novel OFDM-Based Time Domain Quadrature GSM for Visible Light Communication System

In order to improve the spectral efficiency (SE) as well as the receiver performance of band-limited visible light communications (VLCs), two orthogonal frequency division multiplexing (OFDM)-based quadrature generalized multiple-input multiple-output (QG-MIMO) transmission schemes, including time domain (TD) quadrature generalized spatial modulation (TD-QGSM) and TD quadrature generalized spatial multiplexing (TD-QGSMP), are proposed in this paper. Firstly, the constellation symbols in the frequency domain are split into in-phase and quadrature components to perform the OFDM modulation separately. Then, the corresponding time domain signal is spatially mapped on different light emitting diodes (LEDs) for achieving the diversity or multiplexing. In addition, we also propose an illegal vector correction (IVC)-based orthogonal matching pursuit (OMP) detection algorithm to deal with the error propagation and noise amplification effect, where a novel correction criterion is involved for assisting the index vectors estimation and thus for improving the demodulation performance. The simulation results demonstrate that the SE can be significantly improved by the proposed schemes as compared with the existing OFDM-based generalized MIMO schemes, with the TD-QGSM increasing by at least 56.5% and the TD-QGSMP increasing by at least 72.3%. Moreover, the bit error rate (BER) performance can be further improved when applying the proposed IVC-OMP detection method, which outperforms the traditional maximum-likelihood and maximum ratio combining (ML-MRC) detection by at least 62.5%.

Uncertainty analysis of intra-module environmental stress parameter design in light use efficiency-based gross primary productivity estimation models

Terrestrial gross primary productivity is a key part in the carbon cycle, and the light use efficiency model is a widely employed tool for its estimation. However, since the specific designs of environmental stress parameters in light use efficiency models have obvious disparities, even for a single module (e.g., the temperature stress module), the uncertainties related to intra-module environmental stress parameters in light use efficiency models remain unclear. Thus, we gathered mainstream temperature and water stress parameters in light use efficiency models from existing publications, and employed a compatible framework to assess them from three perspectives: (1) identifying similarities and differences of environmental stress parameters; (2) evaluating the error propagation effect of input data under different environmental stress parameter combinations; and (3) assessing the generalization ability of environmental stress parameters. The results showed that the temperature stress parameters exhibited general homogeneity (shared 67.87% variance), while water stress parameters displayed noticeable internal variations (shared variance below 1.0%). Meanwhile, we revealed that the current flexible parameter combination method in light use efficiency model construction should be more cautious, since the flexible environmental stress parameter combinations would influence the error propagation effect from input data to final gross primary productivity estimations. In addition, our analysis found that the variance of environmental stress parameters was closely coupled to model estimation accuracy, and there was a positive relationship between the unique variance of temperature stress parameters and the ability of temperature stress parameters to describe gross primary productivity variation. Overall, the current environmental stress parameters demonstrated acceptable performance across most situations, except for biomes with high temperatures. This study provided a foundation towards the development of future parameter design, which may also inspire the application of empirical environmental factors in other gross primary productivity models.

Semi-Blind Signal Estimation Using Toeplitz Structure-Based Subspace Method

This paper presents a novel semi-blind signal estimation method for a multi-channel system based on a structure-based subspace technique. The proposed method exploits the Toeplitz structure present in the signal matrix of the communication system model and the presence of some pilot sequences. The use of pilot symbols improves the efficiency and robustness of the algorithm and also eliminates the scalar ambiguity inherently present in blind systems. The devised method estimates the transmitted symbols directly without having to estimate the channel as an intermediate step. This will significantly reduce the error propagation effect and eventually results in an improved accuracy. Simulation results illustrate that the developed method exhibits an attractive performance.

Parameter estimation of LFM signals based on time reversal

In this paper, parameter estimation of linear frequency modulation (LFM) signals containing additive white Gaussian noise is studied. Because the center frequency estimation of an LFM signal is affected by the error propagation effect, resulting in a higher signal to noise ratio (SNR) threshold, a parameter estimation method for LFM signals based on time reversal is proposed. The proposed method avoids SNR loss in the process of estimating the frequency, thus reducing the SNR threshold. The simulation results show that the threshold is reduced by 5 dB compared with the discrete polynomial transform (DPT) method, and the root-mean-square error (RMSE) of the proposed estimator is close to the Cramer-Rao lower bound (CRLB).

KR-GCN: Knowledge-Aware Reasoning with Graph Convolution Network for Explainable Recommendation

Incorporating knowledge graphs (KGs) into recommender systems to provide explainable recommendation has attracted much attention recently. The multi-hop paths in KGs can provide auxiliary facts for improving recommendation performance as well as explainability. However, existing studies may suffer from two major challenges: error propagation and weak explainability. Considering all paths between every user-item pair might involve irrelevant ones, which leads to error propagation of user preferences. Defining meta-paths might alleviate the error propagation, but the recommendation performance would heavily depend on the pre-defined meta-paths. Some recent methods based on graph convolution network (GCN) achieve better recommendation performance, but fail to provide explainability. To tackle the above problems, we propose a novel method named K nowledge-aware R easoning with G raph C onvolution N etwork (KR-GCN). Specifically, to alleviate the effect of error propagation, we design a transition-based method to determine the triple-level scores and utilize nucleus sampling to select triples within the paths between every user-item pair adaptively. To improve the recommendation performance and guarantee the diversity of explanations, user-item interactions and knowledge graphs are integrated into a heterogeneous graph, which is performed with the graph convolution network. A path-level self-attention mechanism is adopted to discriminate the contributions of different selected paths and predict the interaction probability, which improves the relevance of the final explanation. Extensive experiments conducted on three real-world datasets show that KR-GCN consistently outperforms several state-of-the-art baselines. And human evaluation proves the superiority of KR-GCN on explainability.

An Analytical Framework for Error Propagation Effects in Multiprocess Manufacturing

Analysis of multisource errors accompanying process execution and study of the propagation effects of the corresponding multiprocess manufacturing process are the basic prerequisites for optimizing the generation process and improving the quality of the product. This paper proposes an analytical framework for error propagation effects in multiprocess manufacturing, which completely covers error source analysis, mathematical calculation of error propagation accumulation, and description of error propagation effects, which can provide better support for error traceability, error propagation monitoring, and error dissipation in manufacturing production. First of all, establish the process state model into the features of the manufacturing level, analyze the type of multisource error, and its mode of action when executing the process operation; second, establish the mathematical calculation model of error propagation accumulation based on state space equation is established, so as to realize calculation and analysis of error propagation; and finally, establish the link graph and link matrix of error propagation influence in multiprocess manufacturing process are established to further reveal the scope and path of error propagation influence. In this section of the case study, a plate cover part had been taken as an example, the multisource error in its manufacturing was analyzed, and the propagation and accumulation of these error values are obtained by state space and state transition theory. On this basis, the link graph and its corresponding link matrix were used to explicitly describe the error propagation effects during the multiprocess manufacturing of the example part. The example analysis illustrated the proposed error analysis framework could analyze and calculate the multisource error of multiprocess manufacturing and visually describe the error propagation effects, which showed the effectiveness and feasibility of the proposed method framework.

On estimating parameters of a multi-component Chirp Model with equal chirp rates

Multi-component chirp signal models with equal chirp rates appear in various radar applications, e.g., synthetic aperture radar, echo signal of a rapid mobile target, etc. Many sub-optimal estimators have been developed for such models, however, these suffer from the problem of either identifiability or error propagation effect. In this paper, we have developed theoretical properties of the least squares estimators (LSEs) of the parameters of multi-component chirp model with equal chirp rates, where the model is contaminated with linear stationary errors. We also propose two computationally efficient estimators as alternative to LSEs, namely sequential combined estimators and sequential plugin estimators. Strong consistency and asymptotic normality of these estimators have been derived. Interestingly, it is observed that sequential combined estimator of the chirp rate parameter is asymptotically efficient. Extensive numerical simulations have been performed, which validate satisfactory computational and theoretical performance of all three estimators. We have also analysed a simulated radar data with the help of our proposed estimators of multi-component chirp model with equal chirp rates, which performs efficiently in recovery of inverse synthetic aperture radar (ISAR) image of a target from a noisy data.

Squint Spotlight SAR Imaging by Two-Step Scaling Transform-Based Extended PFA and 2-D Autofocus

In this article, a novel imaging algorithm by combining two-step scaling transform (TSST) with structure-aided 2-D autofocus is proposed for the squint spotlight synthetic aperture radar (SAR). First, on the basis of planar wavefront assumption, a modified range-frequency linear scaling transform (MRFLST) and an azimuth-time nonlinear scaling transform (ATNST) are proposed to eliminate the coupling between range-frequency and azimuth-time of the received echo. Furthermore, to improve the efficiency, the MRFLST is implemented by using the principle of chirp scaling (PCS), which involves only complex multiplications and fast Fourier transforms (FFTs) without any interpolation, meanwhile, a constant scaling factor (CSF) selecting criteria is defined to avoid range spectrum aliasing. Then, to correct the phase error caused by the range measurement error and atmospheric propagation effects, the prior 2-D phase error structure implied in the TSST is analyzed. Finally, by integrating the derived 2-D phase error structure and range frequency fragmentation technique, a new 2-D autofocus algorithm is presented to improve the image quality. Simulated and real data experiments are carried out to verify the proposed algorithm.

Hybrid Multiuser Equalizer With Nonlinearity Compensation for Wideband mmWave Massive MIMO Uplink Systems

The use of efficient high-power amplifiers (HPAs) is critical for future millimeter-wave (mmWave) systems. HPAs can become highly nonlinear when operated near saturation, distorting the transmission signal. Therefore, the design of efficient solutions to mitigate nonlinear distortion (NLD) is of paramount importance. This article proposes a hybrid multiuser equalizer for the uplink of wideband mmWave massive MIMO systems. The equalizer is designed to mitigate both the multiuser interference (MUI) and inter-symbol interference (ISI), introduced by the multiple access channel, and the NLD, introduced by the user terminals HPA. The digital part of the hybrid equalizer is iterative and computed on a per subcarrier basis, to minimize the mean squared error between the transmitted and detected data. In the iterative processing, a feedback loop estimates and removes the distortion. The analog part is frequency-flat and its design assumes that the digital part efficiently removes the MUI, ISI and NLD. Performance results show a huge gain of the proposed hybrid nonlinear equalizer, when compared with linear ones. The nonlinear effects are almost completely removed, without significant error propagation effects, allowing a more efficient operation of the HPAs.

Robust control strategy for generalized N-trailer vehicles based on a dual-stage disturbance observer

Articulated vehicles with multiple trailers also called N-Trailers have been widely used for transportation tasks in industrial applications. Solutions for the control problem of N-Trailers have been formulated mostly for vehicles with solely on-axle hitching or off-axle hitching but in field applications such as agriculture, most of the structures used are Generalized N-Trailers (GNT) which have a combination of on- and off-axle hitching. Moreover, most of the solutions in literature were developed and tested for laboratory-scale platforms under ideal indoor conditions. However, in outdoors conditions, the motion performance is commonly degraded by model uncertainties, slipping of wheels, and trailers localization loss product of noisy or inaccurate sensor data. In this context, this paper reports the use of Active Disturbance Rejection Control (ADRC) with a Dual-Stage Disturbance Observer (DS-DO) to improve the backward trajectory-tracking performance of GNT in no ideal conditions, where the DS-DO aims to attenuate the effects of error propagation on the ADRC compensation loop and improve the overall closed-loop performance. The proposed ADRC+DS-DO has been validated in simulation and real experiments showing overall improvements on the controller effort reduction and reduction of up to 57% on the tracking error against a traditional ADRC approach already existent in the literature.

Cooperative DF Protocol for MIMO Systems Using One-Bit ADCs.

This study considers a detection scheme for cooperative multi-input–multi-output (MIMO) systems using one-bit analog-to-digital converters (ADCs) in a decode-and-forward (DF) relay protocol. The use of one-bit ADCs is a promising technique for reducing the power consumption, which is necessary for supporting future wireless systems comprising a large number of antennas. However, the use of a large number of antennas remains still limited to mobile devices owing to their size. Cooperative communication using a DF relay can resolve this limitation; however, detection errors at the relay make it difficult to employ cooperative communication directly. This difficulty is more severe in a MIMO system using one-bit ADCs due to its nonlinear nature. To efficiently address the difficulty, this paper proposes a detection scheme that mitigates the error propagation effect. The upper bound of the pairwise error probability (PEP) of one-bit ADCs is first derived in a weighted Hamming distance form. Then, using the derived PEP, the proposed detection for the DF relay protocol is derived as a single weighted Hamming distance. Finally, the complexity of the proposed detection is analyzed in terms of real multiplications. The simulation results show that the proposed detection method efficiently mitigates the error propagation effect but has a relatively low level of complexity when compared to conventional detection methods.

A Typed Iteration Approach for Spoken Language Understanding

A spoken language understanding (SLU) system usually involves two subtasks: intent detection (ID) and slot filling (SF). Recently, joint modeling of ID and SF has been empirically demonstrated to lead to improved performance. However, the existing joint models cannot explicitly use the encoded information of the two subtasks to realize mutual interaction, nor can they achieve the bidirectional connection between them. In this paper, we propose a typed abstraction mechanism to enhance the performance of intent detection by utilizing the encoded information of SF tasks. In addition, we design a typed iteration approach, which can achieve the bidirectional connection of the encoded information and mitigate the negative effects of error propagation. The experimental results on two public datasets ATIS and SNIPS present the superiority of our proposed approach over other baseline methods, indicating the effectiveness of the typed iteration approach.

Significance of Geometrical Condition in Preserving the Quality of TLS Measurement

To acquire complete three-dimensional (3D) data that cover the whole surface of the scanned object, multiple scan stations are inevitable. Thus, the registration process is compulsory to oriented TLS multi local coordinate into one global coordinate system. Taking into account the fundamental principle of error propagation in network design, an open network solution for TLS measurement is impacted to yield uncertainties. To investigate this issue this study has been carried out to verify the effect of error propagation in the TLS pre-processing procedure. Two types of network configurations have been established: i. Closed; and ii. Open networks. To measure the accuracies of both configurations, 3D traversing has been performed in establishing six (6) reference points. With the aid of the reference points, the results demonstrated that by neglecting the geometrical condition the accuracy of TLS pre-processed data might be reduced up to 33 percent. To preserve the quality of the final real-world representation derived from TLS measurement, it is advisable to take into account this fundamental principal

Error propagation of climate model rainfall to streamflow simulation in the Gidabo sub-basin, Ethiopian Rift Valley Lakes Basin

ABSTRACT This study assesses bias error of rainfall from climate models and related error propagation effects to simulated streamflow in the Gidabo sub-basin, Ethiopia. Rainfall is obtained from a combination of four global and regional climate models (GCM-RCMs), and streamflow is simulated by means of the Hydrologiska Byråns Vattenbalansavdelning (HBV-96) rainfall-runoff model. Five bias correction methods were tested to reduce the rainfall bias. To assess the effects of rainfall bias error propagation, percent bias (PBIAS), difference in coefficient of variation (CV), and 10th and 90th percentile indicators were applied. Findings indicate that the bias of the uncorrected rainfall caused large errors in simulated streamflow. All five bias correction methods improved the HBV-96 model performance in terms of capturing the observed streamflow. Overall, the findings of this study indicate that the magnitude of the error propagation varies subject to the selected performance indicator, bias correction method and climate model.

Renz: An R package for the analysis of enzyme kinetic data

BackgroundComplex enzymatic models are required for analyzing kinetic data derived under conditions that may not satisfy the assumptions associated with Michaelis–Menten kinetics. To analyze these data, several software packages have been developed. However, the complexity introduced by these programs is often dispensable when analyzing data conforming to the canonical Michaelis–Menten model. In these cases, the sophisticated routines of these packages become inefficient and unnecessarily intricated for the intended purpose, reason for which most users resort to general-purpose graphing programs. However, this approach, in addition of being time-consuming, is prone to human error, and can lead to misleading estimates of kinetic parameters, particularly when unweighted regression analyses of transformed kinetic data are performed.ResultsTo fill the existing gap between highly specialized and general-purpose software, we have developed an easy-to-use R package, renz, designed for accurate and efficient estimation of enzyme kinetic parameters. The package provides different methods that can be clustered into four categories, depending on whether they are based on data fitting to a single progress curve (evolution of substrate concentration over time) or, alternatively, based on the dependency of initial rates on substrate concentration (differential rate equation). A second criterion to be considered is whether the experimental data need to be manipulated to obtain linear functions or, alternatively, data are directly fitted using non-linear regression analysis. The current program is a cross-platform, free and open-source software that can be obtained from the CRAN repository. The package is accompanied by five vignettes, which are intended to guide users to choose the appropriate method in each case, as well as providing the basic theoretical foundations of each method. These vignettes use real experimental data to illustrate the use of the package utilities.Conclusionsrenz is a rigorous and yet easy-to-use software devoted to the analysis of kinetic data. This application has been designed to meet the needs of users who are not practicing enzymologists, but who need to accurately estimate the kinetic parameters of enzymes. The current software saves time and minimizes the risk of making mistakes or introducing biases due to uncorrected error propagation effects.