Improve Estimation Performance Research Articles

The moderating effect of project complexity on the relationship between organizational controls and cost estimation performance: A conceptual model

Cost overrun in construction projects is high and expected to rise. The improvement of cost estimation performance is vital as it provides a better chance for the construction projects to avoid cost blowouts. In this paper, an extensive literature review is conducted from online databases such as Web of Science, Scopus, and Google Scholar which focus on organization controls and its mechanisms to improve cost estimation performance in the construction industry. While many prior studies on cost estimation emphasized investigating cost estimation methods to improve the performance of cost estimation, less attention was given to organizational and project factors. Several studies have explored the effects of organizational control on performance but failed to include project complexity in the relationship between organizational controls and performance. Therefore, this paper conceptualizes the effect of organizational controls on cost estimation performance with project complexity as a moderator. This proposition suggests project complexity has advantages and disadvantages on organizational controls. Each control mechanism has its feature and effectiveness in high complexity projects. Hence, managers should select appropriate control mechanisms, such as input control and output control that have high effectiveness on the performance.

Self-supervised learning method for consumer-level behind-the-meter PV estimation

Driven by cost reduction and sustainable policies, the penetration of distributed photovoltaic (PV) systems has deepened in recent years. Most of these PV systems are installed behind the meter (BTM), where utilities cannot monitor their output levels directly. Some supervised methods have been studied to estimate BTM PV generation. These methods, however, cannot achieve accurate estimation without the dependency on training data labeled by additional measurements. As an alternative, a self-supervised learning method is proposed in this paper to train supervised estimation models from unlabeled data. Specifically, our proposed method synthesizes pseudo labels for unlabeled net load measurements using PV generation measurements of a small group of PV sites. Moreover, an end-to-end network architecture is proposed as the base estimation model. Based on a linear embedding of PV generation, the proposed end-to-end architecture can be directly trained with PV generation labels, which leads to a simplified training process and improved estimation performance. Extensive numerical simulations on two datasets from different hemispheres are carried out to verify the effectiveness of the proposed methodology.

Geometry-informed irreversible perturbations for accelerated convergence of Langevin dynamics

We introduce a novel geometry-informed irreversible perturbation that accelerates convergence of the Langevin algorithm for Bayesian computation. It is well documented that there exist perturbations to the Langevin dynamics that preserve its invariant measure while accelerating its convergence. Irreversible perturbations and reversible perturbations (such as Riemannian manifold Langevin dynamics (RMLD)) have separately been shown to improve the performance of Langevin samplers. We consider these two perturbations simultaneously by presenting a novel form of irreversible perturbation for RMLD that is informed by the underlying geometry. Through numerical examples, we show that this new irreversible perturbation can improve estimation performance over irreversible perturbations that do not take the geometry into account. Moreover we demonstrate that irreversible perturbations generally can be implemented in conjunction with the stochastic gradient version of the Langevin algorithm. Lastly, while continuous-time irreversible perturbations cannot impair the performance of a Langevin estimator, the situation can sometimes be more complicated when discretization is considered. To this end, we describe a discrete-time example in which irreversibility increases both the bias and variance of the resulting estimator.

Learning From Architectural Redundancy: Enhanced Deep Supervision in Deep Multipath Encoder–Decoder Networks

Deep encoder-decoders are the model of choice for pixel-level estimation due to their redundant deep architectures. Yet they still suffer from the vanishing supervision information issue that affects convergence because of their overly deep architectures. In this work, we propose and theoretically derive an enhanced deep supervision (EDS) method which improves on conventional deep supervision (DS) by incorporating variance minimization into the optimization. A new structure variance loss is introduced to build a bridge between deep encoder-decoders and variance minimization, and provides a new way to minimize the variance by forcing different intermediate decoding outputs (paths) to reach an agreement. We also design a focal weighting strategy to effectively combine multiple losses in a scale-balanced way, so that the supervision information is sufficiently enforced throughout the encoder-decoders. To evaluate the proposed method on the pixel-level estimation task, a novel multipath residual encoder is proposed and extensive experiments are conducted on four challenging density estimation and crowd counting benchmarks. The experimental results demonstrate the superiority of our EDS over other paradigms, and improved estimation performance is reported using our deeply supervised encoder-decoder.

Joint 2D-DOA estimation of coherent targets using EV sensors in MIMO radar

This letter presents an approach for estimating the joint two-dimensional (2D) direction of arrival (DOA) of coherent targets in monostatic multiple-input and multiple-output (MIMO) radar using the electromagnetic vector (EV) sensors. The approach resolves coherent targets without any preprocessing and locates two or more targets having identical azimuth/elevation angles, and also automatically pairs the 2D directional (i.e., azimuth and elevation) angles and the 2D polarization angles. Compared with the existing state-of-the art methods, the presented approach shows improved estimation performance verified by the simulation results.

Evaluation of propensity score methods for causal inference with high-dimensional covariates.

In recent work, researchers have paid considerable attention to the estimation of causal effects in observational studies with a large number of covariates, which makes the unconfoundedness assumption plausible. In this paper, we review propensity score (PS) methods developed in high-dimensional settings and broadly group them into model-based methods that extend models for prediction to causal inference and balance-based methods that combine covariate balancing constraints. We conducted systematic simulation experiments to evaluate these two types of methods, and studied whether the use of balancing constraints further improved estimation performance. Our comparison methods were post-double-selection (PDS), double-index PS (DiPS), outcome-adaptive LASSO (OAL), group LASSO and doubly robust estimation (GLiDeR), high-dimensional covariate balancing PS (hdCBPS), regularized calibrated estimators (RCAL) and approximate residual balancing method (balanceHD). For the four model-based methods, simulation studies showed that GLiDeR was the most stable approach, with high estimation accuracy and precision, followed by PDS, OAL and DiPS. For balance-based methods, hdCBPS performed similarly to GLiDeR in terms of accuracy, and outperformed balanceHD and RCAL. These findings imply that PS methods do not benefit appreciably from covariate balancing constraints in high-dimensional settings. In conclusion, we recommend the preferential use of GLiDeR and hdCBPS approaches for estimating causal effects in high-dimensional settings; however, further studies on the construction of valid confidence intervals are required.

Vehicle state estimation based on extended Kalman filter and radial basis function neural networks

To improve the reliability of vehicle state parameter estimation, a vehicle state fusion estimation method based on dichotomy is proposed. An extended Kalman filter algorithm is designed based on the vehicle 3 degrees of freedom dynamic model. Meanwhile, considering the influence of dynamic model and sensor noise and its coefficient selection on the estimation results, a radial basis function neural network estimation algorithm is designed. To further improve the reliability of the estimation algorithm, a method of estimation algorithm fusion is proposed based on the idea of mutual compensation between model- and data-driven estimation algorithms. The weights of the estimation results of different algorithms are assigned through the dichotomy. The redundancy and fusion of estimation algorithms can improve estimation performance. The effectiveness of the fusion method is verified by the co-simulation of MATLAB/Simulink and CarSim, and the real vehicle test. The results show that the change trend of the estimation result is consistent with the actual state parameters change trend, and the estimation accuracy after algorithm fusion is significantly improved compared to a single extended Kalman filter or radial basis function.

An Evolutionary Field Theorem: Evolutionary Field Optimization in Training of Power-Weighted Multiplicative Neurons for Nitrogen Oxides-Sensitive Electronic Nose Applications.

Neuroevolutionary machine learning is an emerging topic in the evolutionary computation field and enables practical modeling solutions for data-driven engineering applications. Contributions of this study to the neuroevolutionary machine learning area are twofold: firstly, this study presents an evolutionary field theorem of search agents and suggests an algorithm for Evolutionary Field Optimization with Geometric Strategies (EFO-GS) on the basis of the evolutionary field theorem. The proposed EFO-GS algorithm benefits from a field-adapted differential crossover mechanism, a field-aware metamutation process to improve the evolutionary search quality. Secondly, the multiplicative neuron model is modified to develop Power-Weighted Multiplicative (PWM) neural models. The modified PWM neuron model involves the power-weighted multiplicative units similar to dendritic branches of biological neurons, and this neuron model can better represent polynomial nonlinearity and they can operate in the real-valued neuron mode, complex-valued neuron mode, and the mixed-mode. In this study, the EFO-GS algorithm is used for the training of the PWM neuron models to perform an efficient neuroevolutionary computation. Authors implement the proposed PWM neural processing with the EFO-GS in an electronic nose application to accurately estimate Nitrogen Oxides (NOx) pollutant concentrations from low-cost multi-sensor array measurements and demonstrate improvements in estimation performance.

2-D DOA Estimation of Incoherently Distributed Sources Considering Gain-Phase Perturbations in Massive MIMO Systems

In massive multiple-input multiple-output (MIMO) systems, accurate direction-of-arrival (DOA) estimation is important for the base station (BS) to perform effective downlink beamforming. So far, there have been few reports on DOA estimation considering gain-phase perturbations in massive MIMO systems. However, gain-phase perturbations indeed exist in practical applications and cannot be ignored. In this paper, an efficient method for two-dimensional (2-D) DOA estimation of incoherently distributed (ID) sources considering array gain-phase perturbations is proposed for massive MIMO systems. Firstly, a shift invariance structure is established in the subspace framework, and a constrained optimization problem is formulated to estimate the nominal azimuth and elevation DOAs as well as gain-phase perturbations with closed-form expressions, under the assumption that some of the BS antennas are well calibrated; secondly, the corresponding angular spreads are obtained with the aid of the estimated gain-phase perturbations. Theoretical analysis and an approximate Cramér-Rao bound are also provided. An improved estimation performance is achieved by the proposed method as demonstrated by numerical simulations.

Driveline Modeling With Transmission Loss and Robust Torque Observer Design for Dual Clutch Transmission

This paper presents a design of driveline torque observer with dual clutch transmission (DCT). A goal of this study is to improve estimation performance of the observer. To increase the observer’s performance, it is important to make an accurate driveline model and a robust observer. First, to increase the accuracy of the model, we propose lumped driveline efficiency which is a parameter that expresses driveline transmission loss. In addition, a recursive least square estimation (RLSE) algorithm is proposed to estimate the parameter using driving data. Then, the model is updated using the estimated parameter value. Second, a reduced order observer is proposed to estimate transmitted torques of driveline in both driving and gear shifting process. Moreover, the observer, which is robust against measurement noise and disturbance, is designed through frequency domain analysis. For frequency domain analysis, an eigenstructure assignment method is applied to tune observer gains. Performance of the proposed RLSE algorithm and the observer are verified through simulation and testbench experiments.

Oscillometry-Based Blood Pressure Estimation Using Convolutional Neural Networks

Blood pressure measurement is required to monitor the cardiovascular state of a person, and it is commonly conducted in a noninvasive way using oscillometry-based blood pressure monitors (BPM). Blood pressure can be estimated by analyzing the oscillometric waveform (OMW) in the BPM, and many methods have been examined to increase their estimation accuracy. In this study, we proposed a new method that enhances estimation accuracy and requires no external user information, such as age and gender, in the test phase. In the method, the entire OMW was considered as an input to reduce information loss via feature extraction, and convolutional neural networks were utilized to effectively analyze the high-dimensional input. Additionally, the proposed method included a novel ensemble method to further increase the estimation accuracy. The performance of the proposed method was evaluated and compared with other studies via subject-independent tests considering real situations in which it is difficult to obtain preliminary information on a test subject. Data from 64 subjects were used in the test. The mean absolute error of the proposed method was 3.12 and 3.98 mmHg for systolic and diastolic blood pressure, respectively, which was superior to those reported in other studies conducted in similar conditions. Individuals can measure their blood pressure with higher precision using the proposed method with improved estimation performance. This can aid in reducing the risk of cardiovascular diseases.

Impact of sensor placement in soil water estimation*

Soil moisture estimation is an essential element in the implementation of a closed-loop irrigation system. The determination of the best locations to install the sensors such that good state estimation can be obtained is an important problem. In our previous work, this issue has been addressed by employing the modal degree of observability based on extensive simulations. It was found that optimally placed sensors can lead to much-improved soil moisture estimation performance. However, it is unclear whether the significantly improved estimation performance can still be observed in actual applications. In this work, we consider an actual agricultural field in Lethbridge, Alberta, Canada, and study the impact of sensor placement in soil water estimation. Soil moisture measurements from 42 soil moisture sensors installed at different depths were collected for one growing season. First, a three-dimensional agro-hydrological model with heterogeneous soils is developed. Then, a state estimator designed based on the extended Kalman filter (EKF) is adopted to estimate the soil water content. Subsequently, we apply the modal degree of observability to the three-dimensional system and determine where the best sensor locations are. Different scenarios are considered to estimate the soil water content and the estimation results are analyzed for all the scenarios.

Adaptive Discretization Using Golden Section to Aid Outlier Detection for Software Development Effort Estimation

The software engineering researchers have worked on different dimensions to facilitate better software effort estimates, including those focusing on dataset quality improvement. In this research, we specially investigated the effectiveness of outlier removal to improve estimation performance of 5 machine learning (ML) methods (Support Vector Regression, Random Forest, Ridge Regression, K-Nearest Neighbor, and Gradient Boosting Machines) for software development effort estimation (SDEE). We propose a novel discretization method based on Golden Section (dubbed as Golden Section based Adaptive Discretization, GSAD) to identify optimal number of outliers for SDEE dataset. The results signify the importance of optimal number of outliers’ removal to improve estimations. Moreover, the results obtained after applying GSAD technique have been compared with IQR and Cooks’ distance based outlier identification methods over 4 datasets: ISBSG Release 2021, UCP, NASA93 and China. The empirical results confirm that the performance of ML based SDEE methods is generally improving by employing GSAD and the proposed GSAD method has the ability to compete with the other prevalent outlier identification methods.

Joint Estimation of Target Range and Velocity for Radar System via Complex-Valued Kernel Quasi-Newton Method

This article deals with the online joint estimation of the target range and velocity for the monostatic radar system with considering the nonlinear relationship between the target parameters and the observed echo. The nonlinear estimation problem of the two parameters is, therefore, formulated in the reproducing kernel Hilbert space (RKHS) with multiple inputs and multiple outputs. Herein, the complex-valued random Fourier feature (CRFF) method is applied to approximate the kernel method in a fixed-dimensional architecture to address the issue of dimensionality growth in complex-valued kernel learning. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">complex-valued random Fourier feature kernel quasi-Newton</i> (CRFFKQN) algorithm is proposed by solving a quadratic optimization problem in the random Fourier features space, which utilizes the information of all estimation errors up to the current iteration, thus resulting in the significant estimation performance improvement over the existing algorithms that only use the current information to estimate the target parameters. Numerical simulations are performed to illustrate the superiorities of the proposed algorithm against some counterparts in terms of estimation accuracy and efficiency.

Scheme for optimizing a direct current component in a photonics-assisted OFDM MMW system.

This Letter proposes a new scheme for optimizing a direct current (DC) component to enable both the frequency offset estimation and system performance improvement by controlling the IQ modulator to adjust the DC component in a photonics-assisted OFDM MMW system. Compared with the traditional method, a large DC component is used as the pilot frequency for frequency offset estimation. When a small DC component is used, the system performance is better. A 3 Gbaud 16QAM OFDM signal is delivered over 70 m wireless, enabling a 3.5 dB sensitivity improvement at the bit error rate threshold of 1×10-2 in the E-band.

Performance improvement of PMSM rotor position estimation by using adaptive hybrid filter based PLL

To achieve a satisfactory sensorless control performance of permanent magnetic synchronous machine (PMSM), an effective rotor position estimation method is a critical factor. As a useful tool for phase estimation technique, there exists extensive application of phase-locked loops (PLLs) in this task. Nevertheless, since harmonics are existed in the back electromotive force (EMF), the accuracy of position estimation method based on PLL is still low. In this paper, an adaptive hybrid filter based PLL is proposed to enhance the estimation performance. The proposed adaptive hybrid filter is frequency-adaptive, which means the performance is not affected under varying-speed operating conditions. Moreover, all the harmonic disturbances can be totally eliminated. The proposed method is validated under simulation. The results confirm the effectiveness of the proposed method.

Estimation of Joint Angle From sEMG and Inertial Measurements Based on Deep Learning Approach.

Continuous kinematics estimation from surface electromyography (sEMG) allows more natural and intuitive human-machine collaboration. Recent research has suggested the use of multimodal inputs (sEMG signals and inertial measurements) to improve estimation performance. This work focused on assessing the use of angular velocity in combination with myoelectric signals to simultaneously and continuously predict 12 joint angles in the hand. Estimation performance was evaluated for five functional and grasping movements in 20 subjects. The proposed method is based on convolutional and recurrent neural networks using transfer learning (TL). A novel aspect was the use of a pretrained deep network model from basic joint hand movements to learn new patterns present in functional motions. A comparison was carried out with the traditional method based solely on sEMG. Although the performance of the algorithm slightly improved with the use of the multimodal combination, both strategies had similar behavior. The results indicated a significant improvement for a single task: opening a bottle with a tripod grasp.

Improved Performance of The CHIRPS Monthly Rainfall Estimation Extraction from Google Earth Engine (GEE) platform in South Sulawesi Region

The integration of the availability and processing of The Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) data by the Google Earth Engine (GEE) platform is used in this study to extract the estimated monthly rainfall in South Sulawesi. Several areas are selected based on the characteristics of the rainy period cycle representing South Sulawesi, namely Makassar, Masamba, Wajo, and Bone. Monthly rainfall estimation data of CHIRPS in the year 2019 were validated by monthly observed rainfall at the same period showing the CHIRPS rainfall estimation has not been maximized with correlation coefficient values are 0.94, 0.63, 0.65, 0.75, and RMSE percentage 54%, 52%, 95%, 64% for each of the study areas. Then the increase in rainfall estimation performance is carried out by applying multiple linear regression method and considering both monthly observed and estimated rainfall during 30 years from 1989 to 2018, latitude and longitude point as well as elevation in every location. The results show an increase of correlation coefficient to 0.95, 0.74, 0.74, and 0.87 and a general decrease of RMSE percentage to 53%, 39%, 80%, and 67%. Thus, monthly rainfall estimation performance improvement is successfully achieved in various rainy period cycles of the study area.

Sufficient dimension reduction for populations with structured heterogeneity.

A key challenge in building effective regression models for large and diverse populations is accounting for patient heterogeneity. An example of such heterogeneity is in health system risk modeling efforts where different combinations of comorbidities fundamentally alter the relationship between covariates and health outcomes. Accounting for heterogeneity arising combinations of factors can yield more accurate and interpretable regression models. Yet, in the presence of high-dimensional covariates, accounting for this type of heterogeneity can exacerbate estimation difficulties even with large sample sizes. To handle these issues, we propose a flexible and interpretable risk modeling approach based on semiparametric sufficient dimension reduction. The approach accounts for patient heterogeneity, borrows strength in estimation across related subpopulations to improve both estimation efficiency and interpretability, and can serve as a useful exploratory tool or as a powerful predictive model. In simulated examples, we show that our approach often improves estimation performance in the presence of heterogeneity and is quite robust to deviations from its key underlying assumptions. We demonstrate our approach in an analysis of hospital admission risk for a large health system and demonstrate its predictive power when tested on further follow-updata.

Dual CNN-Based Channel Estimation for MIMO-OFDM Systems

Recently, convolutional neural network (CNN)-based channel estimation (CE) for massive multiple-input multiple-output communication systems has achieved remarkable success. However, complexity even needs to be reduced, and robustness can even be improved. Meanwhile, existing methods do not accurately explain which channel features help the denoising of CNNs. In this paper, we first compare the strengths and weaknesses of CNN-based CE in different domains. When complexity is limited, the channel sparsity in the angle-delay domain improves denoising and robustness whereas large noise power and pilot contamination are handled well in the spatial-frequency domain. Thus, we develop a novel network, called dual CNN, to exploit the advantages in the two domains. Furthermore, we introduce an extra neural network, called HyperNet, which learns to detect scenario changes from the same input as the dual CNN. HyperNet updates several parameters adaptively and combines the existing dual CNNs to improve robustness. Experimental results show improved estimation performance for the time-varying scenarios. To further exploit the correlation in the time domain, a recurrent neural network framework is developed, and training strategies are provided to ensure robustness to the changing of temporal correlation. This design improves channel estimation performance but its complexity is still low.