Increase Estimation Accuracy Research Articles

Learning-Based, Fine-Grain Power Modeling of System-Level Hardware IPs

Accurate power and performance models are needed to enable rapid, early system-level analysis and optimization. There is, however, a lack of fast yet fine-grain power models of hardware components at such high levels of abstraction. In this article, we present novel learning-based approaches for extending fast functional simulation models of accelerators and other hardware intellectual property components (IPs) with accurate cycle-, block-, and invocation-level power estimates. Our proposed power modeling approach is based on annotating functional hardware descriptions with capabilities that, depending on observability, allow capturing data-dependent resource, block, or input and output (I/O) activity without a significant loss in simulation speed. We further leverage advanced machine learning techniques to synthesize abstract power models using novel decomposition techniques that reduce model complexities and increase estimation accuracy. Results of applying our approach to various industrial-strength design examples show that our power models can predict cycle-, basic block-, and invocation-level power consumption to within 10%, 9%, and 3% of a commercial gate-level power estimation tool, respectively, all while running at several order of magnitude faster speeds of 1-10Mcycles/sec. Model training and synthesis takes less than 34 minutes in all cases, including up to 30 minutes for training data and trace generation using gate-level simulations.

On Accuracy of Calibration Estimators Supported by Auxiliary Variables from Past Periods Based on Simulation Analyses

In sample surveys there is often a need to estimate not only population characteristics, but subpopulation characteristics as well. We consider the problem of estimating the total value in domains (subpopulations). In this case, the Horvitz‑Thompson estimator could be used. Nevertheless, it does not use any additional information about population units, which are usually known. To increase estimation accuracy we propose to use calibration estimators with auxiliary variables from the current and past periods. In the simulation studies based on real and generated data, we show the influence of using auxiliary information from past periods on the accuracy, and compare properties of two calibration estimators of domain totals in longitudinal surveys.

Factor Analysis for Spectral Estimation.

Power spectrum estimation is an important tool in many applications, such as the whitening of noise. The popular multitaper method enjoys significant success, but fails for short signals with few samples. We propose a statistical model where a signal is given by a random linear combination of fixed, yet unknown, stochastic sources. Given multiple such signals, we estimate the subspace spanned by the power spectra of these fixed sources. Projecting individual power spectrum estimates onto this subspace increases estimation accuracy. We provide accuracy guarantees for this method and demonstrate it on simulated and experimental data from cryo-electron microscopy.

Allocating Travel Times Recorded from Sparse GPS Probe Vehicles into Individual Road Segments

Abstract Probe-based data collection system, which deploys movable sensors in traffic streams to collect traffic records, is commonly used for area-wide measurement of travel times and conditions of traffics on road networks. However, due to sparse distribution of polling positions from a low probe sampling frequency in practice, the sampled points from probe vehicles do not necessarily match with the end points of each link, and the travel time between two consecutive polling points needs to be positioned into an individual link for link travel time calculation. This paper presents the new technique that addresses the allocation of travel times based on data gathered from sparse GPS probe vehicles. The travel time allocation procedure by applying the relationship among instantaneous speeds, sampled positions and tracked times is proposed. The proposed technique is discussed and compared with a ground truth benchmark of high resolution field data recorded from urban roadway corridor in both congested and uncongested traffic conditions. Results suggest that addressing travel time allocation problems by the proposed method significantly outperforms the benchmark method in both complete link and local levels which are demonstrated by an increasing estimation accuracy up to 60%. It is also observed from the local level analysis that the accuracy figures are lower at the intersections comprising high stopped delays, which normally contain highly fluctuated speed profiles. Nonetheless, the proposed technique outperforms the baseline approach in both intersections with low or high stopped delays. The proposed technique offers superior results to the baseline technique in all scenarios, particularly on the segments with highly fluctuated speed profiles.

Estimation of relative permeability curves using an improved Levenberg-Marquardt method with simultaneous perturbation Jacobian approximation

Relative permeability controls the flow of multiphase fluids in porous media. The estimation of relative permeability is generally solved by Levenberg-Marquardt method with finite difference Jacobian approximation (LM-FD). However, the method can hardly be used in large-scale reservoirs because of unbearably huge computational cost. To eliminate this problem, the paper introduces the idea of simultaneous perturbation to simplify the generation of the Jacobian matrix needed in the Levenberg-Marquardt procedure and denotes the improved method as LM-SP. It is verified by numerical experiments and then applied to laboratory experiments and a real commercial oilfield. Numerical experiment indicates that LM-SP uses only 16.1% computational cost to obtain similar estimation of relative permeability and prediction of production performance compared with LM-FD. Laboratory experiment also shows the LM-SP has a 60.4% decrease in simulation cost while a 68.5% increase in estimation accuracy compared with the earlier published results. This is mainly because LM-FD needs 2n (n is the number of controlling knots) simulations to approximate Jacobian in each iteration, while only 2 simulations are enough in basic LM-SP. The convergence rate and estimation accuracy of LM-SP can be improved by averaging several simultaneous perturbation Jacobian approximations but the computational cost of each iteration may be increased. Considering the estimation accuracy and computational cost, averaging two Jacobian approximations is recommended in this paper. As the number of unknown controlling knots increases from 7 to 15, the saved simulation runs by LM-SP than LM-FD increases from 114 to 1164. This indicates LM-SP is more suitable than LM-FD for multivariate problems. Field application further proves the applicability of LM-SP on large real field as well as small laboratory problems.

Partial spectral search‐based DOA estimation method for co‐prime linear arrays

A computationally efficient method for direction-of-arrival (DOA) estimation is proposed for co-prime linear arrays. For each DOA, multiple peaks are generated in the spatial spectrum for each subarray, which are proven to be uniformly distributed in the transformed domain. By searching over a limited sector to find an arbitrary peak, all the others can be recovered without a spectral search. Finally, the DOAs can be uniquely estimated by finding the common peaks of the two decomposed subarrays. The proposed method provides increased estimation accuracy with a substantially reduced computational burden. Simulation results validate the effectiveness of the proposed method.

A three- stage algorithm for software cost and time estimation in fuzzy environment

The software productions are very expensive projects with uncertainty. For these reasons, the estimation of time and cost of software isvery important for both producers and consumers. In this paper an efficient threestage algorithm is developed for software production cost and time estimation. In the first stage, the required personmonth for implementation of software production are obtained by COCOMO and function point methods. We integrated these two methods to consider all aspects in software production and increase estimation accuracy. In the second stage the required duration for completion of each step of production (planning, analysis, design and so on) is obtained by paired comparisons matrix. In third stage, tables of complete time and cost of software are concluded by GERT network in project control and work break structure (WBS). In whole of all stages of this paper, triangular fuzzy numbers are used to express uncertainty existed in succession and repetition of each production step, time of beginning, ending, the duration of each task and costs of them. Retrieved results examined by 30 practical projects and conclude accuracy of 93 percent for time estimation and 92 percent for cost one. Also suggested algorithm is more accurate than COCOMOІІ 2000 algorithm as 50 percent based on examined problems.

Evaluating the relationship between biomass, percent groundcover and remote sensing indices across six winter cover crop fields in Maryland, United States

Winter cover crops are an essential part of managing nutrient and sediment losses from agricultural lands. Cover crops lessen sedimentation by reducing erosion, and the accumulation of nitrogen in aboveground biomass results in reduced nutrient runoff. Winter cover crops are planted in the fall and are usually terminated in early spring, making them susceptible to senescence, frost burn, and leaf yellowing due to wintertime conditions. This study sought to determine to what extent remote sensing indices are capable of accurately estimating the percent groundcover and biomass of winter cover crops, and to analyze under what critical ranges these relationships are strong and under which conditions they break down. Cover crop growth on six fields planted to barley, rye, ryegrass, triticale or wheat was measured over the 2012–2013 winter growing season. Data collection included spectral reflectance measurements, aboveground biomass, and percent groundcover. Ten vegetation indices were evaluated using surface reflectance data from a 16-band CROPSCAN sensor. Restricting analysis to sampling dates before the onset of prolonged freezing temperatures and leaf yellowing resulted in increased estimation accuracy. There was a strong relationship between the normalized difference vegetation index (NDVI) and percent groundcover (r2=0.93) suggesting that date restrictions effectively eliminate yellowing vegetation from analysis. The triangular vegetation index (TVI) was most accurate in estimating high ranges of biomass (r2=0.86), while NDVI did not experience a clustering of values in the low and medium biomass ranges but saturated in the higher range (>1500kg/ha). The results of this study show that accounting for index saturation, senescence, and frost burn on leaves can greatly increase the accuracy of estimates of percent groundcover and biomass for winter cover crops.

High‐resolution DOA estimation for closely spaced correlated signals using unitary sparse Bayesian learning

A novel method is proposed to effectively solve the challenging problem of direction-of-arrival (DOA) estimation for closely spaced correlated signals. A centro-Hermitian extended matrix is exploited to double the number of data samples, and then is transformed into a real-valued data matrix. An improved sparse Bayesian learning scheme is utilised to estimate DOAs by recovering the real-valued jointly row-sparse solution matrix with a reduced computational burden. The proposed method not only provides increased estimation accuracy but also has improved angular separation performance. Simulation results validate the effectiveness of the proposed method.

A unitary ESPRIT scheme of joint angle estimation for MOTS MIMO radar.

The transmit array of multi-overlapped-transmit-subarray configured bistatic multiple-input multiple-output (MOTS MIMO) radar is partitioned into a number of overlapped subarrays, which is different from the traditional bistatic MIMO radar. In this paper, a new unitary ESPRIT scheme for joint estimation of the direction of departure (DOD) and the direction of arrival (DOA) for MOTS MIMO radar is proposed. In our method, each overlapped-transmit-subarray (OTS) with the identical effective aperture is regarded as a transmit element and the characteristics that the phase delays between the two OTSs is utilized. First, the measurements corresponding to all the OTSs are partitioned into two groups which have a rotational invariance relationship with each other. Then, the properties of centro-Hermitian matrices and real-valued rotational invariance factors are exploited to double the measurement samples and reduce computational complexity. Finally, the close-formed solution of automatically paired DOAs and DODs of targets is derived in a new manner. The proposed scheme provides increased estimation accuracy with the combination of inherent advantages of MOTS MIMO radar with unitary ESPRIT. Simulation results are presented to demonstrate the effectiveness and advantage of the proposed scheme.

Consensus-based filter designing for wireless sensor networks with packet loss

In wireless sensor networks (WSNs) and networked control systems (NCSs), packet loss is inevitable. If the node loses measurement data, then the estimation performance will degrade. In this paper, a novel filter based on consensus algorithm for packet loss is designed in order to increase estimation accuracy and reliability. Firstly we develop a packet loss model with a stochastic Bernoulli binary switching sequence. Then a novel filter that can tolerate packet loss is designed with consensus strategy and the orthogonal analysis approach. Finally, simulation results show that comparing with existing methods the proposed filter has superior performance.

Cingulate-Hippocampus Coherence and Trajectory Coding in a Sequential Choice Task

Interactions between cortex and hippocampus are believed to play a role in the acquisition and maintenance of memories. Distinct types of coordinated oscillatory activity, namely at theta frequency, are hypothesized to regulate information processing in these structures. We investigated how information processing in cingulate cortex and hippocampus relates to cingulate-hippocampus coordination in a behavioral task in which rats choose from four possible trajectories according to a sequence. We found that the accuracy with which cingulate and hippocampal populations encode individual trajectories changes with the pattern of cingulate-hippocampal theta coherence over the course of a trial. Initial theta coherence at ~8 Hz during trial onsets lowers by ~1 Hz as animals enter decision stages. At these stages, hippocampus precedes cingulate in processing increased amounts of task-relevant information. We hypothesize that lower theta frequency coordinates the integration of hippocampal contextual information by cingulate neuronal populations, to inform choices in a task-phase-dependent manner.

Low complexity sparse noise reduction method for Loran‐C skywave delay estimation

A sparse noise reduction method applied to Loran-C skywave delay estimation is presented, which is both simple and practicable. Signal sparsity is utilised to reduce the noise inside the Loran frequency band and significant improvement in the performance of estimation is achieved. The proposed method shows increased estimation accuracy with reduced computational complexity owing to no matrix inversion in the computation process.

Breath Alcohol Estimation Training: Behavioral Effects and Predictors of Success

Breath alcohol concentration (BrAC) estimation training has been effective in increasing estimation accuracy in social drinkers. Predictors of estimation accuracy may identify populations to target for training, yet potential predictors typically are not evaluated. In addition, the therapeutic efficacy of estimation training as a preventive strategy for problematic drinking is unknown. Forty-six social drinkers with a recent binge history were randomly assigned to an intervention or control group (n = 23 per group). In each of three sessions (pretraining, training, testing), participants consumed alcohol (0.32, 0.24, 0.16 and 0.08 g/kg, in random order) every 30 min (total dose: 0.8 g/kg). Participants provided five BrAC estimates within 3 h of alcohol administration. The intervention group, but not control group, received internal and external training. During testing, participants provided BrAC estimates, but received no feedback. Participants returned for two follow-up visits to complete self-report measures. BrAC estimation training improved intervention group estimation accuracy within the laboratory. Together, training, low trait anxiety and low risk expectancy predicted high testing accuracy. There were no significant group differences in subsequent alcohol consumption, behavior under the influence or risk expectancy regarding potentially hazardous behaviors. BrAC estimation training is effective in the laboratory but may not translate into naturalistic settings.

Estimating electromechanical modes and mode shapes using the multichannel ARMAX model

A method of estimating the electromechanical modes and mode shapes from multiple synchrophasors is presented in this paper. The approach is based upon identifying the transfer function representation of the state space model of the linearized power system through the estimation of a multichannel AutoRegressive Moving Average eXogenous (ARMAX) model. The relationship between the transfer function and the ARMAX model, which is fully derived here, is shown to be general enough that virtually any multichannel ARMAX estimation algorithm may be applied to this approach. The two-stage least squares (2SLS) algorithm is used here for illustrative purposes. The proposed method is validated with the Monte Carlo procedure using simulated data from a reduced-order model of the Western Electricity Coordinating Council (WECC) system. The performance of the new method is compared with existing multichannel mode and mode shape estimation techniques and is seen to provide increased estimation accuracy with a decrease in computation time. The proposed method is further validated with measured data from the WECC system.

A new approach to assess product lifetime performance for small data sets

Because of cost and time limit factors, the number of samples is usually small in the early stages of manufacturing systems, and the scarcity of actual data will cause problems in decision-making. In order to solve this problem, this paper constructs a counter-intuitive hypothesis testing method by choosing the maximal p-value based on a two-parameter Weibull distribution to enhance the estimate of a nonlinear and asymmetrical shape of product lifetime distribution. Further, we systematically generate virtual data to extend the small data set to improve learning robustness of product lifetime performance. This study provides simulated data sets and two practical examples to demonstrate that the proposed method is a more appropriate technique to increase estimation accuracy of product lifetime for normal or non-normal data with small sample sizes.

Efforts Estimation by Use Case Point using Experience Data

Many software efforts estimation models and methods are invented to make efforts estimation accurate. Unfortunately no model or method is suitable for all kind of project and situations. it is frequently suggested that using experience data, estimation models and checklists can increase software effort estimation accuracy. However, there has been limited empirical research on the subject. It was found that in projects where experience data was utilized in the estimation process, they experienced a lesser magnitude of effort overruns. The use of a checklist also appeared to increase estimation accuracy. The utilization of an estimation model did not appear to have a substantial impact [08].This paper is suggesting that use of estimation model can also produce good estimation results, but historical data is always necessary to assist the estimation. We can use historical data to improve the result of Use Case Point and COCOMO model .In our research we have gain 10% improvement in Use case Point model with use of historical data. This paper is also suggesting that a strong monitoring policy is always required to make your estimation as a success.

The ambivalent mind can be a wise mind: Emotional ambivalence increases judgment accuracy

This article provides evidence that emotional ambivalence, the simultaneous experience of positive and negative emotions, improves judgment accuracy. Two experiments demonstrate that individuals experiencing emotional ambivalence are more accurate in weather temperature forecasts (Experiment 1) and estimation tasks (Experiment 2) than are those experiencing happiness or sadness. Experiment 3a provides suggestive evidence that emotional ambivalence increases individuals' receptivity to alternative perspectives. Experiment 3b provides evidence for the full model: ambivalence increases estimation accuracy by increasing receptivity to alternative perspectives. The experience of ambivalence may be an important tool for encouraging greater receptivity to and consideration of alternative perspectives, and thus greater accuracy in judgments.

OPTIMAL POLARIZATION DESIGN FOR DIRECTION FINDING USING MIMO ELECTROMAGNETIC VECTOR-SENSOR ARRAY

In this paper, transmitter polarization optimization is flrstly proposed to improve the accuracy of azimuth-elevation arrival angles estimation within MIMO electromagnetic vector-sensor array (EMVA). Minimizing of Cramer-Rao bound is used as cost function for the optimal design of transmitting signal polarization. Computer simulation results verify that the optimal polarization design provides increased estimation accuracy of direction flnding in MIMO-EMVA, compared with that of using flxed polarization of transmitting signal. Moreover, the optimal polarization design retains all advantages of using flxed polarization of transmitting signal for MIMO-EMVA direction flnding.

V Bhattacharya Analogy-Based Software Quality Prediction with Project Feature Weights

This paper presents analogy-based software quality estimation with project feature weights. The objective of this research is to predict the quality of project accurately and use the results in future predictions. The focus includes identifying parameters on which the quality of software depends. Estimation of rate of improvement of software quality chiefly depends on the development time. Assigning weights to these parameters to improve upon the results is also in the area of interest. In this paper two different similarity measures namely, Euclidian and Manhattan were the measures used for retrieving the matching cases from the knowledgebase to increases estimation accuracy & reliability. Expert judgment, weights and rating levels were used to assign weights and quality rating levels. The results show that assigning weights to software metrics increases the prediction performance considerably. In order to obtain the results, we have used indigenous tools.