Search results

The purpose of this paper is to develop several predictive models for estimating the structural construction cost and establish range estimation for the structural construction cost using design information available in early stages of residential building projects.

Information about residential building projects is collected based on project documents from construction companies with regard to the design parameters and the actual structural construction costs at completion. Storey enclosure method (SEM) is fundamental for determining the building design parameters, forming the potential variables and developing the cost estimation models using regression analysis. Nonparametric bootstrap method is used to establish range estimation for the structural construction cost.

A model which is developed from an integration of advanced SEM, principle component analysis and regression analysis is robust in terms of predictability. In terms of range estimation, cumulative probability-based range estimates and confidence intervals are established. While cumulative probability-based range estimates provide information about the level of uncertainty included in the estimate, confidence intervals provide information about the variability of the estimate. Such information could be very crucial for management decisions in early stages of residential building projects.

This study could provide practitioners with a better understanding of the uncertainty and variability included in the cost estimate. Hence, they could make effective improvements on cost-related management approaches to enhance project cost performance.

This article examines the accuracy and bias inherent in the wisdom of crowd effect. The purpose is to clarify what kind of bias crowds have when they make predictions. In the theoretical inquiry, the effect of the accumulated absolute deviation was simulated. In the empirical study, the observed biases were examined using data from forecasting foreign exchange rates.

In the theoretical inquiry, the effect of the accumulated absolute deviation was simulated based on mathematical propositions. In the empirical study, the data from 2004 to 2011 were provided by Nikkei, which holds the “Nikkei Yen Derby” competition. In total, 3,657 groups forecasted the foreign exchange rate, and the first prediction was done in early May to forecast the rate at the end of May. The second round took place in June in a similar manner.

The average absolute deviation in May was smaller than that in June. The first round of prediction was more accurate than the second round one. Predictors were affected by the observable real exchange rate, such that they modified their forecasts by referring to the actual data in early June. An actuality bias existed when the participants lost their diverse prospects. Since the standard deviations of the June forecasts were smaller than those of May, the fact-convergence effect was supported.

This article reports novel findings that affect the wisdom of crowd effect—referred to as actuality bias and fact-convergence effect. The former refers to a forecasting bias toward the observable rate near the forecasting date. The latter implies that predictors, as a whole, indicate smaller forecast deviations by observing the realized foreign exchange rate.

Hydrodynamic parameter estimation is significant for the velocity prediction of unmanned surface vehicles. Considering the field data’s uncertain nonlinearities (environmental disturbances and measurement noise), this paper aims to propose a hybrid adaptive parameter estimation (HAPE) strategy.

First, a rough estimation of hydrodynamic parameters is used by the least squares method. Second, an improved adaptive parameter estimation algorithm is applied to compensate for the influence of uncertain nonlinearities and adjust the parameters within the rough range. Finally, it is proved that the calculated velocity asymptotically converges to the actual value during the parameter estimation procedure.

The numerical simulation and pool experiments are conducted in two scenarios of steady turning and sinusoidal thrust to verify the effectiveness of the proposed HAPE method. The results validate that the accuracy of the predicted velocity using the hydrodynamic model obtained by the HAPE strategy is better than the APE algorithm. In addition, the hydrodynamic parameters estimated with the sinusoidal thrust data are more applicable than the steady turning data.

This study proposes a HAPE strategy that considers the uncertain nonlinearities of the field data. This method provides a more accurate predicted velocity. Besides, as far as we know, it is the first time to analyze the influence of different test conditions on the accuracy of the predicted velocity.

This paper aims to present a new vision-based approach for both the identification and the estimation of the relative distance between the unmanned aerial vehicle (UAV) and power pylon. Autonomous power line inspection using small UAVs, has been the focus of many research works over the past couple of decades. Automatic detection of power pylons is a primary requirement to achieve such autonomous systems. It is still a challenging task due to the complex geometry and cluttered background of these structures.

The identification solution proposed, avoids the complexity of classic object recognition techniques. Instead of searching the whole image for the pylon template, low-level geometric priors with robust colour attributes are combined to remove the pylon background. The depth estimation, on the other hand, is based on a new concept which exploits the ego-motion of the inspection UAV to estimate its distance from the pylon using just a monocular camera.

An algorithm is tested on a quadrotor UAV, using different kinds of metallic power pylons. Both simulation and real-world experiments, conducted in different backgrounds and illumination conditions, show very promising results.

In the real tests carried out, the Inertial Navigation System (INS) of the vehicle was used to estimate its ego-motion. A more reliable solution should be considered for longer distances, by either fusing INS and global positioning system data or using visual navigation techniques such as visual odometry.

A simple yet efficient solution is proposed that allows the UAV to reliably identify the pylon, with still a low processing cost. Considering a monocular solution is a major advantage, given the limited payload and processing power of such small vehicles.

Energy demand is an important economic index, and demand forecasting has a significant role when devising energy development plans for cities or countries. GM(1,1) model has become popular because it needs only a few data points to construct a time-series model without statistical assumptions. Several methods have been developed to improve prediction accuracy of the original GM(1,1) model by only estimating the sign of each residual. This study aims to address that this is too tight a restriction for the modification range.

Based on the predicted residual, this study uses the functional-link net (FLN) with genetic-algorithm-based learning to estimate the modification range for its corresponding predicted value obtained from the original GM(1,1) model.

The forecasting ability of the proposed grey prediction model is verified using real energy demand cases from China. Experimental results show that the proposed prediction model performs well compared to other grey residual modification models with sign estimation.

The proposed FLNGM(1,1) model can improve prediction accuracy of the original GM(1,1) model using residual modification. The distinctive feature of the proposed model is to use an FLN to estimate sign and modification range simultaneously for the predicted value based on its corresponding predicted residual obtained from the residual GM(1,1) model.

The author developed a theory of optimal trajectories for air vehicles with variable wing area and conventional wings. He applied a new theory of singular optimal solutions and obtained the optimal flight in many cases. At first glance, the results may seem strange however, this is correct and this paper will show how this new theory may be used. The main idea of the research is in using the vehicle's kinetic energy for increasing the range of missiles and projectiles. The author shows that the range of a ballistic warhead can be increased 3‐4 times if an optimal wing is added to the ballistic warhead, especially a wing with variable area. If increased range is not needed, the warhead mass can be increased. The range of big gun shells can also be increased 3‐9 times. The range of aircraft may be improved 3‐15 percent and more. The results can be used for the design of aircraft, missiles, flying bombs and shells of big guns.

This study provides a systematic literature review and categorization of the costs reported in the literature for the introduction of new vaccines, focusing on sub-Saharan Africa within LMICs, where vaccines are highly needed, financial resources are scarce and data are lacking and scattered.

A systematic literature search of PubMed and Web of Science databases was conducted according to the PRISMA requirements. Searches also included the relevant grey literature. In total, 39 studies were selected and nine cost categories were investigated to build a comprehensive framework.

The paper considers nine cost categories that cover the whole life of the vaccine, from its initial study to its full implementation, including for each of them the relevant subcategories. The systematic review, besides providing specific quantitative data and allowing to assess their variability within each category, points out that delivery, program preparation, administration and procurement costs are the most frequently estimated categories, while the cost of the good sold, costs borne by households and costs associated to AEFI are usually overlooked. Data reported on R&D costs and investment in the production plant differ significantly among the selected contributions.

The literature contributions on cost estimation tend to focus on a precise vaccine, a specific geographic area, or to adopt a narrow approach that captures only a subset of the costs. This article presents a rich and inclusive set of the economic quantitative data on immunization costs in limited-resource countries.

The purpose of this paper is to provide a method for computing the spillover index first proposed by Diebold and Yilmaz (2009), with empirical application on Asian stock markets.

It is based on a VAR-structural-GARCH model.

The results clearly show that the main driver of fluctuations in Asian financial markets is the USA, with China having little connection with other markets. Further, evidence of financial contagion is found during both the 1997 Asian financial crisis and the 2008 global financial crisis.

The method has two advantages: it is both uniquely determined and dynamic.

Frequency diverse array (FDA) radar with uniform frequency offset between antenna elements has been proposed and investigated, which exhibits a range-angle-dependent beampattern. Nevertheless, because of the coupling in range and angle responses, it cannot estimate directly both the range and angle information of a target.The purpose of this paper is to consider a sub-array scheme of range-angle joint estimation of a target for frequency diverse array (FDA) radar.

First, The entire array is divided into two sub-arrays, which employs two different frequency offsets. For aperture extension, each sub-array adopts difference co-array structure . Therefore, the targets range and angle can be estimated directly with the subspace-based multiple signal classification algorithms for the decoupling capability of distance and angle dimensional. The estimation performance is examined by analyzing the Cramer-Rao lower bound (CRLB) versus signal-to-noise ratio (SNR).

Each sub-array adopts difference co-array structure to provide degrees of freedom by only physical sensors when the second-order statistics of the received data is used. And the sub-array is equivalent to two sets of equations to solve two unknown quantities, and the closed solution of the unknown quantity can be directly determined, which cannot be gained by the phase-array radar and basic ULA FDA radar. Finally, numerical simulation results verify the validity of the proposed method.

In this paper, we devise a subarray scheme on FDA radar for range and angle estimation. In order to aperture extension, difference co-array is employed in each subarrays, and more targets can be distinguished than the physical sensors. The range and angle estimation performance is examined by analyzing the CRLB.