Estimation Of Cost Function Research Articles

Flexibility Requirement When Tracking Renewable Power Fluctuation With Peer-to-Peer Energy Sharing

Flexible load at the demand-side has been regarded as an effective measure to cope with volatile distributed renewable generations. To unlock the demand-side flexibility, this paper proposes a peer-to-peer energy sharing mechanism that facilitates energy exchange among users while preserving privacy. We prove the existence and partial uniqueness of the energy sharing market equilibrium and provide a centralized optimization to compute the equilibrium. The centralized optimization is further linearized by a convex combination approach, turning into a multi-parametric linear program (MP-LP) with renewable power output deviations being the parameters. The flexibility requirement of individual users is calculated based on this MP-LP. To be specific, an adaptive vertex generation algorithm is proposed to construct a piecewise linear estimator of the optimal total cost subject to a given error tolerance. Critical regions and optimal strategies are retrieved from the obtained approximate cost function to evaluate the flexibility requirement. The proposed algorithm does not rely on exact characterization of optimal basis invariant sets and thus is not influenced by model degeneracy, a common difficulty faced by existing approaches. Case studies validate the theoretical results and show that the proposed method is scalable.

Questioning the implication of the utility-maximization assumption for the estimation of deprivation cost functions after disasters

Deprivation cost functions (DCFs) allow for the quantification of human suffering after disasters strike. Commonly, DCF estimation methods assume that affected individuals aim to maximize their wellbeing while making rational decisions. However, after disasters, people are often under stress and pressure, possibly traumatized, and they can adopt behaviors that are neither compensatory nor utility based. This paper questions the use of Random Utility Maximization (RUM) to estimate DCFs and compares its results with a Random Regret Minimization (RRM) approach, and a combined method that considers both Regret- and Utility-based decision rules. DCFs for multiple supplies are estimated using stated preference data from two case studies: Colombia and Ecuador. The results suggest that deprivation cost estimations yield significantly different valuations depending on the heuristic used to explain choice behavior, with the DCFs estimated using RUM having higher cost values for a time window shorter than 48 h. This suggests that for longer time windows, RRM is the approach that should be used. Moreover, results show that deprivation costs are context-dependent, and should not be transferred directly. Finally, it is shown that DCFs for multiple commodities can be added separately in the same objective function when planning relief distribution operations. This research is the first attempt to consider different choice heuristics for estimating deprivation cost functions, and it is the first to compare data from multiple locations. The implications of these findings provide disaster managers and planners with new challenges and research directions to improve relief distribution plans.

A New Likelihood Function for Consistent Phase Series Estimation in Distributed Scatterer Interferometry

The proper use of distributed scatterer (DS) can improve both the density and quality of synthetic aperture radar (SAR) interferometry (InSAR) measurements. A critical step in DS interferometry (DSI) is the restoration of a consistent phase series from SAR interferogram stacks. Most state-of-the-art algorithms adopt an approximate likelihood function to calculate the likelihood by replacing the true coherence matrix with its estimation, more specifically, the sample coherence matrix (SCM). However, this approximation has a drawback in that the coherence estimates are greatly biased when the coherence is low. In this study, we derive a new likelihood function without such an approximation. Accordingly, a DSI framework using this function for phase estimation and point selection is provided. In this framework, the new likelihood function serves as a cost function for phase estimation and a quality measure for DS selection. Its performance is investigated by experiments in a simulation study and a real-world case study using Sentinel-1 data over Shenzhen airport in China. The results reveal that the proposed DSI framework outperforms the existing state-of-the-art approaches in different scenarios, in terms of providing a more accurate estimation and improving DS density and coverage.

Learning to Measure: Adaptive Informationally Complete Generalized Measurements for Quantum Algorithms

Many prominent quantum computing algorithms with applications in fields such as chemistry and materials science require a large number of measurements, which represents an important roadblock for future real-world use cases. We introduce a novel approach to tackle this problem through an adaptive measurement scheme. We present an algorithm that optimizes informationally complete positive operator-valued measurements (POVMs) on the fly in order to minimize the statistical fluctuations in the estimation of relevant cost functions. We show its advantage by improving the efficiency of the variational quantum eigensolver in calculating ground-state energies of molecular Hamiltonians with extensive numerical simulations. Our results indicate that the proposed method is competitive with state-of-the-art measurement-reduction approaches in terms of efficiency. In addition, the informational completeness of the approach offers a crucial advantage, as the measurement data can be reused to infer other quantities of interest. We demonstrate the feasibility of this prospect by reusing ground-state energy-estimation data to perform high-fidelity reduced state tomography.

Event-Triggered Model Predictive Adaptive Dynamic Programming for Road Intersection Path Planning of Unmanned Ground Vehicle

Autonomous driving of unmanned ground vehicle (UGV) at road intersection is a challenging task due to the complicated traffic conditions. In this paper, an event-triggered model predictive adaptive dynamic programming (MPADP) algorithm is proposed for path planning of UGV at road intersection. Following the critic-actor scheme of adaptive dynamic programming (ADP), cost function approximation and control policy generation are combined to formulate MPADP. The infinite horizon cost function of ADP is stacked over predictive horizon of model predictive control (MPC), and then the infinite horizon cost function is converted to the finite horizon-stacked cost function in MPADP. By minimizing the approximation error within predictive horizon, the approximation accuracy is enhanced. Considering the limitation of energy consumption, the event-triggered mechanism is designed based on the mismatch of cost function approximation. Three triggering conditions are designed, and the corresponding boundedness of approximation error is proved. Simulation results illustrate the effectiveness, efficiency and feasibility in application of the event-triggered MPADP method for path planning at road intersection.

Analysis of the Effect EXIM Bank Efficiency on Non-Oil Export

The Effect of EXIM Bank Efficiency on Non-Oil Export Abstract According to economic definition, Non-oil export is one of the most important indexes in economy development of each country especially in developing countries. Export import banks (EXIM) can affect the export financing of goods and services, if the bank can have cost efficiency. On the other hand, in this paper the efficiency of Iranian EXIM Bank is estimated by using Stochastic Frontier Analysis (SFA) method. Along with this purpose, On the basis of the results of final model, the output variable includes non-operating income, operating income as well as the input variable including impairment loan, provision of default and branches’ capital were significant in bank efficiency. Also, braches’ capital was one of the main input variables of the model that shows the importance of these variables in different branches of Export Development Bank. Whereas the bank is a specialty bank in non-oil exports section and its main capital is provided by the government, the role of the government in this performance is undeniable. Since the bank is a specialty bank in export domain, the effect of the bank’s efficiency on non-oil export of Iran was studied by using a linear regression. On the strength of the results , the efficiency of the bank in the under study period had an increasing trend. Also, based on the results, a significant correlation was found between the efficiency of Export Development Bank and non-oil exports in the under study period. Introduction Seven criteria for evaluating firm performance are: effectiveness, efficiency, productivity, quality, profitability and Profitability, quality of working life and creativity and innovation. The ratio of the amount of resources that are projected to achieve goals, objectives and activities Be used in proportion to the amount of resources actually used between productivity and the ratio or relationship between The amount of output produced by the firm using a certain amount of inputs is called productivity. Thus, improving the level of productivity is the result of producing more output using a fixed amount of input or producing a fixed amount of output using less input. Methodology The following steps have been taken in the research for fulfilling the research objectives: In the first step, in order to evaluate bank’s performance, the efficiency of various branches was measured using Stochastic Frontier Production Function. Accordingly, 27 branches with continuous activity during the past three years were selected as sample and their monthly efficiency was estimated. For measuring branches’ efficiency, bank’s expenses were considered as a function of input and output variables and then estimated using Frontier 4 software. Various inputs and outputs were fitted in the model and finally non-operating and operating incomes, as outputs, and impairment loan, provision of default and branches’ capital, as bank’s inputs, were identified. Bank efficiency using SFA model As mentioned above, econometric estimation of cost function requires selection of function form. The model was estimated in Cob Douglass and Translog models and research model was selected. Also, the model was estimated with different inputs and outputs. Only the final function form is entered here. General form of Cob Douglass and Translog functions are respectively as follows: is the total cost of ith bank, is output, is input price, is random error and is cost inefficiency. Keywords: EXIM Bank, Efficiency, Non Oil Export, exim bank performance – Stochastic Frontier Analysis Conclusion Whereas inputs aproved in the above model for estimating branches’ efficiency include branches’ capital, impairment loans and provision of default branches' facilities, five strategies are suggested for improving branches’ efficiency and developing non-oil exports. A) Increase of branches’ capital: branches’ capital of Export Development Bank is an exogenous variable as the bank is a governmental one and increase of branches’ capital requires direct governmental support. B) Decrease of impairment loans and provision of default: impairment loans and provision of default are two endogenous variables that are controlled by the bank and its ranches. c)The Central Bank is trying to reduce the restrictions of the rules and regulations related to commercial banks in Exim Bank Iran. d)Reduction and merger of loss-making branches that are unable to cover their costs due to the location and conditions of the branch. e)Increasing the productivity of human and physical capital by holding effective training courses and revising the recruitment and employment of manpower in a traditional way and using scientific methods to attract talented, specialized, motivated and interested manpower and etc.

English

In this paper, we propose a new fractional-order adaptive generalized predictive control (FA-GPC) design based on a fractional order Romero GPC cost function and online estimation of the plant model using the Recursive Least Square (RLS) algorithm. The plant model is supposed to be linear time-invariant with unknown parameters. The proposed controller is applied in numerical simulation to a DC motor system and compared with the classical adaptive generalized predictive control (A-GPC). Simulation results illustrate the effectiveness of the proposed adaptive FA-GPC controller.

A novel method for asynchronous source localisation based on time of arrival measurements

Source localisation is an important component in the application of wireless sensor networks, and plays a key role in environmental monitoring, healthcare and battlefield surveillance and so on. In this article, the source localisation problem based on time-of-arrival measurements in asynchronous sensor networks is studied. Because of imperfect time synchronisation between the anchor nodes and the signal source node, the unknown parameter of start transmission time of signal source makes the localisation problem further sophisticated. The derived maximum-likelihood estimator cost function with multiple local minimum is non-linear and non-convex. A novel two-step method which can solve the global minimum is proposed. First, by leveraging dimensionality reduction, the maximum (minimum) distance maximum (minimum) time-of-arrival matching-based second-order Monte Carlo method is applied to find a rough initial position of the signal source with low computational complexity. Then, the rough initial position value is refined using trust region method to obtain the final positioning result. Comparative analysis with state-of-the-art semidefinite programming and min–max criterion-based algorithms are conducted. Simulations show that the proposed method is superior in terms of localisation accuracy and computational complexity, and can reach the optimality benchmark of Cramér–Rao Lower Bound even in high signal-to-noise ratio environments.

Identifiability and Solvability in Inverse Linear Quadratic Optimal Control Problems

In this paper, the inverse linear quadratic (LQ) problem over finite time-horizon is studied. Given the output observations of a dynamic process, the goal is to recover the corresponding LQ cost function. Firstly, by considering the inverse problem as an identification problem, its model structure is shown to be strictly globally identifiable under the assumption of system invertibility. Next, in the noiseless case a necessary and sufficient condition is proposed for the solvability of a positive semidefinite weighting matrix and its unique solution is obtained with two proposed algorithms under the condition of persistent excitation. Furthermore, a residual optimization problem is also formulated to solve a best-fit approximate cost function from sub-optimal observations. Finally, numerical simulations are used to demonstrate the effectiveness of the proposed methods.

Fast Path Planning for Firefighting UAV Based on A-Star algorithm

When the long distance oil-gas pipeline accident occurs, the fire UAV can give priority to the place where the accident occurs. Emergency investigation and fire rescue, greatly reduces the harm of the accident. However, due to the limitation of the UAV navigation system, the UAV will accumulate the positioning errors over time in the flight process. If the positioning errors can not be corrected in time, it will make the UAV unable to reach the intended destination, thus leading to the failure of the rescue mission. In view of this phenomenon, we propose a UAV path planning scheme considering positioning errors. Taking the UAV’s total flight path as short as possible and errors correction points as few as possible, a multi-objective programming model is established considering errors correction constraints and path constraints. The Euler distance with coefficient is selected as the estimated cost function of A-Star algorithm, and the optimal flight path of UAV is quickly planned through heuristic search. Taking the data of a certain flight area as an example, we have simulation results showing that the A-Star algorithm can quickly and effectively solve the problem of UAV flight path planning considering positioning errors.

A Node-Charge Graph-Based Online Carshare Rebalancing Policy with Capacitated Electric Charging

Viability of electric car-sharing operations depends on rebalancing algorithms. Earlier methods in the literature suggest a trend toward nonmyopic algorithms using queueing principles. We propose a new rebalancing policy using cost function approximation. The cost function is modeled as a p-median relocation problem with minimum cost flow conservation and path-based charging station capacities on a static node-charge graph structure. The cost function is NP complete, so a heuristic is proposed that ensures feasible solutions that can be solved in an online system. The algorithm is validated in a case study of electric carshare in Brooklyn, New York, with demand data shared from BMW ReachNow operations in September 2017 (262 vehicle fleet, 231 pickups per day, and 303 traffic analysis zones) and charging station location data (18 charging stations with four-port capacities). The proposed nonmyopic rebalancing heuristic reduces the cost increase compared with myopic rebalancing by 38%. Other managerial insights are further discussed.

Cost functions are nonconvex in the outputs when the technology is nonconvex: convexification is not harmless

This contribution focuses on testing the empirical impact of the convexity assumption in estimating costs using nonparametric specifications of technology and cost functions. Apart from reviewing the scant available evidence, the empirical results based on two publicly available data sets reveal the effect of the convexity axiom on cost function estimates: cost estimates based on convex technologies turn out to be on average between 21% and 38% lower than those computed on nonconvex technologies. These differences are statistically significant when comparing kernel densities and can be illustrated using sections of the cost function estimates along some output dimension. Finally, also the characterization of returns to scale and economies of scale using production and cost functions for individual units yields conflicting results for between 19% and 31% of individual observations. The theoretical known potential impact as well as these empirical results should make us reconsider convexity in empirical production analysis: clearly, convexity is not harmless.

The sources of regulated productivity in Chinese power plants: An estimation of the restricted cost function combined with DEA approach

China's fossil fuel electric power industry, a traditionally energy- intensive industry, is the focus of recent environmental regulation. In this paper, we propose a framework to measure the effect of environmental regulation on power plants. First, we use the non-parametric distance function to construct the regulatory stringency index with DEA for specific fossil fuel electric power plants. Second, we incorporate the proposed regulatory stringency index into the restricted cost function, to estimate the environmental regulation effect on power plants, including the effect on productivity and elasticities of substitution. This approach allows us to overcome the problem of the data unavailability. Results indicate that there is an approximate U-shaped effect of environmental regulation on productivity growth. Productivity growth is mainly driven by technological progress. The substitution possibility between capital and energy is a good sign for Porter hypothesis in China. These findings provide policy implications for power plants to carry out environmental regulation and improve production performance.

Harmonic-Based Optimal Motion Planning in Constrained Workspaces Using Reinforcement Learning

In this work, we propose a novel reinforcement learning algorithm to solve the optimal motion planning problem. Particular emphasis is given on the rigorous mathematical proof of safety, convergence as well as optimality w.r.t. to an integral quadratic cost function, while reinforcement learning is adopted to enable the cost function's approximation. Both offline and online solutions are proposed, and an implementation of the offline method is compared to a state-of-the-art RRT* approach. This novel approach inherits the strong traits from both artificial potential fields, i.e., reactivity, as well as sampling-based methods, i.e., optimality, and opens up new paths to the age-old problem of motion planning, by merging modern tools and philosophies from various corners of the field.

Costs of mussel farming: A meta-regression analysis

Mussel farming has emerged as a relatively low-cost measure for reducing the content of nutrients in seas, but no systematic assessment has been undertaken of the costs involved. This study estimated the cost of producing mussels by means of a meta-regression analysis. The estimation of the cost function considered different types of mussel, environmental conditions and study characteristics. In total, 23 studies were found with a total of 69 observations. Different mixed-effect regression models were used and robust results were found of statistically significant output elasticities below one, i.e. production costs increase by less than 1% when the mussel quantity increases by 1%, and that the purpose of production, human consumption or nutrient removal, wage and interest rate and ecological conditions in terms of salinity level and temperature had significant effects on the costs. The estimated cost function was transferred to mussel farming for nutrient removal in different countries in the Baltic Sea, which indicated that the costs are relatively low for mussel farming in Russia (Kaliningrad).

Dynamic Imaging Using a Deep Generative SToRM (Gen-SToRM) Model.

We introduce a generative smoothness regularization on manifolds (SToRM) model for the recovery of dynamic image data from highly undersampled measurements. The model assumes that the images in the dataset are non-linear mappings of low-dimensional latent vectors. We use the deep convolutional neural network (CNN) to represent the non-linear transformation. The parameters of the generator as well as the low-dimensional latent vectors are jointly estimated only from the undersampled measurements. This approach is different from traditional CNN approaches that require extensive fully sampled training data. We penalize the norm of the gradients of the non-linear mapping to constrain the manifold to be smooth, while temporal gradients of the latent vectors are penalized to obtain a smoothly varying time-series. The proposed scheme brings in the spatial regularization provided by the convolutional network. The main benefit of the proposed scheme is the improvement in image quality and the orders-of-magnitude reduction in memory demand compared to traditional manifold models. To minimize the computational complexity of the algorithm, we introduce an efficient progressive training-in-time approach and an approximate cost function. These approaches speed up the image reconstructions and offers better reconstruction performance.

Optimal control and learning for cyber‐physical systems

Optimal control and learning for <scp>cyber‐physical</scp> systems

دراسة اقتصادية لأثر الحيازة الفدانية على الکفاءة الإنتاجية والاقتصادية لمحصول الزيتون فى محافظة شمال سيناء

Despite the importance of the olive crop in North Sinai Governorate, this crop is characterized by its low production efficiency in light of the high age of some of its trees, and the presence of local varieties characterized by low productivity, and the main problem of research is limited to the decrease in the cultivated area of ​​the olive harvest from about 52.8 thousand feddans in 2013, To about 35.5 thousand feddans in 2017, a decrease of about 32.8%. The research aims in general to study the effect of feddan holding on the productive and economic efficiency of olive farms in North Sinai Governorate, where the research used the descriptive analysis method to present the topics included in the research, in addition to the quantitative analysis method using some standard models such as multiple regression in its double logarithmic form. In estimating production functions, and in cubic forms in estimating cost functions, The study also relied mainly on primary data through a stratified random sample of 70 farms It is estimated that the fruitful area of ​​the olive harvest increases annually by a statistically significant amount of about 2.6 thousand feddans, which represents about 9% of the annual average, and on the productive indicators of the research sample farms, the average number of trees per acre is about 56 trees, and the general average productivity of the tree is about 81 kg / year, and the general average productivity per feddan was about 4.6 tons / year. The general average age of olive trees in the research sample was about 28.3 years. The general average of the holder's share was about 3.9 feddans It is also evident that the relative importance of the rent item is high among the fixed costs items, as well as the rented labor item, and organic fertilizer is among the variable costs items. It is evident that the first holding category farms (less than 3 feddans) achieved the highest rates of lost income, while the third holding category farms The lowest rates of lost income, which confirms the need for intensification and rationalization of the elements of production within this category. Regarding the profit margins, the results indicated that the general average of the net yield from the olive harvest amounted to about 14.4 thousand pounds, which is equivalent to about 3.1 pounds per kilogram of the crop. The general average of the return over the variable costs of the olive harvest was about 19.1 thousand pounds, which is equivalent to about 4.2 Pounds per kilogram of crop. The general average of the added value was about 17.9 thousand pounds With regard to economic efficiency, the results indicated that every pound spent on fixed and variable production costs achieves an added value of about 1.36 pounds. It was found that the farms of the third holding category for the olive harvest were superior to their counterpart in the first and second categories in profit margins, as well as indicators of economic efficiency. This indicates the importance of expanding olive cultivation to take advantage of the capacity savings in the short and long term.

Tamaño óptimo de las empresas de transporte colectivo que operan en el Área Metropolitana de Mendoza (Argentina), 2005-2012

Este trabajo avanza en el conocimiento de la estructura productiva del servicio de transporte colectivo en el Área Metropolitana de Mendoza (Argentina) durante la etapa previa a un cambio estructural del sistema de movilidad. Para ello, se estudia la existencia de economías de escala a través de la estimación de una función de costos de tipo translog. Se usan datos de panel para las empresas operadoras del servicio mediante buses, en el período 2005-2012. El resultado de este estudio evidencia un mercado que opera en el tramo de producción con deseconomías de escala, donde los costos promedios son crecientes, por lo que se recomienda que el mismo opere a través de un mayor número de empresas de menor tamaño.&#x0D;

Iterative Semidefinite Relaxation for Geolocation of Uncooperative Radars Using Doppler Frequency Measurements

In this article, we propose a novel method utilizing iterative semidefinite relaxation for geolocation of stationary uncooperative radars. In our scenario, the geolocation is to be performed in a receiver located on a moving platform. The proposed method uses frequency measurements that are Doppler-shifted due to the platform motion. First, constructed nonconvex maximum likelihood cost function for position estimation is relaxed to a convex optimization problem by applying linearization to range variables. Then, at each iteration of the method, carrier frequency and position of the radar are estimated jointly. Conducted experiments show that a few iterations are enough for convergence to accurate estimates. The proposed method is computationally less expensive compared to traditional techniques, which require extensive grid search procedures in either position or carrier frequency parameter space. In the experiments, the performance of the proposed method is compared to the state-of-the-art techniques and the Cramer-Rao lower bound (CRLB). It is observed that the proposed method attains the CRLB at low noise levels while still providing accurate solutions at high noise cases. Furthermore, it is also seen that platform-radar geometry has an impact on the proposed method's performance. If the radar lies in the convex hull of the receiver path, the proposed method performs significantly better due to improvement in the linearization of range values.