Estimated Performance Levels Research Articles

Estimation of the Shapley Value of a Peer-to-peer Energy Sharing Game Using Multi-Step Coalitional Stratified Sampling

One of the main objectives of a peer-to-peer energy market is to efficiently manage distributed energy resources while creating additional financial benefits for the participants. Cooperative game theory offers such a framework, and the Shapley value, a cooperative game payoff allocation based on the participants’ marginal contributions made to the local energy coalition, is shown to be fair and efficient. However, its high computational complexity limits the size of the game. In order to improve this peer-to-peer cooperative scheme’s scalability, this paper investigates and adapts a stratified sampling method for the Shapley value estimation. It then proposes a multi-step sampling strategy to further reduce the computation time by dividing the samples into incremental parts and terminating the sampling process once a certain level of estimation performance is achieved. Finally, selected case studies demonstrate the effectiveness of the proposed method, which is able to scale up the game from 20 players to 100 players.

Sensor scheduling for lifetime maximization in centralized state estimation

This paper studies how to maximize the lifetime of a centralized sensor system and meanwhile maintain a certain level of the estimation performance. The model is as follows. A group of sensors measure the states of a dynamic process and transmit the measurements to a remote estimator, which computes the estimates of the states. Constrained by its energy budget, each sensor has limited transmission times and may not transmit data at each time slot. Meanwhile, the time duration to maintain a certain level of the estimation performance of the estimator depends on the scheduling of sensor transmission. The notion of lifetime is defined as the largest time duration within which the system maintains a required estimation performance under a given schedule. This paper aims at studying the optimal scheduling which maximizes the lifetime of the system when a level of estimation performance is required. Both the scenarios of deterministic and stochastic scheduling are considered. In deterministic scheduling, three algorithms are proposed, where the schedules are given by solving convex problems and then discretizing the scheduling variables. In stochastic scheduling, two relaxed problems are considered, where the bounds of the original objective are used. The maximum of lifetime is also studied. Examples are given in the end.

Energy-Efficient Spectrum Sensing for Cognitive Radio Enabled Remote State Estimation Over Wireless Channels

The performance of remote estimation over wireless channels is strongly affected by sensor data losses due to interference. Although the impact of interference can be alleviated by applying cognitive radio technique which features in spectrum sensing and transmitting data only on clear channels, the introduction of spectrum sensing incurs extra energy expenditure. In this paper, we investigate the problem of energy-efficient spectrum sensing for remotely estimating the state of a general linear dynamic system, and formulate an optimization problem which minimizes the total sensor energy consumption while guaranteeing a desired level of estimation performance. We model the problem as a mixed integer nonlinear program and propose a simulated annealing based optimization algorithm which jointly addresses when to perform sensing, which channels to sense, in what order and how long to scan each channel. Simulation results demonstrate that the proposed algorithm well balances the sensing energy and transmission energy expenditure and can achieve the desired estimation performance.

The Analysis of the Relation between Eight-Grade Students’ Estimation Performance in Triangles with Their Teaching Style Expectations and Sources of Motivation

Accepted: 05.06.2014 In this study, eight-grade students’ estimation achievements in triangles were analysed according to motivation types and knowledge type expectations. Three hundred and thirty-seven students from three different elementary schools attended in this study. In order to determine the students’ estimation performances, an estimation test in triangles (ETT) was developed. At the end of the study, it was indicated that the estimation performances of the students in triangles related with high level intrinsic motivation and conceptual knowledge expectation. It was also indicated that the estimation performances of the students who have extrinsic motivation source were lower. Moreover it was pointed out that the students expecting procedural knowledge from their teachers had low level of estimation performance in triangles. This study shows that estimation skills in triangles, one of the subjects of geometry, are related with some important variables like motivation types and conceptual knowledge. There are some important questions that should be answered by future studies like ‘How can students’ estimation skills in geometry be developed? ‘How can it be integrated into curriculum? What are the possible strategies students apply and which factors are they related to?’

A Novel Frequency Offset Estimation Scheme for OFDM Systems

In this paper, we propose a novel frequency offset estimation scheme for orthogonal frequency division multiplexing (OFDM) systems. By correlating the OFDM signals within the coherence phase bandwidth and employing a threshold in the frequency offset estimation process, the proposed scheme is not only robust to the timing offset but also has a reduced complexity compared with that of the conventional scheme. Moreover, a timing offset estimation scheme is also proposed as the next stage of the proposed frequency offset estimation. Numerical results show that the proposed scheme can estimate frequency offset with lower computational complexity and does not require additional memory while maintaining the same level of estimation performance. Keywords—OFDM, frequency offset estimation, threshold.

Effect of Patient Selection Method on Provider Group Performance Estimates

Performance measurement at the provider group level is increasingly advocated, but different methods for selecting patients when calculating provider group performance have received little evaluation. We compared 2 currently used methods according to characteristics of the patients selected and impact on performance estimates. We analyzed Medicare claims data for fee-for-service beneficiaries with diabetes ever seen at an academic multispeciality physician group in 2003 to 2004. We examined sample size, sociodemographics, clinical characteristics, and receipt of recommended diabetes monitoring in 2004 for the groups of patients selected using 2 methods implemented in large-scale performance initiatives: the Plurality Provider Algorithm and the Diabetes Care Home method. We examined differences among discordantly assigned patients to determine evidence for differential selection regarding these measures. Fewer patients were selected under the Diabetes Care Home method (n=3558) than the Plurality Provider Algorithm (n=4859). Compared with the Plurality Provider Algorithm, the Diabetes Care Home method preferentially selected patients who were female, not entitled because of disability, older, more likely to have hypertension, and less likely to have kidney disease and peripheral vascular disease, and had lower levels of predicted utilization. Diabetes performance was higher under Diabetes Care Home method, with 67% versus 58% receiving >1 A1c tests, 70% versus 65% receiving ≥1 low-density lipoprotein (LDL) test, and 38% versus 37% receiving an eye examination. The method used to select patients when calculating provider group performance may affect patient case mix and estimated performance levels, and warrants careful consideration when comparing performance estimates.

Reliable Evaluation of Performance Level for Computer-Aided Diagnostic Scheme

Computer-aided diagnostic (CAD) schemes have been developed for assisting radiologists in the detection of various lesions in medical images. The reliable evaluation of CAD schemes is an important task in the field of CAD research. Many evaluation approaches have been proposed for evaluating the performance of various CAD schemes in the past. However, some important issues in the evaluation of CAD schemes have not been systematically analyzed. The first important issue is the analysis and comparison of various evaluation methods in terms of certain characteristics. The second includes the analysis of pitfalls in the incorrect use of various evaluation methods and the effective approaches to the reduction of the bias and variance caused by these pitfalls. We attempt to address the first important issue in details in this article by conducting Monte Carlo simulation experiments, and to discuss the second issue in the Discussion section. No single evaluation method is universally superior to the others; different situations of CAD applications require different evaluation methods, as recommended in this article. Bias and variance in the estimated performance levels caused by various pitfalls can be reduced considerably by the correct use of good evaluation methods. This article would be useful to researchers in the field of CAD research for selecting appropriate evaluation methods and for improving the reliability of the estimated performance of their CAD schemes.

Comparison of typical evaluation methods for computer‐aided diagnostic schemes: Monte Carlo simulation study

Computer-aided diagnostic (CAD) schemes have been developed for assisting radiologists in the detection of various lesions in medical images. Many evaluation approaches, such as the resubstitution, leave-one-out, cross-validation, and hold-out methods, have been employed for the assessment of the performance of various CAD schemes. For these evaluation methods, some investigators have studied their bias in the estimated performance levels of CAD schemes trained with finite samples. However, systematical study has not been conducted for the comparison of these common evaluation methods in terms of multiple important characteristics such as the bias of the estimated performance, the generalization performance, and the uniqueness of the trained CAD scheme. Therefore, in this study, we examined and compared these important characteristics for various evaluation methods and attempted to provide a guideline for investigators to select appropriate evaluation methods for the assessment of CAD schemes in typical practical situations.

Reduction of bias and variance for evaluation of computer‐aided diagnostic schemes

Computer-aided diagnostic (CAD) schemes have been developed to assist radiologists in detecting various lesions in medical images. In addition to the development, an equally important problem is the reliable evaluation of the performance levels of various CAD schemes. It is good to see that more and more investigators are employing more reliable evaluation methods such as leave-one-out and cross validation, instead of less reliable methods such as resubstitution, for assessing their CAD schemes. However, the common applications of leave-one-out and cross-validation evaluation methods do not necessarily imply that the estimated performance levels are accurate and precise. Pitfalls often occur in the use of leave-one-out and cross-validation evaluation methods, and they lead to unreliable estimation of performance levels. In this study, we first identified a number of typical pitfalls for the evaluation of CAD schemes, and conducted a Monte Carlo simulation experiment for each of the pitfalls to demonstrate quantitatively the extent of bias and/or variance caused by the pitfall. Our experimental results indicate that considerable bias and variance may exist in the estimated performance levels of CAD schemes if one employs various flawed leave-one-out and cross-validation evaluation methods. In addition, for promoting and utilizing a high standard for reliable evaluation of CAD schemes, we attempt to make recommendations, whenever possible, for overcoming these pitfalls. We believe that, with the recommended evaluation methods, we can considerably reduce the bias and variance in the estimated performance levels of CAD schemes.