A note on optimal joint prediction of order statistics

‎ In many experiments, such as biology and quality control problems, sample size cannot always be considered as a constant value. Therefore, the problem of predicting future data when the sample size is an integer-valued random variable can be an important issue. This paper describes the prediction problem of future order statistics based on upper and lower records. Two different cases for the size of the future sample is considered as fixed and random cases‎. To do this, we first derive a general formula for the coverage probability of the prediction interval for each case. For the case that the sample size is a random variable, we consider two different distributions for the sample size, such as ‎binomial and Poisson distributions‎ and we study further details. The numerical computations are also given in this paper‎. Another purpose of this paper is to determine the optimal prediction interval for each case. Finally, the application of the proposed prediction interval is illustrated by analyzing the data in a real-world case study.

In this paper, we discuss the joint estimation and prediction of unobserved order statistics based on a Type-II censored sample from a location-scale family. Using the concept of Loewner order, we simplify the derivations made earlier, and also strengthen in the process some of the existing results. We then study the efficiency of the methods and finally examine the determination of optimal number of order statistics to be observed as well as the performance of non-linear predictors.

Prediction of Ultimate Bearing Capacity and Structural Optimization of Aluminum Alloy Plate Joints Based on Artificial Neural Network

To address the critical energy constraints and complex communication challenges in underwater acoustic sensor networks (UASNs), this letter proposes a joint optimization framework called LCOA-ASPPM. By integrating Layered Cheetah Optimization Algorithm (LCOA) with Attention Optimized Support Vector Regression Power Prediction Model (ASPPM), the framework improves energy efficiency and communication reliability in UASNs. LCOA employs a bio-inspired multi-layer election strategy to optimize cluster head configurations, considering node energy levels, load balancing, spatial distribution, and energy efficiency. ASPPM power prediction system dynamically adjusts transmission power during network operation. It achieves this by applying incremental updates to a pre-trained SVR model, thereby adapting to varying network conditions. Through joint optimization, cluster head elections integrate power prediction results to assess node communication capabilities, while power allocation strategies adapt to cluster structure, thereby creating a mutually reinforcing optimization cycle.

This paper plans to develop an effective machine learning system integrated with the Internet of Things (IoT) to predict the health insurance amount. IoT in healthcare enables interoperability, machine-to-machine communication, information exchange, and data movement that make healthcare service delivery effective. The model includes three phases (a) Feature Extraction, and (b) Weighted Feature Extraction, and (c) Prediction. The feature extraction process computes two statistical measures: First Order Statistics like mean, median, standard deviation, the maximum value of entire data, and minimum value of entire data, and Second-Order Statistics like Kurtosis, skewness, correlation, and entropy. The prediction process deploys a renowned machine learning algorithm called Neural Network (NN). As the main contribution, the weighted feature vector is developed here, where the weight optimally tuned by Modified Whale Optimization Algorithm (WOA). Also, the contribution relies on NN, where the training algorithm replaced with the same modified WOA for weight update. The modified WOA developed here is termed as Fitness dependent Randomized Whale Optimization Algorithm (FR-WOA). At last, the valuable experimental analysis using three datasets confirms the efficient performance of the suggested model.

Prediction of lower limb joint stiffness and optimization of anthropometric parameters in countermovement jump using an anthropometry-informed neural network.

Based on the theories of Back-Propagation (BP) Artificial Neural Networks (ANN) and Genetic Algorithm (GA), combining with multiple statistical analysis method, fatigue life prediction and technological parameter optimization of QFN solder joints were studied in this paper. Firstly, correlation coefficient matrix of the swatch was gained by factor analysis; taking length and width of the pad, stand-off and the solder volume as input parameters, fatigue life as output parameter, Levenberg-Marquardt (LM) algorithm was used to train the BP ANN and network topology was determined through the experiment, then nonlinear relationship between the input and output parameters was established. Network performance was assessed by linear regression method. Finally, the trained ANN was selected as the objective function solver; GA is used to optimize the QFN solder joints in the software of Matlab. The result shows that, the network obtains precision forecast capability when network topology is in the state of 4-6-1, while the experimental error is within 5%, SSE is 0.0054 and MSE is 0.0011.The optimal combination parameters were gained the pad length of 0.8m, the pad width of 0.3283m, the stand-off of 0.1022m, the volume of 0.014m3, the fatigue life of the QFN solder joints increase of 20% while the error is 2.8143%.

Performance optimization and peak load prediction of dissimilar aluminum overlap joints using laser seam stepper

Linear estimation and prediction based on several samples of generalized order statistics from generalized Pareto distributions is considered. Representations of best linear unbiased estimators (BLUEs) and best linear equivariant estimators in location-scale families are derived, as well as corresponding optimal linear predictors. Moreover, we study positivity of the linear estimators of the scale parameter. An example illustrates that the BLUE may attain negative values with positive probability in certain situations.

In this paper we establish an optimal asymptotic linear predictor which does not involve the finite-sample variance-covariance structure. Extensions to the problem of finding the best linear unbiased and simple linear unbiased predictors for k samples are given. Moreover, we obtain alternative linear predictors by modifying the covariance matrix by either an identity matrix or a diagonal matrix. For normal, logistic and Rayleigh samples of size 10, the alternative linear predictors with these modifications have high efficiency when compared with the best linear unbiased predictor.

The rapid determination method of blood clinical biochemical indicators based on near infrared spectral (NIRS) analysis is an important research branch in health monitoring systems. In this paper, the rapid determination method and the optimal analysis model of serum cholesterol were established by using the NIRS technology, partial least squares (PLS) and Savitzky-Golay (SG) smoothing method. Based on the prediction effect of the optimal single wavenumber model, calibration set and prediction set were divided. The calibration and prediction models were established by using PLS method adopting the combination bands of 10000-5300 cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> and 4920-4160 cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> with SG smoothing. By extending the number of smoothing points to 5, 7 ... 61 (odd) and polynomial degree to 2, 3, 4, 5, 6, fourteen smoothing coefficient tables including 400 SG smooth modes were calculated. Based on computer algorithms platform which was developed by authors, PLS models corresponding to all combinations of 400 SG smooth modes and 1-40 PLS factors were constructed. The optimal model was selected according to the prediction effect, and the derivation order is 1, the polynomial degree is 3 or 4, the number of smoothing points is 43, the optimal PLS factor is 13, the prediction correlation coefficient RP is 0.811, and the optimal RMSEP reaches 0.416 mmol/L. The dividing method for calibration set and prediction set, the extending of SG smoothing modes, large-scale joint optimization of SG smoothing modes and PLS factors can be effectively applied to the model optimization of NIRS analysis.

Quality characteristics in manufacturing are correlated and do not follow a normal distribution. This study proposes a quality control method for multivariate manufacturing processes that are based on an improved Mahalanobis–Taguchi System (IMTS). The MTS has no data distribution assumptions and identifies anomalies through the Mahalanobis distance (MD). However, a covariance distance can consider the correlation between variables. Further, to address the shortcomings of the MTS in feature selection and threshold determination. A joint optimization model is proposed in this paper. Under this approach, the IMTS is employed to perform composite analyses on multiple quality characteristics and reduce dimensionality to identify abnormalities and the key quality characteristics that lead to anomalies. Further, various models are compared to construct the optimal non-parametric prediction models for each key quality characteristic. Finally, a conceptual model of process parameter optimization is proposed, which improves the Taguchi method to obtain the optimal combination of process parameters and their importance ranking, as the basis for process adjustment. By applying the proposed method, results show that the IMTS has an abnormality identification rate of 99.5%, which is higher than other methods such as MTS, support vector machine (SVM), back propagation neural network (BPNN), fast correlation-based filter solution SVM (FCBF-SVM) and sequential backward selection BPNN (SBS-BPNN). The dimensionality reduction rate is 0.5, which is higher than MTS, SVM, BPNN, and SBS-BPNN methods. The random forest (RF) algorithm is used for accurate predictions of all five key quality characteristics, the improved Taguchi method guided adjustments to manufacturing processes objectively, effectively, and economically.

Peer-to-peer multi-energy sharing for home microgrids: An integration of data-driven and model-driven approaches

We consider a problem of prediction based on opinions elicited from heterogeneous rational agents with private information. Making an accurate prediction with a minimal cost requires a joint design of the incentive mechanism and the prediction algorithm. To elicit heterogeneous agents’ private information and incentivize agents with different capabilities to act in the principal’s best interest, we design an optimal joint incentive mechanism and prediction algorithm called COPE (COst and Prediction Elicitation), the analysis of which offers several valuable engineering insights. First, when the costs incurred by the agents are linear in the exerted effort, COPE corresponds to a “crowd-contending” mechanism, where the principal only employs the agent with the highest capability. Second, when the costs are quadratic, COPE corresponds to a “crowdsourcing” mechanism that employs multiple agents with different capabilities at the same time. Numerical simulations show that COPE improves the principal’s profit (The improvement is 5%–30% in our simulations), comparing to those mechanisms that assume all agents have equal capabilities. The online appendix is available at https://doi.org/10.1287/opre.2017.1681 .

The failing tube-to-tubesheet joint is identified as a primary quality defect in the fabrication of a shell-and-tube heat exchanger. Operating in conditions of high pressure and temperature, a shell-and-tube heat exchanger may be susceptible to leakage around faulty joints. Owing to the ongoing low performance of the adjacent tube-to-tubesheet expansion, the heat exchanger eventually experiences malfunction. A quality improvement study on the assembly process is necessary in order to delve into the tight-fitting of the tube-to-tubesheet joint. We present a non-linear screening and optimization study of the tight-fitting process of P215NL (EN 10216-4) tube samples on P265GH (EN 10028-2) tubesheet specimens. A saturated fractional factorial scheme was implemented to screen and optimize the tube-to-tubesheet expanded-joint performance by examining the four controlling factors: (1) the clearance, (2) the number of grooves, (3) the groove depth, and (4) the tube wall thickness reduction. The adopted ‘green’ experimental tactic required duplicated tube-push-out test trials to form the ‘lean’ joint strength response dataset. Analysis of variance (ANOVA) and regression analysis were subsequently employed in implementing the Taguchi approach to accomplish the multifactorial non-linear screening classification and the optimal setting adjustment of the four investigated controlling factors. It was found that the tube-wall thickness reduction had the highest influence on joint strength (55.17%) and was followed in the screening hierarchy by the number of grooves (at 30.47%). The groove depth (at 7.20%) and the clearance (at 6.84%) were rather weaker contributors, in spite of being evaluated to be statistically significant. A confirmation run showed that the optimal joint strength prediction was adequately estimated. Besides exploring the factorial hierarchy with statistical methods, an algorithmic (Random Forest) approach agreed with the leading effects line-up (the tube wall thickness and the number of grooves) and offered an improved overall prediction for the confirmation-run test dataset.