Improvement of Cardiac Function by Dry Weight Optimization Based on Interdialysis Inferior Vena Caval Diameter (1)

In hemodialysis (HD) patients, the diameter of the inferior vena cava (IVC) serves for evaluation of the amount of body fluid.

Frequency Weighting Filter Design for Automotive Ride Comfort Evaluation

Few study gives guidance to design weighting filters according to the frequency weighting factors,and the additional evaluation method of automotive ride comfort is not made good use of in some countries.Based on the regularities of the weighting factors,a method is proposed and the vertical and horizontal weighting filters are developed.The whole frequency range is divided several times into two parts with respective regularity.For each division,a parallel filter constituted by a low-and a high-pass filter with the same cutoff frequency and the quality factor is utilized to achieve section factors.The cascading of these parallel filters obtains entire factors.These filters own a high order.But,low order filters are preferred in some applications.The bilinear transformation method and the least P-norm optimal infinite impulse response（IIR） filter design method are employed to develop low order filters to approximate the weightings in the standard.In addition,with the window method,the linear phase finite impulse response（FIR） filter is designed to keep the signal from distorting and to obtain the staircase weighting.For the same case,the traditional method produces 0.330 7 m · s^–2 weighted root mean square（r.m.s.） acceleration and the filtering method gives 0.311 9 m · s^–2 r.m.s.The fourth order filter for approximation of vertical weighting obtains 0.313 9 m · s^–2 r.m.s.Crest factors of the acceleration signal weighted by the weighting filter and the fourth order filter are 3.002 7 and 3.011 1,respectively.This paper proposes several methods to design frequency weighting filters for automotive ride comfort evaluation,and these developed weighting filters are effective.

W eighted sum based method on the optimal weights for weapon evaluation

In order to solve instability problem of calculation precision resulting from the selection of each target weight in evaluating weapon systems, a weighted sum based method is proposed. Specif- ically, the subjective weights depending on experts＇ experience are substituted by the optimal weights. The optimal weights are acquired through constructing a mathematical programming model based on subjective weights and objective weights. The method of solving subjective weights is the same as before, and the objective weights were solved by means of grey theory. The case analysis shows that the method of improved weighted sum can improve the evaluation precision up to more than 5% , and minimize the instability of calculation precision resulting from only using subjective weights. The method that the optimal weights substituted the subjective weights is brought forward in improving evaluation precision for the first time. The ideas of the optimal weights and the pro- posed method are described and analyzed.

A New Class of Hybrid Particle Swarm Optimization Algorithm

A new class of hybrid particle swarm optimization （PSO） algorithm is developed for solving the premature convergence caused by some particles in standard PSO fall into stagnation. In this algorithm, the linearly decreasing inertia weight technique （LDIW） and the mutative scale chaos optimization algorithm （MSCOA） are combined with standard PSO, which are used to balance the global and local exploration abilities and enhance the local searching abilities, respectively. In order to evaluate the performance of the new method, three benchmark functions are used. The simulation results confirm the proposed algorithm can greatly enhance the searching ability and effectively improve the premature convergence.

Multi-Criteria Decision-Making for Power Grid Construction Project Investment Ranking Based on the Prospect Theory Improved by Rewarding Good and Punishing Bad Linear Transformation

Using the improved prospect theory with the linear transformations of rewarding good and punishing bad(RGPBIT),a new investment ranking model for power grid construction projects(PGCPs)is proposed.Given the uncertainty of each index value under the market environment,fuzzy numbers are used to describe qualitative indicators and interval numbers are used to describe quantitative ones.Taking into account decision-maker’s subjective risk attitudes,a multi-criteria decision-making(MCDM)method based on improved prospect theory is proposed.First,the[−1,1]RGPBIT operator is proposed to normalize the original data,to obtain the best andworst schemes of PGCPs.Furthermore,the correlation coefficients between interval/fuzzy numbers and the best/worst schemes are defined and introduced to the prospect theory to improve its value function and loss function,and the positive and negative prospect value matrices of the project are obtained.Then,the optimization model with the maximum comprehensive prospect value is constructed,the optimal attribute weight is determined,and the PGCPs are ranked accordingly.Taking four PGCPs of the IEEERTS-79 node system as examples,an illustration of the feasibility and effectiveness of the proposed method is provided.

Real-time Prediction Model of Amount of Manure in Winter Pig Pen Based on Backpropagation Neural Network

The automatic control of cleaning need to be based on the total amount of manure in the house. Therefore, this article established a prediction model for the total amount of manure in a pig house and took the number of pigs in the house, age, feed intake,feeding time, the time when the ammonia concentration increased the fastest and the daily fixed cleaning time as variable factors for modelling, so that the model could obtain the current manure output according to the real-time input of time. A Backpropagation(BP) neural network was used for training. The cross-validation method was used to select the best hyperparameters, and the genetic algorithm(GA), particle swarm optimization(PSO) algorithm and mind evolutionary algorithm(MEA) were selected to optimize the initial network weights. The results showed that the model could predict the amount of manure in real-time according to the model input. After the cross-validation method determined the hyperparameters, the GA, PSO and MEA were used to optimize the manure prediction model. The GA had the best average performance.

Discovering interlinkages between major cryptocurrencies using high‑frequency data: new evidence from COVID‑19 pandemic

Through the application of the VAR-AGARCH model to intra-day data for three cryptocurrencies(Bitcoin,Ethereum,and Litecoin),this study examines the return and volatility spillover between these cryptocurrencies during the pre-COVID-19 period and the COVID-19 period.We also estimate the optimal weights,hedge ratios,and hedging effectiveness during both sample periods.We find that the return spillovers vary across the two periods for the Bitcoin–Ethereum,Bitcoin–Litecoin,and Ethereum–Litecoin pairs.However,the volatility transmissions are found to be different during the two sample periods for the Bitcoin–Ethereum and Bitcoin–Litecoin pairs.The constant conditional correlations between all pairs of cryptocurrencies are observed to be higher during the COVID-19 period compared to the pre-COVID-19 period.Based on optimal weights,investors are advised to decrease their investments(a)in Bitcoin for the portfolios of Bitcoin/Ethereum and Bitcoin/Litecoin and(b)in Ethereum for the portfolios of Ethereum/Litecoin during the COVID-19 period.All hedge ratios are found to be higher during the COVID-19 period,implying a higher hedging cost compared to the pre-COVID-19 period.Last,the hedging effectiveness is higher during the COVID-19 period compared to the pre-COVID-19 period.Overall,these findings provide useful information to portfolio managers and policymakers regarding portfolio diversification,hedging,forecasting,and risk management.

Grid-Based Pseudo-Parallel Genetic Algorithm and Its Application

Aimed at the problems of premature and lower convergence of simple genetic algorithms （SGA）, three ideas --partition the whole search uniformly, multi-genetic operators and multi-populations evolving independently are introduced, and a grid-based pseudo-parallel genetic algorithms （GPPGA） is put forward. Thereafter, the analysis of premature and convergence of GPPGA is made. In the end, GPPGA is tested by both six-peak camel back function, Rosenbrock function and BP network. The result shows the feasibility and effectiveness of GPPGA in overcoming premature and improving convergence speed and accuracy.

A New Fuzzy Adaptive Algorithm to Classify Imbalanced Data

Classification of imbalanced data is a well explored issue in the data mining and machine learning community where one class representation is overwhelmed by other classes.The Imbalanced distribution of data is a natural occurrence in real world datasets,so needed to be dealt with carefully to get important insights.In case of imbalance in data sets,traditional classifiers have to sacrifice their performances,therefore lead to misclassifications.This paper suggests a weighted nearest neighbor approach in a fuzzy manner to deal with this issue.We have adapted the‘existing algorithm modification solution’to learn from imbalanced datasets that classify data without manipulating the natural distribution of data unlike the other popular data balancing methods.The K nearest neighbor is a non-parametric classification method that is mostly used in machine learning problems.Fuzzy classification with the nearest neighbor clears the belonging of an instance to classes and optimal weights with improved nearest neighbor concept helping to correctly classify imbalanced data.The proposed hybrid approach takes care of imbalance nature of data and reduces the inaccuracies appear in applications of original and traditional classifiers.Results show that it performs well over the existing fuzzy nearest neighbor and weighted neighbor strategies for imbalanced learning.

OPTIMAL SUMMATION INTERVAL AND NONEXISTENCE OF POSITIVE SOLUTIONS TO A DISCRETE SYSTEM

In this paper, we are concerned with properties of positive solutions of the following Euler-Lagrange system associated with the weighted Hardy-Littlewood-Sobolev inequality in discrete form{uj =∑ k ∈Zn vk^q/（1 ＋ ｜j｜）^α（1 ＋ ｜k- j｜）^λ（1 ＋ ｜k｜）^β,（0.1）vj =∑ k ∈Zn uk^p/（1 ＋ ｜j｜）^β（1 ＋ ｜k- j｜）^λ（1 ＋ ｜k｜）^α,where u, v 〉 0, 1 〈 p, q 〈 ∞, 0 〈 λ 〈 n, 0 ≤α ＋ β≤ n- λ,1/p＋1〈λ＋α/n and 1/p＋1＋1/q＋1≤λ＋α＋β/n：=λ^-/n. We first show that positive solutions of（0.1） have the optimal summation interval under assumptions that u ∈ l^p＋1（Z^n） and v ∈ l^q＋1（Z^n）. Then we show that problem（0.1） has no positive solution if 0 〈λˉ pq ≤ 1 or pq 〉 1 and max{（n-λ^-）（q＋1）/pq-1,（n-λ^-）（p＋1）/pq-1} ≥λ^-.

Disequilibrium Analysis of Money Market in China

By the disequilibrium economic theory,this paper first investigates the disequilibrium mathematical economic model of the money market in China.Then,we study the disequilibrium transition of Chinese money market from 1954 to 1993 using a non parametric local fitting technique.Without assuming explicit functional forms,the method of locally weighted maximum likelihood is applied to estimate and test the variations in demand and supply during the period between 1954 to 1993.Furthermore,the disequilibrium state and mechanism of Chinese money market are inspected and the direction,the strength and the fluctuation of the disequilibrium are discussed respectively.Finally the policy suggestions are given about adjusting effectively money demand and supply under the condition of co existing of plan and market economy.In this paper,a brief review on the disequilibrium researches on money market in foreign countries is also given.

Design of Optimal Weight for a Gear Transmission System Using Hybrid Taguchi-Genetic Algorithm

The gear transmission system has been widely applied in mechanical systems, and many high-performance applications of these systems require low weight. With the aid of establishing the optimization model of the gear transmission system that consists of an objective function and some constraints （for example, the bending stress, the contact stress, the torsional strength, etc.）, the optimal weight design of the gear transmission system can be transformed into the optimization problem for the objective function under the constraints. Moreover, both the shaft and the gear of the gear transmission system are considered simultaneously in our design. The hybrid Taguchi-genetic algorithm （HTGA） is employed to find the optimal design variables and the optimal weight of the system. An illustrated example for the single spur gear reducer is given to show that the optimal weight design problem can be successfully solved using the proposed design scheme. It also proves the high efficiency and feasibility of the algorithm in the gear design.

A novel fractional grey forecasting model with variable weighted buffer operator and its application in forecasting China's crude oil consumption

Oil is an important strategic material and civil energy.Accurate prediction of oil consumption can provide basis for relevant departments to reasonably arrange crude oil production,oil import and export,and optimize the allocation of social resources.Therefore,a new grey model FENBGM(1,1)is proposed to predict oil consumption in China.Firstly,the grey effect of the traditional GM(1,1)model was transformed into a quadratic equation.Four different parameters were introduced to improve the accuracy of the model,and the new initial conditions were designed by optimizing the initial values by weighted buffer operator.Combined with the reprocessing of the original data,the scheme eliminates the random disturbance effect,improves the stability of the system sequence,and can effectively extract the potential pattern of future development.Secondly,the cumulative order of the new model was optimized by fractional cumulative generation operation.At the same time,the smoothness rate quasi-smoothness condition was introduced to verify the stability of the model,and the particle swarm optimization algorithm(PSO)was used to search the optimal parameters of the model to enhance the adaptability of the model.Based on the above improvements,the new combination prediction model overcomes the limitation of the traditional grey model and obtains more accurate and robust prediction results.Then,taking the petroleum consumption of China's manufacturing industry and transportation,storage and postal industry as an example,this paper verifies the validity of FENBGM(1,1)model,analyzes and forecasts China's crude oil consumption with several commonly used forecasting models,and uses FENBGM(1,1)model to forecast China's oil consumption in the next four years.The results show that FENBGM(1,1)model performs best in all cases.Finally,based on the prediction results of FENBGM(1,1)model,some reasonable suggestions are put forward for China's oil consumption planning.

A homogenization-planning-tracking method to solve cooperative autonomous motion control for heterogeneous carrier dispatch systems

Taxiing aircraft and towed aircraft with drawbar are two typical dispatch modes on the flight deck of aircraft carriers. In this paper, a novel hierarchical solution strategy, named as the Homogenization-Planning-Tracking(HPT) method, to solve cooperative autonomous motion control for heterogeneous carrier dispatch systems is developed. In the homogenization layer, any towed aircraft system involved in the sortie task is abstracted into a virtual taxiing aircraft. This layer transforms the heterogeneous systems into a homogeneous configuration. Then in the planning layer, a centralized optimal control problem is formulated for the homogeneous system. Compared with conducting the path planning directly with the original heterogeneous system, the homogenization layer contributes to reduce the dimension and nonlinearity of the formulated optimal control problem in the planning layer and consequently improves the robustness and efficiency of the solution process. Finally, in the tracking layer, a receding horizon controller is developed to track the reference trajectory obtained in the planning layer. To improve the tracking performance,multi-objective optimization techniques are implemented offline in advance to determine optimal weight parameters used in the tracking layer. Simulations demonstrate that smooth and collision-free cooperative trajectory can be generated efficiently in the planning phase. And robust trajectory tracking can be realized in the presence of external disturbances in the tracking phase.The developed HPT method provides a promising solution to the autonomous deck dispatch for unmanned carrier aircraft in the future.

An improved cooperative spectrum detection algorithm for cognitive radio

The ability to detect the primary user＇s signal is one of the main performances for cognitive radio networks. Based on the multi-different-cyclic-frequency character- istics of the cyclostationary primary user＇s signal and the cooperation detection advantage of the multi-secondary-user, the paper presents the weighted cooperative spectrum detection algorithm based on cyclostationarity in detail. The core of the algorithm is to detect the primary user＇s signal by the secondary users＇ cooperation detection to the multi-different-cyclic-frequency, and to make a final decision according to the fusion data of the independent secondary users＇ detection results. Meanwhile, in order to improve the detection performance, the paper proposes a method to optimize the weight on basis of the deflection coefficient criterion. The result of simulation shows that the proposed algorithm has better performance even in low signal-to-noise ratio （SNR）.

Development cost prediction of general aviation aircraft using combined estimation technique

Obtaining accurate development cost estimation results of general aviation aircraft is crucial for companies to adopt the best strategy in the development process.To address this problem,this paper proposes a combination of three commonly used single prediction methods.The optimal weight values of the three single prediction methods are determined by utilizing the shortest ideal point method.Ten cost datasets collected from literature are utilized for fitting and testing the combined prediction method,and the weight coefficients of the three individual prediction methods are calculated as 0.6859,0.0035 and 0.3106,respectively.The results of this study indicate that the developed method has better fitting and estimation accuracy than that of the three individual methods,with average fitting and predicting error values of 2.60%and 6.43%,respectively.Additionally,the cost data of military and civil aircraft development from literature are collected for verification.The results further confirm that the proposed method is not only superior to the single prediction methods in terms of high precision but has wider applications.More importantly,this research can provide important reference for general aviation aircraft companies in term of product cost planning and corporate sales strategies.

Effects of inventory classifications on CONWIP system: a case study

This paper studies the effects of integrating four inventory classification methods into constant work-in-process(CONWIP)systems in a multistage batch production.The inventory classification methods are multiple closed-loop CONWIP(MCC),parallel CONWIP(PC),analytical hierarchy procedure,and weighted linear optimization(WLO).Discrete-event simulation models were built.A full-factorial experimental design was employed.Response surface methodology generated suitable regression models for performance comparison of different ABC analysis methods.In the second stage of experiment,the ABC analysis methods from the first stage were fine-tuned and compared.PC and WLO are the most preferred methods because of higher total outputs,lower work-in-process levels,and shorter flow times compared with the other two methods.However,the revenue accrued through WLO is higher than that accrued via PC.This study complements existing literature on price setting in manufacturing organizations by delineating classification of finished goods based on both exogenous and production factors.

ON NEW STRATEGIES TO CONTROL THE ACCURACY OF WENO ALGORITHM CLOSE TO DISCONTINUITIES Ⅱ:CELL AVERAGES AND MULTIRESOLUTION

This paper is the second part of the article and is devoted to the construction and analysis of new non-linear optimal weights for WENO interpolation capable of rising the order of accuracy close to discontinuities for data discretized in the cell averages.Thus,now we are interested in analyze the capabilities of the new algorithm when working with functions belonging to the subspace L1\L2 and that,consequently,are piecewise smooth and can present jump discontinuities.The new non-linear optimal weights are redesigned in a way that leads to optimal theoretical accuracy close to the discontinuities and at smooth zones.We will present the new algorithm for the approximation case and we will analyze its accuracy.Then we will explain how to use the new algorithm in multiresolution applications for univariate and bivariate functions.The numerical results confirm the theoretical proofs presented.