Wind Power Prediction Based on Multi-class Autoregressive Moving Average Model with Logistic Function

The seasonality and randomness of wind present a significant challenge to the operation of modern power systems with high penetration of wind generation. An effective shortterm wind power prediction model is indispensable to address this challenge. In this paper, we propose a combined model, i.e.,a wind power prediction model based on multi-class autoregressive moving average(ARMA). It has a two-layer structure: the first layer classifies the wind power data into multiple classes with the logistic function based classification method;the second layer trains the prediction algorithm in each class. This two-layer structure helps effectively tackle the seasonality and randomness of wind power while at the same time maintaining high training efficiency with moderate model parameters. We interpret the training of the proposed model as a solvable optimization problem. We then adopt an iterative algorithm with a semi-closed-form solution to efficiently solve it. Data samples from open-source projects demonstrate the effectiveness of the proposed model. Through a series of comparisons with other state-of-the-art models, the experimental results confirm that the proposed model improves not only the prediction accuracy,but also the parameter estimation efficiency.

Real-time and wearable functional electrical stimulation system for volitional hand motor function control using the electromyography bridge method

Voluntary participation of hemiplegic patients is crucial for functional electrical stimulation therapy.A wearable functional electrical stimulation system has been proposed for real-time volitional hand motor function control using the electromyography bridge method.Through a series of novel design concepts,including the integration of a detecting circuit and an analog-to-digital converter,a miniaturized functional electrical stimulation circuit technique,a low-power super-regeneration chip for wireless receiving,and two wearable armbands,a prototype system has been established with reduced size,power,and overall cost.Based on wrist joint torque reproduction and classification experiments performed on six healthy subjects,the optimized surface electromyography thresholds and trained logistic regression classifier parameters were statistically chosen to establish wrist and hand motion control with high accuracy.Test results showed that wrist flexion/extension,hand grasp,and finger extension could be reproduced with high accuracy and low latency.This system can build a bridge of information transmission between healthy limbs and paralyzed limbs,effectively improve voluntary participation of hemiplegic patients,and elevate efficiency of rehabilitation training.

基于多尺度卷积神经网络的手机表面缺陷识别方法

针对手机表面缺陷难以精确识别的问题,提出一种兼具Soble算子、逻辑损失函数(logistic loss function,LLF)和多尺度卷积神经网络(multi-scale convolutional neural networks,MSCNN)手机表面缺陷识别方法SL-MSCNN。首先,构建了一种基于Sobel算子的邻域特征增强方法,排除了图像中光照、阴影等无关因素的干扰;其次,设计了一种基于MSCNN的缺陷识别方法,通过获得手机表面图像的多尺度信息,提高了手机表面缺陷的识别精度,同时,引入了LLF,通过降低梯度消失发生的概率加快训练的检测速度。实验结果表明:与其他手机表面缺陷识别方法相比,SL-MSCNN在准确率和效率方面具有更好的使用价值。

Predictive Models for Cumulative Confirmed COVID-19 Cases by Day in Southeast Asia

Coronavirus disease 2019 outbreak has spread as a pandemic since the end of year 2019.This situation has been causing a lot of problems of human beings such as economic problems,health problems.The forecasting of the number of infectious people is required by the authorities of all countries including Southeast Asian countries to make a decision and control the outbreak.This research is to investigate the suitable forecasting model for the number of infectious people in Southeast Asian countries.A comparison of forecasting models between logistic growth curve which is symmetric and Gompertz growth curve which is asymmetric based on the maximumof Coefficient of Determination and theminimumof RootMean Squared Percentage Error is also proposed.The estimation of parameters of the forecasting models is evaluated by the least square method.In addition,spreading of the outbreak is estimated by the derivative of the number of cumulative cases.The findings show that Gompertz growth curve is a suitable forecasting model for Indonesia,Philippines,andMalaysia and logistic growth curve suits the other countries in South Asia.

Wind Power Potential in Interior Alaska from a Micrometeorological Perspective

The wind power potential in Interior Alaska is evaluated from a micrometeorological perspective. Based on the local balance equation of momentum and the equation of continuity we derive the local balance equation of kinetic energy for macroscopic and turbulent systems, and in a further step, Bernoulli’s equation and integral equations that customarily serve as the key equations in momentum theory and blade-element analysis, where the Lanchester-Betz-Joukowsky limit, Glauert’s optimum actuator disk, and the results of the blade-element analysis by Okulov and Sorensen are exemplarily illustrated. The wind power potential at three different sites in Interior Alaska (Delta Junction, Eva Creek, and Poker Flat) is assessed by considering the results of wind field predictions for the winter period from October 1, 2008, to April 1, 2009 provided by the Weather Research and Forecasting (WRF) model to avoid time-consuming and expensive tall-tower observations in Interior Alaska which is characterized by a relatively low degree of infrastructure outside of the city of Fairbanks. To predict the average power output we use the Weibull distributions derived from the predicted wind fields for these three different sites and the power curves of five different propeller-type wind turbines with rated powers ranging from 2 MW to 2.5 MW. These power curves are represented by general logistic functions. The predicted power capacity for the Eva Creek site is compared with that of the Eva Creek wind farm established in 2012. The results of our predictions for the winter period 2008/2009 are nearly 20 percent lower than those of the Eva Creek wind farm for the period from January to September 2013.

Logistic Weighted Profile-Based Bi-Random Walk for Exploring MiRNA-Disease Associations

MicroRNAs(miRNAs)exert an enormous influence on cell differentiation,biological development and the onset of diseases.Because predicting potential miRNA-disease associations(MDAs)by biological experiments usually requires considerable time and money,a growing number of researchers are working on developing computational methods to predict MDAs.High accuracy is critical for prediction.To date,many algorithms have been proposed to infer novel MDAs.However,they may still have some drawbacks.In this paper,a logistic weighted profile-based bi-random walk method(LWBRW)is designed to infer potential MDAs based on known MDAs.In this method,three networks(i.e.,a miRNA functional similarity network,a disease semantic similarity network and a known MDA network)are constructed first.In the process of building the miRNA network and the disease network,Gaussian interaction profile(GIP)kernel is computed to increase the kernel similarities,and the logistic function is used to extract valuable information and protect known MDAs.Next,the known MDA matrix is preprocessed by the weighted K-nearest known neighbours(WKNKN)method to reduce the number of false negatives.Then,the LWBRW method is applied to infer novel MDAs by bi-randomly walking on the miRNA network and the disease network.Finally,the predictive ability of the LWBRW method is confirmed by the average AUC of 0.9393(0.0061)in 5-fold cross-validation(CV)and the AUC value of 0.9763 in leave-one-out cross-validation(LOOCV).In addition,case studies also show the outstanding ability of the LWBRW method to explore potential MDAs.

A MODIFIED INTERPOLATION APPROACH FOR TOPOLOGY OPTIMIZATION

In view of the fact that the follow-up search for an optimal topology is affected by deleting a large number of high-relative-density elements. When the typical density interpolation approach, namely, solid isotropic microstructures with penalization （SIMP）, is employed in the continuum structural topology optimization, a new density interpolation approach based on the logistic function is proposed in this paper. This method can weaken low-relative-density elements while enhancing high-relative-density elements by polarization, and then rationally realize polarization of the intermediate density elements. It can reduce the number of gray-scale elements as much as possible to get the optimal topology with distinct boundaries in conjunction with the sensitivity filtering method based on particle swarm optimization （PSO）. Several typical numerical examples are given to demonstrate this method.

A vibration-based structural damage detection method and its applications to engineering structures

Two major challenges associated with avibration-based damage detection method using changes in natural frequencies are addressed:accurate modeling of structures and the development of a robust inverse algorithm to detect damage,which are defined as the forward and inverse problems,respectively.To resolve the forward problem,new physics-based finite element modeling techniques are developed for fillets in thin-walled beams and for bolted joints,so that complex structures can be accurately modeled with a reasonable model size.To resolve the inverse problem,a logistic function transformation is introduced to convert the constrained optimization problem to an unconstrained one,and a robust iterative algorithm using the Levenberg–Marquardt method is developed to accurately detect the locations and extent of damage.The new methodology can ensure global convergence of the iterative algorithm in solving under--determined system equations and deal with damage detection problems with relatively large modeling error and measurement noise.It is applied to various engineering structures including lightning masts,a space frame structure and one of its components,and a pipeline.The exact locations and extent of damage can be detected in the numerical simulation,and the locations and extent of damage can be successfully detected in experimental damage detection.

Herbaceous peony in warm climate:Modelling stem elongation and growers profit responses to dormancy conditions

We analysed the data collected for herbaceous peony cultivated in a warm climate region and stored in winter under three constant chilling temperatures.We used the quadratic regression model to describe the stem elongation responses to winter dormancy conditions,and the logistic function to describe the weekly stems elongation.The predicted maximal stem length from the first model was used as the input parameter for the second model.More than 4000 data for various(a)chilling constant temperatures during dormancy,(b)dormancy duration,and(c)germination duration,were used.The models were applied to determine the optimal number of chill units.For this purpose,two criteria were used in different versions of the model:the maximal stem length and the maximal profit of farmers.For the two chilling temperatures of 2℃ and 6℃,the optimal values of chill units(in the models of a maximal stem length and maximal profit of farmers)are close to one another,and the values of a maximal stem length and maximal profit are significantly different.In the case of the third chilling temperature of 10℃,the model failed to determine the optimal number of chill units.The method of inverse confidence intervals for testing the significance of the optimal number of chill units was used.