An Improved Bald Eagle Search Algorithm with Cauchy Mutation and Adaptive Weight Factor for Engineering Optimization

The Bald Eagle Search algorithm(BES)is an emerging meta-heuristic algorithm.The algorithm simulates the hunting behavior of eagles,and obtains an optimal solution through three stages,namely selection stage,search stage and swooping stage.However,BES tends to drop-in local optimization and the maximum value of search space needs to be improved.To fill this research gap,we propose an improved bald eagle algorithm(CABES)that integrates Cauchy mutation and adaptive optimization to improve the performance of BES from local optima.Firstly,CABES introduces the Cauchy mutation strategy to adjust the step size of the selection stage,to select a better search range.Secondly,in the search stage,CABES updates the search position update formula by an adaptive weight factor to further promote the local optimization capability of BES.To verify the performance of CABES,the benchmark function of CEC2017 is used to simulate the algorithm.The findings of the tests are compared to those of the Particle Swarm Optimization algorithm(PSO),Whale Optimization Algorithm(WOA)and Archimedes Algorithm(AOA).The experimental results show that CABES can provide good exploration and development capabilities,and it has strong competitiveness in testing algorithms.Finally,CABES is applied to four constrained engineering problems and a groundwater engineeringmodel,which further verifies the effectiveness and efficiency of CABES in practical engineering problems.

Access Request Trustworthiness in Weighted Access Control Framework

Weighted factor is given to access eontrol policies to express the importanceof policy and its effect on access control decision. According to this weighted access controlframework, a trustworthiness model for aceess request is also given. In this model, we give themeasure of trustworthiness factor to access request- by using some idea of uncertainty reasoning ofexpert system, present and prove the parallel propagation formula of request trustworthiness factoramong multiple policies, and get thefinal trustworthiness factor to decide whether authorizing. Inthis model, authorization decision is given according to the calculation of request trustworthinessfactor, which is more understandable, more suitable for real requirement and more powerfulforsecurity enhancement than traditional methods. Meanwhile the finer access control granularity isanother advantage.

Retrieval Single-Doppler Radar Wind with Variational Assimilation Method-Part I: Objective Selection of Functional Weighting Factors

In variational problem, the selection of functional weighting factors (FWF) is one of the key points for discussing many relevant studies. To overcome arbitrariness and subjectivity of the empirical selecting methods used widely at present, this paper tries to put forward an optimal objective selecting method of FWF. The focus of the study is on the weighting factors optimal selection in the variation retrieval single-Doppler radar wind field with the simple adjoint models. Weighting factors in the meaning of minimal variance are calculated out with the matrix theory and the finite difference method of partial differential equation. Experiments show that the result is more objective comparing with the factors obtained with the empirical method.

New weighting factors assignment of evidence theorybased one vidence distance

Evidence theory has been widely used in the information fusion for its effectiveness of the uncertainty reasoning. However, the classical DS evidence theory involves counter-intuitive behaviors when the high conflict information exists. Based on the analysis of some modified methods, Assigning the weighting factors according to the intrinsic characteristics of the existing evidence sources is proposed, which is determined on the evidence distance theory. From the numerical examples, the proposed method provides a reasonable result with good convergence efficiency. In addition, the new rule retrieves to the Yager's formula when all the evidence sources contradict to each other completely.

LDFF,the large molecular weight DNA fragmentation factor,is responsible for the large molecular weight DNA degradation during apoptosis in Xenopus egg extracts

DNA degradation is a biochemical hallmark in apoptosis. It has been demonstrated in many cell types that there are two stages of DNA fragmentation during the apoptotic execution. In the early stage, chromatin DNA is cut into large molecular weight DNA fragments, although the responsible nuclease(s) has not been recognized. In the late stage, the chromatin DNA is cleaved further into short oligonucleosomal fragments by a well-characterized nuclease in apoptosis,the caspase-activated DNase (CAD/DFF40). In this study, we demonstrate that large molecular weight DNA fragmentation also occurs in Xenopus egg extracts in apoptosis. We show that the large molecular weight DNA fragmentation factor (LDFF) is not the Xenopus CAD homolog XCAD. LDFF is activated by caspase-3. The large molecular weight DNA fragmentation activity of LDFF is Mg2+-dependent and Ca2+-independent, can occur in both acidic and neutral pH conditions and can tolerate 45℃ treatment. These results indicate that LDFF in Xenopus egg extracts might be a new DNase (or DNases) responsible for the large DNA fragmentation.

Time Domain Full Waveform Inversion Based on Gradient Preconditioning with an Angle-Dependent Weighting Factor

There are lots of low wavenumber noises in the gradients of time domain full waveform inversion(FWI),which can seriously reduce the accuracy and convergence speed of FWI.Thus,we introduce an angle-dependent weighting factor to precondition the gradients so as to suppress the low wavenumber noises when the multi-scale FWI is implemented in the high frequency.Model experiments show that the FWI based on the gradient preconditioning with an angle-dependent weighting factor has faster convergence speed and higher inversion accuracy than the conventional FWI.The tests on real marine seismic data show that this method can adapt to the FWI of field data,and provide high-precision velocity models for the actual data processing.

Minimum variance adaptive beamforming combined with coherence factor weighting applied to ultrafast active cavitation imaging

The ultrafast active cavitation imaging(UACI)based on plane wave transmission and delay-and-sum(DAS)beamforming has been developed to monitor cavitation events with a high frame rate.However,DAS beamforming leads to images with limited resolution and contrast.In this paper,minimum variance(M V)adaptive beamforming and coherence factor(CF)weighting are combined to achieve an MVCF-based UACI,which can improve the cavitation imaging quality.The detailed algorithm evaluation has been investigated from both simulation and experimental data The simulation data include10point targets and a cyst,while the experimental data are obtained by detecting the dissipation of cavitation bubbles in water excited by a single element transducer with frequency of1.2MHz.The advantages of the proposed methodology as well as the comparison with conventional B-mode,DAS?M V,DAS-CF and MV on the basis of compressive sensing(CS)(called MVCS)beamformers are discussed.The results show that MVCF beamformer has a significant improvement in terms of both resolutions and signal-to-noise ratio(SN R).The MVCF-based UACI has a SNR at21.82dB higher,lateral and axial resolution at2.69times and1.93times?respectively,which were compared with those of B-mode active cavitation mapping.The MVCF-based UACI can be used to image the residual cavitation bubbles with a higher SNR and better spatial resolution

Risk Factors for Low Birth Weight and Preterm Birth：A Population-based Case-control Study in Wuhan,China

Low birth weight（LBW） and preterm birth（PB） are associated with newborn mortality and diseases in adulthood.We explored factors related to LBW and PB by conducting a population-based case-control study from January 2011 to December 2013 in Wuhan,China.A total of 337 LBW newborn babies,472 PB babies,and 708 babies with normal birth weights and born from term pregnancies were included in this study.Information of newborns and their parents was collected by trained investigators using questionnaires and referring to medical records.Univariate and logistic regression analyses with the stepwise selection method were used to determine the associations of related factors with LBW and PB.Results showed that maternal hypertension（OR=6.78,95% CI：2.27–20.29,P=0.001）,maternal high-risk pregnancy（OR=1.53,95% CI：1.06–2.21,P=0.022）,and maternal fruit intake ≥300 g per day during the first trimester（OR=1.70,95% CI：1.17–2.45,P=0.005） were associated with LBW.BMI ≥24 kg/m^2 of mother prior to delivery（OR=0.48,95% CI：0.32–0.74,P=0.001） and gestation ≥37 weeks（OR=0.01,95% CI：0.00–0.02,P〈0.034） were protective factors for LBW.Maternal hypertension（OR=3.36,95% CI：1.26–8.98,P=0.016）,maternal high-risk pregnancy（OR=4.38,95% CI：3.26–5.88,P〈0.001）,maternal meal intake of only twice per day（OR=1.88,95% CI：1.10–3.20,P=0.021）,and mother liking food with lots of aginomoto and salt（OR=1.60,95% CI：1.02–2.51,P=0.040） were risk factors for PB.BMI ≥24 kg/m^2 of mother prior to delivery（OR=0.66,95% CI：0.47–0.93,P=0.018）,distance of house from road ≥36 meters（OR=0.72,95% CI：0.53– 0.97,P=0.028）,and living in rural area（OR= 0.60,95% CI：0.37–0.99,P=0.047） were protective factors for PB.Our study demonstrated some risk factors and protective factors for LBW and PB,and provided valuable information for the prevention of the conditions among newborns.

Analysis of dynamic stress intensity factors of thick-walled cylinder under internal impulsive pressure

Dynamic stress intensity factors are evaluated for thick-walled cylinder with a radial edge crack under internal impulsive pressure. Firstly, the equation for stress intensity factors under static uniform pressure is used as the reference case, and then the weight function for a thick-walled cylinder containing a radial edge crack can be worked out. Secondly, the dynamic stresses in uncracked thick-walled cylinders are solved under internal impulsive pressure by using mode shape function method. The solution consists of a quasi-static solution satisfying inhomogeneous boundary conditions and a dynamic solution satisfying homogeneous boundary condi- tions, and the history and distribution of dynamic stresses in thick-walled cylinders are derived in terms of Fourier-Bessel series. Finally, the dynamic stress intensity factor equations for thick-walled cylinder containing a radial edge crack sub- jected to internal impulsive pressure are given by dynamic weight function method. The finite element method is utilized to verify the results of numerical examples, showing the validity and feasibility of the proposed method.

WEIGHTED KOPPELMAN-LERAY-NORGUET FORMULAS ON A LOCAL q-CONCAVE WEDGE IN A COMPLEX MANIFOLD

A weighted Koppelman-Leray-Norguet formula of (r, s) differential forms on a local q-concave wedge in a complex manifold is obtained. By constructing the new weighted kernels, the authors give a new weighted Koppelman-Leray-Norguet formula without boundary integral of (r, s) differential forms, which is different from the classical one. The new weighted formula is especially suitable for the case of the local g-concave wedge with a non-smooth boundary, so one can avoid complex estimates of boundary integrals and the density of integral may be not defined on the boundary but only in the domain. Moreover, the weighted integral formulas have much freedom in applications such as in the interpolation of functions.

Model Predictive Yaw Control Using Fuzzy-Deduced Weighting Factor for Large-Scale Wind Turbines

Yaw control system plays an important role in helping large-scale horizontal wind turbines capture the wind energy.To track the stochastic and fast-changing wind direction,the nacelle is rotated by the yaw control system.Therein,a difficulty consists in the variation speed of the wind direction much faster than the rotation speed of the nacelle.To deal with this difficulty,model predictive control has been recently proposed in the literature,in which the previewed wind direction is employed into the predictive model,and the estimated captured energy and yaw actuator usage are two contradictive objectives.Since the performance of the model predictive control strat-egy relies largely on the weighting factor that is designed to balance the two objectives,the weighting factor should be carefully selected.In this study,a fuzzy-deduced scheme is proposed to derive the weighting factor of the mod-el predictive yaw control.For the proposed fuzzy-deduced strategy,the variation degree and the increment of the wind direction during the predictive horizon are used as the inputs,and the weighting factor is the output,which is dynamically adjusted.The proposed model predictive yaw control is demonstrated by some simulations using real wind data and its performance is compared with the conventional model predictive control with thefixed weighting factor.Comparison results confirm the outweighing performance of the proposed control strategy over the conventional one.

Effects of Orlistat - induced Weight Loss on Cardiovascular Risk Factors in Obese Chinese Subjects

Objectives To observe the influence of weight loss induced by orlistat on several cardiovascular diseases risk factors in obese Chinese subjects. Methods Sixty obese Chinese patients participated in a 24 week clinical trial. Participants were prescribed a slightly hypocaloric diet and exercise, then they were randomly assigned double -blind treatment with either orlistat 120 mg three times a day or placebo. Their body weight, blood pressure, fasting glucose, insulin, HbA1c, and serum lipid profile were performed before and after the weight loss intervention. Results After 24 weeks, orlistat -treated group lost more of their body weight than placebo group (6. 66 ± 0. 52 kg, 8. 44±4.08% and 1. 98 ± 0. 44 kg, 2. 44±1. 74 % , respectively, P < 0. 05) . Moreover, after treatment, orlistat - treated patients showed significant decreases in serum levels of total cholesterol, low density lipoprotein - cholesterol and high density lipoprotein - cholesterol ( P < 0.01), but in placebo group we found no change. Both systolic blood pressure and diastolic blood pressure fell significantly in orlistat - treated group. Fasting glucose and HOMA - IR in orlistat - treated group was distinctly reduced if compared with placebo group. Conclusions Weight loss resulting from orlistat treatment and slightly hypocaloric diet has produced favorable effects on several cardiovascular risk factors in obese Chinese subjects.

Weight Function Method for Stress Intensity Factor of Internal Surface Semi-elliptical Crack in Elliptical Holes

The hatches for inspecting are usually designed with elliptical holes in airplane structures, so computation of the stress intensity factor of three dimensional crack at elliptical holes is pivotal for damage tolerance analysis of these structures. In this paper, weight function is derived for a two dimensional through cracks at elliptical holes by applying a compounding method. Stress intensity factor formulas for an internal surface semi-elliptical crack in elliptical holes are obtained wing the three dimensional weight function method. Stress intensity factors for an internal surface semi-elliptical crack in elliptical holes under remote tension are computed. At the same time, research on how radius of curvature for elliptical holes affect stress intensity factors was conducted. Stress intensity factors decrease when radius of curvature increases. Some results and conclusions which are of practical value are given.

Cascaded Model Predictive Control of Six-phase Permanent Magnet Synchronous Motor with Fault Tolerant Ability

In the field of high-power electric drives, multiphase motors have the advantages of high power-density, excellent fault tolerance and control flexibility. But their decoupling control and modulation process are much more complicated compared with three-phase motors due to the increased degree of freedom. Finite control set model predictive control can reduce the difficulties of controlling six-phase motors because it does not require modulation process. In this paper, a cascaded model predictive control strategy is proposed for the optimal control of high-power six-phase permanent magnet synchronous motors. Firstly, the current prediction model of torque and harmonic subspaces are established by decoupling the six-phase spatial variables. Secondly, a cascaded cost function with fault-tolerant capability is proposed to eliminate the weighting factor in the cost function. And finally, the proposed strategy is demonstrated through theoretical analysis and experiments. It is validated that the proposed method is able to maintain excellent steady-state control accuracy and fast dynamic response while significantly reduce the control complexity of the system. Besides, it can easily achieve fault-tolerant operation under open-phase fault.

WACPN:A Neural Network for Pneumonia Diagnosis

Community-acquired pneumonia(CAP)is considered a sort of pneumonia developed outside hospitals and clinics.To diagnose community-acquired pneumonia(CAP)more efficiently,we proposed a novel neural network model.We introduce the 2-dimensional wavelet entropy(2d-WE)layer and an adaptive chaotic particle swarm optimization(ACP)algorithm to train the feed-forward neural network.The ACP uses adaptive inertia weight factor(AIWF)and Rossler attractor(RA)to improve the performance of standard particle swarm optimization.The final combined model is named WE-layer ACP-based network(WACPN),which attains a sensitivity of 91.87±1.37%,a specificity of 90.70±1.19%,a precision of 91.01±1.12%,an accuracy of 91.29±1.09%,F1 score of 91.43±1.09%,an MCC of 82.59±2.19%,and an FMI of 91.44±1.09%.The AUC of this WACPN model is 0.9577.We find that the maximum deposition level chosen as four can obtain the best result.Experiments demonstrate the effectiveness of both AIWF and RA.Finally,this proposed WACPN is efficient in diagnosing CAP and superior to six state-of-the-art models.Our model will be distributed to the cloud computing environment.

Low-complexity model predictive control of a four-level active neutral point clamped inverter without weighting factors

The four-level active neutral point clamped(ANPC)inverter has become increasingly widely used in the renewable energy indus-try since it offers one more voltage level without increasing the total number of active switches compared to the three-level ANPC inverter.The model predictive current control(MPCC)is a promising control method for multi-level inverters.However,the conven-tional MPCC suffers from high computational complexity and tedious weighting factor tuning in multi-level inverter applications.A low-complexity MPCC without weighting factors for a four-level ANPC inverter is proposed in this paper.The computational burden and voltage vector candidate set are reduced according to the relationship between voltage vector and neutral point voltage balance.The proposed MPCC shows excellent steady-state and dynamics performances while ensuring the neutral point voltage balancing.The efficacy of the proposed MPCC is verified by simulation and experimental results.

STUDY ON THE METHOD OF CONSTRUCTING RATIONAL CUBIC CURVES AND CURVED SURFACE INCLUDING CONTROLLING PARAMETERS

By adopting the method of controlling parameters this paper describes the construction of various kinds of cubic curve segment and curved surface fragment with rational and non rational parameters, and discusses the relationship between controlling parameters, weighted factors and types, kinds and characteristics of curve segments and curved surface fragments. A mathematical method is provided for CAGD with abundant connotations, broad covering region, convenience, flexibility and direct simplicity.

Effect of local thermal sensation on whole-body thermal sensation under stable non-uniform environment

To reveal the principles of human thermal responses and find out the effects of body parts on whole-body thermal sensation,through a subjective survey,experimental investigations on human responses are carried out when a single body part is thermally stimulated.Cooling airflow is sent to seven body parts,respectively.Totally 94 samples are tested.To eliminate the obvious multicollinearity of thermal sensation among different body parts,the principal component regression approach is adopted to obtain the principal components for the body parts under different experimental conditions.Through regression and analysis of principal components,the weighting factors of the seven body parts are obtained.A predictive model on whole-body thermal sensation is obtained based on the weighting factors.The results show that the different characteristics of trunk and limbs are clearly seen.The weighting factors of local thermal sensation are integrated values,and there is little difference among values of different body parts.

Radar emitter signal recognition based on multi-scale wavelet entropy and feature weighting

In modern electromagnetic environment, radar emitter signal recognition is an important research topic. On the basis of multi-resolution wavelet analysis, an adaptive radar emitter signal recognition method based on multi-scale wavelet entropy feature extraction and feature weighting was proposed. With the only priori knowledge of signal to noise ratio(SNR), the method of extracting multi-scale wavelet entropy features of wavelet coefficients from different received signals were combined with calculating uneven weight factor and stability weight factor of the extracted multi-dimensional characteristics. Radar emitter signals of different modulation types and different parameters modulated were recognized through feature weighting and feature fusion. Theoretical analysis and simulation results show that the presented algorithm has a high recognition rate. Additionally, when the SNR is greater than-4 d B, the correct recognition rate is higher than 93%. Hence, the proposed algorithm has great application value.

Weight Data Fusion Based on Mutual Support Applied in Large Diameter Measurement

The on-line diameter measurement of larger axis workpieces is hard to achieve high precision detection, because of the bad environment of locale, the problem to amend the measuring error by non-uniform temperature field, and the difficulty to collimate and locate by usual method. By improving the measurement accuracy of larger axis accessories, it is useful to raise axis and hole＇s industry produce level. Because of the influence of complex environment in locale and some influential factors which are hard excluded from the large diameter measurement with multi-rolling-wheels method, the measurement results may not support or even contradict each other. To the situation, this paper puts forward a mutual support deviation distinguish data fusion method, including mutual support deviation detection and weight data fusion. The mutual support deviation detection part can effectively remove or weaken the unexpected impact on the measurement results and the weight data fusion part can get more accurate estimate result to the detected data. So the method can further improve the reliability of measurement results and increase the accuracy of the measurement system. By using the weight data fusion based on the mutual support （DFMS） to the simulation and experiment data, both simulation results and experiment results show that the method can effectively distinguish the data influenced by unexpected impact and improve the stability and reliability of measurement results. The new provided mutual support deviation distinguish method can be used to single sensor measurement and multi-sensor measurement, and can be used as a reference in the data distinguish of other area. The DFMS is helpful to realize the diameter measurement expanded uncertainty in 5 ×10^-6D or even higher when the measured axis workpiece＇s diameter is 1-5 m （ 1 m ≤ D ≤5 m ）.