Cross-Cultural Adjustment and Coping:U.S.Students in Taiwan Residents Higher Education

The aim of this study was to investigate the adjustment problems of students from the United States enrolled in universities in the East,specifically in Taiwan,their problems related to cultural adaptation,and the process of adjustment to student life in Taiwan.Under investigation were cultural adjustment and coping skills as these students transitioned from West to East.Qualitative data were collected from interviews with participants and faculty members as well as participant observations.Results indicated that U.S.students found their own ways to acclimate to their new academic setting as well as to social relations,cross-cultural issues,and the linguistic environment in Taiwan to achieve effective adaptation.They made changes in themselves to cope with all situations they encountered.This study provides suggestions for international students abroad in Taiwan,for the Taiwan Residents government,and for universities or colleges in terms of what they should offer to current and future international students.

Adaptive subsequence adjustment with evolutionary asymmetric path-relinking for TDRSS scheduling

Due to the limited transmission resources for data relay in the tracking and data relay satellite system （TDRSS）, there are many job requirements in busy days which will be discarded in the conventional job scheduling model. Therefore, the improvement of scheduling efficiency in the TDRSS can not only help to increase the resource utilities, but also to reduce the scheduling failure ratio. A model of nonhomogeneous parallel machines scheduling problems with time window （NPM-TW） is firstly built up for the TDRSS, considering the distinct features of the variable preparation time and the nonhomogeneous transmission rates for different types of antennas on each tracking and data relay satellite （TDRS）. Then, an adaptive subsequence adjustment （ASA） framework with evolutionary asymmetric path-relinking （EvAPR） is proposed to solve this problem, in which an asymmetric progressive crossover operation is involved to overcome the local optima by the conventional job inserting methods. The numerical results show that, compared with the classical greedy randomized adaptive search procedure （GRASP） algorithm, the scheduling failure ratio of jobs can be reduced over 11% on average by the proposed ASA with EvAPR.

Controlling chaos using Takagi-Sugeno fuzzy model and adaptive adjustment

In this paper, an approach to the control of continuous-time chaotic systems is proposed using the Takagi-Sugeno （TS） fuzzy model and adaptive adjustment. Sufficient conditions are derived to guarantee chaos control from Lyapunov stability theory. The proposed approach offers a systematic design procedure for stabilizing a large class of chaotic systems in the literature about chaos research. The simulation results on Rossler＇s system verify the effectiveness of the proposed methods.

Method of adaptive PLL bandwidth adjustment without phase slipping

In a system based on the phase lock loop（PLL）, a trade-off must be made between the tracking precision and the dynamic performance if constant parameters are adopted. To overcome this drawback, a new method called no phase slipping adaptive bandwidth（NPS-AB） is proposed, which can adjust the loop bandwidth adaptively for different working conditions. As a result, both the tracking precision and the dynamic performance can be achieved concurrently. NPS-AB has two features to keep the loop stable： one is the capability of quick response to dynamics; the other is a series of additional constraints when the bandwidth is switched. Compared with other methods, there is no phase slipping during the adjustment process for NPS-AB. The phase integer ambiguity can be avoided and the phase value is kept valid. It is meaningful for carrier ranging systems. Simulation results show that NPS-AB can deal with sudden dynamics and keep the pseudo-range value stable in the entire dynamic process.

Adaptive algorithm for solving different types and different precisions and nonlinear surveying and mapping parameters adjustment

Approximate linear methods and nonlinear methods were adopted usually for solving models of nonlinear surveying and mapping parameters adjustment. But, these iterative algorithms need to compare harsh initial value. A kind of new algorithm-adaptive algorithm based on analyzing the general methods was put forward. The new algorithm has quick rate of convergence and low dependence for initial value, so it can avoid calculating complex second derivative of the target function. The results indicate that its performance is better than those of the others.

Adaptability of anticipatory postural adjustments associated with voluntary movement

The control of balance is crucial for efficiently performing most of our daily motor tasks, such as those involving goal-directed arm movements or whole body displacement. The purpose of this article is twofold. Firstly, it is to recall how balance can be maintained despite the different sources of postural perturbation arising during voluntary movement. The importance of the so-called "anticipatory postural adjustments"(APA), taken as a "line of defence" against the destabilizing effect induced by a predicted perturbation, is emphasized. Secondly, it is to report the results of recent studies that questioned the adaptability of APA to various constraints imposed on the postural system. The postural constraints envisaged here are classified into biomechanical(postural stability, superimposition of motor tasks),(neuro) physiological(fatigue), temporal(time pressure) and psychological(fear of falling, emotion). Overall, the results of these studies point out the capacity of the central nervous system(CNS) to adapt the spatio-temporal features of APA to each of theseconstraints. However, it seems that, depending on the constraint, the "priority" of the CNS was focused on postural stability maintenance, on body protection and/or on maintenance of focal movement performance.

Adaptive Graph Embedding With Consistency and Specificity for Domain Adaptation

Domain adaptation(DA) aims to find a subspace,where the discrepancies between the source and target domains are reduced. Based on this subspace, the classifier trained by the labeled source samples can classify unlabeled target samples well.Existing approaches leverage Graph Embedding Learning to explore such a subspace. Unfortunately, due to 1) the interaction of the consistency and specificity between samples, and 2) the joint impact of the degenerated features and incorrect labels in the samples, the existing approaches might assign unsuitable similarity, which restricts their performance. In this paper, we propose an approach called adaptive graph embedding with consistency and specificity(AGE-CS) to cope with these issues. AGE-CS consists of two methods, i.e., graph embedding with consistency and specificity(GECS), and adaptive graph embedding(AGE).GECS jointly learns the similarity of samples under the geometric distance and semantic similarity metrics, while AGE adaptively adjusts the relative importance between the geometric distance and semantic similarity during the iterations. By AGE-CS,the neighborhood samples with the same label are rewarded,while the neighborhood samples with different labels are punished. As a result, compact structures are preserved, and advanced performance is achieved. Extensive experiments on five benchmark datasets demonstrate that the proposed method performs better than other Graph Embedding methods.

Switching mechanism and optimisation research on a pressure-attitude adaptive adjusting coal seam water jet slotter

To investigate the attitude-switching mechanisms of existing jet slotters,which integrate drilling,punching and slotting operations,and to improve its fracture ability,we used the power bond diagram theory to analyse the dynamic flow pressure,and force of slotters.A mathematical model was developed for the dynamic characteristics of slotter systems.Furthermore,to study the effect of the main characteristic parameters on the ability of the nozzle to erode sandstone,multi-orthogonal experiments were carried out.And the optimised slots were applied in later practical operations.The research results show that the inlet fluid passed through the time-varying orifice to generate pressure differential thrust,which overcame the spring force,pushed the valve core to open the side nozzle,and closed the rear cavity channel thereby realising the switch of the slotter attitude.An optimal plan was established to balance the diameter,depth,and volume of punching,and a rock-breaking plan was developed for the slotter.Subsequently,the optimised water jet slotter was practically used in coal seam gas drainage.Compared with conventional dense drilling,water jet slotting technology significantly improves the ability,efficiency,and effect of increasing the permeability of the coal seam.

Adaptive Antenna和Switched Beam相结合的新型智能天线

提出了一种上行链路采用Adaptive Beam,下行链路采用Switched Beam的新颖智能天线。其中上行链路是通过由信道估计得到的导向矢量形成指向各用户的Adaptive Beam,下行链路则是通过估计得到的用户分布形成Switched Beam。该智能天线系统既可以抑制上行链路中多用户信号的干扰,也可以使下行波束易于对准用户,使系统有尽可能高的容量且便于实现。

Adaptive multi-feature tracking in particle swarm optimization based particle filter framework

This paper proposes a particle swarm optimization（PSO） based particle filter（PF） tracking framework,the embedded PSO makes particles move toward the high likelihood area to find the optimal position in the state transition stage,and simultaneously incorporates the newest observations into the proposal distribution in the update stage.In the proposed approach,likelihood measure functions involving multiple features are presented to enhance the performance of model fitting.Furthermore,the multi-feature weights are self-adaptively adjusted by a PSO algorithm throughout the tracking process.There are three main contributions.Firstly,the PSO algorithm is fused into the PF framework,which can efficiently alleviate the particles degeneracy phenomenon.Secondly,an effective convergence criterion for the PSO algorithm is explored,which can avoid particles getting stuck in local minima and maintain a greater particle diversity.Finally,a multi-feature weight self-adjusting strategy is proposed,which can significantly improve the tracking robustness and accuracy.Experiments performed on several challenging public video sequences demonstrate that the proposed tracking approach achieves a considerable performance.

Satellite Constellation Design with Adaptively Continuous Ant System Algorithm

The ant system algorithm （ASA） has proved to be a novel meta-heuristic algorithm to solve many multivariable problems. In this paper, the earth coverage of satellite constellation is analyzed and a n ＋ 1^ -fold coverage rate is put forward to evaluate the coverage performance of a satellite constellation. An optimization model of constellation parameters is established on the basis of the coverage performance. As a newly developed method, ASA can be applied to optimize the constellation parameters. In order to improve the ASA, a rule for adaptive number of ants is proposed, by which the search range is obviously enlarged and the convergence speed increased. Simulation results have shown that the ASA is more quick and efficient than other methodV211.71s.

Design of the Control System of Adaptive Balancing Device Based on PLC

Due to the well condition and the un-expected imbalance movement of the pumping unit in use, the energy consumes a lot. The existing balancing equipment cannot adjust and monitor the pumping units in real time. Therefore this paper introduces the new adaptive balancing equipment—fan-shaped adaptive balancing intelligent device, projects a design of such control system based on PLC, and determines the principle of the control system, the execution software and the design flow. Site commissioning effect on Daqing Oilfield shows this fan-shaped adaptive balancing intelligent device can effectively adjust and monitor the pumping unit in real time, the balance even adjusts from 0.787 to 0.901, and integrated energy saving rate is 14.2%. It is approved that this control device is professionally designed, with strong compatibility, and high reliability.

Improving Adaptive Learning Rate of BP Neural Network for the Modelling of 3D Woven Composites Using the Golden Section Law

Focused on various BP algorithms with variable learning rate based on network system error gradient, a modified learning strategy for training non-linear network models is developed with both the incremental and the decremental factors of network learning rate being adjusted adaptively and dynamically. The golden section law is put forward to build a relationship between the network training parameters, and a series of data from an existing model is used to train and test the network parameters. By means of the evaluation of network performance in respect to convergent speed and predicting precision, the effectiveness of the proposed learning strategy can be illustrated.

Fuzzy adaptive genetic algorithm based on auto-regulating fuzzy rules

There are defects such as the low convergence rate and premature phenomenon on the performance of simple genetic algorithms (SGA) as the values of crossover probability (Pc) and mutation probability (Pm) are fixed. To solve the problems, the fuzzy control method and the genetic algorithms were systematically integrated to create a kind of improved fuzzy adaptive genetic algorithm (FAGA) based on the auto-regulating fuzzy rules (ARFR-FAGA). By using the fuzzy control method, the values of Pc and Pm were adjusted according to the evolutional process, and the fuzzy rules were optimized by another genetic algorithm. Experimental results in solving the function optimization problems demonstrate that the convergence rate and solution quality of ARFR-FAGA exceed those of SGA, AGA and fuzzy adaptive genetic algorithm based on expertise (EFAGA) obviously in the global search.

基于Adaptive LASSO Logistic倾向得分模型的网络调查样本推断方法

网络调查是大数据背景下一种重要的抽样调查方法,然而大多数网络调查样本属于非概率样本,其入样概率未知,需要进行建模估计。之前的研究大多通过构建Logistic倾向得分模型计算入样概率,但是Logistic倾向得分模型通常适用于协变量或混杂变量较少的情况,存在较多协变量或混杂变量时如何进行倾向得分建模推断是一个亟待解决的问题。针对此问题,文章充分考虑经典的变量选择方法Adaptive LASSO的降维特点,提出对网络调查样本建立Adaptive LASSO Logistic倾向得分模型估计倾向得分,进一步利用倾向得分逆加权、未加权与加权均值、未加权与加权中位数的分组调整方法,从而估计总体。研究表明:基于Adaptive LASSO Logistic倾向得分模型的总体均值估计的偏差、方差与均方误差都比基于Logistic倾向得分模型的总体均值估计的偏差、方差与均方误差小。

AN IMPROVED ADAPTIVE THRESHOLD MODULATION METHOD FOR JITTER REDUCTION IN SDH/SONET

Adaptive threshold modulation is widely adopted in SDH/SONET network for pointer processing and mapping. When the processing rate is very high, the performance of an all digital implementation is limited by the phase error resolution. Phase error re-sampling technique is adopted here for the all digital implementation of an improved adaptive threshold modulation, which can work in greatly reduced operating speed with high jitter and wander performance. The improved method is adopted in AU-4 and TU-12 pointer processors and the simulated performance is given.

Design and Research of Efficient Adaptive Spray Plant Protection UAV

When the plant protection UAV is spraying on plants, the operator usually performs visual inspection on the vertical distance between the UAV and the plant to adjust the spray height to complete the application. Visual inspection by human eyes is easy to cause the error of spray deposition and deposition density. In order to improve the full utilization of drugs and the spraying efficiency of plant protection UAV, an efficient adaptive spray plant protection UAV was designed, and the vertical distance from the UAV to the plant was collected by ultrasonic wave. Meantime, the plant density was detected in real time, and the flight attitude of plant protection UAV and application rate of spray nozzle were automatically adjusted. The experimental results showed that the designed highly efficient adaptive spray plant protection UAV had fast response speed, precise spray location and less drug loss.

Fine-grained detection of caged-hen head states using adaptive Brightness Adjustment in combination with Convolutional Neural Networks

Timely identification and tracking of abnormal hens in stacked cages are of great significance for precision treatment and the elimination of sick individuals.The head features of the caged-hens are used to overcome observation difficulties caused by the cage and feathers blocking,but it is still hard to identify similar head states.To solve this problem,the fine-grained detection of caged-hens head states was developed using adaptive Brightness Adjustment in combination with Convolutional Neural Networks(FBA-CNN).Grid Region-based CNN(R-CNN),a convolution neural network(CNN),was optimized with the Squeeze-and-Excitation(SE)and Depthwise Over-parameterized Convolutional(DO-Conv)to detect layer heads from cages and to accurately cut them as single-head images.The brightness of each single-head image was adjusted adaptively and classified through the deep convolution neural network based on SE-Resnet50.Finally,we returned to the original image to realize multi-target detection with coordinate mapping.The results showed that the AP@0.5 of layer head detection using the optimized Grid R-CNN was 0.947,the accuracy of classification with SE-Resnet50 was 0.749,the F1 score was 0.637,and the mAP@0.5 of FBA-CNN was 0.846.In summary,this automated method can accurately identify different layer head states in layer cages to provide a basis for follow-up studies of abnormal behavior including dyspnea and cachexia.

典型复合材料结构固化变形仿真预测与控制验证

针对典型复合材料结构固化成型过程中变形难以控制的问题,本文对典型复合材料结构的固化变形进行仿真预测,从固化工艺和模具补偿两方面对固化变形加以控制和验证。固化工艺方面以各设计点变形数据为基础确定了最优固化工艺曲线,模具补偿方面提出了一种构件有限元模型自适应调整的方法,综合考虑固化工艺参数与模具型面补偿采用了一种基于全局补偿量的协同控制方法。结果表明,通过仿真模拟L形构件的固化变形误差为12.4%,借助响应面优化算法得到的L形构件最优固化工艺曲线其固化变形预测值与各试验设计点最大变形的最小值偏差不超过3.3%;T形加筋壁板有限元模型经自适应调整后,对于下表面与目标型面之间的偏差距离,数值模拟值与试验测量值的最大相对误差为17.20%。通过全局补偿量的协同控制方法对半筒形壁板的模具进行补偿,其固化变形最大值相比于传统单一模具型面补偿控制方法降低了接近90%。

基于改进蚁群算法的自动落布车路径规划

针对自动落布车在使用蚁群算法(ant colony algorithm,ACA)进行路径规划过程中出现的收敛次数多、收敛速度较慢且容易陷入局部最优的问题,提出一种改进蚁群算法(improved ant colony algorithm,IACA)。首先对信息素挥发系数ρ进行自适应调整,令其做动态变化,克服算法的收敛次数过多,加快算法收敛速度,减少算法的收敛时间;其次引入细菌觅食算法中趋化操作的趋化步长因子对信息素更新公式进行改进,削减算法迭代的后期信息素浓度值,使算法在后期跳出局部最优值,提高算法全局搜索能力。利用MATLAB将改进后的算法在3种不同的栅格环境中进行仿真验证。结果表明:相比传统蚁群算法,改进后的算法收敛次数减少81.1%,最小路径长度减少6.3%,收敛时间减少20.7%。最后搭建ROS小车实验平台,利用ROS机器人系统对改进蚁群算法在模拟的织布车间环境中进行实验验证。结果表明:对比传统蚁群算法,改进蚁群算法在寻优时间上减少了8.6%。