Conservation genomics provides insights into genetic resilience and adaptation of the endangered Chinese hazelnut, Corylus chinensis

Global climate change has increased concerns regarding biodiversity loss.However,many key conservation issues still required further research,including demographic history,deleterious mutation load,adaptive evolution,and putative introgression.Here we generated the first chromosome-level genome of the endangered Chinese hazelnut,Corylus chinensis,and compared the genomic signatures with its sympatric widespread C.kwechowensis-C yunnanensis complex.We found large genome rearrangements across all Corylus species and identified species-specific expanded gene families that may be involved in adaptation.Population genomics revealed that both C.chinensis and the C.kwechowensis-C.yunnanensis complex had diverged into two genetic lineages,forming a consistent pattern of southwestern-northern differentiation.Population size of the narrow southwestern lineages of both species have decreased continuously since the late Miocene,whereas the widespread northern lineages have remained stable(C.chinensis) or have even recovered from population bottlenecks(C.kwechowensis-C.yunnanensis complex) during the Quaternary.Compared with C.kwechowensis-C. yunnanensis complex,C.chinensis showed significantly lower genomic diversity and higher inbreeding level.However,C.chinensis carried significantly fewer deleterious mutations than C.kwechowensis-C. yunnanensis complex,as more effective purging selection reduced the accumulation of homozygous variants.We also detected signals of positive selection and adaptive introgression in different lineages,which facilitated the accumulation of favorable variants and formation of local adaptation.Hence,both types of selection and exogenous introgression could have mitigated inbreeding and facilitated survival and persistence of C.chinensis.Overall,our study provides critical insights into lineage differentiation,local adaptation,and the potential for future recovery of endangered trees.

Research on Adaptive TSSA-HKRVM Model for Regression Prediction of Crane Load Spectrum

For the randomness of crane working load leading to the decrease of load spectrum prediction accuracy with time,an adaptive TSSA-HKRVM model for crane load spectrum regression prediction is proposed.The heterogeneous kernel relevance vector machine model(HKRVM)with comprehensive expression ability is established using the complementary advantages of various kernel functions.The combination strategy consisting of refraction reverse learning,golden sine,and Cauchy mutation+logistic chaotic perturbation is introduced to form a multi-strategy improved sparrow algorithm(TSSA),thus optimizing the relevant parameters of HKRVM.The adaptive updatingmechanismof the heterogeneous kernel RVMmodel under themulti-strategy improved sparrow algorithm(TSSA-HKMRVM)is defined by the sliding window design theory.Based on the sample data of the measured load spectrum,the trained adaptive TSSA-HKRVMmodel is employed to complete the prediction of the crane equivalent load spectrum.Applying this method toQD20/10 t×43m×12mgeneral bridge crane,the results show that:compared with other prediction models,although the complexity of the adaptive TSSA-HKRVMmodel is relatively high,the prediction accuracy of the load spectrum under long periods has been effectively improved,and the completeness of the load information during thewhole life cycle is relatively higher,with better applicability.

Simulation and Experimental Design of Load Adaptive Braking System on Two Wheeler

The braking quality is considered the main execution of the adaptive control framework that impacts the vehicle safety and rides solace astoundingly notably the stopping distance.This research work aims to create a pattern and design of an electromechanically adjusted lever that multiplies the applied braking force depending on the inputs given by the sensors to reduce the stopping distance of the vehicle.It is carried out using two main parts of the two-wheeler vehicle:thefirst part deals with the detection of load acting on the vehicle and identifying the required braking force to be applied,and the second part deals with the micro-controller which activates the stepper motor for varying the mechanical leverage ratio from various loads on the vehicle using two actively movable wedges.The electromechanically operated variable braking force system is developed to actuate the braking system based on the load on the motorcycle.The MATLAB simulation and experimental work are carried out for various loading(driver and pillion)conditions on a two-wheeler.The results indicate that the proposed electronically operated braking system is more effective than the conventional braking system for various loads and vehicle speeds.Specifically,the stopping distance of the vehicle is decreased significantly by about 4.9%between the con-ventional braking system and the simulated proposed system.Further,the experi-mental results show that the stopping distance is condensed by about 4.1%.The validation between simulated and experimental results revealed a great deal with the least error percentage of about 0.8%.

Status-Aware Resource Adaptation in Information-Centric and Software-Defined Network

In recent years, realising a ＂clean- state＂ design for the future Internet has become an important research focus. An architecture com- bining an Information-Centric Network （ICN） and Sottware-Defmed Network （SDN） （IC-SDN） has gradually attracted more attention. How- ever, the existing studies regarding IC-SDN still lack support in terms of the ＂network status awareness＂ function, resulting in unreasonable resource allocation. In this paper, we propose a new status-aware resource adaptation sche- me, i.e. a status-aware module is embedded into basic elements （Forwarding Node （FN） and Resource adaption Manager （RM））. The FNs collect the network status dynamically for the controller to reallocate network resources accor- ding to the fluctuations in environmental con- ditions. Simulation results show that, compared with the existing IC-SDN mechanism, the pro- posed scheme reduced the link bandwidth var- iance by 56% and the content delivery latency by 40%. The proof-of-concept implementation demonstrates the feasibility of our proposed sc- heme for small-scale deployment.

Adaptive load forecasting of the Hellenic electric grid

Designers are required to plan for future expansion and also to estimate the grid's future utilization. This means that an effective modeling and forecasting technique, which will use efficiently the information contained in the available data, is required, so that important data properties can be extracted and projected into the future. This study proposes an adaptive method based on the multi-model partitioning algorithm (MMPA), for short-term electricity load forecasting using real data. The grid's utilization is initially modeled using a multiplicative seasonal ARIMA (autoregressive integrated moving average) model. The proposed method uses past data to learn and model the normal periodic behavior of the electric grid. Either ARMA (autoregressive moving average) or state-space models can be used for the load pattern modeling. Load anomalies such as unexpected peaks that may appear during the summer or unexpected faults (blackouts) are also modeled. If the load pattern does not match the normal be-havior of the load, an anomaly is detected and, furthermore, when the pattern matches a known case of anomaly, the type of anomaly is identified. Real data were used and real cases were tested based on the measurement loads of the Hellenic Public Power Cooperation S.A., Athens, Greece. The applied adaptive multi-model filtering algorithm identifies successfully both normal periodic behavior and any unusual activity of the electric grid. The performance of the proposed method is also compared to that produced by the ARIMA model.

Dynamic load-altering attack detection based on adaptive fading Kalman filter in power systems

This paper presents an effective and feasible method for detecting dynamic load-altering attacks(D-LAAs)in a smart grid.First,a smart grid discrete system model is established in view of D-LAAs.Second,an adaptive fading Kalman filter(AFKF)is designed for estimating the state of the smart grid.The AFKF can completely filter out the Gaussian noise of the power system,and obtain a more accurate state change curve(including consideration of the attack).A Euclidean distance ratio detection algorithm based on the AFKF is proposed for detecting D-LAAs.Amplifying imperceptible D-LAAs through the new Euclidean distance ratio improves the D-LAA detection sensitivity,especially for very weak D-LAA attacks.Finally,the feasibility and effectiveness of the Euclidean distance ratio detection algorithm are verified based on simulations.

Adaptive bit and power loading algorithm with low complexity in MIMO-OFDM systems

Two adaptive power and bit loading algorithms to maximize the throughput of MIMO-OFDM systems in frequency selective fading environment are proposed. The two algorithms allocate bit based on maximizing the overall throughput. One algorithm allocates power based on guaranteeing that the bit error rate （BER） of each sub-carrier and the total allocated power remain below a target BER threshold and a power threshold, respectively; another one allocates power based on guaranteeing that the mean BER of sub-carriers and the total allocated power remain below a target BER threshold and a power threshold, respectively. The simulation results show that the proposed algorithms can achieve faster throughput with lower computational complexity, which indicates that the proposed algorithms are effective when compared to some existing algorithms.

Indirect Adaptive Robust Trajectory Tracking Control of Hard Rock TBM with Load Variation of Tunneling Face

Posture adjustment of open-type hard rock tunnel boring machine(TBM) can be achieved by properly adjusting the hydraulic pressure of gripper cylinder and torque cylinders. However, the time-varying inhomogeneous load acting on tunneling face of TBM and complex stratum working condition can cause the trajectory deviation. In this paper,the position and posture rectification kinematics and dynamics models of TBM have been established in order to track the trajectory. Moreover, there are uncertain parameters and uncertain loads from complex working conditions in the dynamic model. An indirect adaptive robust control strategy is applied to achieve precise position and posture trajectory tracking control. Simulation results show when the position deviation only occurs in Y-axis and the current orientation is parallel with the designed axis, the deviation can be corrected by controlling the pressure of gripper cylinder and the actual trajectory meets the designed axis when TBM is pushed forward 0.14 m in X-axis. If the deviation only occurs in Z-axis, then the deviation can be corrected by controlling torque cylinders. If the position deviation occurs both in Y-axis and Z-axis at the same time, the pressure of gripper cylinder and torque cylinders should be controlled at the same time to rectify the deviation. Simulation results are shown to illustrate the e ectiveness and robustness of the proposed controller. This research proposes an indirect adaptive robust controller that can track the planned tracking trajectory smoothly and rapidly.

Adaptive Bit Loading Scheme with Semi-Blind Channel Estimation for OFDMSystems

An adaptive bit loading and power-allocation scheme is proposed in order to augment the performance of the system based on orthogonal frequency division multiplexing （OFDM）, which is based on the maximum power margin. Coinciding with the adaptive loading scheme, a semi-blind channel estimation algorithm using subspace decomposition method is proposed, which uses the information in the cyclic prefix. An initial channel state information is estimated by using the training sequences with the method of interpolation filtering. The proposed adaptive scheme is simulated on an OFDM wireless local area network（WLAN） system in a time-varying channel. The performance is compared to the constant loading scheme.

An adaptive load torque observer using hyperstability for PMSM servo system

An adaptive load torque observer is presented to compensate the torque ripple in PMSM servo system. A simple adaptive scheme is derived using Popov ＇ s hyperstability theory. The torque ripple detected by the observer is compensated by a feed forwarding equivalent current which gives fast response. The noisy current information is exempt from the observer to avoid its deterioration to the quality of the observer. The speed measurement delay is considered by using observed speed sinee the instantaneous velocity can＇t be estimated precisely at low speed because of too few position pulses from the absolute encoder during one time interval. Simulation and experimental results demonstrate that the proposed method can improve the dynamic performance of PMSM servo system satisfyingly.

PARALLEL ADAPTIVELY MODIFIED CHARACTERISTIC BASIS FUNCTION METHOD BASED ON STATIC LOAD BALANCE

Characteristic Basis Function Method (CBFM) is a novel approach for analyzing the ElectroMagnetic (EM) scattering from electrically large objects. Based on dividing the studied object into small blocks, the CBFM is suitable for parallel computing. In this paper, a static load balance parallel method is presented by combining Message Passing Interface (MPI) with Adaptively Modified CBFM (AMCBFM). In this method, the object geometry is partitioned into distinct blocks, and the serial number of blocks is sent to related nodes according to a certain rule. Every node only needs to calculate the information on local blocks. The obtained results confirm the accuracy and efficiency of the proposed method in speeding up solving large electrical scale problems.

Topology Optimization of Stiffener Layout Design for Box Type Load-Bearing Component under Thermo-Mechanical Coupling

The structure optimization design under thermo-mechanical coupling is a difficult problem in the topology optimization field.An adaptive growth algorithm has become a more effective approach for structural topology optimization.This paper proposed a topology optimization method by an adaptive growth algorithm for the stiffener layout design of box type load-bearing components under thermo-mechanical coupling.Based on the stiffness diffusion theory,both the load stiffness matrix and the heat conduction stiffness matrix of the stiffener are spread at the same time to make sure the stiffener grows freely and obtain an optimal stiffener layout design.Meanwhile,the objectives of optimization are the minimization of strain energy and thermal compliance of the whole structure,and thermo-mechanical coupling is considered.Numerical studies for square shells clearly show the effectiveness of the proposed method for stiffener layout optimization under thermo-mechanical coupling.Finally,the method is applied to optimize the stiffener layout of box type load-bearing component of themachining center.The optimization results show that both the structural deformation and temperature of the load-bearing component with the growth stiffener layout,which are optimized by the adaptive growth algorithm,are less than the stiffener layout of shape‘#’stiffener layout.It provides a new solution approach for stiffener layout optimization design of box type load-bearing components under thermo-mechanical coupling.

移动边缘计算不确定性任务持续卸载及资源分配方法

移动边缘计算场景中任务的不确定性增加了任务卸载及资源分配的复杂性和难度.鉴于此,提出一种移动边缘计算不确定性任务持续卸载及资源分配方法.首先,构建一种移动边缘计算不确定性任务持续卸载模型,通过基于持续时间片划分的任务多批次处理技术应对任务的不确定性,并设计多设备计算资源协同机制提升对计算密集型任务的承载能力.其次,提出一种基于负载均衡的自适应策略选择算法,避免计算资源过度分配导致信道拥堵进而产生额外能耗.最后,基于泊松分布实现了对不确定任务场景模型的仿真,大量实验结果表明时间片长度减小能够降低系统总能耗.此外,所提算法能够更有效地实现任务卸载及资源分配,相较于对比算法,最大可降低能耗11.8%.

基于动态自适应图神经网络的电动汽车充电负荷预测

电动汽车充电站负荷波动的不确定性与长时间预测任务给提升充电负荷预测精度带来巨大的挑战。文中提出一种基于动态自适应图神经网络的电动汽车充电负荷预测算法。首先,构建了一个充电负荷信息时空关联特征提取层,将多头注意力机制与自适应相关图结合生成具有时空关联性的综合特征表达式,以捕获充电站负荷的波动性;然后,将提取的特征输入到时空卷积层,捕获时间和空间之间的耦合关系;最后,通过切比雪夫多项式图卷积与多尺度时间卷积提升模型耦合长时间序列之间的能力。以Palo Alto数据集为例,与现有方法相比,所提算法在4种波动情况下的平均预测误差大幅降低。

基于多尺度分量特征学习的用户级超短期负荷预测

针对用户级负荷波动性强,一步分解后数据维度增加导致运行效率降低以及精度提升有限等问题,该文提出一种新的多尺度分量特征学习框架,用于用户级超短期负荷预测。构建基于自适应噪声的完整经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)、排列熵(permutation entropy,PE)以及变分模态分解(variational mode decomposition,VMD)的自适应二次模态分解框架,捕捉周期性等时序特征,并降低其非平稳特性;采用多维特征融合的方式挖掘各本征模态函数之间的耦合关系,丰富特征信息;利用改进的多尺度空间注意力(multiscale spatial attention,MSA)模块沿时间、空间以及通道等多尺度提取时空特征及多分量间耦合关系,进而便于卷积神经网络(convolutional neural network,CNN)学习多分量特征。基于江苏省南京市房地产业、教育业以及商务服务业共12位用户的实际负荷数据进行算例分析,各行业平均绝对百分误差分别为5.82%、4.54%以及8.78%,与效果最好的对照模型相比,分别降低了10.46%、6%以及7.48%,验证了该文模型具有较高的预测精度和良好的泛化性能。

ADAFT:SDN大规模流表的适应性深度聚合存储架构

为解决软件定义网络(SDN)数据平面中的三态内容可寻址存储器(TCAM)资源紧张问题,提出了一种基于内容表项树的SDN流表深度聚合方法,进而构建一种SDN大规模流表的适应性深度聚合存储架构ADAFT。该架构放宽了聚合表项之间的汉明距离要求,构建内容表项树聚合动作集不同的流表项,显著提高了流表聚合程度。设计了一种TCAM装载率感知的内容表项树动态限高机制,以降低流表查找开销。同时,提出了一种TCAM装载率感知的表项聚合适应性选择策略,以均衡流表聚合程度和查找开销。实验结果表明,ADAFT架构的流表压缩率明显高于现有方法,最高可达65.74%。

双绕组感应发电机交直流集成发电系统的自抗扰控制策略

针对双绕组感应发电机交直流集成发电系统,为实现简单易调、性能优越、负载适应力强的发电控制,该文提出一种基于线性自抗扰控制器的交直流电压控制策略,通过交流电压和直流电压与控制绕组和功率绕组的传递函数模型,构建交直流电压的线性扩张状态观测器,同时对交流电压有效值、直流电压和电压扰动进行观测,并利用电压和扰动观测值设计线性误差反馈控制律,对交直流电压进行控制。最后,通过仿真和实验验证该控制策略提高电压扰动抑制能力和负载适应性,同时各模块参数可独立设计、控制器结构简单,避免需要较多传感器及降低调参难度。

高分三号数据分布式自适应负载均衡并行转换算法研究

针对PolSARpro软件自带高分三号数据转换模块无法自适应分布式差异化硬件环境问题,提出了一种按照该软件的数据格式要求进行分布式自适应负载均衡并行转换算法,将高分三号多极化数据快速、精确地进行定标与格式转换,对不同硬件配置环境自适应,能够抗数据偏斜,并且通过KingMap V10.5平台实现了算法并在实际数据中进行测试,试验结果表明性能提升了约12.7倍,验证了算法的可行性、高效性和正确性。

基于参数估计-滑模阻抗控制的SEA性能分析

重载四足机器人的足部与地面接触过程和步态转换过程中会受到不确定的冲击载荷作用,易导致足部机构载荷过大从而造成结构的冲击损坏。因此,针对使用液压串联弹性执行器(series elastic actuators,SEA)作为足部末端在非结构环境下动态性能差的问题,提出了基于环境参数估计的滑模阻抗控制方法(environmental parameter estimation sliding mode,EPESM)。以阀控液压缸的活塞位移传递函数为基础建立了基于位置内环的SEA阻抗控制模型,并以PID作为基础控制器;为改善SEA阻抗控制的动态性能,根据Lyapunov第二法构建稳定的自适应环境参数估计方法对SEA期望位置进行前馈补偿;为提升自适应环境参数估计方法在SEA工作过程中不同阶段的动态性能和环境变化适应性,使用模糊控制方法对自适应环境参数估计方法中的自适应参数进行寻优;以SEA状态方程为基础构建滑模控制器与PID控制器进行动态性能对比分析。仿真结果表明:在变SEA弹簧刚度工况和变环境刚度下,EPESM阻抗控制的响应速度明显更快,可将调节时间从平均5 s缩短到1 s内,能更快地达到预期位移和预期接触力,且能略微降低稳态误差,使接触力误差保持在±6 N内。在动态跟踪工况下,EPESM阻抗控制的动态性能更好,在快速进入跟踪状态后,可以长时间保持0.2 s以内的相位滞后和5.2%的幅值误差。

含逆变型分布式电源和分支负荷的配电网自适应电流差动保护

电流差动保护具有良好的选择性和灵敏性。但是,逆变型分布式电源(inverter-interfaced distributed generator,IIDG)和分支负荷接入改变了配电网的故障特性,现有配电网的电流差动保护面临拒动或误动的风险。为解决以上问题,该文在计及P/Q控制的IIDG和分支负荷的故障等值建模基础上,从故障点、IIDG和分支负荷的相对位置出发,挖掘配电网线路两端正序电流差的幅值特征以及正序电流故障分量的相位特征。在此基础上,提出一种正序电流幅值与故障分量相角阈值配合的双比例制动系数自适应差动保护方案,并且利用幅相平面分析所提保护判据的可靠性和灵敏性。最后通过PSCAD/EMTDC验证了所提保护方案的有效性,仿真结果表明该方案适应不同故障类型、位置和过渡电阻,且不受IIDG以及分支负荷接入的影响。