中国山水画自由透视的空间表现探析

本文阐释了中国山水画的自由透视视像,并以此来探讨中国山水画对视觉合理与舒适的追求。这种非科学的自适状态的艺术表现语言以及主观自由表现的当代文化意义,对当下中国山水画空间表现的理论研究及创作实践都具有指导价值和借鉴意义。

Lithium battery state of charge and state of health prediction based on fuzzy Kalman filtering

This paper presents a more accurate battery state of charge(SOC)and state of health(SOH)estimation method.A lithium battery is represented by a nonlinear two-order resistance-capacitance equivalent circuit model.The model parameters are estimated by searching least square error optimization algorithm.Precisely defined by this method,the model parameters allow to accurately determine the capacity of the battery,which in turn allows to specify the SOC prediction value used as a basis for the SOH value.Application of the extended Kalman filter(EKF)removes the need of prior known initial SOC,and applying the fuzzy logic helps to eliminate the measurement and process noise.Simulation results obtained during the urban dynamometer driving schedule(UDDS)test show that the maximum error in estimation of the battery SOC is 0.66%.Battery capacity is estimate by offline updated Kalman filter,and then SOH will be predicted.The maximum error in estimation of the battery capacity is 1.55%.

Intestinal mucosal atrophy and adaptation

Mucosal adaptation is an essential process in gut ho- meostasis. The intestinal mucosa adapts to a range of pathological conditions including starvation, short-gut syndrome, obesity, and bariatric surgery. Broadly, these adaptive functions can be grouped into proliferation and differentiation. These are influenced by diverse interactions with hormonal, immune, dietary, nervous, and mechanical stimuli. It seems likely that clinical out- comes can be improved by manipulating the physiol- ogy of adaptation. This review will summarize current understanding of the basic science surrounding adapta- tion, delineate the wide range of potential targets for therapeutic intervention, and discuss how these might be incorporated into an overall treatment plan. Deeper insight into the physiologic basis of adaptation will identify further targets for intervention to improve clini- cal outcomes.

Low Complexity Remote Radio Unit Selection and Adaptive Bit Partition Algorithm in Cooperative Distributed MIMO Systems

For multi-user cooperative Distributed MIMO (D-MIMO) systems, a low-complexity Remote Radio Unit (RRU) selection and adaptive bit partition algorithm is proposed to maximize the transmission Signal-to-Interference-Noise Ratio (SINR). Considering limited feedback, each user can adaptively select an RRU cluster to maintain the best communication quality. Under this condition, only one codebook is utilized for quantizing the Channel State Information (CSI) with variable dimensions, which effectively reduces the codebook storage amount. Furthermore, we propose an adaptive bit partition algorithm, which separately allocates bits to quantize the desired channels and interference channels. The optimal solution is achieved through an optimization theory to minimize the effect of inter-cell interference. Simulation results show that the proposed algorithm substantially improves the performance compared to other non-adaptive schemes.

Adaptive Feedback Stabilization with Quantized State Measurements for a Class of Chaotic Systems

We investigate asymptotical stabilization for a class of chaotic systems by means of quantization measurements of states.The quantizer adopted in this paper takes finite many values.In particular,one zoomer is placed at the input terminal of the quantizer,and another zoomer is located at the output terminal of the quantizer.The zoomers possess a common adjustable time-varying parameter.By using the adaptive laws for the time-varying parameter and estimating boundary error of values of quantization,the stabilization feedback controller with the quantized state measurements is proposed for a class of chaotic systems.Finally,some numerical examples are given to demonstrate the validity of the proposed methods.

STATE-FEEDBACK ADAPTIVE STABILIZING CONTROL DESIGN FOR A CLASS OF HIGH-ORDER NONLINEAR SYSTEMS WITH UNKNOWN CONTROL COEFFICIENTS

In this paper, a new approach is successfully addressed to design the state-feedback adaptive stabilizing control law for a class of high-order nonlinear systems in triangular form and with unknown and nonidentical control coefficients, whose stabilizing control has been investigated recently under the knowledge that the lower bounds of the control coefficients are exactly known. In the present paper, without any knowledge of the lower bounds of the control coefficients, based on the adaptive technique and appropriately choosing design parameters, we give the recursive design procedure of the stabilizing control law by utilizing the approach of adding a power integrator together with tuning functions. The state-feedback adaptive control law designed not only preserves the equilibrium at the origin, but also guarantees the global asymptotic stability of the closed-loop states and the uniform boundedness of all the other closed-loop signals.

Adaptive Stabilization for a Class of Feedforward Systems with Zero-Dynamics

This paper investigates adaptive state feedback stabilization for a class of feedforward nonlinear systems with zero-dynamics, unknown linear growth rate and control coefficient. For design convenience, the state transformation is first introduced and the new system is obtained. Then, the estimation law is constructed for the unknown control coefficient, and the state feedback controller is proposed with a gain updated on-line. By appropriate choice of the estimation law for the control coefficient and the dynamic gain, the states of the closed-loop system are globally bounded, and the state of the original system converges to zero. Finally, a simulation example is given to illustrate the correctness of the theoretical results.

An efficient equivariant adaptive separation via independence algorithm for acoustical source separation and identification

To balance the convergence rate and steadystate error of blind source separation(BSS) algorithms, an efficient equivariant adaptive separation via independence(Efficient EASI) algorithm is proposed based on separating indicator, which was derived from the convergence condition of EASI, and can be used to evaluate the separation degree of separated signals. Furthermore, a nonlinear monotone increasing function between suitable step sizes and separating indicator is constructed to adaptively adjust step sizes, and forgetting factor is employed to weaken effects of data at the initial stage. Numerical case studies and experimental studies on a test bed with shell structures are provided to validate the efficiency improvement of the proposed method. This study can benefit for vibration & acoustic monitoring and control, and machinery condition monitoring and fault diagnosis.

Microcanonical Phase Diagram of the BEG and Ising Models

The density of states of long-range Blume-Emery-Criffiths （BEG） and short-range lsing models are obtained by using Wang-Landau sampling with adaptive windows in energy and magnetization space. With accurate density of states, we are able to calculate the mierocanonical specific heat of fixed magnetization introduced by Kastner et al. in the regions of positive and negative temperature. The microcanonical phase diagram of the Ising model shows a continuous phase transition at a negative temperature in energy and magnetization plane. However the phase diagram of the long-range model constructed by peaks of the microeanonieal specific heat looks obviously different from the Ising chart.

Adaptive robust beamformer for multi-pair two-way relay networks with imperfect channel state information

In wideband multi-pair two-way relay networks, the performance of beamforming at a relay station(RS) is intimately related to the accuracy of the channel state information(CSI) available. The accuracy of CSI is determined by Doppler spread, delay between beamforming and channel estimation, and density of pilot symbols,including transmit power of pilot symbols. The coefficient of the Gaussian-Markov CSI error model is modeled as a function of CSI delay, Doppler spread, and signal-to-noise ratio, and can be estimated in real time. In accordance with the real-time estimated coefficients of the error model, an adaptive robust maximum signal-to-interferenceand-noise ratio(Max-SINR) plus maximum signal-to-leakage-and-noise ratio(Max-SLNR) beamformer at an RS is proposed to track the variation of the CSI error. From simulation results and analysis, it is shown that: compared to existing non-adaptive beamformers, the proposed adaptive beamformer is more robust and performs much better in the sense of bit error rate(BER); with increase in the density of transmit pilot symbols, its BER and sum-rate performances tend to those of the beamformer of Max-SINR plus Max-SLNR with ideal CSI.