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Parameter extraction of photovoltaic (PV) models plays a vital role in simulation, evaluation and control of PV systems. It requires to identify the parameters of different PV models quickly and accurately. In this paper, an improved differential evolution by reusing the past individual vectors and adaptive mutation strategy is proposed to extract PV parameters. In the proposed method, the successful difference vectors from previous generations are introduced to produce the offspring in the next generations to improve the performance of differential evolution. In addition, to obtain a nice result, an adaptive mutation strategy is considered to establish a good balance of exploration and exploitation. The proposed method is applied to identify the parameters of different PV models, such as single diode, double diode, and PV models. Comparison results demonstrate that the proposed method obtains the competitive performance on accuracy, reliability and convergence when compared with other state-of-the-art methods.

Curriculum Learning (CL) selects the training samples from the easy to difficult to boost the classification results. Most existing variates of CL measure the difficulty level of an example in an intuitive way, i.e., the loss value between the prediction and the ground truth. This way ignores the different distances to every class boundary, which is implied in the prediction vectors. In this paper, we propose a novel CL framework, named Balance Loss Curriculum Learning(BLCL), to reveal the comprehensive difficulty level of an example and improve the curriculum process based on the deep architectures. We follow the teacher-student learning module and train the teacher & student networks simultaneously. The predictions learned by the teacher network indicates the boundary information and is set as the difficulty measurement to guide the student network. The student network focuses on the easy samples according to the boundary information by decreasing the loss from difficult examples as well as increasing the loss from easy examples. Through extensive and rigor experiments, we demonstrate the effectiveness and generality of our framework.

In this paper, we consider a reconfigurable intelligent surface (RIS) aided millimeter wave (mmWave) multiple-input multiple-output (MIMO) system. The system can obtain the huge gain via joint active beamforming at the base station (BS) and passive beamforming at the RIS. However, due to weather and atmospheric effects, outdoor RIS antenna elements are subject to full or partial blockages from a plethora of particles like dirt, salt, ice, and water droplets. These blockages can cause an approximate squared power/SNR loss for the system. Different from the conventional array diagnosis, the RIS has no signal processing capability. Thus, we propose the joint array diagnosis and channel estimation techniques containing two stages to solve the problem. At the first stage the channel parameters at user equipment (UE) and BS are estimated using an iterative reweighted (IR) method. At the second stage, the array blockage coefficient vector and the effective sparse channel parameters at RIS are jointly estimated via solving a two-timescale non-convex optimization problem. We propose two algorithms, i.e., a batch algorithm (BA) and a two-timescale online joint array diagnosis and channel estimation (TOJADCE) algorithm to solve the problem and compare the performance of these two algorithms. Finally, to speed up the convergence of long-term variable and improve estimation performance, we propose a noise reduction (NR) algorithm. The simulations verify the effectiveness of our proposed algorithms.

Wideband sensing-based cognitive radio with simultaneous wireless information and power transfer can be designed for efficient spectrum and energy usage. We first aim to maximize the sum energy harvested by all the energy harvesters subject to constraints on rate, transmit power, interference, and subchannel assignment. Due to the non-convexity of the formulated problem, we relax the integer variable and introduce an auxiliary variable to transform the original problem into a convex problem. Then, the Lagrangian and subgradient methods are adopted to obtain the optimal solutions. However, this scheme may lead to a severe fairness issue among different links. In view of this fact, we further propose an energyharvesting scheme for max-min fairness. In particular, we aim to maximize the energy harvested by the worst case individual link. We show that this problem can be solved in a similar manner to the problem of sum harvested energy maximization. Simulation results are presented to verify the convergence and fairness performance of the proposed algorithm, and to reveal a novel tradeoff between the network harvested energy and sensing time.