For the problem of narrow soft-switching range and low efficiency of dual active bridge (DAB) converter in wide voltage range, this paper proposes a single-sided asymmetric duty modulation (SSADM) strategy, which contains only two control degrees of freedom, and significantly improves the soft-switching performance and efficiency at light load. Firstly, the potential advantages of asymmetric duty modulation (ADM) method are described through waveforms. The principle of determining which full bridge to adopt the ADM is given and the working principle of SSADM is described. Secondly, a linear pulse logic combination analysis of SSADM in time domain is performed to establish a complete steady-state mathematical model. To solve the analytical expression for the optimal solution of SSADM, the peak-to-peak value of the inductor current with simple expression is chosen as the optimization objective, and the global optimal solution of SSADM is solved based on the Karush-Kuhn-Tucker (KKT) condition, and the voltage closed-loop control strategy of SSADM is presented. In addition, the soft-switching range of SSADM is analyzed. Finally, the experimental platform of DAB converter based on SiC device is built. Compared with other modulation strategies, SSADM shows better soft-switching performance with significant efficiency improvement, which is verified by the experimental results.

ITER (International Thermonuclear Experimental Reactor) static magnetic field (SMF) test facility requires a DC power supply with low voltage, high current, and high stability. Due to the limitation ofswitching loss, there is a contradiction between the output current capability and the output ripple. Large output current usually leads to low switching frequency, and low switching frequency will generate a large number of harmonics. To solve the problems, a topology based on the interleaving parallel buck converter is used and tested in this paper. Moreover, the topology is realized with only a small number of switching metal-oxide-semiconductor field effect transistors (MOSFETs). This article introduces the system design scheme and control method in detail. The analysis of harmonic and simulation are carried out. The validity of proposed scheme and control strategy were confirmed by experiments, the power supply system can supply large current of 15kA and has ability of low ripple.

The introduction of new intelligent components in the power system produces more harmonics, and UPFC can adjust them well. For improving the effectiveness of performance parameters of the power system, a reactive power optimization method based on particle swarm optimization considering UPFC is proposed. Through the paper, the energy parameter with UPFC is calculated under the way of the power injection model, and the objective function is to minimize the energy loss in the system. On this basis, reactive power optimization is realized by particle swarm optimization. Under the background of the simulation of the IEEE9 bus, the results of system parameters before and after using particle swarm optimization in UPFC are analyzed. It is found that the proposed method has a good advantage to improve the system network loss and optimize the system quality, which has good practical value.

While significant progress has been made in heterogeneous treatment effect (HTE) estimation, the evaluation of HTE estimators remains underdeveloped. In this article, we propose a robust evaluation framework based on relative error, which quantifies performance differences between two HTE estimators. We first derive the key theoretical conditions on the nuisance parameters that are necessary to achieve a robust estimator of relative error. Building on these conditions, we introduce novel loss functions and design a neural network architecture to estimate nuisance parameters and obtain robust estimation of relative error, thereby achieving reliable evaluation of HTE estimators. We provide the large sample properties of the proposed relative error estimator. Furthermore, beyond evaluation, we propose a new learning algorithm for HTE that leverages both the previously HTE estimators and the nuisance parameters learned through our neural network architecture. Extensive experiments demonstrate that our evaluation framework supports reliable comparisons across HTE estimators, and the proposed learning algorithm for HTE exhibits desirable performance.

Offline policy learning aims to optimize individualized decisions using historical data. However, conventional methods primarily focus on maximizing expected rewards while neglecting individual-level counterfactual harm—cases where the assigned treatment leads to worse outcomes than the control. This can result in overly aggressive policies that harm some individuals. In this article, we propose a counterfactual harm-aware policy learning framework that effectively balances expected reward with individual-level harm. To quantify harm, we introduce two measures: treatment harm rate (THR), capturing the proportion of individuals harmed, and treatment harm quantity (THQ), capturing the severity of harm, particularly for continuous outcomes. A key challenge is that both THR and THQ depend on the joint distribution of potential outcomes, which is generally unidentifiable, even in randomized controlled trials. To address this, we introduce an additive latent variable model under which we establish the identifiability of THR and THQ and develop the corresponding estimators. These estimators are then used to learn policies under explicit harm constraints. Extensive experiments demonstrate the effectiveness of the proposed approach.

In recommender systems, a common problem is the presence of various biases in the collected data, which deteriorates the generalization ability of the recommendation models and leads to inaccurate predictions. Doubly robust (DR) learning has been studied in many tasks in RS, with the advantage that unbiased learning can be achieved when either a single imputation or a single propensity model is accurate. In this paper, we propose a multiple robust (MR) estimator that can take the advantage of multiple candidate imputation and propensity models to achieve unbiasedness. Specifically, the MR estimator is unbiased when any of the imputation or propensity models, or a linear combination of these models is accurate. Theoretical analysis shows that the proposed MR is an enhanced version of DR when only having a single imputation and propensity model, and has a smaller bias. Inspired by the generalization error bound of MR, we further propose a novel multiple robust learning approach with stabilization. We conduct extensive experiments on real-world and semi-synthetic datasets, which demonstrates the superiority of the proposed approach over state-of-the-art methods.