Sustainable agricultural microgrids have emerged as a promising solution to address reliable power supply issues in remote regions by integrating renewable energy sources and energy storage systems. However, the inherent intermittency of renewable resources and the lack of reliable communication infrastructure hinder efficient energy coordination and system stability. This paper proposes a composite model predictive control based decentralized coordination strategy for sustainable agricultural microgrids using hydrogen-battery hybrid energy storage systems. This strategy achieves decentralized dynamic power sharing with optimized transient performance and guaranteed large-signal stability, without relying on real-time communication. The proposed control method incorporates an MPC controller to enhance the dynamic performance of the system and a high-order sliding mode observer to estimate disturbances caused by renewable generation and agricultural load uncertainties. This approach enables smooth power sharing, suppresses current ripple, and ensures system robustness. Additionally, the design is tailored to the characteristics of remote agricultural microgrids by supporting long-term energy storage and mitigating short-term renewable power fluctuations. The effectiveness of the proposed method is verified by simulations and experiments.

Electronic Design Automation (EDA) plays a crucial role in classical chip design and significantly influences the development of quantum chip design. However, traditional EDA tools cannot be directly applied to quantum chip design due to vast differences compared to the classical realm. Several EDA products tailored for quantum chip design currently exist, yet they only cover partial stages of the quantum chip design process instead of offering a fully comprehensive solution. Additionally, they often encounter issues such as limited automation, steep learning curves, challenges in integrating with actual fabrication processes, and difficulties in expanding functionality. To address these issues, we developed a full-stack EDA tool specifically for quantum chip design, called EDA-Q. The design workflow incorporates functionalities present in existing quantum EDA tools while supplementing critical design stages such as device mapping and fabrication process mapping, which users expect. EDA-Q utilizes a unique architecture to achieve exceptional scalability and flexibility. The integrated design mode guarantees algorithm compatibility with different chip components, while employing a specialized interactive processing mode to offer users a straightforward and adaptable command interface. Application examples demonstrate that EDA-Q significantly reduces chip design cycles, enhances automation levels, and decreases the time required for manual intervention. Multiple rounds of testing on the designed chip have validated the effectiveness of EDA-Q in practical applications.

Single-phase inverters have been widely used in industrial applications; yet, improper controller design can lead to stability issues. Conventionally, active damping methods and time-domain stability control methods are employed to stabilize the inverter system. However, these methods often require additional sensors, which may increase the overall cost and physical size of the inverter system. This paper proposes a state-space reconstruction-based stability control design method (S2R-SCDM) and a reconstructed inverter-side inductor current and reconstructed capacitor voltage feedback (RIIC&RCVF) control method to stabilize the single-phase inverter control without additional sampling sensors. This approach provides a cost-effective solution of stability control method for single-phase inverters. Simulation results validate the effectiveness and accuracy of the proposed S2R-SCDM and RIIC&RCVF control method.

Grid-forming (GFM) converters can achieve self-synchronization oriented by the active power balance, which is a promising solution for the high penetration of power electronics. Unfortunately, GFM control suffers from the synchronous oscillation (SO) issue, which may result in system instability. This paper proposes a disturbance observer-based model predictive power synchronization approach to suppress SOs of GFM converters. The mechanism of SO is investigated by the small-signal model of the grid-tied GFM converter, and it is revealed that the SO is induced by the electromagnetic dynamics of the power transfer in the transmission line and the power synchronization dynamics dominate this issue. Then, a model predictive power synchronization controller is proposed for mitigating SOs. In addition, a disturbance observer is developed to compensate the influence of disturbances/uncertainties in the system to improve the performance of power tracking. The proposed control approach is verified by simulations.

Cloud detection is an important and challenging problem in atmospheric remote sensing. This study improves the cloud detection performance of the previously proposed cross-track infrared sounder (CrIS) full spectrum resolution (FSR) cloud detection index (FCDI) by changing the channel pairing criteria and using a new feature refining attention network (FRAnet). Instead of using the standard deviation of the simulated CrIS channel's brightness temperatures (BTs) as pairing index, this study adopts the root mean square error (RMSE) of the fitting results of the candidate pairs to select channel pairs. Since the channel's BT standard deviation mainly reflects the channel's reliability while the RMSE can directly reflect the pairing and fitting quality. Different RMSE thresholds are specified to represent the physical characteristics of different band groups, i.e., the long wave to short wave (LW-SW), LW to medium wave (MW), and MW-SW. After the new channel pairs being selected, the attention mechanism based FRAnet is applied to optimal utilize the FCDIs obtained from over 200 pairs. 40-day data is used for this study, and a precise algorithm is applied to match the CrIS data to the visible infrared imaging radiometer suite (VIIRS) data. As a result, new cloud labels are used. Simulation results show a largely improved cloud detection accuracy from the previously achieved ~ 80% to ~87.8%.

Accurate modeling of personalized head-related transfer functions (HRTFs) is difficult but critical for applications requiring spatial audio. However, this remains challenging as experimental measurements require specialized equipment, numerical simulations require accurate head geometries and robust solvers, and data-driven methods are hungry for data. In this paper, we propose a new deep learning method that combines measurements and numerical simulations to take the best of three worlds. By learning the residual difference and establishing a high quality spatial basis, our method achieves consistently 2 dB to 2.5 dB lower spectral distortion (SD) compared to the state-of-the-art methods.

The lack of data is a major problem in individual HRTF modeling. There are many HRTF databases, but each database only has limited HRTFs with different characteristics, such as distance-dependent HRTFs or individual HRTFs. How to effectively model HRTFs through several different databases is an important task. In this paper, a method for predicting individual distance-dependent HRTFs using a few anthropometric parameters is proposed. By modeling the HRTFs in CIPIC database, which contains individual HRTFs in 1 meter, and the PKU&IOA database, which contains KEMAR HRTFs in eight distances, we predict the individual HRTFs in arbitrary directions and distances. The objective experiments show that the proposed model has less spectral distortions than distance variation function model. The subjective experiments show that the proposed model can predict the individual HRTFs in arbitrary directions and distances.

Cloud detection is important in satellite remote sensing. It still remains challenging even though many algorithms and methods have been developed in the past years. Previously, we proposed a cloud detection index, FCDI for the S-NPP Cross-track Infrared Sounder (CrIS) full spectral resolution (FSR) data using three machine learning (ML) algorithms. Comparison with the cloud products from the Visible Infrared Imaging Radiometer Suite (VIIRS) showed that the cloud detection accuracy of FCDI of each ML algorithm was close and the best accuracy among them was about 80%. This study proposes improvements to the previous FCDI procedure. The pairing criteria of FCDI channels are redesigned to generate a more comprehensive list of the channel pairs among the longwave, mid-wave and shortwave IR bands. In this way, more atmospheric vertical levels can be represented by FCDI. In order to cover the annual variation of the real atmospheric conditions, CrIS FSR data from four seasons are utilized to derive, train, and validate the FCDI. Based on our previous study, the multilayer perceptron (MLP) is selected. Currently, the total accuracy of cloud detection using the FCDI has been improved to 84.5%. Comparison and discussion of the FCDIs are included.

Head-related transfer function (HRTF) plays an important role in the construction of 3D auditory display. This article presents an individual HRTF modeling method using deep neural networks based on spatial principal component analysis. The HRTFs are represented by a small set of spatial principal components combined with frequency and individual-dependent weights. By estimating the spatial principal components using deep neural networks and mapping the corresponding weights to a quantity of anthropometric parameters, we predict individual HRTFs in arbitrary spatial directions. The objective and subjective experiments evaluate the HRTFs generated by the proposed method, the principal component analysis (PCA) method, and the generic method. The results show that the HRTFs generated by the proposed method and PCA method perform better than the generic method. For most frequencies the spectral distortion of the proposed method is significantly smaller than the PCA method in the high frequencies but significantly larger in the low frequencies. The evaluation of the localization model shows the PCA method is better than the proposed method. The subjective localization experiments show that the PCA and the proposed methods have similar performances in most conditions. Both the objective and subjective experiments show that the proposed method can predict HRTFs in arbitrary spatial directions.

In this paper, a method for modeling distance dependent head-related transfer functions is presented. The HRTFs are first decomposed by spatial principal component analysis. Using deep neural networks, we model the spatial principal component weights of different distances. Then we realize the prediction of HRTFs in arbitrary spatial distances. The objective and subjective experiments are conducted to evaluate the proposed distance model and the distance variation function model, and the results have shown that the proposed model has less spectral distortions than distance variation function model, and the virtual sound generated by the proposed model has better performance in terms of distance localization.

In this paper we use ubiquitous smart-chairs to evaluate live presentations. We validate the hypothesis that the audiences' activities can be recognized with pressure sensors under chairs' legs (74.6% accuracy rate from 8 typical activities in 8 live presentations, each with 6 chairs seated), and ce