Investigation of Gallium Nitride Based on Power Semiconductor Devices in Polarization Super Junction Technology

Over the last decade, gallium nitride (GaN) has emerged as an excellent material for the next generation of power devices. GaN transistors, switching losses are very low, thanks to the small parasitic capacitances and switching charges. Device scaling and monolithic integration enable a high-frequency operation, with consequent advantages in terms of miniaturization. For high power/high voltage operation, GaN�based Polarization Super-Junction (PSJ) architectures demonstrate great potential. The aim of this thesis is devoted to the development of PSJ technology. Detailed analysis of the on-state behaviour of the fabricated Ohmic Gate (OG) and Schottky Gate (SG) PSJ HFETs is presented. Theoretical models for calculating the sheet densities of 2DEG and 2DHG are proposed and calibrated with numerical simulations and experimental results. To calculate the R (on, sp) of PSJ HFETs, two different gate structures (Ohmic gate and Schottky gate) are considered herein. The scaling tendency of power devices enables the emergence of multi-channel PSJ concepts. Therefore, lateral and vertical multi-channel PSJ devices based on practical implementation are also investigated. Presented calculated and simulated results show that both lateral and vertical multi-channel PSJ technologies can be well suited to break the unipolar one-dimensional material limits of GaN by orders of magnitude and achieve an excellent trade-off between R (on, sp) and voltage blocking capability provided composition and thickness control can be realised. A novel multi-polarization channel is applied to realize normally-off and high�performance vertical GaN device devices for low voltage applications based on the multi-channel PSJ and vertical MOSFET concepts. This structure is made with 2DHG introduced to realize the enhancement mode channel instead of p-GaN as in conventional vertical GaN MOSFETs. As the 2DHG depends upon growth conditions, p-type doping activation issues can be overcome. The Mg-doped layer is only used to reduce the short-channel effects, as the 2DHG layer is too thin. Two more 2DEG layers P a g e | iv are formed through AlGaN/GaN/AlGaN/GaN polarization structure, which minimizes the on-state resistance. The calculation results show this novel vertical GaN MOSFET – termed SV GaN FET - has the potential to break the GaN material limit in the trade-off between R (on, sp) and breakdown voltage at low voltages. The comprehensive set of development based on the PSJ concept gives a comprehensive overview of next-generation power electronics

Effects of Probiotics on Gut Microbiota in Type 2 Diabetes Patients

Objective: To study the effect of probiotics on gut microbiota in Type 2 diabetes patients and its clinical application value. Methods: Select Type 2 diabetes patients to take orally probiotics for 24 weeks, collect stool samples of subjects at the baseline and end of the trial, identify and analyze gut microbiota of each sample by 16srRNA high-throughput sequencing, and compare the changes of blood glucose, blood lipid and insulin resistance before and after the intervention. Results: A total of 75 patients completed clinical observations. 16srRNA high-throughput sequencing showed that the proportion of the subjects with increased Actinobacteria and Tenericutes at the end of the trial has increased (37.8% and 75.7% respectively). The genus level analysis showed that the number of subjects with increased intestinal probiotics and with decreased conditioned pathogens all increased. Cluster analysis before and after intervention showed that the gut microbiota of samples in the same group had a higher similarity. Compared with the subjects at the baseline status, at the end of the trial after the intervention, fasting blood glucose (FBG) of the subjects significantly decreased (P<0.05), the proportion of the subjects with triglyceride (TG) and cholesterol up to standard increased, and HOMA-IR was significantly improved (P<0.05). Conclusions: Probiotics can regulate the gut microbiota of Type 2 diabetes patients, promote fasting blood glucose (FBG) to reach the standard and improve insulin resistance, and help improve lipid metabolism

ST-P3: End-to-end Vision-based Autonomous Driving via Spatial-Temporal Feature Learning

Many existing autonomous driving paradigms involve a multi-stage discrete pipeline of tasks. To better predict the control signals and enhance user safety, an end-to-end approach that benefits from joint spatial-temporal feature learning is desirable. While there are some pioneering works on LiDAR-based input or implicit design, in this paper we formulate the problem in an interpretable vision-based setting. In particular, we propose a spatial-temporal feature learning scheme towards a set of more representative features for perception, prediction and planning tasks simultaneously, which is called ST-P3. Specifically, an egocentric-aligned accumulation technique is proposed to preserve geometry information in 3D space before the bird's eye view transformation for perception; a dual pathway modeling is devised to take past motion variations into account for future prediction; a temporal-based refinement unit is introduced to compensate for recognizing vision-based elements for planning. To the best of our knowledge, we are the first to systematically investigate each part of an interpretable end-to-end vision-based autonomous driving system. We benchmark our approach against previous state-of-the-arts on both open-loop nuScenes dataset as well as closed-loop CARLA simulation. The results show the effectiveness of our method. Source code, model and protocol details are made publicly available at https://github.com/OpenPerceptionX/ST-P3.Comment: ECCV 202

Spontaneous imbibition behavior in porous media with various hydraulic fracture propagations: A pore-scale perspective

Hydraulic fracturing technology can improve the geologic structure of unconventional oil and gas reservoirs, yielding a complex fracture network resulting from the synergistic action of hydraulic and natural fractures. However, the impact of spontaneous imbibition associated with hydraulic fracture propagation on the reservoir matrix remains poorly understood. In this study, combining the Cahn-Hilliard phase field method with the Navier-Stokes equations, pore-scale modeling was employed to capture the evolution of the oil-water interface during dynamic spontaneous imbibition for hydraulic fracture propagation in a two-end open mode. This pore-scale modeling approach can effectively circumvent the challenges of conducting spontaneous imbibition experiments on specimens partitioned by hydraulic fractures. A direct correlation was established between the pressure difference curve and the morphology of discharged oil phase in the primary hydraulic fracture, providing valuable insights into the distribution of oil phase in spontaneous imbibition. Furthermore, it was shown that secondary hydraulic fracture propagation expands the longitudinal swept area and enhances the utilization of natural fractures in the transverse swept area during spontaneous imbibition. When secondary hydraulic fracture propagation results in the interconnection of upper and lower primary hydraulic fractures, competitive imbibition occurs in the matrix, leading to reduced oil recovery compared to the unconnected models. Our results shed light upon the spontaneous imbibition mechanism in porous media with hydraulic fracture propagation, contributing to the refinement and application of hydraulic fracturing techniques.Document Type: Original articleCited as: Zhou, Y., Guan, W., Zhao, C., Zou, X., He, Z., Zhao, H. Spontaneous imbibition behavior in porous media with various hydraulic fracture propagations: A pore-scale perspective. Advances in Geo-Energy Research, 2023, 9(3): 185-197. https://doi.org/10.46690/ager.2023.09.0

Differentially Private Numerical Vector Analyses in the Local and Shuffle Model

Numerical vector aggregation plays a crucial role in privacy-sensitive applications, such as distributed gradient estimation in federated learning and statistical analysis of key-value data. In the context of local differential privacy, this study provides a tight minimax error bound of $O(\frac{ds}{n\epsilon^2})$, where $d$ represents the dimension of the numerical vector and $s$ denotes the number of non-zero entries. By converting the conditional/unconditional numerical mean estimation problem into a frequency estimation problem, we develop an optimal and efficient mechanism called Collision. In contrast, existing methods exhibit sub-optimal error rates of $O(\frac{d^2}{n\epsilon^2})$ or $O(\frac{ds^2}{n\epsilon^2})$. Specifically, for unconditional mean estimation, we leverage the negative correlation between two frequencies in each dimension and propose the CoCo mechanism, which further reduces estimation errors for mean values compared to Collision. Moreover, to surpass the error barrier in local privacy, we examine privacy amplification in the shuffle model for the proposed mechanisms and derive precisely tight amplification bounds. Our experiments validate and compare our mechanisms with existing approaches, demonstrating significant error reductions for frequency estimation and mean estimation on numerical vectors.Comment: Full version of "Hiding Numerical Vectors in Local Private and Shuffled Messages" (IJCAI 2021

Short-term interval prediction of PV power based on quantile regression-stacking model and tree-structured parzen estimator optimization algorithm

In recent years, the photovoltaic (PV) industry has grown rapidly and the scale of grid-connected PV continues to increase. The random and fluctuating nature of PV power output is beginning to threaten the safe and stable operation of the power system. PV power interval forecasting can provide more comprehensive information to power system decision makers and help to achieve risk control and risk decision. PV power interval forecasting is of great importance to power systems. Therefore, in this study, a Quantile Regression-Stacking (QR-Stacking) model is proposed to implement PV power interval prediction. This integrated model uses three models, extreme gradient boosting (Xgboost), light gradient boosting machine (LightGBM) and categorical boosting (CatBoost), as the base learners and Quantile Regression-Long and Short Term Memory (QR-LSTM) model as the meta-learner. It is worth noting that in order to determine the hyperparameters of the three base learners and one meta-learner, the optimal hyperparameters of the model are searched using a Tree-structured Parzen Estimator (TPE) optimization algorithm based on Bayesian ideas. Meanwhile, the correlation coefficient is applied to determine the input characteristics of the model. Finally, the validity of the proposed model is verified using the actual data of a PV plant in China

Auto-Focus Contrastive Learning for Image Manipulation Detection

Generally, current image manipulation detection models are simply built on manipulation traces. However, we argue that those models achieve sub-optimal detection performance as it tends to: 1) distinguish the manipulation traces from a lot of noisy information within the entire image, and 2) ignore the trace relations among the pixels of each manipulated region and its surroundings. To overcome these limitations, we propose an Auto-Focus Contrastive Learning (AF-CL) network for image manipulation detection. It contains two main ideas, i.e., multi-scale view generation (MSVG) and trace relation modeling (TRM). Specifically, MSVG aims to generate a pair of views, each of which contains the manipulated region and its surroundings at a different scale, while TRM plays a role in modeling the trace relations among the pixels of each manipulated region and its surroundings for learning the discriminative representation. After learning the AF-CL network by minimizing the distance between the representations of corresponding views, the learned network is able to automatically focus on the manipulated region and its surroundings and sufficiently explore their trace relations for accurate manipulation detection. Extensive experiments demonstrate that, compared to the state-of-the-arts, AF-CL provides significant performance improvements, i.e., up to 2.5%, 7.5%, and 0.8% F1 score, on CAISA, NIST, and Coverage datasets, respectively

Spear or Shield: Leveraging Generative AI to Tackle Security Threats of Intelligent Network Services

Generative AI (GAI) models have been rapidly advancing, with a wide range of applications including intelligent networks and mobile AI-generated content (AIGC) services. Despite their numerous applications and potential, such models create opportunities for novel security challenges. In this paper, we examine the challenges and opportunities of GAI in the realm of the security of intelligent network AIGC services such as suggesting security policies, acting as both a ``spear'' for potential attacks and a ``shield'' as an integral part of various defense mechanisms. First, we present a comprehensive overview of the GAI landscape, highlighting its applications and the techniques underpinning these advancements, especially large language and diffusion models. Then, we investigate the dynamic interplay between GAI's spear and shield roles, highlighting two primary categories of potential GAI-related attacks and their respective defense strategies within wireless networks. A case study illustrates the impact of GAI defense strategies on energy consumption in an image request scenario under data poisoning attack. Our results show that by employing an AI-optimized diffusion defense mechanism, energy can be reduced by 8.7%, and retransmission count can be decreased from 32 images, without defense, to just 6 images, showcasing the effectiveness of GAI in enhancing network security