The medium voltage (MV) DC collection and transmission technology for photovoltaics (PVs) offers numerous advantages, such as low transmission line losses, fewer power conversion stages, high efficiency, and good stability. The DC/DC boost converter is the core equipment of the collection system. Employing a resonant converter with a wide adjustable input voltage range can eliminate a maximum power point tracking (MPPT) stage, enabling single-stage PV collection. This paper proposes a clamped resonant DC/DC boost converter for PV collection, which ensures quasi-zero current switching (QZCS) and zero voltage switching (ZVS) over the entire input voltage range. A wide adjustable input voltage control strategy with low current stress and corresponding parameter design methods are provided. Finally, the effectiveness of the proposed scheme is verified through a 250kW simulation.

Data quality is a critical driver of large language model performance, yet existing model-based selection methods focus almost exclusively on English, neglecting other languages that are essential in the training mix for multilingual LLMs. We introduce MuRating, a scalable framework that transfers high-quality English data-quality signals into a multilingual autorater, capable of handling 17 languages. MuRating aggregates multiple English autoraters via pairwise comparisons to learn unified document quality scores, then projects these judgments through translation to train a multilingual evaluator on monolingual, cross-lingual, and parallel text pairs. Applied to web data, MuRating selects balanced subsets of English and multilingual content to pretrain LLaMA-architecture models of 1.2B and 7B parameters. Compared to strong baselines, including QuRater, FineWeb2-HQ, AskLLM, DCLM, our approach increases average accuracy on both English benchmarks and multilingual evaluations. Extensive analyses further validate that pairwise training provides greater stability and robustness than pointwise scoring, underscoring the effectiveness of MuRating as a general multilingual data-selection framework.

Due to the epidemic COVID-19 spread and Intelligent Transportation System (ITS) development, investigators are now to conduct their research over the generated Transportation Big Data (TBD) in many critical areas, such as medical supplies, food supplies, as well as logistics supplies. At present, Vehicular Edge Computing (VEC) is an emerging paradigm to integrate resources from vehicles, road-site units, base stations, and cloud center to promote the performance of TBD tasks scheduling and running. In this paper, we design a three-layered TBD task processing architecture with a federated learning mechanism for credit priority-based task scheduling and running. In our design, we consider the efficiency of task offloading and misbehavior attack problems simultaneously. We propose a vehicular federated learning framework combined with Multi-Layer Perceptron (MLP) credit measurement, which can preserve the privacy of vehicles and obtain the related features for vehicular credit prediction. We also propose a task offloading algorithm to solve the optimization problem for credit priority task offloading between edge computing servers and vehicles. The proposed solution can prioritize tasks and assign sufficient resources for reliable and active task requesters. Experimental results expose that the proposed mechanism outperforms the state-of-the-art solutions when considering efficiency and attack simultaneously for TBD tasks scheduling and running.

Many applications of complex event processing (CEP) in Cloud can tolerate analytical errors to some extent, and it provides us an opportunity to optimize real-time analytics using methods of approximate query processing over big data streams. In this article, we present a novel rules-based sampling technique, which supports to construct sketch over one-pass and high-speed asynchronous data streams and provides accurate answers for different types of analytical queries. Moreover, we propose two methods of distributed sketching implementation, i.e., D-AQP$_b$and D-AQP$_i$, to make our approach to be compatible with batch processing and interactive processing architectures respectively, and be appropriate for stream processing systems in Cloud. Experimental results with real-world and synthetic datasets indicate that our approach can obtain more accurate estimates and improve two times of system throughput when compared with state-of-the-art Hadoop-based approximate engine BlinkDB. When compared with current batch processing systems Spark and stream processing system Spark-Streaming, our methods of D-AQP$_b$and D-AQP$_i$can achieve 2 and 4 orders of magnitude improvement on query response time respectively.

Fuzzing is a well-known technique for efficiently finding software vulnerabilities. Unfortunately, due to syntax check, even the state-of-the-art fuzzers are not very efficient at discovering hard-to-trigger bugs in applications that expect highly structured inputs. Grammar-based fuzzers, while effective, often require expert knowledge and incur significant computational overhead. In this paper, we present LAFuzz, an automated fuzzer that generates high-quality seed inputs, which utilizes a variety of deep neural network model with different setup to efficiently fuzz programs that expect structured or unstructured inputs. We achieve this by combining mutation-based fuzzing and generation-based fuzzing offline. Our evaluation on 8 popular real-world applications demonstrated that LAFuzz-LSTM and LAFuzz-Attention significantly outperform AFL, a state-of-the-art fuzzer, on most cases both at discovering more crashes and achieving higher code coverage. In total, LAFuzz-LSTM and LAFuzz-Attention can effectively improve the code coverage over AFL by 7.55% and 7.67%; and both fuzzers can consistently discover 30.19% as well as 82.39% more unique crashes. Furthermore, extensive evaluation also showed that LAFuzz provides a great compatibility and expansibility.

Cascaded H-bridge (CHB) and modular multilevel converter (MMC) are effective solutions for medium and high voltage motor drive applications. However, the choice between the two converters is not particularly clear. This paper presents a loss evaluation of cascaded H-bridge and modular multilevel converter as a reference for loss calculation. A brief description of CHB and MMC are provided and the explicit expression of conduction loss and switching loss for MMC and CHB is derived respectively to show the relationship between the loss and the main variables of MMC and CHB directly based on carrier phase-shifting PWM (CPS-PWM). In the derivation of switching loss, a novel switching loss calculation method based on PWM is proposed to simplify the theoretical calculation process. Finally, the total loss comparison of the two converters based on different conditions are shown for a better selection in motor drive application.

Recently, modular multilevel converters (MMC) have attracted extensive research interest in the field of HVDC and motor drives. However in motor drives, it appears that the inherent problem of the fluctuation of capacitor voltage particularly at low frequencies limits its further application. This paper proposes a new back-to-back hybrid MMCs topology, of which one MMC is directly connected to the grid and another MMC that controlled to drive an AC motor is connected to the previous one with a DC bus. This paper proposes the corresponding control strategies to drive the motor in wide speed range with basically constant fluctuation of capacitor voltage under constant-load operation at the same time. The grid-side MMC is controlled to generate constant DC current to the DC bus while the motor-side MMC is regulated to generate a constant AC output current to maintain the load torque. The effectiveness of the topology and its control strategies are verified by Matlab/Simulink.

The series HVDC tap based on modular multilevel converter (MMC) has been proposed to provide power supply for rural places. However, most of current capacitor voltage balancing control strategies are not suitable for series MMC tap. In this paper, the principle of power transfer between phases, arms and sub-modules (SMs) in a series MMC are analyzed. A hierarchical capacitor voltage balancing control strategy is then proposed which can automatically balance the voltage of each SM capacitor. Particularly, the control layers are decoupled from each other to improve the stability of the system. Finally, correctness of the proposed control strategy is proved by simulation results.

In multi-view learning applications, like multimedia analysis and information retrieval, we often encounter the corrupted view problem in which the data are corrupted by two different types of noises, i.e., the intra- and inter-view noises. The noises may affect these applications that commonly acquire complementary representations from different views. Therefore, how to denoise corrupted views from multi-view data is of great importance for applications that integrate and analyze representations from different views. However, the heterogeneity among multi-view representations brings a significant challenge on denoising corrupted views. To address this challenge, we propose a general framework to jointly denoise corrupted views in this paper. Specifically, aiming at capturing the semantic complementarity and distributional similarity among different views, a novel Heterogeneous Linear Metric Learning (HLML) model with low-rank regularization, leave-one-out validation, and pseudo-metric constraints is proposed. Our method linearly maps multi-view data to a high-dimensional feature-homogeneous space that embeds the complementary information from different views. Furthermore, to remove the intra- and inter-view noises, we present a new Multi-view Semi-supervised Collaborative Denoising (MSCD) method with elementary transformation constraints and gradient energy competition to establish the complementary relationship among the heterogeneous representations. Experimental results demonstrate that our proposed methods are effective and efficient.