Frontiers of Information Technology & Electronic Engineering最新文献

Electronic healthcare systems can offer convenience but face the risk of data forgery and information leakage. To solve these issues, we propose an identity-based searchable attribute signcryption in lattice for a blockchain-based medical system (BCMS-LIDSASC). BCMS-LIDSASC achieves decentralization and anti-quantum security in the blockchain environment, and provides fine-grained access control and searchability. Furthermore, smart contracts are used to replace traditional trusted third parties, and the interplanetary file system (IPFS) is used for ciphertext storage to alleviate storage pressure on the blockchain. Compared to other schemes, BCMS-LIDSASC requires smaller key size and less storage, and has lower computation cost. It contributes to secure and efficient management of medical data and can protect patient privacy and ensure the integrity of electronic healthcare systems.

提出旨在增强智能教育的大型语言与领域特定模型协作(LDMC)框架。LDMC框架充分利用大型领域通用模型的综合全面知识,将其与小型领域特定模型的专业和学科知识相结合,并融入来自学习理论模型的教育学知识。这种整合产生的多重知识表达促进了个性化和自适应的教育体验。在智能教育背景下探讨了LDMC框架的各种应用,包括群体学习、个性化辅导、课堂管理等。LDMC融合了多种规模模型的智能,代表了一种先进而全面的教育辅助框架。随着人工智能的不断发展,该框架有望在智慧教育领域展现较大潜力。

Graph neural networks (GNNs) have achieved remarkable performance in a variety of graph-related tasks. Recent evidence in the GNN community shows that such good performance can be attributed to the homophily prior; i.e., connected nodes tend to have similar features and labels. However, in heterophilic settings where the features of connected nodes may vary significantly, GNN models exhibit notable performance deterioration. In this work, we formulate this problem as prior-data conflict and propose a model called the mixture-prior graph neural network (MPGNN). First, to address the mismatch of homophily prior on heterophilic graphs, we introduce the non-informative prior, which makes no assumptions about the relationship between connected nodes and learns such relationship from the data. Second, to avoid performance degradation on homophilic graphs, we implement a soft switch to balance the effects of homophily prior and non-informative prior by learnable weights. We evaluate the performance of MPGNN on both synthetic and real-world graphs. Results show that MPGNN can effectively capture the relationship between connected nodes, while the soft switch helps select a suitable prior according to the graph characteristics. With these two designs, MPGNN outperforms state-of-the-art methods on heterophilic graphs without sacrificing performance on homophilic graphs.

Abstract

In the post-Moore era, the development of active phased array antennas will inevitably trend towards active array microsystems. In this paper, the characteristics and composition of the active array antenna are briefly described. Owing to the high efficiency, low profile, and light weight of the active array microsystems, the application prospects and advantages in the engineering of multi-functional airborne radar, spaceborne radar, and communication systems are analyzed. Moreover, according to the characteristics of the post-Moore era of integrated circuits, scientific and technological problems in the active array microsystems are presented, including multi-scale, multi-signal, and multi-physics field coupling. The challenges are also discussed, such as new architectures and algorithms, miniaturization of passive components, novel materials and processes, ultra-wideband technology, and new interdisciplinary technological applications. This paper is expected to inspire in-depth research on active array microsystems.

Abstract

To realize low harmonic distortion of the vibration waveform output from electromagnetic vibrators, we propose a vibration harmonic suppression technology based on an improved sensorless feedback control method. Without changing the original driving circuit, the alternating current (AC) equivalent resistance of the driving coil is used to obtain high-precision vibration velocity information, and then a simple and reliable velocity feedback control system is established. Through the study of the effect of different values of key parameters on the system, we have achieved an effective expansion of the velocity characteristic frequency band of low-frequency vibration, resulting in an enhanced harmonic suppression capability of velocity feedback control. We present extensive experiments to prove the effectiveness of the proposed method and make comparisons with conventional control methods. In the frequency range of 0.01–1.00 Hz, without using any sensors, the method proposed in this study can reduce the harmonic distortion of the vibration waveform by about 40% compared to open-loop control and by about 20% compared to a conventional sensorless feedback control method.

This paper investigates the problem of outlier-resistant distributed fusion filtering (DFF) for a class of multi-sensor nonlinear singular systems (MSNSSs) under a dynamic event-triggered scheme (DETS). To relieve the effect of measurement outliers in data transmission, a self-adaptive saturation function is used. Moreover, to further reduce the energy consumption of each sensor node and improve the efficiency of resource utilization, a DETS is adopted to regulate the frequency of data transmission. For the addressed MSNSSs, our purpose is to construct the local outlier-resistant filter under the effects of the measurement outliers and the DETS; the local upper bound (UB) on the filtering error covariance (FEC) is derived by solving the difference equations and minimized by designing proper filter gains. Furthermore, according to the local filters and their UBs, a DFF algorithm is presented in terms of the inverse covariance intersection fusion rule. As such, the proposed DFF algorithm has the advantages of reducing the frequency of data transmission and the impact of measurement outliers, thereby improving the estimation performance. Moreover, the uniform boundedness of the filtering error is discussed and a corresponding sufficient condition is presented. Finally, the validity of the developed algorithm is checked using a simulation example.

元宇宙可被视为一个社会化和虚拟化的网络空间, 与现实世界平行但互动. 得益于云计算和数字孪生的快速发展, 元宇宙正在将带有传统控制和滤波范式的工业自动化系统转变为信息物理社会融合系统. 在此情况下, 未来的工业自动化系统可能是在一定时间和空间范围内具有强大计算能力的现实世界系统与虚拟孪生系统的集成. 在该领域中, 元宇宙重点涉及信息传输管理、 用户的行为识别以及控制、 滤波和决策, 且性能和成本将是系统在网络空间和现实世界中行为的综合反映. 毫无疑问, 虚拟网络空间和现实世界之间的信息交换的内在特征, 唤起了对安全控制和滤波的新需求. 如何保证期望的系统性能和实现理想的参数设计, 已然成为该领域研究的重大挑战. 然而, 目前对元宇宙的研究主要集中在解决其社会关切和寻求其在各个领域的应用, 其在控制领域的发展仍处于初级阶段.

当网络空间与现实世界交换感兴趣的信息时, 恶意攻击不可避免, 这可能导致数据不可靠或隐私泄露. 因此, 定义合适的评估标准来揭示网络攻击的影响, 并提供面向工程的安全控制和滤波方案, 具有重要意义. 本期专题旨在推进工业元宇宙中安全控制与滤波的理论研究和技术开发, 服务于智能制造的发展需要.

感谢所有作者对此专题的贡献, 感谢所有为作者提供宝贵意见的评审人. 衷心感谢《信息与电子工程前沿(英文)》期刊执行副主编王飞跃教授鼓励我们组织本期专题, 以及编辑人员的全程支持.

This paper is concerned with the scaled formation control problem for multi-agent systems (MASs) over fixed and switching topologies. First, a modified resilient dynamic event-triggered (DET) mechanism involving an auxiliary dynamic variable (ADV) based on sampled data is proposed. In the proposed DET mechanism, a random variable obeying the Bernoulli distribution is introduced to express the idle and busy situations of communication networks. Meanwhile, the operation of absolute value is introduced into the triggering condition to effectively reduce the formation error. Second, a scaled formation control protocol with the proposed resilient DET mechanism is designed over fixed and switching topologies. The scaled formation error system is modeled as a time-varying delay system. Then, several sufficient stability criteria are derived by constructing appropriate Lyapunov–Krasovskii functionals (LKFs). A co-design algorithm based on the sparrow search algorithm (SSA) is presented to design the control gains and triggering parameters jointly. Finally, numerical simulations of multiple unmanned aerial vehicles (UAVs) are presented to validate the designed control method.

Text generation is an essential research area in artificial intelligence (AI) technology and natural language processing and provides key technical support for the rapid development of AI-generated content (AIGC). It is based on technologies such as natural language processing, machine learning, and deep learning, which enable learning language rules through training models to automatically generate text that meets grammatical and semantic requirements. In this paper, we sort and systematically summarize the main research progress in text generation and review recent text generation papers, focusing on presenting a detailed understanding of the technical models. In addition, several typical text generation application systems are presented. Finally, we address some challenges and future directions in AI text generation. We conclude that improving the quality, quantity, interactivity, and adaptability of generated text can help fundamentally advance AI text generation development.

Prompt learning has attracted broad attention in computer vision since the large pre-trained vision-language models (VLMs) exploded. Based on the close relationship between vision and language information built by VLM, prompt learning becomes a crucial technique in many important applications such as artificial intelligence generated content (AIGC). In this survey, we provide a progressive and comprehensive review of visual prompt learning as related to AIGC. We begin by introducing VLM, the foundation of visual prompt learning. Then, we review the vision prompt learning methods and prompt-guided generative models, and discuss how to improve the efficiency of adapting AIGC models to specific downstream tasks. Finally, we provide some promising research directions concerning prompt learning.