Frontiers of Information Technology & Electronic Engineering最新文献

This paper investigates the recoil control of the deepwater drilling riser system with nonlinear tension force and energy-bounded friction force under the circumstances of limited network resources and unreliable communication. Different from the existing linearization modeling method, a triangle-based polytope modeling method is applied to the nonlinear riser system. Based on the polytope model, to improve resource utilization and accommodate random data loss and communication delay, an asynchronous gain-scheduled control strategy under a hybrid event-triggered scheme is proposed. An asynchronous linear parameter-varying system that blends input delay and impulsive update equation is presented to model the nonlinear networked recoil control system, where the asynchronous deviation bounds of scheduling parameters are calculated. Resorting to the Lyapunov–Krasovskii functional method, some solvable conditions of disturbance attenuation analysis and recoil control design are derived such that the resulting networked system is exponentially mean-square stable with prescribed H_∞ performance. The obtained numerical results verified that the proposed nonlinear networked control method can achieve a better recoil response of the riser system with less transmission data compared with the linear control method.

Abstract

Smart city situational awareness has recently emerged as a hot topic in research societies, industries, and governments because of its potential to integrate cutting-edge information technology and solve urgent challenges that modern cities face. For example, in the latest five-year plan, the Chinese government has highlighted the demand to empower smart city management with new technologies such as big data and Internet of Things, for which situational awareness is normally the crucial first step. While traditional static surveillance data on cities have been available for decades, this review reports a type of relatively new yet highly important urban data source, i.e., the big mobile data collected by devices with various levels of mobility representing the movement and distribution of public and private agents in the city. We especially focus on smart city situational awareness enabled by synthesizing the localization of hundreds of thousands of mobile software Apps using the Global Positioning System (GPS). This technique enjoys advantages such as a large penetration rate (∼50% urban population covered), uniform spatiotemporal coverage, and high localization precision. We first discuss the pragmatic requirements for smart city situational awareness and the challenges faced. Then we introduce two suites of empowering technologies that help fulfill the requirements of (1) cybersecurity insurance for smart cities and (2) spatiotemporal modeling and visualization for situational awareness, both via big mobile data. The main contributions of this review lie in the description of a comprehensive technological framework for smart city situational awareness and the demonstration of its feasibility via real-world applications.

Abstract

Consensus is one of the fundamental distributed control technologies for collaboration in multi-agent systems such as collaborative handling in intelligent manufacturing. In this paper, we study the problem of resilient average consensus for multi-agent systems with misbehaving nodes. To protect consensus value from being influenced by misbehaving nodes, we address this problem by detecting misbehaviors, mitigating the corresponding adverse impact, and achieving the resilient average consensus. General types of misbehaviors are considered, including attacks, accidental faults, and link failures. We characterize the adverse impact of misbehaving nodes in a distributed manner via two-hop communication information and develop a deterministic detection compensation based consensus (D-DCC) algorithm with a decaying fault-tolerant error bound. Considering scenarios wherein information sets are intermittently available due to link failures, a stochastic extension named stochastic detection compensation based consensus (S-DCC) algorithm is proposed. We prove that D-DCC and S-DCC allow nodes to asymptotically achieve resilient accurate average consensus and unbiased resilient average consensus in a statistical sense, respectively. Then, the Wasserstein distance is introduced to analyze the accuracy of S-DCC. Finally, extensive simulations are conducted to verify the effectiveness of the proposed algorithms.

Diffusion models, a family of generative models based on deep learning, have become increasingly prominent in cutting-edge machine learning research. With distinguished performance in generating samples that resemble the observed data, diffusion models are widely used in image, video, and text synthesis nowadays. In recent years, the concept of diffusion has been extended to time-series applications, and many powerful models have been developed. Considering the deficiency of a methodical summary and discourse on these models, we provide this survey as an elementary resource for new researchers in this area and to provide inspiration to motivate future research. For better understanding, we include an introduction about the basics of diffusion models. Except for this, we primarily focus on diffusion-based methods for time-series forecasting, imputation, and generation, and present them, separately, in three individual sections. We also compare different methods for the same application and highlight their connections if applicable. Finally, we conclude with the common limitation of diffusion-based methods and highlight potential future research directions.

Cross-lingual summarization (CLS) is the task of generating a summary in a target language from a document in a source language. Recently, end-to-end CLS models have achieved impressive results using large-scale, high-quality datasets typically constructed by translating monolingual summary corpora into CLS corpora. However, due to the limited performance of low-resource language translation models, translation noise can seriously degrade the performance of these models. In this paper, we propose a fine-grained reinforcement learning approach to address low-resource CLS based on noisy data. We introduce the source language summary as a gold signal to alleviate the impact of the translated noisy target summary. Specifically, we design a reinforcement reward by calculating the word correlation and word missing degree between the source language summary and the generated target language summary, and combine it with cross-entropy loss to optimize the CLS model. To validate the performance of our proposed model, we construct Chinese-Vietnamese and Vietnamese-Chinese CLS datasets. Experimental results show that our proposed model outperforms the baselines in terms of both the ROUGE score and BERTScore.

目前文本生成图像的研究已显示出与普通画家类似的水平,但与艺术家绘画水平相比仍有很大改进空间;艺术家水平的绘画通常将多个意象的特征融合到一个意象中,以表示多层次语义信息。在预实验中,我们证实了这一点,并咨询了3个具有不同艺术欣赏能力的群体的意见,以确定画家和艺术家之间绘画水平的区别。之后,利用这些观点帮助人工智能绘画系统从普通画家水平的图像生成改进为艺术家水平的图像生成。具体来说,提出一种无需任何进一步预训练的、基于文本的多阶段引导方法,帮助扩散模型在生成的图像中向多层次语义表示迈进。实验中的机器和人工评估都验证了所提方法的有效性。此外,与之前单阶段引导方法不同,该方法能够通过控制不同阶段之间的指导步数来控制各个意象特征在绘画中的表现程度。

We demonstrate a low-noise, high-gain, and large-dynamic-range photodetector (PD) based on a junction field-effect transistor (JFET) and a charge amplifier for the measurement of quantum noise in Bell-state detection (BSD). Particular photodiode junction capacitance allows the silicon N-channel JFET 2sk152 to be matched to the noise requirement for charge amplifier A250. The electronic noise of the PD is effectively suppressed and the signal-to-noise ratio (SNR) is up to 15 dB at the analysis frequency of 2.75 MHz for a coherent laser power of 50.08 µW. By combining of the inductor and capacitance, the alternating current (AC) and direct current (DC) branches of the PD can operate linearly in a dynamic range from 25.06 µW to 17.50 mW. The PD can completely meet the requirements of SNR and dynamic range for BSD in quantum optics experiments.

Cyber-physical systems (CPSs) take on the characteristics of both multiple rates of information collection and processing and the dependency on information exchanges. The purpose of this paper is to develop a joint recursive filtering scheme that estimates both unknown inputs and system states for multi-rate CPSs with unknown inputs. In cyberspace, the information transmission between the local joint filter and the sensors is governed by an adaptive event-triggered strategy. Furthermore, the desired parameters of joint filters are determined by a set of algebraic matrix equations in a recursive way, and a sufficient condition verifying the boundedness of filtering error covariance is found by resorting to some algebraic operation. A state fusion estimation scheme that uses local state estimation is proposed based on the covariance intersection (CI) based fusion conception. Lastly, an illustrative example demonstrates the effectiveness of the proposed adaptive event-triggered recursive filtering algorithm.

In this paper, the distributed optimization problem is investigated for a class of general nonlinear model-free multi-agent systems. The dynamical model of each agent is unknown and only the input/output data are available. A model-free adaptive control method is employed, by which the original unknown nonlinear system is equivalently converted into a dynamic linearized model. An event-triggered consensus scheme is developed to guarantee that the consensus error of the outputs of all agents is convergent. Then, by means of the distributed gradient descent method, a novel event-triggered model-free adaptive distributed optimization algorithm is put forward. Sufficient conditions are established to ensure the consensus and optimality of the addressed system. Finally, simulation results are provided to validate the effectiveness of the proposed approach.

The performance of existing maneuvering target tracking methods for highly maneuvering targets in cluttered environments is unsatisfactory. This paper proposes a hybrid-driven approach for tracking multiple highly maneuvering targets, leveraging the advantages of both data-driven and model-based algorithms. The time-varying constant velocity model is integrated into the Gaussian process (GP) of online learning to improve the performance of GP prediction. This integration is further combined with a generalized probabilistic data association algorithm to realize multi-target tracking. Through the simulations, it has been demonstrated that the hybrid-driven approach exhibits significant performance improvements in comparison with widely used algorithms such as the interactive multi-model method and the data-driven GP motion tracker.