Frontiers of Information Technology & Electronic Engineering最新文献_第9页

Asynchronous gain-scheduled control of deepwater drilling riser system with hybrid event-triggered sampling and unreliable communication 具有混合事件触发采样和不可靠通信功能的深水钻井立管系统的异步增益调度控制

IF 3 3区工程技术

Frontiers of Information Technology & Electronic Engineering Pub Date : 2023-12-29 DOI: 10.1631/fitee.2300625

Na Pang, Dawei Zhang, Shuqian Zhu

{"title":"Asynchronous gain-scheduled control of deepwater drilling riser system with hybrid event-triggered sampling and unreliable communication","authors":"Na Pang, Dawei Zhang, Shuqian Zhu","doi":"10.1631/fitee.2300625","DOIUrl":"https://doi.org/10.1631/fitee.2300625","url":null,"abstract":"<p>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 <i>H</i><sub>∞</sub> 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.</p>","PeriodicalId":12608,"journal":{"name":"Frontiers of Information Technology & Electronic Engineering","volume":"1 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139063507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Empowering smart city situational awareness via big mobile data 通过移动大数据增强智慧城市态势感知能力

IF 3 3区工程技术

Frontiers of Information Technology & Electronic Engineering Pub Date : 2023-12-29 DOI: 10.1631/fitee.2300453

{"title":"Empowering smart city situational awareness via big mobile data","authors":"","doi":"10.1631/fitee.2300453","DOIUrl":"https://doi.org/10.1631/fitee.2300453","url":null,"abstract":"<h3>Abstract</h3> <p>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.</p>","PeriodicalId":12608,"journal":{"name":"Frontiers of Information Technology & Electronic Engineering","volume":"22 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139068430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards resilient average consensus in multi-agent systems: a detection and compensation approach 实现多代理系统中的弹性平均共识：一种检测和补偿方法

IF 3 3区工程技术

Frontiers of Information Technology & Electronic Engineering Pub Date : 2023-12-28 DOI: 10.1631/fitee.2300467

{"title":"Towards resilient average consensus in multi-agent systems: a detection and compensation approach","authors":"","doi":"10.1631/fitee.2300467","DOIUrl":"https://doi.org/10.1631/fitee.2300467","url":null,"abstract":"<h3>Abstract</h3> <p>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.</p>","PeriodicalId":12608,"journal":{"name":"Frontiers of Information Technology & Electronic Engineering","volume":"43 7 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139063610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Diffusion models for time-series applications: a survey 时间序列应用的扩散模型：调查

IF 3 3区工程技术

Frontiers of Information Technology & Electronic Engineering Pub Date : 2023-12-28 DOI: 10.1631/fitee.2300310

Lequan Lin, Zhengkun Li, Ruikun Li, Xuliang Li, Junbin Gao

{"title":"Diffusion models for time-series applications: a survey","authors":"Lequan Lin, Zhengkun Li, Ruikun Li, Xuliang Li, Junbin Gao","doi":"10.1631/fitee.2300310","DOIUrl":"https://doi.org/10.1631/fitee.2300310","url":null,"abstract":"<p>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.</p>","PeriodicalId":12608,"journal":{"name":"Frontiers of Information Technology & Electronic Engineering","volume":"1 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139063505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing low-resource cross-lingual summarization from noisy data with fine-grained reinforcement learning 利用细粒度强化学习从嘈杂数据中增强低资源跨语言摘要能力

IF 3 3区工程技术

Frontiers of Information Technology & Electronic Engineering Pub Date : 2023-12-27 DOI: 10.1631/fitee.2300296

Yuxin Huang, Huailing Gu, Zhengtao Yu, Yumeng Gao, Tong Pan, Jialong Xu

{"title":"Enhancing low-resource cross-lingual summarization from noisy data with fine-grained reinforcement learning","authors":"Yuxin Huang, Huailing Gu, Zhengtao Yu, Yumeng Gao, Tong Pan, Jialong Xu","doi":"10.1631/fitee.2300296","DOIUrl":"https://doi.org/10.1631/fitee.2300296","url":null,"abstract":"<p>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.</p>","PeriodicalId":12608,"journal":{"name":"Frontiers of Information Technology & Electronic Engineering","volume":"23 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139063504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multistage guidance on the diffusion model inspired by human artists’ creative thinking 从人类艺术家的创造性思维中汲取灵感，对扩散模型进行多阶段指导

IF 3 3区工程技术

Frontiers of Information Technology & Electronic Engineering Pub Date : 2023-12-27 DOI: 10.1631/fitee.2300313

Wang Qi, Huanghuang Deng, Taihao Li

引用次数: 0

A low-noise, high-gain, and large-dynamic-range photodetector based on a JFET and a charge amplifier 基于 JFET 和电荷放大器的低噪声、高增益和大动态范围光电探测器

IF 3 3区工程技术

Frontiers of Information Technology & Electronic Engineering Pub Date : 2023-12-27 DOI: 10.1631/fitee.2300340

Jinrong Wang, Shuang’e Wu, Chengdong Mi, Yaner Qiu, Xin’ai Bai

引用次数: 0

Recursive filtering of multi-rate cyber-physical systems with unknown inputs under adaptive event-triggered mechanisms 在自适应事件触发机制下对具有未知输入的多速率网络物理系统进行递归过滤

IF 3 3区工程技术

Frontiers of Information Technology & Electronic Engineering Pub Date : 2023-12-27 DOI: 10.1631/fitee.2300565

Ying Sun, Miaomiao Fu, Jingyang Mao, Guoliang Wei

引用次数: 0

Event-triggered distributed optimization for model-free multi-agent systems 无模型多代理系统的事件触发分布式优化

IF 3 3区工程技术

Frontiers of Information Technology & Electronic Engineering Pub Date : 2023-12-12 DOI: 10.1631/fitee.2300568

Shanshan Zheng, Shuai Liu, Licheng Wang

引用次数: 0

Hybrid-driven Gaussian process online learning for highly maneuvering multi-target tracking 用于高机动性多目标跟踪的混合驱动高斯过程在线学习

IF 3 3区工程技术

Frontiers of Information Technology & Electronic Engineering Pub Date : 2023-12-07 DOI: 10.1631/fitee.2300348

Qiang Guo, Long Teng, Tianxiang Yin, Yunfei Guo, Xinliang Wu, Wenming Song

引用次数: 0