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Recursive Feasibility of Continuous-Time Model Predictive Control Without Stabilising Constraints

IEEE CONTROL SYSTEMS LETTERS 2021年第1期5卷 265-270页

作者： Esterhuizen, Willem Worthmann, Karl Streif, Stefan Tech Univ Chemnitz Automat Control & Syst Dynam Lab D-09126 Chemnitz Germany Tech Univ Ilmenau Inst Math D-98693 Ilmenau Germany

We consider sampled-data Model Predictive Control (MPC) of nonlinear continuous-time control systems. We derive sufficient conditions to guarantee recursive feasibility and asymptotic stability without stabilising costs and/or constraints. Moreover, we present formulas to explicitly estimate the required length of the prediction horizon based on the concept of (local) cost controllability. For the linear-quadratic case, cost controllability can be inferred from standard assumptions. In addition, we extend results on the relationship between the horizon length and the distance of the initial state to the boundary of the viability kernel from the discrete-time to the continuous-time setting.

关键词： Asymptotic stability Kernel Controllability Predictive control Optimization prediction algorithms Model predictive control state constraints recursive feasibility asymptotic stability sampled-data systems

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BUFFER CONSIDERATIONS FOR DATA COMPRESSION OF NONSTATIONARY VIDEO DATA

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IEEE TRANSACTIONS ON COMMUNICATION TECHNOLOGY 1970年第2期CO18卷 136-&页

作者： POPP, DJ McDonnell Douglas Astronautics Company Saint Louis MO USA

Including a buffer storage into a telemetry data compression system has proven to be both a requirement and a nemesis. Some video compression simulation results which help define buffer requirements for transmitted bit-rate reductions of 2:1 and 3:1 are summarized. The zero-order predictor algorithm with a buffer-controlled variable aperture was found to provide both efficient buffer utilization and acceptable means of controlling buffer "underflow" and "overflow." The investigation was limited to worst-case nonstationary video data.

关键词： Data compression Video compression Telemetry Buffer storage Computational modeling Communications technology Statistical distributions Signal to noise ratio Buffer overflow prediction algorithms

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ML-Powered KQI Estimation for XR Services: A Case Study on 360-Video

IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY

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IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY 2024年 5卷 4075-4097页

作者： Penaherrera-Pulla, O. S. Baena, Carlos Fortes, Sergio Barco, Raquel Univ Malaga Telecommun Res Inst TELMA ETS Ingn Telecomunicac Malaga 29010 Spain

The emergence of cutting-edge technologies and services such as Extended Reality (XR) promises to change how people approach everyday living. At the same time, the emergence of modern and decentralized architectural approaches has ushered in a new generation of mobile networks, such as 5G, as well as outlining the roadmap for B5G (Beyond-5G) and further advancements. These networks are expected to be the enablers for the realization of the metaverse and other futuristic services. In this context, quantifying the service performance is a key enabler for dynamic, environment-adaptive, and proactive network management. This work presents an ML-based (Machine Learning) framework that uses data from the network, such as radio measurements, statistics, and configuration parameters to infer the best ML models that fit diverse XR Key Quality Indicators (KQIs). The output models integrate feature engineering techniques that enhance model size and performance. The proposed framework comprises data preprocessing, model definition, training, tuning, and validation. Additionally, to select the best combination algorithm this work introduces a metric called PET_{score}, which evaluates algorithm candidates in terms of error performance and prediction time. These are considerations that are needed for time-sensitive services like XR's. To validate our proposal, the 360-video service has been chosen to demonstrate the potential of this ML framework with a real XR use case. In addition, the dataset generated for the use case evaluation is publicly accessible and properly referenced. Furthermore, this work serves as a foundation for future research on end-to-end (E2E) quality of experience (QoE)-based network management in conjunction with other enabling technologies, including network slicing, virtualization, and multi-access edge computing (MEC).

关键词： Extended reality Quality of experience Measurement 5G mobile communication Solid modeling prediction algorithms Biological system modeling Machine learning mobile communications extended reality virtual reality quality of experience multimedia 360-video key quality indicators 5G B5G

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ASYMPTOTIC-BEHAVIOR OF THE EXTENDED KALMAN FILTER AS A PARAMETER ESTIMATOR FOR LINEAR-SYSTEMS

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 1979年第1期24卷 36-50页

作者： LJUNG, L Department of Electrical Engineering Linköping University Linkoping Sweden

The extended Kalman filter is an approximate filter for nonlinear systems, based on first-order linearization. Its use for the joint parameter and state estimation problem for linear systems with unknown parameters is well known and widely spread. Here a convergence analysis of this method is given. It is shown that in general, the estimates may be biased or divergent and the causes for this are displayed. Some common special cases where convergence is guaranteed are also given. The analysis gives insight into the convergence mechanisms and it is shown that with a modification of the algorithm, global convergence results can be obtained for a general case. The scheme can then be interpreted as maximization of the likelihood function for the estimation problem, or as a recursive prediction error algorithm.

关键词： Parameter estimation Linear systems Convergence State estimation Nonlinear filters Nonlinear systems Estimation theory Recursive estimation Algorithm design and analysis prediction algorithms

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Integrating functional scoring and regulatory data to predict the effect of non-coding SNPs in a complex neurological disease

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BRIEFINGS IN FUNCTIONAL GENOMICS 2024年第2期23卷 138-149页

作者： Felicio, Daniela Alves-Ferreira, Miguel Santos, Mariana Quintas, Marlene Lopes, Alexandra M. Lemos, Carolina Pinto, Nadia Martins, Sandra Univ Porto ICBAS Mol & Cell Biol Doctoral Program Porto Portugal CGPP IBMC i3 Porto Portugal Univ Porto IBMC i3S Porto Portugal Univ Porto Inst Biomed Sci Porto Portugal IPATIMUP i3 Porto Portugal

Most SNPs associated with complex diseases seem to lie in non-coding regions of the genome;however, their contribution to gene expression and disease phenotype remains poorly understood. Here, we established a workflow to provide assistance in prioritising the functional relevance of non-coding SNPs of candidate genes as susceptibility loci in polygenic neurological disorders. To illustrate the applicability of our workflow, we considered the multifactorial disorder migraine as a model to follow our step-by-step approach. We annotated the overlap of selected SNPs with regulatory elements and assessed their potential impact on gene expression based on publicly available prediction algorithms and functional genomics information. Some migraine risk loci have been hypothesised to reside in non-coding regions and to be implicated in the neurotransmission pathway. In this study, we used a set of 22 non-coding SNPs from neurotransmission and synaptic machinery-related genes previously suggested to be involved in migraine susceptibility based on our candidate gene association studies. After prioritising these SNPs, we focused on non-reported ones that demonstrated high regulatory potential: (1) VAMP2_rs1150 (3 ' UTR) was predicted as a target of hsa-mir-5010-3p miRNA, possibly disrupting its own gene expression;(2) STX1A_rs6951030 (proximal enhancer) may affect the binding affinity of zinc-finger transcription factors (namely ZNF423) and disturb TBL2 gene expression;and (3) SNAP25_rs2327264 (distal enhancer) expected to be in a binding site of ONECUT2 transcription factor. This study demonstrated the applicability of our practical workflow to facilitate the prioritisation of potentially relevant non-coding SNPs and predict their functional impact in multifactorial neurological diseases.

关键词： migraine susceptibility non-coding SNPs regulatory annotation epigenomic databases prediction algorithms

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Assessing the effect of underreporting energy intake on dietary patterns and weight status

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JOURNAL OF THE AMERICAN DIETETIC ASSOCIATION 2007年第1期107卷 64-71页

作者： Bailey, Regan L. Mitchell, Diane C. Miller, Carla Smiciklas-Wright, Helen Penn State Univ Dept Nutr Sci University Pk PA 16801 USA Penn State Univ Penn State Diabet Res Ctr University Pk PA 16802 USA Penn State Univ Diet Assessment Ctr University Pk PA 16802 USA

Objectives To identify misreporting among older rural adults using a prediction algorithm and to compare dietary patterns of underreporters and plausible reporters. Design In this cross-sectional study, diet information was assessed by five 24-hour recalls collected over 10 months. All foods were classified into 24 food subgroups. Demographic, health, and anthropometric data were collected via home visit. Setting Rural Pennsylvania. Subjects One hundred seventy-nine community-dwelling adults, aged 66 to 87 years. Statistical analysis Cluster analysis. Results Underreporters (n=43) were more likely than plausible reporters (n=133) to be overweight and less educated but did not differ by sex. Underreporters consumed fewer servings across the majority of food groups. Two dietary patterns were determined for all and plausible reporters, in both cases one of higher and one of lower nutrient density. Using only plausible reporters to determine dietary patterns was very similar to using all reporters. The correlation between energy intake and weight status was improved for plausible-reporting women, but not men. Conclusions Dietary patterns of plausible reporters were generally similar to that of all reporters;however, correlations with energy intake and weight status improved for women using only plausible reporters. Individuals may not accurately report dietary intake. Those obtaining diet reports should be aware of reporting errors before making decisions about dietary adequacy.

关键词： Body Weight Diet Energy Intake Nutrition Assessment Self Disclosure 体重膳食能量摄取营养评价自我揭露 Energy Intake Self Disclosure prediction algorithms Food Habits Body Weight Meals Journalists Nutrition Assessment

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Impact of Pre-existing Comorbidities on Long-term Outcomes in Kidney Transplant Recipients

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TRANSPLANTATION PROCEEDINGS 2018年第10期50卷 3232-3241页

作者： Kleinsteuber, A. Halleck, F. Khadzhynov, D. Staeck, A. Lehner, L. Duerr, M. Glander, P. Schmidt, D. Budde, K. Staeck, O. Charite Univ Med Berlin Div Nephrol & Internal Intens Care Med Berlin Germany

Background. Outcomes of patients with end-stage renal disease are mainly affected by their comorbidities. Detailed data evaluating the impact of pre-transplant comorbidities on long-term outcome after kidney transplantation are largely missing. Methods. In a long-term retrospective analysis, we investigated 839 deceased donor kidney transplant recipients (KTRs) who received transplants between 1999 and 2014. The prevalence and impact of the most relevant comorbidities were studied in detail. Results. At the time of transplantation, 25% of KTRs had coronary artery disease (CAD), 16% had diabetes mellitus (DM), 11% had peripheral arterial disease (PAD), 8% had chronic heart failure (CHF), and 7% had cerebrovascular disease (CVD). KTRs with pre-existing CAD, DM, PAD, and CHF showed a significantly inferior patient survival. Multivariate analysis adjusting for all relevant factors and comorbidities confirmed CAD as most hazardous independent risk factor for premature death (hazard ratio [HR] 1.70;P =.002). A multivariate analysis revealed CHF and PAD as independent risk factors for death censored graft loss (HR 2.20;P =.003 and HR 1.80;P =.013). Diabetes was independently and significantly associated with T-cell- (HR 1.46;P =.020) and antibody-mediated rejections (HR 2.27;P =.030). Conclusions. Detailed quantification of the impact of pre-transplant comorbidities may facilitate the evaluation of transplant candidates, guide post-transplant follow-up, and may help to further refine prediction algorithms and allocation systems.

关键词： PAD Comorbidity PADI4 gene Risk Factors DFFB gene Kidney Transplantation Transplant Recipient DHX40 gene Heart Rate Congestive Heart Failure Computer-Aided Design prediction algorithms Transplants

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Research on Escaping the Big-Data Traps in O2O Service Recommendation Strategy

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IEEE TRANSACTIONS ON BIG DATA 2021年第1期7卷 199-213页

作者： Xiao Xue Shuai Huangfu Zhang, Lejun Wang, Shufang Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China Henan Polytech Univ Sch Comp Sci & Technol Jiaozuo 454010 Henan Peoples R China Henan Polytech Univ Jiaozuo 454010 Henan Peoples R China Yangzhou Univ Sch Comp Sci & Technol Yangzhou Jiangsu Peoples R China Tianjin Normal Univ Tianjin Peoples R China

Internet business can be divided into two categories: pure online business and Online to Offline (O2O) business. Currently, the recommendation technology for online business is maturing, such as news, movies, products, and so forth. However, traditional recommendation technology can easily cause the overcrowding at some O2O services because of the big data traps. In the end, the users' experience with the O2O service recommendation is useless or very poor because they have to wait for a long time and can't enjoy the service immediately. Hence, how to improve the performance of O2O service recommendation has become a vital problem. To solve the problem, this paper proposes a research framework based on the continuous feedback learning mechanism between cyber layer and social layer. Then, the continuous feedback ideas are implemented in the design of the O2O service recommendation strategy step by step. Furthermore, the computational experiment system is constructed to perform performance analysis of these service strategies. The results show that our research framework is conductive to help O2O service recommendation to escape the big-data traps and to improve user experience.

关键词： Big Data Business prediction algorithms Clustering algorithms Learning systems Collaboration Filtering algorithms Online to offline continuous feedback learning service recommendation strategy computational experiment

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Parallel MPC for Linear Systems With State and Input Constraints

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IEEE CONTROL SYSTEMS LETTERS 2022年 7卷 229-234页

作者： Shi, Jiahe Jiang, Yuning Oravec, Juraj Houska, Boris ShanghaiTech Univ Sch Informat Sci & Technol Shanghai 201210 Peoples R China Ecole Polytech Fed Lausanne Automat Control Lab CH-1015 Lausanne Switzerland Slovak Univ Technol Bratislava Fac Chem & Food Technol Bratislava 81237 Slovakia

This letter proposes a parallelizable algorithm for linear-quadratic model predictive control (MPC) problems with state and input constraints. The algorithm itself is based on a parallel MPC scheme that has originally been designed for systems with input constraints. In this context, one contribution of this letter is the construction of time-varying yet separable constraint margins ensuring recursive feasibility and asymptotic stability of sub-optimal parallel MPC in a general setting, which also includes state constraints. Moreover, it is shown how to tradeoff online run-time guarantees versus the conservatism that is introduced by the tightened state constraints. The corresponding performance of the proposed method as well as the cost of the recursive feasibility guarantees is analyzed in the context of controlling a large-scale mechatronic system. This is illustrated by numerical experiments for a large-scale control system with more than 100 states and 60 control inputs leading to run-times in the millisecond range.

关键词： Real-time systems Standards Springs Robustness Quadratic programming Predictive control prediction algorithms Model predictive control parallel computing real-time control recursive feasibility

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Set-Valued State Estimator With Sparse and Delayed Measurements for Uncertain Discrete-Time Linear Systems

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IEEE CONTROL SYSTEMS LETTERS 2024年 8卷 904-909页

作者： Meslem, Nacim Hably, Ahmad Wang, Zhenhua Raissi, Tarek Univ Grenoble Alpes GIPSA Lab CNRS Grenoble INP F-38000 Grenoble France Harbin Inst Technol Dept Control Sci & Engn Harbin 150001 Peoples R China CEDRIC Conservatoire Natl Arts & Metiers Paris France

This letter deals with online set-membership estimation problem of the state vector of linear discrete-time linear systems with sparse and delayed data. This problem is addressed in a bounded error context where both state disturbance and measurements noise are modeled by bounded boxes. A prediction-correction strategy is adopted to design a Luenberger-like observer where an outer approximation of the reachable set of the estimation error can be computed offline. In addition, by applying interval analysis and under a nonrestrictive observability assumption, both bounding and convergence features of the introduced interval-based state estimator are demonstrated. A numerical case study example is exposed to support the theoretical results by simulation results and to highlight the performance of the proposed estimation approach in the presence of sparse and delayed data.

关键词： Vectors Noise measurement Linear systems prediction algorithms Noise Indexes Estimation error Interval analysis linear systems prediction-correction principle set-valued state estimation sparse measurements

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