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Multivariate learning framework for long-term adaptation in the artificial pancreas

BIOENGINEERING & TRANSLATIONAL MEDICINE 2019年第1期4卷 61-74页

作者： Shi, Dawei Dassau, Eyal Doyle, Francis J., III Harvard Univ Harvard John A Paulson Sch Engn & Appl Sci Cambridge MA 02138 USA

The long-term use of the artificial pancreas (AP) requires an automated insulin delivery algorithm that can learn and adapt with the growth, development, and lifestyle changes of patients. In this work, we introduce a data-driven AP adaptation method for improved glucose management in a home environment. A two-phase Bayesian optimization assisted parameter learning algorithm is proposed to adapt basal and carbohydrate-ratio profile, and key feedback control parameters. The method is evaluated on the basis of the 111-adult cohort of the FDA-accepted UVA/Padova type 1 diabetes mellitus simulator through three scenarios with lifestyle disturbances and incorrect initial parameters. For all the scenarios, the proposed method is able to robustly adapt AP parameters for improved glycemic regulation performance in terms of percent time in the euglycemic range [70, 180] mg/dl without causing risk of hypoglycemia in terms of percent time below 70 mg/dl.

关键词： artificial pancreas data-driven algorithms parameter adaptation safety-critical control

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The Online Knapsack Problem with Departures

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PROCEEDINGS OF THE ACM ON MEASUREMENT AND ANALYSIS OF COMPUTING SYSTEMS 2022年第3期6卷 1–32页

作者： Sun, Bo Yang, Lin Hajiesmaili, Mohammad Wierman, Adam Lui, John C. S. Towsley, Don Tsang, Danny H. K. Chinese Univ Hong Kong Hong Kong Peoples R China Nanjing Univ Nanjing Peoples R China Univ Massachusetts Amherst Amherst MA USA CALTECH Pasadena CA 91125 USA Hong Kong Univ Sci & Technol Guangzhou Guangzhou Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China

The online knapsack problem is a classic online resource allocation problem in networking and operations research. Its basic version studies how to pack online arriving items of different sizes and values into a capacity-limited knapsack. In this paper, we study a general version that includes item departures, while also considering multiple knapsacks and multi-dimensional item sizes. We design a threshold-based online algorithm and prove that the algorithm can achieve order-optimal competitive ratios. Beyond worst-case performance guarantees, we also aim to achieve near-optimal average performance under typical instances. Towards this goal, we propose a data-driven online algorithm that learns within a policy-class that guarantees a worst-case performance bound. In trace-driven experiments, we show that our data-driven algorithm outperforms other benchmark algorithms in an application of online knapsack to job scheduling for cloud computing.

关键词： online knapsack problems knapsack with departures data-driven algorithms competitive ratio cloud job scheduling

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A data-driven method for heat loss estimation from district heating service pipes using heat meter- and GIS data

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ENERGY 2023年第1期277卷

作者： Fester, Jakob Ostergaard, Peter Friis Bentsen, Fredrik Nielsen, Brian Kongsgaard Danish Technol Inst Gregersensvej 1 DK-2630 Taastrup Denmark Verdo Varme A S Agerskellet 7 DK-8920 Randers Denmark Grundfos Poul Due Jensens vej 7 DK-8850 Bjerringbro Denmark

District heating (DH) pipes are often older than 20 years and of unknown condition, imposing a strong demand for detailed knowledge about their current state to help cost-effective ongoing renovation and reduce water and energy losses. Here, we develop a scalable method that exploits hourly resolved remotely read heat meter data, pipe coordinates, and soil temperature measurements to continuously estimate the heat loss of service pipes. The method does not require the measured or simulated flows in the full system, making it possible to omit the usual step of establishing a complete thermohydraulic network model which is currently hindering implementation on larger scale. By exploiting open-source soil temperature data, the estimated heat loss is normalized to output the transmission coefficient, U, in W/(m center dot K), allowing for direct intercomparison between pipes of different length and assessment of timely evolution across seasons. Estimated heat losses are validated by complementary techniques, using simulation of data sets and highly time-resolved data from a test area with additional mea-surement equipment installed. Finally, a full-scale application is demonstrated over a year on 1089 service pipes, including detection of a spontaneously emerged leakage, revealed through the model sensitivity towards the consequential changes in heat loss coefficient, U.

关键词： District heating pipe networks Digital tools Smart meter data GIS-data data-driven algorithms Big data Heat loss calculation Asset management Renovation planning

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Comparative analysis of data-driven electric vehicle battery health models across different operating conditions

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ENERGY 2024年 309卷

作者： Kumar, Roushan Das, Kaushik Krishna, Anurup Univ Petr & Energy Studies Sch Engn Mech Cluster Dehra Dun 248007 India Indian Inst Technol BHU Dept Elect & Commun Engn Varanasi 221005 India

The work covers the development of a data-driven algorithm and computes the performance of learning models for lithium-ion battery state of health (SOH) estimation. A wide range of environmental and temperature conditions (15 degree celsius, 25 degree celsius, and 35 degree celsius) at different charging and discharging rates of 1C and 2C are used for electric vehicle battery health estimation. The result of the tested data of cell 'a' is validated with a different set of cell 'b' on identical test parameters, and the results are tabulated and compared. At 25 degree celsius, the mean absolute errors for the regression algorithms decision tree (DT), k-nearest neighbor (KNN), and random forest (RF) are 3.78641E-03, 3.62524E-03, and 6.16931E-03. The mean absolute percent error for regression algorithms DT, KNN, and RF is 1.48921E-03, 1.40631E-03, and 2.40260E-03. The root mean square error for regression algorithms DT, KNN, and RF is 1.26813E-02, 9.73320E-03, and 1.17238E-02, and the mean squared error for regression algorithms DT, KNN, and RF is 1.60816E-04, 9.47351E-05, and 1.37448E-04. The results show that the KNN and DT methods accurately estimate the SOH under diversified operating conditions in comparison with RF methods and can foster advanced battery health monitoring systems.

关键词： State of health Lithium-ion battery Charge cycle Discharge cycle Machine learning data-driven algorithms

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Damage Identification in Structural Health Monitoring: A Brief Review from its Implementation to the Use of data-driven Applications

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SENSORS 2020年第3期20卷 733-733页

作者： Tibaduiza Burgos, Diego A. Gomez Vargas, Ricardo C. Pedraza, Cesar Agis, David Pozo, Francesc Univ Nacl Colombia Dept Ingn Elect & Elect Cra 45 26-85 Bogota 111321 Colombia Politecn Grancolombiano Inst Univ Escuela Tecnol Informac & Comunicac Bogota 111321 Colombia Univ Nacl Colombia Dept Ingn Sistemas & Ind Cra 45 26-85 Bogota 111321 Colombia UPC EEBE Dept Matemat Control Modeling Identificat & Applicat CoDAlab CDBEduard Maristany 16 Barcelona 08019 Spain

The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making;however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.

关键词： data-driven algorithms damage identification structural health monitoring sensors

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Predicting Positions of People in Human-Robot Conversational Groups 22

Predicting Positions of People in Human-Robot Conversational...

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17th Annual ACM/IEEE International Conference on Human-Robot Interaction (HRI)

作者： Hedayati, Hooman Szafir, Daniel Univ North Carolina Chapel Hill Dept Comp Sci Chapel Hill NC 27599 USA

ISBN: (数字)9781665407311

ISBN: (纸本)9781665407311

Robots that operate in social settings must be able to recognize, understand, and reason about human conversational groups (i.e., F-formations). While several algorithms have been developed for identifying such groups, there has been little research on how robots might reason about inaccuracies following group classification (e.g., recognizing only 4 of 5 group members). We address this gap through a data-driven approach that builds knowledge of human group positioning. By analyzing multiple conversational group data sets, we have developed a system for identifying high probability regions that indicate areas where people are likely to stand in a group relative to a single anchor participant. We use knowledge of these regions to train two models, which we implement on a social robot. The first model can estimate the true size of a partially-observed conversational group (i.e., a group where only some of the participants were detected). Our second model can predict the locations where any undetected participants are likely to reside. Together, these models may improve F-formation detection algorithms by increasing robustness to noisy input data.

关键词： F-formations Social robot Human-Robot Interaction (HRI) Conversational groups data-driven algorithms

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RECURSIVE data-STRUCTURES IN APL

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COMMUNICATIONS OF THE ACM 1979年第2期22卷 79-96页

作者： GULL, WE JENKINS, MA QUEENS UNIV DEPT COMP & INFORMAT SCIKINGSTON K7L 3N6ONTARIOCANADA

Presents the mathematical study of approaches for defining nested arrays in APL, a computer program language. Discussion on the extension of data structure capability of APL; data structure of APL; Characteristics of ... 详细信息

关键词： APL arrays data-driven algorithms nested arrays recursive data structures theory of arrays trees

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Enhancing Electric Vehicle Diagnostics Through Constant Speed Subrange Detection for Noise-Reduced Analysis 22

Enhancing Electric Vehicle Diagnostics Through Constant Spee...

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IEEE 22nd Mediterranean Electrotechnical Conference (MELECON)

作者： El Hadraoui, Hicham Guennouni, Nasr El Maghraoui, Adila El Bazi, Nabil Zegrari, Mourad Chebak, Ahmed Mohammed VI Polytech Univ Green Tech Inst Benguerir Morocco UM6P Green Tech Inst Benguerir Morocco Hassan II Univ Settat Morocco Hassan II Univ Casablanca Casablanca Morocco

ISBN: (纸本)9798350387032;9798350387025

Electric Vehicles (EVs) represent a paradigm shift in the automotive industry, offering a sustainable alternative to traditional engines. However, the efficient diagnosis of EV performance remains a formidable challenge, exacerbated by the intrinsic noise interference associated with speed variations. This paper introduces an innovative approach to mitigate these diagnostic challenges by harnessing constant speed subranges as a strategic diagnostic window. The methodology centers on the identification of the most frequent constant speed subranges, which serve as optimal periods for signal recording from EV electric motors. We propose a novel algorithm that identifies these subranges, initially trained, tested, and validated using the Worldwide Harmonized Light Vehicles Test Cycle (WLTC) dataset, the model is compared with four other models and show less variation ratio in the detected subranges. The crux of this approach lies in the recording and analysis of key motor signals - current, vibration, among others - within these identified subranges. The data derived from these periods are then utilized to train diagnostic algorithms, specifically tuned to the nuances of each subrange. The implementation of this strategy in real-world driving conditions involves the dynamic selection of appropriate diagnostic models aligned with the detected speed subranges. This targeted approach diminishes the impact of noise interference and paves the way for personalized data-driven diagnostics. The proposed method aims to reduces the complexity and enhances the efficacy of diagnostic algorithms, offering a tailored diagnostic solution that adapts to individual driving patterns.

关键词： Electric Vehicle Diagnostics Noise Interference Personalized Diagnostics data-driven algorithms

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Combined Model-Free Adaptive Control with Fuzzy Component by Virtual Reference Feedback Tuning for Tower Crane Systems 7

Combined Model-Free Adaptive Control with Fuzzy Component by...

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7th International Conference on Information Technology and Quantitative Management (ITQM) - Information Technology and Quantitative Management Based on Artificial Intelligence

作者： Roman, Raul-Cristian Precup, Radu-Emil Bojan-Dragos, Claudia-Adina Szedlak-Stinean, Alexandra-Iulia Politehn Univ Timisoara Dept Automat & Appl Informat Bd V Parvan 2 Timisoara 300223 Romania

A novel mix of two data-driven algorithms is proposed in this paper. The mix of the algorithms aims to exploit the main advantage of data-driven Virtual Reference Feedback Tuning (VRFT) algorithm, that is represented by the automatic computation of the optimal parameters using a metaheuristic Grey Wolf Optimizer (GWO) for the Compact Form Dynamic Linearization (CFDL) version of the authors' Model-Free Adaptive Control Takagi-Sugeno Fuzzy Algorithm (CFDL-PDTSFA), so the parameters of the CFDL-PDTSFA are optimally tuned in a model-free manner via VRFT. Three specific optimization problems are defined and solved by Model-Free Adaptive Control, VRFT and GWO algorithms. The new resulted algorithm is validated using experimental results to the arm angular position of the nonlinear tower crane system laboratory equipment. (C) 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-ne-nd/4.0/) Peer-review under responsibility of the scientific committee of the 7th International Conference on Information Technology and Quantitative Management (ITQM 2019)

关键词： data-driven algorithms model-free adaptive fuzzy control tower crane systems virtual reference feedback tuning

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Optimal Reissue Policies for Reducing Tail Latency 17

Optimal Reissue Policies for Reducing Tail Latency

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29th ACM Symposium on Parallelism in algorithms and Architectures (SPAA)

作者： Kaler, Tim He, Yuxiong Elnikety, Sameh MIT CSAIL Cambridge MA 02139 USA Microsoft Res Redmond WA USA

ISBN: (纸本)9781450345934

Interactive services send redundant requests to multiple different replicas to meet stringent tail latency requirements. These additional (reissue) requests mitigate the impact of non-deterministic delays within the system and thus increase the probability of receiving an on-time response. There are two existing approaches of using reissue requests to reduce tail latency. (1) Reissue requests immediately to one or more replicas, which multiplies the load and runs the risk of overloading the system. (2) Reissue requests if not completed after a fixed delay. The delay helps to bound the number of extra reissue requests, but it also reduces the chance for those requests to respond before a tail latency target. We introduce a new family of reissue policies, Single-Time / Random (SINGLER), that reissue requests after a delay d with probability q. SINGLER employs randomness to bound the reissue rate, while allowing requests to be reissued early enough so they have sufficient time to respond, exploiting the benefits of both immediate and delayed reissue of prior work. We formally prove, within a simplified analytical model, that SINGLER is optimal even when compared to more complex policies that reissue multiple times. To use SINGLER for interactive services, we provide efficient algorithms for calculating optimal reissue delay and probability from response time logs through data-driven approach. We apply iterative adaptation for systems with load-dependent queuing delays. The key advantage of this data-driven approach is its wide applicability and effectiveness to systems with various design choices and workload properties. We evaluated SINGLER policies thoroughly. We use simulation to illustrate its internals and demonstrate its robustness to a wide range of workloads. We conduct system experiments on the Redis key-value store and Lucene search server. The results show that for utilizations ranging from 40-60%, SINGLER reduces the 99th-percentile latency of Redis by 30-7

关键词： performance optimization interactive services tail-latency distributed computing reissue policy data-driven algorithms redundancy

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