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5th International Conference on HCI in Mobility, Transport, and Automotive Systems, MobiTAS 2023, held as part of the 25th International Conference on Human-Computer Interaction, HCII 2023

作者： Liu, Junyu Duan, Yicun Bi, Zhuoran Ming, Xiaoxing Jin, Wangkai Song, Zilin Peng, Xiangjun User-Centric Computing Group Ningbo China

ISBN: (纸本)9783031359071

Emerging Autonomous Vehicles (AV) breed great potential to exploit data-driven techniques for adaptive and personalized Human-Vehicle Interactions. However, the lack of high-quality and rich data supports limits the opportunities to explore the design space of data-driven techniques, and validate the effectiveness of concrete mechanisms. Our goal is to initialize the efforts to deliver the building block for exploring data-driven Human-Vehicle Interaction designs. To this end, we present BROOK dataset, a multi-modal dataset with facial video records. We first brief our rationales to build BROOK dataset. Then, we elaborate on how to build the current version of BROOK dataset via a year-long study, and give an overview of the dataset. Next, we present two example studies using BROOK to justify the applicability of BROOK dataset. We also believe BROOK dataset can foster an extensive amount of follow-up studies, and it’s available at https://***/Junyu-Liu-Nate/BROOK-dataset. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： data-driven techniques datasets Human-Vehicle Interaction

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data-driven techniques for Low-Cost Sensor Selection and Calibration for the Use Case of Air Quality Monitoring

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SENSORS 2022年第3期22卷 1093-1093页

作者： Kureshi, Rameez Raja Mishra, Bhupesh Kumar Thakker, Dhavalkumar John, Reena Walker, Adrian Simpson, Sydney Thakkar, Neel Wante, Agot Kirsten Univ Bradford Fac Engn & Informat Bradford BD7 1DP W Yorkshire England City Bradford Metropolitan Dist Council Bradford BD1 1HX W Yorkshire England IBM India Pvt Ltd Kolkata 700156 India IVL Swedish Environm Res Inst S-11428 Stockholm Sweden

With the emergence of Low-Cost Sensor (LCS) devices, measuring real-time data on a large scale has become a feasible alternative approach to more costly devices. Over the years, sensor technologies have evolved which has provided the opportunity to have diversity in LCS selection for the same task. However, this diversity in sensor types adds complexity to appropriate sensor selection for monitoring tasks. In addition, LCS devices are often associated with low confidence in terms of sensing accuracy because of the complexities in sensing principles and the interpretation of monitored data. From the data analytics point of view, data quality is a major concern as low-quality data more often leads to low confidence in the monitoring systems. Therefore, any applications on building monitoring systems using LCS devices need to focus on two main techniques: sensor selection and calibration to improve data quality. In this paper, data-driven techniques were presented for sensor calibration techniques. To validate our methodology and techniques, an air quality monitoring case study from the Bradford district, UK, as part of two European Union (EU) funded projects was used. For this case study, the candidate sensors were selected based on the literature and market availability. The candidate sensors were narrowed down into the selected sensors after analysing their consistency. To address data quality issues, four different calibration methods were compared to derive the best-suited calibration method for the LCS devices in our use case system. In the calibration, meteorological parameters temperature and humidity were used in addition to the observed readings. Moreover, we uniquely considered Absolute Humidity (AH) and Relative Humidity (RH) as part of the calibration process. To validate the result of experimentation, the Coefficient of Determination (R-2), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) were compared for both AH and RH. The experimental resu

关键词： Low-Cost Sensor (LCS) calibration data-driven techniques drift analysis air quality

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State of Health Estimation of Lithium-ion Batteries Based on data-driven techniques 56

State of Health Estimation of Lithium-ion Batteries Based on...

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56th International Universities Power Engineering Conference (UPEC) - Powering Net Zero Emissions

作者： El-Dalahmeh, Ma'd Lillystone, Joseph Al-Greer, Maher El-Dalahmeh, Mo'ath Teesside Univ Sch Comp Engn & Digital Technol Middlesbrough Cleveland England

ISBN: (纸本)9781665443890

Accurate prediction of the state of health of lithium-ion batteries is essential to ensure safe and reliable operation with minimum maintenance cost. However, estimating the state of health of lithium-ion batteries is a challenge due to the complex and nonlinear characteristics of battery degradation during its lifetime. To this end, data-driven techniques are becoming a more attractive promising approach to provide accurate state of health estimation of the lithium-ion battery because of their simplicity and ability to cope with the nonlinearity dynamic behaviour of the lithium-ion battery. Therefore, this paper compares the performance of three data-driven algorithms: the nonlinear autoregressive neural network, convolutional neural network, and long short-term memory network in the state of health estimation. The performance of the proposed algorithms is analysed by using 16 lithium-ion cells cycled under various operating conditions from the NASA Ames Research center. The comparison results demonstrate that the long short-term memory network outperforms the other two methods with a maximum root mean square error of 0.50% and mean absolute error of 0.36%.

关键词： Lithium-ion battery state of health estimation degradation data-driven techniques capacity estimation

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Forecasting fault events for predictive maintenance using data-driven techniques and ARMA modeling

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COMPUTERS & INDUSTRIAL ENGINEERING 2018年 115卷 41-53页

作者： Baptista, Marcia Sankararaman, Shankar de Medeiros, Ivo. P. Nascimento, Cairo, Jr. Prendinger, Helmut Henriques, Elsa M. P. Univ Lisbon Inst Super Tecn P-1049001 Lisbon Portugal SGT Inc NASA Ames Res Ctr Moffett Field CA 94035 USA Embraer SA Technol Dev Dept Sao Jose Dos Campos Brazil Inst Tecnol Aeronaut BR-12228900 Sao Jose Dos Campos SP Brazil Natl Inst Informat Chiyoda Ku 2-1-2 Hitotsubashi Tokyo 1018430 Japan

Presently, time-based airline maintenance scheduling does not take fault predictions into account, but happens at fixed time-intervals. This may result in unnecessary maintenance interventions and also in situations where components are not taken out of service despite exceeding their designed risk of failure. To address this issue we propose a framework that can predict when a component/system will be at risk of failure in the future, and therefore, advise when maintenance actions should be taken. In order to facilitate such prediction, we employ an auto-regressive moving average (ARMA) model along with data-driven techniques, and compare the performance of multiple data-driven techniques. The ARMA model adds a new feature that is used within the data driven model to give the final prediction. The novelty of our work is the integration of the ARMA methodology with data-driven techniques to predict fault events. This study reports on a real industrial case of unscheduled removals of a critical valve of the aircraft engine. Our results suggest that the support vector regression model can outperform the life usage model on the evaluation measures of sample standard deviation, median error, median absolute error, and percentage error. The generalized linear model provides an effective approach for predictive maintenance with comparable results to the baseline. The remaining data-driven models have a lower overall performance.

关键词： Real case study Aircraft prognostics Predictive maintenance data-driven techniques ARMA modeling Life usage modeling

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Estimating forest carbon fluxes using four different data-driven techniques based on long-term eddy covariance measurements: Model comparison and evaluation

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SCIENCE OF THE TOTAL ENVIRONMENT 2018年 627卷 78-94页

作者： Dou, Xianming Yang, Yongguo China Univ Min & Technol Minist Educ Key Lab Coalbed Methane Resources & Reservoir For Xuzhou 221116 Peoples R China China Univ Min & Technol Sch Resources & Geosci Xuzhou 221116 Peoples R China

With the recent availability of large amounts of data from the global flux towers across different terrestrial ecosystems based on the eddy covariance technique, the use of data-driven techniques has been viable. In this study, two advanced techniques, namely adaptive neuro-fuzzy inference system (ANFIS) and extreme learning machine (ELM), were developed and investigated for their viability in estimating daily carbon fluxes at the ecosystem level. All the data used in this study were based upon the long-term chronosequence observations derived from the flux towers in eight forest ecosystems. Both ANFIS and ELM methods were further compared with the most widely used artificial neural network (ANN) and support vector machine (SVM) methods. Moreover, we also focused on probing into the effects of internal parameters on their corresponding approaches. Our estimates showed that most variation in each carbon flux could be effectively explained by the developed models at almost all the sites. Moreover, the forecasting accuracy of each method was strongly dependent upon their respective internal algorithms. The best training function for ANN model can be acquired through the trial and error procedure. The SVM model with the radial basis kernel function performed considerably better than the SVM models with the polynomial and sigmoid kernel functions. The hybrid ELM models achieved similar predictive accuracy for the three fluxes and were consistently superior to the original ELM models with different transfer functions. In most instances, both the subtractive clustering and fuzzy c-means algorithms for the ANFIS models outperformed the most popular grid partitioning algorithm. It was demonstrated that the newly proposed ELM and ANFIS models were able to produce comparable estimates to the ANN and SVM models for forecasting terrestrial carbon fluxes. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Carbon fluxes Forest ecosystems Flux towers data-driven techniques Adaptive neuro-fuzzy inference system Extreme learning machine

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Modeling and forecasting building energy consumption: A review of data-driven techniques

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SUSTAINABLE CITIES AND SOCIETY 2019年 48卷 101533-000页

作者： Bourdeau, Mathieu Zhai, Xiao Qiang Nefzaoui, Elyes Guo, Xiaofeng Chatellier, Patrice Shanghai Jiao Tong Univ Inst Refrigerat & Cryogen Shanghai 200240 Peoples R China Univ Paris Est ESIEE Paris 2 Bd Blaise Pascal F-93162 Noisy Le Grand France Univ Paris Est ESYCOM EA 2552 CNAM ESIEE ParisUPEMLV F-77454 Marne La Vallee France Univ Paris Est IFSTTAR 14-20 Bd Newton Champs Sur Marne Marne La Vallee France

Building energy consumption modeling and forecasting is essential to address buildings energy efficiency problems and take up current challenges of human comfort, urbanization growth and the consequent energy consumption increase. In a context of integrated smart infrastructures, data-driven techniques rely on data analysis and machine learning to provide flexible methods for building energy prediction. The present paper offers a review of studies developing data-driven models for building scale applications. The prevalent methods are introduced with a focus on the input data characteristics and data pre-processing methods, the building typologies considered, the targeted energy end-uses and forecasting horizons, and accuracy assessment. A special attention is also given to different machine learning approaches. Based on the results of this review, the latest technical improvements and research efforts are synthesized. The key role of occupants' behavior integration in data-driven modeling is discussed. Limitations and research gaps are highlighted. Future research opportunities are also identified.

关键词： Building energy consumption Building load forecasting data-driven techniques Machine learning

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Modeling and Predicting Carbon and Water Fluxes Using data-driven techniques in a Forest Ecosystem

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FORESTS 2017年第12期8卷 498-498页

作者： Dou, Xianming Yang, Yongguo China Univ Min & Technol Key Lab Coalbed Methane Resources & Reservoir For Minist Educ Xuzhou 221116 Peoples R China China Univ Min & Technol Sch Resources & Geosci Xuzhou 221116 Peoples R China

Accurate estimation of carbon and water fluxes of forest ecosystems is of particular importance for addressing the problems originating from global environmental change, and providing helpful information about carbon and water content for analyzing and diagnosing past and future climate change. The main focus of the current work was to investigate the feasibility of four comparatively new methods, including generalized regression neural network, group method of data handling (GMDH), extreme learning machine and adaptive neuro-fuzzy inference system (ANFIS), for elucidating the carbon and water fluxes in a forest ecosystem. A comparison was made between these models and two widely used data-driven models, artificial neural network (ANN) and support vector machine (SVM). All the models were evaluated based on the following statistical indices: coefficient of determination, Nash-Sutcliffe efficiency, root mean square error and mean absolute error. Results indicated that the data-driven models are capable of accounting for most variance in each flux with the limited meteorological variables. The ANN model provided the best estimates for gross primary productivity (GPP) and net ecosystem exchange (NEE), while the ANFIS model achieved the best for ecosystem respiration (R), indicating that no single model was consistently superior to others for the carbon flux prediction. In addition, the GMDH model consistently produced somewhat worse results for all the carbon flux and evapotranspiration (ET) estimations. On the whole, among the carbon and water fluxes, all the models produced similar highly satisfactory accuracy for GPP, R and ET fluxes, and did a reasonable job of reproducing the eddy covariance NEE. Based on these findings, it was concluded that these advanced models are promising alternatives to ANN and SVM for estimating the terrestrial carbon and water fluxes.

关键词： carbon fluxes evapotranspiration forest ecosystem data-driven techniques group method of data handling extreme learning machine adaptive neuro-fuzzy inference system

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Exploring the Mechanism of Surface and Ground Water through data-driven techniques with Sensitivity Analysis for Water Resources Management

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WATER RESOURCES MANAGEMENT 2016年第13期30卷 4789-4806页

作者： Tsai, Wen-Ping Chiang, Yen-Ming Huang, Jun-Lin Chang, Fi-John Natl Taiwan Univ Dept Bioenvironm Syst Engn Taipei 10617 Taiwan Zhejiang Univ Inst Hydrol & Water Resources Coll Civil Engn & Architecture Hangzhou 310058 Zhejiang Peoples R China

The over extraction of groundwater in central-western and southwestern Taiwan has resulted in serious land subsidence for decades. For making countermeasures in response to land subsidence, this study collects long-term hydrological data to explore the relationships between surface water and groundwater in various monitoring stations, and then constructs one-month-ahead forecast models by using data-driven techniques for the water resources management of the Zhuoshui River basin in Taiwan. The results demonstrate that the constructed models can accurately forecast monthly groundwater levels. The sensitivity analysis is next conducted on the input variables of the constructed models by using the partial derivative method. The analysis results reveal that streamflow is a predominant factor for groundwater level variation, and therefore streamflow management made by the upstream weir of the river would influence groundwater level variations. This study further implements several scenario analyses based on the interactive mechanism between groundwater and surface water in response to future climatic conditions and weir discharge management, respectively. The results of scenario analyses indicate that the groundwater recharge zone spreads along the Zhuoshui River while lateral and vertical recharge sources would cause different quantities and distribution patterns of groundwater recharge. Besides, an increase in weir discharge would improve groundwater recharge quantities with groundwater level variations at 0.12 m and 0.06 m in wet and dry seasons, respectively. As a consequence, the operation of weir discharge would play an import role in sustainable development of water resources management in the study area.

关键词： data-driven techniques Groundwater level Sensitivity analysis Scenario analysis Water resources management

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Comparative case study of life usage and data-driven prognostics techniques using aircraft fault messages

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COMPUTERS IN INDUSTRY 2017年 86卷 1-14页

作者： Baptista, Marcia de Medeiros, Ivo P. Malere, Joao P. Nascimento, Cairo, Jr. Prendinger, Helmut Henriques, Elsa M. P. Univ Lisbon Inst Super Tecn P-1049001 Lisbon Portugal Embraer SA Dev Technol Dept Sao Jose Dos Campos Brazil ITA BR-12228900 Sao Jose Dos Campos SP Brazil Natl Inst Informat Chiyoda Ku 2-1-2 Hitotsubashi Tokyo 1018430 Japan

Prognostics are a key activity in repair and maintenance operations. A recent approach to condition-based maintenance is the data-driven approach. This approach has been mostly based on past failure time measures, and sensed measurements of component degradation to derive estimates of the remaining useful life of equipment. An alternative source of data, rarely used in these models, is the stream of automatic messages derived from diagnostics systems, which consist of fault codes indicating abnormal events or deviations from optimal operation. Despite the richness and concise nature of these messages, their difficult interpretation poses significant challenges to its use in prognostics. This paper aims to show that data-driven prognostics based on this type of messages can be better suited to maintenance than time-based approaches. We illustrate this comparison with an industrial case study involving the removal times of a bleed valve from the aircraft air management system. Our experimental results reveal a significant accuracy improvement over the contrasting time-based models. We also establish the contribution to this improvement of the data-driven methods and message-related predictors. (C) 2016 Published by Elsevier B.V.

关键词： Case study Aircraft prognostics data-driven techniques Life usage modeling Fault messages

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Estimation of reference evapotranspiration using data driven techniques under limited data conditions

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MODELING EARTH SYSTEMS AND ENVIRONMENT 2017年第4期3卷 1449-1461页

作者： Pandey, P. K. Nyori, Topi Pandey, Vanita North Eastern Reg Inst Sci & Technol Dept Agr Engn Itanagar 791109 Arunachal Prade India

Estimation of reference evapotranspiration (ET0) is a major task in irrigation and water resources development and management. In this context, the FAO56 Penman-Montieth (FAO56PM) equation, one of the most accurate equation is used, which however requires a high number of climatic parameters that are not always available for most meteorological stations. The present study measures the effectiveness of selected machine learning techniques [evolutionary regression (ER), artificial neural network (ANN), multi nonlinear regression (MLNR), and support vector machines (SVMs)] for the estimation of ET0 under limited data conditions. Different models were developed for estimating ET 0, including various sets of daily climatic variables, namely maximum and minimum air temperature, extraterrestrial radiation, sunshine hours, wind speed and relative humidity Statistical evaluation of different results obtained from selected techniques showed that the best performance was obtained when six meteorological variables, i.e., (T-max, T-min, R-a, S-h, W-s, RHmin) were used as input, with highest R-2 = 0.9931 by ANN4 followed by ER4 with R-2 = 0.9886. The ER and ANN performed similarly though ANN performed slightly better against FAO56PM model. The ER was recommended over ANN because ER gives a physical algebraic expression, which can be further replicated without modelling tools and computers. Further, its gives the intermittent calculation visibility and understanding, also that ER technique is easy to use and more practical in application. However, ANN models need modelling tools and computers with the very skilled user and inside calculations like a black box. ANN also require simulation process to get any output.

关键词： Reference evapotranspiration data-driven techniques ANNs ER MLNR SVMs Gamma test technique

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