bluuebunny/crossref_metadata_2025_split huggingface dataset:
Impact of Aromatic Structures and Content in Formulated Fuel for Jet Engine Applications on Particulate Matter Emissions
Authors: Zheng Lukai , Singh Paramvir , Cronly James , Ubogu Emamode A. , Ahmed Ihab , Ling Chenxing , Zhang Yang , Khandelwal Bhupendra 
Published: January 2021

Abstract
Fuel formulation with the particular selection of fuel components is a promising approach that offers the reduction of harmful emissions without altering the combustion system performance. Each fuel component has its own combustion characteristics and hence contribution to emissions. Aromatic is one of the main components of fossil-based fuels and has a strong correlation with the formation of particulate matter (PM) emissions. Besides, aromatics presence in fuel is essential for the compatibility of fuel with the combustion system and maintaining the energy density of the fuel. In this regard, a Rolls-Royce combustor rig was used to test 16 aromatics blended with jet fuels in three different proportions. Moreover, a novel approach of flame luminosity imaging is employed to measure the PM emissions through the soot propensity profile. The results show that PM emissions increase with the proportional increase of aromatics. The di- and cyclo-aromatics produced significantly higher PM emissions compared to alkyl-benzenes. 3-Isopropylcumene tends to lowest PM formation and thus is a consideration as a selection of aromatic type in future fuels for lower PM emissions. Furthermore, it was also observed that PM number concentration measured by the extractive method with DMS 500 instrument correlates well with imaging methods for all the tested fuels. The present study provides information on particular selection of aromatic for future fuel development.
Observer Design for the Series Interconnection of Li-Ion Battery Cells Subject to Reduced Voltage Information
Authors: Couto Luis D. , Schons Silvane , Coutinho Daniel , Kinnaert Michel 
Published: January 2021

Abstract
This work studies the estimation of internal states and voltages in a battery pack composed by lithium-ion cells subject to limited voltage and temperature measurements. A reduced-order electrochemical model for each cell is used. The considered cells are heterogeneous. They are electrically interconnected in series and thermally coupled. The resulting model is linearized and reformulated as a descriptor system. Such a model provides a unified modeling framework accounting for both battery cell dynamics and algebraic states arising from standard Kirchhoff’s laws. An observability study is exploited to design an observable descriptor system that requires the measurement of the surface temperature of one cell together with the total voltage. We resort to Lyapunov theory and the algebraic relationship between the descriptor system states and the measurements to derive a state observer that meets the following conditions. The error system is input-to-state stable and the effect of cell-to-cell variations on a specified performance variable is minimized. The state observer is obtained by solving a set of LMIs. The proposed scheme is able to estimate the state of each individual cell, including state of charge and inner temperature, as well as the unmeasured voltages. The effectiveness of the approach is tested in simulation.
High-Fidelity Teleoperated Scaled Vehicles for Research and Development of Intelligent Transportation Technologies
Authors: Wang Cong , Wang Bohan , Zhao Leidi , Maranon Carlos , Goswamy Nishaant , Lee Jo Young , Wang Guiling 
Published: January 2021

Abstract
This paper presents a systematic design of high-fidelity tele-operated scaled vehicles to be used as a research and development platform for intelligent transportation technologies. Compared to computer simulation and full-scale physical tests, the use of high-fidelity scaled setups provides advantages on testing time and financial effectiveness. The physical design of the vehicles features a 1:14 scale with realistic appearance licensed by car manufacturers. Customized steering system and propulsion control provide high-fidelity maneuver characteristics. Remote control is deployed using a target-host structure over WiFi and can provide seamless switching between human driving and autonomous/assisted driving on the host side. Several possible solutions for real-time panoramic vision feedback are explored, with a tri-camera design based on parallel acquisition interfaces adopted. An adaptive color compression technique is developed to shorten the video streaming latency. A customized miniature LIDAR system is introduced to provide an ultra-small package for on-board installation. As a solution balancing between test fidelity and costs, the proposed scaled vehicles are especially suitable for validation tests during the early stage of research and development. With a long-term goal of developing a multi-vehicle traffic network test platform, ongoing and future work on the construction of scaled buildings and road systems is also discussed.
Multi-Level Hierarchical Estimation for Thermal Management Systems of Electrified Vehicles
Authors: Tannous Pamela J. , Alleyne Andrew G. 
Published: January 2021

Abstract
This paper presents a multi-level model-based hierarchical estimation framework for complex thermal management systems of electrified vehicles. System dynamics are represented by physics-based lumped parameter models derived from a graph-based modeling approach. The complexity of hierarchical models is reduced by applying an aggregation-based model order reduction technique that preserves the physical correspondence between a physical system and its reduced-order model. The paper presents a case study in which a hierarchical observer is designed to estimate the dynamics of a candidate system. The hierarchical observer is connected to a hierarchical controller for closed-loop control. A comparison between the proposed hierarchical observer and a centralized observer shows that a hierarchical observer enables a reduction in the required computational power.
Marker Based Row Alignment Control for an Agricultural Scouting Robot
Authors: Li Qiang , Kong Xiangling , Xu Yunjun 
Published: January 2021

Abstract
In recent years, autonomous robots have been gradually introduced into various agricultural operations to address the ever-increasing labor shortage problem. Accurate navigation from one row to another is one of the many challenging tasks for an autonomous robot scouting in semi-structured agricultural fields. In this study, a marker-based row alignment control is proposed for the cross-bed motion of a scouting robot in strawberry fields. Specifically, a feature-based computer vision algorithm is used to detect primitive markers placed at the end of each planting bed. Then the image coordinates of detected markers are used to guide the robot to move away from one row and then align with the next one. The proposed method is low cost and robust with respect to varying lighting conditions, and has been validated in a local strawberry farm.
Parametric Modeling and Optimal Control of a Combined Heating and Power System With Energy Storage
Authors: De Pascalis Claudia Lucia , Stockar Stephanie 
Published: January 2021

Abstract
Cogeneration is a well-known and cost effective solution for generating power and heat within the same plant, leading to improved overall efficiency and reduced generation cost. Combined heating and power systems can facilitate the penetration of renewable energy sources in medium size applications through the integration of electric and thermal energy storage units. Due to the complexity of the plant as well as significantly variability in power demand and generation, the design and operation of such systems requires a systematic co-optimization of plant and controller for guaranteeing near optimal performance. In this scenario, this paper presents a physics-based parametric modeling approach for the characterization of the main components of a 1MW combined heating and power system that includes renewable sources, electric and thermal storage devices. To demonstrate the model flexibility and potential benefits achieved by an optimal sizing, the system energy management is optimized using Dynamic Programming. The operational costs for different configurations are compared showing that an optimization of the energy management strategy in conjunction with an improved system sizing lead to more than 6% of reduction in the operational cost.
Error Dynamics Design via a Repetitive Loop for UDE-Based Robust Control to Reject Periodic Disturbances
Authors: Wang Yeqin , Dong Yiting , Dai Jiguo , Ren Beibei , Zhong Qing-Chang 
Published: January 2021

Abstract
The uncertainty and disturbance estimator (UDE)-based robust control has a two-degree-of-freedom nature through the design of the error dynamics and the design of the UDE filters. In the conventional design to handle periodic disturbances or mixed sinusoidal disturbances, high-order UDE filters incorporated with the internal model principle (IMP) or time-delay filters (TDF) are adopted to achieve the asymptotic reference tracking and the asymptotic disturbance rejection. In this paper, a new error dynamics design combined with a repetitive loop is proposed for the UDE-based robust control to achieve the asymptotic rejection of both step disturbances and periodic disturbances. The disturbance rejection performance is investigated through the two-degree-of-freedom nature, and the practical implementation of the proposed design is illustrated to eliminate the infinite bandwidth of the repetitive loop. The proposed design is validated through the simulation studies of a battery charging system with comparison to different reported designs of the conventional UDE-based robust control.
Development and Implementation of a New Optimal Supplemental Lighting Control Strategy in Greenhouses
Authors: Mosharafian Sahand , Afzali Shirin , Mohammadpour Velni Javad , van Iersel Marc W. 
Published: January 2021

Abstract
The use of supplemental lighting is an effective way for increasing greenhouse productivity. Recently, using light-emitting diodes (LEDs), capable of precise and quick dimmability, has increased in greenhouses. However, electricity cost of lighting can be significant, and hence, it is necessary to find optimal lighting strategies to minimize supplemental lighting costs. In this paper, we model supplemental lighting in the greenhouse equipped with LEDs as a constrained optimization problem, and we aim at minimizing electricity costs of supplemental lighting. We consider not only plant daily light integral (DLI) need during its photoperiod but also sunlight prediction and variable electricity pricing in our model. We use Markov chain to predict sunlight irradiance throughout the day. By taking sunlight prediction information into account, we avoid supplying more light than plants require. Therefore, our lighting strategy provides sufficient light for plant growth while minimizing electricity costs during the day. We propose an algorithm to find optimal supplemental lighting strategy and evaluate its performance through exhaustive simulation studies using a whole year data and compare it to a heuristic method, which aims to supply a fixed photosynthetic photon flux density (PPFD) to plants at each time-step during the day. We also implement our proposed lighting strategy on Raspberry Pi using Python programming language. Our prediction-based lighting approach shows (on average) about 23% electricity cost reduction compared to a heuristic method throughout the year for a site located at West Virginia.
Distributed Particle Filter With Online Model Learning for Localization Using Time-Difference-of-Arrival (TDOA) Measurements
Authors: Panetta Chandler J. , Ennasr Osama N. , Tan Xiaobo 
Published: January 2021

Abstract
The problem of localizing a moving target arises in various forms in wireless sensor networks. Deploying multiple sensing receivers and using the time-difference-of-arrival (TDOA) of the target’s emitted signal is widely considered an effective localization technique. Traditionally, TDOA-based algorithms adopt a centralized approach where all measurements are sent to a predefined reference node for position estimation. More recently, distributed TDOA-based localization algorithms have been shown to improve the robustness of these estimates. For target models governed by highly stochastic processes, the method of nonlinear filtering and state estimation must be carefully considered. In this work, a distributed TDOA-based particle filter algorithm is proposed for localizing a moving target modeled by a discrete-time correlated random walk (DCRW). We present a method for using data collected by the particle filter to estimate the unknown probability distributions of the target’s movement model, and then apply the distribution estimates to recursively update the particle filter’s propagation model. The performance of the distributed approach is evaluated through numerical simulation, and we show the benefit of using a particle filter with online model learning by comparing it with the non-adaptive approach.
Receding Horizon Control for a 2D Point-Mass Hopping Model Navigating on Terrain With Stepping Stones and Stairs
Authors: Zamani Ali , Bhounsule Pranav A. 
Published: January 2021

Abstract
We consider the problem of a 2D point mass model navigating a complex terrain comprising of stepping stones and stairs while optimizing an energy metric. We solve the problem using receding horizon control as follows. We preview a fixed distance ahead at mid-flight. Then we optimize the number of steps, the controls, and foot placement location, choosing the solution with least energy cost. However, we implement only the solution for the first step which takes the model to the next mid-flight. This process continues until the model reaches the end of the terrain. We improve on past approaches by (1) considering a fixed distance preview as done by humans instead of fixed time or fixed steps, and (2) adding obstacles as a cost and elevation change as a condition within the model dynamics, thus avoiding mixed-integer formulations which are computationally expensive. The resulting problem is solved using constrained nonlinear programming. We demonstrate that the approach works for randomly chosen terrain consisting of stepping stones and stairs.
A Novel Approach to Time Series Forecasting Using Model-Free Adaptive Control Framework
Authors: Narayan Meenakshi , Fey Ann Majewicz 
Published: January 2021

Abstract
Sensor data predictions could significantly improve the accuracy and effectiveness of modern control systems; however, existing machine learning and advanced statistical techniques to forecast time series data require significant computational resources which is not ideal for real-time applications. In this paper, we propose a novel forecasting technique called Compact Form Dynamic Linearization Model-Free Prediction (CFDL-MFP) which is derived from the existing model-free adaptive control framework. This approach enables near real-time forecasts of seconds-worth of time-series data due to its basis as an optimal control problem. The performance of the CFDL-MFP algorithm was evaluated using four real datasets including: force sensor readings from surgical needle, ECG measurements for heart rate, and atmospheric temperature and Nile water level recordings. On average, the forecast accuracy of CFDL-MFP was 28% better than the benchmark Autoregressive Integrated Moving Average (ARIMA) algorithm. The maximum computation time of CFDL-MFP was 49.1ms which was 170 times faster than ARIMA. Forecasts were best for deterministic data patterns, such as the ECG data, with a minimum average root mean squared error of (0.2±0.2).
Output-Feedback Linear Parameter Varying Control of Permanent Magnet Synchronous Motors
Authors: Lee Jaecheol , Tasoujian Shahin , Grigoriadis Karolos , Franchek Matthew 
Published: January 2021

Abstract
This paper investigates the design of an output feedback linear parameter-varying (LPV) gain-scheduled controller for the speed regulation of a surface permanent magnet synchronous motor (SPMSM). Motor dynamics is defined in the stationary reference (α – β) frame and a parameter-varying model formulation is provided to describe the SPMSM nonlinear dynamics. In this context, a robust gain-scheduled LPV output-feedback dynamic controller is designed to satisfy the asymptotic stability of the closed-loop system and meet desired performance requirements, and guarantee robustness against norm-bounded uncertainties and disturbances. The real-time impact of temperature variation on the winding resistance and magnet flux during motor operations are considered in the LPV modelling and the subsequent control design to address demagnetization effects in the motor response. The controller synthesis conditions are formulated in a convex linear matrix inequality (LMI) constraint framework. Finally, the validity of the proposed control strategy is assessed in simulation studies, and the results are compared to the results of the conventional field-oriented control (FOC) method. The results of the simulation demonstrate that the proposed controller provides improved transient response with respect to settling time, overshoot, and disturbance rejection in tracking the velocity profile under the influence of parameter and temperature variations and disturbances.
Evaluation of an implemented new insulin chart to improve quality and safety of diabetes care in a large university hospital: a follow-up study
Authors: Kopanz Julia , Sendlhofer Gerald , Lichtenegger Katharina , Semlitsch Barbara , Riedl Regina , Pieber Thomas R , Tax Christa , Brunner Gernot , Plank Johannes 
Published: January 2021

ObjectivesTo evaluate structure, documentation, treatment quality of a new implemented standardised insulin chart in adult medical inpatient wards at a university hospital.DesignA before–after study (3 to 5 months after implementation) was used to compare the quality of old versus new insulin charts.SettingUniversity Hospital Graz, Austria.ParticipantsHealthcare professionals (n=237) were questioned regarding structure quality of blank insulin charts.InterventionsA new standardised insulin chart was implemented and healthcare professionals were trained regarding features of this chart. Data from insulinised inpatients were evaluated regarding documentation and treatment quality of filled-in insulin charts (n=108 old insulin charts vs n=100 new insulin charts).Main outcomes and measuresThe primary endpoint was documentation error for insulin administration.ResultsHealthcare professionals reported an improved structure quality of the new insulin chart with a Likert type response scale increase in all nine items. Documentation errors for insulin administration (primary endpoint) occurred more often on old than new insulin charts (77% vs 5%, p<0.001). Documentation errors for insulin prescription were more frequent on old insulin charts (100% vs 42%) whereas documentation errors for insulin management rarely occurred in any group (10% vs 8%). Patients of both chart evaluation groups (age: 71±11 vs 71±12 years, 47% vs 42% women, 75% vs 87% type 2 diabetes for old vs new charts, respectively) had a mean of 4±2 good diabetes days. Overall, 26 vs 18 hypoglycaemic episodes (blood glucose (BG) <4.0 mmol/L (72 mg/dL), p=0.28), including 7 vs 2 severe hypoglycaemic episodes (BG <3.0 mmol/L (54 mg/dL), p=0.17) were documented on old versus new insulin charts.ConclusionsThe implementation of a structured documentation form together with training measures for healthcare professionals led to less documentation errors and safe management of glycaemic control in hospitalised patients in a short time follow-up. A rollout at further medical wards is recommended, and sustainability in the long-term has to be demonstrated.
Clinicopathological analysis of 5146 eyelid tumours and tumour-like lesions in an eye centre in South China, 2000–2018: a retrospective cohort study
Authors: Wang Linyan , Shan Yi , Dai Xizhe , You Na , Shao Ji , Pan Xiangji , Gao Tao , Ye Juan 
Published: January 2021

ObjectivesTo describe the clinicopathological characteristics of patients with eyelid tumours and tumour-like lesions in South China, investigate possible factors affecting tumour constitution.DesignRetrospective cohort study.SettingAll patients diagnosed with eyelid tumours were included from a high-volume tertiary eye care centre from South China which caring for over 2000 patients per day. All biopsied specimens were reviewed by two senior pathologists and were classified according to the fourth edition of the WHO Classification of Skin Tumours.ParticipantA total of 5146 cases of eyelid lesions were reviewed from 2000 to 2018, being classified by histogenesis and pathologic diagnosis, being compared with data from previous literature containing different races.Main outcome measuresAge-specific and gender-specific incidence constitutions, time trends, tumour location, distribution in different age groups and relationship with Sociodmographic Index (SDI) were calculated.ResultsBenign tumours accounted for 85.08% (4378) of all cases, among which, nevus was most common (33.07%). Eight of top 10 benign lesions had higher occurrence in upper eyelids. The R² value of linear regression in patient annual number of benign lesions were 0.946 (p<0.01) for male and 0.914 (p<0.01) for female. More than 33.60% (1471/4378) were made up by patients younger than 40 years. The number of patients undergoing removal of benign lesions decreased with age. Among the malignant lesions, basal cell carcinoma (BCC) was most prevalent (48.70%), followed by sebaceous gland carcinoma (34.24%) and majority (81.8%) occurred in patients above 60 years.ConclusionsOver the past 19 years, most eyelid tumours occurred at our centre were benign lesions. The number of patients presenting with benign lesions increased in both genders, especially among young females who were more likely to request surgeries. Among malignant lesions, BCC remains the most common type, appears a higher incidence in countries with higher SDI.