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A microscopic fuel consumption model with respect to comprehensive fuel consumption key impact factors that can reflect dynamic engine operations and various real-world driving conditions is essential for fuel efficient model-based vehicle and powertrain control (MB-VPC). Existing microscopic fuel consumption models are mostly steady state models with limited vehicle parameters as dynamic variables. Data-driven solutions can make the model comprehensive by introducing broad impact factors. However, without the required accessible online computational capability and fast model adaptation mechanisms, those data-driven solutions cannot quickly adapt to unseen driving conditions and engine condition changes based on real-world vehicle data to support MB-VPC. In this paper, a connected vehicle-based data mining (CV-DM) framework is proposed to achieve online adaptive dynamic fuel consumption modeling through knowledge sharing over CVs and the CV remote data center. Based on the CV-DM framework, CV-supported Distributed Meta-regression (CV-DMR) algorithms are developed to realize a fast few-shot adaptation with limited training data. Extensive proof-of-concept experiments are conducted with steady-state and transient vehicle engine data. Compared to the baseline physical model and the existing non-adaptive grey-box model, prediction accuracy is improved by 47%–85% and 38%–80% respectively with only limited training data needed for the subject vehicle. Accordingly, a fuel savings of up to 9.4% can be achieved owing to the improvement of prediction accuracy. [ABSTRACT FROM AUTHOR]

Most of the existing music recommendation systems use collaborative or content based recommendation engines. However, the music choice of a user is not only dependent to the historical preferences or music contents. But also dependent to the mood of that user. This paper proposes an emotion based music recommendation framework that learns the emotion of a user from the signals obtained via wearable physiological sensors. In particular, the emotion of a user is classified by a wearable computing device which is integrated with a galvanic skin response (GSR) and photo plethysmography (PPG) physiological sensors. This emotion information is feed to any collaborative or content based recommendation engine as a supplementary data. Thus, existing recommendation engine performances can be increased using these data. Therefore, in this paper emotion recognition problem is considered as arousal and valence prediction from multi-channel physiological signals. Experimental results are obtained on 32 subjects’ GSR and PPG signal data with/out feature fusion using decision tree, random forest, support vector machine and k-nearest neighbors algorithms. The results of comprehensive experiments on real data confirm the accuracy of the proposed emotion classification system that can be integrated to any recommendation engine. [ABSTRACT FROM AUTHOR]

In embedded real-time systems, the schedulability analysis is an important method to verify whether the real-time constraints are satisfied. Especially, engine control systems in the automotive industry are particular challenging regarding their real-time analysis. Some of the tasks of such systems are triggered at predetermined angular values of the crankshaft. Thus, the frequency of activation increases for higher crankshaft rotation speeds. To avoid overloads, these tasks reduce their computational demand for higher rotation speeds. The variable rate-dependent behavior (VRB) task model has been proposed as a means of modeling such rate-dependent behavior. In this paper, we introduce a new method for the exact computation of interference for VRB-tasks. A number of experimental results are reported to validate our approach and compare it with existing analysis. These experimental results illustrate that our method outperforms previous results in terms of precision and runtime. [ABSTRACT FROM PUBLISHER]

This paper investigates the integrated optimization of internal powertrain energy management and external vehicles coordination for multiple autonomous and connected hybrid electric vehicles (HEVs). Most existing research on hybrid vehicles powertrain energy management only considers a single vehicle scenario and is independent from vehicle coordination control. With the advancement of connected and autonomous vehicles, optimizing both the vehicle level coordination and powertrain level power management together can bring in substantial benefits in the overall energy efficiency. In this paper, to enable such an integrated optimization scheme, first, the models of the external vehicle level dynamics and internal powertrain dynamics are built for each vehicle. Second, by integrating the dynamics of these two interactive levels together, an augmented system is formed. Then, the HEVs energy management problem and the decentralized vehicle coordination for a set of HEVs are addressed in a unified framework using dynamic programming. The results show that, as a result of the integrated optimization, the vehicles are able to operate close to the most fuel-efficient region, and the battery can avoid being depleted or over charged while maintaining the same state of charge at the beginning and the end. Moreover, much less fuel consumption can be achieved for the driving cycle considered, compared with the separated optimization method. [ABSTRACT FROM PUBLISHER]

This paper proposes the use of an adaptive control of a hybrid electric vehicle with dual clutch transmission (HDCT). First, this paper shows mathematical equations for the nonlinear system. Then it presents the linearized model for the proposed system. The control objective of the model reference adaptive controller (MRAC) considered in this paper is to minimize fuel consumption and reduce torque interruption in a hybrid electric vehicle. The MRAC can be used to control the electric motor during changes in speed and gear, and the system can adapt to a model that simulates different driver patterns. The effects of using different model responses as input combinations are analyzed in an effort to exploit the over-actuation feature of the system, as is the sensitivity of the performance to various design factors. The simulation results for an HDCT demonstrate that the MRAC achieves reduced torque interruption and less vehicle jerk compared to the conventional method of operation. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) and their application raise new questions and demands. Accordingly, the charging process of EVs is a novelty in everyday life when using such vehicles. This paper presents results of user charging behavior within a field test. In the field test, 500 people with above average yearly mileage took part in integrating the vehicles into their everyday lives. For the results of this paper, the charging behavior of 200 test people is taken into account. The differences in user behavior between battery electric vehicles (BEVs) and extended range electric vehicles (EREVs) are discussed. The results indicate that there are various opportunities for grid services based on EVs, as the vehicles are connected or parked close to a charging station the majority of the time. Moreover, the results also show deviating user behavior for the BEVs and EREVs. [ABSTRACT FROM AUTHOR]

In mixed-criticality (MC) systems, multiple activities with various certification requirements (thus with different criticality levels) can co-exist on shared hardware platforms, where multicore processors have emerged as the de facto computing engines. In this paper, by using the partitioned earliest-deadline-first with virtual deadlines (EDF-VDs) scheduler for a set of periodic MC tasks running on multicore systems, we derive a criticality-aware utilization bound for efficient feasibility tests and then identify its characteristics. Our analysis shows that the bound increases with increasing number of cores and decreasing system criticality level. We show that, since the utilizations of MC tasks at different criticality levels can vary considerably, the utilization contribution of a task on different cores may have large variations and thus can significantly affect the system schedulability under the EDF-VD scheduler. Based on these observations, we propose a novel and efficient criticality-aware task partitioning algorithm (CA-TPA) to compensate for the inherent pessimism of the utilization bound. In order to improve the system schedulability, the task priorities are determined according to their utilization contributions to the system in CA-TPA. Moreover, by analyzing the utilization variations of tasks at different levels, we develop several heuristics to minimize the utilization increment and balance the workload on cores. The simulation results show that the CA-TPA scheme is very effective in achieving higher schedulability ratio and yielding balanced workloads. The actual implementation in Linux operating system further demonstrates the applicability of CA-TPA with lower run-time overhead, compared to the existing partitioning schemes. [ABSTRACT FROM PUBLISHER]

Ferry contributing a significant amount of greenhouse gas is one of the critical vessels to be electrified. Designing a power system for a ferry with hybrid-shaft generators is different from designing a power system for other vessels because of its fixed route. More clearly, ferries repeatedly travel between their port of origin and port of destination, and before the next voyage, the battery must be recharged to the initial state so that the optimal energy management scheme can be repeatedly applied. Furthermore, the flexibility of hybrid-shaft generators, which allow more fuel saving, increases design complexity. In this paper, a mixed-integer non-linear programming problem is first formulated, and a power management algorithm with an initialization step for fulfilling the battery recharging requirement and a refinement step for minimizing the fuel consumption is proposed. The simulation results obtained from data of an actual ferry show that the proposed power management algorithm can fully recharge the battery and consumes less fuel than a rule-based power management scheme. Simulations also reveal that fuel consumption depends on available shore power, highlighting the necessity to develop charging infrastructure for practical electrification. Because of its speed, the algorithm can support hardware sizing, e.g., battery sizing. [ABSTRACT FROM AUTHOR]

This paper presents a constrained robust output feedback $\mathrm{{H}}\infty /\mathrm{{H}}_{2}$ control-based coordinated delay controller (ROHD) to deal with the controller area network (CAN) induced asynchronous delays which exist in the mode transition process (MTP) of a dual motor plug-in hybrid electric vehicle (DM-PHEV). Due to the multiple actuators of DM-PHEV and the limitation of network transmission rate, the control of MTP from electric driving mode (EM) to hybrid driving mode (HM) based on CAN bus inevitably induces random asynchronous delays. Firstly, the problem of MTP with CAN-induced delay is formulated and a mathematical model of MTP with asynchronous delays is built. In order to deal with the model uncertainty and the interference introduced by the measurement signals, ROHD controller is designed to achieve a fast, smooth and stable MTP in the presence of CAN-induced delays. Simulation and hardware-in-the-loop (HiL) test demonstrate that the torque demand and ride comfort of MTP with network-induced delays controlled by proposed controllers are better than those controlled by traditional robust controller. [ABSTRACT FROM AUTHOR]

A domestic combined heat and power (CHP) system is an effective residential distributed energy generation technology. Nano-CHP is a growing technology that simultaneously provides heat and electricity to households. An electric machine (EM) connected to an internal combustion engine (ICE) is used to produce the electricity, whereas an ICE delivers heat. Any conventional ICE exhibits pulsating torque that causes vibration and noise in the system and reduces the powertrain lifetime. An additional task for the electric drive of smoothing the speed oscillations by controlling the EM as an active torque damping system, which applies an inverse torque sequence to the crankshaft, is here proposed. In this paper, different torque damping techniques have been explored with the aim of finding out the best solution; the investigated techniques have been taken from research in the field of hybrid electric vehicles. In order to model the torque engine oscillation phenomenon, an effective but simple ICE model has been developed. It allowed the comparison of the different techniques by simulations; some of them have been later validated by experimental tests carried out on a dedicated laboratory test bench composed of a real ICE connected to an EM. [ABSTRACT FROM PUBLISHER]

Neatly represented and properly classified load profiles are fundamental to many control optimization techniques of modern power systems, especially in a distribution area. This paper presents a novel load profile management software framework for boosting the efficiency of power systems operation. The proposed framework encodes and classifies load profiles in real-time. Imperfections as well as time-shifts in the input (measured power consumption levels) are tolerated by the suggested system, thus always providing accurate, fast and reliable output. The framework’s fully component based structure allows easy customizations of the encoding as well as the classification engines. The default encoding engine is based on an artificial neural network, a variant known as a deep learning auto-encoder comprised from stacked sparse auto-encoders. The default classifier engine is based on an implementation of a locality sensitive hashing algorithm. The developed methodology was tested on the real case of a set of anonymous customers supplied by a power distribution company. The paper also contains an elaboration about the experiences gained during the design, implementation and testing phase of this system as well as a detailed engineering use case of the framework’s applicability. [ABSTRACT FROM PUBLISHER]

Internal combustion engines exhibit fast pulsating short-time dynamics due to the reciprocating cylinder motion, around mean operating points that change comparatively slow due to inputs such as throttle and load. Comparatively, simple mean value engine models (MVEM) describe the slow changes of the averaged states for automotive control and fault diagnosis. In this paper, a bank of state estimators based on MVEMs is used for fault residual generation. Three faults: 1) throttle mass air-flow sensor fault; 2) exhaust gas recirculation valve sensor fault; and 3) exhaust leak fault are considered here. These faults are significant as they affect emission levels. Optimized thresholds for residual classification are derived for minimizing false alarm rates and missed detection rates. The diagnosis logic, based on the principles of structured residuals proposed in literature, is extended here for multiple hypotheses testing. Furthermore, the Dempster–Shafer theory is used to associate a confidence measure with the decision conclusions and this is shown to improve isolation. Performance is demonstrated with automotive engine data obtained from a four-cylinder instantaneous spark-ignition engine (gasoline) system model, developed in the simulation software AMESim. [ABSTRACT FROM PUBLISHER]

Quality score (QS) plays a critical role in sponsored search advertising (SSA) auctions, and in practice is closely correlated to the historical click-through rate (CTR) of an advertisement. The CTR-QS correlation may impose great influence on advertisers’ positioning strategies of selecting the targeting slots in the sponsored list. In the literature, however, QS is implicitly assumed to be an independent variable and exogenously assigned by Web search engines, so that little theoretical or managerial insights can be offered to help understand the positioning dynamics in SSA auctions with CTR-QS correlation. We strive to bridge this research gap in this paper. Based on a discrete time-dependent optimal control model, which explicitly captures the relationship between the historical CTR and QS, we determine the optimal strategy for revenue-maximizing advertisers’ QS-based positioning decisions through a policy-iteration-based numerical approximation method. We also investigate two practically-used heuristic strategies, namely the greedy and farsighted positioning strategies, aiming to examine and help understand advertisers’ real-world positioning dynamics. Our analysis indicates that both the optimal and greedy positioning strategies lead advertisers to monotonically increase or decrease their targeting slots over time, which may cause a polarization trend emerging in SSA markets. Meanwhile, the farsighted positioning strategy can accelerate the polarization. Our simulations show that both the greedy and farsighted strategies have good revenue performance. Our findings indicate that advertisers should monotonically adjust their targeting positions to maximize their revenue in CTR-QS correlated SSA auctions. Our findings also highlight the need for Web search engine companies to set a lowered weight for historical CTRs or use position-normalized CTRs in their QS measurements, so as to suppress the polarization trend. [ABSTRACT FROM AUTHOR]

Home-based rehabilitation has evolved in recent years as a cost-effective and convenient alternative to traditional clinical rehabilitation. This has consequently created the need to design reliable assessment and adaptation mechanisms that are able to measure and analyze the patient’s performance and to accordingly make proper adjustments that conform to the abilities of the patient during the training. This paper proposes a novel context-based adaptive home-based rehabilitation framework to optimize recovery based on patient’s performance and ambient conditions. Adaptation is mathematically modeled to configure the rehabilitation task based on user’s psychophysiological and environmental conditions. A prototype of the framework is tested for three weeks by two patients with wrist deficiencies. The results obtained are promising and indicate that the proposed framework may be very beneficial for home-based rehabilitation. [ABSTRACT FROM PUBLISHER]

Prediction of event such as failure event or critical warning event plays an important role in reliability engineering. In this paper, we present a semiparametric method to predict event occurrences for an individual unit in real time using recurrent event data. The intensity of event occurrence is modeled using the extended Cox proportional-hazard model and the distinction of units is achieved using an additional frailty parameter. The method presented features an online updating scheme, and therefore, can provide the real-time prediction of the occurrence of next event. We demonstrate the efficacy of frailty and the updating scheme through comprehensive numerical experiments and a case study based on real-world data. [ABSTRACT FROM AUTHOR]

The fiber-laser imaging of gas turbine exhaust species project aims to provide a video-rate imaging (100 frames/s) diagnostic tool for application to the exhaust plumes of the largest civil aero-jet engines. This remit, enabled by chemical species tomography (CST) currently targeting carbon dioxide (CO2), requires system design that facilitates expansion of multiple parameters. Scalability is needed in order to increase imaging speeds and spatial resolutions and extends the system toward other pertinent gases such as the oxides of nitrogen and sulfur and unburnt hydrocarbons. This paper presents a fully scalable, noninvasive instrument for installation in a commercial engine testing facility, technical challenges having been tackled iteratively through bespoke optical and mechanical design, and it specifically presents the high-speed data acquisition (DAQ) system required. Measurement of gas species concentration is implemented by tunable diode laser absorption with wavelength modulation spectroscopy (TDLAS-WMS) using a custom, high-speed 10–40-MS/s/channel 14-bit DAQ. For CO2 tomography, the system uses six angular projections of 21 beams each. However, the presented DAQ has capacity for 192 fully parallel 10-Hz–3-MHz differential inputs, achieving a best-case signal-to-noise ratio (SNR) of 56.5 dB prior to filtering. A 12 Ethernet-connected digitization nodes based on field-programmable gate array technology with software control are distributed around a 7-m-diameter mounting “ring.” Hence, the high data rates of 8.96-Gb/s per printed circuit board and 107.52 Gb/s for the whole system can be reduced by using local digital lock-in amplifiers. We believe that this DAQ system is unique in both the TDLAS and CST literatures. [ABSTRACT FROM AUTHOR]

Object detection in embedded systems is important for many contemporary applications that involve vision and scene analysis. In this paper, we propose a novel architecture for object detection implemented in FPGA, based on the Stripe Memory Engine (SME), and point out shortcomings of existing architectures. SME processes a stream of image data so that it stores a narrow stripe of the input image and its scaled versions and uses a detector unit which is efficiently pipelined across multiple image positions within the SME. We show how to process images with up to 4K resolution at high frame rates using cascades of SMEs. As a detector algorithm, the SMEs use boosted soft cascade with simple image features that require only pixel comparisons and look-up tables; therefore, they are well suitable for hardware implemenation. We describe the components of our architecture and compare it to several published works in several configurations. As an example, we implemented face detection and license plate detection applications that work with HD images ($1280\times 720$ pixels) running at over 60 frames/s on Xilinx Zynq platform. We analyzed their power consumption, evaluated the accuracy of our detectors, and compared them to Haar Cascades from OpenCV that are often used by other authors. We show that our detectors offer better accuracy as well as performance at lower power consumption. [ABSTRACT FROM AUTHOR]

An analog neural network computing engine based on CMOS-compatible charge-trap transistor (CTT) is proposed in this paper. CTT devices are used as analog multipliers. Compared to digital multipliers, CTT-based analog multiplier shows significant area and power reduction. The proposed computing engine is composed of a scalable CTT multiplier array and energy efficient analog–digital interfaces. By implementing the sequential analog fabric, the engine’s mixed-signal interfaces are simplified and hardware overhead remains constant regardless of the size of the array. A proof-of-concept 784 by 784 CTT computing engine is implemented using TSMC 28-nm CMOS technology and occupies 0.68 mm2. The simulated performance achieves 76.8 TOPS (8-bit) with 500 MHz clock frequency and consumes 14.8 mW. As an example, we utilize this computing engine to address a classic pattern recognition problem—classifying handwritten digits on MNIST database and obtained a performance comparable to state-of-the-art fully connected neural networks using 8-bit fixed-point resolution. [ABSTRACT FROM AUTHOR]

This paper investigates the design of a series-parallel hybrid electric powertrain that includes an electric variable transmission (EVT) and two mechanical gears that connect the EVT to the rest of the powertrain. The EVT is implemented as a dual rotor electric machine (EM), which from a design perspective can be studied as two separate EMs. The goal is to optimize the size of the two EMs and the two mechanical gears, while minimizing fuel consumption, subject to performance requirements on top vehicle speed and acceleration time. A practical optimization methodology is proposed that decouples the multi-objective problem into three different control subproblems: a slow dynamic subproblem for fuel economy, a fast dynamic subproblem for acceleration performance and a static optimization subproblem for the top speed performance. Efficient dynamic programming formulations are presented to solve the two dynamic subproblems. Two different scenarios are discussed and analyzed, where the first one presents the influence of the EMs’ sizes on the vehicle performance and the second one delivers near optimal configurations of the EVT powertrain for different gear ratios. A case study is also carried out to compare the sizing results between the proposed method and an existing benchmark method, showing that the two EMs could be reduced by 45% and 11.87% respectively, while powertrain performance could still be maintained at the same level. [ABSTRACT FROM AUTHOR]

In this paper, an online gear shift and power split strategy for parallel hybrid electric vehicles (HEVs) is proposed to optimize the fuel consumption. The gear shift control is implemented with a neural network (NN). The training of the NN is performed with data obtained from dynamic programming (DP) for different driving cycles. Simulation results present that the gear shift sequences and fuel consumption obtained from the NN and DP are very similar, underlining the near-optimal gear shift control for HEVs based on the NN. Furthermore, the power split control is realized with action-dependent heuristic dynamic programming (ADHDP). ADHDP does not need a system model and allows learning continuously. The power split control is therefore robust with respect to uncertainties and disturbances, as well as adaptive with respect to various driving situations and driver behaviors. The gear shift control based on the NN and the power split control based on ADHDP are combined to an adaptive energy management strategy with possible real-time application. It enables online learning of the controller and prevents the curse of dimensionality from DP calculation. Simulations for different driving cycles and comparisons with optimal solutions by DP indicate that the proposed energy management strategy is robust, adaptive, and near optimal. [ABSTRACT FROM AUTHOR]

Free-piston engine coupled with linear generator is a new kind of thermoelectric energy converter. It has been highly concerned due to its high efficiency and low emission in recent years. In this paper, a tubular moving-coil linear generator is developed for the application of free-piston engines. The two-dimensional finite-element model of the linear generator is established, and its electromagnetic characteristics are analyzed. A power converter is designed to control the operating state and the electric current of the linear generator. A prototype is developed and used in a free-piston engine with a bore of 102 mm, and the main performance parameters are tested. According to the simulated and tested results, the linear generator can realize the stable running of free-piston engines. The peak power of the linear generator in a cycle is about 16.77 kW, and the averaged generating power of a cycle is 3.37 kW at the working frequency of 10 Hz. The generating efficiency at peak power of a cycle is about 95.2%, the averaged generating efficiency of a cycle is about 87.5%. [ABSTRACT FROM AUTHOR]

Connected vehicle (CV) technology allows traffic information sharing that can be utilized to improve fuel benefits of hybrid electric vehicles (HEVs). However, there are challenges from the powertrain optimization perspective. First, optimizing powertrain operations is computational heavy due to the nonlinearities, constraints, and high dimensionality of the problem, especially with long optimization time horizon. Secondly, a slow powertrain optimization means the solution may not be optimal anymore at current time if the traffic states change. In this paper, a fast, near global-optimal, and practical HEV powertrain optimization utilizing vehicle speed prediction in CV environment is systematically formulated and solved. First, the minimum fuel rate and battery state-of-charge rate are calculated for different engine and battery power-split ratios. Then, the nonlinear minimum fuel rate and battery state-of-charge rate are piecewise-linearized by introducing dimensionless variables. The engine operating range, engine dynamics, and battery charge-sustaining constraint are represented by the dimensionless variables and treated as linear constraints. This transformation exploits the problem's structure, known as Separable Programming, which is solved efficiently as a large-dimension constrained linear problem. Fast optimization allows utilization of repeated speed predictions to maintain prediction accuracy. Results show comparable fuel economy with Dynamic Programming with significantly less computation time. Average fuel savings of 4.0% and 10.4% over Pontryagin's Minimum Principle and Rule-Based optimization methods are observed. Experimental results show the feasibility of the optimized engine operating points with fuel benefits over the rule-based method. Simulations with vehicle speed prediction and limited Gaussian uncertainties to emulate CV settings also show satisfactory fuel benefits. [ABSTRACT FROM AUTHOR]

According to a recent Deloitte study, the COVID-19 pandemic continues to place a huge strain on the global health care sector. Covid-19 has also catalysed digital transformation across the sector for improving operational efficiencies. As a result, the amount of digitally stored patient data such as discharge letters, scan images, test results or free text entries by doctors has grown significantly. In 2020, 2314 exabytes of medical data was generated globally. This medical data does not conform to a generic structure and is mostly in the form of unstructured digitally generated or scanned paper documents stored as part of a patient’s medical reports. This unstructured data is digitised using Optical Character Recognition (OCR) process. A key challenge here is that the accuracy of the OCR process varies due to the inability of current OCR engines to correctly transcribe scanned or handwritten documents in which text may be skewed, obscured or illegible. This is compounded by the fact that processed text is comprised of specific medical terminologies that do not necessarily form part of general language lexicons. The proposed work uses a deep neural network based self-supervised pre-training technique: Robustly Optimized Bidirectional Encoder Representations from Transformers (RoBERTa) that can learn to predict hidden (masked) sections of text to fill in the gaps of non-transcribable parts of the documents being processed. Evaluating the proposed method on domain-specific datasets which include real medical documents, shows a significantly reduced word error rate demonstrating the effectiveness of the approach. [ABSTRACT FROM AUTHOR]

Although real-time analytics on the up-to-date dataset has become an emerging demand, many big data systems are still designed for offline analytics. Particularly, for critical applications like Fintech, transactional data ingestion ensures a timely, always-correct, and scalable dataset. To carry out append-only ingestion, existing OLTP/HTAP systems are based on strict transactions with imperfect scalability, while NoSQL-like systems support scalable but relaxed transactions. How to ensure essential transactional guarantees without harming scalability seems to be a non-trivial issue. This paper proposes Karst to bring transactional data ingestion for existing offline analytics. We notice that blocking two-phase commit (2PC) to resolve transactional data ingestion is a performance killer for the partitioned analytical systems. Karst introduces a scalable protocol called metadata-oriented commit (MOC) that converts each distributed transaction into multiple partial transactions to avoid 2PC. Moreover, to ingest massive data into plenty of partitions, Karst also employs lazy persistence, lightweight logging, and optimized data traffic. In experiments, Karst could achieve up to about 2x $\sim$ ∼ 10x performance over relevant systems and also shows remarkable scalability. [ABSTRACT FROM AUTHOR]

High-performance multicast-enabled packet forwarding engines (PFEs), as an essential component of high-end switches, use a polynomial-time membership query algorithm to determine which port(s) the data packet should be forwarded. The currently widely used query algorithm is Bloom Filter (BF), which has been proven to have many fatal flaws. Another error-free membership query algorithm includes Scalar-pair Vectors Routing Forwarding (SVRF), Fractional-N Scalar-pair Vectors Routing Forwarding (Frac-N SVRF), and the Per-Port Prime Filter Array (P3FA) also have some shortcomings in space and time efficiencies. In this paper, we proposed a hybrid strategy: Divaricate Heterogeneous SVRF (DH-SVRF) scheme, which based on the P3FA and Frac-N SVRF, which randomly divides all member ships into N groups, and each group has the same structure and is independent of each other to obtain higher time efficiency and space utilization. Finally, we also discussed the selection of the optimal egress-diversity threshold. Through mathematical modeling and simulation, we validate that the proposed DH-SVRF scheme is superior to the SVRF/Frac-N SVRF and traditional BF in terms of scalability, space utilization, and time efficiency in specific conditions such as appropriate egress-diversity thresholds. [ABSTRACT FROM AUTHOR]

The robot’s mobility and intelligence have expanded its application in recent years. Specifically, indoor tracking is a fundamental function of public service robots in nursing homes, hospitals, and warehouses. Existing vision-based tracking requires visual information, which may be unavailable and introduce privacy issues in practical deployment. To this end, in this paper, we propose $\mathsf {LTrack}$ , a long-range tracking system based on LoRa, an emerging low-power wide-area networking (LPWAN) technology, with a single transceiver pair. Note that commodity LoRa devices cannot estimate the angle of arrival (AoA) of signals due to hardware limitations. We design a virtual circular antenna array in the mobile rotating anchor via a lightweight hardware modification to multiplex the only RF channel in the low-cost LoRa device. The difference of time of flight (TDoF) measured in the circular antenna array is fused with the rotating orientation to estimate the target AoA. We also redesign and optimize the primitive LoRa ranging engine based on systematic analysis. Further, we present a real-time mobile target tracking algorithm based on the Doppler frequency shift to combat the uncertainty introduced by the target movement. We have developed the prototype of $\mathsf {LTrack}$ , which consists of a mobile rotating anchor, a LoRa tag, and a commercial robot. The system is evaluated in both LOS and NOLS indoor scenarios. Experiments show that $\mathsf {LTrack}$ supports robust tracking with a median error of 0.12 m and 0.45 m in a $\text{137}\,\text{m}^2$ lab space and a $\text{600}\,\text{m}^2$ corridor, respectively. [ABSTRACT FROM AUTHOR]

This paper presents coordinated power management strategies for a hybrid electric boat (HEB) based on dynamic load's power distribution approach using diesel generators and batteries. Two variable-speed diesel generator sets connecting to dc-bus via fully controlled rectifiers serve as the main power supply source; one lithium-battery pack linked to dc-bus through a bidirectional DC/DC converter serves as an auxiliary power supply source. The power requirement of the thrusters and other onboard equipment is represented by a load power profile. The main contribution of this paper is focused on the load power sharing of the HEB between the two variable-speed diesel generators and the lithium-battery pack, through the fluctuations extracted from load power and their assignation to the batteries. Another important issue is that when the total load demand is low, one of the diesel generator set will be switched off to improve the diesel-fuel efficiency. The output powers of the diesel generators and the batteries are controlled based on the power distribution strategy via the converters controlling. The performances of the proposed hybrid power system control are evaluated through simulations in MATALB/Simulink, and through reduced-scale experimental tests carried out for a specific driving cycle of the HEB. [ABSTRACT FROM PUBLISHER]

Fuel consumption of fossil-based road vehicles is significantly affected by the way vehicles are driven. The same is true for automated vehicles with longitudinal control. This paper presents a periodic servo-loop longitudinal control algorithm for an adaptive cruise control (ACC) system to minimize fuel consumption in car-following scenarios. The fuel-saving mechanism of pulse-and-glide (PnG) operation is first discussed for the powertrain with internal combustion engine and step-gear transmission. The servo-loop controller is then designed based on a periodic switching map for real-time implementation and adjusted with a range-bounded feedback regulator to enhance the robustness to model mismatch. Simulations in both uniform and natural traffic flows demonstrate that this algorithm achieves a significant fuel-saving benefit in automated car-following scenarios up to 8.9% in naturalistic traffic flow (when coasting at neutral gear), compared with a linear quadratic (LQ) controller. Meanwhile, its intervehicle range is preferably bounded so that the negative impact on safety and traffic smoothness is contained. [ABSTRACT FROM PUBLISHER]

Online set intersection operations have been widely used in network processing tasks, such as Quality of Service differentiation, firewall processing, and packet/traffic classification. The major challenge for online set intersection is to sustain line-rate processing speed; accelerating set intersection using state-of-the-art hardware devices is of great interest to the research community. In this paper, we present a novel high-performance set intersection approach on FPGA. In our approach, each element in any set is represented by a combination of Group ID (GID) and Bit Stride (BS); all the sets are intersected using linear merge techniques and bitwise AND operations. We map our online set intersection algorithm onto hardware; this is done by constructing modular Processing Element (PE) and concatenating multiple PEs into a tree-based parallel architecture. In order to improve the throughput on a state-of-the-art FPGA, we feed all the inputs to FPGA in a streaming fashion with the help of the synchronization GIDs. Post place-and-route results show that, for a typical set intersection problem in network processing, our design can intersect \texteight sets, each of up to $32$ K elements, at a throughput of $47.4$ Thousand Intersections Per Second (KIPS) and a latency of $94.8\,\mu$ s per batch of inputs. Compared to the classic linear merge or bitwise AND techniques on state-of-the-art multi-core processors, our designs on FPGA achieves up to $66\times$ throughput improvement and $80\times$ latency reduction. [ABSTRACT FROM AUTHOR]

With the advent of the Internet and Internet-connected devices, modern business applications can experience rapid increases as well as variability in transactional workloads. Database replication has been employed to scale performance and improve availability of relational databases but past approaches have suffered from various issues including limited scalability, performance versus consistency tradeoffs, and requirements for database or application modifications. This paper presents Hihooi, a replication-based middleware system that is able to achieve workload scalability, strong consistency guarantees, and elasticity for existing transactional databases at a low cost. A novel replication algorithm enables Hihooi to propagate database modifications asynchronously to all replicas at high speeds, while ensuring that all replicas are consistent. At the same time, a fine-grained routing algorithm is used to load balance incoming transactions to available replicas in a consistent way. Our thorough experimental evaluation with several well-established benchmarks shows how Hihooi is able to achieve almost linear workload scalability for transactional databases. [ABSTRACT FROM AUTHOR]

This paper presents a design of driveline torque observer with dual clutch transmission (DCT). A goal of this study is to improve estimation performance of the observer. To increase the observer’s performance, it is important to make an accurate driveline model and a robust observer. First, to increase the accuracy of the model, we propose lumped driveline efficiency which is a parameter that expresses driveline transmission loss. In addition, a recursive least square estimation (RLSE) algorithm is proposed to estimate the parameter using driving data. Then, the model is updated using the estimated parameter value. Second, a reduced order observer is proposed to estimate transmitted torques of driveline in both driving and gear shifting process. Moreover, the observer, which is robust against measurement noise and disturbance, is designed through frequency domain analysis. For frequency domain analysis, an eigenstructure assignment method is applied to tune observer gains. Performance of the proposed RLSE algorithm and the observer are verified through simulation and testbench experiments. [ABSTRACT FROM AUTHOR]

This paper investigates a vehicular platoon control problem with prescribed tracking performance in the presence of actuator saturation, uncertain parameters, and unknown disturbances. Two adaptive sliding mode control schemes based on leader–predecessor and leader–bidirectional information flows are presented to ensure string stability and strong string stability, respectively, with a prescribed tracking performance. The actuator saturation nonlinearity is addressed by approximating it with a smooth hyperbolic tangent function. The effects of uncertain parameters and exogenous disturbances are dealt with by introducing a set of adaptation laws. The effectiveness of the proposed control schemes is demonstrated via numerical simulation results. [ABSTRACT FROM AUTHOR]

This paper presents an overview of technology related to on-board microgrids for the more electric aircraft. All aircraft use an isolated system, where security of supply and power density represents the main requirements. Different distribution systems (ac and dc) and voltage levels coexist, and power converters have the central role in connecting them with high reliability and high power density. Ensuring the safety of supply with a limited redundancy is one of the targets of the system design since it allows increasing the power density. This main challenge is often tackled with proper load management and advanced control strategies, as highlighted in this paper. [ABSTRACT FROM AUTHOR]

This paper proposes an integrated method of three-phase ac excitation and high-frequency voltage signal injection (HFVSI) for sensorless controlled starting of brushless synchronous machines (BSM) used as starter/generator in variable frequency ac power systems of civil aircraft. Fixed 400 Hz of the three-phase ac power is adopted both for the ac excitation and HFVSI in the initial starting process to eliminate the bulky rotor position sensor for BSM. The resulting 6th sequence harmonic voltage determined by the rotating rectifier is utilized as the HFVSI into the field-winding of main generator without any extra high-frequency signal injection. The rotor position is estimated by the high-frequency response signals extracted from the armature windings of main generator. Due to the nonlinear rotating rectifier linked in the HFSI chain, a novel frequency-insensitive asynchronous demodulation strategy is proposed in this paper for rotor position estimation. Furthermore, the initial rotor position detection is calibrated by polarity decision of the induced currents of the armature windings within the establishment procedure of field current of the main generator at standstill. The effectiveness of the ac excitation and feasibility of rotor position estimation for sensorless starting control of BSM are validated by the simulation and experimental results. [ABSTRACT FROM AUTHOR]

A hybrid electric unmanned aircraft (HEUA) uses the aerial thermal engine and energy storage system (ESS)-driven electric motor to meet the load demand, thus can be viewed as a “mobile multienergy microgrid.” Conventionally, for a given flight mission, only the energy of an HEUA is optimized while the flight speed is fixed; thus, the overall energy efficiency may be limited. This paper aims to coordinately schedule the energy consumption and the flight speed of an HEUA toward optimal economic and environmental objectives. First, a coordinated optimization model is proposed to coordinately schedule the energy consumption (including the power output of the thermal engine and the ESS) and flight speed (i.e., the true air speed) of an HEUA for simultaneously minimizing the fuel consumption (FC) and polluted gas emission (PGE). Second, a decomposition-based solution method is developed to convert the original nonlinear multiobjective optimization model into a bilevel programming model which can be solved iteratively. Simulation results demonstrate that, compared with the conventional single energy scheduling, the proposed coordinated scheduling method can achieve much better performance in terms of FC and PGE. [ABSTRACT FROM AUTHOR]

In this paper, the computation of eco-driving cycles for electric, conventional, and hybrid vehicles using receding horizon and optimal control is studied. The problem is formulated as consecutive-optimization problems aiming at minimizing the vehicle energy consumption under traffic and speed constraints. The impact of the look-ahead distance and the optimization frequency on the optimal speed computation is studied to find a tradeoff between the optimality and the computation time of the algorithm. For the three architectures considered, simulation results show that in urban driving conditions, a look-ahead distance of 300–500 m leads to a sub-optimality less than $1\%$ in the energy consumption compared to the global solution. For highway driving conditions, a look-ahead distance of 1–1.5 km leads to a sub-optimality less than  $2\%$ compared to the global solution. [ABSTRACT FROM AUTHOR]

This paper presents a predictive control design for diesel vehicle platoons, such as medium- and heavy-duty truck platoons, to achieve lower NOx emissions. By incorporating vehicle longitudinal dynamics control and powertrain estimation with aftertreatment system control, an enhanced emission performance can be attained for the entire platoon considering different operating conditions for individual vehicles. The proposed method utilizes exhaust gas predictive information within a preview horizon to generate optimal control actions such that the tradeoff between NOx reduction efficiency and NH3 slip in the aftertreatment systems can be well-managed. Performance improvement is demonstrated through simulation studies based on an experimental validated powertrain and aftertreatment system model. [ABSTRACT FROM AUTHOR]

Road gradient and vehicle velocity are critical information in deciding the power demand of hybrid electric vehicles (HEVs), and greatly impact the powertrain energy management performances. Generally, the road gradient is assumed to be known in previous studies. This paper presents a data-driven autoregressive integrated moving average (ARIMA) based method, aiming to predict the short-term future velocity and road gradient in real time for the predictive energy management of HEVs. The established ARIMA-based learning model provides vehicle velocity and road gradient prediction references during each control horizon for the controller. Model predictive control is employed to construct the predictive energy management strategy, with dynamic programming to resolve the optimal powertrain control problem during each control horizon. Real driving cycle and road gradient data are collected via experiments, and used to establish the ARIMA predictors. Simulation results show that the ARIMA model is able to predict the future velocity and road gradient with reasonable accuracy. With the ARIMA predictor used in the predictive energy management strategy, the HEV fuel consumption is effectively reduced by about 5%–7% compared with when no predictor used. [ABSTRACT FROM AUTHOR]

In this paper, the configuration of a novel plug-in hybrid electric vehicle (PHEV) system is proposed. For the novel PHEV system, the main operating modes and the control strategy, including rules for mode switching and energy management, are established. A backward model of the vehicle involving the power sources, transmission, and control system is worked out in MATLAB/Simulink/Stateflow environment. The distribution of operating points of each power source, as well as the fuel consumptions of the vehicle under new European driving cycle (NEDC) and FTP75 testing cycles are simulated and compared with typical hybrid systems such as parallel, series, and Toyota hybrid system (THS). The results indicate that the proposed system can operate at high fuel economy by adopting the rule-based energy management strategy along with the equivalent consumption minimization strategy, which proves the feasibility and effectiveness of the configuration and control strategies. [ABSTRACT FROM AUTHOR]

For the online energy optimization problem of plug-in hybrid electric vehicles (P-HEVs), this paper proposes a heuristic dynamic programming (HDP) based online energy management strategy, to minimize the fuel consumption of the P-HEV. First of all, considering the uncertain nonlinear dynamic process of a vehicle in the actual traffic environment, we adopt the back propagation neural network (BPNN) to construct the dynamic model of the P-HEV. Then, on this basis, we utilize the HDP to establish an energy management controller with the aim of minimizing energy consumption of the P-HEV. Moreover, the energy management controller is implemented by an online energy management strategy algorithm. To verify the effect of the controller, we employ a practical route in Beijing road network to simulate the BPNN model of the P-HEV and the proposed energy management strategy. The experimental results show several advantages of our strategy. First, compared to the analytic model, the BPNN model can reflect the real dynamic process of the P-HEV with a higher precision. Second, the assigned torques by the strategy can effectively make the vehicle track the desired vehicle-speeds, and the tracking accuracy of the vehicle-speed is higher than 98%. Besides, on the premise of ensuring the real-time performance, the proposed strategy can further reduce the fuel consumption and emissions of the P-HEV when compared with the existing online energy management strategies, although its fuel consumption is more than that of the offline global optimization energy management strategy by 4% approximately. [ABSTRACT FROM AUTHOR]

Substantial challenges in power systems operation and control as a result of the intermittent and stochastic nature of wind power generation can be significantly alleviated by proficient very short-term wind power prediction interval (WPPI) models. In WPPI models, minimization of cost functions is conducted to train prediction engines and consequently tune their parameters. The prevalent cost functions of prediction engines in WPPI models are mainly non-differentiable and non-convex, and therefore the training process becomes problematic. To transcend such a crucial barrier, this paper addresses a new very short-term WPPI framework based on a bi-level formulation and benefiting from a differentiable and convex cost function. The prediction engine is trained by classical global optimization of the cost function in the lower-level problem, while hyperparameters that control the quality of the WPPIs are injected thereto from the upper-level problem. The hyperparameters can be tuned such that the most useful WPPIs are constructed from the lower-level problem depending on the power system operator's preferences. Lessening the need to heuristically tune a large number of prediction engine parameters is the foremost contribution of this work to the WPPI literature. The superior performance of the proposed WPPI is verified in the multistep ahead prediction of real wind power generation data in comparison to well-tailored benchmark models. [ABSTRACT FROM AUTHOR]

This paper describes a high-performance neutral point voltage balancing technique for a neutral point clamped (NPC) converter. Conventional neutral point voltage balancing methods do not function well under low power factor, low pulse ratio, and near-unity modulation index operation conditions. These conditions are essentially dominant operation conditions for aircraft starter/generator systems. This paper introduces an alternative space vector modulation technique for three-level NPC converters in an aircraft starter generator system. The selection of voltage space vectors is optimized for high modulation index and low power factor operation. Disturbances caused by a low pulse ratio are also compensated. The proposed method maintains neutral point voltage balance and ripple minimization over the full range of operating conditions. This paper also provides a detailed analysis of the sources of neutral point voltage imbalances and ripples in high-speed drives with deep flux weakening. Simulation results obtained from a Simulink/PLECS model and experimental results obtained from a 45 kVA, 32 krpm aircraft starter generator test rig prove that the proposed method eliminates the neutral point voltage imbalance and significantly reduces the neutral point voltage ripple. [ABSTRACT FROM AUTHOR]

Velocity profile optimization of on-road vehicles is one of the main eco-driving techniques, which has great potential to extend the capability of powertrain and automatic longitudinal control by minimizing the energy consumption. Due to the multi factors affecting the driving trajectory and longer prediction horizon comparing with other traditional control, the calculation of a velocity profile optimization often requires a large number of computations. In this paper, a hierarchical control (HC) strategy of velocity optimization is proposed to reduce computation burden with little accuracy loss. In the HC strategy, a specific driving task is divided into several operation of modes as acceleration (A), constant speed (C), deceleration (D), and braking (B). The shift timing of the driving modes are optimized by formulating a nonlinear programming problem in a master controller. Then, engine torque, gear position, and brake force are optimized in each driving mode. Results indicate that the computation time of velocity profile optimization using the proposed HC strategy is reduced by 90% of the ones using the basic centralized optimal controller while the resulting velocities are similar. It is also shown that an improvement of 30% in fuel economy is achieved compared with the real-life human-driven velocity profiles. [ABSTRACT FROM AUTHOR]

Clouds offer flexible and economically attractive compute and storage solutions for enterprises. However, the effectiveness of cloud computing for high-performance computing (HPC) systems still remains questionable. When clouds are deployed on lossless interconnection networks, like InfiniBand (IB), challenges related to load-balancing, low-overhead virtualization, and performance isolation hinder full potential utilization of the underlying interconnect. Moreover, cloud data centers incorporate a highly dynamic environment rendering static network reconfigurations, typically used in IB systems, infeasible. In this paper, we present a framework for a self-adaptive network architecture for HPC clouds based on lossless interconnection networks, demonstrated by means of our implemented IB prototype. Our solution, based on a feedback control and optimization loop, enables the lossless HPC network to dynamically adapt to the varying traffic patterns, current resource availability, workload distributions, and also in accordance with the service provider-defined policies. Furthermore, we present IBAdapt, a simplified ruled-based language for the service providers to specify adaptation strategies used by the framework. Our developed self-adaptive IB network prototype is demonstrated using state-of-the-art industry software. The results obtained on a test cluster demonstrate the feasibility and effectiveness of the framework when it comes to improving Quality-of-Service compliance in HPC clouds. [ABSTRACT FROM AUTHOR]

With the development of more electric aircraft, higher demand for electrical energy is put forward in generation systems. The constant speed drive is eliminated in variable frequency ac (VFAC) generation systems, which makes integrated starter-generator (SG) can be realized. Considering the inherent defects of wound rotor synchronous SG systems, such as the complex starting excitation methods and rotating rectifiers, this paper proposes a VFAC SG system based on hybrid excitation synchronous machines (HESMs) for aircraft applications. The key technology of HESM-based VFAC SG system is to regulate the field current in such a way that three major modes of VFAC SG system operation for aircraft applications, namely, engine starting operation, transition mode, and generating operation, can be realized effectively. The simulation model of HESM-based VFAC SG system is built and analyzed. A prototype HESM-based VFAC SG system has been designed and developed for experimental verification. The starting operation, transition mode, and generating operation of the prototype HESM-based VFAC SG system are implemented. The feasibility of HESMs to VFAC SG system for safety-critical aircraft applications has been validated. [ABSTRACT FROM AUTHOR]

Power electronic converter-interfaced distributed energy resources (DERs) are being increasingly deployed for achieving high energy efficiency, power quality, and flexibility of power system operation and controls. They facilitate access to a wide array of energy sources, including renewables, fuel cells, microturbines, variable speed engine-generator sets, etc. However, recent tests carried out at the Consortium for Electric Reliability Technology Solutions (CERTS) Microgrid have indicated that their deployment in the mixed source microgrid can cause a cascading collapse during extreme events. To investigate the problem, simulation models of two types of DERs are developed in PSCAD/EMTDC software and validated with the experimental test results. Furthermore, the validated models are used to study a cascading collapse problem in a large-scale mixed source microgrid on the benchmark IEEE 33-bus test system. In this paper, three alternative techniques are evaluated to prevent the cascading collapse in the large-scale microgrid caused by fast-acting inverter-based DERs. [ABSTRACT FROM AUTHOR]

Mining trucks are sophisticated equipment that must operate continuously under difficult environmental conditions at the limit of their capacities. Hence, any tool that helps to evaluate the stress of mechanical components and/or overloading of electrical components will be of great benefit. This paper presents the development of an integrated electric–mechanical model of a hauling truck capable of determining all the variables of interest of the powertrain, traction, and retarding systems. The developed model is first evaluated on idealized uphill and downhill routes, providing a clear view of the performance of each component. These results are then applied to the analysis of a complete downhill–uphill truck cycle in a copper mine. Finally, selected sectors of the mine road are evaluated in order to compare field records with the corresponding signals of the developed model. Results show a good agreement with errors between 2% and 5%. [ABSTRACT FROM AUTHOR]

This correspondence paper introduces a novel multi-objective controller, which regulates A/C system operation in a tradeoff between vehicle cabin comfort and fuel consumption for a conventional vehicle with internal combustion engine. The controller has been developed and tested in a simulated environment, where an energy-based model of the A/C system is combined with a thermal dynamic model of the cabin, which considers heat transfer to the environment. The control algorithm proposed herein is compared with two widely used control techniques in the industry, respectively the thermostat and PI control, under different driving cycles. This novel method is implementable in real-time, and simulation results show a reduction of up to 2% in A/C system fuel consumption compared to existing methods with similar thermal performance. [ABSTRACT FROM AUTHOR]

The performance of energy management systems in hybrid electric vehicles (HEVs) is highly related to drivers’ driving style. This paper proposes a driving-style-oriented adaptive equivalent consumption minimization strategy (AECMS-style) in order to improve fuel economy for HEVs. For this purpose, first, a statistical pattern recognition approach is proposed to classify drivers into six groups from moderate to aggressive using kernel density estimation and entropy theory. Then, the effects of driving style on energy management strategies are discussed by analyzing the performance of the equivalent consumption minimization strategy (ECMS). Based on the comprehensive analysis, we design a new optimal equivalent factor adjustment rule for the AECMS-style and also redesign the braking strategy of motors at driving charging mode for different driving styles. Finally, five drivers with typical driving styles participate in experiments to show the effectiveness of our proposed method. Experimental results demonstrate that the AECMS-style can improve the fuel economy and charging sustainability of HEVs, compared with ECMS. [ABSTRACT FROM AUTHOR]

This paper presents a fast fuzzy logic control algorithm for a Takagi–Sugeno–Kang fuzzy controller, which is applied to integrated \textLED drivers with the purpose of getting a good dynamic response and the reduction in the low-frequency (120 Hz) ripple. The algorithm uses the high-speed mathematical engine of a digital signal controller \text(DSC) that is capable to achieve fast multiplications during a common one-cycle instruction. Therefore, the execution time of the controller is improved with the use of a \textDSC. A scaling procedure is included in the program in order to use and to improve the accuracy of the multiplications and divisions in the \textDSC mathematical engine. A low-range \textDSC was used to program the proposed controller; therefore, the cost is affordable for \textLED driver applications. The fuzzy control curve was approximated to the sliding mode control \text(SMC) curve with the purpose of getting some of the good advantages of the \textSMC strategy like a good dynamic performance. The proposed controller is used to regulate the \textLED current. Simulation and experimental results are shown [ABSTRACT FROM PUBLISHER]