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The prototype O system seamlessly integrates Web searching and browsing on touch-enabled tablets using two-handed gesture interaction, much like handling and marking content on a piece of paper. The system also leverages contextual information to infer user intent and significantly improve search results over traditional keyword-based approaches, enabling users to go beyond websites' "walled garden"' and more fully explore information objects. [ABSTRACT FROM PUBLISHER]

Graph engines have been used in many applications to handle big graphs efficiently. The majority of the research to improve their performance has focused primarily on the design of efficient graph processing. This paper claims, however, the focus should be given also to graph storage design. This is because good storage design can improve both CPU performance and I/O performance of graph engines. In this paper, we propose an efficient data layout for single-machine based graph engines. We identify the common node access pattern of the graph algorithms running on single-machine based graph engines. Based on this finding, we propose the breadth-first (BF) data layout which places the nodes processed together in the same or adjacent storage space so that they can be accessed together as much as possible. The experimental results show that the BF data layout improves both CPU and I/O performances significantly in all single-machine based graph engines. [ABSTRACT FROM AUTHOR]

In the controller development process, it is required to verify whether control functions operate normally, without fault or anomaly. Many control functions with complex system structures require various actual tests and test data analysis for verification. This paper presents an anomaly detection algorithm to verify control functions and to more efficiently analyze test data of a hybrid control unit (HCU) of a hybrid electric vehicle (HEV). The anomaly detection algorithm automatically detects anomaly of control functions from the test data, instead of manual labor by engineers. The examined target HCU control functions in this paper are the engine clutch engagement/disengagement control function and the engine start cooperative control function, which are crucial functions of transmission mounted electric drive (TMED) HEVs. A data-driven approach using a one-class support vector machine (SVM) is used to make it easy to apply various control functions. Actual vehicle test data is examined by the algorithm to verify the control functions at an actual vehicle level. The developed anomaly detection algorithm demonstrates feasibility and effectiveness of the proposed algorithm in detecting not only prior-known anomalies but also prior-unknown anomalies. Because other vehicle control functions have similar characteristics with the target functions of this paper, this algorithm is expected to be successfully applied to other HCU control functions. [ABSTRACT FROM AUTHOR]

This paper examines the problem of predictive gear scheduling for fuel consumption minimization in connected/automated heavy trucks. The literature highlights the fuel economy benefits of such predictive scheduling, but there is a need to optimize such scheduling online, in real time. To address this need, we begin by using dynamic programming (DP) to schedule gear shifting offline, in a manner that achieves a globally optimal Pareto tradeoff between the conflicting objectives of minimizing fuel consumption and shift frequency. The computational cost of DP is unfavorable for online implementation, but we present two algorithms addressing this challenge. Both algorithms rely on the fact that in the Pareto limit where fuel consumption minimization is the sole objective, DP furnishes a simple static shift map. Our first algorithm trains a recurrent neural network to prune the shift schedule generated by this map. The second algorithm performs this pruning in a direct manner tailored to reduce the schedule's rain flow count. We simulate these algorithms for different drive cycles. Both algorithms achieve a reasonable tradeoff between fuel consumption and gear shift frequency. However, the rain flow count algorithm is both more effective in approaching the DP-based Pareto front and more computationally efficient. [ABSTRACT FROM AUTHOR]

The engine size of modern passenger transport aircraft is principally determined by take-off conditions, since initial acceleration requires maximum engine power. An electromagnetic launch (EML) system could provide some or all of the energy required at takeoff so that the aircraft engine power requirement and fuel consumption may be significantly reduced. So far, EML for aircraft has been adopted only for military applications to replace steam catapults on the deck of aircraft carriers. This paper will describe the potential application of EML to propel civil aircraft on the runways of modern airports. A comparison of synchronous and asynchronous electrical motor systems designed to launch an A320-200 sized aircraft is presented. The paper also describes a solution of the transient heat transfer problem applied to the conductive components of EML systems. [ABSTRACT FROM PUBLISHER]

Using high-level synthesis techniques, this paper proposes an adaptable high-performance streaming dataflow engine for sparse matrix dense vector multiplication (SpMV) suitable for embedded FPGAs. As the SpMV is a memory-bound algorithm, this engine combines the three concepts of loop pipelining, dataflow graph, and data streaming to utilize most of the memory bandwidth available to the FPGA. The main goal of this paper is to show that FPGAs can provide comparable performance for memory-bound applications to that of the corresponding CPUs and GPUs but with significantly less energy consumption. The experimental results indicate that the FPGA provides higher performance compared to that of embedded GPUs for small and medium-size matrices by an average factor of 3.25 whereas the embedded GPU is faster for larger size matrices by an average factor of 1.58. In addition, the FPGA implementation is more energy efficient for the range of considered matrices by an average factor of 8.9 compared to the embedded CPU and GPU. A case study based on adapting the proposed SpMV optimization to accelerate the support vector machine (SVM) algorithm, one of the successful classification techniques in the machine learning literature, justifies the benefits of utilizing the proposed FPGA-based SpMV compared to that of the embedded CPU and GPU. The experimental results show that the FPGA is faster by an average factor of 1.7 and consumes less energy by an average factor of 6.8 compared to the GPU. [ABSTRACT FROM AUTHOR]

During the development or deployment of discrete event simulation systems, many sudden changes in requirements emerge. To embrace the changes rapidly and reduce development costs, attaching reusable, and black-boxed components to existing systems has been regarded as one of the most effective approaches because modifications at the code level are generally costly and risky. However, since this approach requires components that are an exact fit, it may not be easy to avoid the modifications at the source code level actually. Moreover, the required components may not exist. Hence, this paper applies black-box extensibility to simulation systems in order to avoid the modifications. This paper proposes adaptive discrete event simulation systems using event control models for extending and modifying semantics through event-based simulation interface. With the proposed work, the simulation events are modulated, deleted, and generated by the event-oriented control functions in the event control model to embrace the changes of requirements. It can substitute for the modifications at the code level and extend the existing behavioral semantics. As a result, the proposed work provides a new alternative step in the development process with reusable components to avoid modifications at the source code level. The new step will lead to rapid adaptations of existing simulation systems. To support the effectiveness of this approach, this paper will describe applicable examples based on our empirical studies. [ABSTRACT FROM AUTHOR]

The fuel efficiencies of Hybrid Electric Vehicles (HEVs) depend heavily on the control concepts, especially on energy management strategies, because the system efficiency of the vehicle is determined by the exploitation of the electric energy. Control concepts based on optimal control theories like Dynamic Programming (DP) or Pontryagin's Minimum Principle (PMP) have been widely studied over the last two decades, and it has been proven that these control concepts provide near-optimal solutions if future driving conditions are given prior to actual driving. However, it is difficult to apply to real vehicles unless the future driving conditions are known. In this paper, a well-known control strategy based on PMP has been developed, which needs only current driving conditions. The significance of the study is that the control concept introduced herein was evaluated in the IEEE VTS Challenge 2018 competition. It produced the best results in terms of the total energy consumption, where 52 controllers had been competing to minimize the energy consumption in unknown driving cycles. Although the control system was designed from traditional PMP-based control, it becomes a practicable and effective solution by using an adaptive concept for balancing State Of Charge (SOC) of a battery. This study is an extended version of the study presented during IEEE VPPC 2018 and provides the development process of the control concept used in the controller during the IEEE VTS Challenge 2018. [ABSTRACT FROM AUTHOR]

In automotive embedded real-time systems, e.g., the engine control unit, tasks are activated whenever the engine arrives at a specific angular position. In consequence the frequency of activation changes with the speed of the engine. Additionally, the execution times and deadlines may also depend on the engine speed. In recently published works analytical methods for tasks with this rate-dependent behavior were introduced. Though those methods do not consider angular phases between tasks. For instance, in a typical engine control unit one event is displaced a certain angular position after an event of another task. This paper introduces a sufficient analysis, which considers such angular phases. A number of experimental results are reported to compare our method with existing analysis. These illustrate the improvement in terms of precision and runtime. [ABSTRACT FROM AUTHOR]

This paper presents a self-learning strategy to control the deactivation of cylinders in large displacement automotive engines. Cylinder deactivation is a useful strategy to reduce emissions and improve fuel consumption without limiting the maximum engine power. A cylinder deactivation algorithm has to balance the target of reducing emissions with drivability concerns. Changing the cylinder configuration at the wrong time or too often negatively affects drivability and fuel efficiency. The paper proposes a self-learning algorithm inspired by the Markov Chain formalism. The proposed algorithm predicts the future fuel consumption and determines the best cylinder configuration based on that prediction. The prediction algorithm is learnt online using past inputs and thus adapts to the specific driver and conditions. Two versions of the algorithm are proposed that use two different inputs. The approaches are validated in simulation on an engine test-bed, showing that the self-learning algorithm yields fuel savings up to 10% without affecting drivability in the New European Driving Cycle. [ABSTRACT FROM AUTHOR]

This study deals with a new integrated control sys-tem using active torque vectoring (ATV) and electronic stability control (ESC) for enhancement of vehicle directional stability and steerability. In this system, the stabilizing yaw moment is calculated using a novel constrained nonlinear controller with both input and state constraints. The proposed controller is designed using the prediction of continuous nonlinear vehicle model. It guarantees the vehicle directional stability by restricting the side slip angle in the admissible range when the yaw rate tracks its desired response for improved steerability. The calculated yaw moment is generated as driving and braking forces by integrated ATV and ESC systems. According to the integration policy, the required yaw moment is initially generated by ATV in the rear wheels to keep the vehicle driving performance. Because of limitation of ATV, if more yaw moment is required, the ESC system applies asymmetric braking pressure to front wheels to compensate the scarcity of ATV. By the proposed strategy, the small reduction of vehicle velocity is achieved and the vehicle drivability performance is maintained. In order to determine the full capacity of ATV, the active differential dynamics is considered in this paper. Simulation studies are conducted to evaluate the efficiency of proposed constrained controller in comparing with the unconstrained controller and a conventional nonlinear model predictive controller (NMPC) developed in the recent papers using 14-DOF vehicle model. By analyzing the results, it is clear that the proposed controller is much faster and easy to solution and implementation. [ABSTRACT FROM AUTHOR]

The design of fusion engines is a subject of great importance in a variety of fields. In this paper, we focus on the problem of linear fusion at the feature level for multiple signal matrices with noises, with the features being extremal eigenvectors. When given multiple similarity matrices, the objective is to find an estimate of the latent signal eigenspace. The concentration result for the inner product of features from different matrix samples is developed, utilizing the random matrix theory. Based on of the theoretical results, we proposed an efficient algorithm, EigFuse, to solve the constrained data-driven optimization problem with different level of noises. Our method is of high efficiency by comparing it with state-of-the-art baseline approaches with multiple noise levels. Comprehensive experiments on several synthetic as well as real-life networks demonstrate our method’s superior performance. [ABSTRACT FROM AUTHOR]

Many role-playing games feature character creation systems where players are allowed to edit the facial appearance of their in-game characters. This paper proposes a novel method to automatically create game characters based on a single face photo. We frame this “artistic creation” process under a self-supervised learning paradigm by leveraging the differentiable neural rendering. Considering the rendering process of a typical game engine is not differentiable, an “imitator” network is introduced to imitate the behavior of the engine so that the in-game characters can be smoothly optimized by gradient descent in an end-to-end fashion. Different from previous monocular 3D face reconstruction which focuses on generating 3D mesh-grid and ignores user interaction, our method produces fine-grained facial parameters with a clear physical significance where users can optionally fine-tune their auto-created characters by manually adjusting those parameters. Experiments on multiple large-scale face datasets show that our method can generate highly robust and vivid game characters. Our method has been applied to two games and has now provided over 10 million times of online services. [ABSTRACT FROM AUTHOR]

This paper presents an integrated control strategy for a starter/generator (S/G) system based on five-phase dual-stator winding induction machine (FPDWIM). The FPDWIM has a cage-type rotor and two sets of stator windings. One is a five-phase control winding (CW), and the other is a five-phase power winding (PW). In the starting mode, the FPDWIM works as a motor. The CW provides both active power and reactive power to drive the engine. In the generating mode, the CW mainly handles reactive power while the PW outputs active power. To achieve the integration of the starting and generating controls, indirect CW-flux-oriented control (ICWFOC) to operate in both starting and generating modes is proposed in this paper. In starting mode, the CW current and flux are controlled to output a constant starting torque; while in the generating mode, both CW and PW dc bus voltages are regulated. In this way, the principles and structures of the control strategies in both modes are compatible, resulting in a simpler implementation and improved performance. With the proposed control strategy, the system can complete the starting–generating operation with a smoother transition process. Simulation and experimental results are compared to validate the proposed control strategy. [ABSTRACT FROM AUTHOR]

Domestic electric water heaters (DEWHs) can provide operational flexibility for load control due to their energy storage capacity. Load forecasting for aggregated DEWHs is important for providing information of baseline load and controlling electricity demand profile without negative impact to the normal end use. Advanced metering infrastructures nowadays provide more possibilities to further enhance forecasting with bottom-up method. This article proposes a bottom-up forecasting with Markov-based error reduction method to predict power consumption of aggregated DEWHs for multiple forecast horizons. DEWHs are randomly divided into small aggregations, whose power consumption is forecasted by independent forecast engines. In this paper, the engines are K-means and wavelet decomposition-based neural networks. After summing all forecasting of small aggregations up, a new Markov-based error reduction method is proposed to extract features in residuals and mitigate forecasting error accumulation introduced by the summation, providing opportunities to further improve forecasting accuracy for the total DEWH load. Differing from traditional Markov-based error reduction, two new compensation parameters (compensation coefficient, and compensation threshold) are proposed. They are determined by using particle swarm optimization algorithm. Experiments on real and simulated DEWH loads verified the effectiveness of the proposed forecasting method. The proposed method improved the forecast accuracy over selected benchmark algorithms by about 20% to 80%, according to four performance metrics: mean absolute error, mean absolute percentage error, root-mean-square error, normalized form RMSE. The aggregation effects on performance were also analyzed in theory and tested with simulated DEWHs, providing a good indication of the forecast dependence on the aggregation size. [ABSTRACT FROM AUTHOR]

Placement and packing are two important but separated optimization steps in a conventional field programmable gate array (FPGA) implementation flow. A packing engine clusters logic elements, like lookup tables and flip-flops, into configurable logic blocks, while a placement engine determines their physical locations in FPGA layouts. This paper presents a new paradigm for FPGA placement without an explicit packing stage. In the proposed framework, the solution spaces of placement and packing are simultaneously explored in a smooth and elegant way. Our experiments on ISPD 2016 and 2017 benchmark suites demonstrate the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

SQLite has been deployed in millions of mobile devices from Web to smartphone applications on various mobile operating systems. However, due to the uncoordinated IO interactions with the underlying file system (e.g., ext4), SQLite is not efficient with low transactions per second. In this paper, we for the first time propose a new SQLite-like database engine, called SQLiteKV, which adopts the LSM-tree-based data structure but retains the SQLite operation interfaces. With its SQLite interface, SQLiteKV can be utilized by existing applications without any modification, while providing high performance with its LSM-tree-based data structure. We separate SQLiteKV into the front-end and back-end. In the front-end, we develop a light-weight SQLite-to-KV compiler to solve the semantic mismatch, so SQL statements can be efficiently translated into KV operations. We also design a novel coordination caching mechanism with memory defragmentation so query results can be effectively cached inside SQLiteKV by alleviating the discrepancy of data management between front-end SQLite statements and back-end data organization. In the back-end, we adopt an LSM-tree-based key-value database engine, and propose a lightweight metadata management scheme to mitigate the memory requirement. We have implemented and deployed SQLiteKV on a Google Nexus 6P smartphone. Experimental results with various workloads show that SQLiteKV outperforms SQLite up to six times. [ABSTRACT FROM AUTHOR]

This paper proposes a novel method for detection and isolation of wastegate (WG) faults in a turbocharged (TC) gasoline engine. This paper starts with a fault effect analysis on the WG faults, including WG stuck open and stuck closed, which is an early step in a detection strategy design. Then, a second-order sliding-mode observer (SOSMO) is proposed to capture the exhaust manifold dynamics. The observer uses experimentally validated engine models to estimate the WG position and a virtual force. The virtual force represents the external disturbances that disrupt the WG operation and enables the proposed SOSMO to estimate the WG position robust to the faults. Using this force, a detection and isolation algorithm is presented and experimentally verified on a modern TC gasoline engine. Test bench results show that the proposed strategy can successfully detect and isolate the two different WG faults. [ABSTRACT FROM PUBLISHER]

Using a special type of sliding mode controller, a new type of traction control system (TCS) for hybrid four-wheel drive vehicles is developed. This paper makes two major contributions. First, a new electric powertrain architecture with an in-wheel motor at the front wheels and a clutch on the rear of the transmission is proposed for maximum traction force. The in-wheel motors are controlled to cycle near the optimal slip point. Based on the cycling patterns of the front wheels, the desired wheel speed for rear wheel is defined. The rear wheels are controlled to track this defined speed by controlling the clutch torque. Unlike conventional TCS algorithms, the proposed method exploits clutch control instead of brake control. Second, a special type of sliding mode controller which uses a nonlinear characteristic of the tire is proposed. An important distinction between the proposed sliding mode control method and other conventional feedback controllers is that the former does not depend on feedback error but provides the same functionality. Therefore, the practical aspects are emphasized in this paper. The developed method is confirmed in simulations, and the results reveal that the proposed method opens up opportunities for new types of TCS. [ABSTRACT FROM PUBLISHER]

This paper aims to explore the performance potential of a novel dual-mode power-split hybrid powertrain. First, the steady-state power split characteristics for the proposed hybrid powertrain in different modes are analyzed. The mode switching strategy is developed to maximize powertrain efficiency, which is verified by using dynamic programming based on direct transmit points (DTPs). Moreover, a novel multi-objective evolutionary algorithm based on the decomposition (MOEA/D) method used in a nested way with dynamic programming is proposed to solve the multi-objective optimization of the PS-HEVs for the first time, and the computational efficiency and superiority of the proposed algorithm is compared with the commonly used NSGA-II algorithm. The results show that the MOEA/D based multi-objective optimization framework has similar performance in the search for the Pareto frontier but significantly higher computational efficiency than that of the NSGA-II algorithm. The obtained Pareto frontier provides optimal design candidates for hybrid powertrain systems. [ABSTRACT FROM AUTHOR]

This paper deals with the design of the power train for a hybrid electric motorcycle (HEM). The HEM power train is composed of an internal combustion engine coupled to a synchronous electric machine (EM). The EM is fed by a standard three-phase inverter connected to a high-voltage battery pack. Low weight and volume are the main requirements for HEMs, and the energy storage system (ESS) is the most critical part. Different kinds of ESSs are compared to choose the best solution balancing cost, volume, and weight. A methodology of investigation is proposed in order to recognize all the data necessary to find out the ESS. Moreover, the design choices adopted for the electric drive are reported. In order to predict EM, power converter, and battery pack performances, efficiency maps have been computed. The whole power train has been prototyped. Experimental tests of the HEM prototype on test bench and on a real racetrack are presented. All the experimental measurements highlight a significant performance enhancement of the new HEM with respect to the original one. The HEM acceleration time and top velocity have been improved without affecting too much the original motorcycle weight. [ABSTRACT FROM AUTHOR]

The computationally demanding dynamic programming (DP) algorithm is frequently used in academic research to solve the energy management problem of a hybrid electric vehicle (HEV). This paper is exclusively focused on how the computational demand of such a computation can be reduced. The main idea is to use a local approximation of the gridded cost-to-go and derive an analytic solution for the optimal torque split decision at each point in the time and state grid. Thereby, it is not necessary to quantize the torque split and identify the optimal decision by interpolating in the cost-to-go. Two different approximations of the cost-to-go are considered in this paper: 1) a local linear approximation and 2) a quadratic spline approximation. The results indicate that computation time can be reduced by orders of magnitude with only a slight degradation in simulated fuel economy. Furthermore, with a spline approximated cost-to-go, it is also possible to significantly reduce the memory storage requirements. A parallel plug-in HEV is considered in this paper, but the method is also applicable to an HEV. [ABSTRACT FROM PUBLISHER]

The twin goals of maximizing fuel economy (FE) and improving consumer acceptance by reducing the cost of the energy storage system (ESS) in a series hybrid electric vehicle (SHEV) powertrain is addressed here by using energy storage as a means for filtering drive-cycle power demands on the engine, rather than an energy source for supplying an all-electric drive mode. The concept is intended to minimize, if not eliminate, the battery in the SHEV without resorting to full-range proportional control of the engine and generator. An initial optimization study reported for a mid-size SHEV showed that a 4.5-kWh lithium-ion battery pack was still required. In this paper, a sports-car-class SHEV was studied, where the challenge is to reduce the size of the ESS even more because the available space allocation is only one fourth that of the mid-size vehicle. In this paper, a controller is developed, which allows a hybridized SHEV to be realized with a light ESS. The controller includes a duty-cycling feature that manages the engine performance in multiple efficient regions and a bandwidth-limited (BWL) proportional controller feature that limits low-bandwidth battery current. The performance of the controller has been validated for a SHEV powertrain model with an 80- \textV\mathrm{dc} 1.125-kWh battery, plus an 80- \textV\mathrm{dc} 46.4-F ultracapacitor module using a customized Autonomie vehicle model. The results show that the combined FE of the new design is increased by 13%, compared with the corresponding FE in the equivalent conventional vehicle. Additional FE improvement is possible by reducing the engine size further to reflect the average power demand imposed by real drive cycles. [ABSTRACT FROM AUTHOR]

This paper presents a general framework for the analysis of dyadic cooperation of finite-state machines (FSMs) performing code acquisition, as applied to navigation and communication spread-spectrum systems. Our aim is to show how code acquisition performance can be improved by relying on the unidirectional or bidirectional exchange of timing information between two FSMs. We develop an analytical framework based on the flow-graph approach to derive closed forms for the mean and variance of the acquisition time of various cooperating structures. An approximate form of the cumulative distribution function (CDF) is also provided. This framework is suitable for multiple applications, including peer-to-peer navigation, hierarchical codes, assisted global navigation satellite systems (GNSS), multiple navigation signals, and others. As a study case, we provide quantitative results for a dual-band Galileo receiver, which demonstrate the superiority of dyadic cooperation approaches in acquiring synchronization in both the E1 and E5 bands. [ABSTRACT FROM PUBLISHER]

This paper proposes a blended rule-based energy management system (EMS) for a plug-in hybrid electric vehicle (PHEV). The proposed EMS is formulated over driving information and vehicle trip energy and not over vehicle speed profiles, as usually seen. The proposed EMS design structure and strategy is described followed by its evaluation. This is the first time a platform for a rule-based acausal EMS has been designed. Performance metrics such as the fuel economy and the number of engine stop–starts are compared with conventional rule-based EMS over real-world destinations with uncertain trip demand. Its performance metrics compared with a conventional EMS for a full parallel PHEV was found to be superior in this paper. [ABSTRACT FROM PUBLISHER]

A proper channel modeling methodology that characterizes the statistics of extreme events is key in the design of a system at an ultra-reliable regime of operation. The strict constraint of ultra-reliability corresponds to the packet error rate (PER) in the range of 10−9−10−5 within the acceptable latency on the order of milliseconds. Extreme value theory (EVT) is a robust framework for modeling the statistical behavior of extreme events in the channel data. In this paper, we propose a methodology based on EVT to model the extreme events of a non-stationary wireless channel for the ultra-reliable regime of operation. This methodology includes techniques for splitting the channel data sequence into multiple groups concerning the environmental factors causing non-stationarity, and fitting the lower tail distribution of the received power in each group to the generalized Pareto distribution (GPD). The proposed approach also consists of optimally determining the time-varying threshold over which the tail statistics are derived as a function of time, and assessing the validity of the derived Pareto model. Finally, the proposed approach chooses the best model with minimum complexity that represents the time variation behavior of the non-stationary channel data sequence. Based on the data collected within the engine compartment of Fiat Linea under various engine vibrations and driving scenarios, we demonstrate the capability of the proposed methodology in providing the best fit to the extremes of the non-stationary data. The proposed approach significantly outperforms the channel modeling approach using the stationary channel assumption in characterizing the extreme events. [ABSTRACT FROM AUTHOR]

This paper presents predictive energy management of hybrid electric vehicles (HEVs) via computationally efficient multi-layer control. First, we formulate an optimization problem by considering driveability and a penalty for using service brakes in the objective function to optimize gear, engine on/off, engine clutch state, and power-split decisions subject to constraints on the battery state of charge (SOC) and charge sustenance. Then, we split it into two control layers, including a supervisory control in a higher layer and a local power-split control in a lower layer. In the supervisory layer, a gear and powertrain mode manager (PM) is designed, and optimal gear, engine on/off and clutch states are obtained by using a combination of dynamic programming (DP) and Pontryagin's minimum principle (PMP). Moreover, a real-time iteration Secant method is proposed to calculate optimal battery costate such that the constraint on charge sustenance is satisfied. In the local controller layer, a linear quadratic tracking method (LQT) is used to optimally split power between the engine and the electric machine and keep battery SOC within its bounds. [ABSTRACT FROM AUTHOR]

The security of a system-on-chip (SoC) can be compromised by exploiting the vulnerabilities of the finite state machines (FSMs) in the SoC controller modules through fault injection attacks. These vulnerabilities may be unintentionally introduced by traditional FSM design practices or by CAD tools during synthesis. In this paper, we first analyze how the vulnerabilities in an FSM can be exploited by fault injection attacks. Then, we propose a security-aware FSM design flow for ASIC designs to mitigate them and prevent fault attacks on FSM. Our proposed FSM design flow starts with a security-aware encoding scheme which makes the FSM resilient against fault attacks. However, the vulnerabilities introduced by the CAD tools cannot be addressed by encoding schemes alone. To analyze for such vulnerabilities, we develop a novel technique named analyzing vulnerabilities in FSM. If any vulnerability exists, we propose a secure FSM architecture to address the issue. In this paper, we mainly focus on setup-time violation-based fault attacks which pose a serious threat on FSMs; though our proposed flow works for advanced laser-based fault attacks as well. We compare our proposed secure FSM design flow with traditional FSM design practices in terms of cost, performance, and security. We show that our FSM design flow ensures security while having a negligible impact on cost and performance. [ABSTRACT FROM AUTHOR]

Neural networks generally require significant memory capacity/bandwidth to store/access a large number of synaptic weights. This paper presents design of an energy-efficient neural network inference engine based on adaptive weight compression using a JPEG image encoding algorithm. To maximize compression ratio with minimum accuracy loss, the quality factor of the JPEG encoder is adaptively controlled depending on the accuracy impact of each block. With 1% accuracy loss, the proposed approach achieves $63.4{\times }$ compression for multilayer perceptron (MLP) and $31.3 {\times }$ for LeNet-5 with the MNIST dataset, and $15.3 {\times }$ for AlexNet and $10.2 {\times }$ for ResNet-50 with ImageNet. The reduced memory requirement leads to higher throughput and lower energy for neural network inference ($3 {\times }$ effective memory bandwidth and $22 {\times }$ lower system energy for MLP). [ABSTRACT FROM AUTHOR]

Electrospraying has found multiple applications in various fields of engineering, science, and everyday life. This paper presents the study of behavior of electrosprayed dielectric liquids, such as gasoline or diesel fuels, in high-pressure environments. Computer models were created and utilized to evaluate the use of electric fields in spatial and temporal manipulation of sprayed electrostatically charged droplets. The focus of the study was to investigate the feasibility of such concept in cylindrical geometries, where the target application of this technique is to improve fuel economy and reduce harmful exhaust emissions of internal combustion engines. [ABSTRACT FROM AUTHOR]

Binary neural networks (BNNs) are promising to deliver accuracy comparable to conventional deep neural networks at a fraction of the cost in terms of memory and energy. In this paper, we introduce the XNOR neural engine (XNE), a fully digital configurable hardware accelerator IP for BNNs, integrated within a microcontroller unit (MCU) equipped with an autonomous I/O subsystem and hybrid SRAM/standard cell memory. The XNE is able to fully compute convolutional and dense layers in autonomy or in cooperation with the core in the MCU to realize more complex behaviors. We show post-synthesis results in 65- and 22-nm technology for the XNE IP and post-layout results in 22 nm for the full MCU indicating that this system can drop the energy cost per binary operation to 21.6 fJ per operation at 0.4 V, and at the same time is flexible and performant enough to execute state-of-the-art BNN topologies such as ResNet-34 in less than 2.2 mJ per frame at 8.9 frames/s. [ABSTRACT FROM AUTHOR]

Eco-driving through multiple intersections can have significant fuel benefit for road transportation. Many existing studies assume that a vehicle runs at a constant speed between intersections, which can lead to a large error in fuel prediction and trajectory optimization. By directly taking powertrain dynamics into consideration, this paper studies generic operating rules of fuel-optimal operation for connected vehicles traveling between arbitrary two red-signalized intersections. An optimal control problem is formulated to minimize engine fuel consumption, which is numerically solved by the Legendre pseudospectral algorithm. It was found that the optimal driving operation between two red-signalized intersections takes the form of either a two-stage solution, i.e., accelerating and decelerating, or a three-stage solution, i.e., accelerating, constant speed cruising, and decelerating, depending on the distance between the intersections and the speed limit. A quasi-optimal operating rule is proposed to approximate the optimal solutions, achieving much faster computational speed and less than ±1.5% fuel consumption error compared to the numerical method. The effectiveness of the quasi-optimal rule is demonstrated by applying it to selected multiintersection passing scenarios. [ABSTRACT FROM AUTHOR]

This paper proposes a systematic approach to simulating stochastic electromagnetic transients in emtp, through the reliable numerical solution of stochastic differential equations. Actually, numerical integration schemes for stochastic differential equations (sdes) are not available in emtp. It is shown how to set up the Semi-Implicit Backward Euler scheme specific for stochastic differential equations. The implemented interaction strategy between the engine for the solution of electrical circuits and the engine for the Transient Analysis of Control Systems of emtp is exploited. This is a bolt from the blue feature of this simulator that makes it the only one on the shelf able to handle stochastic differential equations. Exploiting this unveiled capability, some basic and tutorial case studies are presented. They pave the way for the simulation of more complex circuits and systems that go beyond the scope of this work. Simulation files are provided as supplementary material and they are available on GitHub. [ABSTRACT FROM AUTHOR]

This paper presents a novel active power control scheme for SRMs that are used as S/G units at MEAs; the proposed method uses a vector-controlled SAF, facilitating the acceleration of the SRM during starter mode and the efficient power control of the SRG at various operational points during generator mode. Moreover, an energy storage unit is incorporated in the SAF, aiming at enhancing the SRM's starting torque production and taking full advantage of the SRG's energy generation capability, through the appropriate phases’ magnetization regulation that is imposed by the developed adaptive SAF vector control. In addition, the proposed scheme provides enhanced FRTC during both modes of operation. The mathematical analysis is verified through MATLAB/Simulink simulations, while the performance of the S/G concept under study is evaluated via real-time CHIL tests, with the use of the dSPACE 1202 (MicroLabBox) platform and the 16-bit dsPIC30F4011 microcontroller. [ABSTRACT FROM AUTHOR]

In the modern era of artificial intelligence, increasingly sophisticated artificial neural networks (ANNs) are implemented, which pose challenges in terms of execution speed and power consumption. To tackle this problem, recent research on reduced-precision ANNs opened the possibility to exploit analog hardware for neuromorphic acceleration. In this scenario, photonic-electronic engines are emerging as a short-medium term solution to exploit the high speed and inherent parallelism of optics for linear computations needed in ANN, while resorting to electronic circuitry for signal conditioning and memory storage. In this paper we introduce a precision-scalable integrated Photonic-Electronic Multiply-Accumulate Neuron (PEMAN). The proposed device relies on (i) an analog photonic engine to perform reduced-precision multiplications at high speed and low power, and (ii) an electronic front-end for accumulation and application of the nonlinear activation function by means of a nonlinear encoding in the analog-to-digital converter (ADC). The device has been numerically validated through cosimulations to perform multiply-accumulate operations (MAC). Simulations are based on the iSiPP50G SOI process for the photonic engine and a commercial 28 nm CMOS process for the electronic front-end. The PEMAN exhibits a multiplication accuracy of 6.1 ENOB up to 10 GMAC/s, while it can perform computations up to 56 GMAC/s with a reduced accuracy down to 2.1 ENOB. The device can trade off speed and power consumption with resolution, significantly outperforming its analog electronics counterparts both in terms of speed and energy consumption. With respect to other photonic ANNs, the PEMAN has comparable speed and energy consumption with a higher resolution, while outperforming them by a hundredfold in the fan-in, which opens the possibility to accelerate more complex networks. [ABSTRACT FROM AUTHOR]

Pixel-level 2D object semantic understanding is an important topic in computer vision and could help machine deeply understand objects (e.g., functionality and affordance) in our daily life. However, most previous methods directly train on correspondences in 2D images, which is end-to-end but loses plenty of information in 3D spaces. In this paper, we propose a new method on predicting image corresponding semantics in 3D domain and then projecting them back onto 2D images to achieve pixel-level understanding. In order to obtain reliable 3D semantic labels that are absent in current image datasets, we build a large scale keypoint knowledge engine called KeypointNet, which contains 103,450 keypoints and 8,234 3D models from 16 object categories. Our method leverages the advantages in 3D vision and can explicitly reason about objects self-occlusion and visibility. We show that our method gives comparative and even superior results on standard semantic benchmarks. [ABSTRACT FROM AUTHOR]

Domain-independent planning decouples a planning task specification from planning engines. As the specification is usually describing only the physics of the environment, actions and a goal, the planning engines being generic solvers designed to solve any planning task tend to struggle with tasks that can be easily solved by domain-specific algorithms. Additional control knowledge can, to large extent, bridge such a performance gap. Instead of providing a specific planner supporting a given form of control knowledge, control knowledge can be directly encoded within the planning task specification and thus can be exploited by generic planners. In this paper, we propose Attributed Transition-Based Domain Control Knowledge (ATB-DCK) that is represented by a finite state automaton with attributed states, referring to specific states of objects, connected by transitions imposing constraints on action applicability. ATB-DCK, roughly speaking, represents the “grammar” of solution plans that guides the search. We show that ATB-DCK can be compiled into a classical planning task and thus it complements domain-independent planning techniques. Using several domains from the International Planning Competitions as benchmarks, we demonstrate that this approach often considerably improves efficiency of existing state-of-the-art planning engines. [ABSTRACT FROM AUTHOR]

In the past decade, high performance compute capabilities exhibited by heterogeneous GPGPU platforms have led to the popularity of data parallel programming languages such as CUDA and OpenCL. Developing high performance parallel programming solutions using such languages involve a steep learning curve due to the complexity of the underlying heterogeneous compute devices and their impact on performance. This has led to the emergence of several High Performance Computing frameworks which provide high-level abstractions for easing the development of data-parallel applications on heterogeneous platforms. However, the scheduling decisions undertaken by such frameworks only exploit coarse-grained concurrency in data parallel applications. In this paper, we propose PySchedCL, a framework which explores fine-grained concurrency aware scheduling decisions that harness the power of heterogeneous CPU/GPU architectures efficiently. We showcase the efficacy of such scheduling mechanisms over existing coarse-grained dynamic scheduling schemes by conducting extensive experimental evaluations for a diverse set of popular Deep Learning benchmarks. [ABSTRACT FROM AUTHOR]

The energy management strategy (EMS) of a hybrid electric vehicle is a supervisory control meant to steer power flows among the different energy sources of the powertrain. The EMS is designed for the purpose of reaching the maximum potential of a given powertrain architecture. Fuel consumption is not the only criterion to be considered for evaluating the global performance of an EMS, since the EMS also impacts other features such as drivability, through engine starts/stops and gear shifts. This paper proposes a general approach to include drivability constraints in the EMS of powertrains provided with discrete automated transmissions. As a first step, determinist dynamic programming is used to compute the optimal solution of the resulting optimization problem. This optimal solution is then used to assess the performance of the proposed online strategy, which is based on the equivalent consumption minimization strategy. Finally, simulation results are completed by a discussion of experimental results, as the proposed online strategy has been implemented in a prototype vehicle designed at Renault and tested on a dynamometric bench. [ABSTRACT FROM AUTHOR]

In this paper, a nonlinear model predictive controller (NMPC) with a multiagent heuristic optimizer, which is called dynamic particle swarm optimization (DPSO), is proposed to reduce the cold-start hydrocarbon (HC) emission of an automotive spark-ignited engine. In general, the cold-start HC emission reduction has been proven to be a very challenging control problem that has attracted increasing attention from the automotive research community. The main contribution of this paper lies in the implementation of a model-based predictive control scheme that uses a discrete nonlinear control-oriented model for calculating the control commands. In the first part of the experiments, the developed control-oriented model is validated with some data obtained through experiments. Then, by applying the controller to different cold-starting conditions, the values of exhaust gas temperature T\mathrm{exh} and engine-out HC emissions are controlled to reduce the cumulative tailpipe emissions \textHC\mathrm{cum}. To ascertain the veracity of the proposed control scheme, the same problem is solved using Pontryagin's minimum principle. The conducted simulations indicate the applicability of DPSO as an effective solver at the heart of the NMPC. Moreover, the results of model-in-the-loop simulation and hardware-in-the-loop testing demonstrate the acceptable performance of the proposed control scheme for real-time applications, which is based on the NMPC method for the considered problem. [ABSTRACT FROM PUBLISHER]

Planetary gears (PGs) have been used in power-split hybrid and plug-in hybrid vehicles, sometimes with clutches to enable multimode operations. Chevrolet Volt is a well-known production vehicle using such technologies; it achieves excellent performance in both charge-depleting (CD) and charge-sustaining modes. In this paper, a detailed analysis of the General Motors (GM) Voltec powertrain used in the 2016 Chevrolet Volt (Volt Gen 2) is presented. We search through a large topological space, which includes a total of around 1 million design candidates to find different powertrain layouts that achieve the same or better acceleration and fuel economy in comparison with the benchmark: a simulated Volt Gen 2. The key technologies enabling such large-scale design exercise are discussed, and preliminary findings are reported. The case study results show that 49 designs achieve better charge-sustaining, CD, and launching performances than the benchmark, which clearly demonstrated the value of the proposed design methodology. [ABSTRACT FROM PUBLISHER]

Hybrid electric vehicles (HEVs) are capable of improving fuel economy with reduced emissions over traditional vehicles powered only by the internal combustion (IC) engine. HEV durability is often significantly limited by the useful life of the battery, and battery life can be significantly reduced if the battery is often operated outside its allowed discharging/charging limits. This could occur particularly at extremely low temperature, leading to permanent damage and reduced useful life of the battery. To reduce battery overdischarging duration for extended battery life, this paper proposes a stochastic predictive boundary management (SPBM) strategy to proactively make the engine power available to the powertrain based on the predicted torque requirement and its prediction error variance. The effectiveness of the proposed SPBM is validated in simulations, and its performance is compared with the baseline power follower control strategy (PFCS) and predictive boundary management (PBM) under five typical driving cycles with selected initial battery temperature. The simulation results show that the proposed control strategy dramatically reduces the battery overdischarging energy under all five typical driving cycles over both PFCS and PBM, where the average reduction is 82% under aggressive US06 and ARB02 driving cycles, 67% under NYCC and FTP driving cycles, and 37% under the IM240 highway driving cycle. Note that the adaptive prediction and its error variance can be calculated in real time with fairly low computational load. [ABSTRACT FROM PUBLISHER]

This paper studies the fuel-optimal cruising strategies of parallel hybrid electric vehicles (HEVs) and their underlying mechanisms. To achieve fuel-optimal operations, a discontinuous nonlinear optimal control problem is formulated and solved using the Legendre pseudospectral method and the knotting technique. Three optimal cruising strategies in free/fixed-speed cruising scenarios are proposed: vehicle speed pulse-and-glide strategy, state-of-charge (SoC) pulse-and-glide (PnG) strategy, and constant-speed strategy. The performance and optimal behavior of the engine and the motor are presented, and their fuel-saving mechanisms are explained. Finally, two principles to compromise between fuel economy and ride comfort are proposed and studied. [ABSTRACT FROM PUBLISHER]

The paper describes application of unmanned robot applied to automotive test (URAT) to save time, costs, and to improve accuracy of tests. A hierarchical coordinated control method based on fuzzy logic theory is proposed for URAT, to realize the coordinated control and the accurate speed tracking of driving test cycle during all kinds of conditions. The system structure is briefly described in the paper. Similar to a skilled human driver, a control method based on Saridis hierarchical architecture to coordinate multiple robot manipulators systems for the automotive test system is presented. The coordinated movements for throttle mechanical leg, clutch mechanical leg, brake mechanical leg are designed. On top of that, the fuzzy speed controller for URAT is used to accurately track the desired vehicle velocity under different test conditions. Experiments have been conducted by using a Ford FOCUS car. The proposed control method of URAT is experimentally proved and compared with other control methods and with human driver performances. [ABSTRACT FROM PUBLISHER]

In this paper, concurrent design of energy management (EM) and traction control algorithms for a vehicle equipped with a parallel hybrid powertrain is studied. This paper focuses on designing the two control algorithms together as one control design problem, which are traditionally considered separately. First, optimal control actions and operating points are obtained by applying dynamic programming (DP). Then, this information is used for developing a rule-based supervisory controller. Our objective is to minimize the fuel consumption and the wheel slip simultaneously. Two control problems are also solved separately and compared with the concurrent solution. Results show that promising benefits can be obtained by using the concurrent design approach rather than considering two control problems separately. Under the same conditions, the vehicle with the concurrent supervisory controller is 16% more efficient in fuel consumption and experiences 12% less wheel slip, assuming slippery road friction conditions. [ABSTRACT FROM PUBLISHER]

This paper presents a novel approach for time-varying parameter adaptive throttle and brake control for vehicle speed tracking. A control algorithm has been developed based on a linearized longitudinal vehicle model with characteristic lumped parameters. The lumped parameters are slowly time varying, except when a vehicle experiences gear shift. Combined parameter adaptation and throttle/brake control algorithm have been developed. The performance of the proposed control algorithm has been evaluated via simulations and vehicle tests. Since the proposed control algorithm has been designed using a generic form of the vehicle model, it can be implemented for different classes of vehicles with no information about the vehicle powertrain and the brake system. It has been shown from both simulations and vehicle tests that the vehicle speed tracking performance is robust to external disturbance. [ABSTRACT FROM PUBLISHER]

Eco-driving at signalized intersections has significant potential for energy saving. In this paper, we focus on eco-departure operations of connected vehicles equipped with an internal combustion engine and a step-gear automatic transmission. A Bolza-type optimal control problem (OCP) is formulated to minimize engine fuel consumption. Due to the discrete gear ratio, this OCP is a nonlinear mixed-integer problem, which is challenging to handle by most existing optimization methods. The Legendre pseudospectral method combining the knotting technique is employed to convert it into a multistage interconnected nonlinear programming problem, which then solves the optimal engine torque and transmission gear position. The fuel-saving benefit of the optimized eco-departing operation is validated by a passenger car with a five-speed transmission. For real-time implementation, a near-optimal departing strategy is proposed to quickly determine the behavior of the engine and transmission. When a string of vehicles are departing from an intersection, the acceleration of the leading vehicle(s) should be considered to control the following vehicles. This issue is also addressed in this paper. [ABSTRACT FROM PUBLISHER]

Modern engines feature a considerable number of adjustable control parameters. With this increasing number of degrees of freedom (DoFs) for engines and the consequent considerable calibration effort required to optimize engine performance, traditional manual engine calibration or optimization methods are reaching their limits. An automated and efficient engine optimization approach is desired. In this paper, interdisciplinary research on a multiobjective evolutionary algorithm (MOEA)-based global optimization approach is developed for a homogeneous charge compression ignition (HCCI) engine. The performance of the HCCI engine optimizer is demonstrated by the cosimulation between an HCCI engine Simulink model and a Strength Pareto Evolutionary Algorithm 2 (SPEA2)-based multiobjective optimizer Java code. The HCCI engine model is developed by Simulink and validated with different engine speeds (1500–2250 r/min) and indicated mean effective pressures (IMEPs) (3–4.5 bar). The model can simulate the HCCI engine's indicated specific fuel consumption (ISFC) and indicated specific hydrocarbon (ISHC) emissions with good accuracy. The introduced MOEA optimization is an approach to efficiently optimize the engine ISFC and ISHC simultaneously by adjusting the settings of the engine's actuators automatically through the SPEA2. In this paper, the settings of the HCCI engine's actuators are intake valve opening (IVO) timing, exhaust valve closing (EVC) timing, and relative air-to-fuel ratio $\lambda$. The cosimulation study and experimental validation results show that the MOEA engine optimizer can find the optimal HCCI engine actuators' settings with satisfactory accuracy and a much lower time consumption than usual. [ABSTRACT FROM PUBLISHER]

Despite recent advances in aircraft maintenance technology, the breakdowns and catastrophic accidents due to human failure during maintenance activities will continue to occur. This paper argues that the impact of human error is amplified by the increased frequency of activities as a result of premature maintenance (PRM). PRM refers to predated or early maintenance tasks resulting in additional activities over a long horizon. A novel methodology is proposed to monetize the impact of PRM and show its contribution to total maintenance cost. The proposed methodology is inspired by the proportional hazard model to capture the effects of human failure on the system’s hazard rate. Our findings show that the contribution of PRM to the total operating cost is noticeable; this justifies a level of flight delay or even cancellation due to accommodate on-time maintenance activities. [ABSTRACT FROM AUTHOR]

In recent years, spatial-keyword queries have attracted much attention with the fast development of location-based services. However, current spatial-keyword techniques are disk-based, which cannot fulfill the requirements of high throughput and low response time. With the surging data size, people tend to process data in distributed in-memory environments to achieve low latency. In this paper, we present the distributed solution, i.e., Skia (Spatial-Keyword In-memory Analytics), to provide a scalable backend for spatial-textual analytics. Skia introduces a two-level index framework for big spatial-textual data including: (1) efficient and scalable global index, which prunes the candidate partitions a lot while achieving small space budget; and (2) four novel local indexes, that further support low latency services for exact and approximate spatial-keyword queries. Skia can support common spatial-keyword queries via traditional SQL programming interfaces. The experiments conducted on large-scale real datasets have demonstrated the promising performance of the proposed indexes and our distributed solution. [ABSTRACT FROM AUTHOR]