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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 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]

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]

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]

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]

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]

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]

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]

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 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]

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]

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]

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]

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]

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]

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]

In the power control process of the range extender, both the engine and the generator are at risk of reaching the torque output limit. The limitation not only comes from the restriction of their own torque output capacity, but also comes from the additional constraints that are established to avoid power reverse undershoot. In order to improve the system performance under time-varying torque constraints, a real-time model predictive controller (MPC) that manipulates the torque commands of the engine and the generator to track the reference power is designed. Different from the traditional penalty function method, the quadratic function is taken as the penalty function in this paper, which greatly improves the efficiency of solving the optimal solution. Meanwhile, the steady-state torque command in the feasible region is taken as the extreme point of the quadratic penalty function, which avoids the penalty coefficient tending to infinity. By adjusting the penalty coefficients of different constraints, the overall optimal solution approaches the boundary of the active inequality constraints. And according to the Karush-Kuhn-Tucker (KKT) condition, it is proved that the optimal solution of the original problem can be obtained through iteration. The effectiveness and practicability of the proposed strategy are evaluated through numerical simulations on Simulink and experiments on a range extender. [ABSTRACT FROM AUTHOR]

Recent improvements in vehicle-to-everything (V2X) communication and onboard computing power have enabled the development of control algorithms that jointly optimize the vehicle velocity and powertrain control in Connected and Automated Vehicles (CAVs), commonly referred to as the Eco-Driving problem. This paper presents a novel and computationally efficient algorithm to optimize the velocity planning and energy management in a CAV with a hybrid electric powertrain. The Eco-Driving problem is formulated as a dynamic, constrained optimization problem in the spatial domain, where information about the upcoming speed limits and road topography is assumed known. This problem is solved by embedding an Equivalent Consumption Minimization Strategy (ECMS) into a Dynamic Programming (DP) optimization to obtain a sub-optimal solution that provides results close to the global optimum at a fraction of the computational cost. Further, a multi-layer hierarchical control architecture is proposed as a path to a causal, real-time implementation. The DP-ECMS algorithm is converted into a Model Predictive Control (MPC) framework by using principles of Approximate Dynamic Programming (ADP). This causal implementation is finally benchmarked to a global optimal solution obtained with DP for different scenarios. [ABSTRACT FROM AUTHOR]

Advanced algorithms can promote the development of energy management strategies (EMSs) as a key technology in hybrid electric vehicles (HEVs). Reinforcement learning (RL) with distributed structure can significantly improve training efficiency in complex environments, and multi-threaded parallel computing provides a reliable algorithm basis for promoting adaptability. Dedicated to trying more efficient deep reinforcement learning (DRL) algorithms, this paper proposed a deep q-network (DQN)-based energy and emission management strategy (E&EMS) at first. Then, two distributed DRL algorithms, namely asynchronous advantage actor-critic (A3C) and distributed proximal policy optimization (DPPO), were adopted to propose EMSs, respectively. Finally, emission optimization was taken into account and then distributed DRL-based E&EMSs were proposed. Regarding dynamic programming (DP) as the optimal benchmark, simulation results show that three DRL-based control strategies can achieve near-optimal fuel economy and outstanding computational efficiency, and compared with DQN, two distributed DRL algorithms have improved the learning efficiency by four times. [ABSTRACT FROM AUTHOR]

Belief propagation (BP)-based stereo matching has popular owing to its regularity and ability to yield promising results. Some commonly observed hardware-implementation challenges pertaining to the use of this algorithm are large memory requirements and trade-offs between speed and chip area, along with an increasing disparity range. The paper presents a hardware- and memory-efficient architecture for building a BP-based disparity estimation system capable of overcoming issues associated with large disparity ranges. The proposed architecture is memory-efficient owing to the regularity of its underlying algorithm. In addition, the improved hardware efficiency can be attributed to processing element modifications to demonstrate shareable characteristics. Results obtained in this study reveal a 67.8% reduction in required memory corresponding to a time–area term complexity of $O(L(logL)^{2})$ , where L denotes the disparity range. This result is in stark contrast to the $O(L^{2}logL)$ and $O(L^{2})$ complexities observed in extant studies. Compared to state-of-the-art implementations, the proposed architecture offers an 86.2% gate count reduction for message update units at a disparity range of 512. These results confirm the proposed architecture’s suitability for use in large disparity scenarios. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]

As a fundamental task of hybrid electric vehicles (HEVs), energy management strategies are critical for improving the performance. This paper proposes a new queue-Dyna reinforcement learning (RL) algorithm based energy management strategy (EMS), which can substantially reduce the online learning time while guarantees the control performance compared with widely used Q-learning. To solve the existing problems of direct and indirect RL based EMSs, a blended RL algorithm, Dyna, is introduced first. By reusing the actual experience to construct a model online, Dyna integrates direct and indirect RL, and thus has the advantages of both. Furthermore, two novel strategies of backward focusing and prioritized sweeping are incorporated in the Dyna framework, developing the queue-Dyna algorithm. To the best of our knowledge, it's the first attempt of adopting queue-Dyna in the EMS of a HEV. A comparative simulation of direct RL, indirect RL, Dyna and queue-Dyna is implemented, and the results demonstrate the proposed algorithm achieves a great improvement in fast learning and maintains satisfied fuel consumption. At last, a hardware-in-the-loop experiment verified the real-time performance of the proposed EMS. [ABSTRACT FROM AUTHOR]

Predicting the click-through rate of an advertisement is a critical component of online advertising platforms. In sponsored search, the click-through rate estimates the probability that a displayed advertisement is clicked by a user after she submits a query to the search engine. Commercial search engines typically rely on machine learning models trained with a large number of features to make such predictions. This inevitably requires a lot of engineering efforts to define, compute, and select the appropriate features. In this paper, we propose two novel approaches (one working at character level and the other working at word level) that use deep convolutional neural networks to predict the click-through rate of a query-advertisement pair. Specifically, the proposed architectures consider as input only the textual content appearing in a query-advertisement pair and the page position at which the advertisement appears on the search result page of the query, and produce as output a click-through rate prediction. By comparing the character-level model with the word-level model, we show that language representation can be learnt from scratch at character level when trained on enough data. Through extensive experiments using billions of query-advertisement pairs of a popular commercial search engine, we demonstrate that both approaches significantly outperform a baseline model built on well-selected text features and a state-of-the-art word2vec-based approach. We also show the importance of the position feature in the proposed approaches in improving the prediction accuracy. When combining the predictions of the deep models introduced in this study with the prediction of the model in production of the same commercial search engine, we significantly improve the accuracy and the calibration of the click-through rate prediction of the production system. We also show the potential of leveraging the CTR prediction of the proposed deep learning models for query-ad relevance modeling and query-ad matching tasks in sponsored search. [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]

Analytical optimal solution to the energy management of hybrid electric vehicles is of interest from theoretical and practical perspectives. Particularly, effort has been made to derive analytical solution to the energy management problem for series hybrid electric vehicles using Pontryagin's minimum principle (PMP). However, admissibility of the system input was not fully explored in determining the optimal input candidates. In this paper, the analytical solution for the same problem is found by partitioning the positive power demand set into four subsets, where the solution is derived for each case separately according to the corresponding admissible input set. The analytical solution is verified through comparison with numerical solution for a series hybrid electric wheel loader, and two different drive cycles are considered for this purpose. From the proposed solution, effective equivalence factor bounds are found and used to construct an adaptive equivalent consumption minimization strategy. The proposed strategy and the analytical solution are implemented together for the same vehicle to demonstrate their effectiveness in dealing with real-world applications. Simulations are performed for 12 drive cycles, and the results are compared to the ones achieved by PMP-based optimal control where the optimization is done numerically. Simulation results suggest that the proposed methodology is relatively fast and has satisfactory performance in presence of drive cycle uncertainty. It is observed that the proposed method fulfills charge sustenance, and the achieved fuel consumption figures are very close to the optimal benchmarks found by the non-causal method. [ABSTRACT FROM AUTHOR]

This paper presents models and optimization algorithms to compute the fuel-optimal energy management strategies for a parallel hybrid electric powertrain on a given driving cycle. Specifically, we first identify a mixed-integer model of the system, including the engine on/off signal and the gear-shift commands. Thereafter, by carefully relaxing the fuel-optimal control problem to a linear program, we devise an iterative algorithm to rapidly compute the minimum-fuel energy management strategies including the optimal gear-shift trajectory. We validate our approach by comparing its solution with the globally optimal one obtained solving the mixed-integer linear program and with the one resulting from the implementation of the optimal strategies in a high-fidelity nonlinear simulator. We showcase the effectiveness of the presented algorithm by assessing the impact of different powertrain configurations and electric motor size on the achievable fuel consumption. Our numerical results show that the proposed algorithm can assess fuel-optimal control strategies with low computational burden, and that powertrain design choices significantly affect the achievable fuel consumption of the vehicle. [ABSTRACT FROM AUTHOR]

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]

Fully variable valve actuation (FVVA) technology can improve engine performance by changing engine valve timing and valve lift in different conditions. To find the optimal valve profile parameters, engine power and emissions have to be taken into account due to the contradiction between them. Therefore, the paper presented a multi-objective decision making (MODM) strategy for valve profile parameters optimization. All profile parameters both intake valves and exhaust valves were optimized synchronously. We carried out the global synchronous MODM strategy with the Radial Basis Function (RBF) response surface method (RSM). First, a FVVA system was introduced. For the FVVA system, all valve profile parameters are variable. Second, based on GT-suite, an engine model was built and its valve sub-models were rebuilt to achieve the FVVA system. Some simulation results were used to train RSM. Third, based on the simulation results, engine valve parameters were analyzed and some response surface methods were compared. The results show that RBF method has a good agreement with simulation results. At the end, global MODM strategy which optimized both intake valves and exhaust valves synchronously for engine valve parameters was implemented at 2400 RPM based on Non-dominated Sorting Genetic Algorithm (NSGA-Ⅱ) and RBF response surfaces obtained. With the obtained response surfaces, multi-objective virtual optimization which is relied on the obtained response surfaces rather than the GT-suite model was carried out. The virtual optimization results show that engine power can be improved by 1.63%, and the engine NOX emissions can be reduced by 2.75%. To verify the virtual optimization results, we carried out the simulation with the optimized parameters, and the virtual optimization results have a good agreement with results which were obtained from the simulation of the GT-suite model. Besides, the RSM-based virtual multi-objective optimization keeps faster optimization speed. [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]

As the functions of vehicles are more computerized for the safety and convenience of drivers, attack surfaces of vehicle are accordingly increasing. Many attack results have shown that an attacker could intentionally control vehicles. Most of them exploit the vulnerability that controller area network (CAN) protocol, a de-facto standard for the in-vehicle networks, does not support message origin authentication. Although a number of methods to resolve this security vulnerability have been suggested, they have their each limitation to be applied into the current system. They have required either the modification of the CAN standard or dramatical communication load increase, which is infeasible in practice. In this paper, we propose a novel identification method, which works in the physical layer of the in-vehicle CAN network. Our method identifies electronic control units (ECUs) using inimitable characteristics of electrical CAN signals enabling detection of a malicious ECU. Unlike previous attempts to address the security problem in the in-vehicle CAN network, our method works by simply adding a monitoring unit to the existing network, making it deployable in current systems and compliant with required CAN standards. Our experimental results show that our method is able to correctly identify ECUs. In case of misclassfication rate for ECU idnetification, our method yields 0.36% in average which is approximate four times lower than the method proposed by P.-S. Murvay et al. This paper is also the first to identify potential attack models that systems should be able to detect. [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]

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]

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]

Hybrid vehicle energy management is often studied in simulation as an optimal control problem. Under strict convexity assumptions, a solution can be developed using Pontryagin's minimum principle. In practice, however, many engineers do not formally check these assumptions resulting in the possible occurrence of so-called unexplained “numerical issues.” This paper intends to explain and solve these issues. Due to the binary controlled-state variable considered (e.g., switching on/off an internal combustion engine) and the use of a lookup table with linear interpolation (e.g., engine fuel consumption map), the corresponding Hamiltonian function can have multiple minima. Optimal control is not unique. Moreover, it is defined as being singular. Consequently, an infinite number of optimal state trajectories can be obtained. In this paper, a control law is proposed to easily construct a few of them. [ABSTRACT FROM PUBLISHER]

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]

This paper investigates the fault-tolerant control problem for heterogeneous vehicular platoons where input quantization and dead-zone nonlinearity are also involved. Due to the occurrence of actuator faults, the maximum acceleration changes, which may invalid the traditional quadratic spacing policy. To tackle the dilemma, an improved quadratic spacing policy with the lower bound of fault factor is proposed. Furthermore, the improved quadratic spacing policy removes the assumption of zero initial spacing errors. Based on the new spacing policy, an adaptive fault-tolerant platoon control scheme is developed, by employing radial basis function neural networks (RBFNN) and PID-type sliding mode control method. The given scheme is proved to be capable of guaranteeing individual vehicle stability, string stability and traffic flow stability. The effectiveness of the scheme is verified through comparison simulation studies. [ABSTRACT FROM AUTHOR]