Hardware acceleration for power efficient deep packet inspection

The rapid growth of the Internet leads to a massive spread of malicious attacks like viruses and malwares, making the safety of online activity a major concern. The use of Network Intrusion Detection Systems (NIDS) is an effective method to safeguard the Internet. One key procedure in NIDS is Deep Packet Inspection (DPI). DPI can examine the contents of a packet and take actions on the packets based on predefined rules. In this thesis, DPI is mainly discussed in the context of security applications. However, DPI can also be used for bandwidth management and network surveillance. DPI inspects the whole packet payload, and due to this and the complexity of the inspection rules, DPI algorithms consume significant amounts of resources including time, memory and energy. The aim of this thesis is to design hardware accelerated methods for memory and energy efficient high-speed DPI. The patterns in packet payloads, especially complex patterns, can be efficiently represented by regular expressions, which can be translated by the use of Deterministic Finite Automata (DFA). DFA algorithms are fast but consume very large amounts of memory with certain kinds of regular expressions. In this thesis, memory efficient algorithms are proposed based on the transition compressions of the DFAs. In this work, Bloom filters are used to implement DPI on an FPGA for hardware acceleration with the design of a parallel architecture. Furthermore, devoted at a balance of power and performance, an energy efficient adaptive Bloom filter is designed with the capability of adjusting the number of active hash functions according to current workload. In addition, a method is given for implementation on both two-stage and multi-stage platforms. Nevertheless, false positive rates still prevents the Bloom filter from extensive utilization; a cache-based counting Bloom filter is presented in this work to get rid of the false positives for fast and precise matching. Finally, in future work, in order to estimate the effect of power savings, models will be built for routers and DPI, which will also analyze the latency impact of dynamic frequency adaption to current traffic. Besides, a low power DPI system will be designed with a single or multiple DPI engines. Results and evaluation of the low power DPI model and system will be produced in future

Meta-heuristic algorithms in car engine design: a literature survey

Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system

A dual adaptive tunable vibration absorber using MREs for vehicle powertrain vibration control

This paper presents a dual Adaptive Tuned Vibration Absorber (ATVA) using a magnetorheological elastomer (MRE) for powertrain torsional vibration control. The MRE used in this device is a soft MRE with a significant MR effect. By using the MRE, the ATVA can work in a wide frequency range. In this paper, the dual ATVA is proposed rather than a single ATVA because a single ATVA, at a fixed location, cannot deal with resonances happening to several powertrain vibration modes. Also, the dual ATVA concept design is presented to validate its effectiveness. In addition the soft MRE shear modulus is approximated by a polynomial of magnetic flux intensity B and the approximation was experimentally validated. The simulation results showed that with the ATVA, powertrain vibration response is significantly suppressed. Furthermore, the effect of the dual ATVA parameters such as inertia moment, stiffness and damping coefficients and ATVA locations were examined. The dual ATVA will be useful device for powertrain vibration suppression. © 2010 SPIE

Development of Real-time Optimal Control Strategy of Hybrid Transit Bus Based on Predicted Driving Pattern

The control strategy of a hybrid electric vehicle (HEV) has been an active research area in the past decades. The main goal of the optimal control strategy is to maximize the fuel economy and minimize exhaust emissions while also satisfying the expected vehicle performance. Dynamic programming (DP) is an algorithm capable of finding the global optimal solution of HEV operation. However, DP cannot be used as a real-time control approach as it requires pre-known driving information. The equivalent consumption minimization strategy (ECMS) is a real-time control approach, but it always results in local optima due to the non-convex cost function. In my research, a ECMS with DP combined model (ECMSwDP) was proposed, which was a compromise between optimality and real-time capability. In this approach, the optimal equivalent factor (lambda) for a real-time ECMS controller can be derived using ECMSwDP for a given driving condition. The optimal lambda obtained using ECMSwDP was further processed to derive the lambda map, which was a function of vehicle operation and driving information. Six lambda maps were generated corresponding to the developed representative driving patterns. At each distance segment of a drive cycle, the suitable lambda value is available from one of the six lambda maps based on the identified driving pattern and current vehicle operation.;An adaptive ECMS (A-ECMS) model with a driving pattern identification model is developed to achieve the real-time optimal control for a HEV. A-ECMS was capable of controlling the ratio of power provided by the ICE and battery of a hybrid vehicle by selecting the lambda based on the identified lambda map. The effect on fuel consumption of the control strategies developed using the rule-based controller, ECMSwDP, A-ECMS, and DP was simulated using the parallel hybrid bus model developed in this research. The control strategies developed using A-ECMS are able to significantly improve the fuel economy while maintaining the battery charge sustainability. The corrected fuel economy observed with A-ECMS with a penalty function and the average lambda of RDPs was 5.55%, 13.67%, and 19.19% gap to that observed with DP when operated over the Beijing cycle, WVU-CSI cycle, and the actual transit bus route, respectively. The corrected fuel economy observed with A-ECMS with lambda maps of the RDPs was 4.83%, 10.61%, and 14.33% gap to that observed with DP when operated on the Beijing cycle, WVU-CSI cycle, and actual transit bus route, respectively. The simulation results demonstrated that the proposed A-ECMS approaches have the capability to achieve real time suboptimal control of a HEV while maintaining the charge sustainability of the battery

自動車エンジンのロバスト制御に関する研究

八戸工業大

Studies on SI engine simulation and air/fuel ratio control systems design

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.More stringent Euro 6 and LEV III emission standards will immediately begin execution on 2014 and 2015 respectively. Accurate air/fuel ratio control can effectively reduce vehicle emission. The simulation of engine dynamic system is a very powerful method for developing and analysing engine and engine controller. Currently, most engine air/fuel ratio control used look-up table combined with proportional and integral (PI) control and this is not robust to system uncertainty and time varying effects. This thesis first develops a simulation package for a port injection spark-ignition engine and this package include engine dynamics, vehicle dynamics as well as driving cycle selection module. The simulations results are very close to the data obtained from laboratory experiments. New controllers have been proposed to control air/fuel ratio in spark ignition engines to maximize the fuel economy while minimizing exhaust emissions. The PID control and fuzzy control methods have been combined into a fuzzy PID control and the effectiveness of this new controller has been demonstrated by simulation tests. A new neural network based predictive control is then designed for further performance improvements. It is based on the combination of inverse control and predictive control methods. The network is trained offline in which the control output is modified to compensate control errors. The simulation evaluations have shown that the new neural controller can greatly improve control air/fuel ratio performance. The test also revealed that the improved AFR control performance can effectively restrict engine harmful emissions into atmosphere, these reduce emissions are important to satisfy more stringent emission standards

Nonlinear Control Strategies for Advanced Vehicle Thermal Management Systems

Advanced thermal management systems for internal combustion engines can improve coolant temperature regulation and servo-motor power consumption to positively impact the tailpipe emissions, fuel economy, and parasitic losses by better regulating the combustion process with multiple computer controlled components. The traditional thermostat valve, coolant pump, and clutch-driven radiator fan are upgraded with servo-motor actuators. When the system components function harmoniously, desired thermal conditions can be accomplished in a power efficient manner. Although the vehicle\u27s mechanical loads can be driven by electric servo-motors, the power demands often require large actuator sizes and electrical currents. Integrating hydraulically-driven actuators in the cooling circuit offers higher torques in a smaller package space. Hydraulics are widely applied in transportation and manufacturing systems due to their high power density, design flexibility for power transmission, and ease of computer control. In this dissertation, several comprehensive nonlinear control architectures are proposed for transient temperature tracking in automotive cooling circuits. First, a single loop experimental cooling system has been fabricated and assembled which features a variable position smart valve, variable speed electric coolant pump, variable speed electric radiator fan, engine block, radiator, steam-based heat exchanger, and various sensors. Second, a multiple loop experimental cooling system has been assembled which features a variable position smart thermostat valve, two variable speed electric pumps, variable speed electric radiator fan, engine block, transmission, radiator, steam-based heat exchanger, and sensors. Third, a single loop experimental hydraulic-based thermal system has been assembled which features a variable speed hydraulic coolant pump and radiator fan, radiator, and immersion heaters. In the first and second configured systems, the steam-based heat exchanger emulates the engine\u27s combustion process and transmission heat. For the third test platform, immersion heating coils emulate the combustion heat. For the first configured system, representative numerical and experimental results are discussed to demonstrate the thermal management system operation in precisely tracking desired temperature profiles and minimizing electrical power consumption. The experimental results show that less than 0.2°K temperature tracking error can be achieved with a 14% improvement in the system component power consumption. In the second configured system, representative experimental results are discussed to investigate the functionality of the multi-loop thermal management system under normal and elevated ambient temperatures. The presented results clearly show that the proposed robust controller-based thermal management system can accurately track prescribed engine and transmission temperature profiles within 0.13°K and 0.65°K, respectively, and minimize electrical power consumption by 92% when compared to the traditional factory control method. Finally, representative numerical and experimental results are discussed to demonstrate the performance of the hydraulic actuators-based advance thermal management system in tracking prescribed temperature profiles (e.g., 42% improvement in the temperature tracking error) and minimizing satisfactorily hydraulic power consumption when compared to other common control method