Intelligent DDoS packet filtering in high-speed networks

Currently high-speed networks have been attacked by successive waves of Distributed Denial of Service (DDoS) attacks. There are two major challenges on DDoS defense in the high-speed networks. One is to sensitively and accurately detect attack traffic, and the other is to filter out the attack traffic quickly, which mainly depends on high-speed packet classification. Unfortunately most current defense approaches can not efficiently detect and quickly filter out attack traffic. Our approach is to find the network anomalies by using neural network, deploy the system at distributed routers, identify the attack packets, and then filter them quickly by a Bloom filter-based classifier. The evaluation results show that this approach can be used to defend against both intensive and subtle DDoS attacks, and can catch DDoS attacks’ characteristic of starting from multiple sources to a single victim. The simple complexity, high classification speed and low storage requirements make it especially suitable for DDoS defense in high-speed networks.

Not just for romance: applications of speed dating in social work education

In this article we address how a contemporary adaptation of the 'speed dating' model was used for educational purposes with two cohorts of social work students. We outline the dimensions of 'speed dating' as a contemporary social phenomenon, then address how this model relates specifically to groupwork process, and can be used to facilitate social work student learning. The curriculum for two classroom group activities using the 'speed dating' model are outlined, the first to develop university level study skills, the second for debriefing field placement learning experiences. Finally we examine why the 'speed dating' metaphor was successful in provoking a playful yet constructively creative space for students to engage in groupwork process.

An intelligent energy management model for a parallel hybrid vehicle under combined loads

To exploit the benefits offered by parallel HEVs, an intelligent energy management model is developed and evaluated in this paper. Despite most existing works, the developed model incorporates combined wind/drag, slope, rolling, and accessories loads to minimise the fuel consumption under varying driving conditions. A slope prediction unit is also employed. The engine and the electric motor can output power simultaneously under a heavy-load or a slopped road condition. Two simulation were conducted namely slopped-windy-prediction and slopped-windy-prediction-hybrid. The results indicate that the vehicle speed and acceleration is smoother where the hybrid component was included. The average fuel consumption for the first and second simulations were 7.94 and 7.46 liter/100 km, respectively.

Look-ahead intelligent energy management of a parallel hybrid electric vehicle

Improving fuel efficiency in vehicles can reduce the energy consumption concerns associated with operating the vehicles. This paper presents a model for a parallel hybrid electric vehicle. In the model, the flow of energy starts from wheels and spreads toward engine and electric motor. A fuzzy logic based control strategy is implemented for the vehicle. The controller manages the energy flow from the engine and the electric motor, controlling transmission ratio, adjusting speed, and sustaining battery's state of charge. The controller examines the vehicle speed, demand torque, slope difference, state of charge of battery, and engine and electric motor rotation speeds. It then determines the best values for continuous variable transmission ratio, speed, and torque. A slope window method is formed that takes into account the look-ahead slope information, and determines the best vehicle speed. The developed model and control strategy are simulated using real highway data relating to Nowra-Bateman Bay in Australia, and SAE Highway Fuel Economy Driving Schedule. The simulation results are presented and discussed. It is shown that the use of the proposed fuzzy controller reduces the fuel consumption of the vehicle.

Intelligent 3D programmable surface

Creating a highly programmable surface operating at relatively high speed and in real time is an area of research with many challenges. Such a system has applications in the field of optical telescopes, product manufacturing, and giant 3D-screens and billboards for advertising and artwork. This paper covers certain aspects of a keynote presentation at ISDT 2010 including system design, modularity, programmability and the system control intelligence. An overview of the system architecture, actuator design, electronics and distributed control will provide an insight into how the system is controlled and self-tuned for a number of applications. A simulation environment that has been developed to streamline system reconfiguration will also be presented, demonstrating translation of complex mathematical functions into 3D shapes virtually before being displayed on the physical surface.

Intelligent control and look-ahead energy management of hybrid electric vehicles

A review of the state of knowledge in the field of control and energy management in HEVs is carried out. The key innovation of the project is the development of a model of a PHEV using the real road data with an intelligent look-ahead online controller. Another novelty of this work is the method of route planning. It combines the information of vehicle sensors such as accelerometer and speedometer with the data of a GPS to create a road grade map for use within the look-ahead energy management strategy in the vehicle. For the PHEV, an adaptive cruise controller is modelled and an optimisation method is applied to obtain the best speed profile during a trajectory. Finally, the nonlinear model of the vehicle is applied with the sliding mode controller. The effect of using this controller is compared with the universal cruise controller. The stability of the system is studied and proved.

Adaptive intelligent energy management system of plug-in hybrid electric vehicle

Efficient energy management in hybrid vehicles is the key for reducing fuel consumption and emissions. To capitalize on the benefits of using PHEVs (Plug-in Hybrid Electric Vehicles), an intelligent energy management system is developed and evaluated in this paper. Models of vehicle engine, air conditioning, powertrain, and hybrid electric drive system are first developed. The effect of road parameters such as bend direction and road slope angle as well as environmental factors such as wind (direction and speed) and thermal conditions are also modeled. Due to the nonlinear and complex nature of the interactions between PHEV-Environment-Driver components, a soft computing based intelligent management system is developed using three fuzzy logic controllers. The crucial fuzzy engine controller within the intelligent energy management system is made adaptive by using a hybrid multi-layer adaptive neuro-fuzzy inference system with genetic algorithm optimization. For adaptive learning, a number of datasets were created for different road conditions and a hybrid learning algorithm based on the least squared error estimate using the gradient descent method was proposed. The proposed adaptive intelligent energy management system can learn while it is running and makes proper adjustments during its operation. It is shown that the proposed intelligent energy management system is improving the performance of other existing systems. © 2014 Elsevier Ltd.