Mathematical control of complex systems

Copyright © 2013 ZidongWang et al.This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Vision-Based Lane-Changing Behavior Detection Using Deep Residual Neural Network

Accurate lane localization and lane change detection are crucial in advanced driver assistance systems and autonomous driving systems for safer and more efficient trajectory planning. Conventional localization devices such as Global Positioning System only provide road-level resolution for car navigation, which is incompetent to assist in lane-level decision making. The state of art technique for lane localization is to use Light Detection and Ranging sensors to correct the global localization error and achieve centimeter-level accuracy, but the real-time implementation and popularization for LiDAR is still limited by its computational burden and current cost. As a cost-effective alternative, vision-based lane change detection has been highly regarded for affordable autonomous vehicles to support lane-level localization. A deep learning-based computer vision system is developed to detect the lane change behavior using the images captured by a front-view camera mounted on the vehicle and data from the inertial measurement unit for highway driving. Testing results on real-world driving data have shown that the proposed method is robust with real-time working ability and could achieve around 87% lane change detection accuracy. Compared to the average human reaction to visual stimuli, the proposed computer vision system works 9 times faster, which makes it capable of helping make life-saving decisions in time

Integrated Pest Management Portfolios in UK Arable Farming: Results of a Farmer Survey

BACKGROUND. Farmers are faced with a wide range of pest management (PM) options which can be adopted in isolation or alongside complement or substitute strategies. This paper presents the results of a survey of UK cereal producers focusing on the character and diversity of PM strategies currently used by, or available to, farmers. In addition, the survey asked various questions pertaining to agricultural policy participation, attitude toward environmental issues, sources of PM advice and information and the important characteristics of PM technologies. RESULTS. The results indicate that many farmers do make use of a suite of PM techniques and that their choice of integrated PM (IPM) portfolio appears to be jointly dictated by farm characteristics and Government policy. Results also indicate that portfolio choice does affect the number of subsequent insecticide applications per crop. CONCLUSIONS. These results help to identify the type of IPM portfolios considered adoptable by farmers and highlight the importance of substitution in IPM portfolios. As such, these results will help to direct R&D effort toward the realisation of more sustainable PM approaches and aid the identification of potential portfolio adopters. These findings highlight the opportunity a revised agri-environmental policy design could generate in terms of by enhancing coherent IPM portfolio adoption.Pest management; pesticide alternatives; technology and portfolio approaches;

A cloned linguistic decision tree controller for real-time path planning in hostile environments

AbstractThe idea of a Cloned Controller to approximate optimised control algorithms in a real-time environment is introduced. A Cloned Controller is demonstrated using Linguistic Decision Trees (LDTs) to clone a Model Predictive Controller (MPC) based on Mixed Integer Linear Programming (MILP) for Unmanned Aerial Vehicle (UAV) path planning through a hostile environment. Modifications to the LDT algorithm are proposed to account for attributes with circular domains, such as bearings, and discontinuous output functions. The cloned controller is shown to produce near optimal paths whilst significantly reducing the decision period. Further investigation shows that the cloned controller generalises to the multi-obstacle case although this can lead to situations far outside of the training dataset and consequently result in decisions with a high level of uncertainty. A modification to the algorithm to improve the performance in regions of high uncertainty is proposed and shown to further enhance generalisation. The resulting controller combines the high performance of MPC–MILP with the rapid response of an LDT while providing a degree of transparency/interpretability of the decision making

Efficiency versus Robustness of Markets - Why improving market efficiency should not be the only objective of market regulation

The efficiency of capital markets has been questioned almost as long as the efficient market hypothesis had been worked out. Numerous critics have been formulated against this hypothesis, questioning notably the behavioural assumptions underlying the efficient market hypothesis. The present contribution does not focus on the behavioural assumptions but rather looks at the implications of focusing purely on the objective of market efficiency when considering market design questions. Hence it aims at discussing the following, possibly rather fundamental issue: Is the objective of efficiency, which has guided most of the market reforms in the last decades, sufficient? Or has it to be complemented by the objective of robustness? Mathematical and engineering control theory has developed the concept of robust control (e.g. Zhou and Doyle, 1998) and it has been shown that there is always a trade-off between the efficiency of a control system and its robustness (cf. e.g. Safonov, 1981, Doyle et al., 1988). The efficiency of the system describes its reactions to disturbance signals. The lower the integral loss function over the so-called transfer or sensitivity function, the less a system is affected by disturbances such as demand fluctuations, and the more efficient is the control. The economic equivalent clearly is the maximisation of welfare, which results in an efficient economic system. Robustness by contrast is defined as stability of the control system in the presence of model uncertainty (deviations in the model parameters or misperceptions of the underlying system). These concepts are applied to the financial markets in their interaction with the real economy. The financial markets being understood as the controllers of real world activity through investments, the implications of misperceptions in the financial sphere are analysed both theoretically and in an application example. From the theory it may readily derived that financial markets providing efficient, i.e. welfare-optimal solutions, must have limitations with respect to robustness. Also in the application example it turns out that in the presence of potential misperception a reduction of irreversible cost shares in investments may lead to an increase in overall expected system costs. Hence improvements in (conventional) market efficiency may be counter-productive by facilitating misallocation of capital as a consequence of misperceptions in the financial markets. This leads to the conclusion that a sole focus on the efficiency objective in market design is problematic and some of the recent turmoil in financial markets may be explained by the lack of consideration given to robustness issues.market efficiency, robustness, optimal control, stochastic dynamic growth