RISE-Based Adaptive Control with Mass-Inertia Parameter Estimation for Aerial Transportation of Multi-Rotor UAVs

This paper proposes an adaptive tracking strategy with mass-inertia estimation for aerial transportation problems of multi-rotor UAVs. The dynamic model of multi-rotor UAVs with disturbances is firstly developed with a linearly parameterized form. Subsequently, a cascade controller with the robust integral of the sign of the error (RISE) terms is applied to smooth the control inputs and address bounded disturbances. Then, adaptive estimation laws for mass-inertia parameters are designed based on a filter operation. Such operation is introduced to extract estimation errors exploited to theoretically guarantee the finite-time (FT) convergence of estimation errors. Finally, simulations are conducted to verify the effectiveness of the designed controller. The results show that the proposed method provides better tracking and estimation performance than traditional adaptive controllers based on sliding mode control algorithms and gradient-based estimation strategies

An Architecture for IoT-Enabled Smart Transportation Security System: A Geospatial Approach

Internet of Things (IoT) in urban transportation systems have been ubiquitously embedded into a variety of devices and transport entities. The IoT-enabled smart transportation system (STS) has thus gained growing tractions amongst scholars and practitioners. However, several IoT challenges in relation to cyber–physical security are exposed due to the heterogeneity, complexity and decentralisation of the IoT network. There also exist geospatial security concerns with respect to the embeddings of 5G networks into public infrastructures that are interconnected with the transport system via IoT. To address these concerns, this article aims to apply geospatial modelling approach to propose a smart transportation security systems (STSSs). It is modelled and simulated by undertaking an experimental study in the city of Beijing, China. The simulation outcome of the proposed architecture is expected to offer a strategic guide for strategic security management of urban smart transportation

Rethink Baseline of Integrated Gradients from the Perspective of Shapley Value

Numerous approaches have attempted to interpret deep neural networks (DNNs) by attributing the prediction of DNN to its input features. One of the well-studied attribution methods is Integrated Gradients (IG). Specifically, the choice of baselines for IG is a critical consideration for generating meaningful and unbiased explanations for model predictions in different scenarios. However, current practice of exploiting a single baseline fails to fulfill this ambition, thus demanding multiple baselines. Fortunately, the inherent connection between IG and Aumann-Shapley Value forms a unique perspective to rethink the design of baselines. Under certain hypothesis, we theoretically analyse that a set of baseline aligns with the coalitions in Shapley Value. Thus, we propose a novel baseline construction method called Shapley Integrated Gradients (SIG) that searches for a set of baselines by proportional sampling to partly simulate the computation path of Shapley Value. Simulations on GridWorld show that SIG approximates the proportion of Shapley Values. Furthermore, experiments conducted on various image tasks demonstrate that compared to IG using other baseline methods, SIG exhibits an improved estimation of feature's contribution, offers more consistent explanations across diverse applications, and is generic to distinct data types or instances with insignificant computational overhead.Comment: 12 page