CERES :: Browsing by Author "Guo, Weisi"

Browsing by Author "Guo, Weisi"

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Open Access
A novel physics-informed recurrent neural network approach for state estimation of autonomous platforms
(IEEE, 2024-06-30) Perrusquía, Adolfo; Guo, Weisi
State estimation of autonomous platforms is a crucial element in the design and test of perception algorithms. The nonlinear nature of autonomous platforms makes hard to design accurate state estimation algorithms without using linearization techniques, large amount of data or knowledge of the physical parameters of the platform. This paper reports a novel state estimation algorithm of autonomous platforms. The proposed approach is based on a physics informed recurrent neural network (PIRNN) that combines the power of recurrent nets with an estimate structure of the autonomous platform model. This estimated model regularises the weights’ manifold of the network for the accurate estimation of the states. Boundedness of the proposed PIRNN is verified using Lyapunov stability theory as long as the physics-informed signals satisfy a persistent of excitation condition. Simulations are conducted to test the PIRNN model and show its benefits and challenges.
Open Access
A survey of artificial intelligence-related cybersecurity risks and countermeasures in Mobility-as-a-Service
(Institute of Electrical and Electronics Engineers (IEEE), 2024-08-05) Chu, Kai-Fung; Yuan, Haiyue; Yuan, Jinsheng; Guo, Weisi; Balta-Ozkan, Nazmiye; Li, Shujun
Mobility-as-a-service (MaaS) integrates different transport modalities and can support more personalization of travelers’ journey planning based on their individual preferences, behaviors and wishes. To fully achieve the potential of MaaS, a range of artificial intelligence (AI) (including machine learning and data mining) algorithms are needed to learn personal requirements and needs to optimize the journey planning of each traveler and all travelers as a whole, to help transport service operators and relevant governmental bodies to operate and plan their services, and to detect and prevent cyberattacks from various threat actors, including dishonest and malicious travelers and transport operators. The increasing use of different AI and data processing algorithms in both centralized and distributed settings opens the MaaS ecosystem up to diverse cyber and privacy attacks at both the AI algorithm level and the connectivity surfaces. In this article, we present the first comprehensive review on the coupling between AI-driven MaaS design and the diverse cybersecurity challenges related to cyberattacks and countermeasures. In particular, we focus on how current and emerging AI-facilitated privacy risks (profiling, inference, and third-party threats) and adversarial AI attacks (evasion, extraction, and gamification) may impact the MaaS ecosystem. These risks often combine novel attacks (e.g., inverse learning) with traditional attack vectors (e.g., man-in-the-middle attacks), exacerbating the risks for the wider participation actors and the emergence of new business models.
Open Access
Adversarial reconfigurable intelligent surface against physical layer key generation
(IEEE, 2023-04-12) Wei, Zhuangkun; Li, Bin; Guo, Weisi
The development of reconfigurable intelligent surfaces (RIS) has recently advanced the research of physical layer security (PLS). Beneficial impacts of RIS include but are not limited to offering a new degree-of-freedom (DoF) for key-less PLS optimization, and increasing channel randomness for physical layer secret key generation (PL-SKG). However, there is a lack of research studying how adversarial RIS can be used to attack and obtain legitimate secret keys generated by PL-SKG. In this work, we show an Eve-controlled adversarial RIS (Eve-RIS), by inserting into the legitimate channel a random and reciprocal channel, can partially reconstruct the secret keys from the legitimate PL-SKG process. To operationalize this concept, we design Eve-RIS schemes against two PL-SKG techniques used: (i) the CSI-based PL-SKG, and (ii) the two-way cross multiplication based PL-SKG. The channel probing at Eve-RIS is realized by compressed sensing designs with a small number of radio-frequency (RF) chains. Then, the optimal RIS phase is obtained by maximizing the Eve-RIS inserted deceiving channel. Our analysis and results show that even with a passive RIS, our proposed Eve-RIS can achieve a high key match rate with legitimate users, and is resistant to most of the current defensive approaches. This means the novel Eve-RIS provides a new eavesdropping threat on PL-SKG, which can spur new research areas to counter adversarial RIS attacks.
Open Access
Analysing region of attraction of load balancing on complex network
(Oxford Academic, 2022-06-29) Zou, Mengbang; Guo, Weisi
Many complex engineering systems network together functional elements to balance demand spikes but suffer from stability issues due to cascades. The research challenge is to prove the stability conditions for any arbitrarily large and dynamic network topology with any complex balancing function. Most current analyses linearize the system around fixed equilibrium solutions. This approach is insufficient for dynamic networks with multiple equilibria, for example, with different initial conditions or perturbations. Region of attraction (ROA) estimation is needed in order to ensure that the desirable equilibria are reached. This is challenging because a networked system of non-linear dynamics requires compression to obtain a tractable ROA analysis. Here, we employ master stability-inspired method to reveal that the extreme eigenvalues of the Laplacian are explicitly linked to the ROA. This novel relationship between the ROA and the largest eigenvalue in turn provides a pathway to augmenting the network structure to improve stability. We demonstrate using a case study on how the network with multiple equilibria can be optimized to ensure stability.
Open Access
Ant-behavior inspired intelligent nanonet for targeted drug delivery in cancer therapy
(IEEE, 2020-04-01) Lin, Lin; Huang, Fupeng; Yan, Hao; Liu, Fuqiang; Guo, Weisi
Targeted drug delivery system is believed as one of the most promising solutions for cancer treatment due to its low-dose requirement and less side effects. However, both passive targeting and active targeting rely on systemic blood circulation and diffusion, which is actually not the real “active” drug delivery. In this paper, an ant-behavior inspired nanonetwork composing of intelligent nanomachines is proposed. A big intelligent nanomachine take small intelligent nanomachines and drugs to the vicinity of of the tumor area. The small intelligent nanomachines can coordinate with each other to find the most effective path to the tumor cell for drug transportation. The framework and mechanism of this cooperative network are proposed. The route finding algorithm is presented. The convergence performance is analytically analyzed where the influence of the factors such as molecule degradation rate, home-destination distance, number of small nanomachines to the convergence is presented. Finally the simulation results validate the effectiveness of the proposed mechanism and analytical analysis
Open Access
Assessing the impact of major historical events on urban landscapes via local entropy measures
(IEEE, 2021-10-15) Mazzamurro, Matteo; Guo, Weisi
In this paper we show how Shannon entropy, an intuitive and versatile measure of uniformity of a probability distribution, can be adapted to quantify the heterogeneity of land use and population density in and around human settlements. Using a raster data set of estimates of historical population density and land use, we show that local entropy measures capture salient aspects of the evolution of urban systems. Through the case studies of the UK, India, and Italy we reconnect the temporal evolution of the measures to some of the main socioeconomic and political changes and epidemic events these countries went through during the last three centuries. We argue that the diffusion of technological innovations is more apparently correlated to changes in the measures than epidemic events in themselves. We discuss the potential significance and limitations of this finding in understanding changes in urban systems in the context of the ongoing COVID-19 pandemic.
Open Access
Automatic quantification of settlement damage using deep learning of satellite images
(IEEE, 2021-10-15) Lu, Lili; Guo, Weisi
Humanitarian disasters and political violence cause significant damage to our living space. The reparation cost to homes, infrastructure, and the ecosystem is often difficult to quantify in real-time. Real-time quantification is critical to both informing relief operations, but also planning ahead for rebuilding. Here, we use satellite images before and after major crisis around the world to train a robust baseline Residual Network (ResNet) and a disaster quantification Pyramid Scene Parsing Network (PSPNet). ResNet offers robustness to poor image quality and can identify areas of destruction with high accuracy (92 %), whereas PSPNet offers contextualised quantification of built environment damage with good accuracy (84%). As there are multiple damage dimensions to consider (e.g. economic loss and fatalities), we fit a multi-linear regression model to quantify the overall damage. To validate our combined system of deep learning and regression modeling, we successfully match our prediction to the ongoing recovery in the 2020 Beirut port explosion. These innovations provide a better quantification of overall disaster magnitude and inform intelligent humanitarian systems of unfolding disasters.
Embargo
Characterizing China's road network development from a spatial entropy perspective
(Elsevier, 2024-03-28) Pan, Jiamin; Zhao, Xia; Guo, Weisi; Feng, Yuhao; Liu, Yu; Zhu, Jiangling; Fang, Jingyun
Understanding the spatial characteristics of road networks is crucial for the planning of road networks. Although road networks have been frequently evaluated in combination with various socioeconomic factors, the current road network development in developing countries still needs to be better understood. Combining road network density and spatial entropy for 2728 counties in China, we provide a comprehensive assessment of road networks and their key influencing factors. Our results indicate a significant spatial heterogeneity of the road network development, especially on both sides of the Heihe-Tengchong line. Demographic-economic and topographic variables jointly explained 54.2% and 65.1% of the spatial variations in road network density and entropy, respectively. Road entropy increases with road network density in line with a saturation curve from provincial to national scales, which offers guidance for future road planning. Using a K-means clustering analysis, we categorized China's road networks into four groups corresponding to the development stages. Our findings improve the current understanding of road network development in China and provide important implications for national road network planning in the future.
Open Access
Classification of RF transmitters in the presence of multipath effects using CNN-LSTM
(IEEE, 2024-06-09) Patil, Pradnya; Wei, Zhuangkun; Petrunin, Ivan; Guo, Weisi
Radio frequency (RF) communication systems are the backbone of many intelligent transport and aerospace operations, ensuring safety, connectivity, and efficiency. Accurate classification of RF transmitters is vital to achieve safe and reliable functioning in various operational contexts. One challenge in RF classification lies in data drifting, which is particularly prevalent due to atmospheric and multipath effects. This paper provides a convolutional neural network based long short-term memory (CNN-LSTM) framework to classify the RF emitters in drift environments. We first simulate popular-used RF transmitters and capture the RF signatures, while considering both power amplifier dynamic imperfections and the multipath effects through wireless channel models for data drifting. To mitigate data drift, we extract the scattering coefficient and approximate entropy, and incorpo-rate them with the in-phase quadrature (I/Q) signals as the input to the CNN-LSTM classifier. This adaptive approach enables the model to adjust to environmental variations, ensuring sustained accuracy. Simulation results show the accuracy performance of the proposed CNN-LSTM classifier, which achieves an overall 91.11% in the presence of different multipath effects, bolstering the resilience and precision of realistic classification systems over state of the art ensemble voting approaches.
Open Access
A closed-loop output error approach for physics-informed trajectory inference using online data
(IEEE, 2022-09-21) Perrusquía, Adolfo; Guo, Weisi
While autonomous systems can be used for a variety of beneficial applications, they can also be used for malicious intentions and it is mandatory to disrupt them before they act. So, an accurate trajectory inference algorithm is required for monitoring purposes that allows to take appropriate countermeasures. This article presents a closed-loop output error approach for trajectory inference of a class of linear systems. The approach combines the main advantages of state estimation and parameter identification algorithms in a complementary fashion using online data and an estimated model, which is constructed by the state and parameter estimates, that inform about the physics of the system to infer the followed noise-free trajectory. Exact model matching and estimation error cases are analyzed. A composite update rule based on a least-squares rule is also proposed to improve robustness and parameter and state convergence. The stability and convergence of the proposed approaches are assessed via the Lyapunov stability theory under the fulfilment of a persistent excitation condition. Simulation studies are carried out to validate the proposed approaches.
Open Access
Closed-loop output error approaches for drone’s physics informed trajectory inference
(IEEE, 2023-02-22) Perrusquía, Adolfo; Guo, Weisi
The design of adequate countermeasures against drone's threats needs accurate trajectory estimation to avoid economic damage to the aerospace industry and national infrastructure. As trajectory estimation algorithms need highly accurate physics informed models or off-line learning algorithms, radical innovation in on-line trajectory inference is required. In this paper, a novel drone's physics informed trajectory inference algorithm is proposed. The algorithm constructs a physic informed model and infers the drone's trajectories simultaneously using a closed-loop output error architecture. Two different approaches are proposed based on a physics structure and an admittance filtering model which considers: i) full states measurements and ii) partial states measurements. Stability and convergence of the proposed schemes are assessed using Lyapunov stability theory. Simulations studies are carried out to demonstrate the scope and high inference capabilities of the proposed approach.
Open Access
Control layer security: a new security paradigm for cooperative autonomous systems
(IEEE, 2023-07-21) Guo, Weisi; Wei, Zhuangkun; Gonzalez, Oscar; Perrusquía, Adolfo; Tsourdos, Antonios
Autonomous systems often cooperate to ensure safe navigation. Embedded within the centralised or distributed coordination mechanisms are a set of observations, unobservable states, and control variables. Security of data transfer between autonomous systems is crucial for safety, and both cryptography and physical layer security methods have been used to secure communication surfaces - each with its drawbacks and dependencies. Here, we show for the first time a new wireless Control Layer Security (CLS) mechanism. CLS exploits mutual physical states between cooperative autonomous systems to generate cipher keys. These mutual states are chosen to be observable to legitimate users and not sufficient to eavesdroppers, thereby enhancing the resulting secure capacity. The CLS cipher keys can encrypt data without key exchange or a common key pool, and offers very low information leakage. As such the security of digital data channels is now dependent on physical state estimation rather than wireless channel estimation. This protects the estimation process from wireless jamming and channel entropy dependency. We review for first time what kind of signal processing techniques are used for hidden state estimation and key generation, and the performance of CLS in different case studies.
Open Access
Control layer security: exploiting unobservable cooperative states of autonomous systems for secret key generation
(IEEE, 2024-02-26) Wei, Zhuangkun; Guo, Weisi
The rapid growth of autonomous systems (ASs) with data sharing means new cybersecurity methods have to be developed for them. Existing computational complexity-based cryptography does not have information-theoretical bounds and poses threats to superior computational attackers. This post-quantum cryptography issue indeed motivated the rapid advances in using common physical layer properties to generate symmetrical cipher keys (known as PLS). However, PLS remains sensitive to attackers (e.g., jamming) that destroy its prerequisite wireless channel reciprocity. When ASs are in cooperative tasks (e.g., rescuing searching, and formation flight), they will behave cooperatively in the control layer. Inspired by this, we propose a new security mechanism called control layer security (CLS), which exploits the correlated but unobservable states of cooperative ASs to generate symmetrical cipher keys. This idea is then realized in the linearized UAV cooperative control scenario. The theoretical correlation coefficients between Alice's and Bob's states are computed, based on which common feature selection and key quantization steps are designed. The results from simulation and real UAV experiments show i) an approximately 90% key agreement rate is achieved, and ii) even an Eve with the known observable states and systems fails to estimate the unobservable states and the secret keys relied upon, due to the multiple-to-one mapping from unobservable states (pitch, roll and yaw angles) to the observable states (3D trajectory). This demonstrates CLS as a promising candidate to secure the communications of ASs, especially in the adversarial radio environment with attackers that destroys the prerequisite for current PLS.
Open Access
Cost inference of discrete-time linear quadratic control policies using human-behaviour learning
(IEEE, 2022-06-30) Perrusquía, Adolfo; Guo, Weisi
In this paper, a cost inference algorithm for discrete-time systems using human-behaviour learning is pro-posed. The approach is inspired in the complementary learning that exhibits the neocortex, hippocampus, and striatum learning systems to achieve complex decision making. The main objective is to infer the hidden cost function from expert's data associated to the hippocampus (off-policy data) and transfer it to the neocortex for policy generalization (on-policy data) in different systems and environments. The neocortex is modelled by a Q-learning and a least-squares identification algorithms for on-policy learning and system identification. The cost inference is obtained using a one-step gradient descent rule and an inverse optimal control algorithm. Convergence of the cost inference algorithm is discussed using Lyapunov recursions. Simulations verify the effectiveness of the approach.
Open Access
Deep learning methods for solving linear inverse problems: Research directions and paradigms
(Elsevier, 2020-08-07) Bai, Yanna; Chen, Wei; Chen, Jie; Guo, Weisi
The linear inverse problem is fundamental to the development of various scientific areas. Innumerable attempts have been carried out to solve different variants of the linear inverse problem in different applications. Nowadays, the rapid development of deep learning provides a fresh perspective for solving the linear inverse problem, which has various well-designed network architectures results in state-of-the-art performance in many applications. In this paper, we present a comprehensive survey of the recent progress in the development of deep learning for solving various linear inverse problems. We review how deep learning methods are used in solving different linear inverse problems, and explore the structured neural network architectures that incorporate knowledge used in traditional methods. Furthermore, we identify open challenges and potential future directions along this research line.
Open Access
A deep mixture of experts network for drone trajectory intent classification and prediction using non-cooperative radar data
(IEEE, 2024-01-01) Fraser, Benjamin; Perrusquía, Adolfo; Panagiotakopoulos, Dimitrios; Guo, Weisi
The intent prediction of unmanned aerial vehicles (UAVs) also known as drones is a challenging task due to the different mission profiles and tasks that the drone can perform. To alleviate this issue, this paper proposes a deep mixture of experts network to classify and predict drones trajectories measured from non-cooperative radars. Telemetry data of open-access datasets are converted to simulated radar tracks to generate a pool of heterogeneous trajectories and construct three independent datasets to train, validate, and test the proposed architecture. The network is composed of two main components: i) a deep network that predicts the class associated to the input trajectories and ii) a set of deep experts models that learns the extreme bounds of the trajectories in different future time steps. The proposed approach is tested and compared with different deep models to verify its effectiveness under different flight profiles and time-windows.
Open Access
Deep reinforcement learning for optimal hydropower reservoir operation
(American Society of Civil Engineers, 2021-05-21) Xu, Wei; Meng, Fanlin; Guo, Weisi; Li, Xia
Optimal operation of hydropower reservoir systems is a classical optimization problem of high dimensionality and stochastic nature. A key challenge lies in improving the interpretability of operation strategies, i.e., the cause–effect relationship between system outputs (or actions) and contributing variables such as states and inputs. This paper reports for the first time a new deep reinforcement learning (DRL) framework for optimal operation of reservoir systems based on deep Q-networks (DQNs), which provides a significant advance in understanding the performance of optimal operations. DQN combines Q-learning and two deep artificial neural networks (ANNs), and acts as the agent to interact with the reservoir system through learning its states and providing actions. Three knowledge forms of learning considering the states, actions, and rewards were constructed to improve the interpretability of operation strategies. The impacts of these knowledge forms and DRL learning parameters on operation performance were analyzed. The DRL framework was tested on the Huanren hydropower system in China, using 400-year synthetic flow data for training and 30-year observed flow data for verification. The discretization levels of reservoir water level and energy output yield contrasting effects: finer discretization of water level improved performance in terms of annual hydropower generated and hydropower production reliability; however, finer discretization of hydropower production can reduce search efficiency, and thus the resulting DRL performance. Compared with benchmark algorithms including dynamic programming, stochastic dynamic programming, and decision tree, the proposed DRL approach can effectively factor in future inflow uncertainties when determining optimal operations and can generate markedly higher hydropower. This study provides new knowledge of the performance of DRL in the context of hydropower system characteristics and data input features, and shows promise for potentially being implemented in practice to derive operation policies that can be updated automatically by learning from new data.
Open Access
Deep reinforcement learning of passenger behavior in multimodal journey planning with proportional fairness
(Springer, 2023-07-20) Chu, Kai-Fung; Guo, Weisi
Multimodal transportation systems require an effective journey planner to allocate multiple passengers to transport operators. One example is mobility-as-a-service, a new mobility service that integrates various transport modes through a single platform. In such a multimodal and diverse journey planning problem, accommodating heterogeneous passengers with different and dynamic preferences can be challenging. Furthermore, passengers may behave based on experiences and expectations, in the sense that the transport experience affects their state and decision of the next transport service. Current methods of treating each journey planning optimization as a non-time varying single experience problem cannot adequately model passenger experience and memories over many journeys over time. In this paper, we model passenger experience as a Markov model where prior experiences have a transient effect on future long-term satisfaction and retention rate. As such, we formulate a multi-objective journey planning problem that considers individual passenger preferences, experiences, and memories. The proposed approach dynamically determines utility weights to obtain an optimal journey plan for individual passengers based on their status. To balance the profit received by each transport operator, we present a variant-based proportional fairness. Our experiments using real-world and synthetic datasets show that our approach enhances passenger satisfaction, compared to baseline methods. We demonstrate that the overall profit is increased by 2.3 times, resulting in a higher retention rate caused by higher satisfaction levels. Our proposed approach can facilitate the participation of transport operators and promote passenger acceptance of MaaS.
Open Access
Design and hardware-in-the-loop evaluation of a time dissemination framework for drone operations in urban environments
(IEEE, 2023-11-10) Negru, Sorin Andrei; Petrunin, Ivan; Guo, Weisi; Tsourdos, Antonios
With the advent of UAVs (Unmanned Aerial Vehicles) several companies started to offer services, in urban, semi-urban, and rural regions. Although GNSS (Global Navigation Satellite System) can disseminate time information to different platforms, external factors may degrade the signal quality and lead to erroneous time synchronization. The paper is presenting a resilient time dissemination framework, using a wireless 802.11ax protocol and NTP (Network Time Protocol) for the synchronization aspect. A time server, formed by a rubidium clock, a GNSS receiver, and time information provided by NPL (UK’s National Physical Laboratory) traceable to UTC, dictates the time to all the users within the WLAN (Wireless Local Area Network). To evaluate the proposed framework, a lab-based HIL (Hardware in the Loop) simulation is performed using two Jetson Nanos as CC (Companion Computer) and a Pixhawk 2.4 as FCU (Flight Control Unit) representing the end-users in the dissemination framework. In this way, all the communication links are tested and evaluated. Results showed that the two platforms can be synchronized to the time server as an alternative time source, achieving an average RTT (Round Trip Delay) of 8 ms from the Research and Innovation timing node to the FCU, and an average time offset of -0.2 ms.
Open Access
Discovering latent spatial invariance of urban wireless data using compression and deep learning
(IEEE, 2020-07-27) Guo, Weisi
Increasingly available high resolution geospatial wireless demand data is available from high density base stations, wireless localisation, and geo-tagged social media posts. Mapping the evolving spatiotemporal demand is critical for a wide range of infrastructure services, including future network planning and operations. However, monitoring geospatial data demand across a whole city is computationally and financially expensive. Here, we show that geospatial traffic demand data from both 0.4 million Twitter posts and 3.2 million base stations records can be compressed to spatially invariant points in London. These points correspond to major sources of human movement activity that act as either facilitators (e.g. public multi-modal transport hubs) or drivers (e.g. tourist attractions and business hubs). This demonstrates that by monitoring these spatially invariant critical points, we can obtain an accurate understanding of the human demand dynamics elsewhere in the city. Indeed, the operator which maps the dynamics between these points uncover the latent human connected dynamics embedded in complex urban ecosystems. We use both the latest signal processing technique of Graph Fourier Transform (GFT) and a AutoDecoder inspired deep learning neural network to demonstrate spatially invariant compression and both error-free and noisy recovery. These promising results show that we can exploit the connected structure of complex cities to dramatically reduce data monitoring.