| 1. |
- Gao, Yipeng, et al.
(författare)
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Research on the Security of Visual Reasoning CAPTCHA
- 2021
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Ingår i: PROCEEDINGS OF THE 30TH USENIX SECURITY SYMPOSIUM. - : USENIX ASSOC. - 9781939133243 ; , s. 3291-3308
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Konferensbidrag (refereegranskat)abstract
- CAPTCHA is an effective mechanism for protecting computers from malicious bots. With the development of deep learning techniques, current mainstream text-based CAPTCHAs have been proven to be insecure. Therefore, a major effort has been directed toward developing image-based CAPTCHAs, and image-based visual reasoning is emerging as a new direction of such development. Recently, Tencent deployed the Visual Turing Test (VTT) CAPTCHA. This appears to have been the first application of a visual reasoning scheme. Subsequently, other CAPTCHA service providers (Geetest, NetEase, Dingxiang, etc.) have proposed their own visual reasoning schemes to defend against bots. It is, therefore, natural to ask a fundamental question: are visual reasoning CAPTCHAs as secure as their designers expect? This paper presents the first attempt to solve visual reasoning CAPTCHAs. We implemented a holistic attack and a modular attack, which achieved overall success rates of 67.3% and 88.0% on VTT CAPTCHA, respectively. The results show that visual reasoning CAPTCHAs are not as secure as anticipated; this latest effort to use novel, hard AI problems for CAPTCHAs has not yet succeeded. Based on the lessons we learned from our attacks, we also offer some guidelines for designing visual CAPTCHAs with better security.
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| 2. |
- Dai, L., et al.
(författare)
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Distributed stochastic MPC for systems with parameter uncertainty and disturbances
- 2018
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Ingår i: International Journal of Robust and Nonlinear Control. - : John Wiley and Sons Ltd. - 1049-8923 .- 1099-1239. ; 28:6, s. 2424-2441
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Tidskriftsartikel (refereegranskat)abstract
- A distributed stochastic model predictive control algorithm is proposed for multiple linear subsystems with both parameter uncertainty and stochastic disturbances, which are coupled via probabilistic constraints. To handle the probabilistic constraints, the system dynamics is first decomposed into a nominal part and an uncertain part. The uncertain part is further divided into 2 parts: the first one is constrained to lie in probabilistic tubes that are calculated offline through the use of the probabilistic information on disturbances, whereas the second one is constrained to lie in polytopic tubes whose volumes are optimized online and whose facets' orientations are determined offline. By permitting a single subsystem to optimize at each time step, the probabilistic constraints are then reduced into a set of linear deterministic constraints, and the online optimization problem is transformed into a convex optimization problem that can be performed efficiently. Furthermore, compared to a centralized control scheme, the distributed stochastic model predictive control algorithm only requires message transmissions when a subsystem is optimized, thereby offering greater flexibility in communication. By designing a tailored invariant terminal set for each subsystem, the proposed algorithm can achieve recursive feasibility, which, in turn, ensures closed-loop stability of the entire system. A numerical example is given to illustrate the efficacy of the algorithm. Copyright
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| 3. |
- Dai, Li, et al.
(författare)
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Distributed Stochastic MPC of Linear Systems With Additive Uncertainty and Coupled Probabilistic Constraints
- 2017
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Ingår i: IEEE Transactions on Automatic Control. - : IEEE. - 0018-9286 .- 1558-2523. ; 62:7, s. 3474-3481
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Tidskriftsartikel (refereegranskat)abstract
- This technical note develops a new form of distributed stochastic model predictive control (DSMPC) algorithm for a group of linear stochastic subsystems subject to additive uncertainty and coupled probabilistic constraints. We provide an appropriate way to design the DSMPC algorithm by extending a centralized SMPC (CSMPC) scheme. To achieve the satisfaction of coupled probabilistic constraints in a distributed manner, only one subsystem is permitted to optimize at each time step. In addition, by making explicit use of the probabilistic distribution of the uncertainties, probabilistic constraints are converted into a set of deterministic constraints for the predictions of nominal models. The distributed controller can achieve recursive feasibility and ensure closed-loop stability for any choice of update sequence. Numerical examples illustrate the efficacy of the algorithm.
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| 4. |
- Dai, L., et al.
(författare)
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Stochastic self-triggered model predictive control for linear systems with probabilistic constraints
- 2018
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Ingår i: Automatica. - : Elsevier. - 0005-1098 .- 1873-2836. ; 92, s. 9-17
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Tidskriftsartikel (refereegranskat)abstract
- A stochastic self-triggered model predictive control (SSMPC) algorithm is proposed for linear systems subject to exogenous disturbances and probabilistic constraints. The main idea behind the self-triggered framework is that at each sampling instant, an optimization problem is solved to determine both the next sampling instant and the control inputs to be applied between the two sampling instants. Although the self-triggered implementation achieves communication reduction, the control commands are necessarily applied in open-loop between sampling instants. To guarantee probabilistic constraint satisfaction, necessary and sufficient conditions are derived on the nominal systems by using the information on the distribution of the disturbances explicitly. Moreover, based on a tailored terminal set, a multi-step open-loop MPC optimization problem with infinite prediction horizon is transformed into a tractable quadratic programming problem with guaranteed recursive feasibility. The closed-loop system is shown to be stable. Numerical examples illustrate the efficacy of the proposed scheme in terms of performance, constraint satisfaction, and reduction of both control updates and communications with a conventional time-triggered scheme.
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| 5. |
- Gao, Yulong, et al.
(författare)
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Computing Probabilistic Controlled Invariant Sets
- 2021
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Ingår i: IEEE Transactions on Automatic Control. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 0018-9286 .- 1558-2523. ; 66:7, s. 3138-3151
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Tidskriftsartikel (refereegranskat)abstract
- This article investigates stochastic invariance for control systems through probabilistic controlled invariant sets (PCISs). As a natural complement to robust controlled invariant sets (RCISs), we propose finite-, and infinite-horizon PCISs, and explore their relation to RICSs. We design iterative algorithms to compute the PCIS within a given set. For systems with discrete spaces, the computations of the finite-, and infinite-horizon PCISs at each iteration are based on linear programming, and mixed integer linear programming, respectively. The algorithms are computationally tractable, and terminate in a finite number of steps. For systems with continuous spaces, we show how to discretize the spaces, and prove the convergence of the approximation when computing the finite-horizon PCISs. In addition, it is shown that an infinite-horizon PCIS can be computed by the stochastic backward reachable set from the RCIS contained in it. These PCIS algorithms are applicable to practical control systems. Simulations are given to illustrate the effectiveness of the theoretical results for motion planning.
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| 6. |
- Gao, Yulong, et al.
(författare)
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Distributed Freeway Ramp Metering : Optimization on Flow Speed
- 2017
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Ingår i: 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017. - : IEEE. - 9781509028733
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Konferensbidrag (refereegranskat)abstract
- This paper studies the distributed freeway ramp metering problem, for which the cell transmission model (CTM) is utilized. Considering the jam density and the upper bounds on the queue lengths and the ramp metering, we first provide feasibility conditions with respect to the external demand to ensure the controllability of the freeway. Assuming that the freeway is controllable, we formulate an optimization problem which tradeoffs the maximum average flow speed and the minimum waiting queue for each cell. Although the cells of the CTM are dynamically coupled, we propose a distributed backward algorithm for the optimization problem and prove that the solution to the problem is a Nash equilibrium. Furthermore, if the optimization problem is simplified to only maximization of the average flow speed, we argue that the obtained explicit distributed controller is globally optimal. A numerical example is given to illustrate the effectiveness of the proposed control algorithm.
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| 7. |
- Gao, Yulong, et al.
(författare)
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Invariant cover : Existence, cardinality bounds, and computation
- 2021
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Ingår i: Automatica. - : PERGAMON-ELSEVIER SCIENCE LTD. - 0005-1098 .- 1873-2836. ; 129
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Tidskriftsartikel (refereegranskat)abstract
- An invariant cover quantifies the information needed by a controller to enforce an invariance specification. This paper investigates some fundamental problems concerning existence and computation of an invariant cover for uncertain discrete-time linear control systems subject to state and control constraints. We develop necessary and sufficient conditions on the existence of an invariant cover for a polytopic set of states. The conditions can be checked by solving a set of linear programs, one for each extreme point of the state set. Based on these conditions, we give upper and lower bounds on the minimal cardinality of the invariant cover, and design an iterative algorithm with finite-time convergence to compute an invariant cover. We further show in two examples how to use an invariant cover in the design of a coder-controller pair that ensures invariance of a given set for a networked control system with a finite communication data rate.
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| 8. |
- Gao, Yulong, et al.
(författare)
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Probabilistic Characterization of Target Set and Region of Attraction for Discrete-time Control Systems
- 2020
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Ingår i: IEEE International Conference on Control and Automation, ICCA. - : IEEE Computer Society. ; , s. 594-599
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Konferensbidrag (refereegranskat)abstract
- This paper proposes a new notion of stabilization in probability for discrete-time stochastic systems that may be with unbounded disturbances and bounded control input. This new notion builds on two sets: target set and region of attraction. The target set is a set within which the controller is able to keep the system state with a certain probability. The region of attraction is a set from which the controller is able to drive the system state to the target set with a prescribed probability. We investigate the probabilistic characterizations of these two sets for linear stochastic control systems. We provide sufficient conditions for a compact set to be a target set with a given horizon and probability level. Given a target set, we use two methods to characterize the region of attraction: one is based on the solution to a stochastic optimal first-entry time problem while the other is based on stochastic backward reachable sets. For linear scalar systems, we provide analytic representations for the target set and the region of attraction. Simulations are given to illustrate the effectiveness of the theoretical results.
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| 9. |
- Gao, Yulong, et al.
(författare)
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Reachability-based Human-in-the-Loop Control with Uncertain Specifications
- 2020
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Ingår i: IFAC PAPERSONLINE. - : Elsevier BV. - 2405-8963. ; , s. 1880-1887
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Konferensbidrag (refereegranskat)abstract
- We propose a shared autonomy approach for implementing human operator decisions onto an automated system during multi-objective missions, while guaranteeing safety and mission completion. A mission is specified as a set of linear temporal logic (LTL) formulae. Then, using a novel correspondence between LTL and reachability analysis, we synthesize a set of controllers for assisting the human operator to complete the mission, while guaranteeing that the system maintains specified spatial and temporal properties. We assume the human operator's exact preference of how to complete the mission is unknown. Instead, we use a datadriven approach to infer and update the automated system's internal belief of which specified objective the human intends to complete. If, while the human is operating the system, she provides inputs that violate any of the invariances prescribed by the LTL formula, our verified controller will use its internal belief of the human operator's intended objective to guide the operator back on track. Moreover, we show that as long as the specifications are initially feasible, our controller will stay feasible and can guide the human to complete the mission despite some unexpected human errors. We illustrate our approach with a simple, but practical, experimental setup where a remote operator is parking a vehicle in a parking lot with multiple parking options. In these experiments, we show that our approach is able to infer the human operator's preference over parking spots in real-time and guarantee that the human will park in the spot safely.
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| 10. |
- Gao, Yulong, et al.
(författare)
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Risk-Aware Optimal Control for Automated Overtaking With Safety Guarantees
- 2021
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Ingår i: IEEE Transactions on Control Systems Technology. - : Institute of Electrical and Electronics Engineers Inc.. - 1063-6536 .- 1558-0865. ; , s. 1-13
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Tidskriftsartikel (refereegranskat)abstract
- This article proposes a solution to the overtaking control problem where an automated vehicle tries to overtake another vehicle with uncertain motion. Our solution allows the automated vehicle to robustly overtake a human-driven vehicle under certain assumptions. Uncertainty in the predicted motion makes the automated overtaking problem hard to solve due to feasibility issues that arise from the fact that the overtaken vehicle (e.g., a vehicle driven by an aggressive driver) may accelerate to prevent the overtaking maneuver. To counteract them, we introduce the weak assumption that the predicted velocity of the overtaken vehicle respects a supermartingale, meaning that its velocity is not increasing in expectation during the maneuver. We show that this formulation presents a natural notion of risk. Based on the martingale assumption, we perform a risk-aware reachability analysis by analytically characterizing the predicted collision probability. Then, we design a risk-aware optimal overtaking algorithm with guaranteed levels of collision avoidance. Finally, we illustrate the effectiveness of the proposed algorithm with a simulated example.
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