Risk-bounded motion planning for autonomous driving in dynamic environments presents significant research challenges. Ensuring continuous navigation towards a destination while making real-time decisions is a nonconve...
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ISBN:
(纸本)9798331518509;9798331518493
Risk-bounded motion planning for autonomous driving in dynamic environments presents significant research challenges. Ensuring continuous navigation towards a destination while making real-time decisions is a nonconvex problem. This paper presents a graph-based local planning method constrained by user-specific driving preference, represented as a risk-bound criterion for motion planning. First, we propose a lattice graph construction method that adheres to the vehicle's curvature constraints. Then, we formulate the trajectory planning problem as an integer-linearprogramming task, addressed by our novel risk-bounded and prediction-aware constrained shortest path. Our solution accounts for both static and dynamic obstacles in urban settings, adhering to traffic regulations. At the core of our approach is a conservative spatiotemporal risk assessment mechanism, which evaluates collisions considering the uncertain delay from speed control of the ego vehicle and predicted trajectories of dynamic obstacles. We implemented our solution using the CARLA simulator and the ROS2 platform, within a comprehensive framework encompassing global planning, local planning, and vehicle control. The effectiveness of our approach is demonstrated through notable collision avoidance, improved path-tracking, and enhanced risk-bounded planning capabilities.
This study presents an innovative extension to Home Healthcare Scheduling and Routing Problem (HHCRSP), introducing an Optional Starting Point (OSP) that allows for personalization of routes from the caregivers' h...
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ISBN:
(纸本)9798350373981;9798350373974
This study presents an innovative extension to Home Healthcare Scheduling and Routing Problem (HHCRSP), introducing an Optional Starting Point (OSP) that allows for personalization of routes from the caregivers' homes directly to patients. We developed a model using Mixed integer linear programming (MILP) and a metaheuristic method inspired by the Greedy Randomized Adaptive Search Procedure (GRASP). This combination aims to make scheduling and routing more efficient, saving time and improving the quality of care for patients at home. Our results show that this new method can significantly enhance how home healthcare is delivered, making it more flexible and effective for both caregivers and patients.
Electric vehicles (EVs) provide a promising solution to global pollution by replacing conventional vehicles. Enough public charging stations must be built to encourage the broad adoption of electric vehicles (EVs). Ad...
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ISBN:
(纸本)9798350387414
Electric vehicles (EVs) provide a promising solution to global pollution by replacing conventional vehicles. Enough public charging stations must be built to encourage the broad adoption of electric vehicles (EVs). Additionally, efficient scheduling and planning of EV arrivals at charging stations with a variety of power sources is essential for improving overall operating efficiency. This paper presents a framework for optimizing both the profit and throughput of a charging station that incorporates a solar photovoltaic system and an energy storage system. We present an integer linear programming model to find the optimal solution. We propose a heuristic strategy to efficiently find near-optimal solutions. Our experimental results demonstrate that the proposed approach can provide a reasonably good solution and for small inputs, the performance deviation is less than 5%. For larger inputs, a comparison of the heuristic and baseline is presented. In certain scenarios, the heuristic performs well, while the baseline exhibits better performance in others. We can use both approaches and select the one that yields better results.
integerprogramming (IP), as the name suggests is an integer-variable-based approach commonly used to formulate real-world optimization problems with constraints. Currently, quantum algorithms reformulate the IP into ...
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We study the question whether copies of S-1 in SU(3) can be amalgamated in a compact group. This is the simplest instance of a fundamental open problem in the theory of compact groups raised by George Bergman in 1987....
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We study the question whether copies of S-1 in SU(3) can be amalgamated in a compact group. This is the simplest instance of a fundamental open problem in the theory of compact groups raised by George Bergman in 1987. Considerable computational experiments suggest that the answer is positive in this case. We obtain a positive answer for a relaxed problem using theoretical considerations.
This paper presents a feed forward artificial neural network that identifies the order of the dynamics of a unit step response. The main contribution of this paper is demonstrating that a system trained on only intege...
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ISBN:
(纸本)9798350373981;9798350373974
This paper presents a feed forward artificial neural network that identifies the order of the dynamics of a unit step response. The main contribution of this paper is demonstrating that a system trained on only integer order (first and second order) systems can identify fractional order responses with a high degree of accuracy. The details of the design of structure of the neural network, the training method and the training sets, as well as statistics describing the accuracy of the fractional predictions are presented. Also using the neural network to identify fractional dynamics for a large scale networked system from the authors' prior work is presented as further validation and a demonstration of the applicability of the results. This demonstrates the potential for practicing engineers to use similar machine learning tools trained on "standard" systems with the ability to distinguish when features such as fractional order dynamics are significant and warrant deeper consideration for the design or control of such a system.
The rapid expansion of computing needs from emerging applications pushes a large amount of deployment of computing infrastructures and corresponding energy cost and greenhouse gas emissions of computing generate great...
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ISBN:
(纸本)9781728190549
The rapid expansion of computing needs from emerging applications pushes a large amount of deployment of computing infrastructures and corresponding energy cost and greenhouse gas emissions of computing generate great concern. In this paper, we study how to maximize the platform profit by optimizing task scheduling in the Space-Air-Ground integrated Computing Power Network supplied by green energy while considering both the user requirements and dynamics of green energy. First, we formalize the problem as a binary integer linear programming problem that is NP-hard. The problem is then further modeled as a Markov decision process. Considering the dual dynamics of user requests and the generation of green energy, we propose a task scheduling strategy based on deep reinforcement learning, which can predict power generation based on the current operating status of each hydroelectric power station and also provide a scheduling strategy. Extensive experiments demonstrate that the proposed algorithm performs better than the baseline algorithms.
Opacity is an important information flow property that is concerned with the secret leakage of a system to a malicious observer called an "intruder". Usually, opacity analyses are made under static or dynami...
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ISBN:
(纸本)9783031497360;9783031497377
Opacity is an important information flow property that is concerned with the secret leakage of a system to a malicious observer called an "intruder". Usually, opacity analyses are made under static or dynamic observation, i.e., the observability of events in a system is fixed or changeable over time by a mask. In this paper, we address the verification of language-based opacity in the context of discrete-event systems under Orwellian observation. We consider an Orwellian partial observability model, where some unobservable events, not visible when occurring, may become noticeable in the future. Specifically, we propose a set of unobservable events that are no longer unobservable once an event in another particular disjoint event subset is triggered. First, we define and solve an integer linear programming problem to verify language-based opacity in discrete event systems using labeled Petri nets. We then propose a new Orwellian projection function that is event-based, i.e., the system is allowed to re-interpret the observation of the already triggered events when a particular observable event occurs. Finally, the verification of language-based opacity in discrete event systems under Orwellian projection is addressed.
This paper proposes a scalable filterless horseshoe-and-spur architecture optimized using a novel and exact integer linear programming (ILP) framework. The proposed solution allows network expansion with minimal disru...
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ISBN:
(纸本)9798350377330;9798350377323
This paper proposes a scalable filterless horseshoe-and-spur architecture optimized using a novel and exact integer linear programming (ILP) framework. The proposed solution allows network expansion with minimal disruptions and less cost compared to simple horseshoes.
The v-function of a graded filtration I = {I[k]}k≥0 is introduced. Under the assumption that I is Noetherian, we prove that the v-function v(I[k]) is an eventually quasi-linear function. This result applies to severa...
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