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"Human-machine Authority Allocation in Indirect Cooperative Shared Stee" by Hongbo Wang, Lizhao Feng et al.

In the man-machine co-driving, most of the existing indirect cooperative shared steering control(ICSSC) strategies adopt fixed driver models and are designed based on rules. However, the fixed driver model is difficult to match with the actual situation, and the rule-based strategy is hard to be designed under the multi-dimensional feature input and the multi-objective conditions and require complicated parameters adjustment. A driver model that conforms to the driving characteristics of drivers with actual driving data is established, and an ICSSC strategy is proposed based on reinforcement learning in this paper, so as to realize the dynamic allocation of human-machine steering driving weight. Firstly, the vehicle dynamics model is established according to the vehicle longitudinal, lateral and yaw dynamics, the driver driving data is collected, and then the trajectory tracking MPC (Model Predictive Control) steering controller is designed. Secondly, DQN (Deep Q-Network), DDPG (Deep D ....

Model Predictive Control , Deepq Network , Deep Deterministic Policy Gradient , Twin Delayed Deep Deterministic Policy Gradient , Heuristic Algorithms , Human Machine Systems , Ndirect Shared Steering Control Strategy , An Machine Co Driving , Multi Objective , Reinforcement Learning , Resource Management , Vehicle Dynamics , Weight Distribution ,

"Exploiting environmental information using HsMMs for smartphone user t" by Shuai Sun, Yan Li et al.

The extensive deployment of wireless infrastructure provides alternative low-cost methods for location awareness of mobile phone users in indoor environments by processing the received signal strength (RSS) of the mobile phone. In such a signal processing framework, hidden Markov models (HMMs) are often used to model the uncertainties of RSS data and incorporate environmental information into localization. Since hidden semi-Markov models (HsMMs) outperform HMMs in their ability to model state duration more flexibly, employing HsMMs for indoor user positioning is a promising research direction. In this aspect, a user’s personal preference of staying in a particular area, and the functionality of certain areas, such as a dining room, as well as navigation landmarks, can be utilized in the HsMM to assist localization. This paper proposes an online HsMM forward recursion algorithm to incorporate these information for real-time smartphone user tracking. We apply the proposed HsMM forward ....

Environmental Information , Orward Recursion , Heuristic Algorithms , Hidden Markov Models , Idden Semi Markov Model , Indoor Positioning , Location Awareness , Time Measurement , Viterbi Algorithm ,

"Green Multi-Stage Upgrade of Switches and Controllers for Bundled-Link" by Lely Hiryanto, Sieteng Soh et al.

The switches in legacy networks perform both control and data forwarding operations. In contrast, in a software defined network (SDN), a controller is responsible for network control and each SDN-switch (s-switch) only forwards data packets. Advantageously, controllers can be upgraded easily to run state-of-the-art network configuration and management solutions. In this context, this paper addresses the novel problem of upgrading a legacy network into an SDN over multiple stages that span months or years. More specifically, it aims to minimize energy consumption by optimizing switch upgrades, controller placement, and traffic routing subject to an operator’s budget, traffic delay tolerance, and controller capacity. We formulate the problem as a mixed integer program (MIP), and develop a heuristic solution that ensures a single path is used between switches and up to two link-disjoint paths are used between an s-switch and its controller. Our simulation results show that increasing an ....

Control Systems , Ontroller Placement , Energy Consumption , Nergy Saving , Reen Routing , Heuristic Algorithms , Ybrid Sdn , Network Planning , Process Control , Software Defined Networking , Switch Upgrade , Telecommunications Traffic ,