Supervised Classification of Operator Functional State Based on Physiological Data: Application to Drones Swarm Piloting

To improve the safety and the performance of operators involved in risky and demanding missions (like drone operators), human-machine cooperation should be dynamically adapted, in terms of dialogue or function allocation. To support this reconfigurable cooperation, a crucial point is to assess online the operator’s ability to keep performing the mission. The article explores the concept of Operator Functional State (OFS), then it proposes to operationalize this concept (combining context and physiological indicators) on the specific activity of drone swarm monitoring, carried out by 22 participants on simulator SUSIE. With the aid of supervised learning methods (Support Vector Machine, k-Nearest Neighbors, and Random Forest), physiological and contextual are classified into three classes, corresponding to different levels of OFS. This classification would help for adapting the countermeasures to the situation faced by operators.

Latency Reduction in Vehicular Sensing Applications by Dynamic 5G User Plane Function Allocation with Session Continuity

Vehicle automation is driving the integration of advanced sensors and new applications that demand high-quality information, such as collaborative sensing for enhanced situational awareness. In this work, we considered a vehicular sensing scenario supported by 5G communications, in which vehicle sensor data need to be sent to edge computing resources with stringent latency constraints. To ensure low latency with the resources available, we propose an optimization framework that deploys User Plane Functions (UPFs) dynamically at the edge to minimize the number of network hops between the vehicles and them. The proposed framework relies on a practical Software-Defined-Networking (SDN)-based mechanism that allows seamless re-assignment of vehicles to UPFs while maintaining session and service continuity. We propose and evaluate different UPF allocation algorithms that reduce communications latency compared to static, random, and centralized deployment baselines. Our results demonstrated that the dynamic allocation of UPFs can support latency-critical applications that would be unfeasible otherwise.

Redesigning the Municipal Solid Waste Supply Chain Considering the Classified Collection and Disposal: A Case Study of Incinerable Waste in Beijing

The output of municipal solid waste is growing rapidly, which has brought tremendous pressure to urban development. The supply chain of municipal solid waste (MSW) in China mainly contains three processes: collection, transportation, and disposal. The waste is sorted at the collection and disposed of according to the classification. However, it is mixed at the transportation stage. Mixed transportation remixes the separately collected waste, which seriously affects the disposal effect. The supply chain of MSW urgently needs to be redesigned to improve the MSW disposal effect. First of all, on the ground of the waste treatment situation, we redesigned the supply chain of MSW in China. Secondly, combined with the redesign of the MSW supply chain, this paper established the function allocation model for collection stations, making a collection station only gather one type of waste, and built the transportation path planning model for vehicles, reducing the impact of waste storage on residents. Finally, based on the data of Xuanwu District in Beijing, the supply chain redesigning practical example of incinerable waste was given. The supply chain redesigning model in this paper not only makes full use of the existing infrastructure but also improves the disposal effect of waste. The supply chain redesigning model has practical application value.

Coordinated control of distributed drive electric vehicle by TVC and ESC based on function allocation

Safe and comfortable driving experience includes the improvement of handling performance and stability control. This paper proposed a coordinated controller based on the function allocation for the handling performance and stability of distributed drive electric vehicles. The proposed controller has three layers. The upper control layer designs a dynamic stability envelope boundary suitable for various driving conditions through the phase plane method, and on this basis, the function allocation rules of torque vector control (TVC) strategy and electronic stability control (ESC) strategy were formulated. The medium control layer used the robust [Formula: see text] dynamic output feedback control method and the improved particle swarm optimization (PSO) parameter self-adjusting method to calculate the additional yaw moment required by the TVC strategy and the ESC strategy, respectively. The two types of additional yaw moment are implemented by the in-wheel motor and the hydraulic brake mechanism respectively. The lower control layer optimized the four-wheel torque and braking force based on the optimal tire load rate using the quadratic programming method. The proposed coordinated controller was performed in the CarSim/Simulink co-simulation platform and tested in a real vehicle platform. The results show that the proposed controller can improve the vehicle dynamic response according to the driver’s intention, thus bringing the better handling performance and stability.