Decentralized Adaptive Formation via Consensus-Oriented Multi-Agent Communication

Adaptive multi-agent formation control, which requires the formation to flexibly adjust along with the quantity variations of agents in a decentralized manner, belongs to one of the most challenging issues in multi-agent systems, especially under communication-limited constraints. In this paper, we propose a novel Consensus-based Decentralized Adaptive Formation (Cons-DecAF) framework. Specifically, we develop a novel multi-agent reinforcement learning method, Consensus-oriented Multi-Agent Communication (ConsMAC), to enable agents to perceive global information and establish the consensus from local states by effectively aggregating neighbor messages. Afterwards, we leverage policy distillation to accomplish the adaptive formation adjustment. Meanwhile, instead of pre-assigning specific positions of agents, we employ a displacement-based formation by Hausdorff distance to significantly improve the formation efficiency. The experimental results through extensive simulations validate that the proposed method has achieved outstanding performance in terms of both speed and stability.Comment: 6 pages, 5 figure

Foreign Object Detection for Electric Vehicle Wireless Charging

Wireless power transfer technology is being widely used in electric vehicle wireless-charging applications, and foreign object detection (FOD) is an important module that is needed to satisfy the transmission and safety requirements. FOD mostly includes two key parts: metal object detection (MOD) and living object detection (LOD), which should be implemented during the charging process. In this paper, equivalent circuit models of a metal object and a living object are proposed, and the FOD methods are reviewed and analyzed within a unified framework based on the proposed FOD models. A comparison of these detection methods and future challenges is also discussed. Based on these analyses, detection methods that employ an additional circuit for detection are recommended for FOD in electric vehicle wireless-charging applications

Interoperability Study of Wireless Charging System Based on Generalized DD Coil

In recent years, with the popularization of intelligent auxiliary driving technology, wireless charging technology has garnered widespread attention due to its advantages in automation. However, there are still many issues to be resolved before the widespread adoption of wireless charging technology, with a key issue being the interoperability of coupling structures. To achieve adaptive interoperability, this paper proposes a wireless charging system based on dual generalized double-D (DD) coils. The system introduces a receiving structure composed of two generalized DD coils, which can naturally decouple the two coils. At the same time, the system topology employs a series connection of half-bridge rectifiers to enhance the system’s equivalent mutual inductance. Mathematical models and simulation verifications are provided. The proposed approach has been validated through simulations, showing that the system can automatically achieve interoperability with unipolar coils, DD coils, and quadrupolar coils

Asymmetric Equivalences in Fuzzy Logic

We introduce a new class of operations called asymmetric equivalences. Several properties of asymmetric equivalence operations have been investigated. Based on the asymmetric equivalence, quasi-metric spaces are constructed on [0, 1]. Finally, we discuss symmetrization of asymmetric equivalences

An improved submodule unified pulse modulation scheme for a hybrid modular multilevel converter

This paper presents an improved submodule unified pulse width modulation (SUPWM) scheme for a hybrid modular multilevel converter (MMC) composed of half-bridge submodules (HBSMs) and full-bridge submodules (FBSMs). The proposed SUPWM scheme can achieve an output voltage of (2N+1) (where N is the number of submodules in each arm) levels, which is the same as that of the carrier-phase-shifted PWM (CPSPWM) scheme. Meanwhile, the proposed SUPWM scheme can alleviate the uneven loss distributions between the left leg and right leg in FBSMs of the hybrid MMC. Moreover, the capacitor voltages of the sub-modules can be well balanced without complicated closed-loop voltage balancing controllers. The validity of the proposed SUPWM scheme is verified by both the simulated and experimental results

An Adversarial Attack Method against Specified Objects Based on Instance Segmentation

The deep model is widely used and has been demonstrated to have more hidden security risks. An adversarial attack can bypass the traditional means of defense. By modifying the input data, the attack on the deep model is realized, and it is imperceptible to humans. The existing adversarial example generation methods mainly attack the whole image. The optimization iterative direction is easy to predict, and the attack flexibility is low. For more complex scenarios, this paper proposes an edge-restricted adversarial example generation algorithm (Re-AEG) based on semantic segmentation. The algorithm can attack one or more specific objects in the image so that the detector cannot detect the objects. First, the algorithm automatically locates the attack objects according to the application requirements. Through the semantic segmentation algorithm, the attacked object is separated and the mask matrix for the object is generated. The algorithm proposed in this paper can attack the object in the region, converge quickly and successfully deceive the deep detection model. The algorithm only hides some sensitive objects in the image, rather than completely invalidating the detection model and causing reported errors, so it has higher concealment than the previous adversarial example generation algorithms. In this paper, a comparative experiment is carried out on ImageNet and coco2017 datasets, and the attack success rate is higher than 92%

A Momentum-Based Local Face Adversarial Example Generation Algorithm

Small perturbations can make deep models fail. Since deep models are widely used in face recognition systems (FRS) such as surveillance and access control, adversarial examples may introduce more subtle threats to face recognition systems. In this paper, we propose a practical white-box adversarial attack method. The method can automatically form a local area with key semantics on the face. The shape of the local area generated by the algorithm varies according to the environment and light of the character. Since these regions contain major facial features, we generated patch-like adversarial examples based on this region, which can effectively deceive FRS. The algorithm introduced the momentum parameter to stabilize the optimization directions. We accelerated the generation process by increasing the learning rate in segments. Compared with the traditional adversarial algorithm, our algorithms are very inconspicuous, which is very suitable for application in real scenes. The attack was verified on the CASIA WebFace and LFW datasets which were also proved to have good transferability

A New ZVS Tuning Method for Double-Sided LCC Compensated Wireless Power Transfer System

This paper presents a new zero voltage switching (ZVS) tuning method for the double-sided inductor/capacitor/capacitor (LCC) compensated wireless power transfer (WPT) system. An additional capacitor is added in the secondary side of the double-sided LCC compensation network in order to tune the network to realize ZVS operation for the primary-side switches. With the proposed tuning method, the turn off current of the primary-side switches at the low input voltage range can be reduced compared with the previous ZVS tuning method. Consequently, the efficiency of the WPT at the low input voltage range is improved. Moreover, the relationship between the input voltage and the output power is more linear than that of the previous ZVS tuning method. In addition, the proposed method has a lower start-up voltage. The analysis and validity of the proposed tuning method are verified by simulation and experimental results. A WPT system with up to 3.5 kW output power is built, and 95.9% overall peak efficiency is achieved

Constructing Cellulose Diacetate Aerogels with Pearl-Necklace-like Skeleton Networking Structure

Cellulose and its derivative aerogels have attracted much attention due to their renewable and biodegradable properties. However, the significant shrinkage in the supercritical drying process causes the relatively high thermal conductivity and low mechanical property of cellulose and its derivatives aerogels. Considering the pearl-necklace-like skeleton network of silica aerogels, which can improve thermal insulation property and mechanical property. Herein, we propose a new strategy for fabricating cellulose diacetate aerogels (CDAAs) with pearl-necklace-like skeletons by using tert-butanol (TBA) as exchange solvent after experiencing the freezing-drying course. CDAAs obtained have the low density of 0.09 g cm−3, the nanopore size in the range of 10–40 nm, the low thermal conductivity of 0.024 W m−1 K−1 at ambient conditions, and the excellent mechanical properties (0.18 MPa at 3% strain, 0.38 MPa at 5% strain). Ultimately, CDAAs with moderate mechanical property paralleled to cellulose-derived aerogels obtained from supercritical drying process are produced, only simultaneously owning the radial shrinkage of 6.2%. The facile method for fabricating CDAAs could provide a new reference for constructing cellulose/cellulose-derived aerogels and other biomass aerogels