Motion Compensation for Near-Range Synthetic Aperture Radar Applications

The work focuses on the analysis of influences of motion errors on near-range SAR applications and design of specific motion measuring and compensation algorithms. First, a novel metric to determine the optimum antenna beamwidth is proposed. Then, a comprehensive investigation of influences of motion errors on the SAR image is provided. On this ground, new algorithms for motion measuring and compensation using low cost inertial measurement units (IMU) are developed and successfully demonstrated

Design and Implementation on Multi-Function Smart Wheelchair

With the increase of population aging, chronic diseases and accidental injuries, more and more people are facing the plight of diminished or even lost walking ability. As a kind of mobile service robot, the smart wheelchair has strong environmental adaptability, smooth motion control and friendly human-computer interaction experience, and is an indispensable tool in rehabilitation engineering and elderly assistance engineering, which has important research value and social significance. In this paper, the system structure framework of the multi-function smart wheelchair and the specific technical scheme of each module are formulated based on the modular design idea, and the intelligent control hardware platform with Arduino UNO controller as the core is built to complete the control software writing, and an intelligent wheelchair for the elderly and disabled guardianship is designed. This multi-function smart wheelchair combined with IoT technology can realize distance navigation, obstacle avoidance, medication reminder, sign measurement, remote monitoring, GPS positioning, and upload information through OneNET cloud platform, which can view the location of the wheelchair and the safety status of the elderly from the map in real time in the cell phone APP and PC backstage, in order to improve the control and monitoring capability of the wheelchair

Distributed large scale systems: a multi-agent RL-MPC architecture

Universidat Politécnica de Cataluya. Programa de Doctorat: Automàtica, Robòtica I Visiò.[EN]: This thesis describes a methodology to deal with the interaction between MPC controllers in a distributed MPC architecture. This approach combines ideas from Distributed Artificial Intelligence (DAI) and Reinforcement Learning (RL) in order to provide a controller interaction based on cooperative agents and learning techniques. The aim of this methodology is to provide a general structure to perform optimal control in networked distributed environments, where multiple dependencies between subsystems are found. Those dependencies or connections often correspond to control variables. In that case, the distributed control has to be consistent in both subsystems. One of the main new concepts of this architecture is the negotiator agent. Negotiator agents interact with MPC agents to determine the optimal value of the shared control variables in a cooperative way using learning techniques (RL). The optimal value of those shared control variables has to accomplish a common goal, probably different from the specific goal of each agent sharing the variable. Two cases of study, in which the proposed architecture is applied and tested are considered, a small water distribution network and the Barcelona water network. The results suggest this approach is a promising strategy when centralized control is not a reasonable choice.[ES]: Esta tesis describe una metodología para hacer frente a la interacción entre controladores MPC en una arquitectura MPC distribuida. Este enfoque combina las ideas de Inteligencia Artificial Distribuida (DIA) y aprendizaje por refuerzo (RL) con el fin de proporcionar una interacción entre controladores basado en agentes de cooperativos y técnicas de aprendizaje. El objetivo de esta metodología es proporcionar una estructura general para llevar a cabo un control óptimo en entornos de redes distribuidas, donde se encuentran varias dependencias entre subsistemas. Esas dependencias o conexiones corresponden a menudo a variables de control. En ese caso, el control distribuido tiene que ser coherente en ambos subsistemas. Uno de los principales conceptos novedosos de esta arquitectura es el agente negociador. Los agentes negociadores actúan junto con agentes MPC para determinar el valor óptimo de las variables de control compartidas de forma cooperativa utilizando técnicas de aprendizaje (RL). El valor óptimo de esas variables compartidas debe lograr un objetivo común, probablemente diferente de los objetivos específicos de cada agente que está compartiendo la variable. Se consideran dos casos de estudio, en el que la arquitectura propuesta se ha aplicado y probado, una pequeña red de distribución de agua y la red de agua de Barcelona. Los resultados sugieren que este enfoque es una estrategia prometedora cuando el control centralizado no es una opción razonable.Peer Reviewe

Study on Mechanism and Improvement of Triple Frequency Noise of Rotary Compressor

With the continuous improvement of social life, people have more stringent noise requirements for home air conditioners. As the kernel of an air conditioner, compressor provides power for the whole system, inevitably generating vibration and noise. Therefore, Reducing the vibration and noise of the compressor is great significance for the noise reduction of the air conditioner. Generally, vibration is mainly transferred through the suction and exhaust pipes to the air conditioning pipe system. However, due to the complicated configuration, there are intensive modals for the pipe system, especially those in low frequency range, which may lead to resonance and large acoustic radiation. This paper studies the generation and transmission mechanism of triple frequency vibration of compressor, the compressor exhaust pressure fluctuation stimulates the exhaust pipe to vibrate, and then results in vibration of the air conditioning pipe systems, and vibration generated by the rotor is transferred to intake pipe via the accumulator, and cause the pipe systems to vibrate. Based on this research, we find some main factors which influence the triple frequency vibration and noise of the compressor, which are the exhaust pressure pulsation, the natural frequency of the rotor-crankshaft system swing, the natural frequency of the accumulator swing. Then, above factors which affect the compressor vibration and noise are analyzed and improved separately, and conducted noise tests on the improved compressor at 90Hz. The results show that the compressor noise are reduced by 29.8% around 250Hz

ChainsFormer: A Chain Latency-aware Resource Provisioning Approach for Microservices Cluster

The trend towards transitioning from monolithic applications to microservices has been widely embraced in modern distributed systems and applications. This shift has resulted in the creation of lightweight, fine-grained, and self-contained microservices. Multiple microservices can be linked together via calls and inter-dependencies to form complex functions. One of the challenges in managing microservices is provisioning the optimal amount of resources for microservices in the chain to ensure application performance while improving resource usage efficiency. This paper presents ChainsFormer, a framework that analyzes microservice inter-dependencies to identify critical chains and nodes, and provision resources based on reinforcement learning. To analyze chains, ChainsFormer utilizes light-weight machine learning techniques to address the dynamic nature of microservice chains and workloads. For resource provisioning, a reinforcement learning approach is used that combines vertical and horizontal scaling to determine the amount of allocated resources and the number of replicates. We evaluate the effectiveness of ChainsFormer using realistic applications and traces on a real testbed based on Kubernetes. Our experimental results demonstrate that ChainsFormer can reduce response time by up to 26% and improve processed requests per second by 8% compared with state-of-the-art techniques.Comment: 15 page