Energy expenditure for quadrotor control has a likelihood of being costly given parameter-dependent controllers that are less than optimal. The cost can grow proportionally when applied to multiple quadrotors for trac...
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ISBN:
(纸本)9798350361087;9798350361070
Energy expenditure for quadrotor control has a likelihood of being costly given parameter-dependent controllers that are less than optimal. The cost can grow proportionally when applied to multiple quadrotors for tracking and collaborative navigation tasks. This research aims to establish a basic approach to tuning PID (Proportional-Integral-Derivative) parameters for a simulated quadrotor drone. A PID controller for autonomy provides a straightforward method for correcting robotic movement based on its current state. However, applying a PID system to a flight controller poses challenges with an inherently under-actuated system, which includes the likelihood of large overshoots and lengthy adjustment times. To address this, we utilize PSO (Particle Swarm optimization) for optimizing PID parameters in a simulated quadrotor. The PSO is employed to find optimal PID values for thrust, yaw, and translational movement on x- and y-positions by identifying converging values across randomly created particles. We conducted a set of experiments and compared it to the default PID controller. The experiments demonstrate converging properties for particles that achieve minimal fitness scores, particularly in reducing overshoot. The results indicate that the optimized PID controller outperforms the default PID controller without optimization. Using optimized PID controllers can decrease the amount of positional error during flight and when adjusting position with collaborative navigation and collision avoidance algorithms.
The proceedings contain 27 papers. The topics discussed include: a general-purpose analog computer to population protocol compiler;compile-time optimization of the energy consumption of numerical computations;effectiv...
ISBN:
(纸本)9798400704925
The proceedings contain 27 papers. The topics discussed include: a general-purpose analog computer to population protocol compiler;compile-time optimization of the energy consumption of numerical computations;effective HPC programming via domain specific abstractions and compilation;high-level synthesis for complex applications: the Bambu approach;relaxed threshold implementations;using a performance model to implement a superscalar CVA6;seeing beyond the order: a LEN5 to sharpen edge microprocessors with dynamic scheduling;integrating systemC-AMS power modeling with a RISC-V ISS for virtual prototyping of battery-operated embedded devices;a gigabit, DMA-enhanced open-source ethernet controller for mixed-criticality systems;and model theft attack against a tinyML application running on an ultra-low-power open-source SoC.
Serverless computing promises automatic resource provisioning to relieve the burden of developers. Yet, developers still have to manually configure resources on current serverless platforms to satisfy application-leve...
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Automated storage and retrieval systems (ASRS) are important in distribution centers and warehouses. To decrease cost or CO2 emissions it is natural to optimize various aspects of an ASRS. In this work, we provide a c...
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ISBN:
(纸本)9781665493130
Automated storage and retrieval systems (ASRS) are important in distribution centers and warehouses. To decrease cost or CO2 emissions it is natural to optimize various aspects of an ASRS. In this work, we provide a concept for a two-phase optimization combining two important optimization tasks in ASRS: Given multiple rearrangement jobs, we first sequence these jobs to minimize the total travelling distance of the cranes. We continue the optimization by computing optimal trajectories for the sequence to guarantee energy efficient driving of the cranes. We describe our algorithms for a complex ASRS architecture with two cranes on parallel rails in one aisle. Additionally, we describe how to use our results for parallelization of crane movements in the considered warehouse architecture.
In this paper, we study the nearest prototype classification, which is a classification task using labeled prototypes. In previous work, we have revised our margin-maximization method for the nearest prototype classif...
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ISBN:
(纸本)9783031682070;9783031682087
In this paper, we study the nearest prototype classification, which is a classification task using labeled prototypes. In previous work, we have revised our margin-maximization method for the nearest prototype classification. Its optimization problem is formulated using DC (Difference of Convex) functions and solved using CCP (Convex-Concave Procedure), which is a k-means-like algorithm. In this paper, we apply the revised method for fuzzy nearest prototype classification, in which the function selecting the closest prototype is fuzzified, and a label of a given instance is predicted using more than one nearest prototype. Through a numerical study, we examine the characteristics of the proposed method.
In IT service operations such as service help desk, the primary task is to resolve customer queries satisfactorily within the stipulated service level agreements (SLA). These customer queries, referred to as tickets o...
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ISBN:
(纸本)9798350367102;9798350367096
In IT service operations such as service help desk, the primary task is to resolve customer queries satisfactorily within the stipulated service level agreements (SLA). These customer queries, referred to as tickets often contain sensitive and non-sensitive information. The disclosure of sensitive information even to an authorized agents is a privacy concern and could increase the risk of insider threat. In this work, we propose a framework to restrict the data exposure to authorized agents in such IT service operations. To facilitate privacy-enabled service operations, we assess the risk associated with the disclosure of attributes using its vulnerability and provide a masking strategy to reduce the data exposure. However, fully masking the key attributes within the ticket could hinder the resolution time and potentially lead to SLA violations. To overcome this, we propose an optimization model for partial masking which takes into consideration the attribute vulnerability and privacy requirement of an application, to minimize the overall data exposure. We provide an illustration on how this masking schemes can be implemented.
In today's fast-paced world, teachers seek continuous improvement for their courses to match their context using e-learning systems. With the aid of technologies, teachers have access to vast amount of resources w...
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Prior studies have proposed various methods to mitigate the computational burden of design optimization in induction machines (IM) through finite element analysis (FEA). However, they often face high computational com...
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ISBN:
(纸本)9798350348958;9798350348965
Prior studies have proposed various methods to mitigate the computational burden of design optimization in induction machines (IM) through finite element analysis (FEA). However, they often face high computational complexity, where the computation time increases significantly with the number of input parameters considered. Additionally, these methods mostly focus exclusively on the individual physics of IMs (electromagnetic or thermal aspect) without addressing their interdependent influences. To confront these issues, this study explores the utilization of artificial neural networks (ANN) for the IMs' design optimization, taking into account their Multiphysics aspects. The goal is to leverage ANNs power to efficiently tackle complex design optimization challenges while reducing computation time, thus simplifying achieving optimal electro-thermal performance. Through in-depth analysis and modeling, the research illuminates ANN's potential in developing superior designs, while also meeting speed and accuracy criteria. The findings introduce a novel approach that integrates advanced computational tools with traditional design methods to enhance IM performance across various applications with implications for other machine types.
Active magnetic compensation is required to achieve near-zero magnetic environment for Magnetocardiography (MCG). The coupling relations between the magnetic fields of passive field cause difficulties in compensation....
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ISBN:
(纸本)9798350348958;9798350348965
Active magnetic compensation is required to achieve near-zero magnetic environment for Magnetocardiography (MCG). The coupling relations between the magnetic fields of passive field cause difficulties in compensation. In this paper, we establish a multidirectional magnetic field decoupling model which build adjustive vector and unidirectional magnetic field matrix (AV-UMFM) equations. Based on Particle Swarm optimization (PSO) algorithm, the adjustive vector of the model can be acquired despite ill-conditioned problems. Finally, adjustive vector controls currents to simultaneously compensate for multidirectional residual magnetic fields and builds magnetic compensation system.
Cyber-physical production systems have emerged with the rise of Industry 4.0 in different industrial fields. Especially the food sector, where inhomogeneous input products like beer/yeast suspensions with different qu...
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ISBN:
(纸本)9781665493130
Cyber-physical production systems have emerged with the rise of Industry 4.0 in different industrial fields. Especially the food sector, where inhomogeneous input products like beer/yeast suspensions with different qualities and properties have yet slowed down automation, has potential for this evolution. This contribution presents optimization methods for a dynamical cross-flow filtration plant which is driven by an advanced control concept in combination with data driven product monitoring via inline near infrared spectroscopy (NIR) in order to improve energy savings and filtration performance. Using a hierarchical control and optimizationstructure, the non stationary batch process is steered towards a high production rate with low energy consumption for a variety of different input products.
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