Laplacian dynamics on signed digraphs have a richer behavior than those on nonnegative digraphs. In particular, for the so-called 'repelling' signed Laplacians, the marginal stability property (needed to achie...
详细信息
Fine-grained plant pathology classification is an important task for precision agriculture, but at the same time, it is challenging due to the subtle difference in plant categories. Variances in the lighting condition...
Fine-grained plant pathology classification is an important task for precision agriculture, but at the same time, it is challenging due to the subtle difference in plant categories. Variances in the lighting conditions, position, and stages of disease symptoms usually lead to degradation of classification accuracy. Knowledge distillation is a popular method to improve the model performance to deal with the indistinguishable image classification problem. It aims to have a well-optimised small student network guided by a large teacher network. Existing knowledge distillation methods mainly consider training a teacher network that needs a high storage space and considerable computing resources. Self-knowledge distillation methods have been proposed to distil knowledge from the same network. Although self-knowledge distillation saves time and space compared with knowledge distillation, it only learns label knowledge. In this paper, we propose a novel self-distillation method to recognize the fine-grained plant category, which considers holistic knowledge based on the Squeeze and Excitation Network. We label this new method as holistic self-distillation because it captures knowledge through spatial features and labels. The performance validation of the proposed approach is performed on two public fine-grained plant datasets: Plant Pathology 2021 and Plant Pathology 2020 with the accuracy of 98.22% and 90.72% respectively. We also present experiments on the state-of-the-art algorithm (ResNet-50). The classification results demonstrate the effectiveness of the proposed approach with respect to accuracy.
In laser powder bed fusion additive manufacturing, using fixed values for the process parameters (beam power, velocity, and other parameters) may not lead to homogeneously distributed heat in all locations in the buil...
In laser powder bed fusion additive manufacturing, using fixed values for the process parameters (beam power, velocity, and other parameters) may not lead to homogeneously distributed heat in all locations in the build, especially around complex design features. This could lead to builds with defects, leading to poor mechanical and micro-structure properties. To guarantee heat homogeneity, the process parameters need to be actively controlled to adapt to different locations in the build. Builds with varying geometrical features would need different control strategies. In this work, we propose to use reinforcement learning (RL) to control, for the first time, simultaneously multiple AM process parameters to achieve consistent melting properties. Our results show that using RL as a multiple-input multiple-output control system achieves a more consistent meltpool geometry.
This paper proposes a hybrid computational framework for fault detection during the coil winding manufacturing process by using a combination of Discrete Event Simulation (DES) model with a Supervised Machine Learning...
This paper proposes a hybrid computational framework for fault detection during the coil winding manufacturing process by using a combination of Discrete Event Simulation (DES) model with a Supervised Machine Learning (SML) algorithm. The conventional End of the Line (EoL) tests are insufficient in detecting faults during process resulting in increased manufacturing costs and lead times. The proposed methodology utilises a Knowledge Distillation (KD) approach to address the challenges associated with the technique and optimise the student model's performance by employing architecture search and data augmentation. Multiple SML algorithms were evaluated to determine their effectiveness in predicting faults during manufacturing. The random forest algorithm demonstrated superior performance due to its ability to handle complex data and identify the impact of interdependencies of process parameters on the final product quality. The method was validated by conducting physical experiments on a linear coil-winding machine, and the results indicated that the random forest algorithm has the potential to decrease simulation time from 2 minutes to less than a second. The proposed methodology has the potential to reduce manufacturing time, enhance stator quality, and ultimately improve their reliability and safety.
A function of grid-connected VSGs is facilitating the recovery of sagging voltages to nominal levels, achieved by supplying reactive power (Q) to the grid. During normal operation, however, a VSG is required to supply...
A function of grid-connected VSGs is facilitating the recovery of sagging voltages to nominal levels, achieved by supplying reactive power (Q) to the grid. During normal operation, however, a VSG is required to supply active power (P) at unity power factor and, therefore, in order to supply the Q required in the event of a sagging voltage, the VSG must change its control mode from P-supply to PQ- or Q-supply. Such transitions may induce high current flows—caused by the sudden reduction of the voltage during the sag—which are potentially damaging to the power semiconductor devices of the VSG; if these effects are to be avoided, the supplied current must be limited to its rated value, but this may result in a low power transfer from the VSG to the grid. To circumvent these problems, in this paper we propose a LVRT strategy using an active SFCL. We show that the proposed strategy, which combines a PQ decoupling scheme with an active SFCL, ensures maximum power transfer with an improved transient response during LVRT.
Soft-growing robots are emerging with numerous potential applications because of their superior capability of frictionless navigation. However, their success is hindered by their tendency to buckle under the tension r...
Soft-growing robots are emerging with numerous potential applications because of their superior capability of frictionless navigation. However, their success is hindered by their tendency to buckle under the tension required to retract them via inversion. In this paper, we propose a simple and scalable tubular backbone to facilitate retracting the robot body without buckling. With this backbone, compressive forces at the robot's tip are mitigated and a limit is placed on the effective length for retraction during the application of tension. We first present the selection of the backbone and the development of such a retractable soft-growing robot. Along with the characterization of the working principles behind this buckling-free mechanism, success was observed with the use of the backbone in retraction tests. The effects of different parameters such as robot body lengths, air pressures, curvatures, and retraction modes on the performance were also investigated. This backbone approach requires no bulky or in-situ mechatronic components inside the robot body and thus may be used in medical applications which appreciate simple, compact, and in-situ electronic-free designs.
High penetration of renewable energy (RE) generations in power systems results into a low inertia-weak power grid. To increase inertia of the latter systems, the RE to grid interfacing inverters can be operated to mim...
High penetration of renewable energy (RE) generations in power systems results into a low inertia-weak power grid. To increase inertia of the latter systems, the RE to grid interfacing inverters can be operated to mimic synchronous generators. This technology is known as virtual synchronous generator (VSG). However, as the grid weakens there is severe coupling between active power (P) and reactive power (Q). Hence, a VSG requires a PQ decoupling technique for its successful operation under this case (connection to the weak power grid). Therefore, this paper proposes a virtual power circle with variable center and radius for independent control of both P and Q. The method is implemented using virtual impedance. The efficacy of the proposed scheme to decouple PQ is validated using a synchronverter connected to the weak grid in MATLAB/Simulink environment.
This paper considers a linear quadratic Gaussian (LQG) control problem with constraints on system inputs and random packet losses occurring on the communication channel between plant and controller. It is well known t...
详细信息
This paper considers a linear quadratic Gaussian (LQG) control problem with constraints on system inputs and random packet losses occurring on the communication channel between plant and controller. It is well known that, in the absence of constraints, the Separation Principle between estimator and controller holds when the channel employs a TCP-like protocol but not so under a UDP-like protocol. This paper gives a counterexample that shows that, under a model predictive control (MPC) scheme that handles the constraints, the Separation Principle does not hold even in the TCP-like case. Theoretical analysis characterizes and reveals a trade-off between estimation errors in the estimator and prediction errors in the controller. Counterintuitively, the poorer on-average performance of the estimator in the UDP case may be compensated by smaller prediction errors in the controller.
The numerous toolboxes within MATLAB [12] to aid student engagement and learning largely require deep understanding of coding, as well as the associated engineering. This paper discusses some work in progress which is...
详细信息
ISBN:
(数字)9798350314403
ISBN:
(纸本)9798350314410
The numerous toolboxes within MATLAB [12] to aid student engagement and learning largely require deep understanding of coding, as well as the associated engineering. This paper discusses some work in progress which is focussing on developing files and resources for a control 101 course [17] which are both engaging and also have much lower pedestals of understanding before use. The intention is focus on attracting student interest and communicating core concepts before students need to begin the longer process of deep learning. Thus this toolbox fills a large gap in the current provision.
This paper builds on a body of work in the community which is focussed on sharing learning and teaching resources, especially those which might support a first course in control. Here attention is given to some of the...
详细信息
This paper builds on a body of work in the community which is focussed on sharing learning and teaching resources, especially those which might support a first course in control. Here attention is given to some of the mathematical, analytical and numerical computations which are required to support simple system and feedback analysis and design. The aim is to provide resources which allow students to focus on core concepts and understanding so that the numerical computations are not an obstacle to their investigations. More specifically, this paper focuses on a number of MATLAB livescript files which have been produced to help students visualise the impact of parameter and design choices on system behaviour, while simultaneously empowering them to understand the source code and thus upskill them for the future. The paper gives an overview of the livescripts available so users can decide whether these could be useful in their own context; all are freely available on the author's website (Rossiter, 2021).
暂无评论