In this paper, a model-free data-driven prescribedtime-performance (DDPTP) algorithm is developed for the underactuated autonomous surface vehicle (ASV). By defining a leader to provide the dynamic reference signal, t...
详细信息
An accurate estimation of the state of charge (SoC) of lithium titanate (LTO) batteries is required for their effective operation and management. In this study, we propose an unscented Kalman filter (UKF) approach for...
An accurate estimation of the state of charge (SoC) of lithium titanate (LTO) batteries is required for their effective operation and management. In this study, we propose an unscented Kalman filter (UKF) approach for estimating the SoC of LTO batteries, which are challenging to assess due to the nonlinear voltage-SoC relationship and aging impact. Our approach uses a state and measurement model based on LTO’s electrochemical characteristics and employs sigma points and weights to address nonlinearities. According to the findings of our research, the UKF-based methodology has high accuracy, rapid convergence, and resilience to discharge rate, outperforming or matching the capabilities of existing state-of-the-art approaches. This work provides a novel and effective solution for LTO battery SoC estimation, useful for applications in electric vehicles, energy storage, and smart grid energy systems.
Collision Avoidance System (CAS) is important for drone safety. CAS consists of three steps e.g., obstacle sensing, collision prediction, and collision avoidance. Collision prediction enables drones to gain informatio...
Collision Avoidance System (CAS) is important for drone safety. CAS consists of three steps e.g., obstacle sensing, collision prediction, and collision avoidance. Collision prediction enables drones to gain information, process information, and estimate whether the object has a risk of collision. Convolution Neural Network (CNN) is one of the methods that can be employed for collision prediction. However, CNN is a method that needs a large data in the training. Dario Pedro et al. provided a dataset called the CoLANet dataset that consists of VDOs of collision drones. Subsequently, they proposed a new algorithm called Neural Network Pipeline which has a Convolution Neural Network (CNN) part to extract the feature from a couple of images. CNN extracts images by using MobileNetV2 as a pre-trained model. They chose MobileNetV2 due to training performance from another dataset. This paper aims to assess the performance of lightweight CNN models using the CoLANet dataset. The models will be trained on the Keras library with parameters of fewer than ten million. The models will be validated by Confusion Matrix and Receiver Operating Characteristics. In conclusion, we examine which pre-trained CNN model has the best performance and suggest ongoing work.
This paper considers the problem of long-term target tracking in complex scenes when tracking failures are unavoidable due to illumination change,target deformation,scale change,motion blur,and other *** specifically,...
详细信息
This paper considers the problem of long-term target tracking in complex scenes when tracking failures are unavoidable due to illumination change,target deformation,scale change,motion blur,and other *** specifically,a target tracking algorithm,called re-detection multi-feature fusion,is proposed based on the fusion of scale-adaptive kernel correlation filtering and *** target tracking algorithm trains three kernel correlation filters based on the histogram of oriented gradients,colour name,and local binary pattern features and then obtains the fusion weight of response graphs corresponding to different features based on average peak correlation energy criterion and uses weighted average to complete the position estimation of the tracked *** order to deal with the problem that the target is occluded and disappears in the tracking process,a random fern classifier is trained to perform re-detection when the target is *** comparing the OTB-50 target tracking dataset,the experimental results show that the proposed tracker can track the target well in the occlusion attribute video sequence in the OTB-100 test dataset and has a certain improvement in tracking accuracy and success rate compared with the traditional correlation filter tracker.
This paper investigates the distributed fault-tolerant containment control(FTCC)problem of nonlinear multi-agent systems(MASs)under a directed network *** proposed control framework which is independent on the global ...
详细信息
This paper investigates the distributed fault-tolerant containment control(FTCC)problem of nonlinear multi-agent systems(MASs)under a directed network *** proposed control framework which is independent on the global information about the communication topology consists of two *** from most existing distributed fault-tolerant control(FTC)protocols where the fault in one agent may propagate over network,the developed control method can eliminate the phenomenon of fault *** on the hierarchical control strategy,the FTCC problem with a directed graph can be simplified to the distributed containment control of the upper layer and the fault-tolerant tracking control of the lower ***,simulation results are given to demonstrate the effectiveness of the proposed control protocol.
The development of an accurate soft sensor modeling method in the process industry remains a great challenge because the coupling relationship between variables is always intricate and difficult to model. In this work...
The development of an accurate soft sensor modeling method in the process industry remains a great challenge because the coupling relationship between variables is always intricate and difficult to model. In this work, a dynamic graph learning (DGL) soft sensor is proposed to alleviate this problem. The proposed model realizes the ability of the soft sensor to perceive the coupling relationship in real time by automatically learning the dynamic graph. Then, a causal convolutional mechanism and a multi-hop graph attention mechanism are used to systematically construct the dependencies of variables in the spatial-temporal dimension and model their variation patterns effectively. Finally, the proposed method is tested on the penicillin fermentation process and shown to be feasible and effective. The results showed that the change of the dynamic graph in the spatial-temporal dimension was in line with the process mechanism.
Energy management system (EMS) is an important tool for energy efficiency and reliability of the power system. The optimal power dispatch of energy resources can be obtained using the nonlinear model predictive contro...
详细信息
ISBN:
(数字)9798350387193
ISBN:
(纸本)9798350387209
Energy management system (EMS) is an important tool for energy efficiency and reliability of the power system. The optimal power dispatch of energy resources can be obtained using the nonlinear model predictive control (NMPC). It is formulated as an optimization problem subject to linear and nonlinear constraints. Although the existing system works well under the current conditions, power loss has not been considered. Power loss in transmission lines occurs due to their resistance and a high amount of power flow to loads connecting to the system. The power loss leads to decreased efficiency in the electrical system and reduces the lifespan of equipment because of the high temperature from loss. This paper aims to analyze the power loss in the Mae Hong Son (MHS) microgrid. To achieve the research objectives, we employ the EMS of MHS with NMPC and calculate power loss in transmission lines for a period of 7 days. EMS considers three objective functions, namely, total operating cost, total carbon dioxide emission, and combined economic and emission dispatch and take into account of three seasons consisting of rainy, winter, and summer.
With the advent of the 6G era, precise positioning and reliable, energy-efficient data transmission have become critical for the Internet of Things. However, high-frequency signals like millimeter-wave and terahertz a...
详细信息
ISBN:
(数字)9798331533991
ISBN:
(纸本)9798331534004
With the advent of the 6G era, precise positioning and reliable, energy-efficient data transmission have become critical for the Internet of Things. However, high-frequency signals like millimeter-wave and terahertz are easily obstructed, making traditional LOS-based methods ineffective in complex environments. To overcome this, NLOS localization methods and Reconfigurable Intelligent Surfaces offer promising solutions by dynamically modifying the wireless environment. This paper introduces a Direct Position Determination method that integrates Deep Neural Networks (DNN) with the Sparrow Search Algorithm (SSA) to tackle the challenges of high computational complexity and low localization accuracy in RIS-aided systems. An NLOS scenario with RIS is established, and a far-field channel model is derived. The method uses DNN to model the nonlinear relationship between the likelihood function and user position, and then employs SSA to optimize the DNN-based fitness function, efficiently solving the objective function. Experimental results show that the DNN-SSA method consistently achieves high localization accuracy across different scenarios, even at low SNR levels, demonstrating its effectiveness and potential for 6G high-precision localization.
Modbus over TCP/IP has become one of the most utilised industrial protocols for networking critical infrastructure. It is crucial to test how the ABB 800xA DCS controller's Modbus TCP/IP protocol functions at both...
Modbus over TCP/IP has become one of the most utilised industrial protocols for networking critical infrastructure. It is crucial to test how the ABB 800xA DCS controller's Modbus TCP/IP protocol functions at both the component-level and the functional-level. Whereas manual testing takes a lot of time to build up a test environment. For example connecting the hardware like end devices, launching the controller (which executes the settings and programs created in the DCS software), configuring the DCS software tool (which enables editing, compilation, testing, and downloading of the desired controller programs to the controller), developing or loading controller programs which has different configurations, modifying the application variables, and also manually providing the input values into the desired registers or coils using Modbus slaves. Therefore, there is a great need to automate the entire process so that it becomes less repetitious, more effective, and requires less human effort. After comparing some available tools, We decided on SpecFlow as the point of entry into the automation framework. By connecting business-readable behavioral needs to the actual implementation, this tool "seeks to narrow the interaction gap between domain-specific experts and developers," according to its description. Our approach takes an array of steps defined in the Gherkin language using the SpecFlow framework as its input and embeds them inside step definition files developed using the C# language and *** framework. Additionally, PyModbusTCP served as the Modbus slave in the above project, providing complete test automation. PyModbusTCP is a slave-generation library built on the Python programming language. The Modbus Client object is used to provide access to the Modbus/TCP server.
This paper focuses on the merging-following control of vehicles standing on different lanes in presence of performance requirements and limited communication resources. A constraint of tracking error is developed to p...
详细信息
ISBN:
(数字)9798350387780
ISBN:
(纸本)9798350387797
This paper focuses on the merging-following control of vehicles standing on different lanes in presence of performance requirements and limited communication resources. A constraint of tracking error is developed to prescribe the performance, and then addressed via error mapping transformation, leading to a prescribed performance control (PPC) algorithm that can make the both distance and angular errors between consecutive vehicles evolve within the prescribed region. Then an event-triggered control protocol driven by the performance function is incorporated to reduce the signal transmission frequency between the controller and actuator with a performance-communication balance. Based on the Lyapunov stability proof, it is verified that all signals in the vehicular system remain uniformly ultimately bounded. A numerical simulation is presented to illustrate the effectiveness of the proposed scheme.
暂无评论