Multivariate time series (MTS) forecasting has been extensively applied across diverse domains, such as weather prediction and energy consumption. However, current studies still rely on the vanilla point-wise self-att...
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The PBFT consensus algorithm commonly used in the consortium chain has the disadvantages of three-stage consensus process, resulting in high communication costs. This paper proposes an enhanced PBFT algorithm that uti...
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Path planning in dynamic environments holds paramount significance within the realm of mobile robotics. The Rapidly-Exploring Random Tree (RRT) algorithm stands as one of the most extensively employed path planning al...
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ISBN:
(数字)9798350387780
ISBN:
(纸本)9798350387797
Path planning in dynamic environments holds paramount significance within the realm of mobile robotics. The Rapidly-Exploring Random Tree (RRT) algorithm stands as one of the most extensively employed path planning algorithms, distinguished by its probabilistic completeness. However, the algorithm suffers from the long time to obtain the initial solution and the blindness of the expansion. To address these shortcomings, an improved RRT algorithm based on dynamic goal-biased sampling method (Dyn-RRT) is proposed to enhance the algorithm’s orientation and reduce the time obtaining the initial solution. To bolster the algorithm’s utility in dynamic settings, Dynamic Window Approach (DWA) is integrated into the Dyn-RRT algorithm to enhance adaptability to the dynamic milieu. In addition, simulation experiments show that proposed dynamic goal-biased sampling method is also applicable to other RRT series algorithms and can greatly reduce the time obtaining the initial solution.
The excessive length and high semantic complexity of station equipment fault handling report texts are addressed to the extent that effective text recognition is difficult to achieve. A station equipment fault handlin...
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Fruit and Vegetable Recognition with Calorie Estimation based on Mobilenetv2 is a pioneering research endeavor aimed at leveraging deep learning techniques to enhance dietary monitoring and health management. Building...
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ISBN:
(数字)9798350383867
ISBN:
(纸本)9798350383874
Fruit and Vegetable Recognition with Calorie Estimation based on Mobilenetv2 is a pioneering research endeavor aimed at leveraging deep learning techniques to enhance dietary monitoring and health management. Building upon the success of neural network models in various domains, this study explores the application of Mobilenetv2 and EfficientNet architecture for accurately identifying fruits and vegetables from images and estimating their respective caloric content. The research dataset comprises meticulously curated images of diverse fruits and vegetables, ensuring comprehensive coverage across different categories. Through rigorous experimentation and evaluation, the proposed model demonstrates remarkable accuracy in fruit and vegetable recognition, achieving an impressive accuracy rate of 97.6%. Moreover, the incorporation of calorie estimation adds a novel dimension to dietary analysis, enabling users to make informed decisions regarding their nutritional intake. The findings of this research not only contribute to the advancement of computer vision techniques but also hold significant implications for personalized nutrition tracking and health- conscious applications.
Image segmentation has impressive progress in the past several *** good segmentation usually follows pixelwise well-annotated labels which is ***,the robustness would not be guaranteed due to lack-ofdiversity *** work...
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ISBN:
(数字)9789887581536
ISBN:
(纸本)9781665482561
Image segmentation has impressive progress in the past several *** good segmentation usually follows pixelwise well-annotated labels which is ***,the robustness would not be guaranteed due to lack-ofdiversity *** work usually focuses on pixels individually and pay less attention to the neighbor *** local context would be scarce and the global context is not utilized following these *** proposal a method,named Forest Semantic Segmentation Network(FSSNet) to address these *** organizes original version and augmented version of images,as two inputs into student branch and teacher branch,and force the two outputs being consistent to strengthen the robustness of our ***,we not only consider pixel itself and also the neighbor pixels because the context of neighbor pixels helps understanding the *** utilizes contrastive loss with memory bank to involve global context in training which will make pixels closer to others in same category and far away from pixels of different categories.A bank filter is suggested to improve the quality of features in the memory *** also proposal a new sample strategy to improve the effect of contrastive loss and reduce the *** method can improve accuracy and strengthen the robustness with affordable extra computation during training process,and no additional computation during inference toward *** to benchmark,the proposed approach can improve the mIoU by 3.1% on our challenging dataset.
A predictive simulation is built on a conceptual model (e.g., to identify relevant constructs and relationships) and serves to estimate the potential effects of 'what-if' scenarios. Developing the conceptual m...
ISBN:
(纸本)9798350369663
A predictive simulation is built on a conceptual model (e.g., to identify relevant constructs and relationships) and serves to estimate the potential effects of 'what-if' scenarios. Developing the conceptual model and plausible scenarios has long been a time-consuming activity, often involving the manual processes of identifying and engaging with experts, then performing desk research, and finally crafting a compelling narrative about the potential futures captured as scenarios. Automation could speed-up these activities, particularly through text mining. We performed the first review on automation for simulation scenario building. Starting with 420 articles published between 1995 and 2022, we reduced them to 11 relevant works. We examined them through four research questions concerning data collection, extraction of individual elements, connecting elements of insight and (degree of automation of) scenario generation. Our review identifies opportunities to guide this growing research area by emphasizing consistency and transparency in the choice of datasets or methods.
Some systems,in spite of having multiple outputs,have only one control input,which makes their control a *** novel controllers are proposed that utilise an adaptive finite-time sliding mode control(AFSMC)scheme for a ...
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Some systems,in spite of having multiple outputs,have only one control input,which makes their control a *** novel controllers are proposed that utilise an adaptive finite-time sliding mode control(AFSMC)scheme for a class of single-input multiple-output(SIMO)nonlinear systems in the presence of unknown mismatched *** alleviate the inherent chattering phenomenon of sliding mode control,new forms of the two designed controllers are suggested by using new sliding *** only can the proposed AFSMC scheme stabilise the system in a finite time,but also it can provide estimated data of the uncertainty upper bound in the *** stability theory is used to obtain finite-time stability analysis of the closed-loop ***,simulation results are carried out in Simulink/MATLAB for a four-dimensional autonomous hyper-chaotic system with mismatched uncertainties as an example of SIMO uncertain nonlinear systems to reveal the effectiveness of the proposed controllers.
The rapid growth of the Internet of Things (IoT) has led to widespread deployment of IoT systems in domains such as smart homes, healthcare, and transportation. However, IoT systems often operate under uncertainty, ma...
The rapid growth of the Internet of Things (IoT) has led to widespread deployment of IoT systems in domains such as smart homes, healthcare, and transportation. However, IoT systems often operate under uncertainty, making it difficult to predict and control their behavior. In this paper, we propose an adaptive decision making approach for IoT systems in uncertain, dynamic environments. We present a framework with perception, decision and execution layers to handle uncertainty in IoT systems. The perception layer senses the environment and system state. The decision layer employs an optimized deep Q-network algorithm (Ad-DQN) specifically designed to handle uncertain environments, enabling it to make informed decisions based on learned experiences. The execution layer implements the actions. We demonstrate the framework on an intelligent air conditioning system as a case study of an IoT system operating under uncertainty. The Ad-DQN based decision layer adapts the air conditioning control policy to maximize comfort while minimizing energy usage. Experiments show our method outperforms traditional DQN method in uncertain environments.
Advancements in cyber-physical systems (CPSs) makes CPSs essential entities in society today, and have made them prominent across all fields. For example, the healthcare industry has evolved technologically and has ef...
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