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.
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.
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.
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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Tomato is one of the most popular crops worldwide. The success of a tomato crop is highly dependent on the health of the plants. Nutrient deficiency surveillance is typically conducted through visual inspections, whic...
Tomato is one of the most popular crops worldwide. The success of a tomato crop is highly dependent on the health of the plants. Nutrient deficiency surveillance is typically conducted through visual inspections, which can be challenging for inexperienced farmers and home gardeners to work accurately. There is a growing need for a quick, reliable, and accurate nutrient deficiency identification system to address this issue. The proposed method is based on image processing and deep learning technologies that are highly effective for image classification tasks. Two models were trained using a dataset collected from tomato plants in Sri Lanka and evaluated using Mask Region-based Convolutional Neural Network (Mask R-CNN) and, You Only Look Once (YOLO) for deficiency classification, and results obtained 92% and 98% accuracy, respectively. Deficiency dispersion level expressed as a percentage using Mask R-CNN and followed by image processing techniques. Overall, this proposed system offers a convenient and accessible tool for farmers and home gardeners to monitor and maintain the health of their tomato plants, enabling them to achieve optimal yields and ensure profitable returns.
In the era of social media, platforms have become integral to various domains, particularly business, where trends significantly influence decision-making processes. Despite numerous studies, effective decision-making...
ISBN:
(数字)9781837243150
In the era of social media, platforms have become integral to various domains, particularly business, where trends significantly influence decision-making processes. Despite numerous studies, effective decision-making in social networks remains a challenge. This study addresses these issues by proposing a novel model for analyzing decision-making strategies. A publicly available dataset containing social media user reviews of various products, including attributes such as identification, labels, country, and sentiment, is utilized. The dataset undergoes preprocessing and normalization, incorporating techniques such as tokenization, lemmatization, stop-word removal, and punctuation elimination. Deep learning methodologies are applied for model development and analysis, leveraging Python and the PyCharm framework. The proposed model is rigorously validated using state-of-the-art techniques and evaluated through extensive testing to measure its performance in terms of accuracy. Comparative analysis with recent methods underscores the effectiveness of the model. The findings of this study offer valuable insights for improving decision-making strategies, guiding new product development, and integrating diverse analytical models in social network contexts.
Professional networking sites like LinkedIn have made the hiring process “falser” in the context of several "duplicate" profiles; therefore, attempts are being made to change this area by using blockchain ...
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Professional networking sites like LinkedIn have made the hiring process “falser” in the context of several "duplicate" profiles; therefore, attempts are being made to change this area by using blockchain technology and machine learning for the dual-validation system of the profile authenticity. Then, for this purpose, a user interface based on a browser extension is developed such that it becomes very easy for recruiters to interface with it. The blockchain technology authenticates certification credentials, and AI/ML models analyze LinkedIn profile data to validate whether it is a genuine or fake profile based on pre-trained criteria. By leveraging blockchain’s immutable nature, credential authenticity is safeguarded, ensuring that once verified, certifications cannot be tampered with. Meanwhile, machine learning models continuously analyze behavioral data such as abnormal connection growth and unusual activity patterns to detect signs of fraudulent profiles.
Sharing of scattered, heterogeneous and massive design resources by using the design resources integration and sharing platform can improve the collaborative design efficiency of complex product development process. T...
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In modern air traffic management, generating synthetic flight trajectories has emerged as a promising solution for addressing data scarcity, protecting sensitive information, and supporting large-scale analyses. In th...
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