Object segmentation is one of the main activities for the robot to create a sense of its environment. this task is a precursor to other activities, such as autonomous navigation in a given environment. through sensors...
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Large Language models (LLMs) have demonstrated significant effectiveness across various NLP tasks, including text ranking. this study assesses the performance of large language models (LLMs) in listwise reranking for ...
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Autonomous robotics and mechatronics have drastically changed the manufacturing and healthcare sectors by increasing productivity, precision, and flexibility. this work addresses the pressing need for new methods to a...
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the vehicle image retrieval in highway scenes is influenced by the complexity and diversity of vehicle types, as well as the impact of lighting and climate changes on vehicle image clarity. the paper aims to achieve h...
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ISBN:
(纸本)9798350344738;9798350344721
the vehicle image retrieval in highway scenes is influenced by the complexity and diversity of vehicle types, as well as the impact of lighting and climate changes on vehicle image clarity. the paper aims to achieve high-precision vehicle retrieval in complex scenes on highways. Firstly, the paper constructs a vehicle image retrieval dataset of Southeast University (SEU) including whole vehicle image test set, vehicle face image test set, vehicle window test set, vehicle license plate test set. Secondly, three variants of CNN models are investigated for the vehicle image retrieval in expressway scenarios, including VGG16-VIR-ES, ResNet50-VIR-ES and DenseNet121-VIR-ES. Finally, based on vehicle image retrieval dataset of Southeast University (SEU), the research of compare experiment on VGG16-VIR-ES, ResNet50-VIR-ES and DenseNet121-VIR-ES are carried out. the experimental results show that DenseNet121-VIR-ES outperform the other models, and the accuracy of the vehicle image retrieval method based on DenseNet121 network is 92.12%, 91.55%, 92.86% and 84.98% for the whole vehicle image test set, vehicle image test set, window image test set and license plate image test set, respectively.
the rapid development of artificial intelligence technology has drawn widespread attention to the problem of target recognition in open environments. Traditional target recognition methods are easily affected by envir...
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In recent years, increasing the energy efficiency of buildings has become one of the objectives of facility managers. Advanced control methods can be used to improve the efficiency of Heating, Ventilation, and Air Con...
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With a large increase in the amount of data that are transferred via publicly available computer networks, the global demand for new protection and prevention methods could be observed in recent studies of many resear...
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ISBN:
(数字)9781665468589
ISBN:
(纸本)9781665468596;9781665468589
With a large increase in the amount of data that are transferred via publicly available computer networks, the global demand for new protection and prevention methods could be observed in recent studies of many research groups. the paper deals with anomaly detection, focusing on cybersecurity applications, as there are only few papers that address this topic. Four methods, such as DBSCAN, One-class SVM, LSTM and Isolation forest were used to solve this problem. During the experimental part, the implementation and experiments were performed to examine the performance on common dataset to assess the ability and further possible applications.
In this paper we take into account the variation of the model parameters, during the regulation process, in the discrete time sliding mode control approach. Moreover, the system is affected by unknown, but bounded ext...
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ISBN:
(数字)9781665468589
ISBN:
(纸本)9781665468596;9781665468589
In this paper we take into account the variation of the model parameters, during the regulation process, in the discrete time sliding mode control approach. Moreover, the system is affected by unknown, but bounded external disturbances that do not need to satisfy the matching conditions. the controller guarantees the fastest, monotonic and finite time convergence of the representative point to the switching hyperplane, simultaneously ensuring constraints satisfaction on both state and input signal. Sufficient condition that assures all mentioned properties is formally proved and the simulation example demonstrates advantages of the theoretical considerations.
Operating autonomous vehicles safely and efficiently in crowded pedestrian environments is a challenging task, especially with partial observations. this is because autonomous vehicles cannot always determine the curr...
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ISBN:
(数字)9781665468800
ISBN:
(纸本)9781665468800
Operating autonomous vehicles safely and efficiently in crowded pedestrian environments is a challenging task, especially with partial observations. this is because autonomous vehicles cannot always determine the current state of the pedestrians and take optimal actions due to limitations of onboard sensors such as visual occlusion. Furthermore, scenarios involving partial observations are normally not included in training data for navigation models as it requires extra hybrid training for various partial observations scenarios. To address this challenge, we propose a novel method that incorporates a transformer-based human multi-modal trajectory prediction model into a deep reinforcement learning framework to determine the optimal driving strategy. Furthermore, we introduce a novel metric and use a subset of observations from an existing pedestrian dataset for the evaluation of crowd prediction and navigation methods under various partial observations scenarios. We also validate the robustness and efficiency of our proposed method through several experiments and compare to the state-of-the-art methods.
Fault detection in electric drives is crucial for ensuring operational reliability and minimizing downtime. this paper provides a brief overview of the methods based on machine learning used for fault detection in ele...
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