Purpose-Path planning is an important part of UAV mission *** main purpose of this paper is to overcome the shortcomings of the standard particle swarm optimization(PSO)such as easy to fall into the local optimum,so t...
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Purpose-Path planning is an important part of UAV mission *** main purpose of this paper is to overcome the shortcomings of the standard particle swarm optimization(PSO)such as easy to fall into the local optimum,so that the improved PSO applied to the UAV path planning can enable the UAV to plan a better quality ***/methodology/approach-Firstly,the adaptation function is formulated by comprehensively considering the performance constraints of the flight target as well as the UAV ***,the standard PSO is improved,and the improved particle swarm optimization with multi-strategy fusion(MFIPSO)is *** method introduces class sigmoid inertia weight,adaptively adjusts the learning factors and at the same time incorporates K-means clustering ideas and introduces the Cauchy perturbation ***,MFIPSO is applied to UAV path ***-Simulation experiments are conducted in simple and complex scenarios,respectively,and the quality of the path is measured by the fitness value and straight line rate,and the experimental results show that MFIPSO enables the UAV to plan a path with better ***/value-Aiming at the standard PSO is prone to problems such as premature convergence,MFIPSO is proposed,which introduces class sigmoid inertia weight and adaptively adjusts the learning factor,balancing the global search ability and local convergence ability of the *** idea of K-means clustering algorithm is also incorporated to reduce the complexity of the algorithm while maintaining the diversity of particle *** addition,the Cauchy perturbation is used to avoid the algorithm from falling into local ***,the adaptability function is formulated by comprehensively considering the performance constraints of the flight target as well as the UAV itself,which improves the accuracy of the evaluation model.
In the evolving landscape of surveillance and security applications, the task of person re-identification(re-ID) has significant importance, but also presents notable difficulties. This task entails the process of acc...
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In the evolving landscape of surveillance and security applications, the task of person re-identification(re-ID) has significant importance, but also presents notable difficulties. This task entails the process of accurately matching and identifying persons across several camera views that do not overlap with one another. This is of utmost importance to video surveillance, public safety, and person-tracking applications. However, vision-related difficulties, such as variations in appearance, occlusions, viewpoint changes, cloth changes, scalability, limited robustness to environmental factors, and lack of generalizations, still hinder the development of reliable person re-ID methods. There are few approaches have been developed based on these difficulties relied on traditional deep-learning techniques. Nevertheless, recent advancements of transformer-based methods, have gained widespread adoption in various domains owing to their unique architectural properties. Recently, few transformer-based person re-ID methods have developed based on these difficulties and achieved good results. To develop reliable solutions for person re-ID, a comprehensive analysis of transformer-based methods is necessary. However, there are few studies that consider transformer-based techniques for further investigation. This review proposes recent literature on transformer-based approaches, examining their effectiveness, advantages, and potential challenges. This review is the first of its kind to provide insights into the revolutionary transformer-based methodologies used to tackle many obstacles in person re-ID, providing a forward-thinking outlook on current research and potentially guiding the creation of viable applications in real-world scenarios. The main objective is to provide a useful resource for academics and practitioners engaged in person re-ID. IEEE
The reduced-activation ferritic/martensitic(RAFM)steel CLF-1 has been designed as a candidate structural material for nuclear fusion energy *** engineering mechanical design,the effects of temperature on the strain di...
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The reduced-activation ferritic/martensitic(RAFM)steel CLF-1 has been designed as a candidate structural material for nuclear fusion energy *** engineering mechanical design,the effects of temperature on the strain distribution of CLF-1 steel during uniaxial tensile tests were explored within the temperature range from room temperature to 650°C using uniaxial tensile tests combined with in situ digital image correlation ***-concentrated regions alternately distributed±45°along the tensile direction could be attributed to the shear stress having the maximum value at±45°along the tensile direction and the coordinated deformation of the *** total strain distribution changed from a normal distribution to a lognormal distribution with increasing deformation owing to the competition between the elastic and plastic strains at all test *** localization has a strong relationship with temperature at the same engineering strain because of the temperature effects on dynamic strain aging(DSA).The stronger the DSA effect,the stronger the strain *** increasing temperature,the stronger the strain localization at the same strain,the weaker the plasticity,that is,DSA-induced embrittlement,and the slower the strength decline,that is,DSA-induced hardening.
We have witnessed the emergence of superhuman intelligence thanks to the fast development of large language models(LLMs) and multimodal language models. As the application of such superhuman models becomes increasingl...
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We have witnessed the emergence of superhuman intelligence thanks to the fast development of large language models(LLMs) and multimodal language models. As the application of such superhuman models becomes increasingly popular, a critical question arises: how can we ensure they still remain safe, reliable, and aligned well with human values encompassing moral values, Schwartz's Values, ethics, and many more? In this position paper, we discuss the concept of superalignment from a learning perspective to answer this question by outlining the learning paradigm shift from large-scale pretraining and supervised fine-tuning, to alignment training. We define superalignment as designing effective and efficient alignment algorithms to learn from noisy-labeled data(point-wise samples or pair-wise preference data) in a scalable way when the task is very complex for human experts to annotate and when the model is stronger than human experts. We highlight some key research problems in superalignment, namely, weak-to-strong generalization, scalable oversight, and evaluation. We then present a conceptual framework for superalignment, which comprises three modules: an attacker which generates the adversary queries trying to expose the weaknesses of a learner model, a learner which refines itself by learning from scalable feedbacks generated by a critic model with minimal human experts, and a critic which generates critics or explanations for a given query-response pair, with a target of improving the learner by criticizing. We discuss some important research problems in each component of this framework and highlight some interesting research ideas that are closely related to our proposed framework, for instance, self-alignment, self-play, self-refinement, and more. Last, we highlight some future research directions for superalignment, including the identification of new emergent risks and multi-dimensional alignment.
With the continuous growth of the population, crowd counting plays a crucial role in intelligent monitoring systems for the Internet of Things (IoT) and smart city development. Accurate monitoring of crowd density not...
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From the perspective of state-channel interaction,standard quantum teleportation can be viewed as a communication process characterized by both input and output,functioning as a quantum depolarizing *** achieve a prec...
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From the perspective of state-channel interaction,standard quantum teleportation can be viewed as a communication process characterized by both input and output,functioning as a quantum depolarizing *** achieve a precise quantification of the quantumness introduced by this channel,we examine its uncertainties,which encompass both statedependent and state-independent ***,for qudit systems,we provide general formulas for these *** analyze the uncertainties associated with standard quantum teleportation when induced by isotropic states,Werner states,and X-states,and we elucidate the correlation between these uncertainties and the parameters of the specific mixed *** findings demonstrate the validity of quantifying these uncertainties.
With the rapid development and widespread application of information, computer, and communication technologies, Cyber-Physical-Social Systems (CPSS) have gained increasing importance and attention. To enable intellige...
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This study proposes a malicious code detection model DTL-MD based on deep transfer learning, which aims to improve the detection accuracy of existing methods in complex malicious code and data scarcity. In the feature...
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In recent years, great success has been achieved in many tasks of natural language processing (NLP), e.g., named entity recognition (NER), especially in the high-resource language, i.e., English, thanks in part to the...
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Long-term multivariate time series forecasting is an important task in engineering applications. It helps grasp the future development trend of data in real-time, which is of great significance for a wide variety of f...
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Long-term multivariate time series forecasting is an important task in engineering applications. It helps grasp the future development trend of data in real-time, which is of great significance for a wide variety of fields. Due to the non-linear and unstable characteristics of multivariate time series, the existing methods encounter difficulties in analyzing complex high-dimensional data and capturing latent relationships between multivariates in time series, thus affecting the performance of long-term prediction. In this paper, we propose a novel time series forecasting model based on multilayer perceptron that combines spatio-temporal decomposition and doubly residual stacking, namely Spatio-Temporal Decomposition Neural Network (STDNet). We decompose the originally complex and unstable time series into two parts, temporal term and spatial term. We design temporal module based on auto-correlation mechanism to discover temporal dependencies at the sub-series level, and spatial module based on convolutional neural network and self-attention mechanism to integrate multivariate information from two dimensions, global and local, respectively. Then we integrate the results obtained from the different modules to get the final forecast. Extensive experiments on four real-world datasets show that STDNet significantly outperforms other state-of-the-art methods, which provides an effective solution for long-term time series forecasting.
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