Discovering Travel Spatiotemporal Pattern Based on Sequential Events Similarity

Chen, Juanjuan; Huang, Liying; Chen, YangQuan; Zheng, Nijia

doi:10.1155/2020/6632956

Cited by 52 publications

(1 citation statement)

References 10 publications

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“…Therefore, this design benefits from the vertical lift‐off from rotary‐wing designs and gains the added aerodynamic efficiencies and extended flight time of the fixed‐wing design. Other designs include tilt‐wing (Cetinsoy et al, 2012), tilt‐rotor (Chen, Huang, et al, 2020), tilt‐body (Ro et al, 2007), and ducted fan (Deng et al, 2020). A comparison between all the designs discussed above is shown in Table 2.…”

Section: Design Environmental and Legislative Constraintsmentioning

confidence: 99%

Autonomous aerial robotics for package delivery: A technical review

Saunders

Saeedi

2023

Journal of Field Robotics

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Small unmanned aerial vehicles (UAVs) have gained significant interest in the last decade. More specifically these vehicles have the capacity to impact package delivery logistics in a disruptive way. This paper reviews research problems and state‐of‐the‐art solutions that facilitate package delivery. Different aerial manipulators and grippers are listed along with control techniques to address stability issues. Landing on a platform is next discussed which encompasses static and dynamic platforms. Landing on a dynamic platform presents further challenges. This includes delayed control responses and poor precision of the relative motion between the platform and the aerial vehicle. Subsequently, risks such as weather conditions, state estimation, and collision avoidance to ensure safe transit is considered. Finally, delivery UAV routing is investigated which categorizes the topic into two areas: drone operations and drone–truck collaborative operations. Additionally, we compare the solutions against design, environmental, and legal constraints.

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Section: Design Environmental and Legislative Constraintsmentioning

confidence: 99%

Autonomous aerial robotics for package delivery: A technical review

Saunders

Saeedi

2023

Journal of Field Robotics

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Research on children's cognitive development for learning disabilities using recommendation method

Shen

2020

Concurrency and Computation

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In order to achieve effective cognitive training that can significantly improve the learning abilities of children with learning disabilities, we consider the computer‐based assistive technologies and propose a personalized cognitive training items recommendation method based on children's preferences and evaluation values. First, we devise an improved cognitive ability evaluation method to evaluate children's cognitive ability values. Second, we transform these values to corresponding cognitive levels using K‐means clustering algorithm and form comprehensive cognitive level maps to reveal the strengths and weaknesses of children in terms of cognitive abilities. Finally, an improved collaborative filtering recommendation method combining children's preferences and evaluation values are represented to generate a superior training proposal. The proposed method is implemented on our cognitive development platform (CogDaily) which is an assistive system and used for testing and practicing. The fifth‐grade students with learning disabilities in mathematics in a primary school in Hangzhou are selected as subjects. They are divided into test group and control group for 2 weeks of testing through this platform. The analysis results of the changes of cognitive abilities before and after the experiment show that the accurate recommendation proposal can effectively enhance children's cognitive abilities and improve their learning abilities.

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SEV‐Net: Residual network embedded with attention mechanism for plant disease severity detection

Zhao

Chen

et al. 2021

Concurrency and Computation

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Summary Early and accurate assessment of plant disease severity is key to preventing disease attack. Traditional detection methods rely on manual vision to distinguish between types of disease infection, but this is time consuming, laborious and inaccurate. To address this problem, this paper proposes a deep learning‐based attentional network model (SEV‐Net) for plant disease severity identification and classification. The network embeds the improved channel and spatial attention module into the residual block of ResNet. The proposed attention module reduces the redundancy of information between channels and focuses on the most information‐rich regions of the feature map. In this experiment, SEV‐Net achieved an accuracy of 97.59% and 95.37% for multiple and single plant (Tomato) disease severity classification, which was better than existing attentional networks (SE‐Net and CBAM). Moreover, the combination of visualization techniques showed that SEV‐Net was adept at distinguishing small variations between plant diseases, proving the feasibility and effectiveness of the network. Furthermore, we have also designed and developed an Android application for real‐time classification of plant disease severity. The system deploys the SEV‐Net network model, which has higher classification accuracy and faster recognition speed.

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Discovering Travel Spatiotemporal Pattern Based on Sequential Events Similarity

Cited by 52 publications

References 10 publications

Autonomous aerial robotics for package delivery: A technical review

Autonomous aerial robotics for package delivery: A technical review

Research on children's cognitive development for learning disabilities using recommendation method

SEV‐Net: Residual network embedded with attention mechanism for plant disease severity detection

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