Cognitive computing and wireless communications on the edge for healthcare service robots

Wan, Shaohua; Gu, Zonghua; Ni, Qiang

doi:10.1016/j.comcom.2019.10.012

Cited by 187 publications

(86 citation statements)

References 46 publications

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“…The output of softmax layer θ shows the result of classification, e.g., θ c k means the probability that the input example belongs to class c k . To avoid overfitting, we use dropout to improve regularization, which can improve the performance of neural network by preventing the coefficient of feature detectors [32][33][34][35]. We apply 50% dropout between concatenate layer and the first fully connected layer.…”

Section: The Feature Fusion Deep Learning Methodsmentioning

confidence: 99%

Learning deep networks with crowdsourcing for relevance evaluation

Yin²,

et al. 2020

J Wireless Com Network

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In this paper, we propose a novel relevance evaluation method using labels collected from crowdsourcing. The proposed method not only predicts the relevance between query texts and responses in information retrieval systems but also performs the label aggregation tasks simultaneously. It first merges two kinds of heterogeneous data (i.e., image and query text) and constructs a CNN-like deep neural network. Then, on the top of its softmax layer, an additional layer was built to model the crowd workers. Finally, classification models for relevance prediction and aggregated labels for training examples can be simultaneously learned from noisy labels. Experimental results show that the proposed method significantly outperforms other state-of-the-art methods on a real-world dataset.

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Section: The Feature Fusion Deep Learning Methodsmentioning

confidence: 99%

Learning deep networks with crowdsourcing for relevance evaluation

Yin²,

et al. 2020

J Wireless Com Network

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“…These robots can be more effective in providing diagnosis and treatment when compares to a human caregiver. Further, mobile robots have tremendous advantages in healthcare facilities (Wan et al, 2019;Yi et al, 2008). In an epidemic outbreak, the healthcare facilities will be flourished with patients.…”

Section: Robotics Technologiesmentioning

confidence: 99%

Restructured society and environment: A review on potential technological strategies to control the COVID-19 pandemic

Elavarasan¹,

Pugazhendhi

2020

Science of The Total Environment

240

205

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The technology shift occurred in society during the COVID-19 pandemic and its influence. • Technology-based strategies in controlling the pandemic and to support the society. • Key technologies include Artificial Intelligence and Machine learning, supply chain, communication and surveillance. • Potential roles of IoT, Drone &Robotic technologies and mobile applications. • Influence of existing technologies, challenges and future impacts on society.

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“…Among them, the support vector machine (SVM) has a good application effect. SVM has been used in many fields, such as pattern recognition, regression, and equalization [35][36][37]. Based on SVM, Qian et al provided a driver identification method with data of steering, brake, and acceleration pedals [38].…”

Section: Introductionmentioning

confidence: 99%

A driver’s car-following behavior prediction model based on multi-sensors data

Wang

et al. 2020

J Wireless Com Network

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The prerequisite for the effective operation of vehicle collision warning system is that the necessary operation is not implemented. Therefore, the behavior prediction that the driver should perform when the preceding vehicle braking is the key to improve the effectiveness of the warning system. This study was conducted to acquire characteristics in the car-following behavior when confronted by the braking of the preceding vehicle, including the reaction time and operation behavior, and establish a behavior prediction model. A driving experiment on the expressway was conducted using devices, such as millimeter-wave radars and controller area network (CAN) bus data, to acquire 845 segments of car following when the brake lamps of the car ahead are on. Data analysis demonstrates that the mean of time distance of car following, mean of car-following distance, and time-to-collision (TTC) mean are closely related with whether or not the driver slowed the car down. The operation states of the driver were divided into keeping the unchanged state of the degree of accelerator pedal opening, loosening of accelerator pedal without braking, braking, and other special situations with the input variables of car-following distance, speed of driver's car, relative speed, time distance, and TTC using the support vector machine (SVM) method to build a prediction model for the operation behavior of the driver. The verification result showed that the model predicts driving behavior with an accuracy rate of 80%. It reflects the actual decision-making process of the driver, especially the normal operation of the driver, to loosen the accelerator pedal without braking. This model can help to optimize the algorithm of the rear-end accident warning system and improve intelligent system acceptance.

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Cognitive computing and wireless communications on the edge for healthcare service robots

Cited by 187 publications

References 46 publications

Learning deep networks with crowdsourcing for relevance evaluation

Learning deep networks with crowdsourcing for relevance evaluation

Restructured society and environment: A review on potential technological strategies to control the COVID-19 pandemic

A driver’s car-following behavior prediction model based on multi-sensors data

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