Preschool Education Interactive System Based on Smart Sensor Image Recognition

The dissolution test has become the most important quality index in the research and development of solid formulation, especially the evaluation of drug bioequivalence. However, it had low operability, was tedious, and was always overlooked. The previously related studies required a fixed tablet and analysed the recorded video by disso GUARO PRO and Microsoft Paint™. Therefore, we have developed a novel image recognition system to automatically track the moving tablet and analyse the volume change at the same time. Image recognition technology is often used to monitor the dissolution process. The camera system with visible light and infrared camera functions was placed on the dissolution tester. The system collects the plate image for binary processing and then records and calculates its pixel area, which can automatically record the volume change of the tablet in the dissolution test, no matter disintegration or corrosion.

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“…The following four steps are used to achieve image recognition [ 16 to 25 ] in the image analysis program.…”

Section: Methodsmentioning

confidence: 99%

Monitoring and Analysis Solid Formulation Dissolution Phenomenon with Image Recognition Technologies

Wang

Kwong

Liu

et al. 2022

Computational Intelligence and Neuroscience

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“…One of the primary principles of attack methodology is to impact the final output as much as possible by using the slightest change to the input. The level of adversarial distortion will accumulate along with the depth, which has been proved by some previous studies [1,17,24,26]. Slight as the malicious perturbation is, adversarial examples are sensitive to even low-level random noises.…”

Section: Adversarial Example Detectionmentioning

confidence: 84%

“…The continuous upgrading of DNNs provides an opportunity to efficiently process the enormous unstructured data generated by the wide-spreading imaging sensors in IoT systems [1,2]. However, recent studies [3][4][5] have shown that deep neural networks (DNNs) are vulnerable to adversarial attacks, which apply subtle and unperceivable perturbations to input examples and can completely fool the deep learning model.…”

Section: Introductionmentioning

confidence: 99%

“…According to the manipulation objects, we divide the mainstream defense methods into three categories: (1) enhancing the robustness of deep learning models by modifying the model itself, (2) detecting adversarial examples by independent widgets, and (3) removing the perturbations in adversarial examples directly. Adversarial training [3,[10][11][12] is now the state-of-the-art approach targeting to enhance the robustness of deep models.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Denoising by Decorated Noise: An Interpretability-Based Framework for Adversarial Example Detection

Zhao

Zhan

Cheng

et al. 2023

Wireless Communications and Mobile Computing

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The intelligent imaging sensors in IoT benefit a lot from the continuous renewal of deep neural networks (DNNs). However, the appearance of adversarial examples leads to skepticism about the trustworthiness of DNNs. Malicious perturbations, even unperceivable for humans, lead to incapacitations of a DNN, bringing about the security problem in the information integration of an IoT system. Adversarial example detection is an intuitive solution to judge if an input is malicious before acceptance. However, the existing detection approaches, more or less, have some shortcomings like (1) modifying the network structure, (2) extra training before deployment, and (3) requiring some prior knowledge about attacks. To address these problems, this paper proposes a novel framework to filter out the adversarial perturbations by superimposing the original images with the noises decorated by a new gradient-independent visualization method, namely, score class activation map (Score-CAM). We propose to trim the Gaussian noises in a way with more explicit semantic meaning and stronger explainability, which is different from the previous studies based on intuitive hypotheses or artificial denoisers. Our framework requires no extra training and gradient calculation, which is friendly to embedded devices with only inference capabilities. Extensive experiments demonstrate that the proposed framework is sufficiently general to detect a wide range of attacks and apply it to different models.

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“…The majority of the time, teachers will carry out preschool education activities based on the user's needs for concentration training, such as clapping games, operational activities, mirror games, and other enjoyable activities, or group dances, aerobics, cheerleading, stacking arhats, rubber band jumping, archery, and other sports activities, or develop independent activity plans in accordance with student individual differences [10][11][12][13][14][15][16][17][18]. It is necessary to analyze relevant activity video images to support the follow-up teachers in sorting out the principles and strategies for observing and analyzing children's behaviors in activities in order to achieve effective observation of teachers and children in preschool education activities intended to cultivate children's concentration.…”

Section: Introductionmentioning

confidence: 99%

Image Segmentation and Target Extraction of Preschool Educational Activity Space for Improving Children's Concentration

Jin¹,

Ma²,

Niu³

et al. 2022

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Concentration is crucial for children to nurture good personality and develop well. To observe teachers and children in concentration-oriented preschool education activities, it is necessary to analyze the video images of relevant activities. This paper the image segmentation and target extraction of preschool education activity space for improving children's concentration. After discussing the relationship between children's concentration and preschool educational activity intervention, the authors introduced the frequency-tuned saliency algorithm into the constructed Gaussian mixture model, constructed the spatial information of the images on the preschool educational activity space for improving children's concentration, and successfully segmented these images. Since the target children are small and numerous, have color overlap with the background, and face strong light interference, the ViBe algorithm with complex scenes, i.e., ViBe+, was selected to quickly detect the multiple child targets in complex preschool education activity environments. Experimental results verify the effectiveness of the proposed algorithm.

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Preschool Education Interactive System Based on Smart Sensor Image Recognition

Cited by 4 publications

References 23 publications

Monitoring and Analysis Solid Formulation Dissolution Phenomenon with Image Recognition Technologies

Monitoring and Analysis Solid Formulation Dissolution Phenomenon with Image Recognition Technologies

Denoising by Decorated Noise: An Interpretability-Based Framework for Adversarial Example Detection

Image Segmentation and Target Extraction of Preschool Educational Activity Space for Improving Children's Concentration

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