Feature Map Distillation of Thin Nets for Low-Resolution Object Recognition

Coma caused by craniocerebral injury is a common condition of neurosurgical acute injury. There is no specific method to promote awakening in a clinic. Early comprehensive treatment may be helpful to patients. The common methods are hyperbaric oxygen (HBO) and low-frequency repetitive transcranial magnetic stimulation (rTMS). However, the application effect and mechanism of rTMS combined with HBO on coma patients with traumatic brain injury need to be further studied. The brain stem auditory evoked potential (BAEP) is examined by the Kennedy coma recovery scale (CRS-R), the recovery of brain function and the state of consciousness are evaluated, and the therapeutic effect is evaluated by the Glasgow Coma Scale (GCS). Cerebrospinal fluid NE level, MCA blood flow velocity, and left brainstem and right brainstem auditory evoked potential are used to evaluate brain rehabilitation. RTMS combined with HBO could shorten the wake-up time, improve the wake-up rate, improve the GCS score and CRS-R score, shorten the brain wave latency time of the left and right brainstem, increase the NE level of cerebrospinal fluid, and decrease the blood flow velocity of MCA. RTMS combines with HBO can improve the nerve excitability of brain cells, reduce the disturbance of consciousness, promote the functional recovery of brain injury, and has a certain role in promoting the awakening of patients with traumatic brain injury coma.

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“…A coma occurs when the score is below 8. The lower the score, the more serious the disturbance of consciousness [ 8 ].…”

Section: The Proposed Methodsmentioning

confidence: 99%

Effect of Low Frequency Repetitive Transcranial Magnetic Stimulation (rTMS) Combined with Hyperbaric Oxygen (HBO) on Awakening of Coma Patients with Traumatic Brain Injury

Liu

Bao

et al. 2022

Journal of Healthcare Engineering

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“…e value of big data has not yet been revealed through mature operation mode. We need to treat this problem rationally, any new technology will experience a bubble stage at the early stage of development, and this is the inevitable result of the massive influx of resources [27][28][29][30]. When people's lives are completely recorded by data, data driven will become an indispensable element of existence.…”

Section: Related Workmentioning

confidence: 99%

Study on Intelligently Designed Business Innovation Service Models Driven by Big Data

Liu

Shang

Wan³

2022

Mathematical Problems in Engineering

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Although conventional business models have been increasingly affected in front of the big data technology application, it has also brought new opportunities and challenges for enterprise development. In order to create a higher value, enterprises should keep pace with the times and actively develop business innovation service models. The greatest value brought by data is to help enterprises find potential business value. It can provide a broader user market and channels, avoid homogeneous competition, and realize the integration of upstream and downstream value chains. In addition, it abandons the extensive development under the traditional model and allows enterprises to return to real value services, which is also an irresistible trend of business model transformation. This paper studies and analyzes business innovation service models. First, the business model as required is presented, and the management system and risk evaluation method are introduced. Then, the construction of the business service model is discussed, and the typical big data technologies are reviewed. Next, according to the evaluation theory of business model, the index system of business innovation service model is explored, which can examine the development of business model objectively and comprehensively. Last, the operations of the business model under the big data are analyzed. The research on the business model in this paper can be provided with universality and has a certain practical value for the development of business innovation service.

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“…In general, the current research on color harmony modeling involves key technologies, such as feature extraction and machine learning. In terms of feature extraction Huang et al (2022) , proposed a Feature Map Distillation (FMD) framework under which the feature map size of teacher and student networks was different. Zhang et al (2021) integrated several well-proved modules together to learn both short-term and long-term features from video inputs and meanwhile avoid intensive computation.…”

Section: Introductionmentioning

confidence: 99%

Attribute analysis and modeling of color harmony based on multi-color feature extraction in real-life scenes

Wang

Liu²,

Jian³

et al. 2022

Front. Psychol.

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Color harmony is the focus of many researchers in the field of art and design, and its research results have been widely used in artistic creation and design activities. With the development of signal processing and artificial intelligence technology, new ideas and methods are provided for color harmony theory and color harmony calculation. In this article, psychological experimental methods and information technology are combined to design and quantify the 16-dimensional physical features of multiple colors, including multi-color statistical features and multi-color contrast features. Eighty-four subjects are invited to give a 5-level score on the degree of color harmony for 164 multi-color materials selected from the screenshots of film and television scenes. Based on the multi-color physical features and the subjective evaluation experiment, the correlation analysis is firstly carried out, which shows that the overall lightness, difference of the color tones, number of multiple colors, lightness contrast, color tone contrast, and cool/warm contrast are significantly correlated with color harmony. On the other hand, the regression prediction model and classification prediction model of color harmony are constructed based on machine learning algorithms. In terms of regression prediction model, the prediction accuracy of linear models is higher than that of nonlinear models, with 63.9% as the highest, indicating that the multi-color physical features can explain color harmony well. In terms of classification prediction model, the Random Forest (RF) has the best prediction performance, with an accuracy of 80.2%.

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Feature Map Distillation of Thin Nets for Low-Resolution Object Recognition

Cited by 46 publications

References 58 publications

Effect of Low Frequency Repetitive Transcranial Magnetic Stimulation (rTMS) Combined with Hyperbaric Oxygen (HBO) on Awakening of Coma Patients with Traumatic Brain Injury

Effect of Low Frequency Repetitive Transcranial Magnetic Stimulation (rTMS) Combined with Hyperbaric Oxygen (HBO) on Awakening of Coma Patients with Traumatic Brain Injury

Study on Intelligently Designed Business Innovation Service Models Driven by Big Data

Attribute analysis and modeling of color harmony based on multi-color feature extraction in real-life scenes

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