Automatic seizure detection has significance for the diagnosis of epilepsy and the reduction of massive workload for neuroscientists. In the analysis of electroencephalogram (EEG) to achieving the goal of detecting se...
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Automatic seizure detection has significance for the diagnosis of epilepsy and the reduction of massive workload for neuroscientists. In the analysis of electroencephalogram (EEG) to achieving the goal of detecting seizure activity automatically, in this paper, we proposed a novel algorithm. First, the original signal is filtered into a band-pass filter and the linear features are calculated; Then, deep neural network - bidirectional long-short term memory network (Bi-LSTM) is used to train and classify features. Ultimately, obtained the specificity is 97.14%, the sensitivity is 100%, the G-mean is 98.56% and the average accuracy is 98.56%. The experimental results show that the proposed method has good performance and potential for clinical application.
Data mining, especially the extraction of the relationship between genes and proteins, plays an important role in the biomedical field. Several related models have been proposed for data mining in the biomedical domai...
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ISBN:
(数字)9781728162157
ISBN:
(纸本)9781728162164
Data mining, especially the extraction of the relationship between genes and proteins, plays an important role in the biomedical field. Several related models have been proposed for data mining in the biomedical domain. Furthermore, manually curated biomedical knowledge bases, which could assist the task, have been used to enhance the data-mining model. However, due to the limitation of methods, much prior knowledge information is not be fully exploited. In this work, we propose a novel method that reasonably applied the curated prior knowledge for biomedical text mining by dual sentence representation models; one model is for the experimental data and the other one is for the prior knowledge information sentence. We evaluated our method on two community-supported datasets; BioNLP and BioCreative corpora. The experimental results demonstrate that the dual sentence representation model can successfully utilize external prior knowledge information to extract relationship from biomedical text. Our method can achieve state-of-art results and it could be an application of biomedical relation extraction in the future.
Large language models (LLMs) and large visual-language models (LVLMs) have exhibited near-human levels of knowledge, image comprehension, and reasoning abilities, and their performance has undergone evaluation in some...
Large language models (LLMs) and large visual-language models (LVLMs) have exhibited near-human levels of knowledge, image comprehension, and reasoning abilities, and their performance has undergone evaluation in some healthcare domains. However, a systematic evaluation of their capabilities in cervical cytology screening has yet to be conducted. Here, we constructed CCBench, a benchmark dataset dedicated to the evaluation of LLMs and LVLMs in cervical cytology screening, and developed a GPT-based semi-automatic evaluation pipeline to assess the performance of six LLMs (GPT-4, Bard, Claude-2.0, LLaMa-2, Qwen-Max, and ERNIE-Bot-4.0) and five LVLMs (GPT-4V, Gemini, LLaVA, Qwen-VL, and ViLT) on this dataset. CCBench comprises 773 question-answer (QA) pairs and 420 visual-question-answer (VQA) triplets, making it the first dataset in cervical cytology to include both QA and VQA data. We found that LLMs and LVLMs demonstrate promising accuracy and specialization in cervical cytology screening. GPT-4 achieved the best performance on the QA dataset, with an accuracy of 70.5% for close-ended questions and average expert evaluation score of 6.9/10 for open-ended questions. On the VQA dataset, Gemini achieved the highest accuracy for close-ended questions at 67.8%, while GPT-4V attained the highest expert evaluation score of 6.1/10 for open-ended questions. Besides, LLMs and LVLMs revealed varying abilities in answering questions across different topics and difficulty levels. However, their performance remains inferior to the expertise exhibited by cytopathology professionals, and the risk of generating misinformation could lead to potential harm. Therefore, substantial improvements are required before these models can be reliably deployed in clinical practice.
Recently, multi-wavelength narrow linewidth random fiber laser has very interested for every researcher in this field, because of their useful advantages application, such as high-resolution spectroscopy and fiber opt...
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ISBN:
(数字)9781728155586
ISBN:
(纸本)9781728155593
Recently, multi-wavelength narrow linewidth random fiber laser has very interested for every researcher in this field, because of their useful advantages application, such as high-resolution spectroscopy and fiber optic sensing. In this paper, the standard single-mode fiber is used to form a half-opened cavity structure for generating the narrow linewidth random fiber laser and used the FBG-FP as a filter to form the narrow linewidth RFL into multi-wavelength. Firstly, we used the Rayleigh scattering that processed as well as in a standard single-mode fiber to provide random distribution feedback at the same time while using erbium-doped fiber (EDF) to provide the gain or amplification for achieving a broadband random laser output. Then, FBG-FP is added to the half-open cavity random laser structure. The multi-wavelength and narrow linewidth RFL can be achieved when the broadband RFL goes through the FBG-FP. In this paper we have generated the multi-wavelength narrow linewidth random fiber laser which has more than 10 wavelengths and the 3dB bandwidth is less than 0.01 nm and the mode separation of each wavelength is 0.04nm.
For those who love painting but unfortunately have visual impairments, holding a paintbrush to create a work is really a difficult task. For the purpose of solving this problem, a painting navigation system for visual...
For those who love painting but unfortunately have visual impairments, holding a paintbrush to create a work is really a difficult task. For the purpose of solving this problem, a painting navigation system for visually impaired painters is introduced through the live demonstration. When painting, the developed system can endow visually impaired persons with the ability to perceive the surrounding environment, thus helping them realize their dream of painting. To achieve this goal, we designed four main modules viz., QR code based drawing board positioning module, brush real-time positioning module, color recognition module and human-computer interaction module, and integrated them into the system. In the validation experiments, the blindfolded users can successfully create a painting with the help of the developed navigation system. Moreover, the users told us that this system provided them with good experience. In a way, this painting navigation system can be seen as "angel's eyes" of visually impaired painters. The demo video of the proposed painting navigation system is available at: https://***/10.6084/***.9760004.v1.
As the solid oxide fuel cell (SOFC) system work environment is a high-temperature environment for a long time, it is difficult to obtain the SOFC stack internal state change directly. When the fault occurs, it is diff...
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Example-based face sketch synthesis technology generally requires face photo-sketch images with face alignment and size *** break through the limitation,we propose a global face sketch synthesis method:In training,all...
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Example-based face sketch synthesis technology generally requires face photo-sketch images with face alignment and size *** break through the limitation,we propose a global face sketch synthesis method:In training,all training photo-sketch patch pairs are collected together and a photo feature dictionary is learned from the photo *** each atom of the dictionary,its K closest photo-sketch patch pairs are clustered,namely "Anchored Neighborhood".In testing,for each test photo patch,we search its nearest photo patch in the Anchored Neighborhood determined by its closest atom,then the corresponding sketch patch is the *** the same way,we train and test in the high-frequency domain and synthesis the high-frequency ***,the fusion of the initial and the high-frequency results is the final *** experiments on three public face sketch datasets and various real-world photos demonstrate the effectiveness and robustness of the proposed method.
In this paper,a novel methodology is presented to settle the region of interest(ROI) detection problem in vehicle color recognition so as to remove the redundant components of vehicles that interfere greatly with colo...
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In this paper,a novel methodology is presented to settle the region of interest(ROI) detection problem in vehicle color recognition so as to remove the redundant components of vehicles that interfere greatly with color *** order to make full use of the local color and spatial information,vehicle images are divided into different superpixels at *** spatial relationship between superpixels and the outermost pixels is then used for the background removal of vehicle *** comparing with the vehicle window clustering centroids obtained by k-means,the superpixels close to the universal color characteristics of windows are removed so that the dominant color superpixels are ***,a linear Support Vector Machine classifier is trained for color *** experiments demonstrate that the proposed methodology is effective for color region of interest detection and thus contribute to vehicle color recognition.
The task of RGBT tracking aims to take the complementary advantages from visible spectrum and thermal infrared data to achieve robust visual tracking, and receives more and more attention in recent years. Existing wor...
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ISBN:
(数字)9781728150239
ISBN:
(纸本)9781728150246
The task of RGBT tracking aims to take the complementary advantages from visible spectrum and thermal infrared data to achieve robust visual tracking, and receives more and more attention in recent years. Existing works focus on modality-specific information integration by introducing modality weights to achieve adaptive fusion or learning robust feature representations of different modalities. Although these methods could effectively deploy the modality-specific properties, they ignore the potential values of modality-shared cues as well as instance-aware information, which are crucial for effective fusion of different modalities in RGBT tracking. In this paper, we propose a novel Multi-Adapter convolutional Network (MANet) to jointly perform modality-shared, modality-specific and instance-aware feature learning in an end-to-end trained deep framework for RGBT tracking. We design three kinds of adapters within our network. In a specific, the generality adapter is to extract shared object representations, the modality adapter aims at encoding modality-specific information to deploy their complementary advantages, and the instance adapter is to model the appearance properties and temporal variations of a certain object. Moreover, to reduce computational complexity for real-time demand of visual tracking, we design a parallel structure of generic adapter and modality adapter. Extensive experiments on two RGBT tracking benchmark datasets demonstrate the outstanding performance of the proposed tracker against other state-of-the-art RGB and RGBT tracking algorithms.
To realize the commercialization of SOFC, it must be ensured that it can work efficiently and stably. SOFC fault diagnosis becomes an essential part of the research. Due to the strong coupling of faults in the stack, ...
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To realize the commercialization of SOFC, it must be ensured that it can work efficiently and stably. SOFC fault diagnosis becomes an essential part of the research. Due to the strong coupling of faults in the stack, this paper uses neural network algorithm to detect and diagnose faults. The simulation results verify that through the diagnosis of the test sample, the recognition rate of the test sample by the network is found to be 95%, which explains the neural network fault diagnosis model established in this paper on identifying the normal working state of the stack, the electrode stacking of the stack, and the gas leakage fault of the stack has good effectiveness and accuracy.
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