Grammar automatic error detection can help language learners identify whether there are errors in their own written text. For the grammatical error detection task of spoken Chinese in this paper, one of the most obvio...
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In this study, we tackle the critical challenge of imbalanced human acivity datasets, which has long hindered the advancement of human activity recognition (HAR) systems. To address this issue, we propose a novel mult...
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ISBN:
(数字)9798350388343
ISBN:
(纸本)9798350388350
In this study, we tackle the critical challenge of imbalanced human acivity datasets, which has long hindered the advancement of human activity recognition (HAR) systems. To address this issue, we propose a novel multi-sensor data synthesis method based on generative adversarial networks (GANs). This innovative approach allows us to synthetically generate high-quality data for underrepresented behaviors, thereby balancing the dataset and enhancing the performance of downstream HAR tasks. Recognizing the importance of preserving the inherent relationships between sensor modalities, our algorithm considers the common features shared across multiple sensors. Through a spatial transformation mechanism, we are able to seamlessly translate the characteristics of different wearable sensors, ensuring the generated data maintains a high degree of consistency and specificity. To further bolster the authenticity and diversity of the synthetic data, we incorporate an auxiliary classifier that discriminates between real and generated samples. The classification loss is then seamlessly integrated into the GAN's training objective, providing valuable guidance for the network's optimization. Extensive evaluations across three key dimensions - temporal dynamics, data similarity, and category differentiation - demonstrate the HAR-STGAN algorithm's superior performance compared to state-of-the-art alternatives.
Contribution:This paper designs a learning and training platform that can systematically help radiologists learn automated medical image analysis *** platform can help radiologists master deep learning theories and me...
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Contribution:This paper designs a learning and training platform that can systematically help radiologists learn automated medical image analysis *** platform can help radiologists master deep learning theories and medical applications such as the three-dimensional medical decision support system,and strengthen the teaching practice of deep learning related courses in hospitals,so as to help doctors better understand deep learning knowledge and improve the efficiency of auxiliary ***:In recent years,deep learning has been widely used in academia,industry,*** increasing number of companies are starting to recruit a large number of professionals in the field of deep *** numbers of colleges and universities also offer courses related to deep learning to help radiologists learn automated medical image analysis *** now,however,there is no practical training platform that can help radiologists learn automated medical image analysis ***:The platform proposes the basic learning,model combat,business application(BMR)concept,including the learning guidance system and the assessment training system,which constitutes a closed-loop learning guidance mode of“learning-assessment-training-learning”.Findings:The survey results show that most of radiologists met their learning expectations by using this *** platform can help radiologists master deep learning techniques quickly,comprehensively and firmly.
This paper introduces an innovative approach to addressing the Weapon Target Assignment (WTA) problem, utilizing the Deep Q-Learning (DQN) algorithm. The goal is to optimize the allocation between weapons and targets ...
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ISBN:
(数字)9798350391367
ISBN:
(纸本)9798350391374
This paper introduces an innovative approach to addressing the Weapon Target Assignment (WTA) problem, utilizing the Deep Q-Learning (DQN) algorithm. The goal is to optimize the allocation between weapons and targets to achieve the most effective combat outcome. Initially proposed by Manne, the WTA problem is a multi-objective, multi-constraint NP-complete problem, the complexity of which increases with the number and variety of weapons and targets. The DQN model presented in this paper transforms the WTA problem into a reinforcement learning issue, effectively addressing the issues of low efficiency and the propensity to fall into local optima associated with traditional heuristic algorithms in large-scale scenarios.
Qinghai-Tibet Plateau lakes are important carriers of water resources in the‘Asian’s Water Tower’,and it is of great significance to grasp the spatial distribution of plateau lakes for the climate,ecological enviro...
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Qinghai-Tibet Plateau lakes are important carriers of water resources in the‘Asian’s Water Tower’,and it is of great significance to grasp the spatial distribution of plateau lakes for the climate,ecological environment,and regional water ***,the differences in spatial-spectral characteristics of various types of plateau lakes,and the complex background information of plateau both influence the extraction effect of ***,it is a great challenge to completely and effectively extract plateau *** this study,we proposed a multiscale contextual information aggregation network,termed MSCANet,to automatically extract Plateau lake *** consists of three main components:a multiscale lake feature encoder,a feature decoder,and a Multicore Pyramid Pooling Module(MPPM).The multiscale lake feature encoder suppressed noise interference to capture multiscale spatial-spectral information from heterogeneous *** MPPM module aggregated the contextual information of various lakes *** applied the MSCANet to the lake extraction of the Qinghai-Tibet Plateau based on Google data;additionally,comparative experiments showed that the MSCANet proposed had obvious improvement in lake detection accuracy and morphological ***,we transferred the pre-trained optimal model to the Landsat-8 and Sentinel-2A dataset to verify the generalization of the MSCANet.
Photocatalytic degradation of organic pollutants is of great significance for wastewater remediation but is still hindered by the poor catalytic efficiency of the ***,we report a strategy to simultaneously introduce p...
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Photocatalytic degradation of organic pollutants is of great significance for wastewater remediation but is still hindered by the poor catalytic efficiency of the ***,we report a strategy to simultaneously introduce piezocatalysis and to enhance the intrinsic photocatalysis in a single catalyst,which improved the performance for catalytic degradation of methylene blue(MB)***,piezoelectric BiFeO_(3)(BFO)nanotube doped with different contents of Gd and La(Bi_(0.9)(GdxLa_(1−x))0.1FeO_(3))were produced by *** doping led to a higher concentration of surface oxygen vacancy(OV)in Bi_(0.9)Gd_(0.07)La_(0.03)FeO_(3),which effectively increased the piezoelectric field due to the deformation of BFO,and suppressed the recombination of photon-generated electron–hole *** Bi_(0.9)Gd_(0.07)La_(0.03)FeO_(3)nanotube showed excellent catalytic performance under simultaneous light irradiation and ultrasonic excitation,giving an extraordinary 95%degradation of MB within 90 *** findings suggest that the piezoelectric effect combined with defect engineering can enhance the catalytic performance of Bi_(0.9)Gd_(0.07)La_(0.03)FeO_(3)*** could potentially be extended to other catalytic systems for high-performance pollutant treatment.
Underwater optical imaging is prone to image distortion, blurring and other problems arising from thermal disturbance. To tackle down the above-mentioned problems, underwater images affected by thermal disturbance are...
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Entangled quantum states serve as important resources in quantum communication, quantum computing, and quantum sensing. Creating entangled states between remote nodes is referred to as remote entanglement establishmen...
Entangled quantum states serve as important resources in quantum communication, quantum computing, and quantum sensing. Creating entangled states between remote nodes is referred to as remote entanglement establishment (REE). REE typically consists of three types of quantum operations: entanglement generation, distillation, and swapping. By carefully designing the sequence describing the order of these operations, this paper investigates REE in a repeater chain under the requirement that the fidelity of the established entanglements be above a desired threshold. Specifically, the paper derives an asymptotically achievable upper bound on the maximum REE rate.
We report a real-time 1 kbps stealthy transmission in the 10 Gbps QPSK public communication. The stealth data is embedded in dither signals of bias control. The scheme is compatible with existing optical transmission ...
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The amount and variety of raw data generated in the agriculture sector from numerous sources, including soil sensors and local weather stations, are proliferating. However, these raw data in themselves are meaningless...
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