This paper proposes a parity recognition of blade number and manoeuvre intention classification algorithm of rotor target based on the convolutional neural network(CNN) using micro Doppler features. Firstly, the time-...This paper proposes a parity recognition of blade number and manoeuvre intention classification algorithm of rotor target based on the convolutional neural network(CNN) using micro Doppler features. Firstly, the time-frequency spectrograms are acquired from the radar echo by the short-time Fourier transform.Secondly, based on the obtained spectrograms, a seven-layer CNN architecture is built to recognize the blade-number parity and classify the manoeuvre intention of the rotor target. The constructed architecture contains a leaky rectified linear unit and a dropout layer to accelerate the convergence of the architecture and avoid over-fitting. Finally, the spectrograms of the datasets are divided into three different ratios, i.e., 20%, 33% and 50%,and the cross validation is used to verify the effectiveness of the constructed CNN architecture. Simulation results show that, on the one hand, as the ratio of training data increases, the recognition accuracy of parity and manoeuvre intention is improved at the same signal-to-noise ratio(SNR);on the other hand, the proposed algorithm also has a strong robustness: the accuracy can still reach 90.72% with an SNR of – 6 dB.展开更多
For the problem of insufficient feature interaction between intent classification and slot filling in spoken language understanding tasks,this paper proposes a method that uses ChatGPT to generate more diverse samples...For the problem of insufficient feature interaction between intent classification and slot filling in spoken language understanding tasks,this paper proposes a method that uses ChatGPT to generate more diverse samples,combined with a contrastive learning approach,to improve the model architecture and strengthen the interaction between intent and slot features.Specifically,prompts are designed for ChatGPT to generate diverse synthetic data with the same slots but different intents,and with the same int ents but different slots.A contrastive learning module is further designed,in which positive and negative intent-slot sample pairs are constructed via the ChatGPT-based mixed data augmentation method.The feature space distribution is optimized using a w eighted InfoNCE loss,enhancing the aggregation of similar features and the separation of dissimilar ones.Meanwhile,a multi-task joint training framework is employed to simultaneously optimize the cross-entropy loss for intent classification and the cont rastive loss,enabling deeper semantic interaction between intents and slots,thereby improving the overall model performance.Experimental results on the ATIS and SNIPS datasets demonstrate that the proposed method significantly outperforms traditional ba seline models in both intent detection accuracy and slot filling F1 score.In addition,ablation studies confirm the effectiveness of the contrastive learning and mixed data augmentation components.Overall,this work introduces a contrastive learning mech anism to effectively address the insufficient label-feature interaction in spoken language understanding tasks,offering a novel approach for optimizing multi-task dialogue systems.展开更多
The human-computer dialogue has recently attracted extensive attention from both academia and industry as an important branch in the field of artificial intelligence(AI).However,there are few studies on the evaluation...The human-computer dialogue has recently attracted extensive attention from both academia and industry as an important branch in the field of artificial intelligence(AI).However,there are few studies on the evaluation of large-scale Chinese human-computer dialogue systems.In this paper,we introduce the Second Evaluation of Chinese Human-Computer Dialogue Technology,which focuses on the identification of a user’s intents and intelligent processing of intent words.The Evaluation consists of user intent classification(Task 1)and online testing of task-oriented dialogues(Task 2),the data sets of which are provided by iFLYTEK Corporation.The evaluation tasks and data sets are introduced in detail,and meanwhile,the evaluation results and the existing problems in the evaluation are discussed.展开更多
基金supported by the National Natural Science Foundation of China (61901514)the Young Talent Program of Air Force Early Warning Academy (TJRC425311G11)。
文摘This paper proposes a parity recognition of blade number and manoeuvre intention classification algorithm of rotor target based on the convolutional neural network(CNN) using micro Doppler features. Firstly, the time-frequency spectrograms are acquired from the radar echo by the short-time Fourier transform.Secondly, based on the obtained spectrograms, a seven-layer CNN architecture is built to recognize the blade-number parity and classify the manoeuvre intention of the rotor target. The constructed architecture contains a leaky rectified linear unit and a dropout layer to accelerate the convergence of the architecture and avoid over-fitting. Finally, the spectrograms of the datasets are divided into three different ratios, i.e., 20%, 33% and 50%,and the cross validation is used to verify the effectiveness of the constructed CNN architecture. Simulation results show that, on the one hand, as the ratio of training data increases, the recognition accuracy of parity and manoeuvre intention is improved at the same signal-to-noise ratio(SNR);on the other hand, the proposed algorithm also has a strong robustness: the accuracy can still reach 90.72% with an SNR of – 6 dB.
文摘For the problem of insufficient feature interaction between intent classification and slot filling in spoken language understanding tasks,this paper proposes a method that uses ChatGPT to generate more diverse samples,combined with a contrastive learning approach,to improve the model architecture and strengthen the interaction between intent and slot features.Specifically,prompts are designed for ChatGPT to generate diverse synthetic data with the same slots but different intents,and with the same int ents but different slots.A contrastive learning module is further designed,in which positive and negative intent-slot sample pairs are constructed via the ChatGPT-based mixed data augmentation method.The feature space distribution is optimized using a w eighted InfoNCE loss,enhancing the aggregation of similar features and the separation of dissimilar ones.Meanwhile,a multi-task joint training framework is employed to simultaneously optimize the cross-entropy loss for intent classification and the cont rastive loss,enabling deeper semantic interaction between intents and slots,thereby improving the overall model performance.Experimental results on the ATIS and SNIPS datasets demonstrate that the proposed method significantly outperforms traditional ba seline models in both intent detection accuracy and slot filling F1 score.In addition,ablation studies confirm the effectiveness of the contrastive learning and mixed data augmentation components.Overall,this work introduces a contrastive learning mech anism to effectively address the insufficient label-feature interaction in spoken language understanding tasks,offering a novel approach for optimizing multi-task dialogue systems.
文摘The human-computer dialogue has recently attracted extensive attention from both academia and industry as an important branch in the field of artificial intelligence(AI).However,there are few studies on the evaluation of large-scale Chinese human-computer dialogue systems.In this paper,we introduce the Second Evaluation of Chinese Human-Computer Dialogue Technology,which focuses on the identification of a user’s intents and intelligent processing of intent words.The Evaluation consists of user intent classification(Task 1)and online testing of task-oriented dialogues(Task 2),the data sets of which are provided by iFLYTEK Corporation.The evaluation tasks and data sets are introduced in detail,and meanwhile,the evaluation results and the existing problems in the evaluation are discussed.
