Artificial intelligence technology is introduced into the simulation of muzzle flow field to improve its simulation efficiency in this paper.A data-physical fusion driven framework is proposed.First,the known flow fie...Artificial intelligence technology is introduced into the simulation of muzzle flow field to improve its simulation efficiency in this paper.A data-physical fusion driven framework is proposed.First,the known flow field data is used to initialize the model parameters,so that the parameters to be trained are close to the optimal value.Then physical prior knowledge is introduced into the training process so that the prediction results not only meet the known flow field information but also meet the physical conservation laws.Through two examples,it is proved that the model under the fusion driven framework can solve the strongly nonlinear flow field problems,and has stronger generalization and expansion.The proposed model is used to solve a muzzle flow field,and the safety clearance behind the barrel side is divided.It is pointed out that the shape of the safety clearance under different launch speeds is roughly the same,and the pressure disturbance in the area within 9.2 m behind the muzzle section exceeds the safety threshold,which is a dangerous area.Comparison with the CFD results shows that the calculation efficiency of the proposed model is greatly improved under the condition of the same calculation accuracy.The proposed model can quickly and accurately simulate the muzzle flow field under various launch conditions.展开更多
深度学习是人工智能领域的热门研究方向之一,它通过构建多层人工神经网络模仿人脑对数据的处理机制。大型语言模型(large language model,LLM)基于深度学习的架构,在无需编程指令的情况下,能通过分析大量数据以获得理解和生成人类语言...深度学习是人工智能领域的热门研究方向之一,它通过构建多层人工神经网络模仿人脑对数据的处理机制。大型语言模型(large language model,LLM)基于深度学习的架构,在无需编程指令的情况下,能通过分析大量数据以获得理解和生成人类语言的能力,被广泛应用于自然语言处理、计算机视觉、智慧医疗、智慧交通等诸多领域。文章总结了LLM在医疗领域的应用,涵盖了LLM针对医疗任务的基本训练流程、特殊策略以及在具体医疗场景中的应用。同时,进一步讨论了LLM在应用中面临的挑战,包括决策过程缺乏透明度、输出准确性以及隐私、伦理问题等,随后列举了相应的改进策略。最后,文章展望了LLM在医疗领域的未来发展趋势,及其对人类健康事业发展的潜在影响。展开更多
目的对比分析人工智能阅片与人工阅片在诊断骨质疏松性椎体压缩骨折中的效能。方法连续收集了2023年1月至2023年12月80例骨质疏松性椎体压缩骨折患者及20例无骨折但存在非特异性腰疼的患者的资料纳入了该项研究。根据患者的计算机断层扫...目的对比分析人工智能阅片与人工阅片在诊断骨质疏松性椎体压缩骨折中的效能。方法连续收集了2023年1月至2023年12月80例骨质疏松性椎体压缩骨折患者及20例无骨折但存在非特异性腰疼的患者的资料纳入了该项研究。根据患者的计算机断层扫描(computed tomography,CT)影像分别进行人工智能软件诊断和3名不同年资的脊柱外科临床医生(高级职称、中级职称、初级职称各1人)人工阅片诊断。比较不同检测方法的诊断效能。结果各组灵敏度、特异度、阳性预测值、阴性预测值、受试者工作特征(receiver operating characteristic,ROC)曲线下面积(area under the curve,AUC)、Kappa值分别为AI阅片:0.975、0.900、0.975、0.900、0.938、0.875;高级职称:0.950、0.900、0.974、0.818、0.925、0.819;中级职称:0.825、0.850、0.957、0.548、0.837、0.560;初级职称:0.750、0.750、0.923、0.429、0.751、0.390。结论人工智能的诊断水平与高年资医生诊断水平相当,明显高于中级及初级临床医生的诊断水平。展开更多
传统时序预测模型通常仅关注捕捉复杂时序中的趋势和模式,而忽略了变量间的相互作用,限制了该模型在复杂时序预测中应用.提出一种Dualformer双模型并联方案,该模型并联iTransformer(inverted transformer)和PatchTST(patch time series ...传统时序预测模型通常仅关注捕捉复杂时序中的趋势和模式,而忽略了变量间的相互作用,限制了该模型在复杂时序预测中应用.提出一种Dualformer双模型并联方案,该模型并联iTransformer(inverted transformer)和PatchTST(patch time series transformer),通过激活函数替代前馈神经网络,并通过多层感知机计算输出结果.Dualformer利用注意力机制同时捕捉复杂时序中的时间维度和变量维度信息,关注时间趋势与多变量交互.实验结果显示,Dualformer在复杂时序预测效果上显著优于对比模型iTransformer、PatchTST和DLinear(decomposition linear),在实际应用中可显著提高复杂时序预测的准确度,具有广泛应用前景.展开更多
基金Supported by the Natural Science Foundation of Jiangsu Province of China(Grant No.BK20210347)Supported by the National Natural Science Foundation of China(Grant No.U2141246).
文摘Artificial intelligence technology is introduced into the simulation of muzzle flow field to improve its simulation efficiency in this paper.A data-physical fusion driven framework is proposed.First,the known flow field data is used to initialize the model parameters,so that the parameters to be trained are close to the optimal value.Then physical prior knowledge is introduced into the training process so that the prediction results not only meet the known flow field information but also meet the physical conservation laws.Through two examples,it is proved that the model under the fusion driven framework can solve the strongly nonlinear flow field problems,and has stronger generalization and expansion.The proposed model is used to solve a muzzle flow field,and the safety clearance behind the barrel side is divided.It is pointed out that the shape of the safety clearance under different launch speeds is roughly the same,and the pressure disturbance in the area within 9.2 m behind the muzzle section exceeds the safety threshold,which is a dangerous area.Comparison with the CFD results shows that the calculation efficiency of the proposed model is greatly improved under the condition of the same calculation accuracy.The proposed model can quickly and accurately simulate the muzzle flow field under various launch conditions.
文摘深度学习是人工智能领域的热门研究方向之一,它通过构建多层人工神经网络模仿人脑对数据的处理机制。大型语言模型(large language model,LLM)基于深度学习的架构,在无需编程指令的情况下,能通过分析大量数据以获得理解和生成人类语言的能力,被广泛应用于自然语言处理、计算机视觉、智慧医疗、智慧交通等诸多领域。文章总结了LLM在医疗领域的应用,涵盖了LLM针对医疗任务的基本训练流程、特殊策略以及在具体医疗场景中的应用。同时,进一步讨论了LLM在应用中面临的挑战,包括决策过程缺乏透明度、输出准确性以及隐私、伦理问题等,随后列举了相应的改进策略。最后,文章展望了LLM在医疗领域的未来发展趋势,及其对人类健康事业发展的潜在影响。
文摘目的对比分析人工智能阅片与人工阅片在诊断骨质疏松性椎体压缩骨折中的效能。方法连续收集了2023年1月至2023年12月80例骨质疏松性椎体压缩骨折患者及20例无骨折但存在非特异性腰疼的患者的资料纳入了该项研究。根据患者的计算机断层扫描(computed tomography,CT)影像分别进行人工智能软件诊断和3名不同年资的脊柱外科临床医生(高级职称、中级职称、初级职称各1人)人工阅片诊断。比较不同检测方法的诊断效能。结果各组灵敏度、特异度、阳性预测值、阴性预测值、受试者工作特征(receiver operating characteristic,ROC)曲线下面积(area under the curve,AUC)、Kappa值分别为AI阅片:0.975、0.900、0.975、0.900、0.938、0.875;高级职称:0.950、0.900、0.974、0.818、0.925、0.819;中级职称:0.825、0.850、0.957、0.548、0.837、0.560;初级职称:0.750、0.750、0.923、0.429、0.751、0.390。结论人工智能的诊断水平与高年资医生诊断水平相当,明显高于中级及初级临床医生的诊断水平。
文摘传统时序预测模型通常仅关注捕捉复杂时序中的趋势和模式,而忽略了变量间的相互作用,限制了该模型在复杂时序预测中应用.提出一种Dualformer双模型并联方案,该模型并联iTransformer(inverted transformer)和PatchTST(patch time series transformer),通过激活函数替代前馈神经网络,并通过多层感知机计算输出结果.Dualformer利用注意力机制同时捕捉复杂时序中的时间维度和变量维度信息,关注时间趋势与多变量交互.实验结果显示,Dualformer在复杂时序预测效果上显著优于对比模型iTransformer、PatchTST和DLinear(decomposition linear),在实际应用中可显著提高复杂时序预测的准确度,具有广泛应用前景.
