Based on the explicit finite element(FE) method and platform of ABAQUS,considering both the inhomogeneity of soils and concave-convex fluctuation of topography,a large-scale refined two-dimensional(2D) FE nonlinear an...Based on the explicit finite element(FE) method and platform of ABAQUS,considering both the inhomogeneity of soils and concave-convex fluctuation of topography,a large-scale refined two-dimensional(2D) FE nonlinear analytical model for Fuzhou Basin was established.The peak ground motion acceleration(PGA) and focusing effect with depth were analyzed.Meanwhile,the results by wave propagation of one-dimensional(1D) layered medium equivalent linearization method were added for contrast.The results show that:1) PGA at different depths are obviously amplified compared to the input ground motion,amplification effect of both funnel-shaped depression and upheaval areas(based on the shape of bedrock surface) present especially remarkable.The 2D results indicate that the PGA displays a non-monotonic decreasing with depth and a greater focusing effect of some particular layers,while the 1D results turn out that the PGA decreases with depth,except that PGA at few particular depth increases abruptly; 2) To the funnel-shaped depression areas,PGA amplification effect above 8 m depth shows relatively larger,to the upheaval areas,PGA amplification effect from 15 m to 25 m depth seems more significant.However,the regularities of the PGA amplification effect could hardly be found in the rest areas; 3) It appears a higher regression rate of PGA amplification coefficient with depth when under a smaller input motion; 4) The frequency spectral characteristic of input motion has noticeable effects on PGA amplification tendency.展开更多
The present study focused on analyzing the technical efficiency office farms in southwest of Niger. The data from January to March 2015 survey of 148 ms in three districts of south-western of Niger were analyzed by us...The present study focused on analyzing the technical efficiency office farms in southwest of Niger. The data from January to March 2015 survey of 148 ms in three districts of south-western of Niger were analyzed by using DEA-Tobit two-step method. In the f＇ust step, data envelopment analysis （DEA） was applied to estimate technical, pure technical and scale efficiency. In the second step, Tobit regression was used to identify factors affecting technical efficiency. The results showed that rice producers in southwest of Niger could reduce their inputs by 52% and still produce the same level of rice output. The Tobit regression showed that factors, such as farm size, experience in rice farming, membership of cooperative, main occupation and land ownership had a direct impact on technical efficiency.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
属性级情感分析作为一种细粒度情感分析方法,目前在许多应用场景中都具有重要作用.然而,随着社交媒体和在线评论的日益广泛以及各类新兴领域的出现,使得跨领域属性级情感分析面临着标签数据不足以及源领域与目标领域文本分布差异等挑战...属性级情感分析作为一种细粒度情感分析方法,目前在许多应用场景中都具有重要作用.然而,随着社交媒体和在线评论的日益广泛以及各类新兴领域的出现,使得跨领域属性级情感分析面临着标签数据不足以及源领域与目标领域文本分布差异等挑战.目前已有许多数据增强方法试图解决这些问题,但现有方法生成的文本仍存在语义不连贯、结构单一以及特征与源领域过于趋同等问题.为了克服这些问题,提出一种基于大语言模型(large language model,LLM)数据增强的跨领域属性级情感分析方法.所提方法利用大模型丰富的语言知识,合理构建针对跨领域属性级别情感分析任务的引导语句,挖掘目标领域与源领域相似文本,通过上下文学习的方式,使用领域关联关键词引导LLM生成目标领域有标签文本数据,用以解决目标领域数据缺乏以及领域特异性问题,从而有效提高跨领域属性级情感分析的准确性和鲁棒性.所提方法在多个真实数据集中进行实验,实验结果表明,该方法可以有效提升基线模型在跨领域属性级情感分析中的表现.展开更多
油中溶解气体分析(dissolved gas analysis,DGA)监测技术数据质量的管理、维护及保障体系不完善,导致目前油中溶解气体数据质量存在一定缺陷。提出一种适合油中溶解气体时序数据的趋势预测方法,通过对油中溶解气体在线监测时序数据的特...油中溶解气体分析(dissolved gas analysis,DGA)监测技术数据质量的管理、维护及保障体系不完善,导致目前油中溶解气体数据质量存在一定缺陷。提出一种适合油中溶解气体时序数据的趋势预测方法,通过对油中溶解气体在线监测时序数据的特征进行深入分析,选择了泛化性能优越的统计模型,并借鉴加性模型的优点,对存在缺失值的油中溶解气体数据进行拟合,并对预测效果进行分析。同时,与XGBoos(textreme gradient boosting,XGBoost)模型预测效果进行对比,通过实例对比了两者在预测效果上的差异。展开更多
基金Project(2011CB013601) supported by the National Basic Research Program of ChinaProject(51378258) supported by the National Natural Science Foundation of China
文摘Based on the explicit finite element(FE) method and platform of ABAQUS,considering both the inhomogeneity of soils and concave-convex fluctuation of topography,a large-scale refined two-dimensional(2D) FE nonlinear analytical model for Fuzhou Basin was established.The peak ground motion acceleration(PGA) and focusing effect with depth were analyzed.Meanwhile,the results by wave propagation of one-dimensional(1D) layered medium equivalent linearization method were added for contrast.The results show that:1) PGA at different depths are obviously amplified compared to the input ground motion,amplification effect of both funnel-shaped depression and upheaval areas(based on the shape of bedrock surface) present especially remarkable.The 2D results indicate that the PGA displays a non-monotonic decreasing with depth and a greater focusing effect of some particular layers,while the 1D results turn out that the PGA decreases with depth,except that PGA at few particular depth increases abruptly; 2) To the funnel-shaped depression areas,PGA amplification effect above 8 m depth shows relatively larger,to the upheaval areas,PGA amplification effect from 15 m to 25 m depth seems more significant.However,the regularities of the PGA amplification effect could hardly be found in the rest areas; 3) It appears a higher regression rate of PGA amplification coefficient with depth when under a smaller input motion; 4) The frequency spectral characteristic of input motion has noticeable effects on PGA amplification tendency.
文摘The present study focused on analyzing the technical efficiency office farms in southwest of Niger. The data from January to March 2015 survey of 148 ms in three districts of south-western of Niger were analyzed by using DEA-Tobit two-step method. In the f＇ust step, data envelopment analysis （DEA） was applied to estimate technical, pure technical and scale efficiency. In the second step, Tobit regression was used to identify factors affecting technical efficiency. The results showed that rice producers in southwest of Niger could reduce their inputs by 52% and still produce the same level of rice output. The Tobit regression showed that factors, such as farm size, experience in rice farming, membership of cooperative, main occupation and land ownership had a direct impact on technical efficiency.
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。
文摘属性级情感分析作为一种细粒度情感分析方法,目前在许多应用场景中都具有重要作用.然而,随着社交媒体和在线评论的日益广泛以及各类新兴领域的出现,使得跨领域属性级情感分析面临着标签数据不足以及源领域与目标领域文本分布差异等挑战.目前已有许多数据增强方法试图解决这些问题,但现有方法生成的文本仍存在语义不连贯、结构单一以及特征与源领域过于趋同等问题.为了克服这些问题,提出一种基于大语言模型(large language model,LLM)数据增强的跨领域属性级情感分析方法.所提方法利用大模型丰富的语言知识,合理构建针对跨领域属性级别情感分析任务的引导语句,挖掘目标领域与源领域相似文本,通过上下文学习的方式,使用领域关联关键词引导LLM生成目标领域有标签文本数据,用以解决目标领域数据缺乏以及领域特异性问题,从而有效提高跨领域属性级情感分析的准确性和鲁棒性.所提方法在多个真实数据集中进行实验,实验结果表明,该方法可以有效提升基线模型在跨领域属性级情感分析中的表现.
文摘油中溶解气体分析(dissolved gas analysis,DGA)监测技术数据质量的管理、维护及保障体系不完善,导致目前油中溶解气体数据质量存在一定缺陷。提出一种适合油中溶解气体时序数据的趋势预测方法,通过对油中溶解气体在线监测时序数据的特征进行深入分析,选择了泛化性能优越的统计模型,并借鉴加性模型的优点,对存在缺失值的油中溶解气体数据进行拟合,并对预测效果进行分析。同时,与XGBoos(textreme gradient boosting,XGBoost)模型预测效果进行对比,通过实例对比了两者在预测效果上的差异。
