The classification of lung nodules is a challenging problem as the visual analysis of the nodules and non-nodules revealed homogenous textural patterns.In this work,an Auxiliary Classifier(AC)-Generative Adversarial Net...The classification of lung nodules is a challenging problem as the visual analysis of the nodules and non-nodules revealed homogenous textural patterns.In this work,an Auxiliary Classifier(AC)-Generative Adversarial Network(GAN)based Lung Cancer Classification(LCC)system is developed.The pro-posed AC-GAN-LCC system consists of three modules;preprocessing,Lungs Region Detection(LRD),and AC-GAN classification.A Wienerfilter is employed in the preprocessing module to remove the Gaussian noise.In the LRD module,only the lung regions(left and right lungs)are detected using itera-tive thresholding and morphological operations.In order to extract the lung region only,floodfilling and background subtraction.The detected lung regions are fed to the AC-GAN classifier to detect the nodules.It classifies the nodules into one of the two classes,i.e.,binary classification(such as nodules or non-nodules).The AC-GAN is the extended version of the conditional GAN that predicts the label of a given image.Three different optimization techniques,adaptive gradient optimi-zation,root mean square propagation optimization,and Adam optimization are employed for optimizing the AC-GAN architecture.The proposed AC-GAN-LCC system is evaluated on the Lung Image Database Consortium(LIDC)data-base Computed Tomography(CT)scan images.The proposed AC-GAN-LCC system classifies∼15000 CT slices(7310 non-nodules and 7685 nodules).It pro-vides an overall accuracy of 98.8%on the LIDC database using Adam optimiza-tion by a 10-fold cross-validation approach.展开更多
Background Human-machine dialog generation is an essential topic of research in the field of natural language processing.Generating high-quality,diverse,fluent,and emotional conversation is a challenging task.Based on...Background Human-machine dialog generation is an essential topic of research in the field of natural language processing.Generating high-quality,diverse,fluent,and emotional conversation is a challenging task.Based on continuing advancements in artificial intelligence and deep learning,new methods have come to the forefront in recent times.In particular,the end-to-end neural network model provides an extensible conversation generation framework that has the potential to enable machines to understand semantics and automatically generate responses.However,neural network models come with their own set of questions and challenges.The basic conversational model framework tends to produce universal,meaningless,and relatively"safe"answers.Methods Based on generative adversarial networks(GANs),a new emotional dialog generation framework called EMC-GAN is proposed in this study to address the task of emotional dialog generation.The proposed model comprises a generative and three discriminative models.The generator is based on the basic sequence-to-sequence(Seq2Seq)dialog generation model,and the aggregate discriminative model for the overall framework consists of a basic discriminative model,an emotion discriminative model,and a fluency discriminative model.The basic discriminative model distinguishes generated fake sentences from real sentences in the training corpus.The emotion discriminative model evaluates whether the emotion conveyed via the generated dialog agrees with a pre-specified emotion,and directs the generative model to generate dialogs that correspond to the category of the pre-specified emotion.Finally,the fluency discriminative model assigns a score to the fluency of the generated dialog and guides the generator to produce more fluent sentences.Results Based on the experimental results,this study confirms the superiority of the proposed model over similar existing models with respect to emotional accuracy,fluency,and consistency.Conclusions The proposed EMC-GAN model is capable of generating consistent,smooth,and fluent dialog that conveys pre-specified emotions,and exhibits better performance with respect to emotional accuracy,consistency,and fluency compared to its competitors.展开更多
高比例新能源发展愿景下,为有效缩短储能回报周期、提升新能源消纳以及降低配电网碳排放,提出一种考虑精细化充放电与碳效益的配电网储能多目标双层规划模型。首先,基于Wasserstein距离和梯度惩罚的改进生成对抗网络(Wasserstein genera...高比例新能源发展愿景下,为有效缩短储能回报周期、提升新能源消纳以及降低配电网碳排放,提出一种考虑精细化充放电与碳效益的配电网储能多目标双层规划模型。首先,基于Wasserstein距离和梯度惩罚的改进生成对抗网络(Wasserstein generative adversarial network with gradient penalty,WGAN-GP)以及K-中心聚类算法(K-medoids)生成光伏典型场景。其次,建立储能系统的充放电精细化模型,并基于储能降碳量和全生命周期碳排放量构建碳效益模型。然后,构建考虑精细化充放电与碳效益的双层配电网储能规划运行模型,以日总成本最小为上层目标,对储能进行优化配置;以运行成本最小、电压偏移量最小和储能碳效益最大为下层目标,实现配电网的优化运行。再次,利用跨层关联变量建模将双层模型转化为单层多目标模型,并采用归一化法向约束法(normalized normal constraint,NNC)求解多目标问题,采用熵权-逼近理想解排序法(technique for order preference by similarity to ideal solution,TOPSIS)选取最优折中解。最后,基于IEEE 33节点系统进行算例仿真,验证模型有效性。展开更多
文摘The classification of lung nodules is a challenging problem as the visual analysis of the nodules and non-nodules revealed homogenous textural patterns.In this work,an Auxiliary Classifier(AC)-Generative Adversarial Network(GAN)based Lung Cancer Classification(LCC)system is developed.The pro-posed AC-GAN-LCC system consists of three modules;preprocessing,Lungs Region Detection(LRD),and AC-GAN classification.A Wienerfilter is employed in the preprocessing module to remove the Gaussian noise.In the LRD module,only the lung regions(left and right lungs)are detected using itera-tive thresholding and morphological operations.In order to extract the lung region only,floodfilling and background subtraction.The detected lung regions are fed to the AC-GAN classifier to detect the nodules.It classifies the nodules into one of the two classes,i.e.,binary classification(such as nodules or non-nodules).The AC-GAN is the extended version of the conditional GAN that predicts the label of a given image.Three different optimization techniques,adaptive gradient optimi-zation,root mean square propagation optimization,and Adam optimization are employed for optimizing the AC-GAN architecture.The proposed AC-GAN-LCC system is evaluated on the Lung Image Database Consortium(LIDC)data-base Computed Tomography(CT)scan images.The proposed AC-GAN-LCC system classifies∼15000 CT slices(7310 non-nodules and 7685 nodules).It pro-vides an overall accuracy of 98.8%on the LIDC database using Adam optimiza-tion by a 10-fold cross-validation approach.
文摘Background Human-machine dialog generation is an essential topic of research in the field of natural language processing.Generating high-quality,diverse,fluent,and emotional conversation is a challenging task.Based on continuing advancements in artificial intelligence and deep learning,new methods have come to the forefront in recent times.In particular,the end-to-end neural network model provides an extensible conversation generation framework that has the potential to enable machines to understand semantics and automatically generate responses.However,neural network models come with their own set of questions and challenges.The basic conversational model framework tends to produce universal,meaningless,and relatively"safe"answers.Methods Based on generative adversarial networks(GANs),a new emotional dialog generation framework called EMC-GAN is proposed in this study to address the task of emotional dialog generation.The proposed model comprises a generative and three discriminative models.The generator is based on the basic sequence-to-sequence(Seq2Seq)dialog generation model,and the aggregate discriminative model for the overall framework consists of a basic discriminative model,an emotion discriminative model,and a fluency discriminative model.The basic discriminative model distinguishes generated fake sentences from real sentences in the training corpus.The emotion discriminative model evaluates whether the emotion conveyed via the generated dialog agrees with a pre-specified emotion,and directs the generative model to generate dialogs that correspond to the category of the pre-specified emotion.Finally,the fluency discriminative model assigns a score to the fluency of the generated dialog and guides the generator to produce more fluent sentences.Results Based on the experimental results,this study confirms the superiority of the proposed model over similar existing models with respect to emotional accuracy,fluency,and consistency.Conclusions The proposed EMC-GAN model is capable of generating consistent,smooth,and fluent dialog that conveys pre-specified emotions,and exhibits better performance with respect to emotional accuracy,consistency,and fluency compared to its competitors.
文摘高比例新能源发展愿景下,为有效缩短储能回报周期、提升新能源消纳以及降低配电网碳排放,提出一种考虑精细化充放电与碳效益的配电网储能多目标双层规划模型。首先,基于Wasserstein距离和梯度惩罚的改进生成对抗网络(Wasserstein generative adversarial network with gradient penalty,WGAN-GP)以及K-中心聚类算法(K-medoids)生成光伏典型场景。其次,建立储能系统的充放电精细化模型,并基于储能降碳量和全生命周期碳排放量构建碳效益模型。然后,构建考虑精细化充放电与碳效益的双层配电网储能规划运行模型,以日总成本最小为上层目标,对储能进行优化配置;以运行成本最小、电压偏移量最小和储能碳效益最大为下层目标,实现配电网的优化运行。再次,利用跨层关联变量建模将双层模型转化为单层多目标模型,并采用归一化法向约束法(normalized normal constraint,NNC)求解多目标问题,采用熵权-逼近理想解排序法(technique for order preference by similarity to ideal solution,TOPSIS)选取最优折中解。最后,基于IEEE 33节点系统进行算例仿真,验证模型有效性。
