The existing depth video coding algorithms are generally based on in-loop depth filters, whose performance are unstable and easily affected by the outliers. In this paper, we design a joint weighted sparse representat...The existing depth video coding algorithms are generally based on in-loop depth filters, whose performance are unstable and easily affected by the outliers. In this paper, we design a joint weighted sparse representation-based median filter as the in-loop filter in depth video codec. It constructs depth candidate set which contains relevant neighboring depth pixel based on depth and intensity similarity weighted sparse coding, then the median operation is performed on this set to select a neighboring depth pixel as the result of the filtering. The experimental results indicate that the depth bitrate is reduced by about 9% compared with anchor method. It is confirmed that the proposed method is more effective in reducing the required depth bitrates for a given synthesis quality level.展开更多
Medical Image Fusion is the synthesizing technology for fusing multi-modal medical information using mathematical procedures to generate better visual on the image content and high-quality image output.Medical image f...Medical Image Fusion is the synthesizing technology for fusing multi-modal medical information using mathematical procedures to generate better visual on the image content and high-quality image output.Medical image fusion represents an indispensible role infixing major solutions for the complicated medical predicaments,while the recent research results have an enhanced affinity towards the preservation of medical image details,leaving color distortion and halo artifacts to remain unaddressed.This paper proposes a novel method of fusing Computer Tomography(CT)and Magnetic Resonance Imaging(MRI)using a hybrid model of Non Sub-sampled Contourlet Transform(NSCT)and Joint Sparse Representation(JSR).This model gratifies the need for precise integration of medical images of different modalities,which is an essential requirement in the diagnosing process towards clinical activities and treating the patients accordingly.In the proposed model,the medical image is decomposed using NSCT which is an efficient shift variant decomposition transformation method.JSR is exercised to extricate the common features of the medical image for the fusion process.The performance analysis of the proposed system proves that the proposed image fusion technique for medical image fusion is more efficient,provides better results,and a high level of distinctness by integrating the advantages of complementary images.The comparative analysis proves that the proposed technique exhibits better-quality than the existing medical image fusion practices.展开更多
In the era of Big data,learning discriminant feature representation from network traffic is identified has as an invariably essential task for improving the detection ability of an intrusion detection system(IDS).Owin...In the era of Big data,learning discriminant feature representation from network traffic is identified has as an invariably essential task for improving the detection ability of an intrusion detection system(IDS).Owing to the lack of accurately labeled network traffic data,many unsupervised feature representation learning models have been proposed with state-of-theart performance.Yet,these models fail to consider the classification error while learning the feature representation.Intuitively,the learnt feature representation may degrade the performance of the classification task.For the first time in the field of intrusion detection,this paper proposes an unsupervised IDS model leveraging the benefits of deep autoencoder(DAE)for learning the robust feature representation and one-class support vector machine(OCSVM)for finding the more compact decision hyperplane for intrusion detection.Specially,the proposed model defines a new unified objective function to minimize the reconstruction and classification error simultaneously.This unique contribution not only enables the model to support joint learning for feature representation and classifier training but also guides to learn the robust feature representation which can improve the discrimination ability of the classifier for intrusion detection.Three set of evaluation experiments are conducted to demonstrate the potential of the proposed model.First,the ablation evaluation on benchmark dataset,NSL-KDD validates the design decision of the proposed model.Next,the performance evaluation on recent intrusion dataset,UNSW-NB15 signifies the stable performance of the proposed model.Finally,the comparative evaluation verifies the efficacy of the proposed model against recently published state-of-the-art methods.展开更多
The goal of the present study was to investigate age-related changes in attentional allocation for shared task representations during joint performance;event-related potentials were recorded while participants perform...The goal of the present study was to investigate age-related changes in attentional allocation for shared task representations during joint performance;event-related potentials were recorded while participants performed a modified visual three-stimulus oddball task, both alone and together with another participant. Younger adults and older adults (14 each) participated in the study. Participants were required to identify rare target stimuli while ignoring frequent standards, as well as rare non-targets assigned to a partner’s action (<i>i.e</i>., no-go stimuli for one’s own task). ERP component, nogo-P3 and P3b were measured to investigate the inhibition and the attentional allocation to the partner’s stimuli. Results showed that younger adults elicited larger frontal nogo P3 and parietal P3b for non-targets in the joint than in the individual condition. Contrary to expectation, older adults induced frontal no-go P3 in the joint condition not in the individual condition. In the sharing of the task with another, the result suggested that the efficiency of matching of incoming information with the representation of the other’s task declined with age, whereas aging did not affect the suppression of incorrect preparation of motor responses instigated by this representation.</i.i.e.<>展开更多
随着新能源机车向高效率、智能化方向发展,精准监测动力电池的充放电状态(state of charge,SOC)和健康状态(state of health,SOH)对于保障机车运行安全尤为关键。针对传统独立估计方法在复杂工况下适应性差、难以捕捉时变耦合特性的问题...随着新能源机车向高效率、智能化方向发展,精准监测动力电池的充放电状态(state of charge,SOC)和健康状态(state of health,SOH)对于保障机车运行安全尤为关键。针对传统独立估计方法在复杂工况下适应性差、难以捕捉时变耦合特性的问题,提出一种基于自适应加权多通道长短期记忆网络(long short-term memory,LSTM)与Transformer融合的联合网络架构(MLTA-Net)。该方法构建了涵盖多层次特征的电池健康因子集合,引入等压升时间、最大容量增量电压等关键动态特征,从电化学机理层面强化了老化趋势的表征能力。MLTA-Net模型采用多通道并行架构,分离处理不同类型的电池数据特征,通过LSTM编码器捕获短期时序依赖关系,利用Transformer多头自注意力机制解析全局工况特征,并通过自适应加权融合层进行特征融合,实现电池状态高精度优化估计。实验方法基于磷酸铁锂电池循环老化数据集,在不同老化阶段下对SOH进行估计。研究结果表明,所提方法对电池最大容量衰减均方根误差稳定在0.06%以内,在预测误差、稳定性方面均优于传统方法。在脉冲工况和深度充放电条件下对电池SOC-SOH进行联合估计,预测精度相比单独估计有显著提升,尤其在SOC发生突变的关键时刻误差降低了84.2%,在电池老化阶段展现出更强的鲁棒性和泛化能力。本研究为复杂工况下的SOC-SOH联合估计提供了高效、精准的解决方案,为智能电池管理系统的优化提供了理论参考和技术支持。展开更多
基金Supported by the National Natural Science Foundation of China(61462048)
文摘The existing depth video coding algorithms are generally based on in-loop depth filters, whose performance are unstable and easily affected by the outliers. In this paper, we design a joint weighted sparse representation-based median filter as the in-loop filter in depth video codec. It constructs depth candidate set which contains relevant neighboring depth pixel based on depth and intensity similarity weighted sparse coding, then the median operation is performed on this set to select a neighboring depth pixel as the result of the filtering. The experimental results indicate that the depth bitrate is reduced by about 9% compared with anchor method. It is confirmed that the proposed method is more effective in reducing the required depth bitrates for a given synthesis quality level.
文摘Medical Image Fusion is the synthesizing technology for fusing multi-modal medical information using mathematical procedures to generate better visual on the image content and high-quality image output.Medical image fusion represents an indispensible role infixing major solutions for the complicated medical predicaments,while the recent research results have an enhanced affinity towards the preservation of medical image details,leaving color distortion and halo artifacts to remain unaddressed.This paper proposes a novel method of fusing Computer Tomography(CT)and Magnetic Resonance Imaging(MRI)using a hybrid model of Non Sub-sampled Contourlet Transform(NSCT)and Joint Sparse Representation(JSR).This model gratifies the need for precise integration of medical images of different modalities,which is an essential requirement in the diagnosing process towards clinical activities and treating the patients accordingly.In the proposed model,the medical image is decomposed using NSCT which is an efficient shift variant decomposition transformation method.JSR is exercised to extricate the common features of the medical image for the fusion process.The performance analysis of the proposed system proves that the proposed image fusion technique for medical image fusion is more efficient,provides better results,and a high level of distinctness by integrating the advantages of complementary images.The comparative analysis proves that the proposed technique exhibits better-quality than the existing medical image fusion practices.
基金This work was supported by the Research Deanship of Prince Sattam Bin Abdulaziz University,Al-Kharj,Saudi Arabia(Grant No.2020/01/17215).Also,the author thanks Deanship of college of computer engineering and sciences for technical support provided to complete the project successfully。
文摘In the era of Big data,learning discriminant feature representation from network traffic is identified has as an invariably essential task for improving the detection ability of an intrusion detection system(IDS).Owing to the lack of accurately labeled network traffic data,many unsupervised feature representation learning models have been proposed with state-of-theart performance.Yet,these models fail to consider the classification error while learning the feature representation.Intuitively,the learnt feature representation may degrade the performance of the classification task.For the first time in the field of intrusion detection,this paper proposes an unsupervised IDS model leveraging the benefits of deep autoencoder(DAE)for learning the robust feature representation and one-class support vector machine(OCSVM)for finding the more compact decision hyperplane for intrusion detection.Specially,the proposed model defines a new unified objective function to minimize the reconstruction and classification error simultaneously.This unique contribution not only enables the model to support joint learning for feature representation and classifier training but also guides to learn the robust feature representation which can improve the discrimination ability of the classifier for intrusion detection.Three set of evaluation experiments are conducted to demonstrate the potential of the proposed model.First,the ablation evaluation on benchmark dataset,NSL-KDD validates the design decision of the proposed model.Next,the performance evaluation on recent intrusion dataset,UNSW-NB15 signifies the stable performance of the proposed model.Finally,the comparative evaluation verifies the efficacy of the proposed model against recently published state-of-the-art methods.
文摘The goal of the present study was to investigate age-related changes in attentional allocation for shared task representations during joint performance;event-related potentials were recorded while participants performed a modified visual three-stimulus oddball task, both alone and together with another participant. Younger adults and older adults (14 each) participated in the study. Participants were required to identify rare target stimuli while ignoring frequent standards, as well as rare non-targets assigned to a partner’s action (<i>i.e</i>., no-go stimuli for one’s own task). ERP component, nogo-P3 and P3b were measured to investigate the inhibition and the attentional allocation to the partner’s stimuli. Results showed that younger adults elicited larger frontal nogo P3 and parietal P3b for non-targets in the joint than in the individual condition. Contrary to expectation, older adults induced frontal no-go P3 in the joint condition not in the individual condition. In the sharing of the task with another, the result suggested that the efficiency of matching of incoming information with the representation of the other’s task declined with age, whereas aging did not affect the suppression of incorrect preparation of motor responses instigated by this representation.</i.i.e.<>
文摘随着新能源机车向高效率、智能化方向发展,精准监测动力电池的充放电状态(state of charge,SOC)和健康状态(state of health,SOH)对于保障机车运行安全尤为关键。针对传统独立估计方法在复杂工况下适应性差、难以捕捉时变耦合特性的问题,提出一种基于自适应加权多通道长短期记忆网络(long short-term memory,LSTM)与Transformer融合的联合网络架构(MLTA-Net)。该方法构建了涵盖多层次特征的电池健康因子集合,引入等压升时间、最大容量增量电压等关键动态特征,从电化学机理层面强化了老化趋势的表征能力。MLTA-Net模型采用多通道并行架构,分离处理不同类型的电池数据特征,通过LSTM编码器捕获短期时序依赖关系,利用Transformer多头自注意力机制解析全局工况特征,并通过自适应加权融合层进行特征融合,实现电池状态高精度优化估计。实验方法基于磷酸铁锂电池循环老化数据集,在不同老化阶段下对SOH进行估计。研究结果表明,所提方法对电池最大容量衰减均方根误差稳定在0.06%以内,在预测误差、稳定性方面均优于传统方法。在脉冲工况和深度充放电条件下对电池SOC-SOH进行联合估计,预测精度相比单独估计有显著提升,尤其在SOC发生突变的关键时刻误差降低了84.2%,在电池老化阶段展现出更强的鲁棒性和泛化能力。本研究为复杂工况下的SOC-SOH联合估计提供了高效、精准的解决方案,为智能电池管理系统的优化提供了理论参考和技术支持。
