8921.
8922.
针对现有差分进化（DE）算法在处理种群多样性降低和局部最优问题时,种群大小改进方法的性能不足,提出一种基于双档案种群大小自适应方法（APSA）的差分进化算法（APDE）。首先,构建2个档案分别用于记录在先前进化中丢弃的个体和实验个体;其次,根据种群分布状态变化衡量多样性变化,并在… … 相似文献
黄亚伟;钱雪忠;宋威 《计算机应用》2024,(12):3844-3853
8923.
8924.
针对现有理论串联质谱图预测仅限于预测b、y主干碎片离子以及单一模型难以捕捉肽序列复杂关系的问题,提出一种基于Transformer和门控循环单元（GRU）的肽序列理论串联质谱图预测方法,名为DeepCollider。首先,通过自注意力机制和长距离依赖关系,使用Transforme… … 相似文献
何长久;杨婧涵;周丕宇;边昕烨;吕明明;董迪;付岩;王海鹏 《计算机应用》2024,(12):3958-3964
8925.
8926.
8927.
区块链3.0是区块链技术发展的第3阶段,也是构建价值互联网的内核,它在分片、跨链以及隐私保护等方面的创新使它具有广泛的应用场景和研究价值,受到学术界和产业界相关人士的高度重视。针对区块链3.0的发展、技术与应用,调研并综述近5年国内外关于区块链3.0的相关文献。首先,介绍区块链的… … 相似文献
方鹏;赵凡;王保全;王轶;蒋同海 《计算机应用》2024,(12):3647-3657
8928.
8929.
8930.
8931.
8932.
区块链是一种结合分布式存储、共识机制和密码学原理等技术的新兴数据结构。针对公有链上的数据访问控制问题,提出一种面向公有区块链上的私有区块链（POP）的多权威属性加密（MA-ABE）方案。首先,构建依托公有区块链的私有区块链,并给出具体的数据隐私保护流程;其次,设计联合权威初始化算… … 相似文献
孙科硕;高海英;宋杨 《计算机应用》2024,(12):3699-3708
8933.
8934.
本文针对快速多项式乘法算法与可重构单元的高能效设计问题展开研究,首先对现有的格基后量子密码算法展开研究,提出了一种基于数论变换（Number Theoretic Transform,NTT）的快速多项式乘法算法,并针对其中的核心运算过程,提出了高能效混合基的NTT和INTT（Inverse Number Theoretic Transform）算法,该算法可以利用NTT变换高效实现所有基于有限域的格基后量子密码算法中的多项式乘法.在此基础上,对快速多项式乘法算法运算结构进行研究,在不增加额外运算部件的前提下,通过优化网络连接关系,提出了一种高能效可重构的混合基多项式乘法加速网络,在可灵活实现基2、基3、基4的NTT/INTT算法的同时,将基3与基4的NTT运算效率提升了一倍.本文针对混合基NTT运算过程中的访存冲突问题展开研究,从理论上分析了冲突产生的原因,在此基础上分析提出了一种高能效混合基的内存管理方案,设计了相应的地址生成逻辑.本文提出的内存访问方案是原地内存访问的一种,硬件固化后仍可实现不同的多项式乘法算法的内存管理.实验结果表明,在55 nm CMOS工艺下,完成维度为256,模数小于216的多项式乘法运算仅需0.785μs,最高工作频率可达到476 MHz,功耗为83.6 mW,面积时间积（Area Time Product,ATP）为152.604 kGE·μs.与当前现有研究相比,本文提出的结构的ATP值降低了40%以上.… … 相似文献
别梦妮;李伟;陈韬;李慧琴;杜怡然;南龙梅 《电子学报》2024,52(12):3957-3966
8935.
连续监测心电信号对于加强心血管疾病的早期筛查和诊断至关重要.然而,现有的心电信号监测方法存在依赖昂贵设备、依赖用户执行特殊操作、应用场景受限等弊端,无法满足广泛人群在日常生活状态下长期连续监测心电的迫切需求.为了克服上述问题,本研究提出了一种基于改进的非负矩阵分解技术的抗运动干扰… … 相似文献
曹烨彤;李凡;刘晓晨;谢睆冉;陈慧杰 《电子学报》2024,52(12):4153-4165
8936.
当前基于深度学习的中文长文本摘要生成的研究存在以下问题：（1）生成模型缺少信息引导,缺乏对关键词汇和语句的关注,存在长文本跨度下关键信息丢失的问题;（2）现有中文长文本摘要模型的词表常以字为基础,并不包含中文常用词语与标点,不利于提取多粒度的语义信息.针对上述问题,本文提出了融合… … 相似文献
郭哲;张智博;周炜杰;樊养余;张艳宁 《电子学报》2024,52(12):3914-3930
8937.
ObjectiveWith the development of face recognition technology, face images have been used as identity verification in many fields. As importa… … 相似文献
《中国图象图形学报》2024,29(12):3644-3656
8938.
ObjectiveFederated learning enables multiple parties to collaboratively train a machine learning model without communicating their local data. In practical applications, the data between nodes usually follow a non-independent identical distribution （non-IID）. In the local update, each client model will be optimized toward its local optima （i.e., fitting its individual feature distribution） instead of the global optimal objective and raises a client update drift. Meanwhile, in global updates that aggregate these diverged local models, the server model is further distracted by the set of mismatching local optima, which subsequently leads to a global drift at the server model. To solve the problems of slow global convergence and increasing number of training communication rounds caused by non-IID data, this paper proposes a joint dynamic correction federated learning algorithm （FedJDC） that is optimized from the client and server.MethodTo reduce the influence of non-IID on federated learning, this paper carries out a joint optimization from the two aspects of local model update and global model update and proposes the FedJDC algorithm. This paper then uses the cosine similarity between the local and global update directions to measure the offset of each participating client. Afterward, given that each client has a different degree of non-IID, if the degree of the model offset is only determined by the cosine similarity calculated in this round, then the model update may become unstable. Therefore, the FedJDC algorithm defines the cumulative offset and introduces the attenuation coefficient ρ . In calculating the cumulative offset of the model, the current and historical cumulative offsets are taken into account. In addition, by changing ρ to reduce the proportion of the cumulative offset of the current round, the influence of the offset of the current round on the final result can be reduced. This paper also proposes a strategy for dynamically adjusting the constraint terms for local model update offset. Specifically, the constraint terms of the local loss function are dynamically adjusted according to the calculated cumulative offset of the local model, and the algorithm is automatically adapted to various non-IID settings without a careful selection of hyperparameters, thus improving the flexibility of the algorithm. To dynamically change the weight of global model aggregation in each round and effectively improve the convergence speed and model accuracy, this paper also designs a dynamic weighted aggregation strategy that takes the accumulated offset uploaded by all clients as the weight of global model aggregation in each round of communication.ResultThe proposed method is tested on three dataset using different deep learning models. LeNeT5, the VGG16 network model, and the ResNet18 network model are used for training in the MNIST, FMNIST, and CIFAR10 datasets, respectively. Four experiments are designed to prove the effectiveness of the proposed algorithm. To verify the accuracy of FedJDC at different degrees of non-IID, the hyperparameter β of the Dirichlet distribution is varied, and the performance of different algorithms is compared. Experimental results show that FedJDC can improve the model accuracy by 5.48%, 1.62%, 2.10%, and 2.28% on average compared with FedAvg, FedProx, FedAdp, and FedLAW, respectively. To evaluate the communication efficiency of FedJDC, the number of communication rounds is counted as FedJDC reaches a target accuracy, and this number is compared with that obtained by other algorithms. Experimental results show that under different degrees of non-IID, FedJDC can reduce communication rounds by 62.29%, 20.90%, 24.93%, and 20.47% on average compared with FedAvg, FedProx, FedAdp, and FedLAW, respectively. This paper also investigates the effect of the number of local epochs on the accuracy of the final model. Experimental results show that FedJDC outperforms the other four methods under different epochs in terms of final model accuracy. FedJDC also demonstrates better robustness against the larger offset caused by more local update epochs. Ablation experiments also show that each optimization method performs well on all datasets, and FedJDC combines these two strategies to achieve the global optimal performance.ConclusionThis paper optimizes the local and global model offsets from two aspects and proposes a joint dynamic correction algorithm for these offsets in federated learning. The cumulative offset is defined, and the attenuation coefficient is introduced into the calculation of the cumulative offset. Considering the historical and current offset information, the size of the cumulative offset is dynamically adjusted to ensure the stability of the training parameter update. The dynamic constraint strategy takes the cumulative offset calculated by the client in each round as the constraint parameter of the client model. The dynamic weighted aggregation strategy changes the weight of each local model during the global model aggregation based on the cumulative offset of each participating client so as to dynamically update the global model in each round. The combination of the two optimization strategies has achieved good results, effectively alleviated the performance degradation of the federated learning model caused by non-IID data, and provided a good foundation for the further implementation of federated learning in this field.… … 相似文献
《中国图象图形学报》2024,29(12):3727-3738
8939.
ObjectiveInformational warfare has put forward higher requirements for military reconnaissance, and military target identification, as one o… … 相似文献
《中国图象图形学报》2024,29(12):3564-3577
8940.
ObjectiveFine-grained image classification aims to classify a super-category into multiple sub-categories. This task is more challenging tha… … 相似文献
《中国图象图形学报》2024,29(12):3756-3769
关于我们 |
广告服务 |
商务洽谈 |
联系我们 |
免责条款 |
隐私声明 |
网站导航
网站版权所有：中国科学院软件研究所 京ICP备09084417号-9
地址：北京海淀区中关村南四大街4号 邮编：100190 电话：010-62562563 E-mail ： cj_mail@jscas.ac.cn
本系统由:北京勤云科技发展有限公司提供技术支持
网站版权所有：中国科学院软件研究所 京ICP备09084417号-9
地址：北京海淀区中关村南四大街4号 邮编：100190 电话：010-62562563 E-mail ： cj_mail@jscas.ac.cn
本系统由:北京勤云科技发展有限公司提供技术支持
