随着移动设备和物联网技术的普及,时空数据的收集和分析变得越来越重要。轨迹预测,尤其是实时轨迹预测,对于许多应用领域如智能交通和城市规划等至关重要。现有的轨迹预测方法往往无法在保证预测准确性的同时满足实时性的要求,且实时数...随着移动设备和物联网技术的普及,时空数据的收集和分析变得越来越重要。轨迹预测,尤其是实时轨迹预测,对于许多应用领域如智能交通和城市规划等至关重要。现有的轨迹预测方法往往无法在保证预测准确性的同时满足实时性的要求,且实时数据通常是不完整或带有噪声的,要求预测算法必须能够适应不完全的轨迹信息。基于此,提出了一种基于时空数据库的实时启发式轨迹预测模型(Real-time Heuristic Trajectory Prediction Based on Spatio-Temporal Databases,RHTP-STD)。RHTP-STD利用MobilityDB数据库平台存储和管理轨迹数据,通过图构建算法将轨迹数据转换为时空图。RHTP-STD采用启发式算法,融合历史和实时数据,快速预测移动对象的未来轨迹。实验结果表明,RHTP-STD在Argoverse数据集上的预测准确性和实时性均优于现有方法。讨论所提方法在不同应用场景中的适用性,提出了未来的研究方向。展开更多
针对目前所有权转移协议无法完成多标签所有权同时转移等问题,提出一种无可信第三方参与的群组标签所有权转移协议。该协议可满足在同一个原所有者及新所有者的情况下,同时将一组标签的所有权由原所有者转让给新所有者,使用范围更广泛...针对目前所有权转移协议无法完成多标签所有权同时转移等问题,提出一种无可信第三方参与的群组标签所有权转移协议。该协议可满足在同一个原所有者及新所有者的情况下,同时将一组标签的所有权由原所有者转让给新所有者,使用范围更广泛。协议采用R_LWE密码体制实现传输信息加密,为有效降低组标签一端计算量,标签端仅进行加密运算,加密运算则放在归属者一端进行。对协议进行多角度分析,协议在确保安全的前提下,计算量方面优于对比协议,可在现有低成本射频识别(Radio Frequency Identification,RFID)系统中使用。展开更多
Five-valued Boolean functions play an important role in the design of symmetric cryptography.While the design and properties of single-output almost optimal five-valued spectra Boolean functions have been extensively ...Five-valued Boolean functions play an important role in the design of symmetric cryptography.While the design and properties of single-output almost optimal five-valued spectra Boolean functions have been extensively studied over the past few decades,there has been limited research on the construction of almost optimal five-valued spectra vectorial Boolean functions.In this paper,we present a construction method for even-variable 2-output almost optimal five-valued spectra balanced Boolean functions,whose Walsh spectra values belong to the set{0,±2^(n/2),±2^(n/2+1)},at the same time,we discuss the existence of sufficient conditions in the construction.Additionally,this paper presents a novel construction method for balanced single-output Boolean functions with even variables featuring a special five-valued spectral structure,whose Walsh spectra values are constrained to the set{0,±2^(n/2),±3·2^(n/2)}.These functions provide new canonical examples for the study of Boolean function spectral theory.展开更多
Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devi...Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devices have made power load data increasingly complex and volatile.This places higher demands on the prediction and analysis of power loads.In order to improve the prediction accuracy of short-term power load,a CNN-BiLSTMTPA short-term power prediction model based on the Improved Whale Optimization Algorithm(IWOA)with mixed strategies was proposed.Firstly,the model combined the Convolutional Neural Network(CNN)with the Bidirectional Long Short-Term Memory Network(BiLSTM)to fully extract the spatio-temporal characteristics of the load data itself.Then,the Temporal Pattern Attention(TPA)mechanism was introduced into the CNN-BiLSTM model to automatically assign corresponding weights to the hidden states of the BiLSTM.This allowed the model to differentiate the importance of load sequences at different time intervals.At the same time,in order to solve the problem of the difficulties of selecting the parameters of the temporal model,and the poor global search ability of the whale algorithm,which is easy to fall into the local optimization,the whale algorithm(IWOA)was optimized by using the hybrid strategy of Tent chaos mapping and Levy flight strategy,so as to better search the parameters of the model.In this experiment,the real load data of a region in Zhejiang was taken as an example to analyze,and the prediction accuracy(R2)of the proposed method reached 98.83%.Compared with the prediction models such as BP,WOA-CNN-BiLSTM,SSA-CNN-BiLSTM,CNN-BiGRU-Attention,etc.,the experimental results showed that the model proposed in this study has a higher prediction accuracy.展开更多
This paper describes what is thought to be the first generation of a continuous wave deep ultraviolet laser at 275 nm by efficient frequency doubling of a blue-diode-pumped Pr:YLF laser at 550 nm.By employing a novel ...This paper describes what is thought to be the first generation of a continuous wave deep ultraviolet laser at 275 nm by efficient frequency doubling of a blue-diode-pumped Pr:YLF laser at 550 nm.By employing a novel fast-axis collimated blue semiconductor laser as the pump source,combined with a folded cavity and innovation coating technology,and utilizing a Brewster-cut BBO crystal for intracavity frequency doubling,TEM00 mode deep UV laser radiation at 275 nm with an output power of 351 mW is obtained.This marks the first report of achieving 275 nm laser generation based on Pr:LiYF4 to date.展开更多
视频流量逐渐在网络中占据主导地位,且视频平台大多对其进行加密传输。虽然加密传输视频可以有效保护用户隐私,但是也增加了监管有害视频传播的难度.现有的加密视频识别方法基于TCP(Transmission Control Protocol)传输协议头部信息和HT...视频流量逐渐在网络中占据主导地位,且视频平台大多对其进行加密传输。虽然加密传输视频可以有效保护用户隐私,但是也增加了监管有害视频传播的难度.现有的加密视频识别方法基于TCP(Transmission Control Protocol)传输协议头部信息和HTTP/1.1(Hypertext Transfer Protocol Version1.1)的传输模式,提取应用层音视频数据单元传输长度序列来实现视频识别.但是随着基于UDP(User Datagram Protocol)的QUIC(Quick UDP Internet Connections)协议及基于QUIC实现的HTTP/3(Hypertext Transfer Protocol Version 3)协议应用于视频传输,已有方法不再适用.HTTP/3协议缺少类似TCP的头部信息,且使用了多路复用机制,并对几乎所有数据进行了加密,此外,视频平台开始使用多片段合并分发技术,这给从网络流量中精准识别加密视频带来了巨大挑战。本文基于HTTP/3协议中的控制信息特征,提出了从HTTP/3加密视频流中提取数据传输特征并进行修正的方法,最大程度复原出应用层音视频长度特征.面向多片段合并分发导致的海量匹配问题,本文基于明文指纹库设计了键值数据库来实现视频的快速识别.实验结果表明,本文提出的基于HTTP/3传输特性的加密视频识别方法能够在包含36万个真实视频指纹的YouTube大规模指纹库中达到接近99%的准确率,100%的精确率以及99.32%的F1得分,对传输过程中加人了填充顿的Facebook平台,在包含28万个真实视频指纹的大规模指纹库中达到95%的准确率、100%的精确率以及96.45%的F1得分,在具有同样特性的Instagram平台中,最高可达到97.57%的F1得分,且本方法在所有指纹库中的平均视频识别时间均低于0.4秒.本文的方法首次解决了使用HTTP/3传输的加密视频在大规模指纹库场景中的识别问题,具有很强的实用性和通用性.展开更多
文摘随着移动设备和物联网技术的普及,时空数据的收集和分析变得越来越重要。轨迹预测,尤其是实时轨迹预测,对于许多应用领域如智能交通和城市规划等至关重要。现有的轨迹预测方法往往无法在保证预测准确性的同时满足实时性的要求,且实时数据通常是不完整或带有噪声的,要求预测算法必须能够适应不完全的轨迹信息。基于此,提出了一种基于时空数据库的实时启发式轨迹预测模型(Real-time Heuristic Trajectory Prediction Based on Spatio-Temporal Databases,RHTP-STD)。RHTP-STD利用MobilityDB数据库平台存储和管理轨迹数据,通过图构建算法将轨迹数据转换为时空图。RHTP-STD采用启发式算法,融合历史和实时数据,快速预测移动对象的未来轨迹。实验结果表明,RHTP-STD在Argoverse数据集上的预测准确性和实时性均优于现有方法。讨论所提方法在不同应用场景中的适用性,提出了未来的研究方向。
文摘针对目前所有权转移协议无法完成多标签所有权同时转移等问题,提出一种无可信第三方参与的群组标签所有权转移协议。该协议可满足在同一个原所有者及新所有者的情况下,同时将一组标签的所有权由原所有者转让给新所有者,使用范围更广泛。协议采用R_LWE密码体制实现传输信息加密,为有效降低组标签一端计算量,标签端仅进行加密运算,加密运算则放在归属者一端进行。对协议进行多角度分析,协议在确保安全的前提下,计算量方面优于对比协议,可在现有低成本射频识别(Radio Frequency Identification,RFID)系统中使用。
基金National Natural Science Foundation of China(62272360)。
文摘Five-valued Boolean functions play an important role in the design of symmetric cryptography.While the design and properties of single-output almost optimal five-valued spectra Boolean functions have been extensively studied over the past few decades,there has been limited research on the construction of almost optimal five-valued spectra vectorial Boolean functions.In this paper,we present a construction method for even-variable 2-output almost optimal five-valued spectra balanced Boolean functions,whose Walsh spectra values belong to the set{0,±2^(n/2),±2^(n/2+1)},at the same time,we discuss the existence of sufficient conditions in the construction.Additionally,this paper presents a novel construction method for balanced single-output Boolean functions with even variables featuring a special five-valued spectral structure,whose Walsh spectra values are constrained to the set{0,±2^(n/2),±3·2^(n/2)}.These functions provide new canonical examples for the study of Boolean function spectral theory.
文摘Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devices have made power load data increasingly complex and volatile.This places higher demands on the prediction and analysis of power loads.In order to improve the prediction accuracy of short-term power load,a CNN-BiLSTMTPA short-term power prediction model based on the Improved Whale Optimization Algorithm(IWOA)with mixed strategies was proposed.Firstly,the model combined the Convolutional Neural Network(CNN)with the Bidirectional Long Short-Term Memory Network(BiLSTM)to fully extract the spatio-temporal characteristics of the load data itself.Then,the Temporal Pattern Attention(TPA)mechanism was introduced into the CNN-BiLSTM model to automatically assign corresponding weights to the hidden states of the BiLSTM.This allowed the model to differentiate the importance of load sequences at different time intervals.At the same time,in order to solve the problem of the difficulties of selecting the parameters of the temporal model,and the poor global search ability of the whale algorithm,which is easy to fall into the local optimization,the whale algorithm(IWOA)was optimized by using the hybrid strategy of Tent chaos mapping and Levy flight strategy,so as to better search the parameters of the model.In this experiment,the real load data of a region in Zhejiang was taken as an example to analyze,and the prediction accuracy(R2)of the proposed method reached 98.83%.Compared with the prediction models such as BP,WOA-CNN-BiLSTM,SSA-CNN-BiLSTM,CNN-BiGRU-Attention,etc.,the experimental results showed that the model proposed in this study has a higher prediction accuracy.
文摘This paper describes what is thought to be the first generation of a continuous wave deep ultraviolet laser at 275 nm by efficient frequency doubling of a blue-diode-pumped Pr:YLF laser at 550 nm.By employing a novel fast-axis collimated blue semiconductor laser as the pump source,combined with a folded cavity and innovation coating technology,and utilizing a Brewster-cut BBO crystal for intracavity frequency doubling,TEM00 mode deep UV laser radiation at 275 nm with an output power of 351 mW is obtained.This marks the first report of achieving 275 nm laser generation based on Pr:LiYF4 to date.
文摘视频流量逐渐在网络中占据主导地位,且视频平台大多对其进行加密传输。虽然加密传输视频可以有效保护用户隐私,但是也增加了监管有害视频传播的难度.现有的加密视频识别方法基于TCP(Transmission Control Protocol)传输协议头部信息和HTTP/1.1(Hypertext Transfer Protocol Version1.1)的传输模式,提取应用层音视频数据单元传输长度序列来实现视频识别.但是随着基于UDP(User Datagram Protocol)的QUIC(Quick UDP Internet Connections)协议及基于QUIC实现的HTTP/3(Hypertext Transfer Protocol Version 3)协议应用于视频传输,已有方法不再适用.HTTP/3协议缺少类似TCP的头部信息,且使用了多路复用机制,并对几乎所有数据进行了加密,此外,视频平台开始使用多片段合并分发技术,这给从网络流量中精准识别加密视频带来了巨大挑战。本文基于HTTP/3协议中的控制信息特征,提出了从HTTP/3加密视频流中提取数据传输特征并进行修正的方法,最大程度复原出应用层音视频长度特征.面向多片段合并分发导致的海量匹配问题,本文基于明文指纹库设计了键值数据库来实现视频的快速识别.实验结果表明,本文提出的基于HTTP/3传输特性的加密视频识别方法能够在包含36万个真实视频指纹的YouTube大规模指纹库中达到接近99%的准确率,100%的精确率以及99.32%的F1得分,对传输过程中加人了填充顿的Facebook平台,在包含28万个真实视频指纹的大规模指纹库中达到95%的准确率、100%的精确率以及96.45%的F1得分,在具有同样特性的Instagram平台中,最高可达到97.57%的F1得分,且本方法在所有指纹库中的平均视频识别时间均低于0.4秒.本文的方法首次解决了使用HTTP/3传输的加密视频在大规模指纹库场景中的识别问题,具有很强的实用性和通用性.
