开展脉冲重复间隔(Pulse Repetition Interval,PRI)模式识别工作是电子支援系统的一项重要任务。现代复杂电磁环境下,受雷达辐射源部署和接收设备本身影响,雷达脉冲丢失率极高,导致分选后PRI序列调制规律被破坏,现有的PRI模式识别方法...开展脉冲重复间隔(Pulse Repetition Interval,PRI)模式识别工作是电子支援系统的一项重要任务。现代复杂电磁环境下,受雷达辐射源部署和接收设备本身影响,雷达脉冲丢失率极高,导致分选后PRI序列调制规律被破坏,现有的PRI模式识别方法准确率不足。针对上述问题,从PRI序列还原角度出发,并结合PRI序列本质是时序序列的特点,提出GAIN-LSTM(Generative Adversarial Imputation Nets and Long Short Term Memory)网络架构,其先对丢失脉冲位置进行补全操作,恢复PRI调制规律,然后对还原后PRI序列进行调制模式识别。仿真结果表明,提出的GAIN-LSTM网络架构在脉冲丢失率70%时仍保持95%的正确识别率。展开更多
应用中的各种因素可能造成数据缺失,影响后续任务的分析。因此,数据集缺失值的插补尤为重要。相比原本没有插补的处理,错误的插补值也会对分析造成更严重的偏差。针对这种情况,提出新的采用双重判别器的基于条件生成对抗插补网络(C-GAIN...应用中的各种因素可能造成数据缺失,影响后续任务的分析。因此,数据集缺失值的插补尤为重要。相比原本没有插补的处理,错误的插补值也会对分析造成更严重的偏差。针对这种情况,提出新的采用双重判别器的基于条件生成对抗插补网络(C-GAIN)的缺失值插补算法DDC-GAIN(Dual Discriminator based on C-GAIN)。该算法通过一个辅助判别器辅助主判别器判断预测值的真假,即根据一个样本的全局信息判断这个样本生成的真假,更注重特征之间的关系,以此估算预测值。在4个数据集上与5种经典插补算法进行对比实验,结果表明:同样条件下,DDC-GAIN算法在样本量较大时的均方根误差(RMSE)最低;在Default credit card数据集上缺失率为15%时,DDC-GAIN算法的RMSE比次优算法C-GAIN降低了28.99%。这说明利用辅助判别器帮助主判别器学习特征之间的关系是有效的。展开更多
Multi-sensor system is becoming increasingly important in a variety of military and civilian applications. In general, single sensor system can only provide partial information about environment while multi-sensor sys...Multi-sensor system is becoming increasingly important in a variety of military and civilian applications. In general, single sensor system can only provide partial information about environment while multi-sensor system provides a synergistic effect, which improves the quality and availability of information. Data fusion techniques can effectively combine this environmental information from similar and/or dissimilar sensors. Sensor management, aiming at improving data fusion performance by controlling sensor behavior, plays an important role in a data fusion process. This paper presents a method using fisher information gain based sensor effectiveness metric for sensor assignment in multi-sensor and multi-target tracking applications. The fisher information gain is computed for every sensor-target pairing on each scan. The advantage for this metric over other ones is that the fisher information gain for the target obtained by multi-sensors is equal to the sum of ones obtained by the individual sensor, so standard transportation problem formulation can be used to solve this problem without importing the concept of pseudo sensor. The simulation results show the effectiveness of the method.展开更多
The lifting technique is now the most popular tool for dealing with sampled-data controlsystems. However, for the robust stability problem the system norm is not preserved by the liftingas expected. And the result is ...The lifting technique is now the most popular tool for dealing with sampled-data controlsystems. However, for the robust stability problem the system norm is not preserved by the liftingas expected. And the result is generally conservative under the small gain condition. The reason forthe norm di?erence by the lifting is that the state transition operator in the lifted system is zero inthis case. A new approach to the robust stability analysis is proposed. It is to use an equivalentdiscrete-time uncertainty to replace the continuous-time uncertainty. Then the general discretizedmethod can be used for the robust stability problem, and it is not conservative. Examples are givenin the paper.展开更多
Cochlodinium polykrikoides is a notoriously harmful algal species that inflicts severe damage on the aquacultures of the coastal seas of Korea and Japan. Information on their expected movement tracks and boundaries of...Cochlodinium polykrikoides is a notoriously harmful algal species that inflicts severe damage on the aquacultures of the coastal seas of Korea and Japan. Information on their expected movement tracks and boundaries of influence is very useful and important for the effective establishment of a reduction plan. In general, the information is supported by a red-tide（a.k.a algal bloom） model. The performance of the model is highly dependent on the accuracy of parameters, which are the coefficients of functions approximating the biological growth and loss patterns of the C. polykrikoides. These parameters have been estimated using the bioassay data composed of growth-limiting factor and net growth rate value pairs. In the case of the C. polykrikoides, the parameters are different from each other in accordance with the used data because the bioassay data are sufficient compared to the other algal species. The parameters estimated by one specific dataset can be viewed as locally-optimized because they are adjusted only by that dataset. In cases where the other one data set is used, the estimation error might be considerable. In this study, the parameters are estimated by all available data sets without the use of only one specific data set and thus can be considered globally optimized. The cost function for the optimization is defined as the integrated mean squared estimation error, i.e., the difference between the values of the experimental and estimated rates. Based on quantitative error analysis, the root-mean squared errors of the global parameters show smaller values, approximately 25%–50%, than the values of the local parameters. In addition, bias is removed completely in the case of the globally estimated parameters. The parameter sets can be used as the reference default values of a red-tide model because they are optimal and representative. However, additional tuning of the parameters using the in-situ monitoring data is highly required.As opposed to the bioassay data, it is necessary because the bioassay data have limitations in terms of the in-situ coastal conditions.展开更多
针对大数据环境下随机森林算法存在冗余与不相关特征过多、特征子空间信息含量不足以及并行化效率低等问题,提出了结合增益率与堆叠自编码器的并行随机森林算法PRFGRSAE(parallel random forest algorithm combining gain ratio and sta...针对大数据环境下随机森林算法存在冗余与不相关特征过多、特征子空间信息含量不足以及并行化效率低等问题,提出了结合增益率与堆叠自编码器的并行随机森林算法PRFGRSAE(parallel random forest algorithm combining gain ratio and stacked auto encoders)。首先,提出了结合非线性归一化增益率和堆叠自编码器的降维策略DRNGRSAE(dimension reduction combining nonlinear normalization gain ratio and stacked auto encoders),通过过滤特征集中的冗余和不相关特征,并利用堆叠自编码器提取特征,有效减少了冗余以及不相关特征数;其次,提出了结合拉丁超立方抽样与归一化相关度的子空间选择策略SSLF(subspace selection strategy combining Latin hypercube sampling and feature class correlation),通过对特征集进行多层划分抽样,形成空间表达度较高的特征子空间,有效保证了特征子空间的信息含量;最后,提出结合可变动作学习自动机的reducer分配策略DSVLA(distribution strategy based on variable-action learning automata),使每个数据簇均匀分配到reducer进行处理,有效提高了并行化效率。实验结果表明,PRFGRSAE算法的加速比与准确度较IMRF、KSMRF和GAPRF算法都有显著提升,因此该算法应用于大数据处理,特别对包含较多特征的数据集有更高的精准度和并行效率。展开更多
Telecom sectors generally operate at negative voltages to reduce the effect of corrosion caused in the metallic wire due to electrochemical reaction while communicating signals. To feed those lines and to have an effe...Telecom sectors generally operate at negative voltages to reduce the effect of corrosion caused in the metallic wire due to electrochemical reaction while communicating signals. To feed those lines and to have an effective digital data transmission, a power electronic converter referred as Modified Negative Luo Converter (MNLC) is proposed in this paper. MNLC is a high gain converter in which the output voltage increases in geometric progression. This paper portrays a novel concept of a 50 Hz pulse data transmission through RLCG (Resistance-inductance-capacitance with a shunt conductance) transmission line using MNLC. Signal frequency of 50 Hz to be transmitted is anded with a high frequency pulse that charges and discharges MNLC and produces the boosted negative output voltage. The boosted output is again transmitted through the RLCG transmission line from which 50 Hz data pulse is retrieved at the output of the transmission line by comparing with a comparator signal. This sort of MNLC aided data transmission not only introduces less loss in its transmitted data but also overcomes various health hazards of conventional radio frequency (RF) communication. This technique also proves that any data bit stream can be transmitted and retrieved using the proposed high gain DC-DC converter. The simulation model of the proposed system is implemented in MATLAB for various switching frequencies with its prototype of the converter developed and the results are verified.展开更多
针对目前能源交易体系存在安全性较低、平台管理成本较高以及碳配额交易空缺的问题,提出基于量子区块链的碳配额交易模型。通过建立碳排放权分配指标体系,采用灰色关联分析法,实现碳排放权配额的初次分配,保障参与节点碳配额初始分配的...针对目前能源交易体系存在安全性较低、平台管理成本较高以及碳配额交易空缺的问题,提出基于量子区块链的碳配额交易模型。通过建立碳排放权分配指标体系,采用灰色关联分析法,实现碳排放权配额的初次分配,保障参与节点碳配额初始分配的公平性。利用零和博弈数据包络分析(Zero Sum Gains-Data Envelopment Analysis,ZSG-DEA)模型,计算参与交易主体碳配额利用效率和优化方案,有效促进产业结构升级。嵌入碳配额交易奖惩系统,有效保障减排低碳主体的利益。采用量子纠缠态签名智能合约,保障碳配额交易的量子通信安全,进行交易节点间的身份验证。算例分析表明,该交易架构能够有效保障碳配额分配的公平性,提高碳配额的利用效率,保障碳配额交易过程中的交易数据和用户信息安全性,为推动后量子时代的碳配额交易进程提供理论支撑与决策支持。展开更多
文摘开展脉冲重复间隔(Pulse Repetition Interval,PRI)模式识别工作是电子支援系统的一项重要任务。现代复杂电磁环境下,受雷达辐射源部署和接收设备本身影响,雷达脉冲丢失率极高,导致分选后PRI序列调制规律被破坏,现有的PRI模式识别方法准确率不足。针对上述问题,从PRI序列还原角度出发,并结合PRI序列本质是时序序列的特点,提出GAIN-LSTM(Generative Adversarial Imputation Nets and Long Short Term Memory)网络架构,其先对丢失脉冲位置进行补全操作,恢复PRI调制规律,然后对还原后PRI序列进行调制模式识别。仿真结果表明,提出的GAIN-LSTM网络架构在脉冲丢失率70%时仍保持95%的正确识别率。
文摘应用中的各种因素可能造成数据缺失,影响后续任务的分析。因此,数据集缺失值的插补尤为重要。相比原本没有插补的处理,错误的插补值也会对分析造成更严重的偏差。针对这种情况,提出新的采用双重判别器的基于条件生成对抗插补网络(C-GAIN)的缺失值插补算法DDC-GAIN(Dual Discriminator based on C-GAIN)。该算法通过一个辅助判别器辅助主判别器判断预测值的真假,即根据一个样本的全局信息判断这个样本生成的真假,更注重特征之间的关系,以此估算预测值。在4个数据集上与5种经典插补算法进行对比实验,结果表明:同样条件下,DDC-GAIN算法在样本量较大时的均方根误差(RMSE)最低;在Default credit card数据集上缺失率为15%时,DDC-GAIN算法的RMSE比次优算法C-GAIN降低了28.99%。这说明利用辅助判别器帮助主判别器学习特征之间的关系是有效的。
文摘Multi-sensor system is becoming increasingly important in a variety of military and civilian applications. In general, single sensor system can only provide partial information about environment while multi-sensor system provides a synergistic effect, which improves the quality and availability of information. Data fusion techniques can effectively combine this environmental information from similar and/or dissimilar sensors. Sensor management, aiming at improving data fusion performance by controlling sensor behavior, plays an important role in a data fusion process. This paper presents a method using fisher information gain based sensor effectiveness metric for sensor assignment in multi-sensor and multi-target tracking applications. The fisher information gain is computed for every sensor-target pairing on each scan. The advantage for this metric over other ones is that the fisher information gain for the target obtained by multi-sensors is equal to the sum of ones obtained by the individual sensor, so standard transportation problem formulation can be used to solve this problem without importing the concept of pseudo sensor. The simulation results show the effectiveness of the method.
文摘The lifting technique is now the most popular tool for dealing with sampled-data controlsystems. However, for the robust stability problem the system norm is not preserved by the liftingas expected. And the result is generally conservative under the small gain condition. The reason forthe norm di?erence by the lifting is that the state transition operator in the lifted system is zero inthis case. A new approach to the robust stability analysis is proposed. It is to use an equivalentdiscrete-time uncertainty to replace the continuous-time uncertainty. Then the general discretizedmethod can be used for the robust stability problem, and it is not conservative. Examples are givenin the paper.
基金The part of the project "Development of Korea Operational Oceanographic System(KOOS),Phase 2",funded by the Ministry of Oceans and Fisheries,Koreathe part of the project entitled "Cooperative Project on Korea-China Bilateral Committee on Ocean Science",funded by the Ministry of Oceans and Fisheries,Korea and China-Korea Joint Research Ocean Research Center
文摘Cochlodinium polykrikoides is a notoriously harmful algal species that inflicts severe damage on the aquacultures of the coastal seas of Korea and Japan. Information on their expected movement tracks and boundaries of influence is very useful and important for the effective establishment of a reduction plan. In general, the information is supported by a red-tide（a.k.a algal bloom） model. The performance of the model is highly dependent on the accuracy of parameters, which are the coefficients of functions approximating the biological growth and loss patterns of the C. polykrikoides. These parameters have been estimated using the bioassay data composed of growth-limiting factor and net growth rate value pairs. In the case of the C. polykrikoides, the parameters are different from each other in accordance with the used data because the bioassay data are sufficient compared to the other algal species. The parameters estimated by one specific dataset can be viewed as locally-optimized because they are adjusted only by that dataset. In cases where the other one data set is used, the estimation error might be considerable. In this study, the parameters are estimated by all available data sets without the use of only one specific data set and thus can be considered globally optimized. The cost function for the optimization is defined as the integrated mean squared estimation error, i.e., the difference between the values of the experimental and estimated rates. Based on quantitative error analysis, the root-mean squared errors of the global parameters show smaller values, approximately 25%–50%, than the values of the local parameters. In addition, bias is removed completely in the case of the globally estimated parameters. The parameter sets can be used as the reference default values of a red-tide model because they are optimal and representative. However, additional tuning of the parameters using the in-situ monitoring data is highly required.As opposed to the bioassay data, it is necessary because the bioassay data have limitations in terms of the in-situ coastal conditions.
基金Supported by the Natural Science Foundation of Jiangsu Province （ BK2010411 ） and the National International Cooperation Project of China-Korea （2011DFA11310）.
文摘针对大数据环境下随机森林算法存在冗余与不相关特征过多、特征子空间信息含量不足以及并行化效率低等问题,提出了结合增益率与堆叠自编码器的并行随机森林算法PRFGRSAE(parallel random forest algorithm combining gain ratio and stacked auto encoders)。首先,提出了结合非线性归一化增益率和堆叠自编码器的降维策略DRNGRSAE(dimension reduction combining nonlinear normalization gain ratio and stacked auto encoders),通过过滤特征集中的冗余和不相关特征,并利用堆叠自编码器提取特征,有效减少了冗余以及不相关特征数;其次,提出了结合拉丁超立方抽样与归一化相关度的子空间选择策略SSLF(subspace selection strategy combining Latin hypercube sampling and feature class correlation),通过对特征集进行多层划分抽样,形成空间表达度较高的特征子空间,有效保证了特征子空间的信息含量;最后,提出结合可变动作学习自动机的reducer分配策略DSVLA(distribution strategy based on variable-action learning automata),使每个数据簇均匀分配到reducer进行处理,有效提高了并行化效率。实验结果表明,PRFGRSAE算法的加速比与准确度较IMRF、KSMRF和GAPRF算法都有显著提升,因此该算法应用于大数据处理,特别对包含较多特征的数据集有更高的精准度和并行效率。
文摘Telecom sectors generally operate at negative voltages to reduce the effect of corrosion caused in the metallic wire due to electrochemical reaction while communicating signals. To feed those lines and to have an effective digital data transmission, a power electronic converter referred as Modified Negative Luo Converter (MNLC) is proposed in this paper. MNLC is a high gain converter in which the output voltage increases in geometric progression. This paper portrays a novel concept of a 50 Hz pulse data transmission through RLCG (Resistance-inductance-capacitance with a shunt conductance) transmission line using MNLC. Signal frequency of 50 Hz to be transmitted is anded with a high frequency pulse that charges and discharges MNLC and produces the boosted negative output voltage. The boosted output is again transmitted through the RLCG transmission line from which 50 Hz data pulse is retrieved at the output of the transmission line by comparing with a comparator signal. This sort of MNLC aided data transmission not only introduces less loss in its transmitted data but also overcomes various health hazards of conventional radio frequency (RF) communication. This technique also proves that any data bit stream can be transmitted and retrieved using the proposed high gain DC-DC converter. The simulation model of the proposed system is implemented in MATLAB for various switching frequencies with its prototype of the converter developed and the results are verified.
文摘针对目前能源交易体系存在安全性较低、平台管理成本较高以及碳配额交易空缺的问题,提出基于量子区块链的碳配额交易模型。通过建立碳排放权分配指标体系,采用灰色关联分析法,实现碳排放权配额的初次分配,保障参与节点碳配额初始分配的公平性。利用零和博弈数据包络分析(Zero Sum Gains-Data Envelopment Analysis,ZSG-DEA)模型,计算参与交易主体碳配额利用效率和优化方案,有效促进产业结构升级。嵌入碳配额交易奖惩系统,有效保障减排低碳主体的利益。采用量子纠缠态签名智能合约,保障碳配额交易的量子通信安全,进行交易节点间的身份验证。算例分析表明,该交易架构能够有效保障碳配额分配的公平性,提高碳配额的利用效率,保障碳配额交易过程中的交易数据和用户信息安全性,为推动后量子时代的碳配额交易进程提供理论支撑与决策支持。
