基于数值分析的装备体系作战能力研究

装备体系的作战能力是部队战斗力的重要组成部分,是分析部队力量编成、评估部队完成任务能力常用的关键性指标。系统采用层次化、可组构、面向实体的数据存储结构对装备体系进行描述,基于“五力”指数指标体系和量化、聚合模型逐层计算装备单元、作战单元和装备体系的作战能力,既保持了数值分析方法使用简单、计算快速的优点,又较好地解决了不同类型武器装备之间的能力聚合和对比分析等问题,可为制定装备发展规划、构建训练模拟系统等提供基础参考数据。     

无线传感器网络中基于神经网络的数据融合模型

数据融合技术通过减少传感器节点间的数据通信量,可以有效地节省传感器节点能耗,延长无线传感器网络的寿命.提出了独特的基于神经网络的数据融合模型(NNBA),该模型巧妙地将无线传感器网络的分簇层次结构与神经网络的层次结构相结合,将每个簇设计为一个三层感知器神经网络模型,通过神经网络方法从采集到的大量原始数据中提取特征数据,然后将特征数据发送给汇聚节点.以森林火灾实时监测网为应用实例,设计神经元模型及功能函数,并给出NNBA模型的仿真测试结果.     

Attraction ofCarpophilus spp. (Coleoptera: Nitidulidae) to synthetic aggregation pheromones and host-related coattractants in Australian stone fruit orchards: Beetle phenology and pheromone dose studies

Synthetic aggregation pheromones ofCarpophilus hemipterus (L.) andCarpophilus mutilatus Erichson were field tested during a 10-month period in southern New South Wales stone fruit orchards to determineCarpophilus spp. phenology and the effect of two pheromone doses on attraction. Aggregation pheromones synergize the attraction of host volatiles toCarpophilus spp. Four major species,C. hemipterus, C. mutilatus, C. davidsoni Dobson andC. (Urophorus) humeralis (F.), were trapped, with greater numbers of each species inC. hemipterus pheromone/fermenting whole-wheat breaddough-baited traps, than in dough-only-traps. InC. mutilatus pheromone/ fermenting-dough-baited traps, onlyC. mutilatus andC. davidsoni responded in greater numbers than to dough-only traps. Beetles first appeared in traps in late September (early spring) when daily maximum temperatures averaged 17.5C. Trappings reached a peak during October and declined to very low levels in November–December (late spring-early summer). Numbers trapped of all species increased during February–March (late summer–early autumn), presumably due to the presence of abundant host resources (ripening and fallen fruit), and continued at high levels until May (late autumn). An 18-week study demonstrated significantly greater responses byCarpophilus spp. to 5000-g than to 500-g doses of C.hemipterus andC. mutilatus pheromones. Greatest responses to 5000g were recorded forC. hemipterus andC. mutilatus responding to their own pheromones (increased attraction over dough alone of 259x and 21.2x respectively). Implications of the study and the potential for using synthetic aggregation pheromones for managingCarpophilus spp. populations in Australian stone fruit are discussed.     

基于改进3D UNet的前列腺MR图像分割

针对前列腺磁共振 (magnetic resonance, MR)图像边缘模糊、对比度较低,灰度值分布不均衡而导致分割精度较差的问题,提出了一种结合双路径注意力(dual path attention,DPA) 和多尺度特征聚合(multi-scale feature aggregation,MFA) 模块的改进3D UNet网络模型。首先,对数据集进行重采样和裁剪处理以适应模型输入。然后,在3D UNet网络的编码器各层引入DPA 并添加残差连接,加强特征的 编码能力。同时,在网络解码器中加入MFA模块,以充分利用空间上下文信息,增强语义信息。最后,在公开数据集PROMISE12上进行验证,所提出的模型的Dice系数为89.90%,Hausdorff 距离为9.37 mm。相比较于其他模型,所提出模型的分割结果更优,且参数量和运算量更少。     

考虑广域稳态量测时延的全网同时断面生成

互联大电网运行方式复杂多变,广域稳态量测数据采集不同步将引入较大的量测误差。为提高稳态量测数据时标一致性,提出一种基于统一时钟的全网同时断面生成方法。首先,基于广域稳态量测数据时序特征,提出一种固定时间间隔的数据滚动存储及循环更新方法,解决断面数据对齐问题。然后,采用电气介数法识别关键节点,在保证关键节点量测数据实时性的基础上,基于量测量的时空关联特性修正未更新量测数据,从而确保整合后全网同时断面数据的时效性和准确性。最后,基于标准算例和实际电网算例仿真结果,验证了所提方法的正确性和有效性。     

Few-shot object segmentation with a new feature aggregation module

The success of convolutional neural network for object segmentation depends on a large amount of training data and high-quality samples. But annotating such high-quality training data for pixel-wise segmentation is labor-intensive. To reduce the massive labor work, few-shot learning has been introduced to segment objects, which uses a few samples for training without compromising the performance. However, the current few-shot models are biased towards the seen classes rather than being class-irrelevant due to lack of global context prior attention. Therefore, this study aims at proposing a few-shot object segmentation model with a new feature aggregation module. Specifically, the proposed work develops a detail-aware module to enhance the discrimination of details with diversified attributes. To enhance the semantics of each pixel, we propose a global attention module to aggregate detailed features containing semantic information. Furthermore, to improve the performance of the proposed model, the model uses support samples that represents class-specific prototype obtained by respective category prototype block. Next, the proposed model predicts label of each pixel of query sample by estimating the distance between the pixel and prototypes. Experiments on standard datasets demonstrate significance of the proposed model over SOTA in terms of segmentation with a few training samples.     

Discrete GWO Optimized Data Aggregation for Reducing Transmission Rate in IoT

The conventional hospital environment is transformed into digital transformation that focuses on patient centric remote approach through advanced technologies. Early diagnosis of many diseases will improve the patient life. The cost of health care systems is reduced due to the use of advanced technologies such as Internet of Things (IoT), Wireless Sensor Networks (WSN), Embedded systems, Deep learning approaches and Optimization and aggregation methods. The data generated through these technologies will demand the bandwidth, data rate, latency of the network. In this proposed work, efficient discrete grey wolf optimization (DGWO) based data aggregation scheme using Elliptic curve Elgamal with Message Authentication code (ECEMAC) has been used to aggregate the parameters generated from the wearable sensor devices of the patient. The nodes that are far away from edge node will forward the data to its neighbor cluster head using DGWO. Aggregation scheme will reduce the number of transmissions over the network. The aggregated data are preprocessed at edge node to remove the noise for better diagnosis. Edge node will reduce the overhead of cloud server. The aggregated data are forward to cloud server for central storage and diagnosis. This proposed smart diagnosis will reduce the transmission cost through aggregation scheme which will reduce the energy of the system. Energy cost for proposed system for 300 nodes is 0.34μJ. Various energy cost of existing approaches such as secure privacy preserving data aggregation scheme (SPPDA), concealed data aggregation scheme for multiple application (CDAMA) and secure aggregation scheme (ASAS) are 1.3 μJ, 0.81 μJ and 0.51 μJ respectively. The optimization approaches and encryption method will ensure the data privacy.     

基于3D路径聚合高分辨率网络的胰腺分割

目的 胰腺的准确分割是胰腺癌识别和分析的重要前提。现有基于深度学习的主流胰腺分割网络大多是编码—解码结构,对特征图采用先降低再增加分辨率的方式,严重丢失了胰腺位置和细节信息,导致分割效果不佳。针对上述问题,提出了基于3D路径聚合高分辨率网络的胰腺分割方法。方法 首先,为了捕获更多3D特征上下文信息,将高分辨率网络中的2D运算拓展为3D运算;其次,提出全分辨特征路径聚合模块,利用连续非线性变换缩小全分辨率输入图像与分割头网络输出特征语义差异的同时,减少茎网络下采样丢失的位置和细节信息对分割结果的影响;最后,提出多尺度特征路径聚合模块,利用渐进自适应特征压缩融合方式,避免低分辨率特征通道过度压缩导致的信息内容损失。结果 在公开胰腺数据集上,提出方法在Dice系数（Dice similarity coefficient,DSC）、Jaccard系数（Jaccard index,JI）、精确率（precision）和召回率（recall）上相比3D高分辨率网络（3D high-resolution net,3DHRNet）分别提升了1.41%、2.09%、2.35%和0.49%,相比具有代表性编码—解码结构的胰腺分割方法,取得了更高的分割精度。结论 本文提出的3D路径聚合高分辨率网络（3D pathaggregation high-resolution network,3DPAHRNet）具有更强的特征位置和细节信息的保留能力,能够显著改善在腹部CT（computed tomography）图像中所占比例较小的胰腺器官的分割结果。开源代码可在https：//github.com/qiuchengjian/PAHRNet3D获得。     

考虑储能备用的电动汽车负荷连续追踪弃风曲线优化模型

在大力推动高比例可再生能源并网的背景下,风电的强随机波动特征导致的大量弃风问题给电力系统的经济可靠运行带来了挑战,而电动汽车、储能等作为需求侧的灵活性资源参与曲线追踪交易为弃风问题带来了解决方案。首先,分析了电动汽车消纳风电的可行性;然后,对电动汽车聚合负荷消纳风电的交易模式进行了梳理,聚焦连续曲线追踪交易,在充分考虑电动汽车聚合的物理经济约束下建立了混合整数线性规划模型以求解聚合调用方案。由于电动汽车的移动储能能力与追踪效果有限,引入储能系统进行联合优化,采用逐步搜索法在降低聚合成本的同时,得到储能的最优容量与功率配置以及同时优化物理弃风电量与经济成本的聚合方案。算例分析结果表明：考虑储能备用的聚合方法能够提高风电曲线的追踪精度,减小聚合成本,验证了所建模型在连续曲线追踪中的可行性与适用性,可为曲线追踪交易市场的完善与新型电力系统的建设提供借鉴。     

太赫兹网络中基于中继协作转发的高效双信道MAC协议

针对现有多输入多输出(Multiple-Input Multiple-Output, MIMO)太赫兹通信网络双信道MAC协议存在波束重叠干扰和冗余控制开销等问题,提出了一种太赫兹网络中基于中继协作转发的双信道MAC协议(High Efficiency Dual channel MAC Protocol Based on Relay Cooperative Forwarding in Terahertz Networks, HE-RCFMAC)。HE-RCFMAC协议包含动态帧聚合、基于位置信息自适应协作转发和精简RTS(Request To Send)/CTS(Clear To Send)帧三种机制。经三种机制处理后,可有效提升信道利用率,同时减小控制开销,提高数据传输成功率和整体网络吞吐量。仿真结果表明,所提协议与现有的MIMO太赫兹双信道MAC协议相比,MAC层吞吐量、数据传输成功率和信道利用率分别提升了12.82%、12.28%和8.73%,证明了所提协议的有效性。