基于混沌时间序列的变电站设备温度预测

变电站输变线路和设备的温度变化能够反映其老化、负载过高等引起的安全隐患.通过对变电站设备温度数据的非线性分析和预测,实现对设备的有效预警,将避免事故引起的巨大损失.对变电站已测温度数据建立时间序列,利用小数据量法验证变电站设备温度时间序列的混沌特性.研究基于RBF神经网络的混沌时间序列预测并与神经网络预测进行对比,单步预测与多步预测结果均优于神经网络预测.仿真结论证明了基于神经网络的混沌时间序列预测方法的有效性.     

FaceSeg: Automatic Face Segmentation for Real-Time Video

Segmenting human faces automatically is very important for face recognition and verification, security system, and computer vision. In this paper, we present an accurate segmentation system for cutting human faces out from video sequences in real-time. First, a learning based face detector is developed to rapidly find human faces. To speed up the detection process, a face rejection cascade is constructed to remove most of negative samples while retaining all the face samples. Then, we develop a coarse-to-fine segmentation approach to extract the faces based on a min-cut optimization. Finally, a new matting algorithm is proposed to estimate the alpha-matte based on an adaptive trimap generation method. Experimental results demonstrate the effectiveness and robustness of our proposed method that can compete with the well-known interactive methods in real-time.     

基于人工免疫的网络安全态势评估模型

本文将人工免疫原理应用到网络安全态势评估的研究中.通过对入侵检测模型中有关自体、非自体、抗原的形式化描述,建立了抗体细胞的演化机制、动态免疫记忆、免疫监视等免疫机制.依据人体免疫系统抗体浓度的变化与病原体入侵强度的对应关系,建立了网络安全态势实时定量计算模型.     

基于物联网技术的智能物流管理系统的设计与应用

基于物联网技术的智能物流管理系统能实现对物流过程的全面监控与数据实时追踪分析,并提供决策的智能化支持。该系统通过物联网技术,能提高物流服务的质量与效率,为物流行业提供高效、智能化的管理解决方案。系统的设计方法和关键技术包括总体设计框架和物联网中的关键技术应用。同时,通过行业应用示例验证了智能物流管理系统的实际应用价值。基于物联网技术的智能物流管理系统具有广阔的应用前景和发展空间,为相关行业提供了参考和借鉴。     

红芪活性成分免疫调节作用及机制研究进展

免疫功能受损或免疫系统紊乱会导致机体疾病发生,影响人身体健康,基于调节机体免疫治疗疾病已成为临床应用热点。红芪作为甘肃大宗药材,临床应用广泛,具有抗肿瘤、抗炎、免疫调节等多种药理作用。红芪活性成分主要有红芪多糖、黄酮类、萜类、皂苷类等,其中红芪多糖为红芪发挥免疫调节作用主要的物质基础。红芪增强机体免疫力主要体现在增强免疫器官功能,诱导免疫细胞分化、促进免疫因子分泌,提高单核巨噬细胞吞噬功能、调节炎性因子水平以及激活相关通路等方面来达到其抗肿瘤、抗炎作用。本文主要对红芪活性物质免疫调节作用进行阐述,为红芪的进一步临床开发应用提供思路。     

Blood cell counting based on U-Net++ and YOLOv5

Clinical information about a variety of disorders is available through blood cell counting, which is usually done by manual methods. However, manual methods are complex, time-consuming and susceptible to the subjective experience of inspectors. Although many efforts have been made to develop automated blood cell counting algorithms, the complexity of blood cell distribution and the highly overlapping nature of some red blood cells (RBCs) remain significant challenges that limit the improvement of analytical accuracy. Here, we proposed an end-to-end method for blood cell counting based on deep learning. Firstly, U-Net++ was used to segment the whole blood cell image into several regions of interest (ROI), and each ROI contains only one single cell or multiple overlapping cells. Subsequently, YOLOv5 was used to detect blood cells in each ROI. Specifically, we proposed several strategies, including fine classification of RBCs, adaptive adjustment for non-maximal suppression (NMS) threshold and blood cell morphology constraints to improve the accuracy of detection. Finally, the detection outcomes for each ROI were combined and superimposed. The results show that our method can effectively address the issue of high overlap and precisely segment and detect blood cells, with a 98.18% accuracy rate for blood cell counting.     

Relation network based on multi-granular hypergraphs for person re-identification

Applied Intelligence - In recent years, person re-identification (re-ID) has become a widespread research topic that focuses on retrieving target pedestrians from a set of images, typically taken...     

基于多流网络一致性的视频显著性检测

现有的视频显著性检测算法通常采用双流结构提取视频的时空线索,其中运动信息作为双流结构的一个分支,在显著物体发生剧烈或慢速移动时存在运动估计准确率低的问题,并且不合理的训练数据或方案使得权重偏向单个分支结构。提出一种基于多流网络一致性的视频显著性检测算法MSNC。设计并使用一种新的三重网络结构提取预选目标区域的颜色信息、时序信息和先验特征,通过先验特征补偿运动流的缺陷,并提高运动线索的利用率。采用多流一致性融合模型优化三流分支,得到不同特征的最佳融合方案。同时通过循环训练策略平衡三重网络的权重,以避免网络过度拟合单流分支,从而有效地提高运动估计和定位的准确率。在Davis数据集上的实验结果表明,相比PCSA、SSAV、MGA等算法,该算法的鲁棒性更优,其maxF和S-Measure值分别达到0.893和0.912,MAE仅为0.021。