New method for detection fusion of MAC based on DCM and NP rule

The problem of distributed detection fusion using multiple sensors for remote underwater target detection is studied. Considering that multiple access channel （MAC） schemes are able to offer high efficiency in bandwidth usage and consume less energy than the parallel access channel （PAC）, the MAC scheme is introduced into the underwater target detection field. The model of underwater distributed detection fusion based on MAC schemes is established. A new method for detection fusion of MAC based on deflection coefficient maximization （DCM） and Neyman-Pearson （NP） rule is proposed. Under the power constraint of local sensors, this paper uses the DCM theory to derive the optimal weight coefficients and offsets. The closed-form expressions of detection probability and false alarm probability for fusion systems are obtained. The optimal detection performance of fusion systems is analyzed and deeply researched. Both the theory analysis and simulation experiments indicate that the proposed method could improve the detection performance and decrease the error probability effectively under power constraints of local sensors and low signal to noise ratio.

Optimal decision fusion given sensor rules

When all the rules of sensor decision are known, the optimal distributeddecision fusion, which relies only on the joint conditional probability densities, can be derivedfor very general decision systems. They include those systems with interdependent sensorobservations and any network structure. It is also valid for m-ary Bayesian decision problems andbinary problems under the Neyman-Pearson criterion. Local decision rules of a sensor withcommunication from other sensors that are optimal for the sensor itself are also presented, whichtake the form of a generalized likelihood ratio test. Numerical examples are given to reveal someinteresting phenomena that communication between sensors can improve performance of a senordecision, but cannot guarantee to improve the global fusion performance when sensor rules were givenbefore fusing.

基于视觉传达技术的激光图像多级融合方法研究

设计了基于视觉传达技术的激光图像多级融合方法,以获得突出的视觉传达效果。首先采用改进单尺度Retinex算法提取原始激光图的反射图像,并通过高斯-拉普拉斯算法的重构获得的多尺度彩色图像,实现原始激光图像的增强,然后采用深度堆叠卷积神经网络对获得高、低频图像,并依据最大局部方差融高频图像,根据匹配度与阈值的对比融合低频图像,最后实验结果表明:堆叠CNN数量为4时,融合后的激光图像视觉传达效果最优,该方法增强后的激光图像局部细节信息丰富、色彩饱满度好,融合图像的图像最大灰度值频率仅为0.015。

基于模糊数学的多源异构数据融合模型

针对多源异构型数据来源复杂、结构特殊,导致其融合难度较大的问题,为提升数据融合效率与准确性,提出一种基于模糊数学的多源异构数据融合模型.首先,利用联邦加权平均融合策略,整合从各传感器传输至数据级融合层中的元数据,得到数据级融合结果;其次,结合主成分分析法与典型相关分析法,提取出由网络本体语言统一后数据的特征,完成特征级数据融合;再次,根据模糊数学理论建立、更新模糊规则库,通过决策融合算法,得到决策级融合结果;最后,将上述不同层级的数据融合结果结合建立数据融合模型,得到最终的数据融合结果.实验结果表明,该方法的最大协方差值和绝对误差值不超过0.15,最短融合时间仅为12.6 ms.该方法的融合精度和稳定性较好,时效性与抗扰性均具有显著的优越性.

基于卷积神经网络的红外与可见光图像融合方法

针对当前红外与可见光图像融合方法存在融合效果差、效率低等问题,为了获得更优的红外与可见光图像融合结果,提出了基于卷积神经网络的红外与可见光图像融合方法。首先收集待融合红外与可见光图像,采用Retinex算法对图像进行增强操作,提升图像亮度和细节信息,然后采用卷积神经网络提取图像融合特征,并设计红外与可见光图像融合规则,根据规则得到图像融合结果,最后应用多个数据集进行红外与可见光图像融合性能测试实验,结果表明,卷积神经网络的图像融合后整体视觉效果好,重要细节信息丰富,熵和平均梯度的值超过了6,融合时间低于1 s,整体性能优于其它红外与可见光图像对比融合方法。

Multimodal Medical Image Fusion in Non-Subsampled Contourlet Transform Domain

Multimodal medical image fusion is a powerful tool for diagnosing diseases in medical field. The main objective is to capture the relevant information from input images into a single output image, which plays an important role in clinical applications. In this paper, an image fusion technique for the fusion of multimodal medical images is proposed based on Non-Subsampled Contourlet Transform. The proposed technique uses the Non-Subsampled Contourlet Transform (NSCT) to decompose the images into lowpass and highpass subbands. The lowpass and highpass subbands are fused by using mean based and variance based fusion rules. The reconstructed image is obtained by taking Inverse Non-Subsampled Contourlet Transform (INSCT) on fused subbands. The experimental results on six pairs of medical images are compared in terms of entropy, mean, standard deviation, QAB/F as performance parameters. It reveals that the proposed image fusion technique outperforms the existing image fusion techniques in terms of quantitative and qualitative outcomes of the images. The percentage improvement in entropy is 0% - 40%, mean is 3% - 42%, standard deviation is 1% - 42%, QAB/Fis 0.4% - 48% in proposed method comparing to conventional methods for six pairs of medical images.

Target recognition based on modified combination rule

Evidence theory is widely used in the field of target recognition. The invalidation problem of this theory when dealing with highly conflict evidences is a research hotspot. Several alternatives of the combination rule are analyzed and compared. A new combination approach is proposed. Calculate the reliabilities of evidence sources using existing evidences. Construct reliabilities judge matrixes and get the weights of each evidence source. Weight average all inputted evidences. Combine processed evidences with D-S combination rule repeatedly to identify a target. The application in multi-sensor target reeognition as well as the comparison with typical alternatives all validated that this approach can dispose highly conflict evidences efficiently and get reasonable reeognition results rapidly.

Scheme of Cooperative Spectrum Sensing Based on Adaptive Decision Fusion Algorithm

Spectrum sensing is one of the core technologies for cognitive radios （CR）, where reliable detection of the signals of primary users （PUs） is precondition for implementing the CR systems. A cooperative spectrum sensing scheme based on an adaptive decision fusion algorithm for spectrum sensing in CR is proposed in this paper. This scheme can estimate the PU prior probability and the miss detection and false alarm probabilities of various secondary users （SU）, and make the local decision with the Chair-Varshney rule so that the decisions fusion can be done for the global decision. Simulation results show that the false alarm and miss detection probabilities resulted from the proposed algorithm are significantly lower than those of the single SU, and the performance of the scheme outperforms that of the cooperative detection by using the conventional decision fusion algorithms.

Knowledge Fusion and Synchronization over Ubiquitous Ontology Mapping

With the rapid development and popularization of web services, the available information types and structure are becoming more and more complex and challenging. Actually web services involve the need for dynamic integration and transparent knowledge integration, in light of the urgent information changing track. Under this situation, the traditional search engine and information integration cannot finish this challenge, thereby bringing the opportunity for knowledge fusion and synchronization. This paper proposes a multi-matching strategy ontology mapping method for web information, i.e., ubiquitous ontology mapping method (U-Mapping), which can be viewed as a base collection of information on multiple ontologies made to appear anytime and everywhere. This approach is usually built independently by different information providers, avoiding the grammatical and semantic conflict. Finally, the ontology case information can be utilized under the consolidation of the U-Mapping, concerning language technology and machine learning methods.

New Algorithm for CBR-RBR Fusion with Robust Thresholds

The case-based reasoning（CBR） and rule-based reasoning（RBR） fusion systems include a diverse range of fusion methods and their tasks are characterized by interleaving combination of the reasoning procedures. Existing approaches cannot clarify the complex relationships between data from the knowledge sources nor uniformly represent the heterogeneous case and rule knowledge in one fusion space. As a result, existing approaches fail to solve system fragility due to knowledge uncertainty and reasoning unreliability. For the purpose of addressing the difficulties, a novel algorithm for CBR-RBR fusion with robust thresholds（CRFRT） is proposed. Heterogeneous case and rule knowledge are uniformly represented in one defined fusion unitary space. The robust thresholds have been achieved to distinguish the complex relationships between meta-knowledge in the fusion space and to enhance system capacity of knowledge identification. Furthermore, fusion reasoning strategies are constructed for CRFRT and its procedure based on which robust solution of the fusion reasoning problem is obtained. Finally, CRFRT is validated by benchmark problems in machine learning. Compared with other CBR and RBR approaches, the reasoning efficiency and accuracy are increased by 5% and 2.2% respectively. The variations of system accuracy are decreased by 2% to 3.8%. The above results show that the CRFRT algorithm boosts the system＇s effectiveness and robustness. The proposed CRFRT can solve the fragility of complex intelligence decision system and give quality performance for fault diagnosis.

Multimodal Medical Image Fusion Methods Based on Improved Discrete Wavelet Transform

Objective This paper proposed a novel algorithm of discrete wavelet transform(DWT) which is used for multimodal medical image fusion. Methods The source medical images are initially transformed by DWT followed by fusing low and high frequency sub-images. Then, the "coefficient absolute value" that can provide clear and detail parts is adapted to fuse high-frequency coefficients, where as the "region energy ratio" which can efficiently preserve most information of source images is employed to fuse low-frequency coefficients. Finally, the fused image is reconstructed by inverse wavelet transform. Results Visually and quantitatively experimental results indicate that the proposed fusion method is superior to traditional wavelet transform and the existing fusion methods. Conclusion The proposed method is a feasible approach for multimodal medical image fusion which can obtain more efficient and accurate fusions results even in the noise environment.

A NOVEL FRAMEWORK FOR SOCCER GOAL DETECTION BASED ON SEMANTIC RULE

Focusing on the problem of goal event detection in soccer videos,a novel method based on Hidden Markov Model(HMM) and the semantic rule is proposed.Firstly,a HMM for a goal event is constructed.Then a Normalized Semantic Weighted Sum(NSWS) rule is established by defining a new feature of shots,semantic observation weight.The test video is detected based on the HMM and the NSWS rule,respectively.Finally,a fusion scheme based on logic distance is proposed and the detection results of the HMM and the NSWS rule are fused by optimal weights in the decision level,obtaining the final result.Experimental results indicate that the proposed method achieves 96.43% precision and 100% recall,which shows the effectiveness of this letter.

Medical Image Fusion Based on Wavelet Multi-Scale Decomposition

This paper describes a method to decompose multi-scale information from different source medical image using wavelet transformation. The data fusion between CT image and MRI image is implemented based on the coefficients fusion rule which included choice of regional variance and weighted average wavelet information. The result indicates that this method is better than WMF, LEF and RVF on fusion results, details and target distortion.

A new fusion approach based on distance of evidences

Based on the framework of evidence theory, data fusion aims at obtaining a single Basic Probability Assignment (BPA) function by combining several belief functions from distinct information sources. Dempster’s rule of combination is the most popular rule of combinations, but it is a poor solution for the management of the conflict between various information sources at the normalization step. Even when it faces high conflict information, the classical Dempster-Shafer’s (D-S) evidence theory can involve counter-intuitive results. This paper presents a modified averaging method to combine conflicting evidence based on the distance of evidences; and also gives the weighted average of the evidence in the system. Numerical examples showed that the proposed method can realize the modification ideas and also will provide reasonable results with good convergence efficiency.

基于关联规则挖掘的多学科知识融合研究——以新冠肺炎研究领域为例

[目的/意义]挖掘高强度关联学科,揭示多学科知识融合规律,有助于更好地把握和推动多学科知识融合。[方法/过程]从Web of Science核心集获取新冠肺炎主题论文,采用Apriori算法挖掘参考文献所属学科的频繁项集和强关联规则,揭示知识融合特征。以一项强关联规则为例,结合关键词聚类分析,识别该学科组合在知识融合后形成的热门主题。[结果/结论]新冠肺炎研究的知识来源非常广泛,跨学科知识融合十分普遍,关联规则挖掘提供了一种识别和预测强关联学科组合及其知识融合趋势的新方案。

基于特征空间多分类对抗机制的红外与可见光图像融合

为突破传统融合规则带来的性能瓶颈,提出一个基于特征空间多类别对抗机制的红外与可见光图像融合网络.相较于现存方法,其融合规则更合理且性能更好.首先,训练一个引入注意力机制的自编码器网络实现特征提取和图像重建.然后,采用生成式对抗网络(generative adversarial network,GAN)在训练好的编码器提取的特征空间上进行融合规则的学习.具体来说,设计一个特征融合网络作为生成器融合从源图像中提取的特征,然后将一个多分类器作为鉴别器.这种多分类对抗学习可使得融合特征同时逼近红外和可见光2种模态的概率分布,从而保留源图像中最显著的特征.最后,使用训练好的译码器从特征融合网络输出的融合特征重建出融合图像.实验结果表明:与最新的所有主流红外与可见光图像融合方法包括GTF,MDLatLRR,DenseFuse,FusionGAN,U2Fusion相比,所提方法主观效果更好,客观指标最优个数为U2Fusion的2倍,融合速度是其他方法的5倍以上.

多传感特征融合的空调送风温度模糊PID控制方法

为了在不降低室内环境舒适度的前提下减少空调能耗,需要对空调送风温度进行合理控制,提出多传感特征融合的空调送风温度模糊PID控制方法。在分析空调系统结构的基础上,建立多传感器采集拓扑网络,通过欧氏距离构建相似性矩阵,获取不同传感器的支持度,使用加权自适应算法对多传感特征信息进行融合,得到室内环境温度控制信息。利用自适应模糊PID算法,以误差和误差变化率作为室内环境温度的模糊控制规则参数调整依据,通过均方差得到风机转速控制数值,以此实现快速调整和精准控制空调送风温度。测试实验表明,所提方法响应速度快、超调小、成本低,可有有效为空调节能降耗提供基础理论支撑。

基于知识-规则的省级基础地理实体多源数据融合转换方法

湖南省新型基础测绘体系建设正在稳步推进,其中省级基础地理实体改造与建库是重要任务之一。本文在遵循自然资源部相关技术标准的前提下,建立了一套适用于湖南省的地理实体分级分类标准规范,融合1∶1万基础地理信息和第三次全国国土调查年度变更等多源数据,以地理实体编码为统一标识符,提出了基于知识-规则的省级地理实体多源数据融合转换方法。该方法充分利用了已有的存量数据,实现了省级基础地理实体数据的标准化改造,避免了重复生产的资源浪费,极大地提高了基础地理实体生产的效率,有利于基础地理实体在行业中的推广和应用。

基于分数阶小波与引导滤波的多聚焦图像融合方法

针对多聚焦图像融合中存在易丢失细节信息、图像边缘处产生伪影等问题,提出了一种基于离散分数阶小波变换(DFRWT)结合引导滤波的多聚焦图像融合新方法。首先,采用DFRWT将多聚焦源图像进行多尺度分解,得到低频部分与高频部分。其次,为了使图像信息有效地融合,根据小波模系数的能量在不同阶数下的分布特征,选取最适合分数阶阶数,在低频部分应用拉普拉斯能量并获得初始决策,再用引导滤波修正决策图得到融合规则;高频部分采用分数阶空间频率的融合规则。最后,通过DFRWT逆变换获得融合后的图像。新方法与现有5种算法进行视觉对比和定量评估,仿真实验表明,本文方法有效抑制了Gibbs效应和边缘处的伪影效应,视觉效果和客观评价均令人满意,融合图像的质量优于已有的几类经典算法。

南京云锦图案生态审美特性研究

为拓宽南京云锦图案的研究视野,本文从生态美学的研究视角梳理和分析其审美特性,分别对其素材、纹样、口诀、色彩所蕴含的生态美学特征加以分析论证。研究表明,南京云锦图案在素材体系上饱含积极的生态审美整体性特征,在纹样形态上不乏鲜活的生态交融性特征,在纹样口诀上积淀着深厚的“生生律性”特征,在色彩运用上具有鲜明的生态审美自然性特征。以上四方面特性是“活态”传承云锦文化遗产的创新点所在,这一研究成果表明,对云锦图案生态审美元素的研究、保护、传承、应用推广是生态经济时代发展云锦工艺的有益尝试。