Spatial Distribution Feature Extraction Network for Open Set Recognition of Electromagnetic Signal

This paper proposes a novel open set recognition method,the Spatial Distribution Feature Extraction Network(SDFEN),to address the problem of electromagnetic signal recognition in an open environment.The spatial distribution feature extraction layer in SDFEN replaces convolutional output neural networks with the spatial distribution features that focus more on inter-sample information by incorporating class center vectors.The designed hybrid loss function considers both intra-class distance and inter-class distance,thereby enhancing the similarity among samples of the same class and increasing the dissimilarity between samples of different classes during training.Consequently,this method allows unknown classes to occupy a larger space in the feature space.This reduces the possibility of overlap with known class samples and makes the boundaries between known and unknown samples more distinct.Additionally,the feature comparator threshold can be used to reject unknown samples.For signal open set recognition,seven methods,including the proposed method,are applied to two kinds of electromagnetic signal data:modulation signal and real-world emitter.The experimental results demonstrate that the proposed method outperforms the other six methods overall in a simulated open environment.Specifically,compared to the state-of-the-art Openmax method,the novel method achieves up to 8.87%and 5.25%higher micro-F-measures,respectively.

Feature subset selection based on mahalanobis distance: a statistical rough set method

In order to select effective feature subsets for pattern classification, a novel statistics rough set method is presented based on generalized attribute reduction. Unlike classical reduction approaches, the objects in universe of discourse are signs of training sample sets and values of attributes are taken as statistical parameters. The binary relation and discernibility matrix for the reduction are induced by distance function. Furthermore, based on the monotony of the distance function defined by Mahalanobis distance, the effective feature subsets are obtained as generalized attribute reducts. Experiment result shows that the classification performance can be improved by using the selected feature subsets.

Robustness Evaluation of Remote-Sensing Image Feature Detectors with TH Priori-Information Data Set

In this paper,we build a remote-sensing satellite imagery priori-information data set,and propose an approach to evaluate the robustness of remote-sensing image feature detectors.The building TH Priori-Information(TPI)data set with 2297 remote sensing images serves as a standardized high-resolution data set for studies related to remote-sensing image features.The TPI contains 1)raw and calibrated remote-sensing images with high spatial and temporal resolutions(up to 2 m and 7 days,respectively),and 2)a built-in 3-D target area model that supports view position,view angle,lighting,shadowing,and other transformations.Based on TPI,we further present a quantized approach,including the feature recurrence rate,the feature match score,and the weighted feature robustness score,to evaluate the robustness of remote-sensing image feature detectors.The quantized approach gives general and objective assessments of the robustness of feature detectors under complex remote-sensing circumstances.Three remote-sensing image feature detectors,including scale-invariant feature transform(SIFT),speeded up robust features(SURF),and priori information based robust features(PIRF),are evaluated using the proposed approach on the TPI data set.Experimental results show that the robustness of PIRF outperforms others by over 6.2%.

Clinical Features and Haematological Indices of Neonatal Septicaemia in Poor Resource Setting

Background: The burden of neonatal septicaemia has remained high worldwide and even more severe in the developing countries like ours. Clinical manifestation is variable and non-specific thereby resulting in delay in diagnosis. Blood culture which is the gold standard for diagnosis of neonatal septicaemia (NNS) has many drawbacks due to long waiting time for culture process, low yield, improper inoculation adding to the problem of late diagnosis. Haematological parameters have been utilized in rapid and early diagnosis of NNS and prompt treatment thus circumventing problems associated with drawbacks in blood culture. Objective: The study was to identify the common clinical features of neonatal septicaemia and haematological indices that were commonly utilized in rapid diagnosis of NNS, and also to determine their sensitivity, specificity, positive predictive and negative predictive value. Materials and Methods: The study was prospective and neonates that had clinical features suggestive of neonatal septicaemia were enrolled consecutively into the study. The patients were appropriately investigated including blood cultures, CSF cultures and urine among others, also blood sample for packed cell volume (PCV), total white cell count (TWBC), absolute neutrophil count (ANC), absolute platelet count (APC). Immature to mature neutrophil ratio (I/MNR), immature to total neutrophil ratio (I/TNR) and micro-ESR (erythrocyte sedimentation rate) was also done and analyzed. Results: The common clinical symptoms were fever 39.5%, poor feeding 33.6%, excessive cry 38.7%, difficulty in breathing 50.0%, yellowish skin 26.9%, while the common physical signs were hyper/hypothermia 41.1%, tachypnoea 41.2%, septic umbilical stump 64.0%, hepatomegally 37.3% and convulsions 42.0%. Blood culture yield was positive in 41.82% and mortality was as high as 28.00%, the incidence of NNS was 5.9/1000 live births. The haematological parameters as marker of NNS PCV, TWBC, ANC, APC, I/MNR, I/TNR and micro-ESR individually were statistically significant (P < 0.05), also their individual sensitivity, specificity, positive and negative predictive values were highly associated with neonatal septicaemia. However, when they were tested in combinations these markers of neonatal septicaemia had low sensitivity, specificity and their predictive values were weak in excluding NNS. Conclusions: The need for early and rapid diagnosis of NNS is pertinent, culturing of the appropriate specimens remains the only way to identify the aetiological organisms, but is associated with delay. Haematological indices are excellent markers of NNS and analysis is rapid and can easily be done in our laboratory settings, and when utilized efficiently, it would circumvent the delay associated with blood culture for long waiting period for the result, thereby reducing morbidity and mortality.

Geological Features,Mineralization Types and Metallogenic Setting of the Phlaythong Large Iron Deposit,Southern Laos

The Phlaythong large iron deposit in Shampasak of southern Laos,is located in the Kon Tum microblock （Fig.1A）,central-southern part of the Indo-China block,and the geographic coordinate of the central mining area is 14°43′04″ N and 106°07′02″ E.

Application of a new feature extraction and optimization method to surface defect recognition of cold rolled strips

Considering that the surface defects of cold rolled strips are hard to be recognized by human eyes under high-speed circumstances, an automatic recognition technique was discussed. Spectrum images of defects can be got by fast Fourier transform （FFF） and sum of valid pixels （SVP）, and its optimized center region, which concentrates nearly all energies, are extracted as an original feature set. Using genetic algorithm to optimize the feature set, an optimized feature set with 51 features can be achieved. Using the optimized feature set as an input vector of neural networks, the recognition effects of LVQ neural networks have been studied. Experiment results show that the new method can get a higher classification rate and can settle the automatic recognition problem of surface defects on cold rolled strips ideally.

CLUSTERING PROPERTIES OF FUZZY KOHONEN'S SELF-ORGANIZING FEATURE MAPS

A new clustering algorithm called fuzzy self-organizing feature maps is introduced. It can process not only the exact digital inputs, but also the inexact or fuzzy non-digital inputs, such as natural language inputs. Simulation results show that the new algorithm is superior to original Kohonen’s algorithm in clustering performance and learning rate.

Application of Self-Organizing Feature Map Neural Network Based on K-means Clustering in Network Intrusion Detection

Due to the widespread use of the Internet,customer information is vulnerable to computer systems attack,which brings urgent need for the intrusion detection technology.Recently,network intrusion detection has been one of the most important technologies in network security detection.The accuracy of network intrusion detection has reached higher accuracy so far.However,these methods have very low efficiency in network intrusion detection,even the most popular SOM neural network method.In this paper,an efficient and fast network intrusion detection method was proposed.Firstly,the fundamental of the two different methods are introduced respectively.Then,the selforganizing feature map neural network based on K-means clustering(KSOM)algorithms was presented to improve the efficiency of network intrusion detection.Finally,the NSLKDD is used as network intrusion data set to demonstrate that the KSOM method can significantly reduce the number of clustering iteration than SOM method without substantially affecting the clustering results and the accuracy is much higher than Kmeans method.The Experimental results show that our method can relatively improve the accuracy of network intrusion and significantly reduce the number of clustering iteration.

A new ensemble feature selection and its application to pattern classification

Neural network ensemble based on rough sets reduct is proposed to decrease the computational complexity of conventional ensemble feature selection algorithm. First, a dynamic reduction technology combining genetic algorithm with resampling method is adopted to obtain reducts with good generalization ability. Second, Multiple BP neural networks based on different reducts are built as base classifiers. According to the idea of selective ensemble, the neural network ensemble with best generalization ability can be found by search strategies. Finally, classification based on neural network ensemble is implemented by combining the predictions of component networks with voting. The method has been verified in the experiment of remote sensing image and five UCI datasets classification. Compared with conventional ensemble feature selection algorithms, it costs less time and lower computing complexity, and the classification accuracy is satisfactory.

Feature Selection Based on Difference and Similitude in Data Mining

Feature selection is the pretreatment of data mining. Heuristic search algorithms are often used for this subject. Many heuristic search algorithms are based on discernibility matrices, which only consider the difference in information system. Because the similar characteristics are not revealed in discernibility matrix, the result may not be the simplest rules. Although differencesimilitude（DS） methods take both of the difference and the similitude into account, the existing search strategy will cause some important features to be ignored. An improved DS based algorithm is proposed to solve this problem in this paper. An attribute rank function, which considers both of the difference and similitude in feature selection, is defined in the improved algorithm. Experiments show that it is an effective algorithm, especially for large-scale databases. The time complexity of the algorithm is O（｜ C ｜^2｜U ｜^2）.

Binary Representation of Polar Bear Algorithm for Feature Selection

In most of the scientific research feature selection is a challenge for researcher.Selecting all available features is not an option as it usually complicates the research and leads to performance drop when dealing with large datasets.On the other hand,ignoring some features can compromise the data accuracy.Here the rough set theory presents a good technique to identify the redundant features which can be dismissed without losing any valuable information,however,exploring all possible combinations of features will end with NP-hard problem.In this research we propose adopting a heuristic algorithm to solve this problem,Polar Bear Optimization PBO is a metaheuristic algorithm provides an effective technique for solving such kind of optimization problems.Among other heuristic algorithms it proposes a dynamic mechanism for birth and death which allows keep investing in promising solutions and keep dismissing hopeless ones.To evaluate its efficiency,we applied our proposed model on several datasets and measured the quality of the obtained minimal feature set to prove that redundant data was removed without data loss.

Feature Selection for SVM Classifiers Based on Discretization

The rough sets and Boolean reasoning based discretization approach (RSBRA) is no t suitable for feature selection for machine learning algorithms such as neural network or SVM because the information loss due to discretization is large. A mo dified RSBRA for feature selection was proposed and evaluated with SVM classifie rs. In the presented algorithm, the level of consistency, coined from the rough sets theory, is introduced to substitute the stop criterion of circulation of th e RSBRA, which maintains the fidelity of the training set after discretization. The experimental results show the modified algorithm has better predictive accur acy and less training time than the original RSBRA.

GRADIENT OF REFERENCE DIFFERENCE BASED MATCHING ALGORITHM FOR IMAGE FEATURE POINT

During matching on feature point, gray correlation matching technology is utilized to extract multi-peaks as a coarse matching set. A pair of given corresponding reference points within the left and right images is used to calculate gradients of reference difference between the reference points and each feature point within the multi-peaks set. The unique correspondence is determined by criterion of minimal gradients of reference difference. The obtained correspondence is taken as a new pair of reference points to update the reference points continuously until all feature points in the left (or right) image being matched with the right (or left) image. The gradients of reference difference can be calculated easily by means of pre-setting a pair of obvious feature points in the left and right images as a pair of corresponding reference points. Besides, the efficiency of matching can be improved greatly by taking the obtained matching point as a new pair of reference points, and by updating the reference point continuously. It is proved that the proposed algorithm is valid and reliable by 3D reconstruction on two pairs of actual natural images with abundant and weak texture, respectively.

Similarity Measures of Satellite Images Using an Adaptive Feature Contrast Model

Similarity measurement is one of key operations to retrieve “desired” images from an image database. As a famous psychological similarity measure approach, the Feature Contrast (FC) model is defined as a linear combination of both common and distinct features. In this paper, an adaptive feature contrast (AdaFC) model is proposed to measure similarity between satellite images for image retrieval. In the AdaFC, an adaptive function is used to model a variable role of distinct features in the similarity measurement. Specifically, given some distinct features in a satellite image, e.g., a COAST image, they might play a significant role when the image is compared with an image including different semantics, e.g., a SEA image, and might be trivial when it is compared with a third image including same semantics, e.g., another COAST image. Experimental results on satellite images show that the proposed model can consistently improve similarity retrieval effectiveness of satellite images including multiple geo-objects, for example COAST images.

Coherent Point Drift Registration Combined with Image Feature and its Application

A key step of constructing active appearance model is requiring a set of appropriate training shapes with well-defined correspondences.In this paper,we introduce a novel point correspondence method(FB-CPD),which can improve the accuracy of coherent point drift(CPD) by using the information of image feature.The objective function of the proposed method is defined by both of geometric spatial information and image feature information,and the origin Gaussian mixture model in CPD is modified according to the image feature of points.FB-CPD is tested on the 3D prostate and liver point sets through the simulation experiments.The registration error can be reduced efficiently by FB-CPD.Moreover,the active appearance model constructed by FB-CPD can obtain fine segmentation in 3D CT prostate image.Compared with the original CPD,the overlap ratio of voxels was improved from 88.7% to 90.2% by FB-CPD.

基于不一致近邻的模糊粗糙集特征选择

模糊粗糙集可突破经典粗糙集仅能处理离散数据的局限,有效对连续型数值进行特征选择。然而,模糊粗糙集以对象为中心计算,时间复杂度高,难以处理高维和大规模数据。为此,基于水平截集提出一种不一致近邻加速策略。该策略跟踪论域中每个对象的模糊近邻集,持续删减其中不影响计算的近邻,若对象的不一致近邻删减至空,则删减该对象,从而提高算法效率。同时,设计一种基于不一致近邻递减的属性重要度,可有效抑制冗余特征入选,提升效率及分类精度。通过理论证明,所提的加速策略及属性重要度不影响属性入选的次序。在此基础上,提出新的模糊粗糙集特征选择算法。在9个UCI和scikit数据集上进行验证,实验结果表明,该算法不仅有效缩短运行时间,并可取得较高的分类精度,相比FA-FSCE、AVDP和IV-FS-FRS-2算法,运行时间至少可缩短9.44%,尤其在高维和大规模数据上可缩短61.01%~99.54%,在支持向量机和K-近邻算法的分类精度上最高可分别提高11.20%和19.95%。

隐私保护技术特征对用户隐私保护行为意愿的影响研究

为确保数字经济高质量发展,加强移动应用的个人隐私保护至关重要。隐私设置和权限请求设置作为当前移动服务商向用户提供的主要隐私保护技术措施,其有效性受到争议,并未得到用户广泛的使用或采纳,这可能是因为用户无法通过隐私设置选择和控制移动应用收集的个人信息种类、使用目的与共享对象,且权限请求设置操作流程较为复杂。要想切实发挥隐私保护技术的积极效果,其应具备的技术特征不容小觑。本研究从给予用户对个人信息披露的细粒度控制的视角,针对现有隐私设置和权限请求设置提出两种技术特征,即隐私设置可操作性与权限请求设置有效性,并基于信号传递理论,探究这两种技术特征对用户拒绝提供个人信息和提供虚假个人信息意愿(简称“隐私保护行为意愿”)的影响机理。本研究采用基于情景的实验方法,共收集334份有效数据,应用PLS-SEM(partial least squares-structural equation modeling)方法进行实证分析。研究结果发现,本研究提出的两种技术特征对用户的隐私保护行为意愿具有显著的直接负向影响,并通过隐私担忧间接负向影响用户的隐私保护行为意愿;这两种技术特征对用户隐私保护行为意愿具有显著的正向交互作用。本研究丰富和拓展了隐私保护技术设计与用户信息行为研究,并为移动服务商设计有效的隐私保护技术以提升竞争优势提供了启示,从而促进数字经济高质量发展。

纤维肌痛综合征生物标记物的筛选及免疫细胞浸润分析

背景:纤维肌痛综合征作为常见风湿病,其发病与中枢敏化及免疫异常有关,但具体过程尚未阐明,缺乏特异性诊断标志物,不断探索该病的发病机制具有重要的临床意义。目的:基于加权基因共表达网络分析(WGCNA)等生物信息学方法和机器学习算法筛选纤维肌痛综合征潜在的诊断相关标志基因,并分析其免疫细胞浸润特征。方法:对来自基因表达综合数据库(GEO)的纤维肌痛综合征数据集转录谱进行差异分析和WGCNA分析,整合筛选出差异共表达基因,进一步采用机器学习套索回归(LASSO)算法、支持向量机递归特征消除(SVM-RFE)机器学习算法来识别核心生物标志物,并绘制受试者工作特征(ROC)曲线以评估诊断价值。最后,采用单样本基因集富集分析(ssGSEA)和基因集富集分析(GSEA)评估纤维肌痛综合征的免疫细胞浸润情况及通路富集。结果与结论:①对GSE67311数据集按照log2|(FC)|>0,P<0.05的条件进行差异分析后获得8个下调的差异表达基因;进行WGCNA分析后获得正相关性最高(r=0.22,P=0.04)的模块(MEdarkviolet)内含基因497个,负相关性最高(r=-0.41,P=6×10-5)的模块(MEsalmon2)内含基因19个;将差异表达基因与WGCNA的2个高相关性模块基因取交集,获得7个基因。②对上述7个基因进行LASSO回归算法筛选出4个基因,进行SVM-RFE机器学习算法筛选出5个基因,两者取交集后确定了3个核心基因,分别为重组1号染色体开放阅读框150蛋白(germinal center associated signaling and motility like,GCSAML)、整合素β8(Integrin beta-8,ITGB8)和羧肽酶A3(carboxypeptidase A3,CPA3);绘制3个核心基因的ROC曲线下面积分别为0.744,0.739,0.734,提示均具有很好的诊断价值,可作为纤维肌痛综合征的生物标志物。③免疫浸润分析结果显示,与对照组相比纤维肌痛综合征患者记忆B细胞、CD56 bright NK细胞和肥大细胞显著下调(P<0.05),且与上述3个生物标志物显著正相关(P<0.05)。④富集分析结果提示,纤维肌痛综合征的富集途径包括9条,主要与嗅觉传导、神经活性配体-受体相互作用及感染等通路密切相关。⑤上述结果显示,纤维肌痛综合征的发生发展与多基因参与、免疫调节异常及多个通路失调有关,但这些基因与免疫细胞之间的相互作用,以及它们与各通路之间的关系尚需进一步研究。

基于邻域粗集神经网络的大数据特征分类系统

为提升主机元件对大数据的分类准确性,尽可能地避免数据误传,提出基于邻域粗集神经网络的大数据特征分类系统。在邻域粗集神经网络中,完成对邻域系数的粒化处理,通过逼近运算的方式,使神经网络模型快速趋于稳定。选取大数据特征调制信息,借助调制识别器元件控制大数据特征的导出方向,结合关联信道组织完成数据特征的多标合并处理。实验表明,利用该系统可将大数据的单位召回率提升至65%,能够促进主机元件对大数据的准确分类。