The Testing Intelligence System Based on Factor Models and Self-Organizing Feature Maps

Presented is a new testing system based on using the factor models and self-organizing feature maps as well as the method of filtering undesirable environment influence. Testing process is described by the factor model with simplex structure, which represents the influences of genetics and environmental factors on the observed parameters - the answers to the questions of the test subjects in one case and for the time, which is spent on responding to each test question to another. The Monte Carlo method is applied to get sufficient samples for training self-organizing feature maps, which are used to estimate model goodness-of-fit measures and, consequently, ability level. A prototype of the system is implemented using the Raven＇s Progressive Matrices （Advanced Progressive Matrices） - an intelligence test of abstract reasoning. Elimination of environment influence results is performed by comparing the observed and predicted answers to the test tasks using the Kalman filter, which is adapted to solve the problem. The testing procedure is optimized by reducing the number of tasks using the distribution of measures to belong to different ability levels after performing each test task provided the required level of conclusion reliability is obtained.

Fast and Accurate Machine Learning Inverse Lithography Using Physics Based Feature Maps and Specially Designed DCNN

Inverse lithography technology(ILT)is intended to achieve optimal mask design to print a lithography target for a given lithography process.Full chip implementation of rigorous inverse lithography remains a challenging task because of enormous computational resource requirements and long computational time.To achieve full chip ILT solution,attempts have been made by using machine learning techniques based on deep convolution neural network(DCNN).The reported input for such DCNN is the rasterized images of the lithography target;such pure geometrical input requires DCNN to possess considerable number of layers to learn the optical properties of the mask,the nonlinear imaging process,and the rigorous ILT algorithm as well.To alleviate the difficulties,we have proposed the physics based optimal feature vector design for machine learning ILT in our early report.Although physics based feature vector followed by feedforward neural network can provide the solution to machine learning ILT,the feature vector is long and it can consume considerable amount of memory resource in practical implementation.To improve the resource efficiency,we proposed a hybrid approach in this study by combining first few physics based feature maps with a specially designed DCNN structure to learn the rigorous ILT algorithm.Our results show that this approach can make machine learning ILT easy,fast and more accurate.

Visualization of amino acid composition differences between processed protein from different animal species by self-organizing feature maps

Amino acids are the dominant organic components of processed animal proteins,however there has been limited investigation of differences in their composition between various protein sources.Information on these differences will not only be helpful for their further utilization but also provide fundamental information for developing species-specific identification methods.In this study,self-organizing feature maps(SOFM) were used to visualize amino acid composition of fish meal,and meat and bone meal(MBM) produced from poultry,ruminants and swine.SOFM display the similarities and differences in amino acid composition between protein sources and effectively improve data transparency.Amino acid composition was shown to be useful for distinguishing fish meal from MBM due to their large concentration differences between glycine,lysine and proline.However,the amino acid composition of the three MBMs was quite similar.The SOFM results were consistent with those obtained by analysis of variance and principal component analysis but more straightforward.SOFM was shown to have a robust sample linkage capacity and to be able to act as a powerful means to link different sample for further data mining.

Semi-Supervised Clustering Algorithm Based on Deep Feature Mapping

Clustering analysis is one of the main concerns in data mining.A common approach to the clustering process is to bring together points that are close to each other and separate points that are away from each other.Therefore,measuring the distance between sample points is crucial to the effectiveness of clustering.Filtering features by label information and mea-suring the distance between samples by these features is a common supervised learning method to reconstruct distance metric.However,in many application scenarios,it is very expensive to obtain a large number of labeled samples.In this paper,to solve the clustering problem in the few supervised sample and high data dimensionality scenarios,a novel semi-supervised clustering algorithm is proposed by designing an improved prototype network that attempts to reconstruct the distance metric in the sample space with a small amount of pairwise supervised information,such as Must-Link and Cannot-Link,and then cluster the data in the new metric space.The core idea is to make the similar ones closer and the dissimilar ones further away through embedding mapping.Extensive experiments on both real-world and synthetic datasets show the effectiveness of this algorithm.Average clustering metrics on various datasets improved by 8%compared to the comparison algorithm.

An improved deep dilated convolutional neural network for seismic facies interpretation

With the successful application and breakthrough of deep learning technology in image segmentation,there has been continuous development in the field of seismic facies interpretation using convolutional neural networks.These intelligent and automated methods significantly reduce manual labor,particularly in the laborious task of manually labeling seismic facies.However,the extensive demand for training data imposes limitations on their wider application.To overcome this challenge,we adopt the UNet architecture as the foundational network structure for seismic facies classification,which has demonstrated effective segmentation results even with small-sample training data.Additionally,we integrate spatial pyramid pooling and dilated convolution modules into the network architecture to enhance the perception of spatial information across a broader range.The seismic facies classification test on the public data from the F3 block verifies the superior performance of our proposed improved network structure in delineating seismic facies boundaries.Comparative analysis against the traditional UNet model reveals that our method achieves more accurate predictive classification results,as evidenced by various evaluation metrics for image segmentation.Obviously,the classification accuracy reaches an impressive 96%.Furthermore,the results of seismic facies classification in the seismic slice dimension provide further confirmation of the superior performance of our proposed method,which accurately defines the range of different seismic facies.This approach holds significant potential for analyzing geological patterns and extracting valuable depositional information.

Instance Retrieval Using Region of Interest Based CNN Features

Recently, image representations derived by convolutional neural networks(CNN) have achieved promising performance for instance retrieval, and they outperformthe traditional hand-crafted image features. However, most of existing CNN-based featuresare proposed to describe the entire images, and thus they are less robust to backgroundclutter. This paper proposes a region of interest (RoI)-based deep convolutionalrepresentation for instance retrieval. It first detects the region of interests (RoIs) from animage, and then extracts a set of RoI-based CNN features from the fully-connected layerof CNN. The proposed RoI-based CNN feature describes the patterns of the detected RoIs,so that the visual matching can be implemented at image region-level to effectively identifytarget objects from cluttered backgrounds. Moreover, we test the performance of theproposed RoI-based CNN feature, when it is extracted from different convolutional layersor fully-connected layers. Also, we compare the performance of RoI-based CNN featurewith those of the state-of-the-art CNN features on two instance retrieval benchmarks.Experimental results show that the proposed RoI-based CNN feature provides superiorperformance than the state-of-the-art CNN features for in-stance retrieval.

DGConv: A Novel Convolutional Neural Network Approach for Weld Seam Depth Image Detection

We propose a novel image segmentation algorithm to tackle the challenge of limited recognition and segmentation performance in identifying welding seam images during robotic intelligent operations.Initially,to enhance the capability of deep neural networks in extracting geometric attributes from depth images,we developed a novel deep geometric convolution operator(DGConv).DGConv is utilized to construct a deep local geometric feature extraction module,facilitating a more comprehensive exploration of the intrinsic geometric information within depth images.Secondly,we integrate the newly proposed deep geometric feature module with the Fully Convolutional Network(FCN8)to establish a high-performance deep neural network algorithm tailored for depth image segmentation.Concurrently,we enhance the FCN8 detection head by separating the segmentation and classification processes.This enhancement significantly boosts the network’s overall detection capability.Thirdly,for a comprehensive assessment of our proposed algorithm and its applicability in real-world industrial settings,we curated a line-scan image dataset featuring weld seams.This dataset,named the Standardized Linear Depth Profile(SLDP)dataset,was collected from actual industrial sites where autonomous robots are in operation.Ultimately,we conducted experiments utilizing the SLDP dataset,achieving an average accuracy of 92.7%.Our proposed approach exhibited a remarkable performance improvement over the prior method on the identical dataset.Moreover,we have successfully deployed the proposed algorithm in genuine industrial environments,fulfilling the prerequisites of unmanned robot operations.

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.

An Object-based Approach for Two-level Gully Feature Mapping Using High-resolution DEM and Imagery： A Case Study on Hilly Loess Plateau Region, China

Gully feature mapping is an indispensable prerequisite for the motioning and control of gully erosion which is a widespread natural hazard. The increasing availability of high-resolution Digital Elevation Model(DEM) and remote sensing imagery, combined with developed object-based methods enables automatic gully feature mapping. But still few studies have specifically focused on gully feature mapping on different scales. In this study, an object-based approach to two-level gully feature mapping, including gully-affected areas and bank gullies, was developed and tested on 1-m DEM and Worldview-3 imagery of a catchment in the Chinese Loess Plateau. The methodology includes a sequence of data preparation, image segmentation, metric calculation, and random forest based classification. The results of the two-level mapping were based on a random forest model after investigating the effects of feature selection and class-imbalance problem. Results show that the segmentation strategy adopted in this paper which considers the topographic information and optimal parameter combination can improve the segmentation results. The distribution of the gully-affected area is closely related to topographic information, however, the spectral features are more dominant for bank gully mapping. The highest overall accuracy of the gully-affected area mapping was 93.06% with four topographic features. The highest overall accuracy of bank gully mapping is 78.5% when all features are adopted. The proposed approach is a creditable option for hierarchical mapping of gully feature information, which is suitable for the application in hily Loess Plateau region.

Optimization method for a radar situation interface from error-cognition to information feature mapping

With the rapid development of digital and intelligent information systems, display of radar situation interface has become an important challenge in the field of human-computer interaction. We propose a method for the optimization of radar situation interface from error-cognition through the mapping of information characteristics. A mapping method of matrix description is adopted to analyze the association properties between error-cognition sets and design information sets. Based on the mapping relationship between the domain of error-cognition and the domain of design information, a cross-correlational analysis is carried out between error-cognition and design information.We obtain the relationship matrix between the error-cognition of correlation between design information and the degree of importance among design information. Taking the task interface of a warfare navigation display as an example, error factors and the features of design information are extracted. Based on the results, we also propose an optimization design scheme for the radar situation interface.

Feature Mapping and Recuperation by Using Elliptical Basis Function Networks for Robust Speaker Verification

The performance of speaker verification systems is often compromised under real world environments. For example, variations in handset characteristics could cause severe performance degradation. This paper presents a novel method to overcome this problem by using a non linear handset mapper. Under this method, a mapper is constructed by training an elliptical basis function network using distorted speech features as inputs and the corresponding clean features as the desired outputs. During feature recuperation, clean features are recovered by feeding the distorted features to the feature mapper. The recovered features are then presented to a speaker model as if they were derived from clean speech. Experimental evaluations based on 258 speakers of the TIMIT and NTIMIT corpuses suggest that the feature mappers improve the verification performance remarkably.

MIA-UNet:Multi-Scale Iterative Aggregation U-Network for Retinal Vessel Segmentation

As an important part of the new generation of information technology,the Internet of Things(IoT)has been widely concerned and regarded as an enabling technology of the next generation of health care system.The fundus photography equipment is connected to the cloud platform through the IoT,so as to realize the realtime uploading of fundus images and the rapid issuance of diagnostic suggestions by artificial intelligence.At the same time,important security and privacy issues have emerged.The data uploaded to the cloud platform involves more personal attributes,health status and medical application data of patients.Once leaked,abused or improperly disclosed,personal information security will be violated.Therefore,it is important to address the security and privacy issues of massive medical and healthcare equipment connecting to the infrastructure of IoT healthcare and health systems.To meet this challenge,we propose MIA-UNet,a multi-scale iterative aggregation U-network,which aims to achieve accurate and efficient retinal vessel segmentation for ophthalmic auxiliary diagnosis while ensuring that the network has low computational complexity to adapt to mobile terminals.In this way,users do not need to upload the data to the cloud platform,and can analyze and process the fundus images on their own mobile terminals,thus eliminating the leakage of personal information.Specifically,the interconnection between encoder and decoder,as well as the internal connection between decoder subnetworks in classic U-Net are redefined and redesigned.Furthermore,we propose a hybrid loss function to smooth the gradient and deal with the imbalance between foreground and background.Compared with the UNet,the segmentation performance of the proposed network is significantly improved on the premise that the number of parameters is only increased by 2%.When applied to three publicly available datasets:DRIVE,STARE and CHASE DB1,the proposed network achieves the accuracy/F1-score of 96.33%/84.34%,97.12%/83.17%and 97.06%/84.10%,respectively.The experimental results show that the MIA-UNet is superior to the state-of-the-art methods.

CLUSTERING OF DOA DATA IN RADAR PULSE BASED ON SOFM AND CDBW

Clustering is the main method of deinterleaving of radar pulse using multi-parameter.However,the problem in clustering of radar pulses lies in finding the right number of clusters.To solve this problem,a method is proposed based on Self-Organizing Feature Maps(SOFM) and Composed Density between and within clusters(CDbw).This method firstly extracts the feature of Direction Of Arrival(DOA) data by SOFM using the characteristic of DOA parameter,and then cluster of SOFM.Through computing the cluster validity index CDbw,the right number of clusters is found.The results of simulation show that the method is effective in sorting the data of DOA.

Pattern recognition of seismogenic nodes using Kohonen selforganizing map: example in west and south west of Alborz region in Iran

Pattern recognition of seismic and mor- phostructural nodes plays an important role in seismic hazard assessment. This is a known fact in seismology that tectonic nodes are prone areas to large earthquake and have this potential. They are identified by morphostructural analysis. In this study, the Alborz region has considered as studied case and locations of future events are forecast based on Kohonen Self-Organized Neural Network. It has been shown how it can predict the location of earthquake, and identifies seismogenic nodes which are prone to earthquake of M5.5＋ at the West of Alborz in Iran by using International Institute Earthquake Engineering and Seismology earthquake catalogs data. First, the main faults and tectonic lineaments have been identified based on MZ （land zoning method） method. After that, by using pattern recognition, we generalized past recorded events to future in order to show the region of probable future earthquakes. In other word, hazardous nodes have determined among all nodes by new catalog generated Self-organizing feature maps （SOFM）. Our input data are extracted from catalog, consists longitude and latitude of past event between 1980-2015 with magnitude larger or equal to 4.5. It has concluded node D1 is candidate for big earthquakes in comparison with other nodes and other nodes are in lower levels of this potential.

A Study on Classification and Detection of Small Moths Using CNN Model

Currently,there are many limitations to classify images of small objects.In addition,there are limitations such as error detection due to external factors,and there is also a disadvantage that it is difficult to accurately distinguish between various objects.This paper uses a convolutional neural network(CNN)algorithm to recognize and classify object images of very small moths and obtain precise data images.A convolution neural network algorithm is used for image data classification,and the classified image is transformed into image data to learn the topological structure of the image.To improve the accuracy of the image classification and reduce the loss rate,a parameter for finding a fast-optimal point of image classification is set by a convolutional neural network and a pixel image as a preprocessor.As a result of this study,we applied a convolution neural network algorithm to classify the images of very small moths by capturing precise images of the moths.Experimental results showed that the accuracy of classification of very small moths was more than 90%.

An Emotion Analysis Method Using Multi-Channel Convolution Neural Network in Social Networks

As an interdisciplinary comprehensive subject involving multidisciplinary knowledge,emotional analysis has become a hot topic in psychology,health medicine and computer science.It has a high comprehensive and practical application value.Emotion research based on the social network is a relatively new topic in the field of psychology and medical health research.The text emotion analysis of college students also has an important research significance for the emotional state of students at a certain time or a certain period,so as to understand their normal state,abnormal state and the reason of state change from the information they wrote.In view of the fact that convolutional neural network cannot make full use of the unique emotional information in sentences,and the need to label a large number of highquality training sets for emotional analysis to improve the accuracy of the model,an emotional analysismodel using the emotional dictionary andmultichannel convolutional neural network is proposed in this paper.Firstly,the input matrix of emotion dictionary is constructed according to the emotion information,and the different feature information of sentences is combined to form different network input channels,so that the model can learn the emotion information of input sentences from various feature representations in the training process.Then,the loss function is reconstructed to realize the semi supervised learning of the network.Finally,experiments are carried on COAE 2014 and self-built data sets.The proposed model can not only extract more semantic information in emotional text,but also learn the hidden emotional information in emotional text.The experimental results show that the proposed emotion analysis model can achieve a better classification performance.Compared with the best benchmark model gram-CNN,the F1 value can be increased by 0.026 in the self-built data set,and it can be increased by 0.032 in the COAE 2014 data set.

Turnout fault diagnosis based on DBSCAN/PSO-SOM

In order to diagnose the common faults of railway switch control circuit,a fault diagnosis method based on density-based spatial clustering of applications with noise(DBSCAN)and self-organizing feature map(SOM)is proposed.Firstly,the three-phase current curve of the switch machine recorded by the micro-computer monitoring system is dealt with segmentally and then the feature parameters of the three-phase current are calculated according to the action principle of the switch machine.Due to the high dimension of initial features,the DBSCAN algorithm is used to separate the sensitive features of fault diagnosis and construct the diagnostic sensitive feature set.Then,the particle swarm optimization(PSO)algorithm is used to adjust the weight of SOM network to modify the rules to avoid“dead neurons”.Finally,the PSO-SOM network fault classifier is designed to complete the classification and diagnosis of the samples to be tested.The experimental results show that this method can judge the fault mode of switch control circuit with less training samples,and the accuracy of fault diagnosis is higher than that of traditional SOM network.

An improved de-interleaving algorithm of radar pulses based on SOFM with self-adaptive network topology

As a core part of the electronic warfare(EW) system,de-interleaving is used to separate interleaved radar signals. As interleaved radar pulses become more complex and denser, intelligent classification of radar signals has become very important. The self-organizing feature map(SOFM) is an excellent artificial neural network, which has huge advantages in intelligent classification of complex data. However, the de-interleaving process based on SOFM is faced with the problems that the initialization of the map size relies on prior information and the network topology cannot be adaptively adjusted. In this paper, an SOFM with self-adaptive network topology(SANT-SOFM) algorithm is proposed to solve the above problems. The SANT-SOFM algorithm first proposes an adaptive proliferation algorithm to adjust the map size, so that the initialization of the map size is no longer dependent on prior information but is gradually adjusted with the input data. Then,structural optimization algorithms are proposed to gradually optimize the topology of the SOFM network in the iterative process,constructing an optimal SANT. Finally, the optimized SOFM network is used for de-interleaving radar signals. Simulation results show that SANT-SOFM could get excellent performance in complex EW environments and the probability of getting the optimal map size is over 95% in the absence of priori information.

A Modified SOFM Method for Point Cloud Segmentation in Reverse Engineering

The purpose of reverse engineering is to convert a large point cloud into a CAD model. In reverse engineering, the key issue is segmentation, i.e. studying how to subdivide the point cloud into smaller regions, where each of them can be approximated by a single surface. Segmentation is relatively simple, if regions are bounded by sharp edges and small blends; problems arise when smoothly connected regions need to be separated. In this paper, a modified self-organizing feature map neural network （SOFM） is used to solve segmentation problem. Eight dimensional feature vectors （3-dimensional coordinates, 3-dimensional normal vectors, Gaussian curvature and mean curvature） are taken as input for SOFM. The weighted Euclidean distance measure is used to improve segmentation result. The method not only can deal with regions bounded by sharp edges, but also is very efficient to separating smoothly connected regions. The segmentation method using SOFM is robust to noise, and it operates directly on the point cloud. An examples is given to show the effect of SOFM algorithm.

Intelligent identification method and application of seismic faults based on a balanced classification network

This study combined fault identification with a deep learning algorithm and applied a convolutional neural network(CNN)design based on an improved balanced crossentropy(BCE)loss function to address the low accuracy in the intelligent identification of seismic faults and the slow training speed of convolutional neural networks caused by unbalanced training sample sets.The network structure and optimal hyperparameters were determined by extracting feature maps layer by layer and by analyzing the results of seismic feature extraction.The BCE loss function was used to add the parameter which is the ratio of nonfaults to the total sample sets,thereby changing the loss function to find the reference of the minimum weight parameter and adjusting the ratio of fault to nonfault data.The method overcame the unbalanced number of sample sets and improved the iteration speed.After a brief training,the accuracy could reach more than 95%,and gradient descent was evident.The proposed method was applied to fault identification in an oilfield area.The trained model can predict faults clearly,and the prediction results are basically consistent with an actual case,verifying the effectiveness and adaptability of the method.