Influence Factor Analysis of Rocket Launcher Projectile Concentration Based on Feature Extraction

Knowledge discovery using neural approach for SME’s credit risk analysis problem in Turkey

An algorithm for extracting individual signs of a person’s gait according to the data of a mobile phone accelerometer is considered. The developed algorithm includes the stage of data segmentation, correlation selection of motion patterns, and data clustering procedures. By selecting noisy segments, the quality of data clustering and the extraction of individual features is improved. The results of the algorithm work allow us to refine the existing models of changes in human motor behavior in personalized medicine and security systems. The article also discusses the features of data segmentation and the influence of external factors on the measurement results.

Tissue characterization based on NMR parameters is the ultimate goal of NMR imaging. In practice, however, quantitative estimation of specific relaxation components in complex biological tissues is difficult. Noise, limited sampling, and heterogeneity of voxel contents are the main reasons why neither multiexponential nor simplified descriptions of relaxation behavior are entirely relevant. Alternatively, the changes in tissue contrast in a series of NMR images can be analyzed by multivariate methods. Satellite image processing, multispectral analysis, cluster analysis, analysis of principal components, fuzzy set theory, and different types of classification algorithm have been used to identify specific tissue NMR signatures and to improve tissue classification (Vannier et al. 1985; Jungke et al. 1988; Ortendahl and Carlson 1988; Pearlman et al. 1988). In contrast to these methods, factor analysis is not directed at classification but rather at a simplified description of tissues by means of a few parameters which may or may not have a physical meaning. The aim of this contribution is to discuss the relevance of a factor analysis model in NMR imaging and the possibility of physical interpretation of the model parameters.

Objective To discuss the influence factors of abnormal uterine bleeding in adolescents in Jingzhou area, Hubei Province, in order to reduce the incidence rate of abnormal uterine bleeding in adolescents. Methods A total of 5 000 junior high school girls, aged 12-16 years old, were randomly chosen from 13 middle schools in Jingzhou area during November 2012 to May 2013. All of the 5 000 girls completed two self-designed questionnaires (Questionnaire on Menstruation Situation in Adolescent Girls and Influence of Psychological Factors on Abnormal Uterine Bleeding in Adolescents). Questionnaire on Menstruation Situation in Adolescent Girls included the following items: general information (age, family address, body height and weight), menstrual status (menarche age, menstrual cycle, menstrual period, menstrual quantity), and other factors (living environment, menstrual nursing, health situation and nutrition). Influence of Psychological Factors on Abnormal Uterine Bleeding in Adolescents mainly focused on psychological factors. Results A total of 5 000 questionnaires were distributed and 4 564 effective questionnaires were collected (436 girls without menstruation). The effective rate of questionnaires was 91.28%. According to the questionnaire results, the incidence rate of abnormal uterine bleeding in adolescents was 47.69% (2 177/4 564). There was no significant difference of incidence rates of abnormal uterine bleeding in adolescents among different living environments (χ2=4.08, P>0.05). There were significant differences of incidence rates of abnormal uterine bleeding among different health situation, menstrual nursing and psychological pressure (χ2=13.39, 12.82, 10.43; P<0.05). Health situation, menstrual nursing and psychological pressure were risk factors for abnormal uterine bleeding in adolescents (OR=3.65, 95%CI: 2.53-4.34, P<0.05; OR=0.97, 95%CI: 0.86-1.09, P<0.05; OR=0.57, 95%CI: 0.33-0.64, P<0.05). Conclusion Moderate exercises, balanced nutrition, good mentality and menstrual nursing can promote women's reproductive health. Key words: Puberty; Uterine hemorrhage; Factor analysis, statistical

This paper presents a new method of knowledge gathering for decision support in image understanding based on information extracted from the dynamics of saccadic eye movements. The framework involves the construction of a generic image feature extraction library, from which the feature extractors that are most relevant to the visual assessment by domain experts are determined automatically through factor analysis. The dynamics of the visual search are analyzed by using the Markov model for providing training information to novices on how and where to look for image features. The validity of the framework has been evaluated in a clinical scenario whereby the pulmonary vascular distribution on Computed Tomography images was assessed by experienced radiologists as a potential indicator of heart failure. The performance of the system has been demonstrated by training four novices to follow the visual assessment behavior of two experienced observers. In all cases, the accuracy of the students improved from near random decision making (33%) to accuracies ranging from 50% to 68%.

This document briefly describes the systems submitted by the Center for Robust Speech Systems (CRSS) from The University of Texas at Dallas (UTD) in the 2010 NIST Speaker Recognition Evaluation. Our systems primarily use factor analysis as feature extractor [1] and support vector machine (SVM) classification framework. Our main focus in the evaluation is on the telephone trials in the core condition and 10 second train-test condition. Novel elements in our system include a supervised probabilistic principal component analysis (SPPCA) based approach for factor analysis, and an algorithm for optimal selection of the negative samples for training the SVM. I. SYSTEM COMPONENTS In this section, we describe the specific blocks used for building our systems. Later, we will discuss how these parts were joined together to form our sub-systems. A. Feature Extraction The acoustic features used in this submission were identical for all the subsystems. A 60-dimension feature (19 MFCC with log energy + ∆ + ∆∆) using a 25 ms analysis window with 10 ms shift, filtered by feature warping using a 3-s sliding window is employed [2]. To remove the silence frames, a Hungarian phoneme recognizer [3] and an energy based voice activity detection (VAD) method were used. A block diagram of our feature extraction system is shown in Fig.2.

Brain-Computer Interface (BCI) is an innovative communication technique mainly used in biomedical applications for assisting devices. In this context, main aim is help people with severe disabilities restore the movement ability, or replace lost motor function controlling external devices, or communicate with other people leading them to become more self-sufficient. The connection between BCI and wireless communication standards brings BCI into the Internet of Things (IoT), giving the opportunity to better interconnect the brain of both able and disable people with the surrounding physical and cyber worlds: it is called the human in the loop paradigm. The combination of BCI and IoT falls within the wider topic of Cognitive IoT technology, an enriched solution for Industry 4.0 and IoT in industry. The integration of the human role in the IoT can lead to several advantages both in human life and in technological progress. As a novel measurement device, BCI has to be widely characterized in order to improve the reliability of the obtained result. In this work, the authors review the scientific challenges of electroencephalography data acquisition for BCI, the parameters influencing its variability, and the processing techniques for a correct feature extraction and data classification. In particular, first the influence factors and the issues of EEG acquisition are reviewed. Then, the most popular devices used for BCI sensing unit are described. Finally, the authors describe the post processing techniques, the feature extraction algorithms, and the the classifier to properly choose the right signal feature.

Short-term wind speed prediction based on feature extraction with Multi-task Lasso and Multilayer Perceptron

A computer‐aided‐diagnostic system for diagnosing melanoma often uses distinct kinds of features for characterizing the lesions. Extracting distinct features from melanocytic images represents the various characteristics of pigmented lesions. Concatenating such features distinguishes extracted feature's information effectively while eliminating the redundant information amid them, which aids in discriminating the cancerous and noncancerous lesions. This article proposes a framework comprising segmentation, feature extraction, feature fusion, and classification to differentiate benign lesions from melanoma. The proposed framework is four‐fold: beginning with the extraction of ROI from an image, the SLICACO method is used for segmentation. Thereafter, ABCD rule‐based global and local features are extracted for effective melanoma detection. Further, we develop a new hybrid feature fusion strategy, PCAFA, leveraging the benefits of principal component analysis and factor analysis. The method performs early fusion by combining all extracted features within an individual feature vector and fed to a learning model for the prediction. While late fusion is performed using majority voting by combining the outputs of machine learning models. After that, gradient tree boosting, support vector machine, and decision tree models are trained to utilize distinct features of skin lesions for their classification as benign or malignant. The effectiveness of the designed framework is validated on the ISIC2017 benchmark skin lesion dataset based on specificity, sensitivity, and accuracy. The generalizability of the designed framework is gauged by conducting a fair comparison with conventional methods. Evaluated results reveal the potential of the proposed fused feature‐set in discriminating malignant and nonmalignant lesions with an accuracy of 96.8%.

Tourism destinations of physical environment include tourism products such as support services, tourist attractions, and tourism resources, which are available in various scales. These locations are in active competition to raise tourism rate of income. This research uses a model suggesting that destination competition should be evaluated not only by its potential in improving life style and social development, but also through proficiency in resource allocation resulting in long-term economic prosperity. Accordingly, the present study for the first time seeks to analyze the influential factors on the tourism destinations competitiveness in Anzali Special Economic Zone. Required data obtained by a 36-item questionnaire through which the views of 40 tourism experts and specialists selected from Gillan Province Cultural Heritage, Handicraft and Tourism Organization as well as Gillan University. To determine the influential factors, the Factor Analysis Tests were used, while Friedman test was applied to prioritize the effect of components in tourism destination competitive advantage of Anzali Special Economic Zone. The results of this study revealed a crucial importance of tourism attractions and planning components

In spectral analysis, diagnostic absorption features can indicate the existence of specific materials. Absorption parameters such as absorption center, absorption width, and absorption depth can be used in not only identification and quantitative analysis of minerals, but also in retrieval of surface physical properties. Continuum removal (CR) is commonly used to extract absorption features. However, for a band range containing more than one absorption contribution factors, the feature extracted by CR could be a result of comprehensive effect of different factors. In this paper, a new spectral feature extraction method named reference spectral background removal (RSBR) is proposed. Given the reference spectral background, RSBR can eliminate the influence of unwanted contribution factor, and extract the absorption feature of target contribution factor. Using RSBR, the basic absorption feature parameters including the absorption center, absorption width, and absorption depth are extracted. The results are compared with those obtained from the CR. It is shown that RSBR can effectively extract pure absorption features of target material, while more accurate absorption parameters can also be achieved.

Myoformer: sEMG missing signal recovery for gesture recognition based on multi-channel self-attention mechanism

Object detection in very-high-resolution (VHR) remote sensing imagery remains a challenge. Environmental factors, such as illumination intensity and weather, reduce image quality, resulting in poor feature representation and limited detection accuracy. To enrich the feature representation and mine the underlying context information among objects, this article proposes a context-aware convolutional neural network (CA-CNN) model for object detection that includes proposal generation, context feature extraction, feature fusion, and classification. During feature extraction, we propose integrating a context-regions-of-interests (Context-RoIs) mining layer into the CNN model and extracting context features by mapping Context-RoIs mined from the foreground proposals to multilevel feature maps. Finally, the context features extracted from multilevel layers are fused into a single layer, and the proposals represented by the fused features are classified by a softmax classifier. In this article, through numerous experiments, we thoroughly explore the influence of key factors, such as Context-RoIs, different feature scales, and different spatial context window sizes. Because of the end-to-end network design approach, our proposed model simultaneously maintains high efficiency and effectiveness. We conducted all model testing on the public NWPU VHR-10 data set. The experimental results demonstrate that our proposed CA-CNN model achieves significantly improved model performance and better detection results compared with the state-of-the-art methods.

就目前的面板製程而言，所有的製程流程都在無塵室內進行，廠區也將外在環境的影響降至最低，但多數的面板在輸送過程和存放的時候仍會因為異物的掉落或是基台故障等因素，導致產品出現缺陷。例如面板上的電極在生成時因異物存在而導致斷路，或是在面板運送時被機器手臂刮傷等等，這些缺陷若不處理會在螢幕上出現亮/暗點或亮/暗線，又或許出現不規則形狀的亮度不均Mura。這些有異常的面板如果是在液晶胞工程時才被發現的話，能做的修補很有限又或許是直接判為次級品降價出售。這對面板廠商而言即是成本的消耗，但若在陣列電路工程中發現的話，不但修補較為容易，且可以即時對出問題的機器進行調整。故提高面板的良率，不但可以降低生產成本並增加公司獲利，且減少有瑕疵的面板對公司的聲譽也有不少的助益，故對面板廠商而言是最重要的目標。 為了增加良率，多數的公司都設有檢查部門，主要是當製程進行到某一個階段時進行人工的瑕疵分類工作，以防止較大的錯誤繼續發生。但是為了降低人為誤判的影響以及增加處理的速度，以達到全檢的目標，將整個檢查系統自動化是必須的。 本論文的目標即是發展一個自動化瑕疵分類系統。藉由融合各種數位影像處理、統計紋理特徵抽取以及類神經網路辨識和支持向量機器等方法，針對檢查機台所拍攝的瑕疵影像進行自動即時分類，以利產品良率的提升。本研究主要是針對檢查機台於陣列電路工程中、第五道光罩（畫素電極光罩）微影製程後所拍攝到的瑕疵影像進行分析與辨識，發展了一個『畫素電極光罩中微影製程瑕疵影像辨識系統』。此系統可以對六種常見的瑕疵影像（GE殘、SE殘、SD殘、源電極斷路、異物和PE2連）進行分類，讓瑕疵分類達到自動化、即時化。

Gas turbine combustion system (GTCS) works in the highly adverse environmental conditions of high temperature and high pressure. Because GTCS initial temperature is too high to be directly measured, exhaust gas temperature (EGT) is an alternative to detect performance of GTCS. However, various interferences, such as working and ambient conditions as well as compressor and combustion efficiencies and so on, have a comprehensive effect on EGT, which causes low detection accuracy to GTCS. So, it's necessary to build a model that can eliminate the interferences on EGT so as to monitor performance of GTCS effectively. Feature extraction and model structure are keys to modeling. In general, it's based on data-driven or mechanism analysis approaches to build the model. However, the former containing a low information density leads to too many input variables and the overcomplicated model structure. In addition, although the latter has a high information density, modeling of a gas turbine is a difficult task in practice, which still limits the application of model-based approaches. For deficiencies of the existing methods, this paper proposes a method based on data and prior knowledge fusion to build a model called Frequent Pattern model for detecting performance of GTCS. Firstly, it's based on prior knowledge to tentatively select the model feature. And it's found that Average Exhaust Gas Temperature (AET) contains all the influences of various factors on EGT. Secondly, it's determined by data analysis that there is a linear relationship between AET and EGT. Therefore, we conclude that a simple linear model can eliminate the interferences on EGT and characterize the structural differences of different flame tubes, by which performance of the GTCS can be monitored accurately and effectively. Thirdly, a contrast test is done to verify the conclusion. A linear model is built based on the proposed method and a nonlinear model is built only based on data-driven method. By comparison, it's concluded that the proposed method can not only simplify the model but also improve the model accuracy.