Automatic identification of bird targets with radar via patterns produced by wing flapping

Bird identification with radar is important for bird migration research, environmental impact assessments (e.g. wind farms), aircraft security and radar meteorology. In a study on bird migration, radar signals from birds, insects and ground clutter were recorded. Signals from birds show a typical pattern due to wing flapping. The data were labelled by experts into the four classes BIRD, INSECT, CLUTTER and UFO (unidentifiable signals). We present a classification algorithm aimed at automatic recognition of bird targets. Variables related to signal intensity and wing flapping pattern were extracted (via continuous wavelet transform). We used support vector classifiers to build predictive models. We estimated classification performance via cross validation on four datasets. When data from the same dataset were used for training and testing the classifier, the classification performance was extremely to moderately high. When data from one dataset were used for training and the three remaining datasets were used as test sets, the performance was lower but still extremely to moderately high. This shows that the method generalizes well across different locations or times. Our method provides a substantial gain of time when birds must be identified in large collections of radar signals and it represents the first substantial step in developing a real time bird identification radar system. We provide some guidelines and ideas for future research.

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Small Foreign Object Debris Detection for Millimeter-Wave Radar Based on Power Spectrum Features

Sensors ◽

10.3390/s20082316 ◽

2020 ◽

Vol 20 (8) ◽

pp. 2316

Author(s):

Peishuang Ni ◽

Chen Miao ◽

Hui Tang ◽

Mengjie Jiang ◽

Wen Wu

Keyword(s):

Power Spectrum ◽

Millimeter Wave ◽

Pso Algorithm ◽

Classification Performance ◽

Support Vector ◽

Measured Signal ◽

Foreign Object ◽

Millimeter Wave Radar ◽

Ground Clutter ◽

Wave Radar

Foreign object debris (FOD) detection can be considered a kind of classification that distinguishes the measured signal as either containing FOD targets or only corresponding to ground clutter. In this paper, we propose a support vector domain description (SVDD) classifier with the particle swarm optimization (PSO) algorithm for FOD detection. The echo features of FOD and ground clutter received by the millimeter-wave radar are first extracted in the power spectrum domain as input eigenvectors of the classifier, followed with the parameters optimized by the PSO algorithm, and lastly, a PSO-SVDD classifier is established. However, since only ground clutter samples are utilized to train the SVDD classifier, overfitting inevitably occurs. Thus, a small number of samples with FOD are added in the training stage to further construct a PSO-NSVDD (NSVDD: SVDD with negative examples) classifier to achieve better classification performance. Experimental results based on measured data showed that the proposed methods could not only achieve a good detection performance but also significantly reduce the false alarm rate.

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A machine learning model of microscopic agglutination test for diagnosis of leptospirosis

PLoS ONE ◽

10.1371/journal.pone.0259907 ◽

2021 ◽

Vol 16 (11) ◽

pp. e0259907

Author(s):

Yuji Oyamada ◽

Ryo Ozuru ◽

Toshiyuki Masuzawa ◽

Satoshi Miyahara ◽

Yasuhiko Nikaido ◽

...

Keyword(s):

Machine Learning ◽

Dark Field ◽

Classification Performance ◽

Agglutination Test ◽

Microscopic Agglutination Test ◽

Support Vector ◽

Automatic Identification ◽

Identification Algorithm ◽

Machine Learning Model ◽

Negative Images

Leptospirosis is a zoonosis caused by the pathogenic bacterium Leptospira. The Microscopic Agglutination Test (MAT) is widely used as the gold standard for diagnosis of leptospirosis. In this method, diluted patient serum is mixed with serotype-determined Leptospires, and the presence or absence of aggregation is determined under a dark-field microscope to calculate the antibody titer. Problems of the current MAT method are 1) a requirement of examining many specimens per sample, and 2) a need of distinguishing contaminants from true aggregates to accurately identify positivity. Therefore, increasing efficiency and accuracy are the key to refine MAT. It is possible to achieve efficiency and standardize accuracy at the same time by automating the decision-making process. In this study, we built an automatic identification algorithm of MAT using a machine learning method to determine agglutination within microscopic images. The machine learned the features from 316 positive and 230 negative MAT images created with sera of Leptospira-infected (positive) and non-infected (negative) hamsters, respectively. In addition to the acquired original images, wavelet-transformed images were also considered as features. We utilized a support vector machine (SVM) as a proposed decision method. We validated the trained SVMs with 210 positive and 154 negative images. When the features were obtained from original or wavelet-transformed images, all negative images were misjudged as positive, and the classification performance was very low with sensitivity of 1 and specificity of 0. In contrast, when the histograms of wavelet coefficients were used as features, the performance was greatly improved with sensitivity of 0.99 and specificity of 0.99. We confirmed that the current algorithm judges the positive or negative of agglutinations in MAT images and gives the further possibility of automatizing MAT procedure.

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A New Flaw Identification Model of Ultrasonic Signals Based on EMD and Possibility SVM

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.189-193.2764 ◽

2011 ◽

Vol 189-193 ◽

pp. 2764-2769

Author(s):

Xiu Fen Zhao ◽

Ming Ge He ◽

Guo Fu Yin

Keyword(s):

Classification Performance ◽

Support Vector ◽

Automatic Identification ◽

Identification Performance ◽

Model Decomposition ◽

Ultrasonic Signals ◽

Artificial Neural Network Ann ◽

Spreading Potential ◽

Very High ◽

Identification Model

Automatic identification of flaws is very important for ultrasonic nondestructive testing and evaluation of large shafts. A novel automatic identification model of defects is presented. Empirical Model decomposition (EMD) is applied to feature extraction of ultrasonic signals, and possibility Support Vector Machine (SVM) with Dempster-Shafer (DS) theory to perform the identification task. Meanwhile, comparative study on convergent velocity and classified effect is done among SVM and artificial neural network (ANN) with DS models. To validate the method, some experiments are performed and the results show the proposed system has very high identification performance for large shafts and the possibility SVM processes better classification performance and spreading potential than ANN with DS model under the small study sample condition.

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A Machine Learning Approach Towards Standardizing Microscopic Agglutination Test for Diagnosis of Leptospirosis

10.1101/2020.12.08.410712 ◽

2020 ◽

Author(s):

Yuji Oyamada ◽

Ryo Ozuru ◽

Toshiyuki Masuzawa ◽

Satoshi Miyahara ◽

Yasuhiko Nikaido ◽

...

Keyword(s):

Machine Learning ◽

Dark Field ◽

Classification Performance ◽

Agglutination Test ◽

Microscopic Agglutination Test ◽

Support Vector ◽

Automatic Identification ◽

Identification Algorithm ◽

Negative Images ◽

Antibody Titers

AbstractLeptospirosis is a zoonosis caused by the pathogenic bacterium Leptospira. The Microscopic Agglutination Test (MAT) is widely used as the gold standard for diagnosis of leptospirosis. In this method, diluted patient serum is mixed with serotype-determined Leptospiras, and the presence or absence of aggregation is determined under a dark-field microscope to calculate the antibody titer. Problems of the current MAT method are 1) a requirement of examining many specimens per sample, and 2) a need of distinguishing contaminants from true aggregates to accurately identify positivity. Therefore, increasing efficiency and accuracy are the key to refine MAT. It is possible to achieve efficiency and standardize accuracy at the same time by automating the decision making process. In this study, we built an automatic identification algorithm of MAT using a machine learning method to determine aggregation within microscopic images. The machine learned the features from 316 positive and 230 negative MAT images created with sera of Leptospira-infected (positive) and non-infected (negative) hamsters, respectively. In addition to the acquired original images, wavelet-transformed images were also considered as features. We utilized a support vector machine (SVM) as a proposed decision method. We validated the trained SVMs with 210 positive and 154 negative images. When the features were obtained from original or wavelet-transformed images, all negative images were misjudged as positive, and the classification performance was very low with sensitivity of 1 and specificity of 0. In contrast, when the histograms of wavelet coefficients were used as features, the performance was greatly improved with sensitivity of 0.99 and specificity of 0.99. We confirmed that the current algorithm judges the positive or negative of agglutinations in MAT images, that it gave the further possibility of making a much suitable algorithm by adding more indices (e.g., antibody titers and bacterial counts) in the future.

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Binary Spectrum Feature for Improved Classiﬁer Performance

10.36227/techrxiv.12993122 ◽

2020 ◽

Author(s):

Nalika Ulapane ◽

Karthick Thiyagarajan ◽

sarath kodagoda

Keyword(s):

Machine Learning ◽

Classification Performance ◽

Feature Reduction ◽

Sensor Data ◽

Machine Learning Techniques ◽

Support Vector ◽

Svm Classifier ◽

Monitoring Task ◽

Classifier Performance ◽

Spectrum Feature

<div>Classiﬁcation has become a vital task in modern machine learning and Artiﬁcial Intelligence applications, including smart sensing. Numerous machine learning techniques are available to perform classiﬁcation. Similarly, numerous practices, such as feature selection (i.e., selection of a subset of descriptor variables that optimally describe the output), are available to improve classiﬁer performance. In this paper, we consider the case of a given supervised learning classiﬁcation task that has to be performed making use of continuous-valued features. It is assumed that an optimal subset of features has already been selected. Therefore, no further feature reduction, or feature addition, is to be carried out. Then, we attempt to improve the classiﬁcation performance by passing the given feature set through a transformation that produces a new feature set which we have named the “Binary Spectrum”. Via a case study example done on some Pulsed Eddy Current sensor data captured from an infrastructure monitoring task, we demonstrate how the classiﬁcation accuracy of a Support Vector Machine (SVM) classiﬁer increases through the use of this Binary Spectrum feature, indicating the feature transformation’s potential for broader usage.</div><div><br></div>

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Overview of Subsynchronous Oscillation in Grid-connected Wind Farm

Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) ◽

10.2174/2352096513999200407103526 ◽

2020 ◽

Vol 13 (7) ◽

pp. 969-979

Author(s):

Xu Pei-Zhen ◽

Lu Yong-Geng ◽

Cao Xi-Min

Keyword(s):

Power Systems ◽

Wind Turbine ◽

Large Scale ◽

Wind Farm ◽

Wind Farms ◽

Induction Generator ◽

Future Research ◽

Stable Operation ◽

Subsynchronous Oscillation ◽

Different Types

Background: Over the past few years, the subsynchronous oscillation (SSO) caused by the grid-connected wind farm had a bad influence on the stable operation of the system and has now become a bottleneck factor restricting the efficient utilization of wind power. How to mitigate and suppress the phenomenon of SSO of wind farms has become the focus of power system research. Methods: This paper first analyzes the SSO of different types of wind turbines, including squirrelcage induction generator based wind turbine (SCIG-WT), permanent magnet synchronous generator- based wind turbine (PMSG-WT), and doubly-fed induction generator based wind turbine (DFIG-WT). Then, the mechanisms of different types of SSO are proposed with the aim to better understand SSO in large-scale wind integrated power systems, and the main analytical methods suitable for studying the SSO of wind farms are summarized. Results: On the basis of results, using additional damping control suppression methods to solve SSO caused by the flexible power transmission devices and the wind turbine converter is recommended. Conclusion: The current development direction of the SSO of large-scale wind farm grid-connected systems is summarized and the current challenges and recommendations for future research and development are discussed.

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Use of Machine Learning to Investigate the Quantitative Checklist for Autism in Toddlers (Q-CHAT) towards Early Autism Screening

Diagnostics ◽

10.3390/diagnostics11030574 ◽

2021 ◽

Vol 11 (3) ◽

pp. 574

Author(s):

Gennaro Tartarisco ◽

Giovanni Cicceri ◽

Davide Di Pietro ◽

Elisa Leonardi ◽

Stefania Aiello ◽

...

Keyword(s):

Machine Learning ◽

High Performance ◽

Behavioral Science ◽

Autistic Traits ◽

Classification Performance ◽

Recursive Feature Elimination ◽

Diagnostic Tools ◽

Support Vector ◽

K Nearest Neighbors ◽

Autism Screening

In the past two decades, several screening instruments were developed to detect toddlers who may be autistic both in clinical and unselected samples. Among others, the Quantitative CHecklist for Autism in Toddlers (Q-CHAT) is a quantitative and normally distributed measure of autistic traits that demonstrates good psychometric properties in different settings and cultures. Recently, machine learning (ML) has been applied to behavioral science to improve the classification performance of autism screening and diagnostic tools, but mainly in children, adolescents, and adults. In this study, we used ML to investigate the accuracy and reliability of the Q-CHAT in discriminating young autistic children from those without. Five different ML algorithms (random forest (RF), naïve Bayes (NB), support vector machine (SVM), logistic regression (LR), and K-nearest neighbors (KNN)) were applied to investigate the complete set of Q-CHAT items. Our results showed that ML achieved an overall accuracy of 90%, and the SVM was the most effective, being able to classify autism with 95% accuracy. Furthermore, using the SVM–recursive feature elimination (RFE) approach, we selected a subset of 14 items ensuring 91% accuracy, while 83% accuracy was obtained from the 3 best discriminating items in common to ours and the previously reported Q-CHAT-10. This evidence confirms the high performance and cross-cultural validity of the Q-CHAT, and supports the application of ML to create shorter and faster versions of the instrument, maintaining high classification accuracy, to be used as a quick, easy, and high-performance tool in primary-care settings.

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Impact of Dataset Size on Classification Performance: An Empirical Evaluation in the Medical Domain

Applied Sciences ◽

10.3390/app11020796 ◽

2021 ◽

Vol 11 (2) ◽

pp. 796

Author(s):

Alhanoof Althnian ◽

Duaa AlSaeed ◽

Heyam Al-Baity ◽

Amani Samha ◽

Alanoud Bin Dris ◽

...

Keyword(s):

Empirical Evaluation ◽

Classification Performance ◽

Support Vector ◽

Robust Model ◽

Original Distribution ◽

C4.5 Decision Tree ◽

Dataset Size ◽

Overall Performance ◽

Medical Domain ◽

The Impact

Dataset size is considered a major concern in the medical domain, where lack of data is a common occurrence. This study aims to investigate the impact of dataset size on the overall performance of supervised classification models. We examined the performance of six widely-used models in the medical field, including support vector machine (SVM), neural networks (NN), C4.5 decision tree (DT), random forest (RF), adaboost (AB), and naïve Bayes (NB) on eighteen small medical UCI datasets. We further implemented three dataset size reduction scenarios on two large datasets and analyze the performance of the models when trained on each resulting dataset with respect to accuracy, precision, recall, f-score, specificity, and area under the ROC curve (AUC). Our results indicated that the overall performance of classifiers depend on how much a dataset represents the original distribution rather than its size. Moreover, we found that the most robust model for limited medical data is AB and NB, followed by SVM, and then RF and NN, while the least robust model is DT. Furthermore, an interesting observation is that a robust machine learning model to limited dataset does not necessary imply that it provides the best performance compared to other models.

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Raman Spectral Signatures of Serum-Derived Extracellular Vesicle-Enriched Isolates May Support the Diagnosis of CNS Tumors

Cancers ◽

10.3390/cancers13061407 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1407

Author(s):

Matyas Bukva ◽

Gabriella Dobra ◽

Juan Gomez-Perez ◽

Krisztian Koos ◽

Maria Harmati ◽

...

Keyword(s):

Lumbar Disc ◽

Area Under The Curve ◽

Principal Component ◽

Extracellular Vesicle ◽

Classification Performance ◽

Cns Tumors ◽

Clinical Samples ◽

Support Vector ◽

Serum Samples ◽

Raman Spectroscopic

Investigating the molecular composition of small extracellular vesicles (sEVs) for tumor diagnostic purposes is becoming increasingly popular, especially for diseases for which diagnosis is challenging, such as central nervous system (CNS) malignancies. Thorough examination of the molecular content of sEVs by Raman spectroscopy is a promising but hitherto barely explored approach for these tumor types. We attempt to reveal the potential role of serum-derived sEVs in diagnosing CNS tumors through Raman spectroscopic analyses using a relevant number of clinical samples. A total of 138 serum samples were obtained from four patient groups (glioblastoma multiforme, non-small-cell lung cancer brain metastasis, meningioma and lumbar disc herniation as control). After isolation, characterization and Raman spectroscopic assessment of sEVs, the Principal Component Analysis–Support Vector Machine (PCA–SVM) algorithm was performed on the Raman spectra for pairwise classifications. Classification accuracy (CA), sensitivity, specificity and the Area Under the Curve (AUC) value derived from Receiver Operating Characteristic (ROC) analyses were used to evaluate the performance of classification. The groups compared were distinguishable with 82.9–92.5% CA, 80–95% sensitivity and 80–90% specificity. AUC scores in the range of 0.82–0.9 suggest excellent and outstanding classification performance. Our results support that Raman spectroscopic analysis of sEV-enriched isolates from serum is a promising method that could be further developed in order to be applicable in the diagnosis of CNS tumors.

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Deep Learning Associated with Laser-Induced Breakdown Spectroscopy (LIBS) for the Prediction of Lead in Soil

Applied Spectroscopy ◽

10.1177/0003702819826283 ◽

2019 ◽

Vol 73 (5) ◽

pp. 565-573 ◽

Cited By ~ 9

Author(s):

Yun Zhao ◽

Mahamed Lamine Guindo ◽

Xing Xu ◽

Miao Sun ◽

Jiyu Peng ◽

...

Keyword(s):

Deep Learning ◽

Confusion Matrix ◽

Principal Component ◽

Classification Performance ◽

Soil Samples ◽

Laser Induced Breakdown Spectroscopy ◽

Support Vector ◽

Breakdown Spectroscopy ◽

Laser Induced Breakdown ◽

Different Levels

In this study, a method based on laser-induced breakdown spectroscopy (LIBS) was developed to detect soil contaminated with Pb. Different levels of Pb were added to soil samples in which tobacco was planted over a period of two to four weeks. Principal component analysis and deep learning with a deep belief network (DBN) were implemented to classify the LIBS data. The robustness of the method was verified through a comparison with the results of a support vector machine and partial least squares discriminant analysis. A confusion matrix of the different algorithms shows that the DBN achieved satisfactory classification performance on all samples of contaminated soil. In terms of classification, the proposed method performed better on samples contaminated for four weeks than on those contaminated for two weeks. The results show that LIBS can be used with deep learning for the detection of heavy metals in soil.

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