Statistical Distribution Of Data Research Articles

FedSBS: Federated-Learning participant-selection method for Intrusion Detection Systems

Federated Learning (FL) is a decentralized machine learning approach in which multiple participants collaboratively train a model. Participants keep data locally, train their local models, and aggregate them in a single global model in a federated server. Collaborative FL-based Intrusion Detection Systems face challenges on an uneven statistical distribution of data and malicious participants trying to subvert the learning process. The statistical hurdles associated with imbalanced data and malicious participants pose a risk of skewing the training with biased or random data. The inability to effectively manage these statistical inconsistencies may degrade system performance, leading to false intrusion detection or opening avenues for cybersecurity breaches. To overcome these challenges, we propose a training method that employs score-based participant selection and utilizes global momentum for model aggregation. Our method improves the global model performance while mitigating the risks posed by malicious participants. The proposal incorporates a scoring system based on an information gain variant to evaluate each participant’s contribution. The scoring system and an epsilon greedy selection method ensure robust participant selection in each aggregation round. Furthermore, incorporating a global momentum term helps preserve previous knowledge at each aggregation round, contributing to model stability and overall learning. The proposed solution has demonstrated superior performance, delivering 80% F1-Score and 90% accuracy on experiments even in the presence of malicious participants, revealing the robustness and effectiveness of the proposal in mitigating statistical challenges. Consequently, the proposed method significantly enhances the performance of federated learning models, leading to more secure and efficient collaborative intrusion detection systems.

An adaptive spatio-temporal neural network for PM2.5 concentration forecasting

Accurate PM2.5 concentration prediction is essential for environmental control management, therefore numerous air quality monitoring stations have been established, which generate multiple time series with spatio-temporal correlation. However, the statistical distribution of data from different monitoring stations varies widely, which needs to provide higher flexibility in the feature extraction stage. Moreover, the spatio-temporal correlation and mutation characteristics of the time series are difficult to capture. To this end, an adaptive spatio-temporal prediction network (ASTP-NET) is proposed, in which the encoder adaptively extracts the input data features, then captures the spatio-temporal dependencies and dynamic changes of the time series, the decoder part maps the encoded features into a predicted future time series representation, while an objective function is proposed to measure the overall fluctuations of the model’s multi-step prediction. In this paper, ASTP-NET is evaluated based on the Xi'an air quality dataset, and the results show that the model outperforms other baseline methods.

RACBase: A cloud-based database of recycled aggregate concrete durability

This paper presents the first universal cloud-based database of recycled aggregate concrete (RAC) durability, named RACBase. The database collects experimental data on the durability of RAC from published literature all over the globe. Each set of data consists of the literature source, materials information, and the unified test result. The resistance to chloride ion attack, carbonation, sulfate attack, and freeze-thaw for RAC is quantified by the passed electric charge, carbonation depth, compressive strength loss, and durability factor, respectively. Based on the web interface, RACBase provides the functions of searching, adding, acquiring, and storing data, and gives the statistical distribution of data for each durability test. Users can freely access RACBase at https://www.racbase.com and browse and upload their data after online registration. One illustration of building a machine learning model for predicting the chloride resistance of RAC is presented to show the potential application of the database. The extreme gradient boosting model shows superiority to the artificial neural network and random forest models with the R2 of 0.975, RMSE of 246.719, and MAPE of 0.081. RACBase enriches the way for users to conduct in-depth research on the durability of RAC and construct performance prediction models.

Improving the robustness of adversarial attacks using an affine-invariant gradient estimator

As designers of artificial intelligence try to outwit hackers, both sides continue to hone in on AI’s inherent vulnerabilities. Designed and trained from certain statistical distributions of data, deep neural networks (DNNs) remain vulnerable to deceptive inputs that violate a DNN’s statistical, predictive assumptions. Before being fed into a neural network, however, most existing adversarial examples cannot maintain malicious functionality when applied to an affine transformation. For practical purposes, maintaining that malicious functionality serves as an important measure of the robustness of adversarial attacks. To help DNNs learn to defend themselves more thoroughly against attacks, we propose an affine-invariant adversarial attack, which can consistently produce more robust adversarial examples over affine transformations. For efficiency, we propose to disentangle current affine-transformation strategies from the Euclidean geometry coordinate plane with its geometric translations, rotations and dilations; we reformulate the latter two in polar coordinates. Afterwards, we construct an affine-invariant gradient estimator by convolving the gradient at the original image with derived kernels, which can be integrated with any gradient-based attack methods. Extensive experiments on ImageNet, including some experiments under physical condition, demonstrate that our method can significantly improve the affine invariance of adversarial examples and, as a byproduct, improve the transferability of adversarial examples, compared with alternative state-of-the-art methods.

Investigating the relationship between talent management and knowledge sharing among physical education teachers in Fardis city

The purpose of this research was to investigate the relationship between talent management and knowledge sharing among physical education teachers in Fardis city. Descriptive-correlation research method that has been done by field. The statistical population of physical education teachers in Fardis city was determined to be 130 people and the statistical sample was determined as all 130 people and then the statistical sample was selected by simple random and after collecting the questionnaires it was observed that 117 questionnaires could be used. Measurement tools include Talent Management Questionnaire (Sarmad et al., 2011) and Knowledge Sharing Questionnaire (Dixon, 2001). Content and face validity of the questionnaires were confirmed by management professors and then their reliability was 0.83 for the talent management questionnaire and 0.74 for the knowledge sharing questionnaire, respectively. Descriptive and inferential statistics (Pearson correlation coefficient) and Kolmograph-Smirnov test were used for analysis to determine the normality of statistical data distribution. Findings showed that there is a positive and significant relationship between talent management and knowledge sharing. Therefore, it can be concluded that if the staff talent management organization is properly managed and supervised, we will see the sharing of knowledge among employees as well as the responsibility of Physical education teachers.

Influence of Maintenance and Repair of Axle Boxes on the Risks of Their Failure

Purpose. The main purpose of the study is to identify the interconnection between the risks of failure of axle boxes of freight cars and the success of performing a complex task of maintenance and repair. To do this, one should identify the sequence of calculating the values of successful completion of the complex task of maintenance and repair of freight cars, make practical calculations using the example of car repair companies of Prydniprovska Railway regional branch and identify the dependencies of failure risks of the freight cars and the process of successful completion of the task. Methodology. The paper constructs the interconnections of the probabilities of performing and not performing a complex task of maintenance and repair of freight cars. In addition, the calculation sequence of static probability estimates of successful performing and not performing of the set task, static estimates of the average deviation time and the main time of successful performing, as well as its intensity is presented. Findings. The obtained statistical data of the effective implementation of the complex task of maintenance and repair of freight cars at Prydniprovska Railway regional branch and the results of their empirical distribution throughout a period of 90 days, showed that the sample average is 24.1078 cars, the sample variance is 3.28 cars, the value of the standard deviation is 1.81 cars, and the coefficient of variation is 0.075 cars. The influence of diagnostics on the failure risks of axle boxes of freight cars during operation after fulfilling the task of maintenance and repair is also established. Originality. Based on statistical processing of experimental data, for the first time an empirical distribution of statistical data of successful completion of a complex task of maintenance and repair of freight cars was performed, which allows calculating the reliability indicators of axle boxes after maintenance and repair of freight cars. For the first time, the dependence of axle boxes diagnostics on the risk of failure of performing the task of maintenance and repair of freight cars is received, which will allow reducing the risk of failure if the task is successfully completed. Practical value. The use of diagnostics of axle boxes of freight cars during maintenance and repair can reduce the risk of failure by 2… 4.5 times.

Genomic selection for resistance to mammalian bark stripping and associated chemical compounds in radiata pine.

The integration of genomic data into genetic evaluations can facilitate the rapid selection of superior genotypes and accelerate the breeding cycle in trees. In this study, 390 trees from 74 control-pollinated families were genotyped using a 36K Axiom SNP array. A total of 15,624 high-quality SNPs were used to develop genomic prediction models for mammalian bark stripping, tree height, and selected primary and secondary chemical compounds in the bark. Genetic parameters from different genomic prediction methods-single-trait best linear unbiased prediction based on a marker-based relationship matrix (genomic best linear unbiased prediction), multitrait single-step genomic best linear unbiased prediction, which integrated the marker-based and pedigree-based relationship matrices (single-step genomic best linear unbiased prediction) and the single-trait generalized ridge regression-were compared to equivalent single- or multitrait pedigree-based approaches (ABLUP). The influence of the statistical distribution of data on the genetic parameters was assessed. Results indicated that the heritability estimates were increased nearly 2-fold with genomic models compared to the equivalent pedigree-based models. Predictive accuracy of the single-step genomic best linear unbiased prediction was higher than the ABLUP for most traits. Allowing for heterogeneity in marker effects through the use of generalized ridge regression did not markedly improve predictive ability over genomic best linear unbiased prediction, arguing that most of the chemical traits are modulated by many genes with small effects. Overall, the traits with low pedigree-based heritability benefited more from genomic models compared to the traits with high pedigree-based heritability. There was no evidence that data skewness or the presence of outliers affected the genomic or pedigree-based genetic estimates.

Data-Driven Analysis of Cycle-to-Cycle Variations, Scatter, and Furcation in the UH-60A Wind Tunnel Rotor Airloads Measurements

A data-driven clustering algorithm based on proper orthogonal decomposition was applied to assess the scatter found in the UH-60A wind tunnel airloads measurements. Upon verifying the capability of the algorithm, pushrod loads, blade surface pressures, sectional loads, and torsional moments were analyzed. Spatial eigenmodes resulting from the decomposition provided the optimal basis; projection of the individual cycles onto the high singular value modes allowed visualizing the statistical distribution of data over the entire azimuth. While not all cases showed furcation in the data, the bimodal distribution was found in the high-thrust cases, where statistically normal distribution is generally assumed. Consequent clustering of the measured cycles produced an excellent correlation among clusters found in the pushrod loads, blade surface pressure, and torsional moment that suggests a common source for furcation in the data. The cycles assigned to one group repeatedly showed distinguishable variations from the other group in terms of the presence/absence of a dynamic stall vortex, azimuthal occurrence of the stall, chordwise location of separation, reattachment, and so on. When one of the clusters is smaller in size compared to the other, the conventional phase average obscured all the intricate features even when the loads are substantially higher than the larger cluster. In general, clustering the dataset when warranted showed not only higher peak loads but also lower variance for both the clusters across the entire azimuth compared to the conventional simple phase-average results. Computational simulations were conducted using CREATETM -AV Helios towards understanding the underlying flow field. Misinterpreted earlier as under-/overpredictive when compared with the simple phase-average data, Helios results consistently showed significantly improved correlation with one of the two clusters. Combining the clustered results and the flow visualization provided by Helios, aperiodicity in the spatial location and the strength of both the trim tab vortices and tip vortices have been hypothesized as potential sources of furcation.

Evaluation of the Probabilistic Distribution of Statistical Data Used in the Process of Developing Fragility Curves

Evaluation of the Probabilistic Distribution of Statistical Data Used in the Process of Developing Fragility Curves

Validation of artificial intelligence prediction models for skin cancer diagnosis using dermoscopy images: the 2019 International Skin Imaging Collaboration Grand Challenge

BackgroundPrevious studies of artificial intelligence (AI) applied to dermatology have shown AI to have higher diagnostic classification accuracy than expert dermatologists; however, these studies did not adequately assess clinically realistic scenarios, such as how AI systems behave when presented with images of disease categories that are not included in the training dataset or images drawn from statistical distributions with significant shifts from training distributions. We aimed to simulate these real-world scenarios and evaluate the effects of image source institution, diagnoses outside of the training set, and other image artifacts on classification accuracy, with the goal of informing clinicians and regulatory agencies about safety and real-world accuracy.MethodsWe designed a large dermoscopic image classification challenge to quantify the performance of machine learning algorithms for the task of skin cancer classification from dermoscopic images, and how this performance is affected by shifts in statistical distributions of data, disease categories not represented in training datasets, and imaging or lesion artifacts. Factors that might be beneficial to performance, such as clinical metadata and external training data collected by challenge participants, were also evaluated. 25 331 training images collected from two datasets (in Vienna [HAM10000] and Barcelona [BCN20000]) between Jan 1, 2000, and Dec 31, 2018, across eight skin diseases, were provided to challenge participants to design appropriate algorithms. The trained algorithms were then tested for balanced accuracy against the HAM10000 and BCN20000 test datasets and data from countries not included in the training dataset (Turkey, New Zealand, Sweden, and Argentina). Test datasets contained images of all diagnostic categories available in training plus other diagnoses not included in training data (not trained category). We compared the performance of the algorithms against that of 18 dermatologists in a simulated setting that reflected intended clinical use.Findings64 teams submitted 129 state-of-the-art algorithm predictions on a test set of 8238 images. The best performing algorithm achieved 58·8% balanced accuracy on the BCN20000 data, which was designed to better reflect realistic clinical scenarios, compared with 82·0% balanced accuracy on HAM10000, which was used in a previously published benchmark. Shifted statistical distributions and disease categories not included in training data contributed to decreases in accuracy. Image artifacts, including hair, pen markings, ulceration, and imaging source institution, decreased accuracy in a complex manner that varied based on the underlying diagnosis. When comparing algorithms to expert dermatologists (2460 ratings on 1269 images), algorithms performed better than experts in most categories, except for actinic keratoses (similar accuracy on average) and images from categories not included in training data (26% correct for experts vs 6% correct for algorithms, p<0·0001). For the top 25 submitted algorithms, 47·1% of the images from categories not included in training data were misclassified as malignant diagnoses, which would lead to a substantial number of unnecessary biopsies if current state-of-the-art AI technologies were clinically deployed.InterpretationWe have identified specific deficiencies and safety issues in AI diagnostic systems for skin cancer that should be addressed in future diagnostic evaluation protocols to improve safety and reliability in clinical practice.FundingMelanoma Research Alliance and La Marató de TV3.

PENGARUH METODE LATIHAN FARTLEK TERHADAP PENINGKATAN DAYA TAHAN AEROBIC PESERTA EKTRAKURIKULER FUTSAL PUTRA

AbstrakThe problem in this study is the ability of aerobic endurance for futsal players at SMA Muhammadiyah 1 Pontianak, who take part in futsal extracurriculars are still categorized as lacking and the Fartlek method has not been implemented. The purpose of this study was to find the effect of the Fartlek method on aerobic endurance ability. The population in this study were students who took extracurricular activities at SMA Muhammadiyah 1 Pontianak, amounting to 24 people. Sampling in this study using saturated sampling technique, namely the technique of determining the sample if all the population is used as a sample. The form of research used in this study is a Pre Experimental Design with a One-Group Pretest-Posttest Design model, namely a research design that is given a pretest before being given treatment, so that the results of the treatment can be known more accurately. Based on the calculated chi square for pretest = 10,806 and posttest = 8.834, it is smaller than the chi square table (11,070), then the distribution of statistical data for the 24 athletes can be declared normal, F count is smaller than F table 1.179 &lt;2.01 for F table 0 0.05, it can be concluded that the variance of the data to be analyzed is homogeneous. The calculation of the T test obtained the value of t count = 8,306 &gt; t table = 1,714. The percentage increase in aerobic endurance using the fartlek method is 6.75%. Thus, the conclusion of this study is that increasing aerobic endurance by using the fartlek method has been shown to have an effect on increasing the aerobic endurance of students who take part in futsal extracurricular at SMA Muhammadiyah 1 Pontianak.Keywords: Fartlek, endurance

Learning From Network Data Changes for Unsupervised Botnet Detection

The networks of infected devices (a.k.a., botnets) threaten network security due to their dynamic nature and support to different attacks (e.g., Distributed Denial of Services and personal data theft). Detecting botnets is a challenging task because the infected devices (bots) are numerous, widely and geographically spread. Significant attention has been given to improve the efficiency, robustness and adaptability of network security approaches. However, in the literature, botnet detection techniques usually ignore fast changes in statistical data distribution, performing over static windows, i.e., fixed intervals of time or fixed quantity of flows. Changes in statistical data distribution are known as concept drifts and they make the classification models obsolete. Furthermore, those works employing approaches aware of concept drift use supervised machine learning, which is slow, costly, and prone to error. Therefore, this article presents TRUSTED, a system for online and unsupervised botnet detection aware of concept drifts. Unlike other works, the TRUSTED system improves the learning process for botnet detection, applying concept drift in an online and unsupervised classification. Evaluations comprise offline and online scenarios. Results show that the TRUSTED system detects botnets using concept drift identification, reaching 87% to 95% accuracy, precision, recall, and F1-scores.

Improving the Robustness of Adversarial Attacks Using an Affine-Invariant Gradient Estimator

As designers of artificial intelligence try to outwit hackers, both sides continue to hone in on AI's inherent vulnerabilities. Designed and trained from certain statistical distributions of data, AI's deep neural networks (DNNs) remain vulnerable to deceptive inputs that violate a DNN's statistical, predictive assumptions. Before being fed into a neural network, however, most existing adversarial examples cannot maintain malicious functionality when applied to an affine transformation. For practical purposes, maintaining that malicious functionality serves as an important measure of the robustness of adversarial attacks. To help DNNs learn to defend themselves more thoroughly against attacks, we propose an affine-invariant adversarial attack, which can consistently produce more robust adversarial examples over affine transformations. For efficiency, we propose to disentangle current affine-transformation strategies from the Euclidean geometry coordinate plane with its geometric translations, rotations and dilations; we reformulate the latter two in polar coordinates. Afterwards, we construct an affine-invariant gradient estimator by convolving the gradient at the original image with derived kernels, which can be integrated with any gradient-based attack methods. Extensive experiments on ImageNet, including some experiments under physical condition, demonstrate that our method can significantly improve the affine invariance of adversarial examples and, as a byproduct, improve the transferability of adversarial examples, compared with alternative state-of-the-art methods.

Exploring the Impact of Government Interventions on COVID-19 Pandemic Spread in Kuwait

To model the trajectory of the pandemic in Kuwait from February 24, 2020 to February 28, 2021, we used two modeling procedures: Auto Regressive Integrated Moving Average (ARIMA) with structural breaks and Multivariate Adaptive Regression Splines (MARS), and then mapped the key breakpoints of the models to the set of government-enforced interventions. The MARS model, as opposed to the ARIMA model, provides a more precise interpretation of the intervention's effects. It demonstrates that partial and total lockdown interventions were highly effective in reducing the number of confirmed cases. When some interventions, such as enforcing regional curfews, closing workplaces, and imposing travel restrictions, were combined, their impact became significant. MARS method is recommended to be applied when exploring the impact of interventions on the spread of a disease. It does not require any prior assumptions about the statistical distribution of data, does not affect data collinearity, has simple and transparent functions, and allows for a more accurate analysis of intervention results.

Copula based post-processing for improving the NMME precipitation forecasts

Using reliable and timely precipitation forecasts on a monthly or seasonal scale could be useful in many water resources management planning, especially in countries facing drought challenges. Amongst many, the North American Multi-Model Ensemble (NMME) is one of the most well-known models. In this study, a Bayesian method based on Copula functions has been applied to improve NMME precipitation forecasts. This method is based on the existence of a correlation between the raw forecast and observational data. Two main factors affect the results of rainfall improvement based on the selected method. This research has presented innovative methods in these regards namely; 1) the approach of selecting the appropriate statistical distribution for variables and 2) the selection method of improved data according to the conditional probability distribution functions (CPDF). To evaluate the effectiveness of the statistical distribution, firstly the precipitation forecast improvement model has been developed based on the application of parametric (Exponential, Normal, Gamma, LogNormal and General Exreteme Value (GEV)) and non-parametric distributions (Standard Normal Kernel). Then the novel mixed distribution function based on GEV parametric distribution and Standard Normal Kernel (non-parametric distribution) has been suggested. As the second aim, a new method for selecting improved data based on the center of mass of estimated CPDF is presented. The evaluation of the proposed method for estimating the statistical distribution of data and improving the forecast precipitation by the NMME model has been performed in Sistan and Baluchestan province in Iran. In this regard, the data of 1982–2010 for the calibration period and the data of 2012–2016 for the validation of the results have been used. According to the results, the non-parametric distribution best fitted with the data in the time series and selecting the appropriate bandwidth increased the efficiency of this distribution. Besides, due to the weakness of non-parametric distributions in the boundaries, the use of GEV distribution with a high ability to estimate boundary conditions as semi-parametric distribution, led to improved performance of the proposed distribution. Finally, the selection of the improved data based on the center of the mass method has efficiently provided much improvement compared to the maximum likelihood method commonly used.

CRITERIA FOR ESTIMATING THE SENSORIMOTOR REACTION TIME BY THE SMALL UAV OPERATOR

Context. The rapid development of science and technology predetermines a significant expansion of the fields of application of UAVs different purposes. The key to the effective use UAVs is high-quality training of operators, an important element of which is the PPS of candidates, in particular, the assessment of their sensorimotor reactions. This can be achieved by selecting and justifying appropriate criteria. Objective. The goal of the work is the justification criteria for estimating the time sensorimotor reactions of a small UAV operator by analyzing the density distribution of statistical data. Method. A method has been developed to determine criteria for evaluating the time of sensorimotor reactions a small UAV operator based on the accumulation statistical material and its mathematical processing based on the results of a field experiment. The method allows to estimate numerical characteristics the distribution of the average reaction time in three modes: training production, in the conditions overload, in the conditions of overtraining and to obtain a generalized estimation. It was possible, by analyzing the occasional noninterruptible values, which take values within a certain range of values, to establish standards against which the obtained values the sensorimotor reaction time of the small UAV operator are compared and a decision is made on their suitability for training. Results. We obtained statistical series for the modes of assessment: skill development, under obstacle conditions, under conditions skill restructuring. For a visual representation of the series the corresponding histograms the distribution of the average reaction time duration were constructed. In order to eliminate the representativeness error, statistical series alignment was carried out by selecting a theoretical distribution curve for each series, which displays only essential features of the statistical material. For this purpose, we approximated the histogram of distribution by the polynomialf fourth degree. The interval theoretical density of distribution, in which the time sensomotor reaction of an arbitrary person is considered normal, with a given probability reliability such event – 0.95 has been established. To verify the effectiveness of the proposed method, algorithms for estimating the sensorimotor reaction time of a small UAV operator in three modes have been synthesized and the corresponding software that implements the proposed algorithms has been developed. Conclusions. The criteria for evaluating the sensorimotor reaction time for UAV operator to a visual stimulus using specialized software were substantiated. This allowed the previous PPS training candidates to take into account the requirements to the motor skills of the small UAV operator and the specificity his movements. The conducted experiments confirmed the validity of decisions made. Prospects for further research may include expansion of testing modes with justification for appropriate evaluation criteria.

Production forecast and estimation of the recovery factor of the Los Humeros geothermal field, Mexico

Production data from the Los Humeros geothermal field in Mexico are used to develop a forecast method for operation of the field in the future. This method supports understanding the limitations of sustainably exploiting a geothermal reservoir. Using such forecasts, fluid extraction could be scheduled in order to fulfill the steam demand of the installed capacity. Moreover, the forecast of the extraction can diminish the commercial risk involved and therefore clear the way for new investments. Herein, we determined the total extracted heat from the Los Humeros geothermal system, with the aim to forecast the next phase of fluid extraction. We took the information from 29 producing wells to analyze their historic geothermal fluid production. From their statistical distribution of data, we estimated the amount of extracted fluids for a future phase. The size of the heat reserve previous to exploitation was calculated from the forecasted and the extracted heat. Thus, the recovery factor of the system was calculated as the ratio of the accumulated production of heat and the initial heat in place. The general recovery factor for 60 years of production was calculated as 37–44%. Due to the heterogeneity of the system, this study conducted individual assessments. The forecasted heat extraction for the next 30 years of production, amounts to 580 PJ assuming a constant extraction rate between 6 and 56 ton·[h]−1 depending on the particular well. The results of this study potentially complement the existing models of the geothermal exploitation and offer an estimation of the future production of the Los Humeros, which could be a good basis for decision-making and management of the site.

A supervised machine learning method to detect anomalous real-time broiler breeder body weight data recorded by a precision feeding system

A precision feeding (PF) system is an intelligent computer-controlled feeding system that can be used to feed individual broilers, breeders or layers automatically based on measuring real-time body weight (BW). Vast amounts of real-time BW data can be generated every day when birds visit a PF station. However, anomalous observations occurred in real-time BW observations, which were caused by multiple birds entering the station at the same time, upward or downward variation in scale measurement in the recorded data due to the movement of the bird, or a misread for radio frequency identification tag. Known anomalous data should be removed because they have a negative impact on the interpretation of the data. Manually cleaning the anomalies is accurate, but it is time-consuming and labor-intensive. Statistical methods and unsupervised machine learning methods are effective in detecting anomalies to some extent because they just check data distribution. The current study reported a supervised machine learning method to detect anomalies in real-time BW recorded by the PF system. Real-time BW data of 5 broiler breeders from day 15 to 306 were checked and the anomalies were manually labeled. Variables regarding the statistical distribution of data and features regarding the feeding activity recorded by the PF system in each day were extracted from the dataset. Among the 4 machine learning algorithms including k-nearest neighbor (KNN), random forest classifier (RF), support vector machine (SVM), and artificial neural network (ANN), RF produced the highest F1 score (0.9712) and area under the precision-recall curve (0.9948). Compared with 4 other common anomaly detection methods including Z-scores, interquartile range (IQR), density-based spatial clustering of applications with noise (DBSCAN), and local outlier factor (LOF), RF had a higher average F1 score (0.9448), which indicated that RF was a more effective anomaly detection algorithm for this type of data.

Chi-MFlexDT:Chi-square-based multi flexible fuzzy decision tree for data stream classification

In recent years, a number of classification algorithms using fuzzy decision trees have been proposed. A key issue encountered in all of these algorithms is how to select the division feature. In traditional algorithms, it is common to use the information gain criterion. There is a claim that the use of information-based criteria leads to an unfair selection of features that have large number of values while not being suitable for division. This problem causes to decrease accuracy of classification in stream data. However, other algorithms for classifying stream data and extracting fuzzy rules have been proposed but the problem of unfairly selecting features with large amount of values still persists. This paper, present a new algorithm to solve problems of selecting splitting feature in fuzzy decision tree for classification of stream data. In proposed algorithm, we extend multi flexible fuzzy decision tree (MFlexDT) with chi-square based fuzzy partitioning of values. In this paper, we also extend traditional chi-square statistical to fuzzy chi-square statistical data distribution. In evaluating the proposed algorithm, tree depth and accuracy are two factors that affect performance. Experimental results show that the new algorithm has been able to improve the performance of other algorithms.

Investigation of flaw strength distributions from tensile force-strain curves of fiber tows

The flaw strength data are determined using tensile tests on various fiber tow types including, SiC, carbon, glass, basalt and alumina. The plots of p-quintile vs. flaw strength data derived from experimental force-strain curves exhibit linearity. This indicates unambiguously that flaw strength is a Gaussian variate. The statistical distribution of data was also calculated using the Weibull distribution function. Normal and Weibull cumulative distributions of flaw strengths are found to compare fairly well. The significance of normal distribution function and of the p-quintile vs. flaw strength relation for the characterisation of fibre flaw strength is discussed. The p-quintile vs. flaw strength relation is shown to provide theoretically a material characteristic. The normal distribution function is used to construct reference empirical distributions of flaw strengths that allow the evaluation of Weibull plot and Maximum Likelihood Estimation methods as functions of sample size and composition.