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[IEEE 2018 IEEE 17th International Symposium on Network Computing and Applications (NCA) - Cambridge, MA, USA (2018.11.1-2018.11.3)] 2018 IEEE 17th International Symposium on Network Computing and Applications (NCA) - Analysis of Burst Header Packets in Optical Burst Switching Networks
摘要： Optical Burst Switching (OBS) networks provide a practical alternative to optical packet switching and optical circuit switching by separating control information from the primary data, sending the former on a separate control channel. However, this separation also renders OBS networks susceptible to a denial- or degradation-of-service attack (intentional or otherwise) when the data provisioned by a header packet on the control channel does not materialize. This paper addresses the problem of detecting and characterizing such problems and describes a method based on monitoring network traffic on the control and data channels. The method is evaluated on a publicly available dataset.
关键词： classification,optical burst switching,machine learning,quality of service
更新于2025-09-23 15:21:21
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Machine learning for improved data analysis of biological aerosol using the WIBS
摘要： Primary biological aerosol including bacteria, fungal spores and pollen have important implications for public health and the environment. Such particles may have different concentrations of chemical fluorophores and will respond differently in the presence of ultraviolet light, potentially allowing for different types of biological aerosol to be discriminated. Development of ultraviolet light induced fluorescence (UV-LIF) instruments such as the Wideband Integrated Bioaerosol Sensor (WIBS) has allowed for size, morphology and fluorescence measurements to be collected in real-time. However, it is unclear without studying instrument responses in the laboratory, the extent to which different types of particles can be discriminated. Collection of laboratory data is vital to validate any approach used to analyse data and ensure that the data available is utilized as effectively as possible.
关键词： biological aerosol,UV-LIF,WIBS,clustering,machine learning
更新于2025-09-23 15:21:21
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[IEEE 2018 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI) - Las Vegas, NV (2018.4.8-2018.4.10)] 2018 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI) - Estimating Plant Centers Using A Deep Binary Classifier
摘要： Phenotyping is the process of estimating the physical and chemical properties of a plant. Traditional phenotyping is labor intensive and time consuming. These measurements can be obtained faster by collecting aerial images with an Unmanned Aerial Vehicle (UAV) and analyzing them using modern image analysis technologies. We propose a method to estimate plant centers by classifying each pixel as a plant center or not a plant center. We then label the center of each cluster as the plant location. We studied the performance of our method on two datasets. We achieved 84% precision and 90% recall on one dataset consisting of early stage plants and 62% precision and 77% recall on another dataset consisting of later stage plants.
关键词： Color Image Processing,Plant Phenotyping,CNN,Machine Learning
更新于2025-09-23 15:21:21
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Suspended Sediment Concentration Estimation from Landsat Imagery along the Lower Missouri and Middle Mississippi Rivers Using an Extreme Learning Machine
摘要： Monitoring and quantifying suspended sediment concentration (SSC) along major fluvial systems such as the Missouri and Mississippi Rivers provide crucial information for biological processes, hydraulic infrastructure, and navigation. Traditional monitoring based on in situ measurements lack the spatial coverage necessary for detailed analysis. This study developed a method for quantifying SSC based on Landsat imagery and corresponding SSC data obtained from United States Geological Survey monitoring stations from 1982 to present. The presented methodology first uses feature fusion based on canonical correlation analysis to extract pertinent spectral information, and then trains a predictive reflectance–SSC model using a feed-forward neural network (FFNN), a cascade forward neural network (CFNN), and an extreme learning machine (ELM). The trained models are then used to predict SSC along the Missouri–Mississippi River system. Results demonstrated that the ELM-based technique generated R2 > 0.9 for Landsat 4–5, Landsat 7, and Landsat 8 sensors and accurately predicted both relatively high and low SSC displaying little to no overfitting. The ELM model was then applied to Landsat images producing quantitative SSC maps. This study demonstrates the benefit of ELM over traditional modeling methods for the prediction of SSC based on satellite data and its potential to improve sediment transport and monitoring along large fluvial systems.
关键词： suspended sediment,Landsat,water quality,extreme learning machine,machine learning
更新于2025-09-23 15:21:01
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A flexible piezoelectric strain sensor array with laser-patterned serpentine interconnects
摘要： This paper evaluates the classification of multisample problems, such as electromyographic (EMG) data, by making aggregate features available to a per-sample classifier. It is found that the accuracy of this approach is superior to that of traditional methods such as majority vote for this problem. The classification improvements of this method, in conjunction with a confidence measure expressing the per-sample probability of classification failure (i.e., a hazard function) is described and measured. Results are expected to be of interest in clinical decision support system development.
关键词： Bayes methods,machine learning,statistical learning,pattern analysis,decision support systems,supervised learning
更新于2025-09-23 15:21:01
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Evaluation of electrical efficiency of photovoltaic thermal solar collector
摘要： In this study, machine learning methods of artificial neural networks (ANNs), least squares support vector machines (LSSVM), and neuro-fuzzy are used for advancing prediction models for thermal performance of a photovoltaic-thermal solar collector (PV/T). In the proposed models, the inlet temperature, flow rate, heat, solar radiation, and the sun heat have been considered as the input variables. Data set has been extracted through experimental measurements from a novel solar collector system. Different analyses are performed to examine the credibility of the introduced models and evaluate their performances. The proposed LSSVM model outperformed the ANFIS and ANNs models. LSSVM model is reported suitable when the laboratory measurements are costly and time-consuming, or achieving such values requires sophisticated interpretations.
关键词： hybrid machine learning model,Renewable energy,photovoltaic-thermal (PV/T),least square support vector machine (LSSVM),adaptive neuro-fuzzy inference system (ANFIS),neural networks (NNs)
更新于2025-09-23 15:21:01
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[IEEE 2019 International Workshop on Fiber Optics in Access Networks (FOAN) - Sarajevo, Bosnia and Herzegovina (2019.9.2-2019.9.4)] 2019 International Workshop on Fiber Optics in Access Networks (FOAN) - Intelligent Non-woven Textiles Based on Fiber Bragg Gratings for Strain and Temperature Monitoring
摘要： In-vehicle speech-based interaction between a driver and a driving agent should be performed without affecting the driving behavior. A driving agent provides information to the driver and helps his/her driving behavior and non-driving-related tasks, e.g., selecting music and giving weather information. In this paper, we focus on a method for determining utterance timings when a driving agent provides non-driving-related information. If a driving agent provides a driver with non-driving-related information at an inappropriate moment, it will distract his/her driving behavior and deteriorate his/her safety driving. To solve or to mitigate the problem, we propose a novel method for determining the utterance timing of a driving agent on the basis of a double articulation analyzer, which is an unsupervised nonparametric Bayesian machine learning method for detecting contextual change points. To verify the effectiveness of the method, we conduct two experiments. One is an experiment on a short circuit around a park in an urban area, and the other is an experiment on a long course in a town. The results show that the proposed method enables a driving agent to avoid inappropriate timing better than baseline methods.
关键词： Driving agent,machine learning,driving data,driver distraction,nonparametric Bayes
更新于2025-09-23 15:21:01
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Correlations between thermal history and keyhole porosity in laser powder bed fusion
摘要： Additive manufacturing has the potential to revolutionize the production of metallic components as it yields near net shape parts with complex geometries and minimizes waste. At the present day, additively manufactured components face qualification and certification challenges due to the difficulty in controlling defects. This has driven a significant research effort aimed at better understanding and improving processing controls – yielding a plethora of in-situ measurements aimed at correlating defects with material quality metrics of interest. In this work, we develop machine-learning methods to learn correlations between thermal history and subsurface porosity for a variety of print conditions in laser powder bed fusion. Un-normalized surface temperatures (in the form of black-body radiances) are obtained using high-speed infrared imaging and porosity formation is observed in the sample cross-section through synchrotron x-ray imaging. To demonstrate the predictive power of these features, we present four statistical machine-learning models that correlate temperature histories to subsurface porosity formation in laser fused Ti-6Al-4V powder.
关键词： in-situ measurement,keyhole porosity,machine learning,laser powder bed fusion,x-ray imaging
更新于2025-09-23 15:21:01
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[IEEE 2019 IEEE 13th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG) - Sonderborg, Denmark (2019.4.23-2019.4.25)] 2019 IEEE 13th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG) - Modelling properties of solar cells irradiated from different lighting sources
摘要： The hypertriglyceridemic waist (HW) phenotype is strongly associated with type 2 diabetes; however, to date, no study has assessed the predictive power of phenotypes based on individual anthropometric measurements and triglyceride (TG) levels. The aims of the present study were to assess the association between the HW phenotype and type 2 diabetes in Korean adults and to evaluate the predictive power of various phenotypes consisting of combinations of individual anthropometric measurements and TG levels. Between November 2006 and August 2013, 11 937 subjects participated in this retrospective cross-sectional study. We measured fasting plasma glucose and TG levels and performed anthropometric measurements. We employed binary logistic regression (LR) to examine statistically significant differences between normal subjects and those with type 2 diabetes using HW and individual anthropometric measurements. For more reliable prediction results, two machine learning algorithms, naive Bayes (NB) and LR, were used to evaluate the predictive power of various phenotypes. All prediction experiments were performed using a tenfold cross validation method. Among all of the variables, the presence of HW was most strongly associated with type 2 diabetes (p < 0.001, adjusted odds ratio (OR) = 2.07 [95% CI, 1.72–2.49] in men; p < 0.001, adjusted OR = 2.09 [1.79–2.45] in women). When comparing waist circumference (WC) and TG levels as components of the HW phenotype, the association between WC and type 2 diabetes was greater than the association between TG and type 2 diabetes. The phenotypes tended to have higher predictive power in women than in men. Among the phenotypes, the best predictors of type 2 diabetes were waist-to-hip ratio + TG in men (AUC by NB = 0.653, AUC by LR = 0.661) and rib-to-hip ratio + TG in women (AUC by NB = 0.73, AUC by LR = 0.735). Although the presence of HW demonstrated the strongest association with type 2 diabetes, the predictive power of the combined measurements of the actual WC and TG values may not be the best manner of predicting type 2 diabetes. Our findings may provide clinical information concerning the development of clinical decision support systems for the initial screening of type 2 diabetes.
关键词： type 2 diabetes,hypertriglyceridemic waist (HW) phenotype,Anthropometric measurements,triglycerides (TG),predictor,machine learning,data mining
更新于2025-09-23 15:21:01
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Day-ahead photovoltaic power production forecasting methodology based on machine learning and statistical post-processing
摘要： A main challenge towards ensuring large-scale and seamless integration of photovoltaic systems is to improve the accuracy of energy yield forecasts, especially in grid areas of high photovoltaic shares. The scope of this paper is to address this issue by presenting a uni?ed methodology for hourly-averaged day-ahead photovoltaic power forecasts with improved accuracy, based on data-driven machine learning techniques and statistical post-processing. More speci?cally, the proposed forecasting methodology framework comprised of a data quality stage, data-driven power output machine learning model development (arti?cial neural networks), weather clustering assessment (K-means clustering), post-processing output optimisation (linear regressive correction method) and the ?nal performance accuracy evaluation. The results showed that the application of linear regression coe?cients to the forecasted outputs of the developed day-ahead photovoltaic power production neural network improved the performance accuracy by further correcting solar irradiance forecasting biases. The resulting optimised model provided a mean absolute percentage error of 4.7% when applied to historical system datasets. Finally, the model was validated both, at a hot as well as a cold semi-arid climatic location, and the obtained results demonstrated close agreement by yielding forecasting accuracies of mean absolute percentage error of 4.7% and 6.3%, respectively. The validation analysis provides evidence that the proposed model exhibits high performance in both forecasting accuracy and stability.
关键词： Performance,Forecasting,Machine learning,Photovoltaic,Arti?cial neural networks,Clustering
更新于2025-09-23 15:21:01