Against the Trend-An tentative Data Analysis Method using Classical Regression against Machine Learning Approach

The machine learning approach is a new hot topic in recent years that are widely used in different sections, including industries, economy, disaster prediction and politics. After decades’ of development, the available machine learning algorithms are numerous and diverse. Traditional methods such as regression, classical statistical methods, are unfortunately laid aside as non-mainstream. This paper tries to compare the classical regression with machine learning algorithm as classifier. Typical machine learning algorithm support vector machine (SVM) is compared with the classical regression. The classical regression is modified to tailor as classifier. Confidence interval and credibility of prediction from regression is developed to evaluate the prediction uncertainty. Benchmark data from public database is used to demonstrate the performance. The results showed that regression exhibits an efficient computational cost with comparative accuracy

Examination of turbulence impacts on ultra-short-term wind power and speed forecasts with machine learning

Wind turbines’ economic and secure operation can be optimized through accurate ultra-short-term wind power and speed forecasts. Turbulence, considered as a local short-term physical wind phenomenon, affects wind power generation. This paper investigates the use of turbulence intensity for ultra-short-term predictions of wind power and speed with a wind farm in the Arctic, including and excluding wind turbulence, within three hours by employing several different machine learning algorithms. A rigorous and detailed statistical comparison of the predictions is conducted. The results show that the algorithms achieve reasonably accurate predictions, but turbulence intensity does not statistically contribute to wind power or speed forecasts. This observation illustrates the uncertainty of turbulence in wind power generation. Besides, differences between the types of algorithms for ultra-short-term wind forecasts are also statistically insignificant, demonstrating the unique stochasticity and complexity of wind speed and power

Environmental influences on the intensity changes of tropical cyclones over the western North Pacific

The influence of environmental conditions on the intensity changes of tropical cyclones (TCs) over the western North Pacific (WNP) is investigated through examination of 37 TCs during 2000–2011 that interacted directly with the western North Pacific subtropical high (WNPSH). Comprehensive composite analysis of the environmental conditions is performed for two stages of storms: one is categorized as intensifying events (maximum wind speed increases by 15 kn over 48 h) and the other is categorized as weakening events (maximum wind speed decreases by 15 kn over 48 h). Comparison of the composite analysis of these two cases show that environmental conditions associated with the WNPSH play important roles in the intensity changes of TCs over the WNP. When a TC moves along the southern periphery of the WNPSH, the relatively weaker easterly environmental vertical wind shear helps bring warm moist air from the south and southeast to its southeast quadrant within 500 km, which is favorable for the TC to intensify. However, when a TC moves along the western edge of the WNPSH, under the combined influences of the WNPSH and an upper-level westerly trough, a strong westerly vertical shear promotes the intrusion of dry environmental air associated with the WNPSH from the north and northwest, which may lead to the inhibition of moisture supply and convection over the western half of the TC and thus its weakening. These composite results are consistent with those with additional geographic restrictions, suggesting that the dry air intrusion and the vertical wind shear (VWS) associated with the WNPSH, indeed affect the intensity changes of TCs over the WNP beyond the difference related solely to variations in geographical locations. The average sea surface temperature (SST) of 27.6 °C for the weakening events is also lower than an average of 28.9 °C for the strengthening events, but remains above the critical value of 27 °C for TC intensification, suggesting that the SST may be regarded as a less positive factor for the weakening events

Probability distributions for wind speed volatility characteristics: A case study of Northern Norway

The Norwegian Arctic is rich in wind resources. The development of wind power in this region can boost green energy and also promote local economies. In wind power engineering, it is a tremendous advantage to base projects on a sound understanding of the intrinsic properties of wind resources in an area. Wind speed volatility, a phenomenon that strongly affects wind power generation, has not received sufficient research attention. In this paper, a framework for studying short-term wind speed volatility with statistical analysis and probabilistic modeling is constructed for an existing wind farm in Northern Norway. It is found that unlike the characteristics of wind power volatility, wind speed volatility cannot be described by the normal distribution. The reason is that even though the probability distribution of wind speed volatility is centrally symmetric, it is much more centrally concentrated and has thicker tails. After comparing three distributions corresponding to different sampling periods, this paper suggests utilizing the t distribution, with average modeling RMSE less than 0.006 and R2 exceeding 0.995 and with the best modeling scenario of temporal resolution, the 30 mins has an RMSE of 0.0051 and an R2 of 0.997, to more accurately and effectively explore the fluctuating characteristics of wind speed

Modelling the transport of oil after a proposed oil spill accident in Barents Sea and its environmental impact on Alke species

OA Green publisher. Can archive pre-print and post-print or publisher's version/PDF. Link to publisher's version: http://doi.org/10.1088/1755-1315/82/1/012010Accidental oil spills can have significant effect on the coastal and marine environment. As the oil extraction and exploration activities increase in the Barents Sea, it is of increasingly importance to investigate the potential oil spill incidents associated with these activities. In this study, the transport and fate of oil after a proposed oil spill incident in Barents Sea was modelled by oil spill contingency and response model OSCAR. The possibility that the spilled oil reach the open sea and the strand area was calculated respectively. The influence area of the incident was calculated by combining the results from 200 simulations. The possibility that the spilled oil reach Alke species, a vulnerable species and on the National Red List of birds in Barents Sea, was analyzed by combining oil spill modelling results and the Alke species distribution data. The results showed that oil is dominated with a probability of 70-100% in the open sea to reach an area in a radius of 20km from the release location after 14 days of release. The probability reduces with the increasing distances from the release location. It is higher possibility that the spilled oil will reach the Alke species in the strand area than in the open sea in the summer. The total influence area of the release is 11 429 km2 for the surface water and 1528 km2 for the coastal area

Acupuncture and Moxibustion for Inflammatory Bowel Diseases: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

Background. Inflammatory bowel diseases (IBD) are recurrent and refractory which include ulcerative colitis (UC) and Crohn’s disease (CD). Clinical researches about acupuncture and moxibustion treatments for IBD are increasing, while systematic reviews about their efficacy remains in a shortage. This study sought to evaluate the efficacy of acupuncture and moxibustion for IBD. Methods. Seven significant databases both in and abroad were searched for randomized controlled trials (RCTs) which compared acupuncture and moxibustion as the main intervention to pharmacotherapy in treating IBD. A meta-analysis was performed. Results. A total of 43 RCTs were included. Among the 43 included trials, 10 trials compared oral sulphasalazine (SASP) with acupuncture and/or moxibustion treatments. A meta-analysis of the 10 trials indicated that acupuncture and moxibustion therapy was superior to oral SASP. Conclusion. Acupuncture and moxibustion therapy demonstrates better efficacy than oral SASP in treating IBD. However, given the limitations of this systematic review and the included literature, definitive conclusions regarding the exact efficacy of acupuncture and moxibustion treatment for IBD cannot be drawn. Extant RCTs still cannot provide sufficient evidence and multicentre, double-blind RCTs with large sample sizes are needed to provide higher-quality evidence

The silicon isotope composition of Ethmodiscus rexlaminated diatom mats from the tropical West Pacific: Implications for silicate cycling during the Last Glacial Maximum

The cause of massive blooms of Ethmodiscus rex laminated diatom mats (LDMs) in the eastern Philippine Sea (EPS) during the Last Glacial Maximum (LGM) remains uncertain. In order to better understand the mechanism of formation of E. rex LDMs from the perspective of dissolved silicon (DSi) utilization, we determined the silicon isotopic composition of single E. rex diatom frustules (δ30SiE. rex) from two sediment cores in the Parece Vela Basin of the EPS. In the study cores, δ30SiE. rex varies from −1.23‰ to −0.83‰ (average −1.04‰), a range that is atypical of marine diatom δ30Si and that corresponds to the lower limit of reported diatom δ30Si values of any age. A binary mixing model (upwelled silicon versus eolian silicon) accounting for silicon isotopic fractionation during DSi uptake by diatoms was constructed. The binary mixing model demonstrates that E. rex dominantly utilized DSi from eolian sources (i.e., Asian dust) with only minor contributions from upwelled seawater sources (i.e., advected from Subantarctic Mode Water, Antarctic Intermediate Water, or North Pacific Intermediate Water). E. rex utilized only ~24% of available DSi, indicating that surface waters of the EPS were eutrophic with respect to silicon during the LGM. Our results suggest that giant diatoms did not always use a buoyancy strategy to obtain nutrients from the deep nutrient pool, thus revising previously proposed models for the formation of E. rex LDMs

Failure diagnostics using support vector machine

Failure diagnostics is an important part of condition monitoring aiming to identify incipient failures in early stages. Accurate and efficient failure diagnostics can guarantee that the operator makes the correct maintenance decision, thereby reducing the maintenance costs and improving system availability. The Support Vector Machine (SVM) is discussed in this thesis with the purpose of efficiently diagnosing failure. The SVM utilizes the kernel method to transform input data from a lower dimensional space to a higher dimensional space. In the higher dimensional space, the hitherto linearly non separable patterns can be linearly separated, without compromising the computational cost. This facilitates failure diagnostics as in the higher dimensional space, the existing failure or incipient failure is more identifiable. The SVM uses the maximal margin method to overcome the “overfitting” problem. This problem makes the model fit special data sets. The maximal margin method also makes it suitable for solving small sample size problems. In this thesis, the SVM is compared with another well known technique, the Artificial Neural Network (ANN). In the comparative study, the SVM performs better than the ANN. However, as the performance of the SVM critically depends on the parameters of the kernel function, this thesis proposes using an Ant Colony Optimization (ACO) method to obtain the optimal parameters. The ACO optimized SVM is applied to diagnose the electric motor in a railway system. The Support Vector Regression (SVR) is an extension of the SVM. In this thesis, SVR is combined with a time-series to forecast reliability. Finally, to improve the SVM performance, the thesis proposes a multiple kernel SVM. The SVM is an excellent pattern recognition technique. However, to obtain an accurate diagnostics performance, one has to extract the appropriate features. This thesis discusses the features extracted from the time domain and uses the SVM to diagnose failure for a bearing. Another case in this thesis is presented, namely failure diagnostics for an electric motor installed in a railway’s crossing and switching system; in this case, the features are extracted from the power consumption signal. In short, the thesis discuses the use of the SVM in failure diagnostics. Theoretically, the SVM is an excellent classifier or regressor possessing a solid theoretical foundation. Practically, the SVM performs well in failure diagnostics, as shown in the cases presented. Finally, as failure diagnostics critically relies on feature extraction, this thesis considers feature extraction from the time domain.Godkänd; 2011; 20111121 (yuafuq); DISPUTATION Ämnesområde: Drift och underhållsteknik/Operation and Maintenance Opponent: Professor Thomas Lindblad, Institutionen för fysik, Kungliga Tekniska Högskolan, Stockholm Ordförande: Professor Uday Kumar, Institutionen för samhällsbyggnad och naturresurser, Luleå tekniska universitet Tid: Tisdag den 20 december 2011, kl 09.00 Plats: F1031, Luleå tekniska universite

Predicting time to failure using support vector regression

Support Vector Machine (SVM) is a new but prospective technique which has been used in pattern recognition, data mining, etc. Taking the advantage of Kernel function, maximum margin and Lanrangian optimization method, SVM has high application potential in reliability data analysis. This paper introduces the principle and some concepts of SVM. One extension of regular SVM named Support Vector Regression (SVR) is discussed. SVR is dedicated to solve continuous problem. This paper uses SVR to predict reliability for repairable system. Taking an equipment from Swedish railway industry as a case, it is shown that the SVR can predict (Time to Failure) TTF accurately and its prediction performance can outperform Artificial Neural Network (ANN).Godkänd; 2010; 20100823 (ysko

Replacement policy for repairable system under various failure types with finite time horizon

The repairable system suffers various type of failure and each failure type has different repair cost. Assume the failure process of the system as Non-homogenous Poisson Process (NHPP). The system is replaced after it experienced a predetermined number of minimal repairs. Considering finite time horizon, the paper proposes a replacement model for the system. It firstly proves that the failure process of each type of failure also follows NHPP. Then it develops a model to estimate the total cost which covers minimal repair cost for each type of failure and system replacement cost. To obtain the numerical solution, the paper introduces a numerical approach to approximate renewal function and a nonlinear programming model is developed. A numerical example is presented eventually.Godkänd; 2009; 20091214 (yuafuq