Support Vector Machine-Based Aqueduct Safety Assessment

2011 ◽  
Vol 368-373 ◽  
pp. 531-536
Author(s):  
Qiang Qu ◽  
Ming Qi Chang ◽  
Lei Xu ◽  
Yue Wang ◽  
Shao Hua Lu
Keyword(s):  
Support Vector Machine ◽  
Safety Assessment ◽  
Optimal Solution ◽  
Indicator System ◽  
Small Sample ◽  
Assessment Model ◽  
Irrigation District ◽  
Support Vector ◽  
Risk Minimization ◽  
Safety Standards

According to water power, structure and foundation conditions of aqueduct, it has established aqueduct safety assessment indicator system and standards. Based on statistical learning theory, support vector machine shifts the learning problems into a convex quadratic programming problem with structural risk minimization criterion, which could get the global optimal solution, and be applicable to solving the small sample, nonlinearity classification and regression problems. In order to evaluate the safety condition of aqueduct, it has established the aqueduct safety assessment model which is based on support vector machine. It has divided safety standards into normal, basically normal, abnormal and dangerous. According to the aqueduct safety assessment standards and respective evaluation level, the sample set is generated randomly, which is used to build a pair of classifier with many support vectors. The results show that the method is feasible, and it has a good application prospect in irrigation district canal building safety assessment.

A Discrimination Method of Saturated Sand Liquefaction Possibility Based on Support Vector Machine

2014 ◽  
Vol 509 ◽  
pp. 38-43
Author(s):  
Zhong Jie Fan ◽  
Yan Qiu Leng ◽  
Yong Long Xu ◽  
Zheng Jiang Meng ◽  
Ji Wei Xu
Keyword(s):  
Support Vector Machine ◽  
Influence Factors ◽  
Kernel Functions ◽  
Small Sample ◽  
Support Vector ◽  
Saturated Sand ◽  
Risk Minimization ◽  
Sand Liquefaction ◽  
Discriminant Model ◽  
Svm Model

Based on the analysis of influence factors of saturated sand, this paper expounds the limitations of traditional evaluation of liquefaction, and introduces the criterion of support vector machine (SVM) based on the principle of structural risk minimization. According to the main influence factors of sand liquefaction, a SVM discriminant model of sand liquefaction with different kernel functions is established. Through studying small sample data, this model can establish nonlinear mapping relationship between influence factors and liquefaction type. On the basis of seismic data, a radial based kernel function is selected to predict sand liquefaction type. The research results show that the predicted magnitude is identical with the actual result, to prove that it is effective to apply this SVM model to evaluate the level of sand liquefaction.

Leak Detection Method Based on Support Vector Machine

2008 ◽  
Author(s):  
XiaoJing Fan ◽  
LaiBin Zhang ◽  
Wei Liang ◽  
ZhaoHui Wang
Keyword(s):  
Support Vector Machine ◽  
Statistical Learning ◽  
Learning Theory ◽  
Statistical Learning Theory ◽  
Leak Detection ◽  
Small Sample ◽  
Support Vector ◽  
Svm Classifier ◽  
Risk Minimization ◽  
Sample Set

Assumptive and uncertain factors, few leak samples, complex non-linear pipeline systems are the problems often involved in the process of pipeline leak detection. Furthermore, the pressure wave changes of leakage are similar to these of valve regulation and pump closure. Thus it is difficult to establish a reliable model and to distinguish the leak signal pattern from others in pipeline leak detection. The veracity of leak detection system is limited. This paper presents a novel technique based on the statistical learning theory, support vector machine (SVM) for pipeline leak detection. Support Vector Machine (SVM) is learning system that uses a hypothesis space of linear functions in a high dimensional feature space, trained with a learning algorithm from optimization theory that implements a learning bias derived from statistical learning theory. SVM is based on the principle of structure risk minimization as opposed to the principle of empirical risk minimization in conventional techniques. Thus, SVM has good performance for classification over small sample set. In this paper, an overview of the limitations of traditional statistics and the advantage of statistical learning theory will be introduced. In this paper, an SVM classifier is used to classify the signal pattern with few samples. Firstly, the algorithm of the SVM classifier and steps of using the model to identify leakage signals are studied. Secondly, the classification results of the experiment show that SVM classifier has high recognition accuracy. In addition, SVM is compared with neural network method. Then the paper concludes that in terms of classification ability and generalization performance, SVM has clearly advantages than neural network method over small sample set, so SVM is more applicable to pipeline leak detection.

Research Status of Fault Diagnosis Based on Support Vector Machine

2013 ◽  
Vol 475-476 ◽  
pp. 787-791
Author(s):  
Li Mei Liu ◽  
Jian Wen Wang ◽  
Ying Guo ◽  
Hong Sheng Lin
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Small Sample ◽  
Learning Ability ◽  
Practical Significance ◽  
Support Vector ◽  
Limited Information ◽  
Risk Minimization ◽  
Minimization Principle ◽  
Good Learning

Support vector machine has good learning ability and it is good to perform the structural risk minimization principle of statistical learning theory and its application in fault diagnosis of the biggest advantages is that it is suitable for small sample decision. Its nature of learning method is under the condition of limited information to maximize the implicit knowledge of classification in data mining and it is of great practical significance for fault diagnosis. This paper analyzed and summarized the present situation of application of support vector machine in fault diagnosis and made a meaningful exploration on development direction of the future.

Simulation and Performance Analysis of Tilted Time Window and Support Vector Machine Based Learning Object Ranking Method

2020 ◽  
Vol 13 (2) ◽  
pp. 153-164
Author(s):  
Narina Thakur ◽  
Deepti Mehrotra ◽  
Abhay Bansal ◽  
Manju Bala
Keyword(s):  
Support Vector Machine ◽  
Classification Accuracy ◽  
Time Window ◽  
Optimal Solution ◽  
Similarity Score ◽  
Learning Objects ◽  
Retrieval Algorithm ◽  
Support Vector ◽  
Computationally Efficient ◽  
User Query

Objective: Since the adequacy of Learning Objects (LO) is a dynamic concept and changes in its use, needs and evolution, it is important to consider the importance of LO in terms of time to assess its relevance as the main objective of the proposed research. Another goal is to increase the classification accuracy and precision. Methods: With existing IR and ranking algorithms, MAP optimization either does not lead to a comprehensively optimal solution or is expensive and time - consuming. Nevertheless, Support Vector Machine learning competently leads to a globally optimal solution. SVM is a powerful classifier method with its high classification accuracy and the Tilted time window based model is computationally efficient. Results: This paper proposes and implements the LO ranking and retrieval algorithm based on the Tilted Time window and the Support Vector Machine, which uses the merit of both methods. The proposed model is implemented for the NCBI dataset and MAT Lab. Conclusion: The experiments have been carried out on the NCBI dataset, and LO weights are assigned to be relevant and non - relevant for a given user query according to the Tilted Time series and the Cosine similarity score. Results showed that the model proposed has much better accuracy.

scholarly journals G-computation and machine learning for estimating the causal effects of binary exposure statuses on binary outcomes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Florent Le Borgne ◽  
Arthur Chatton ◽  
Maxime Léger ◽  
Rémi Lenain ◽  
Yohann Foucher
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Statistical Power ◽  
Small Sample ◽  
Causal Effects ◽  
Small Samples ◽  
Support Vector ◽  
Sample Sizes ◽  
Super Learner ◽  
Small Sample Sizes

AbstractIn clinical research, there is a growing interest in the use of propensity score-based methods to estimate causal effects. G-computation is an alternative because of its high statistical power. Machine learning is also increasingly used because of its possible robustness to model misspecification. In this paper, we aimed to propose an approach that combines machine learning and G-computation when both the outcome and the exposure status are binary and is able to deal with small samples. We evaluated the performances of several methods, including penalized logistic regressions, a neural network, a support vector machine, boosted classification and regression trees, and a super learner through simulations. We proposed six different scenarios characterised by various sample sizes, numbers of covariates and relationships between covariates, exposure statuses, and outcomes. We have also illustrated the application of these methods, in which they were used to estimate the efficacy of barbiturates prescribed during the first 24 h of an episode of intracranial hypertension. In the context of GC, for estimating the individual outcome probabilities in two counterfactual worlds, we reported that the super learner tended to outperform the other approaches in terms of both bias and variance, especially for small sample sizes. The support vector machine performed well, but its mean bias was slightly higher than that of the super learner. In the investigated scenarios, G-computation associated with the super learner was a performant method for drawing causal inferences, even from small sample sizes.

scholarly journals PREDICTION OF PASSENGER FLOW ON THE HIGHWAY BASED ON THE LEAST SQUARE SUPPOERT VECTOR MACHINE / MAŽIAUSIŲ KVADRATŲ ATRAMINIŲ VEKTORIŲ METODO TAIKYMAS KELEIVIŲ SRAUTUI GREITKELYJE PROGNOZUOTI / ПРИМЕНЕНИЕ МЕТОДА ОПОРНЫХ ВЕКТОРОВ С КВАДРАТИЧНОЙ ФУНКЦИЕЙ ПОТЕРЬ ДЛЯ ПРОГНОЗИРОВАНИЯ ПАССАЖИРСКИХ ПОТОКОВ НА АВТОМАГИСТРАЛЯХ

Transport ◽  
2011 ◽  
Vol 26 (2) ◽  
pp. 197-203 ◽  
Author(s):  
Yanrong Hu ◽  
Chong Wu ◽  
Hongjiu Liu
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Least Square ◽  
Small Samples ◽  
Support Vector ◽  
Risk Minimization ◽  
Structural Risk Minimization ◽  
Passenger Flow ◽  
Regressive Model ◽  
Structural Risk

A support vector machine is a machine learning method based on the statistical learning theory and structural risk minimization. The support vector machine is a much better method than ever, because it may solve some actual problems in small samples, high dimension, nonlinear and local minima etc. The article utilizes the theory and method of support vector machine (SVM) regression and establishes the regressive model based on the least square support vector machine (LS-SVM). Through predicting passenger flow on Hangzhou highway in 2000–2008, the paper shows that the regressive model of LS-SVM has much higher accuracy and reliability of prediction, and therefore may effectively predict passenger flow on the highway. Santrauka Atraminių vektorių metodas (Support Vector Machine – SVM) yra skaičiuojamasis metodas, paremtas statistikos teorija, struktūriniu požiūriu mažinant riziką. SVM metodas, palyginti su kitais metodais, yra patikimesnis metodas, nes juo remiantis galima išspręsti realias problemas, esant įvairioms sąlygoms. Tyrimams naudojama SVM metodo regresijos teorija ir sukuriamas regresinis modelis, kuris grindžiamas mažiausių kvadratų atraminių vektorių metodu (Least Squares Support Vector Machine – LS-SVM). Straipsnio autoriai prognozuoja keleivių srautą Hangdžou (Kinija) greitkelyje 2000–2008 m. Gauti rezultatai rodo, kad regresinis LS-SVM modelis yra labai tikslus ir patikimas, todėl gali būti efektyviai taikomas keleivių srautams prognozuoti greitkeliuose. Резюме Метод опорных векторов (Support Vector Machine – SVM) – это набор аналогичных алгоритмов вида «обучение с учителем», использующихся для задач классификации и регрессионного анализа. Метод SVM принадлежит к семейству линейных классификаторов. Основная идея метода SVM заключается в переводе исходных векторов в пространство более высокой размерности и поиске разделяющей гиперплоскости с максимальным зазором в этом пространстве. Алгоритм работает в предположении, что чем больше разница или расстояние между параллельными гиперплоскостями, тем меньше будет средняя ошибка классификатора. В сравнении с другими методами метод SVM более надежен и позволяет решать проблемы с различными условиями. Для исследования был использован метод SVM и регрессионный анализ, затем создана регрессионная модель, основанная на методе опорных векторов с квадратичной функцией потерь (Least Squares Support Vector Machine – LS-SVM). Авторы прогнозировали пассажирский поток на автомагистрали Ханчжоу (Китай) в 2000–2008 гг. Полученные результаты показывают, что регрессионная модель LS-SVM является надежной и может быть применена для прогнозирования пассажирских потоков на других магистралях.

scholarly journals Sustainability Evaluation of Renewable Energy Incubators Using Interval Type-II Fuzzy AHP-TOPSIS with MEA-MLSSVM

2021 ◽  
Vol 13 (4) ◽  
pp. 1796
Author(s):  
Guangqi Liang ◽  
Dongxiao Niu ◽  
Yi Liang
Keyword(s):  
Support Vector Machine ◽  
Renewable Energy ◽  
Evolutionary Algorithm ◽  
Least Squares ◽  
Fuzzy Ahp ◽  
Indicator System ◽  
Support Vector ◽  
Type Ii ◽  
Sustainability Evaluation ◽  
Interval Type

With the development of renewable energy, renewable energy incubators have emerged continuously. However, these incubators present a crude development model of low-level replication and large-scale expansion, which has triggered a series of urgent problems including unbalanced regional development, low incubation efficiency, low resource utilization, and vicious competition for resources. There are huge challenges for the sustainable development of incubators in the future. A scientific and accurate evaluation approach is of great significance for improving the sustainability of renewable energy incubators. Therefore, this paper proposes a novel method combining an interval type-II fuzzy analytic hierarchy process (AHP) with mind evolutionary algorithm-modified least-squares support vector machine (MEA-MLSSVM). The indicator system is established from two aspects: service capability and operational efficiency. TOPSIS integrated with an interval type-II fuzzy AHP is employed for index weighting and assessment. In the least-squares support vector machine (LSSVM), the traditional radial basis function is replaced with the wavelet transform function (WT), and the parameters are fine-tuned by the mind evolutionary algorithm (MEA). Accordingly, the establishment of a comprehensive sustainability evaluation model for renewable energy incubators is accomplished in this paper. The experimental study reveals that this novel technique has the advantages of scientificity and precision and provides a decision-making basis for renewable energy incubators to realize sustainable operation.

scholarly journals Risk Assessment and Monitoring of Green Logistics for Fresh Produce Based on a Support Vector Machine

2020 ◽  
Vol 12 (18) ◽  
pp. 7569
Author(s):  
Guoquan Zhang ◽  
Guohao Li ◽  
Jing Peng
Keyword(s):  
Risk Assessment ◽  
Support Vector Machine ◽  
Learning Algorithm ◽  
Fresh Produce ◽  
Assessment Model ◽  
Cold Chain ◽  
Risk Indicators ◽  
Support Vector ◽  
Monitoring Model ◽  
Assessment And Monitoring

The sustainability and profitability of fresh produce supply chains are contingent upon several risk factors. This work, therefore, examines several risk indicators that affect the quality and safety of fresh produce in transit, including technological, biological, sustainability, environmental, and emergency risks. Then, we developed a risk assessment and monitoring model that employs a machine learning algorithm, a support vector machine, based on historical monitoring data. The proposed methodology was then applied to simulation and numerical analysis to assess the risks incurred in the strawberry cold chain. After training, the algorithm predicted the risks incurred during transportation with an average accuracy of 90.4%. Therefore, the developed methodology can effectively and accurately perform a risk assessment. Furthermore, the risk assessment model can be applied to other fresh produce due to comprehensive risk indicators. Decision-makers in fresh produce logistics companies can use the developed methodology to identify and mitigate risks incurred, thus improving food safety, reducing product loss, maximizing profits, and realizing sustainable development.

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