Approach and landing are among the most challenging and dangerous tasks required of helicopter pilots. Helicopter accident statistics suggest that the majority of helicopter accidents in the U.S. occur during either t...
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ISBN:
(数字)9781624106095
ISBN:
(纸本)9781624106095
Approach and landing are among the most challenging and dangerous tasks required of helicopter pilots. Helicopter accident statistics suggest that the majority of helicopter accidents in the U.S. occur during either the approach or landing phases of flight and that unstabilized approaches are a leading cause due to the associated increased potential for loss of control, loss of situational awareness, or controlled flight into terrain. In the present work, routine flight data records obtained as part of a voluntary helicopter flight data monitoring program are used to identify, analyze, and evaluate approach events in a variety of helicopter flights from different mission types, such as emergency medical services, flight training, and research. A semi-automated process is developed which enables detection of the approach phase, construction of a nominal approach path, analysis of approach stability, and knowledge discovery through datamining. The process was able to identify 3950 visual and 183 instrument approaches in a set of 3749 operational helicopter flight data records. Analysis of visual approach events suggests that typical approaches are higher and steeper than expected. Thus, the traditional definition of a stabilized approach may not align with typical operations. Additionally, atypical approaches can be classified as highly stabilized, marginally unstabilized, or unstabilized depending on their associated stability measures.
After the digital revolution,large quantities of data have been generated with time through various *** networks have made the process of data analysis very difficult by detecting attacks using suitable *** Intrusion ...
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After the digital revolution,large quantities of data have been generated with time through various *** networks have made the process of data analysis very difficult by detecting attacks using suitable *** Intrusion Detection Systems(IDSs)secure resources against threats,they still face challenges in improving detection accuracy,reducing false alarm rates,and detecting the unknown *** paper presents a framework to integrate datamining classification algorithms and association rules to implement network intrusion *** experiments have been performed and evaluated to assess various machine learning classifiers based on the KDD99 intrusion *** study focuses on several data mining algorithms such as;naïve Bayes,decision trees,support vector machines,decision tables,k-nearest neighbor algorithms,and artificial neural ***,this paper is concerned with the association process in creating attack rules to identify those in the network audit data,by utilizing a KDD99 dataset anomaly *** focus is on false negative and false positive performance metrics to enhance the detection rate of the intrusion detection *** implemented experiments compare the results of each algorithm and demonstrate that the decision tree is the most powerful algorithm as it has the highest accuracy(0.992)and the lowest false positive rate(0.009).
This research developed a more efficient integrated model (IM) based on combining the Nash-Sutcliffe efficiency coefficient (NSEC) and individual datamining (DM) algorithms for the spatial mapping of dust provenance ...
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This research developed a more efficient integrated model (IM) based on combining the Nash-Sutcliffe efficiency coefficient (NSEC) and individual datamining (DM) algorithms for the spatial mapping of dust provenance in the Hamoun-e-Hirmand Basin, southeastern Iran. This region experiences severe wind erosion and includes the Sistan plain which is one of the most PM2.5-polluted regions in the world. Due to a prolonged drought over the last two decades, the frequency of dust storms in the study area is increasing remarkably. Herein, 14 factors controlling dust emissions (FCDEs) including soil characteristics, climatic variables, digital elevation map, normalized difference vegetation index, land use and geology were mapped. Correlation and collinearity among the FCDEs were examined by the Pearson test, tolerance coefficient (TC) and variance inflation factor (VIF), with the results suggesting a lack of collinearity between FCDEs. A tree-based genetic algorithm was applied to prioritize and quantify the importance weights of the FCDEs. Thirteen individual datamining models were applied for mapping dust provenance. The model performance was assessed using root mean square error, mean absolute error and NSEC. Based on clustering analysis, the 13 DM models were grouped into five clusters and then the cluster with the highest NSEC values used in an integrated modelling process. Based on the results, the IM (NSEC = 93%) outperformed the individual DM models (the NSEC values range between 51 and 92%). Using the IM, 11, 5, 7 and 77% of the total study area were classified into low, moderate, high and very high susceptibility classes for dust provenance, respectively. Overall, the results illustrate the benefits of an IM for mapping spatial variation in the susceptibility of catchment areas to act as dust sources.
This paper suggests a novel datamining algorithm for the evaluation of e-learning courses from a Learning Management System. This new algorithm, which is called S-Algo+ (Superposition Algorithm), takes as input the c...
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This paper suggests a novel datamining algorithm for the evaluation of e-learning courses from a Learning Management System. This new algorithm, which is called S-Algo+ (Superposition Algorithm), takes as input the course rankings and the suggestion results from any kind of ranking/hierarchical algorithms and evaluates the validity of a course ranking position. The ranking algorithms estimate the quantity and quality of the course content according to users' actions and interest. S-Algo+ generates an improved final ranking suggestion output, combining the best results of the source ranking algorithms using statistical and mathematic techniques. In this way, the researchers and course instructors can use more accurate results. The efficiency and applicability of the S-Algo+ algorithm was evaluated successfully with a cross-comparison quantitative and qualitative process in a case study at a Greek university. Our new proposed S-Algo+ algorithm may lead to both theoretical and practical advantages. It may also apply not only for course evaluation but for any kind of web application such as e-commerce.
The ability to predict failure is an advantageous educational tool that can be effectively used to counsel student, and this may also be used as a tool for developing, and channelling adequate academic interventions t...
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The ability to predict failure is an advantageous educational tool that can be effectively used to counsel student, and this may also be used as a tool for developing, and channelling adequate academic interventions toward preventing failure and dropout tendencies. Students are generally admitted based on their evaluated academic potentials as measured using their admission criteria scores. This study seeks to identify the relationship, if any, between the admission criteria scores and the graduation grades, and to examine the influence of ethnicity using the geopolitical zone of origin of the student on the predictive accuracy of the models developed using a Nigerian University as a case study. datamining analyses were carried out using four classifiers on the Orange Software, and the results were verified with multiple regression analysis. The maximum classification accuracy observed is 53.2% which indicates that the pre-admission scores alone are insufficient for predicting the graduation result of students but it may serve as a useful guide. By applying over-sampling technique, the accuracy increased to 79.8%. The results establish that the ethnic background of the student is statistically insignificant in predicting their graduation results. Hence, the use of ethnicity in admission processes is therefore not ideal.
The widespread adoption of the Internet has transformed various industries, driving significant systemic reforms across different sectors. This transformation has enhanced the Internet's role in information dissem...
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The security issues of Cyber-Physical power Systems (CPS) have attracted widespread attention from scholars. Vulnerability assessment emerges as an effective method to identify the critical components and thus increas...
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ISBN:
(纸本)9781450386081
The security issues of Cyber-Physical power Systems (CPS) have attracted widespread attention from scholars. Vulnerability assessment emerges as an effective method to identify the critical components and thus increase the system resilience. While efforts have been made to study the vulnerability features of power systems under the occurrence of a single, discrete disturbance or failure at a specific time instant, this paper focuses on identifying the critical components of the cyber-physical system considering time-varying operational states. To investigate the potentially ever-changing CPS vulnerability features, in this paper we construct a database of cascading failure chains using quasi-dynamic simulations to capture the vulnerability relationships among components under time-varying operational states. Then, by adopting sequential miningalgorithms, we mine the most frequent cascading failure patterns and identify the critical components based on the datamining results. Simulation studies are conducted on IEEE 39-bus and IEEE RTS-96 systems to evaluate the effectiveness of the proposed method for the identification of critical components at both cyber and physical layers.
The aim of the research was to determine what relations occur between the stress experienced by parents in the relationship with their children and the personality traits that they shape in them. Personal characterist...
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The aim of the research was to determine what relations occur between the stress experienced by parents in the relationship with their children and the personality traits that they shape in them. Personal characteristics that parents shape in their children have been classified into metatraits described in the Circumplex of Personality model. The study was carried out on a sample of 319 parents of preschool children. Two datamining methods were used: text miningalgorithms and cluster analysis carried out by data mining algorithms. Parents experiencing higher stress more often shaped Self-restraint (Delta Plus) personality features. They tried to ensure that their children did not develop Sensation-Seeking (Delta Minus) features associated with impulsivity and dominance over others. Parents experiencing lower parental stress developed Stability (Alpha Plus) qualities related to social adaptation in their children and tried to ensure that they did not develop the antisocial features of Disinhibition (Alpha Minus).
GPCRs are the largest family of cell surface receptors;many of them remain orphans. The GPCR functions prediction represents a very important bioinformatics task. It consists in assigning to the protein the correspond...
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GPCRs are the largest family of cell surface receptors;many of them remain orphans. The GPCR functions prediction represents a very important bioinformatics task. It consists in assigning to the protein the corresponding functional class. This classification step requires a good protein representation method and a robust classification algorithm. However, the complexity of this task could be increased because of the great number of GPCRs features in most databases, which produce combinatorial explosion. In order to reduce complexity and optimize classification, the authors propose to use bio-inspired metaheuristics for both the feature selection and the choice of the best couple (feature extraction strategy [FES], datamining algorithm [DMA]). The authors propose to use the BAT algorithm for extracting the pertinent features and the genetic algorithm to choose the best couple. They compared the results they obtained with two existing algorithms. Experimental results indicate the efficiency of the proposed system.
Several algorithms strategies based on Rough Set Theory (RST) have been used for the selection of attributes and grouping objects that show similar features. On the other hand, most of these clustering techniques cann...
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ISBN:
(纸本)9781728151977;9781728151960
Several algorithms strategies based on Rough Set Theory (RST) have been used for the selection of attributes and grouping objects that show similar features. On the other hand, most of these clustering techniques cannot deal tackle partitioning. In addition, these processes are computationally complexity and low purity. In this study, the researcher looked at the limitations of the two rough set based techniques used, Information-Theoretic Dependency Roughness (ITDR) and Maximum Indiscernible Attribute (MIA). They also proposed a novel method for selecting clustering attributes, Maximum mean Attribute (MMA). They compared the performance of MMA, ITDR and MIA technique, using UCI and benchmark datasets. Their results validated the performance of the MMA with regards to its purity and computational complexity.
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