A Target Selection Algorithm of Seeker Based on the Prior Information and SVM

2013 ◽  
Vol 734-737 ◽  
pp. 3071-3074
Author(s):  
Guo Dong Zhang ◽  
Zhong Liu
Keyword(s):  
Prior Information ◽  
Simulation Experiment ◽  
Target Selection ◽  
Support Vector ◽  
Selection Algorithm ◽  
Distribution Law ◽  
Surface Ship ◽  
False Target ◽  
Target Characteristics ◽  
Selection Of

Aiming at the phenomenon that the chaff and corner reflector released by surface ship can influence the selection of missile seeker, this paper proposed a multi-target selection method based on the prior information of false targets distribution and Support Vector Machine (SVM). By analyzing the false targets distribution law we obtain two classification principles, which are used to train the SVM studies the true and false target characteristics. The trained SVM is applied to the seeker in the target selection. This method has advantages of simple programming and high classification accuracy, and the simulation experiment in this paper confirms the correctness and effectiveness of this method.

Aero-Engine Adaptive Model Using Recursive Reduced Least Squares Support Vector Regression

2011 ◽  
Vol 65 ◽  
pp. 218-223
Author(s):  
Ling Ping Jiang
Keyword(s):  
Feature Selection ◽  
Least Squares ◽  
Support Vector Regression ◽  
Support Vector ◽  
Adaptive Model ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Sparse Model ◽  
Aero Engine ◽  
Selection Of

In consideration of the problem in traditional aero-engine adaptive model, a new algorithm was proposed based on Recursive Reduced Least Squares Support Vector Regression (RRLSSVR). Feature Selection of model input and flight envelope divided was needed before the model established, then adaptive model was developed in every small envelope. Finally, an adaptive model was applied to validate the effectiveness and feasibility of the proposed feature selection algorithm and sparse model using RRLSSVR.

scholarly journals Study on Reasonable Roadway Position of Working Face under Strip Coal Pillar in Rock Burst Mine

2020 ◽  
Vol 2020 ◽  
pp. 1-21 ◽  
Author(s):  
Hengze Yang ◽  
Zhongping Guo ◽  
Daozhi Chen ◽  
Chao Wang ◽  
Fuyu Zhang ◽  
...  
Keyword(s):  
Simulation Experiment ◽  
Longitudinal Crack ◽  
Coal Pillar ◽  
Distribution Law ◽  
Working Face ◽  
Coal Wall ◽  
Safety Production ◽  
The Stability ◽  
Two Sides ◽  
Selection Of

It is of great significance to study the reasonable position of mining roadway under strip coal pillar for increasing the stability of mining roadway, reducing the waste of resources, and realizing the safety production of working face. Based on the research background of the working face under the strip coal pillar in Jinqiao Coal Mine of Jining, Shandong Province, through theoretical analysis, similar material simulation experiment, and numerical simulation experiment, the stress distribution law, plastic failure range, and rationality of coal pillar setting in different width sections are systematically studied. Finally, the tailentry of working face is determined at the position of 5 m from the bottom of strip coal pillar to 1308 goaf. During the mining period of 1310 working face, the peak value of side abutment pressure is at the position of 3∼4 m; beyond 25 m in front of the coal wall, the deformation of the surrounding rock on the tailentry surface is small. After entering the advanced support section, the deformation of the two sides is mainly longitudinal crack expansion and local shallow small flakes; however, the roof is complete and stable. Therefore, the selection of tailentry location and coal pillar width has played a good role. The research results of this study can provide some reference for similar mine with similar geological and production technical conditions.

A Research on Navigation Star Catalog Selection Algorithm Based on SVM

2013 ◽  
Vol 706-708 ◽  
pp. 613-617
Author(s):  
Fu Cheng Liu ◽  
Zhao Hui Liu ◽  
Wen Liu ◽  
Dong Sheng Liang ◽  
Kai Cui ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Field Of View ◽  
Support Vector ◽  
Selection Algorithm ◽  
Boltzmann Entropy ◽  
Local Criterion ◽  
Star Catalog ◽  
Uniform Sampling ◽  
Global Criterion ◽  
Small Capacity

A navigation star catalog (NSC) selection algorithm via support vector machine (SVM) is proposed in this paper. The sphere spiral method is utilized to generate the sampling boresight directions by virtue of obtaining the uniform sampling data. Then the theory of regression analysis methods is adopted to extract the NSC, and an evenly distributed and small capacity NSC is obtained. Two criterions, namely a global criterion and a local criterion, are defined as the uniformity criteria to test the performance of the NSC generated. Simulations show that, compared with MFM, magnitude weighted method (MWM) and self-organizing algorithm(S-OA), the Boltzmann entropy (B.e) of SVM selection algorithm (SVM-SA) is the minimum, to 0.00207. Simultaneously, under the conditions such as the same field of view (FOV) and elimination of the hole, both the number of guide stars (NGS) and standard deviation (std) of SVM-SA is the least, respectively 7668 and 2.17. Consequently, the SVM-SA is optimal in terms of the NGS and the uniform distribution, and has also a strong adaptability.

Electrophysiological Correlates of Similarity-based Interference during Detection of Visual Forms

2006 ◽  
Vol 18 (6) ◽  
pp. 880-888 ◽  
Author(s):  
Markus Conci ◽  
Klaus Gramann ◽  
Hermann J. Müller ◽  
Mark A. Elliott
Keyword(s):  
Target Selection ◽  
Event Related Potentials ◽  
Form Perception ◽  
Psychological Review ◽  
Physiological Basis ◽  
Related Potentials ◽  
Kanizsa Figure ◽  
Global And Local ◽  
Selection Of ◽  
Form Information

Illusory figure completion demonstrates the ability of the visual system to integrate information across gaps. Mechanisms that underlie figural emergence support the interpolation of contours and the filling-in of form information [Grossberg, S., & Mingolla, E. Neural dynamics of form perception: Boundary completion, illusory figures and neon colour spreading. Psychological Review, 92, 173–211, 1985]. Although both processes contribute to figure formation, visual search for an illusory target configuration has been shown to be susceptible to interfering form, but not contour, information [Conci, M., Müller, H. J., & Elliott, M. A. The contrasting impact of global and local object attributes on Kanizsa figure detection. Submitted]. Here, the physiological basis of form interference was investigated by recording event-related potentials elicited from contour- and surface-based distracter interactions with detection of a target Kanizsa figure. The results replicated the finding of form interference and revealed selection of the target and successful suppression of the irrelevant distracter to be reflected by amplitude differences in the N2pc component (240–340 msec). In conclusion, the observed component variations reflect processes of target selection on the basis of integrated form information resulting from figural completion processes.

scholarly journals Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at Plot Scale

2018 ◽  
Vol 10 (8) ◽  
pp. 1285 ◽  
Author(s):  
Reza Attarzadeh ◽  
Jalal Amini ◽  
Claudia Notarnicola ◽  
Felix Greifeneder
Keyword(s):  
Soil Moisture ◽  
Optical Data ◽  
Estimation Accuracy ◽  
Support Vector ◽  
Practical Applications ◽  
Initial Stage ◽  
Object Based ◽  
Single Polarization ◽  
Selection Of

This paper presents an approach for retrieval of soil moisture content (SMC) by coupling single polarization C-band synthetic aperture radar (SAR) and optical data at the plot scale in vegetated areas. The study was carried out at five different sites with dominant vegetation cover located in Kenya. In the initial stage of the process, different features are extracted from single polarization mode (VV polarization) SAR and optical data. Subsequently, proper selection of the relevant features is conducted on the extracted features. An advanced state-of-the-art machine learning regression approach, the support vector regression (SVR) technique, is used to retrieve soil moisture. This paper takes a new look at soil moisture retrieval in vegetated areas considering the needs of practical applications. In this context, we tried to work at the object level instead of the pixel level. Accordingly, a group of pixels (an image object) represents the reality of the land cover at the plot scale. Three approaches, a pixel-based approach, an object-based approach, and a combination of pixel- and object-based approaches, were used to estimate soil moisture. The results show that the combined approach outperforms the other approaches in terms of estimation accuracy (4.94% and 0.89 compared to 6.41% and 0.62 in terms of root mean square error (RMSE) and R2), flexibility on retrieving the level of soil moisture, and better quality of visual representation of the SMC map.

scholarly journals A Hybrid Barnacles Mating Optimizer Algorithm With Support Vector Machines for Gene Selection of Microarray Cancer Classification

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 64895-64905
Author(s):  
Essam H. Houssein ◽  
Diaa Salama Abdelminaam ◽  
Hager N. Hassan ◽  
Mustafa M. Al-Sayed ◽  
Emad Nabil
Keyword(s):  
Support Vector Machines ◽  
Gene Selection ◽  
Cancer Classification ◽  
Support Vector ◽  
Vector Machines ◽  
Selection Of

LINGUOCULTURAL POTENTIAL OF EUPHEMISMS-AMULETS

2021 ◽  
pp. 68-74
Author(s):  
SERGEY I. ROMANOV ◽  
Keyword(s):  
Target Selection ◽  
Cultural Factors ◽  
Point Of View ◽  
Negative Consequences ◽  
Cultural Community ◽  
Magical Power ◽  
Selection For ◽  
Cultural Code ◽  
Russian Linguistic ◽  
Selection Of

The article deals with a special type of euphemisms-amulets, that is, linguocultural units endowed with the function of protection. There are two types of euphemisms-amulets from the point of view of relevance: obsolete and current units. Obsolete euphemisms- amulets have targets that are not recognized as dangerous by the modern linguistic and cultural community. Current euphemisms-amulets, although not always consciously, are used by representatives of the modern Russian linguistic and cultural community to protect against something bad. The paper establishes that the use of the euphemism-amulet is based on the transla- tion of the target's representation into another cultural code. The work reveals that the euphemisms-amulets are directed not to mitigate an unwanted nomination but how to replace it. An undesirable nomination is endowed with negative magical properties, which is why the linguocultural community imposes a ban on its use. A protective cultural function is superim- posed on the euphemism. The main pragmatic explanation for the use of the euphemism- amulet is the speaker's desire not to predict an encounter with an unwanted object, which is based on belief in the magical power of the word. The factors that determine the linguocultural specificity of euphemisms-amulets are revealed. The first factor is target selection. For the Russian linguocultural community, such targets include a totemic animal, evil forces representing another world, death. The second factor is the selection of nominations for the euphemistic function, which is determined by culturally marked background knowledge, ideas, and typical practices. The communicative- pragmatic platform for the use of euphemisms-amulets is the belief in the magical power of the word, in the fact that the use of the forbidden word can lead to negative consequences (in particular, to cause the appearance of something dangerous, undesirable). The work proves that the identified cultural factors are universal, based on universal archetypes: one's own / another's, permission / prohibition, life / death. At the same time, the fact of the appearance of the euphemism-amulet, the choice of its internal form is determined by national and cultural factors.

Utilizing Data-Driven Models to Predict Brittleness in Tuscaloosa Marine Shale: A Machine Learning Approach

2021 ◽  
Author(s):  
Jamal Ahmadov
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Brittleness Index ◽  
Estimation Methods ◽  
Gradient Boosting ◽  
Average Error ◽  
Support Vector ◽  
Marine Shale ◽  
Effective Manner ◽  
Selection Of

Abstract The Tuscaloosa Marine Shale (TMS) formation is a clay- and liquid-rich emerging shale play across central Louisiana and southwest Mississippi with recoverable resources of 1.5 billion barrels of oil and 4.6 trillion cubic feet of gas. The formation poses numerous challenges due to its high average clay content (50 wt%) and rapidly changing mineralogy, making the selection of fracturing candidates a difficult task. While brittleness plays an important role in screening potential intervals for hydraulic fracturing, typical brittleness estimation methods require the use of geomechanical and mineralogical properties from costly laboratory tests. Machine Learning (ML) can be employed to generate synthetic brittleness logs and therefore, may serve as an inexpensive and fast alternative to the current techniques. In this paper, we propose the use of machine learning to predict the brittleness index of Tuscaloosa Marine Shale from conventional well logs. We trained ML models on a dataset containing conventional and brittleness index logs from 8 wells. The latter were estimated either from geomechanical logs or log-derived mineralogy. Moreover, to ensure mechanical data reliability, dynamic-to-static conversion ratios were applied to Young's modulus and Poisson's ratio. The predictor features included neutron porosity, density and compressional slowness logs to account for the petrophysical and mineralogical character of TMS. The brittleness index was predicted using algorithms such as Linear, Ridge and Lasso Regression, K-Nearest Neighbors, Support Vector Machine (SVM), Decision Tree, Random Forest, AdaBoost and Gradient Boosting. Models were shortlisted based on the Root Mean Square Error (RMSE) value and fine-tuned using the Grid Search method with a specific set of hyperparameters for each model. Overall, Gradient Boosting and Random Forest outperformed other algorithms and showed an average error reduction of 5 %, a normalized RMSE of 0.06 and a R-squared value of 0.89. The Gradient Boosting was chosen to evaluate the test set and successfully predicted the brittleness index with a normalized RMSE of 0.07 and R-squared value of 0.83. This paper presents the practical use of machine learning to evaluate brittleness in a cost and time effective manner and can further provide valuable insights into the optimization of completion in TMS. The proposed ML model can be used as a tool for initial screening of fracturing candidates and selection of fracturing intervals in other clay-rich and heterogeneous shale formations.

Military Targeting in the Context of Self-Defence Actions

2015 ◽  
Vol 84 (1) ◽  
pp. 3-28 ◽  
Author(s):  
James A. Green ◽  
Christopher P.M. Waters
Keyword(s):  
Target Selection ◽  
Jus In Bello ◽  
Customary International Law ◽  
Jus Ad Bellum ◽  
Conceptual Clarity ◽  
Starting Point ◽  
Definition Of ◽  
The Relationship ◽  
Self Defence ◽  
Selection Of

For self-defence actions to be lawful, they must be directed at military targets. The absolute prohibition on non-military targeting under the jus in bello is well known, but the jus ad bellum also limits the target selection of states conducting defensive operations. Restrictions on targeting form a key aspect of the customary international law criteria of necessity and proportionality. In most situations, the jus in bello will be the starting point for the definition of a military targeting rule. Yet it has been argued that there may be circumstances when the jus ad bellum and the jus in bello do not temporally or substantively overlap in situations of self-defence. In order to address any possible gaps in civilian protection, and to bring conceptual clarity to one particular dimension of the relationship between the two regimes, this article explores the independent sources of a military targeting rule. The aim is not to displace the jus in bello as the ‘lead’ regime on how targeting decisions must be made, or to undermine the traditional separation between the two ‘war law’ regimes. Rather, conceptual light is shed on a sometimes assumed but generally neglected dimension of the jus ad bellum’s necessity and proportionality criteria that may, in limited circumstances, have significance for our understanding of human protection during war.

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