Search results

The purpose of this paper is to present an evolutionary clustering algorithm based on mixed measure for complex distributed data.

In this method, the data are first partitioned into some spherical distributed sub‐clusters by using the Euclidean distance as the similarity measurement, and each clustering center represents all the members of corresponding cluster. Then, the clustering centers obtained in the first phase are clustered by using a novel manifold distance as the similarity measurement. The two clustering processes in this method are both based on evolutionary algorithm.

Theoretical analysis and experimental results on seven artificial data sets and seven UCI data sets with different structures show that the novel algorithm has the ability to identify clusters efficiently with no matter simple or complex, convex or non‐convex distribution. When compared with the genetic algorithm‐based clustering and the K‐means algorithm, the proposed algorithm outperformed the compared algorithms on most of the test data sets.

The method presented in this paper represents a new approach to solving clustering problems of complex distributed data. The novel method applies the idea “coarse clustering, fine clustering”, which executes coarse clustering by Euclidean distance and fine clustering by manifold distance as similarity measurements, respectively. The proposed clustering algorithm is shown to be effective in solving data clustering problems with different distribution.

For a better understanding of the preferences and differences of young consumers in emerging wine markets, this study aims to propose a clustering method to segment the super-new generation wine consumers based on their sensitivity to wine brand, origin and price and then conduct user profiles for segmented consumer groups from the perspectives of demographic attributes, eating habits and wine sensory attribute preferences.

We first proposed a consumer clustering perspective based on their sensitivity to wine brand, origin and price and then conducted an adaptive density peak and label propagation layer-by-layer (ADPLP) clustering algorithm to segment consumers, which improved the issues of wrong centers' selection and inaccurate classification of remaining sample points for traditional DPC (DPeak clustering algorithm). Then, we built a consumer profile system from the perspectives of demographic attributes, eating habits and wine sensory attribute preferences for segmented consumer groups.

In this study, 10 typical public datasets and 6 basic test algorithms are used to evaluate the proposed method, and the results showed that the ADPLP algorithm was optimal or suboptimal on 10 datasets with accuracy above 0.78. The average improvement in accuracy over the base DPC algorithm is 0.184. As an outcome of the wine consumer profiles, sensitive consumers prefer wines with medium prices of 100–400 CNY and more personalized brands and origins, while casual consumers are fond of popular brands, popular origins and low prices within 50 CNY. The wine sensory attributes preferred by super-new generation consumers are red, semi-dry, semi-sweet, still, fresh tasting, fruity, floral and low acid.

Young Chinese consumers are the main driver of wine consumption in the future. This paper provides a tool for decision-makers and marketers to identify the preferences of young consumers quickly which is meaningful and helpful for wine marketing.

In this study, the ADPLP algorithm was introduced for the first time. Subsequently, the user profile label system was constructed for segmented consumers to highlight their characteristics and demand partiality from three aspects: demographic characteristics, consumers' eating habits and consumers' preferences for wine attributes. Moreover, the ADPLP algorithm can be considered for user profiles on other alcoholic products.

This paper aims to develop the Hybrid feature descriptor and probabilistic neuro-fuzzy system for attaining the high accuracy in face recognition system. In recent days, facial recognition (FR) systems play a vital part in several applications such as surveillance, access control and image understanding. Accordingly, various face recognition methods have been developed in the literature, but the applicability of these algorithms is restricted because of unsatisfied accuracy. So, the improvement of face recognition is significantly important for the current trend.

This paper proposes a face recognition system through feature extraction and classification. The proposed model extracts the local and the global feature of the image. The local features of the image are extracted using the kernel based scale invariant feature transform (K-SIFT) model and the global features are extracted using the proposed m-Co-HOG model. (Co-HOG: co-occurrence histograms of oriented gradients) The proposed m-Co-HOG model has the properties of the Co-HOG algorithm. The feature vector database contains combined local and the global feature vectors derived using the K-SIFT model and the proposed m-Co-HOG algorithm. This paper proposes a probabilistic neuro-fuzzy classifier system for the finding the identity of the person from the extracted feature vector database.

The face images required for the simulation of the proposed work are taken from the CVL database. The simulation considers a total of 114 persons form the CVL database. From the results, it is evident that the proposed model has outperformed the existing models with an improved accuracy of 0.98. The false acceptance rate (FAR) and false rejection rate (FRR) values of the proposed model have a low value of 0.01.

This paper proposes a face recognition system with proposed m-Co-HOG vector and the hybrid neuro-fuzzy classifier. Feature extraction was based on the proposed m-Co-HOG vector for extracting the global features and the existing K-SIFT model for extracting the local features from the face images. The proposed m-Co-HOG vector utilizes the existing Co-HOG model for feature extraction, along with a new color gradient decomposition method. The major advantage of the proposed m-Co-HOG vector is that it utilizes the color features of the image along with other features during the histogram operation.

It matters to be aware of the important role played by the brain in the progressive constitution and unification of the three major facets of the human being: a biological individual; a social actor; a self‐conscious, reflective, and deliberating subject. The aim is to discuss this role.

The dialogues carried on by each one of these facets with an environment of its own (the material environment; the social milieu; the subject's inner world) are related to the functioning of three distinct levels of integration, organization, and adaptation within the human brain.

The neural substrate of basic affective processes pervades the entire brain and the latter processes play a predominant role in the mediation and integration of the individual's interactions with his/her environments. The degree of “plasticity”, i.e. the sensitivity to the shaping influence of environmental conditions, increases markedly from the lower to the higher level of brain functioning. Any individual characteristic of brain functioning is the outcome of a series of complex and evolving interactions between genetic and environmental factors.

Since brain development highly depends on the early environment (the first years of life), it is of the utmost importance to ensure that every developing brain benefits from optimal environmental conditions.

The paper brings together a series of scientific facts in an integrated and dynamic bio‐psycho‐social perspective which aims at working out a “model of man” thought to be an appropriate basis for any study of human development.

The purpose of this paper is to discuss a data interpolation method of curved surfaces from the point of dimension reduction and manifold learning.

Instead of transmitting data of curved surfaces in 3D space directly, the method transmits data by unfolding 3D curved surfaces into 2D planes by manifold learning algorithms. The similarity between surface unfolding and manifold learning is discussed. Projection ability of several manifold learning algorithms is investigated to unfold curved surface. The algorithms’ efficiency and their influences on the accuracy of data transmission are investigated by three examples.

It is found that the data interpolations using manifold learning algorithms LLE, HLLE and LTSA are efficient and accurate.

The method can improve the accuracies of coupling data interpolation and fluid-structure interaction simulation involving curved surfaces.

The purpose of this paper is to present a Differential Immune Clone Clustering Algorithm (DICCA) to solve image segmentation.

DICCA combines immune clone selection and differential evolution, and two populations are used in the evolutionary process. Clone reproduction and selection, differential mutation, crossover and selection are adopted to evolve two populations, which can increase population diversity and avoid local optimum. After extracting the texture features of an image and encoding them with real numbers, DICCA is used to partition these features, and the final segmentation result is obtained.

This approach is applied to segment all sorts of images into homogeneous regions, including artificial synthetic texture images, natural images and remote sensing images, and the experimental results show the effectiveness of the proposed algorithm.

The method presented in this paper represents a new approach to solving clustering problems. The novel method applies the idea two populations are used in the evolutionary process. The proposed clustering algorithm is shown to be effective in solving image segmentation.

With the increasing demand for surveillance and smart transportation, drone technology has become the center of attraction for robotics researchers. This study aims to introduce a new path planning approach to drone navigation based on topology in an uncertain environment. The main objective of this study is to use the Ricci flow evolution equation of metric and curvature tensor over angular Riemannian metric, and manifold for achieving navigational goals such as path length optimization at the minimum required time, collision-free obstacle avoidance in static and dynamic environments and reaching to the static and dynamic goals. The proposed navigational controller performs linearly and nonlinearly both with reduced error-based objective function by Riemannian metric and scalar curvature, respectively.

Topology and manifolds application-based methodology establishes the resultant drone. The trajectory planning and its optimization are controlled by the system of evolution equation over Ricci flow entropy. The navigation follows the Riemannian metric-based optimal path with an angular trajectory in the range from 0° to 360°. The obstacle avoidance in static and dynamic environments is controlled by the metric tensor and curvature tensor, respectively. The in-house drone is developed and coded using C++. For comparison of the real-time results and simulation results in static and dynamic environments, the simulation study has been conducted using MATLAB software. The proposed controller follows the topological programming constituted with manifold-based objective function and Riemannian metric, and scalar curvature-based constraints for linear and nonlinear navigation, respectively.

This proposed study demonstrates the possibility to develop the new topology-based efficient path planning approach for navigation of drone and provides a unique way to develop an innovative system having characteristics of static and dynamic obstacle avoidance and moving goal chasing in an uncertain environment. From the results obtained in the simulation and real-time environments, satisfactory agreements have been seen in terms of navigational parameters with the minimum error that justifies the significant working of the proposed controller. Additionally, the comparison of the proposed navigational controller with the other artificial intelligent controllers reveals performance improvement.

In this study, a new topological controller has been proposed for drone navigation. The topological drone navigation comprises the effective speed control and collision-free decisions corresponding to the Ricci flow equation and Ricci curvature over the Riemannian metric, respectively.

Single-shot multi-category clothing recognition and retrieval play a crucial role in online searching and offline settlement scenarios. Existing clothing recognition methods based on RGBD clothing images often suffer from high-dimensional feature representations, leading to compromised performance and efficiency.

To address this issue, this paper proposes a novel method called Manifold Embedded Discriminative Feature Selection (MEDFS) to select global and local features, thereby reducing the dimensionality of the feature representation and improving performance. Specifically, by combining three global features and three local features, a low-dimensional embedding is constructed to capture the correlations between features and categories. The MEDFS method designs an optimization framework utilizing manifold mapping and sparse regularization to achieve feature selection. The optimization objective is solved using an alternating iterative strategy, ensuring convergence.

Empirical studies conducted on a publicly available RGBD clothing image dataset demonstrate that the proposed MEDFS method achieves highly competitive clothing classification performance while maintaining efficiency in clothing recognition and retrieval.

This paper introduces a novel approach for multi-category clothing recognition and retrieval, incorporating the selection of global and local features. The proposed method holds potential for practical applications in real-world clothing scenarios.

The purpose of this paper is to establish the influence of oil concentration in oil-in-water emulsions on their flammability on hot surfaces and on their viscosity. The interest in fire test systematization is obviously developing due to many grades and applications of fluids and new design solutions asking for higher parameters in exploitation, including pressure and temperature. Higher temperature and pressure have a synergic effect on fire risk; thus, a special attention has to be given to selecting fluids based on fire tests.

This test simulates a hazardous event when a fluid drops on a hot surface: 10 ml of fluid is dropped during 40-60 seconds on a manifold kept at a constant temperature, from a distance of 300 ± 5 mm above the surface. Tests were done under the procedure of SR EN ISO 20823:2004, with an original equipment. The apparent viscosity of the tested fluids was determined using a rheometer Rheotest 2. The tests were done for the fully mineral oil (Prista MHE-40) and for emulsions with different oil volume in water: 5, 10, 20, 30, 40, 50, 60, 70, 80 and 90 per cent, respectively.

The mineral oil MHE 40 Prista does not burn repeatedly for manifold temperature lower than 440°C, but it burns at 450°C on the clean surface and at 425°C on dirty surface, as obtained after testing the same oil, but at a temperature for which the oil burns. The emulsions do not burn even at 90 per cent oil in water, but the apparent viscosity of the emulsion is too high and unstable, above 20-30 per cent (volume) oil in water. No evident relationship was found between the apparent viscosity of the emulsions and their behavior on hot surface.

The hydraulic fluids were ranked, taking into account the flammability characteristics determined with the help of this test.

This paper aims to reduce the risk of fire in hazardous environments using fire-resistant fluids.

Testing hydraulic fluids under the procedure of SR EN ISO 20823:2004 is required by European and national regulations to avoid large-scale accidents produced by the ignition of hydraulic fluids.

As far as the authors have known, the test procedure was only used for establishing whether a certain fluid passes or does not pass this test. The authors did not find any references for establishing the influence of oil concentration on the flammability characteristics. Also, the equipment has an original design, allowing for a good repeatability and a high protection of the operator.

This paper aims to use the redundancy of a 7-DOF (degree of freedom) serial manipulator to solve motion planning problems along a given 6D Cartesian tool path, in the presence of geometric constraints, namely, obstacles and joint limits.

This paper describes an explicit expression of the task submanifolds for a 7-DOF redundant robot, and the submanifolds can be parameterized by two parameters with this explicit expression. Therefore, the global search method can find the feasible path on this parameterized graph.

The proposed planning algorithm is resolution complete and resolution optimal for 7-DOF manipulators, and the planned path can satisfy task constraint as well as avoiding singularity and collision. The experiments on Motoman SDA robot are reported to show the effectiveness.

This algorithm is still time-consuming, and it can be improved by applying parallel collision detection method or lazy collision detection, adopting new constraints and implementing more effective graph search algorithms.

Compared with other task constrained planning methods, the proposed algorithm archives better performance. This method finds the explicit expression of the two-dimensional task sub-manifolds, so it’s resolution complete and resolution optimal.