scholarly journals Kernel Probabilistic K-Means Clustering

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1892
Author(s):  
Bowen Liu ◽  
Ting Zhang ◽  
Yujian Li ◽  
Zhaoying Liu ◽  
Zhilin Zhang
Keyword(s):  
Step Length ◽  
Performance Comparison ◽  
Equivalent Model ◽  
The Novel ◽  
Programming Technique ◽  
Running Time ◽  
Fuzzy C Means ◽  
Synthetic Datasets ◽  
Lagrangian Optimization ◽  
Novel Model

Kernel fuzzy c-means (KFCM) is a significantly improved version of fuzzy c-means (FCM) for processing linearly inseparable datasets. However, for fuzzification parameter m=1, the problem of KFCM (kernel fuzzy c-means) cannot be solved by Lagrangian optimization. To solve this problem, an equivalent model, called kernel probabilistic k-means (KPKM), is proposed here. The novel model relates KFCM to kernel k-means (KKM) in a unified mathematic framework. Moreover, the proposed KPKM can be addressed by the active gradient projection (AGP) method, which is a nonlinear programming technique with constraints of linear equalities and linear inequalities. To accelerate the AGP method, a fast AGP (FAGP) algorithm was designed. The proposed FAGP uses a maximum-step strategy to estimate the step length, and uses an iterative method to update the projection matrix. Experiments demonstrated the effectiveness of the proposed method through a performance comparison of KPKM with KFCM, KKM, FCM and k-means. Experiments showed that the proposed KPKM is able to find nonlinearly separable structures in synthetic datasets. Ten real UCI datasets were used in this study, and KPKM had better clustering performance on at least six datsets. The proposed fast AGP requires less running time than the original AGP, and it reduced running time by 76–95% on real datasets.

A multi-objective sparse evolutionary framework for large-scale weapon target assignment based on a reward strategy

2021 ◽  
Vol 40 (5) ◽  
pp. 10043-10061
Author(s):  
Xiaoping Shi ◽  
Shiqi Zou ◽  
Shenmin Song ◽  
Rui Guo
Keyword(s):  
Large Scale ◽  
The Novel ◽  
Genetic Operators ◽  
Multi Objective ◽  
Target Assignment ◽  
Solution Selection ◽  
Battlefield Environment ◽  
Sparse Problems ◽  
Novel Model ◽  
Error Parameter

 The asset-based weapon target assignment (ABWTA) problem is one of the important branches of the weapon target assignment (WTA) problem. Due to the current large-scale battlefield environment, the ABWTA problem is a multi-objective optimization problem (MOP) with strong constraints, large-scale and sparse properties. The novel model of the ABWTA problem with the operation error parameter is established. An evolutionary algorithm for large-scale sparse problems (SparseEA) is introduced as the main framework for solving large-scale sparse ABWTA problem. The proposed framework (SparseEA-ABWTA) mainly addresses the issue that problem-specific initialization method and genetic operators with a reward strategy can generate solutions efficiently considering the sparsity of variables and an improved non-dominated solution selection method is presented to handle the constraints. Under the premise of constructing large-scale cases by the specific case generator, two numerical experiments on four outstanding multi-objective evolutionary algorithms (MOEAs) show Runtime of SparseEA-ABWTA is faster nearly 50% than others under the same convergence and the gap between MOEAs improved by the mechanism of SparseEA-ABWTA and SparseEA-ABWTA is reduced to nearly 20% in the convergence and distribution.

A novel fractional discrete grey model with an adaptive structure and its application in electricity consumption prediction

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yitong Liu ◽  
Yang Yang ◽  
Dingyu Xue ◽  
Feng Pan
Keyword(s):  
Fractional Order ◽  
Electricity Consumption ◽  
Grey Model ◽  
The Novel ◽  
Raw Data ◽  
Content Type ◽  
Power Load ◽  
Grey Models ◽  
Novel Model ◽  
Consumption Prediction

PurposeElectricity consumption prediction has been an important topic for its significant impact on electric policies. Due to various uncertain factors, the growth trends of electricity consumption in different cases are variable. However, the traditional grey model is based on a fixed structure which sometimes cannot match the trend of raw data. Consequently, the predictive accuracy is variable as cases change. To improve the model's adaptability and forecasting ability, a novel fractional discrete grey model with variable structure is proposed in this paper.Design/methodology/approachThe novel model can be regarded as a homogenous or non-homogenous exponent predicting model by changing the structure. And it selects the appropriate structure depending on the characteristics of raw data. The introduction of fractional accumulation enhances the predicting ability of the novel model. And the relative fractional order r is calculated by the numerical iterative algorithm which is simple but effective.FindingsTwo cases of power load and electricity consumption in Jiangsu and Fujian are applied to assess the predicting accuracy of the novel grey model. Four widely-used grey models, three classical statistical models and the multi-layer artificial neural network model are taken into comparison. The results demonstrate that the novel grey model performs well in all cases, and is superior to the comparative eight models.Originality/valueA fractional-order discrete grey model with an adaptable structure is proposed to solve the conflict between traditional grey models' fixed structures and variable development trends of raw data. In applications, the novel model has satisfied adaptability and predicting accuracy.

Assessing Bypassed Hydrocarbons

2021 ◽  
Author(s):  
Stanley Oifoghe ◽  
Nora Alarcon ◽  
Lucrecia Grigoletto
Keyword(s):  
Continental Shelf ◽  
Reservoir Characterization ◽  
Gamma Ray ◽  
West African ◽  
The Novel ◽  
The West ◽  
Present Case Study ◽  
Novel Model

Abstract Hydrocarbons are bypassed in known fields. This is due to reservoir heterogeneities, complex lithology, and limitations of existing technology. This paper seeks to identify the scenarios of bypassed hydrocarbons, and to highlight how advances in reservoir characterization techniques have improved assessment of bypassed hydrocarbons. The present case study is an evaluation well drilled on the continental shelf, off the West African Coastline. The targeted thin-bedded reservoir sands are of Cenomanian age. Some technologies for assessing bypassed hydrocarbon include Gamma Ray Spectralog and Thin Bed Analysis. NMR is important for accurate reservoir characterization of thinly bedded reservoirs. The measured NMR porosity was 15pu, which is 42% of the actual porosity. Using the measured values gave a permeability of 5.3mD as against the actual permeability of 234mD. The novel model presented in this paper increased the porosity by 58% and the permeability by 4315%.

scholarly journals CONTROL NOVEL MODEL OF KNEE CPM DEVICE

2009 ◽  
Vol 12 (4) ◽  
pp. 18-29
Author(s):  
Thanh Diep Cong Tu
Keyword(s):  
Weight Ratio ◽  
Artificial Muscle ◽  
Network Control ◽  
Continuous Passive Motion ◽  
The Novel ◽  
Highly Nonlinear ◽  
Cord Injury ◽  
And Control ◽  
Novel Model ◽  
Interesting Alternative

In recent years, CPM - Continuous Passive Motion has been proved to be one of the most effective therapeutic methods for patients who have problems with motion such as spinal cord injury, ankle and knee injury, parkinson and so on. Many commercial CPM devices are found in market but all of them use motors as the main actuators. The lack of human compliance of electric actuators, which are commonly used in these machines, makes them potentially harmful to patients. An interesting alternative, to electric actuators for medical purposes, particularly promising for rehabilitation, is a pneumatic artificial muscle (PAM) actuator because of its high power/weight ratio and compliance properties. However, the highly nonlinear and hysteresis of PAM make it the challenging for design and control. In this study, a PID compensation using neural network control is studied to improve the control performance of the novel model of Knee CPM device.

A scalable eigenspace-based fuzzy c-means for topic detection

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hendri Murfi
Keyword(s):  
Representation Learning ◽  
Detection Methods ◽  
Data Sets ◽  
Topic Detection ◽  
Data Set ◽  
Content Type ◽  
Running Time ◽  
Fuzzy C Means ◽  
Coherence Score ◽  
Value Decomposition

PurposeThe aim of this research is to develop an eigenspace-based fuzzy c-means method for scalable topic detection.Design/methodology/approachThe eigenspace-based fuzzy c-means (EFCM) combines representation learning and clustering. The textual data are transformed into a lower-dimensional eigenspace using truncated singular value decomposition. Fuzzy c-means is performed on the eigenspace to identify the centroids of each cluster. The topics are provided by transforming back the centroids into the nonnegative subspace of the original space. In this paper, we extend the EFCM method for scalability by using the two approaches, i.e. single-pass and online. We call the developed topic detection methods as oEFCM and spEFCM.FindingsOur simulation shows that both oEFCM and spEFCM methods provide faster running times than EFCM for data sets that do not fit in memory. However, there is a decrease in the average coherence score. For both data sets that fit and do not fit into memory, the oEFCM method provides a tradeoff between running time and coherence score, which is better than spEFCM.Originality/valueThis research produces a scalable topic detection method. Besides this scalability capability, the developed method also provides a faster running time for the data set that fits in memory.

The Novel Model of Building and Energy Engineering with New Building Materials Based on IPv6 Technology

2013 ◽  
Vol 738 ◽  
pp. 141-144
Author(s):  
Guo Fang Kuang ◽  
Zhao Feng Sun
Keyword(s):  
Building Materials ◽  
High Efficiency ◽  
New Technology ◽  
The Novel ◽  
Ip Address ◽  
Construction Engineering ◽  
Encryption And Authentication ◽  
Effective Use ◽  
Novel Model ◽  
Energy Engineering

New building materials variety and yield is developing with hitherto unknown speed, construction engineering development if the effective use of new building materials will be excellent performance of new technology. Novel building materials can significantly reduce the weight of buildings, to promote the light construction structure created the conditions. IPv6 is not only a good solution to the problem of the lack of IP address, but also due to the introduction of encryption and authentication mechanisms to make it a better improvement in the network. The paper presents the novel model of building and energy engineering based on IPv6 technology. Experimental results show that the proposed method has high efficiency.

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