An Improved K-Means Algorithm Based on Evidence Distance

The main influencing factors of the clustering effect of the k-means algorithm are the selection of the initial clustering center and the distance measurement between the sample points. The traditional k-mean algorithm uses Euclidean distance to measure the distance between sample points, thus it suffers from low differentiation of attributes between sample points and is prone to local optimal solutions. For this feature, this paper proposes an improved k-means algorithm based on evidence distance. Firstly, the attribute values of sample points are modelled as the basic probability assignment (BPA) of sample points. Then, the traditional Euclidean distance is replaced by the evidence distance for measuring the distance between sample points, and finally k-means clustering is carried out using UCI data. Experimental comparisons are made with the traditional k-means algorithm, the k-means algorithm based on the aggregation distance parameter, and the Gaussian mixture model. The experimental results show that the improved k-means algorithm based on evidence distance proposed in this paper has a better clustering effect and the convergence of the algorithm is also better.

Bayesian inference and Markov chain Monte Carlo based estimation of critical slip distance parameter in rate and state model for fault friction

We present an algorithmic framework to solve an inverse problem using Bayesian inference and Markov chain Monte Carlo sampling. The input of the inverse problem is the acceleration of the slipping seismogenic fault and the output is the probability distribution of the critical slip distance parameter of the rate and state model for fault friction.

Performance Comparison of Several Range-based Techniques for Indoor Localization Based on RSSI

The classical rang-based technique for position estimation is still reliably used for indoor localization. Trilateration and multilateration, which include three or more references to locate the indoor object, are two common examples. These techniques use at least three intersection-locations of the references' distance and conclude that the intersection is the object's position. However, some challenges have appeared when using a simple power-to-distance parameter, i.e., received signal strength indicator (RSSI). RSSI is known for its fluctuated values when used as the localization parameter. The improvement of classical range-based has been proposed, namely min-max and iRingLA algorithms. These algorithms or methods use the approximation in a bounding-box and rings for min-max and iRingLA, respectively. This paper discusses the comparison performance of min-max and iRingLA with multilateration as the classical method. We found that min-max gives the best performance, and in some positions, iRingLA gives the best accuracy error. Hence, the approximation method can be promising for indoor localization, especially when using a simple and straightforward RSSI parameter.

Van der Waals Interactions of Moving Particles with Surfaces of Cylindrical Geometry

General nonrelativistic theory has been developed and the expressions obtained for the tangential (dissipative) and radial (conservative) image forces and van der Waals forces (vdW) acting on charged and neutral particles when they move parallel to the axis of a cylinder with circular cross-section, or in the space between coaxial cylinders. Numerical calculations of vdW forces have been performed for metal (Au) and dielectric (Si) materials of cylinders (filaments) and Cs atoms at velocities ~107m/s. A remarkable result is that in the case of metal cylinders (atomic filaments and chains) the dynamic vdW potential can be repulsive for certain values of the velocity–distance parameter and the characteristic atomic frequency. In the case of a Si material, the dynamic vdW potential increases relative to the static one (by modulus) when the velocity–distance parameter Vω0/R changes from zero to ~1.3 and then tends to zero.

Dependence of inclusive jet production on the anti-kT distance parameter in pp collisions at $$ \sqrt{\mathrm{s}} $$ = 13 TeV

The dependence of inclusive jet production in proton-proton collisions with a center-of-mass energy of 13 TeV on the distance parameter R of the anti-kT algorithm is studied using data corresponding to integrated luminosities up to 35.9 fb−1 collected by the CMS experiment in 2016. The ratios of the inclusive cross sections as functions of transverse momentum pT and rapidity y, for R in the range 0.1 to 1.2 to those using R = 0.4 are presented in the region 84 < pT< 1588 GeV and |y|< 2.0. The results are compared to calculations at leading and next-to-leading order in the strong coupling constant using different parton shower models. The variation of the ratio of cross sections with R is well described by calculations including a parton shower model, but not by a leading-order quantum chromodynamics calculation including nonperturbative effects. The agreement between the data and the theoretical predictions for the ratios of cross sections is significantly improved when next-to-leading order calculations with nonperturbative effects are used.

Improved Density Peaks Clustering Based on Natural Neighbor Expanded Group

Density peaks clustering (DPC) is an advanced clustering technique due to its multiple advantages of efficiently determining cluster centers, fewer arguments, no iterations, no border noise, etc. However, it does suffer from the following defects: (1) difficult to determine a suitable value of its crucial cutoff distance parameter, (2) the local density metric is too simple to find out the proper center(s) of the sparse cluster(s), and (3) it is not robust that parts of prominent density peaks are remotely assigned. This paper proposes improved density peaks clustering based on natural neighbor expanded group (DPC-NNEG). The cores of the proposed algorithm contain two parts: (1) define natural neighbor expanded (NNE) and natural neighbor expanded group (NNEG) and (2) divide all NNEGs into a goal number of sets as the final clustering result, according to the closeness degree of NNEGs. At the same time, the paper provides the measurement of the closeness degree. We compared the state of the art with our proposal in public datasets, including several complex and real datasets. Experiments show the effectiveness and robustness of the proposed algorithm.

Cross-Linked PIM-1 Membranes with Improved Stability to Aromatics

Composite membranes were prepared for nanofiltration of aromatic solvents. Cross-linking with AlCl3 was used to improve the stability of the PIM-1 selective layer in aromatic solvents like toluene, benzene and xylene. Nanofiltration performances of obtained membranes were tested with 4 different aromatic hydrocarbons and with 3 solvents from other classes of solvents. Obtained permeability for aromatic hydrocarbons was above 8,5 kg/m2·h·bar and retention of Remazol brilliant blue R dye with molecular mass 626 was up to 96 %. It was shown that permeability results correlated with Hansen solubility parameter and distance parameter between polymer and solvent. PIM-1 has higher permeability for non-polar hydrocarbons due to higher affinity between polymer and solvent.

Experimental Study on the Behavior of a Bubble in the Vicinity of an Air Bubble Attached to a Fixed Structure

The interaction between a spark-generated bubble and a non-oscillating air bubble attached to a horizontal rigid plate is investigated using a low-voltage spark-discharge setup. Large numbers of fascinating and complicated interactions are given rise during two bubble coupling pulsation due to the deformation property endowed to the plate through an air bubble attached. It is found that, under the combined influence of the cavitation and the structure, some complex phenomenon likes the bubble coalescence, air bubble skirt phenomenon, bubble splitting are given rise during the two bubble interaction procedure. The coupling phenomenon between the spark generated bubble, air bubble and the structure are discussed in detailed The mainly attention are paid to the coupled response of an attached quiescent bubble under oscillating bubble, the effect of the distance parameter and volume ratio parameter on the air bubble shapes are analyzed in detail, and the mechanism behind these phenomenon are investigated. Our study can be a useful scheme in the protection of a naval structure from threats induced by an explosion bubble impact loading.

Texture Analysis and Genetic Algorithms for Osteoporosis Diagnosis

Early diagnosis of osteoporosis can efficiently predict fracture risk. There is a great demand to prevent this disease. The goal of this study was to distinguish osteoporotic cases from healthy controls on 2D bone radiograph images, using texture analysis and genetic algorithms (GAs). Gray Level Co-occurrence Matrix (GLCM), Run length Matrix (RLM) and Binarized Statistical Image Features (BSIF) were used for texture analysis. Features are numerous and parameter-dependent. The related experts can pick out the useful input features for the classifier. It however remains a difficult task and may be inefficient or even harmful as the data pattern is not clear. In this paper, GAs were used to optimize the two parameters of the co-occurrence matrix (distance parameter or pixel separation, orientation or direction) and the number of gray levels used in the preprocessing quantification step. GAs were also used to select the best combination of features extracted from GLCM and RLM matrices. Experiments were conducted on two populations composed of Osteoporotic Patients and Control Subjects. Results show that GAs combined with GLCM and BSIF features can improve the classification rates (ACC = 87.50%) obtained using GLCM (ACC = 77.8%) alone.