Constructions of Optimal Optical Orthogonal Codes with Weights Set 5,7 via Cyclic Packing

Double-weight optical orthogonal codes are variable-weight optical orthogonal codes (OOCs), which have been widely applied in optical networks and systems. Some works have been devoted to optimal n , W , 1 , Q -OOCs with max w : w ∈ W ≤ 6 . So far, there is no explicit construction of optimal n , W , 1 , Q -OOCs with W = 5,7 . It is known that heavier-weight codewords have better code performance than lighter-weight codewords. So, in this paper, we use cyclic packing to construct two infinite classes of optimal OOCs with weights set 5,7 explicitly, for any prime p ≡ 3 mod 4 and p ≥ 7 . In addition, for 1 ≤ t < p − 1 / 2 , by breaking t blocks of size 7 into 3 of 31 p , 5,7 , 1 , 1 / 2 , 1 / 2 -OOCs and 41 p , 5,7 , 1 , 2 / 3 , 1 / 3 -OOCs, we obtain new infinite classes of optimal 31 p , 3,5,7 , 1 , 7 t / p − 1 + 6 t , p − 1 / 2 p − 1 + 6 t , p − 1 − 2 t / 2 p − 1 + 6 t -OOCs and 41 p , 3,5,7 , 1 , 14 t / 3 p − 1 + 4 t , 2 p − 1 / 3 p − 1 + 4 t , p − 1 − 2 t / 3 p − 1 + 4 t -OOCs, respectively.

Spectral Clustering of Mixed-Type Data

Cluster analysis seeks to assign objects with similar characteristics into groups called clusters so that objects within a group are similar to each other and dissimilar to objects in other groups. Spectral clustering has been shown to perform well in different scenarios on continuous data: it can detect convex and non-convex clusters, and can detect overlapping clusters. However, the constraint on continuous data can be limiting in real applications where data are often of mixed-type, i.e., data that contains both continuous and categorical features. This paper looks at extending spectral clustering to mixed-type data. The new method replaces the Euclidean-based similarity distance used in conventional spectral clustering with different dissimilarity measures for continuous and categorical variables. A global dissimilarity measure is than computed using a weighted sum, and a Gaussian kernel is used to convert the dissimilarity matrix into a similarity matrix. The new method includes an automatic tuning of the variable weight and kernel parameter. The performance of spectral clustering in different scenarios is compared with that of two state-of-the-art mixed-type data clustering methods, k-prototypes and KAMILA, using several simulated and real data sets.

An Analysis of Computer-Aided Design Software Course Teaching in Visual Communication Design Major by Integrating Grey Variable Weight Clustering Evaluation Model

Computer-aided design software course has become a compulsory course for visual communication design specialty in many colleges and universities, but there are some problems in its development, such as boring classroom, lack of specialty, weak practice, and single assessment. Due to the inaccuracy of data in the teaching process of traditional CAD software course, this paper puts forward an innovative educational model of CAD software course in visual communication design major. Visual communication design major carries out the career planning of college students under the background of Internet +, comprehensively analyzes the employment situation of college students from the career planning introduction, on the basis of exploring the current situation of computer-aided design software course education in China, and analyzes the curriculum reform of this major students’ career planning.

Parameter Identification of the Yoshida-Uemori Hardening Model for Remanufacturing

The deformation of plastics during production and service means that retired parts often possess different mechanical states, and this can directly affect not only the properties of remanufactured mechanical parts, but also the design of the remanufacturing process itself. In this paper, we describe the stress-strain relationship for remanufacturing, in particular the cyclic deformation of parts, by using the particle swarm optimization (PSO) method to acquire the Yoshida-Uemori (Y-U) hardening model parameters. To achieve this, tension-compression experimental data of AA7075-O, standard PSO, oscillating second-order PSO (OS-PSO) and variable weight PSO (VW-PSO) were acquired separately. The influence of particle numbers on the inverse analysis efficiency was studied based on standard PSO. Comparing the results of PSO variations showed that: 1) standard PSO is able to avoid local solutions and obtain Y-U model parameters to the same degree of precision as the OS-PSO; 2) by adjusting section weight, the VW-PSO could improve local fitting accuracy and adapt to asymmetric deformation; 3) by reducing particle numbers to a certain extent, the efficiency of analysis can be improved while also maintaining accuracy.

VOC and CO Removals by Perovskite Type Nanocatalysts Supported on Commercial Substrates

In this research, it was tried to choose a kind of perovskite catalyst with optimized formulation La0.8Sr0.2Co0.66Fe0.34O3 to remove air pollutants. This perovskite catalyst stabilized on the various supports such as alumina and ZSM-5 with the sol-gel synthesis technique and ceramic monolith by dip-coating method. Four different catalysts by variable weight percentage including PE-Al 10%, PE-Al 20%, PE-Al 30%, and PE-Al 40% were prepared by sol-gel synthesis technique. In this work, the XRD technique was used to confirm the formation of perovskite catalysts’ crystalline phases on the supports. As a result, XRD patterns revealed the formation of the perovskite phase onto the alumina and zeolite supports. Activity tests of these four catalysts were examined in the catalytic oxidation of Toluene and CO using an experimental setup consisting of a tubular flow reactor at the temperature 280-400°C and 100-400°C for the toluene and CO removal systems, respectively. According to the results of the catalysts’ activity test, the alumina supported with 40% w/w perovskite catalyst showed the best performance, and its activity was similar to the activity of the bulk catalyst (over 95% conversion of toluene at about 290°C). For the coated catalysts on a ceramic monolith, the complete removal of carbon monoxide at 50°C was lower than the powdered form. Results from the activity test in a toluene removal system that show coating of the bulk and supported catalysts on ceramic monolith; have an essential impact on the activity test of these catalysts.

Variable Weight Matter–Element Extension Model for the Stability Classification of Slope Rock Mass

The slope stability in an open-pit mine is closely related to the production safety and economic benefit of the mine. As a result of the increase in the number and scale of mine slopes, slope instability is frequently encountered in mines. Therefore, it is of scientific and social significance to strengthen the study of the stability of the slope rock mass. To accurately classify the stability of the slope rock mass in an open-pit mine, a new stability evaluation model of the slope rock mass was established based on variable weight and matter–element extension theory. First, based on the main evaluation indexes of geology, the environment, and engineering, the stability evaluation index system of the slope rock mass was constructed using the corresponding classification criteria of the evaluation index. Second, the constant weight of the evaluation index value was calculated using extremum entropy theory, and variable weight theory was used to optimize the constant weight to obtain the variable weight of the evaluation index value. Based on matter–element extension theory, the comprehensive correlation between the upper and lower limit indexes in the classification criteria and each classification was calculated, in addition to the comprehensive correlation between the rock mass indexes and the stability grade of each slope. Finally, the grade variable method was used to calculate the grade variable interval corresponding to the classification criteria of the evaluation index and the grade variable value of each slope rock mass, so as to determine the stability grade of the slope rock. The comparison results showed that the classification results of the proposed model are in line with engineering practice, and more accurate than those of the hierarchical-extension model and the multi-level unascertained measure-set pair analysis model.