Toward seed-insensitive solutions to local community detection

Local community detection aims at finding a community structure starting from a seed which is a given vertex in a network without global information, such as online social networks that are too large and dynamic to ever be known fully. Nonetheless, the existing approaches to local community detection are usually sensitive to seeds, i.e., some seeds may lead to missing of some true communities. In this paper, we present a seed-insensitive method called GMAC and its variation iGMAC for local community detection. They estimate the similarity among vertices by investigating vertices’ neighborhoods, and reveal a local community by maximizing its internal similarity and minimizing its external similarity simultaneously. Extensive experimental results on both synthetic and real-world data sets verify the effectiveness of our algorithms.     

Increasing recommended effectiveness with markov chains and purchase intervals

Recommendation system is an important component of many websites and has brought huge economic benefits and challenges for online shoppers and e-commerce companies. Existing recommendation systems focus on producing a list of products which users may be interested to purchase, while overlooking the purchase chain and temporal diversity which may increase the likelihood of a purchase decision. In this paper, we propose to utilize the Markov chain to track the chain of users’ purchase behaviors and utilize the purchase intervals to improve the temporal diversity for e-commerce recommender. We design and implement several algorithms and integrate these into our recommendation model. We evaluate our system on a real-world e-commerce dataset. Experimental results demonstrate that our approach significantly improves the accuracy, conversion rate and temporal diversity compared to the state-of-the-art algorithms.     

Collaborative representation with reduced residual for face recognition

Collaboration representation-based classification (CRC) was proposed as an alternative approach to the sparse representation method with similar efficiency. The CRC is essentially a competition scheme for the training samples to compete with each other in representing the test sample, and the training class with the minimum representation residual from the test sample wins the competition in the classification. However, the representation error is usually calculated based on the Euclidean distance between a test sample and the weighted sum of all the same-class samples. This paper exploits alternative methods of calculating the representation error in the CRC methods to reduce the representation residual in a more optimal way, so that the sample classes compete with each other in a closer range to represent the test sample. A large number of face recognition experiments on three face image databases show that the CRC methods with optimized presentation residual achieve better performance than the original CRC, and the maximum improvement in classification accuracy is up to 12 %.     

Exponential synchronization of two totally different chaotic systems based on a unified model

This paper presents an exponential synchronization scheme between two chaotic systems with different structures and parameters. A unified model consisting of a linear dynamic system and a bounded static nonlinear operator is employed to describe these totally different chaotic systems. A novel state feedback control law is established to exponentially synchronize the two unified models with different parameters. Most chaotic systems with different structures and parameters, such as Hopfield neural networks, cellular neural networks, Chua’s circuits, unified chaotic systems, Qi systems, and chaotic recurrent multilayer perceptrons, can be transformed into this unified model with the synchronization controller designed in a unified way. Two numerical examples are exploited to illustrate the effectiveness of the proposed design schemes.     

A power spectrum optimization algorithm inspired by magnetotactic bacteria

Magnetotactic bacteria (MTB) are one kind of bacteria with magnetic particles called magnetosomes in their bodies. These particles often connect together like a chain. The MTB move toward the ideal living conditions under the interaction between magnetic field produced by the magnetic particles chain and that of the earth. In the paper, a new magnetic bacteria algorithm based on power spectrum (PSMBA) for optimization is proposed. The candidate solutions are decided by power spectrum in the algorithm. It mainly includes four steps: power spectrum calculation, bacteria swimming, bacteria rotation and bacteria replacement. The effect of swimming schemes and parameter settings on the performance of PSMBA is studied. And it is compared with GA, PSO and its variants and some other optimization algorithms on 25 benchmark functions including CEC2005. The simulation results show that PSMBA has better performance on most of the problems than most of the compared algorithms.     

A new Chinese character recognition approach based on the fuzzy clustering analysis

In this paper, a new Chinese character recognition (CCR) approach is proposed based on the fuzzy clustering analysis theory. Chinese characters (CCs) have various similar radicals and stroke components, which make it difficult to recognize features in the CCR process. At the same time, the recognition accuracy and the efficiency are lower when the objects to be recognized are complex. In order to solve these problems, a fuzzy clustering analysis method is introduced to enhance the computing efficiency. At first, the CCs including learning samples and testing samples are transformed into binarization templates in the form of matrixes. Then, the minimum distance algorithm is applied to calculate ‘distances’ between the testing sample templates and the learning sample templates. At last, the character recognition can be achieved by searching the minimum distance from the results. The experiment results of the CCR process can prove the effectiveness and accuracy of the new method.     

A new gear fault feature extraction method based on hybrid time–frequency analysis

Gear is one of the popular and important components in the rotary machinery transmission. Vibration monitoring is the common way to take gear feature extraction and fault diagnosis. The gear vibration signal collected in the running time often reflects the characteristics such as non-Gaussian and nonlinear, which is difficult in time domain or frequency domain analysis. This paper proposed a novel gear fault feature extraction method based on hybrid time–frequency analysis. This method combined the Mexican hat wavelet filter de-noise method and the auto term window method at the first time. This method can not only de-noise noise jamming in raw vibration signal, but also extract gear fault features effectively. The final experimental analysis proved the feasibility and the availability of this new method.     

关注非异氰酸酯聚氨酯(NIPU)涂料的应用和发展

介绍了NIPU涂料发展的现状,阐述其特点,主要原材料聚碳酸酯和多元胺的反应动力学,杂化HNIPU的概念及其应用前景。     

煤中吡啶型氮热解机理的量子化学研究

用密度泛函 ( DFT)方法 ,在 UB3LYP/6- 31 G( d)水平上研究了煤中吡啶型氮的热解机理 ,对热解过程中由于官能团周围环境的不同而形成的三类吡啶自由基进行了量子化学计算 ,通过对键的 Mulliken布居数等计算结果的分析 ,分别得到了这三类自由基的热解途径 .并优化得到了反应物、中间体和产物的几何构型 .计算结果表明 ,吡啶型氮主要是以 HCN的形式释放出来的 .NH3 是 HCN二次反应的产物 .