Search results

Accurately judging the quality of marine economic development is the premise of grasping the level and status of marine economic development. In order to scientifically evaluate the development quality of regional marine economy, the purpose of this paper is to select the marine area of Qingdao as the research object, and construct a marine economic development quality evaluation index system with 16 indicators.

The raw data is normalized by the range conversion method, and the weight of the index is determined by the information entropy model. Further, the grey relational analysis (GRA) method is used to evaluate the quality of marine economic development of Qingdao from 2012 to 2017.

The results show that the marine economic development capacity of Qingdao is with the generally increasing trend, the total marine economy is with on the rising trend, the marine storage and transportation capacity, and marine ecological environment are first decreased, and then increased. The utilization of marine resources is generally decreasing, and the comprehensive management of oceans varies with the changes of environment and economy. Therefore, in view of the development capacity of marine economy, the coordinated development of economy and environment should be carried out.

This paper uses the GRA to evaluate the quality of marine economic development and provides a reference for the development of marine economy in Qingdao.

Variable fractional delay filtering is an important technology in signal processing; the research shows that all-pass variable fractional delay (VFD) filters achieve higher design accuracy than FIR VFD filters; therefore, the design, analysis and implementation of all-pass VFD filters are of great importance.

In this paper, a two-stage approach for the design of general 1-D stable VFD all-pass filters is proposed. The method takes the desired group delay range [N−1, N], where N is the filter order.

The design algorithm is decomposed into two design stages: first, a set of fixed delay all-pass filters are designed by minimizing a set of objective functions defined in terms of approximating error criterion and filter stability constraint. Then, the design result is determined by fitting each of the fixed delay all-pass filter coefficients as 1-D polynomials. A design example together with its comparisons with those of the recent literature studies is given to justify the effectiveness of the proposed design method.

An illustrating design example shows that the method proposed can achieve better filter performances than the existing ones.

The purpose of this article is to present a new application of pursuit‐based analysis for diagnosing rolling element bearing faults.

Intelligent diagnosis of rolling element bearing faults in rotating machinery involves the procedure of feature extraction using modern signal processing techniques and artificial intelligence technique‐based fault detection and identification. This paper presents a comparative study of both the basis and matching pursuits when applied to fault diagnosis of rolling element bearings using vibration analysis.

Fault features were extracted from vibration acceleration signals and subsequently fed to a feed forward neural network (FFNN) for classification. The classification rate and mean square error (MSE) were calculated to evaluate the performance of the intelligent diagnostic procedure. Results from the basis pursuit fault diagnosis procedure were compared with the classification result of a matching pursuit feature‐based diagnostic procedure. The comparison clearly illustrates that basis pursuit feature‐based fault diagnosis is significantly more accurate than matching pursuit feature‐based fault diagnosis in detecting these faults.

Intelligent diagnosis can reduce the reliance on experienced personnel to make expert judgements on the state of the integrity of machines. The proposed method has the potential to be extensively applied in various industrial scenarios, although this application concerned rolling element bearings only. The principles of the application are directly translatable to other parts of complex machinery.

This work presents a novel intelligent diagnosis strategy using pursuit features and feed forward neural networks. The value of the work is to ease the burden of making decisions on the integrity of plant through a manual program in condition monitoring and diagnostics particularly of complex pieces of plant.

Dyadic interactions are significant for human life. Most body sensor networks-based research studies focus on daily actions, but few works have been done to recognize affective actions during interactions. The purpose of this paper is to analyze and recognize affective actions collected from dyadic interactions.

A framework that combines hidden Markov models (HMMs) and k-nearest neighbor (kNN) using Fisher kernel learning is presented in this paper. Furthermore, different features are considered according to the interaction situations (positive situation and negative situation).

Three experiments are conducted in this paper. Experimental results demonstrate that the proposed Fisher kernel learning-based framework outperforms methods using Fisher kernel-based approach, using only HMMs and kNN.

The research may help to facilitate nonverbal communication. Moreover, it is important to equip social robots and animated agents with affective communication abilities.

The presented framework may gain strengths from both generative and discriminative models. Further, different features are considered based on the interaction situations.

The purpose of this paper is to introduce an innovative theoretical, numerical and experimental investigations on the HP NGD function. The identified HP NGD topology under study is constituted by first order passive RC-network. The simulations and measurements confirm in very good agreement the HP NGD behaviors of the tested circuits. NGD responses with optimal values of about -1 ns and cut-off frequencies of about 20 MHz are obtained.

The identified HP NGD topology understudy is constituted by a first-order passive Resistor-capacitor RC network. An innovative approach to HP NGD analysis is developed. The analytical investigation from the voltage transfer function showing the meaning of HP properties is established.

This paper introduces innovative theoretical, numerical and experimental investigations on the HP NGD function.

The NGD characterization as a function of the resistance and capacitance parameters is investigated. The feasibility of the HP NGD function is verified with proofs of concept constituted of lumped surface mounted components on printed circuit boards. The simulations and measurements confirm in very good agreement the HP NGD behaviors of the tested circuits. NGD responses with optimal values of about −1 ns and cut-off frequencies of about 20 MHz are obtained.

This paper aims to explore the transformation process and transformation mechanism of carbon steel under the marine environment.

In this paper, the transformation and rust layers corrosion products on 0Cu2Cr carbon steel with different cycles coupon test was investigated and deeply explored by scanning electron microscope, energy dispersive spectrometer, X-ray diffraction.

The results showed that the thickness of rust layers grew from 71.83 µm to 533.7 µm with increasing duration of corrosion. The initial corrosion product was γ-FeOOH, then part of the γ-FeOOH continued growing, and under the capillary action, the other part of the γ-FeOOH transformed to α-FeOOH.

To the best of the authors’ knowledge, this paper puts forward for the first time a new viewpoint of the development of corrosion products of low-carbon steel in two ways. This discovery provides a new idea for the future development of steel for marine engineering.

China has a new rural revitalization strategy to stimulate rural transformation through modernizing rural areas and resolving their social contradictions. While social capital is recognized as an important element to rural revitalization and entrepreneurship, research into the role of psychological capital is less developed. Therefore, this paper assesses the impact of both social and psychological capital on entrepreneurial performance of Chinese new-generation rural migrant entrepreneurs (NGRMEs) who have returned to their homes to develop businesses as part of the rural revitalization revolution.

Based on a survey, data were collected from 525 NGRMEs in Shaanxi province. This paper uses factor analysis to determine variables for a multiple linear regression model to investigate the impacts of dimensions of both social capital and psychological capital on NGRMEs’ entrepreneurial performance.

Through the factor analysis, social capital of these entrepreneurs consists of five dimensions (reputation, participation, networks, trust and support), psychological capital has three dimensions (innovation and risk-taking, self-efficacy and entrepreneurial happiness) and entrepreneurial performance contains four dimensions (financial, customer, learning and growth, and internal business process). Furthermore, the multiple linear regression model empirically verifies that both social capital and psychological capital significantly influence and positively correlate with NGRMEs' entrepreneurial performance.

This study shows the importance of how a mixture of interrelated social and psychological dimensions influence entrepreneurial performance that may contribute to the success of the Chinese rural revitalization strategy. This has serious implications when attempting to improve the lives of over 100 million rural Chinese citizens.

Spam emails classification using data mining and machine learning approaches has enticed the researchers' attention duo to its obvious positive impact in protecting internet users. Several features can be used for creating data mining and machine learning based spam classification models. Yet, spammers know that the longer they will use the same set of features for tricking email users the more probably the anti-spam parties might develop tools for combating this kind of annoying email messages. Spammers, so, adapt by continuously reforming the group of features utilized for composing spam emails. For that reason, even though traditional classification methods possess sound classification results, they were ineffective for lifelong classification of spam emails duo to the fact that they might be prone to the so-called “Concept Drift”. In the current study, an enhanced model is proposed for ensuring lifelong spam classification model. For the evaluation purposes, the overall performance of the suggested model is contrasted against various other stream mining classification techniques. The results proved the success of the suggested model as a lifelong spam emails classification method.

The purpose of this study is to determine the tribological behavior and wear mechanism of a polytetrafluoroethylene (PTFE)/polyester (PET) fabric composite for application as a self-lubricating liner suitable for high-speed and low-load friction conditions.

The effects of different loads and sliding speeds on the friction coefficients and wear characteristics of the composite were studied using reciprocating friction tests. Scanning electron microscopy, extended depth-of-field microscopy, and energy-dispersive X-ray spectrometry was used to analyze the worn surface morphology, wear depth and elemental content of the lubrication films, respectively.

The friction coefficient curves of the composites presented a long-term steady wear stage under different sliding conditions. With increasing sliding speed, the friction coefficient and wear depth of the composite slowly increased. The film-forming mechanism of the composite revealed that the PTFE/PET ply yarn on the composite surface formed complete PTFE lubrication films at the initial sliding stage.

The PTFE/PET fabric composite maintained good friction stability and high-speed adaptability, which demonstrates that the composite has broad application prospects as a highly reliable self-lubricating bearing liner with a long lifespan.

The paper aims to build a new objective evaluation method of fabric pilling by combining an integrated image analysis technology with a deep learning algorithm.

Series of image analysis techniques were adopted. First, a Fourier transform transformed images into the frequency domain. The optimal resolution matrix of an exponential high-pass filter was determined by combining the energy algorithm. Second, the multidimensional discrete wavelet transform determined the optimal division level. Third, the iterative threshold method was used to enhance images to obtain a complete and clear pilling ball images. Finally, the deep learning algorithm was adopted to train data from pilling ball images, and the pilling levels were classified according to the learning features.

The paper provides a new insight about how to objectively evaluate fabric pilling grades. Results of the experiment indicate that the proposed objective evaluation method can obtain clear and complete pilling information and the classification accuracy rate of the deep learning algorithm is 94.2%, whose structures are rectified linear unit (ReLU) activation function, four hidden layers, cross-entropy learning rules and the regularization method.

Because the methodology of the paper is based on woven fabric, the research study’s results may lack generalizability. Therefore, researchers are encouraged to test other kinds of fabric further, such as knitted and unwoven fabrics.

Combined with a series of image analysis technology, the integrated method can effectively extract clear and complete pilling information from pilled fabrics. Pilling grades can be classified by the deep learning algorithm with learning pilling information.