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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.

In response to the challenges posed by the conventional manual flange docking method in the LNG (Liquefied Natural Gas) loading process, such as low positioning accuracy, constraints on production efficiency and safety hazards, this study analyzed the LNG five-axis loading arm’s main functions and structural characteristics.

An automated solution for the joints of the LNG loading arm was designed. The forward kinematic model of the LNG loading arm was established using the Denavit–Hartenberg (D-H) parameter method, and its workspace was analyzed. The Newton–Raphson iteration method was employed to solve the inverse kinematics of the LNG loading arm, facilitating trajectory planning. The relationship between the target position and the joint variables was established to verify the stability of the arm’s motion. Flange center identification was achieved using the Hough transform function. Based on the ROS platform, combined with Gazebo and Rviz, an experimental simulation of automatic docking of the LNG loading arm was conducted.

The docking errors in the XYZ directions were all less than 0.8 mm, meeting the required docking accuracy. Moreover, the motion performance of the loading arm during docking was smooth and free of abrupt changes, validating its capability to accomplish the automatic docking task.

The proposed trajectory planning and automatic docking scheme can be used for the rapid filling of LNG filling arms and LNG tankers to improve the efficiency of LNG transportation. In guiding the docking, the proposed automatic docking scheme is an accurate and efficient way to improve safety.

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 study is to improve the mechanical properties, thermal insulation properties and flame retardant properties of polyethylene terephthalate (PET), the organic montmorillonite (OMMT)/SiO₂ aerogel/PET composites and fibers were prepared, and the effects of the OMMT/SiO₂ aerogel on the structure, thermal conductivity and flame retardance of the OMMT/SiO₂ aerogel/PET composites and their fibers were systematically investigated.

The OMMT/SiO₂ aerogel/PET composites and fibers were prepared by in-situ polymerization and melt spinning using SiO₂ aerogel as thermal insulation filler and OMMT (DK2) as comodified filler.

The experimental results showed that OMMT improved the crystallization properties of PET. Compared with the crystallinity of SiO₂ aerogel/PET composites (34.8%), SiO₂ aerogel/PET composites and their fibers reached 45.1% and 49.2%, respectively. The breaking strength of the OMMT/SiO₂ aerogel/PET composite fibers were gradually increased with the OMMT content. When the content of OMMT was 0.8 wt.%, the fracture strength of the composite fibers reached 4.40 cN/dtex, which was 54% higher than that of the SiO₂ aerogel/PET fiber. In addition, the thermal insulation properties of the composites and their fibers were improved by addition of fillers, and at the same time reached the flame retardant level. The thermal conductivity of the 0.8 wt.% OMMT/SiO₂ aerogel/PET composites was 101.78 mW/(m·K), which was 49.3% and 58.8% lower than that of the SiO₂ aerogel/PET composites and the pure PET, respectively. The thermal conductivity of the fiber fabrics woven from the 0.8 wt.% OMMT/SiO₂ aerogel/PET composites was 28.18 mW/(m·K), which was 29.0% and 44.6% lower than that of the SiO₂ aerogel/PET composite fiber fabrics and PET fiber fabrics. The flame retardancy of the composites was improved, with an limiting oxygen index value of 29.2% for the 0.8 wt.% OMMT/SiO₂ aerogel/PET composites, which was 4.1% higher compared to the SiO₂ aerogel/PET composites, and achieved the flame retardant level.

The SiO₂ aerogel/PET composites and their fibers have good mechanical properties, flame retardant properties and thermal insulation properties, exhibited good potential for application in the field of thermal insulation, such as warm clothing. Nowadays, as the energy crisis is becoming more and more serious, it is very important to improve the thermal insulation properties of PET to reduce energy losses and mitigate the energy crisis.

In this study, PET based composites and their fibers with excellent mechanical properties, thermal insulation properties and flame retardant property were obtained by using three-dimensional network porous silica aerogel with low density and low thermal conductivity as the thermal insulation functional filler and two-dimensional layered OMMT as the synergetic modified filler.

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 prepare an oil-impregnated porous polytetrafluoroethylene (PTFE) composite with advanced tribological properties using citric acid as a novel pore-forming agent.

Citric acid (CA) was used to form pores in PTFE, and then oil-impregnated PTFE composites were prepared. The pore-forming efficiency of CA was evaluated. The possible mechanism of lubrication was proposed according to the tribological properties.

The results show CA is an efficient pore-forming agent and completely removed, and the porosity of the PTFE increases with the increase of the CA content. The oil-impregnated porous PTFE exhibits an excellent tribological performance, an increased wear resistance of 77.29% was realized in comparison with neat PTFE.

This study enhances understanding of the lubrication mechanism of oil-impregnated porous polymers and guides for their tribological applications.

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.