Search results

Recent development of wireless communication devices dictates that miniaturization, multi-functions and high integration are the important factors for antenna structures. This has resulted in the requirement of antennas with dual or multi-frequency operations. Although the dual-band antennas can be achieved through the experience-based configuration selection with the parameter adjustment, it is still a challenging problem to design an antenna with specific dual-frequency operations effectively. The purpose of the paper is to develop an effective design method to guide the design of antennas with specific dual-frequency operations.

The topology optimization is carried out through the material distribution approach, where the patch of the antenna is taken as the design domain. The optimization formulation is established with maximizing the minimum antenna efficiency at the target frequencies. The sensitivity of the antenna efficiency with the design variables is derived, and the optimization problem is solved by a gradient-based algorithm.

Based on the proposed design method, an example of a patch antenna design for specific dual-frequency operations is presented. The performance of the designed antenna is cross-verified by experimentation, where the reflection coefficients (S₁₁) obtained by simulation and experiment show a good agreement. The simulation and the experimentation of the designed antenna show that two operational bands are optimized to occur around the target frequencies, which confirms the effectiveness of the proposed design method.

This paper presents a topology optimization-based design method for patch antennas operating at dual specific frequencies.

The paper aims to propose a novel dual-stage shape memory alloy (SMA) actuated gripper (DAG), of which the grasp performance is improved through primary and secondary actuation.

This paper presents a method of integrating the design of dual-stage actuation modules based on the SMA bias actuation principle to enhance the grasping shape adaptability and force modulation of a DAG. The actuation angle range and grasping performance of the DAG are investigated by thermomechanical analysis and the finite element method based numerical simulation.

The results of present experiments and simulations indicate that the actuation angle scope of the DAG is about 20° under no load, which enables the grasping space occupied by an object in the DAG from 60 mm to 120 mm. The grasping force adjusted by changing the input power of the primary main actuation module and secondary fine-tuning actuation module can reach a maximum of 2 N, which is capable of grasping objects of various sizes, weights, shapes, etc.

The contribution of this paper is to design a DAG based on SMA, and establish the solution methods for the primary main actuation module and secondary fine-tuning actuation module, respectively. It lays a foundation for the research of lightweight and intelligent robotic grippers.

This paper aims to study the energy absorption properties of the thin-walled square tube with lateral piecewise variable thickness under axial crashing and the influence of the tube parameters on energy absorption.

In this work, the energy absorption properties of the thin-walled square tube were analyzed by theoretical, numerical and experimental approach. The numerical results are obtained based on the finite element method. The explicit formulation for predicting the mean crushing force of the tube with lateral piecewise variable thickness was derived based on Super Folding Element method. The limitation of the prediction formulation was analyzed by numerical calculation. The numerical calculation was also used to compare the energy absorption between the tube with lateral piecewise variable thickness and other tubes, and to carry out the parametric analysis.

Results indicate that the thin-walled tube with lateral piecewise variable thickness has higher energy absorption properties than the uniform thickness tubes and the tubes with lateral linear variable thickness. The thickness of the corner is the key factor for the energy absorption of the tubes. The thickness of the non-corner region is the secondary factor. Increasing the corner thickness and decreasing the non-corner thickness can make the energy absorption improved. It is also found that the prediction formulation of the mean crushing force given in this paper can quickly and accurately predict the energy absorption of the square tube.

The outcome of the present research provides a design idea to improve the energy absorption of thin-walled tube by designing cross-section thickness and gives an explicit formulation for predicting the mean crushing force quickly and accurately.

In this paper, a homogenization‐based multi‐scale method for predicting the effective thermal conductivity of porous materials with radiation is presented, which considers the effect of geometry and distribution of pores. Using homogenization method to solve the pure conductive problem of porous materials with periodic structure, the effective thermal conductivity without considering radiation is predicted, and a temperature field in a local domain of a unit cell is obtained. This temperature field is taken as the good approximation of the real temperature distribution, and the radiative thermal conductivity is obtained. The effect of the microstructure, the distribution and geometry of pores on heat transfer of porous materials is discussed. It is concluded that the dimension of the pores is an important influence factor on the thermal transfer property of porous materials if radiation is considered. Increasing the pore’s dimension enhances the contribution of radiation to the heat transfer property of porous materials. For porous materials with cylindrical and spherical pores, the radiative thermal conductivity is proportional to pore’s diameter.

Usually, an optimal topology is obtained by optimizing the material distribution within a prescribed domain; for example, a rectangular domain with a specified length and width for a plane problem. However, the dimensions (i.e. aspect ratio) of a rectangular design domain have significant influence on the resultant optimal topology. In this paper, a minimum Averaged Compliance Density (ACD) based method for topology optimization of structures is proposed. Unlike the conventional topology optimization method, the ACD is taken as the objective function, and the topology and domain dimensions of the structure are optimized simultaneously. As an example, the topology of a cantilever beam with large aspect ratio will be optimized, which is often difficult for traditional topology optimization algorithms. Through optimizing the topology and the dimensions of the design domain, a base structure is obtained, which is repeated to yield the whole, assembled beam. The influence of the relative values of shear force and moment is analyzed numerically. Results show that as the value of the bending moment increases relative to the shear force, the optimal topology changes from a truss‐like structure to a vertically stiffened box‐like structure.

The purpose of this paper is to classify Chinese word semantic relations, which are synonyms, antonyms, hyponyms and meronymys.

Basically, four simple methods are applied, ontology-based, dictionary-based, pattern-based and morpho-syntactic method. The authors make good use of search engine to build lexical and semantic resources for dictionary-based and pattern-based methods. To improve classification performance with more external resources, they also classify the given word pairs in Chinese and in English at the same time by using machine translation.

Experimental results show that the approach achieved an average F1 score of 50.87 per cent, an average accuracy of 70.36 per cent and an average recall of 40.05 per cent over all classification tasks. Synonym and antonym classification achieved high accuracy, i.e. above 90 per cent. Moreover, dictionary-based and pattern-based approaches work effectively on final data set.

For many natural language processing (NLP) tasks, the step of distinguishing word semantic relation can help to improve system performance, such as information extraction and knowledge graph generation. Currently, common methods for this task rely on large corpora for training or dictionaries and thesauri for inference, where limitation lies in freely data access and keeping built lexical resources up-date. This paper builds a primary system for classifying Chinese word semantic relations by seeking new ways to obtain the external resources efficiently.

Based on user-generated content from a Chinese social media platform, this paper aims to investigate multiple methods of constructing user profiles and their effectiveness in predicting their gender, age and geographic location.

This investigation collected 331,634 posts from 4,440 users of Sina Weibo. The data were divided into two parts, for training and testing . First, a vector space model and topic models were applied to construct user profiles. A classification model was then learned by a support vector machine according to the training data set. Finally, we used the classification model to predict users’ gender, age and geographic location in the testing data set.

The results revealed that in constructing user profiles, latent semantic analysis performed better on the task of predicting gender and age. By contrast, the method based on a traditional vector space model worked better in making predictions regarding the geographic location. In the process of applying a topic model to construct user profiles, the authors found that different prediction tasks should use different numbers of topics.

This study explores different user profile construction methods to predict Chinese social media network users’ gender, age and geographic location. The results of this paper will help to improve the quality of personal information gathered from social media platforms, and thereby improve personalized recommendation systems and personalized marketing.

Online comments significantly impact consumer choice and product sales. Existing research focuses on the direct effects of online comments on product sales, whereas studies on the spillover effects of online comments are relatively limited, especially for high-involvement products. This study explores the impact of online comments of competing products on focal product sales in high-involvement products.

Data mining techniques are used to collect 72,367 online comments from the Autohome platform, and sentiment analysis algorithms are used to quantify the textual information for subsequent analysis. Specifically, two panel two-way fixed-effects models are constructed to explore the impact of the average valence and quantity of online comments of competing cars on focal car sales, and analyse this impact in terms of heterogeneity across car price levels, while the moderating effect of online comments of competing cars is explored.

The results show that the average quantity of online comments of competing cars has a significant effect on the sales of the focal car in the overall sample, while the average valence of online comments of competing cars does not have a significant spillover effect. Moreover, the spillover effect varies by car price level. For high-priced cars, the average quantity of online comments of competing cars significantly and negatively affects focal car sales, and the average valence of online comments of competing cars significantly and negatively moderates the effect of the valence of focal car online comments on its sales. For lower-priced cars, online comments of competing cars don’t significantly affect focal car sales.

This study not only enriches the theory of online comments and high-involvement product sales, but also provides reference and guidance for exploring spillover effects of online comments for other products.

Food plays an important role in every culture around the world. Recently, cuisine preference analysis has become a popular research topic. However, most of these studies are conducted through questionnaires and interviews, which are highly limited by the time, cost and scope of data collection, especially when facing large-scale survey studies. Some researchers have, therefore, attempted to mine cuisine preferences based on online recipes, while this approach cannot reveal food preference from people’s perspective. Today, people are sharing what they eat on social media platforms by posting reviews about the meal, reciting the names of appetizers or entrees, and photographing as well. Such large amount of user-generated contents (UGC) has potential to indicate people’s preferences over different cuisines. Accordingly, the purpose of this paper is to explore Chinese cuisine preferences among online users of social media.

Based on both UGC and online recipes, the authors first investigated the cuisine preference distribution in different regions. Then, dish preference similarity between regions was calculated and few geographic factors were identified, which might lead to such regional similarity appeared in our study. By applying hierarchical clustering, the authors clustered regions based on dish preference and ingredient usage separately.

Experimental results show that, among 20 types of traditional Chinese cuisines, Sichuan cuisine is most favored across all regions in China. Geographical proximity is the more closely related to differences of regional dish preference than climate proximity.

Different from traditional definitions of regions to which cuisine belong, the authors found new association between region and cuisine based on dish preference from social media and ingredient usage of dishes. Using social media may overcome problems with using traditional questionnaires, such as high costs and long cycle for questionnaire design and answering.

Over the past 20 years, China's infrastructure has developed at an extraordinary speed. The current literature mainly focuses on the effects of political incentives on the infrastructure. However, this paper indicates that the structural change of China's land regime is an important clue and that the supernormal development of China's infrastructure is an explicable result for that.

This paper theoretically proves that in a politically centralized and economically decentralized economic entity with a public land-ownership regime, the self-financing mechanism formed by local officials through regulation of the land-grant price is the primary factor that influences the optimal supply volume of infrastructure in a region, in addition to political and economic incentives, and whether the self-financing mechanism can be formed or not depends on the structure of a country's land regime, which can help to explain the difference between the development of infrastructure in China and that in other developing countries from a theoretical angle.

The paper suggests that the mode is facing an important transformation toward land reform and new-type urbanization construction, and the replication and promotion of China's experience in infrastructure construction are of further significance under the Belt and Road Initiative as it provides a method for helping developing countries to eliminate infrastructure bottlenecks.

Through the test of multinational panel data, the paper indicates that the structural change of China's land regime around 1990 had an overall effect on the supernormal development of infrastructure in China. The paper indicates that the “land-based development mode” of China's infrastructure indeed contributed to the supernormal development of infrastructure in China, but there are still some shortcomings in this mode.