Search results

This paper investigates query processing in MANETs. Cache techniques and multi‐join database operations are studied. For data caching, a group‐caching strategy is proposed. Using the cache and the index of the cached data, queries can be processed at a single node or within the group containing this single node. For multi‐join, a cost evaluation model and a query plan generation algorithm are presented. Query cost is evaluated based on the parameters including the size of the transmitted data, the transmission distance and the query cost at each single node. According to the evaluations, the nodes on which the query should be executed and the join order are determined. Theoretical analysis and experiment results show that the proposed group‐caching based query processing and the cost based join strategy are efficient in MANETs. It is suitable for the mobility, the disconnection and the multi‐hop features of MANETs. The communication cost between nodes is reduced and the efficiency of the query is improved greatly.

The traditional methods have difficulty to inspection various types of copper strips defects as inclusions, pits and delamination defects under uneven illumination. Therefore, this paper aims to propose an optimal real Gabor filter model for inspection; however, improper selection of Gabor parameters will cause the boundary between the defect and the background image to be not very clear. This will make the defect and the background cannot be completely separated.

The authors proposed an optimal Real Gabor filter model for inspection of copper surface defects under uneven illumination. This proposed method only requires a single filter by calculating the specific convolution energy of the Gabor filter with the image. The Real Gabor filter’s parameter is optimized by particle swarm optimization (PSO), which objective fitness function is maximization of the Gabor filter’s energy average divided by the energy standard deviation, the objective makes a distinction between the defect and normal area.

The authors have verified the effect with different iterations of parameter optimization using PSO, the effects with different control constant of energy and neighborhood window size of real Gabor filter, the experimental results on a number of metal surface have shown the proposed method achieved a well performance in defect recognition of metal surface.

The authors propose a defect detection method based on particle swarm optimization for single Gabor filter parameters optimization. This proposed method only requires a single filter and finds the best parameters of the Gabor filter. By calculating the specific convolution energy of the Gabor filter and the image, to obtain the best Gabor filter parameters and to highlight the defects, the particle swarm optimization algorithm’s fitness objective function is maximize the Gabor filter's average energy divided by the energy standard deviation.

The purpose of this study is to ensure the successful implementation of a landing cushion for the new generation armored vehicles with significantly enhanced quality. Furthermore, to introduce a high-precision landing cushioning analysis model.

To accurately analyze the cushioning performance of the new generation armored vehicles, a nonlinear finite element dynamics model considering the complex travel system was established. The model considered the influence of various nonlinear factors to measure the dynamic response difference between the proposed and traditional models. The cushioning performance of airbags under different landing conditions and their various influence factors were analyzed.

The travel system has a large influence on the key points of the vehicle, whose rear end of the upper deck has a larger acceleration fluctuation compared with the traditional model. The increase in the body material stiffness is helpful to reduce this fluctuation. The established nonlinear finite element model can effectively analyze the landing cushioning performance of airborne armored vehicles. The area of the external airbag vent has a large influence on the cushioning performance, and the cushioning system has excellent cushioning performance under various operating conditions.

This study introduces the travel system, which is ignored by traditional analytical models. The interactions between various types of complex structures are included in the analysis process in its entirety, leading to valuable new conclusions. Quantitatively reveals the analytical errors of traditional simulation models in multiple dimensions and the reasons for their formation. Based on a high-precision simulation model, it is verified that the designed airbag cushioning system has an excellent cushioning effect for the new generation of heavy airborne armored vehicles.

The novelty of this work comes from the need for smooth landing with low overload for a new type of large-load airborne armored vehicle and provides a high-precision model that quantifies the traditional analytical modeling errors and error principle.

The purpose of this study is to describe the proposed alpha solar rotary mechanism (ASRM) and how it is used to accurately modify the solar array of the China Space Station (CSS) in orbit to maintain continuous tracking of the sun to provide power. It also highlights the need to evaluate the performance of the ASRM and predict potential failure modes in various extreme scenarios.

To evaluate the performance of the ASRM, a dynamic model was created and tested under normal and faulty conditions. In addition, a multidirectional stiffness test was conducted on the prototype to verify the accuracy of the ASRM's dynamic model. The high-precision ASRM model was then used to predict potential failure modes and damaged parts in various extreme scenarios.

The simulation results were in good agreement with the test results, with a maximum error of less than 8.85%. The high-precision ASRM's model was able to accurately predict potential failure modes and damaged parts in extreme scenarios, demonstrating the effectiveness of the proposed model and simulation evaluation test.

The proposed high-precision ASRM model and simulation evaluation test provide an effective way to evaluate the structural safety and optimize the design of the spacecraft. This information can be used to improve the performance and reliability of the CSS's solar array and ensure continuous power supply to the station.

This work aims to combine the cloud robotics technologies with deep reinforcement learning to build a distributed training architecture and accelerate the learning procedure of autonomous systems. Especially, a distributed training architecture for navigating unmanned aerial vehicles (UAVs) in complicated dynamic environments is proposed.

This study proposes a distributed training architecture named experience-sharing learner-worker (ESLW) for deep reinforcement learning to navigate UAVs in dynamic environments, which is inspired by cloud-based techniques. With the ESLW architecture, multiple worker nodes operating in different environments can generate training data in parallel, and then the learner node trains a policy through the training data collected by the worker nodes. Besides, this study proposes an extended experience replay (EER) strategy to ensure the method can be applied to experience sequences to improve training efficiency. To learn more about dynamic environments, convolutional long short-term memory (ConvLSTM) modules are adopted to extract spatiotemporal information from training sequences.

Experimental results demonstrate that the ESLW architecture and the EER strategy accelerate the convergence speed and the ConvLSTM modules specialize in extract sequential information when navigating UAVs in dynamic environments.

Inspired by the cloud robotics technologies, this study proposes a distributed ESLW architecture for navigating UAVs in dynamic environments. Besides, the EER strategy is proposed to speed up training processes of experience sequences, and the ConvLSTM modules are added to networks to make full use of the sequential experiences.

The purpose of this paper is to study the effect of alignment of conductive particles on the piezoresistivity of composite based on a theoretical model. The piezoresistivity of composite is associated with the characteristics of conductive network formed by the conductive particles distributed in the composite, which can be changed through aligning the conductive particles.

The orientations of the tunnel resistors formed by each two adjacent conductive particles are dependent on the aligned level of the conductive particles, and different orientations induce different deformations for a tunnel resistor under external strain, which determines the piezoresistivity of the composites. To investigate the resistance behavior of composites with various characteristics of conductive networks, a piezoresistivity model is developed in this paper by considering the aligned level of conductive particles.

The results obtained from the proposed piezoresistivity model indicate that the sensitivity and stability of composites can be enhanced through aligning the conductive particles. Also, the piezoresistivity of composites filled with randomly distributed conductive particles is isotropic, and it turns to be anisotropic when the conductive particles are aligned.

The change and its mechanism of the piezoresistivity upon the aligned level of conductive particles have been pointed out in this paper based on the proposed model. The achievement of this paper will help the people understand, predict and optimize the piezoresistivity of composites, and provide a new approach to design a strain sensor based on the piezoresistivity.

This paper aims to improve the diversity and richness of haptic perception by recognizing multi-modal haptic images.

First, the multi-modal haptic data collected by BioTac sensors from different objects are pre-processed, and then combined into haptic images. Second, a multi-class and multi-label deep learning model is designed, which can simultaneously learn four haptic features (hardness, thermal conductivity, roughness and texture) from the haptic images, and recognize objects based on these features. The haptic images with different dimensions and modalities are provided for testing the recognition performance of this model.

The results imply that multi-modal data fusion has a better performance than single-modal data on tactile understanding, and the haptic images with larger dimension are conducive to more accurate haptic measurement.

The proposed method has important potential application in unknown environment perception, dexterous grasping manipulation and other intelligent robotics domains.

This paper proposes a new deep learning model for extracting multiple haptic features and recognizing objects from multi-modal haptic images.

The purpose of this paper is to explore in depth the impact and transmission mechanism of different international capital flows on domestic employment and wages in China within a systematic framework; also to reveal whether the empirical results can confirm the basic model inferences.

Using dynamic economic model and empirical experiment, this study designs and conducts the analysis within a systematic framework. The authors acquire the needed and credible empirical data.

The international capital inflows will increase the average wage level, and the international capital outflows will significantly reduce the level of domestic wages. The unofficially recorded capital flows would appear negatively related to the domestic wages. Due to the complexities of relevant elements, the impact of different international capital flows on domestic employment is of insignificance. It is noteworthy that the impact of international capital flows on the average wage changes of different provinces will tend to converge to a certain extent.

The results have reflected that the capital flows between the different provinces have no obvious frictions and barriers.

The paper explores in depth the impact and transmission mechanism of different international capital flows on domestic employment and wages within a systematic framework. The empirical analysis related to the China different provinces is an exploratory experiment.

The effect of using microcrystalline cellulose (MCC) as an additive in coating paint films for non-stick coatings was studied in this work. This paper aims to discuss the benefits of MCC blended in the coating paint film that consists of poly(methyl methacrylate) (PMMA) and dammar.

PMMA and dammar mixed at a specific Wt.% ratio with xylene as its solvent. Two sets of mixtures were prepared, where one mixture contained MCC and another, without. The mixtures were applied to metal substrates as coating paint films. The performance of the non-stick coating paint film was observed through the adhesive test between adhesion layers on the coating paint film and also through the cross-hatch test for the adhesion of the non-stick coating paint film to the metal substrate. The results correlate with the surface roughness and glossiness tests.

The results showed that for the coating paint films, Sample B consisted of 80:20 Wt.% ratio of PMMA-dammar with an addition of 5 Wt.% MCC had an excellent performance as non-stick coating paint films. The MCC formed microparticles on the surface of the coating paint film sample and this causes the coating paint film samples with MCC to develop a rougher surface compared to the coating paint film without MCC. Sample B coating paint film had the highest average surface roughness (Ra) of 383 µm. The cross-hatch test showed the coating paint film with the addition of MCC had stronger adhesiveness on the substrate’s surface thus prevents the coating from peeling off from the surface.

The developed coating paint film in this study would be suitable for outdoor applications to prevent illegal advertisements and stickers.

MCC added to the coating paint film improves the surface performance as a non-stick coating.