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A double-module semi-submersible platform for scientific experiment has been applied firstly in practical engineering. Hinge-type connectors are used to link the two modules reliably and release the relative pitch freedom, and the strength of connector structures determines directly the integrity and safety of the platform. In this paper, the three-dimensional finite element model of the platform structure is developed including the local features of connectors, and main elastic modes are obtained. By using the Potential flow theory and the Green Function method, the hydrodynamic coefficients of the model are determined. Based on the three-dimensional hydroelastic theory, the response amplitude operators (RAOs) of generalized coordinates associated with different order modes are investigated. That indicates the structural deformation of connectors is excited mainly by modes of horizontal bending moment and torsion, and the stress responses in the longitudinal bulkheads close to bearings are generally marked. Using the modal superposition method, the RAOs of stress in the typical joints of connector structures are calculated under the given wave directions. Considering the JONSWAP spectrum and the amplitudes of stress responses of connector structures following Rayleigh distribution in short term, the strength of the connector structure is predicted in short term. In order to verify the correctness of the strength prediction method presented in this paper, more than thirty stress sensors are installed on the typical joints of connector structures of the double-module semi-submersible platform located in the real sea. Those stress responses are monitored during the typical typhoon activity, and the statistical data is compared with the predicted results from the theoretical model. Both the distribution trend and magnitudes of the results from the two methods are principally consistent that validates the given model. This theoretical method may provide an effective tool to analyze the strength of connector structures of multiple-module offshore platform.

In this work, three D–A–π–A stereoscopic phenothiazine dyes JA0, JA6 and JA7 were prepared and applied in dye-sensitized solar cells. Spirobifluorene and long carbon chain units were introduced onto the donors of phenothiazine dyes to construct stereoscopic structures, this strategy can effectively suppress dye charge recombination by decreasing back reaction of the injected electrons with triiodide species in the electrolyte, the results show that the Voc values of the DSSCs based on JA0 and JA6 were both over 800 mV. To improve the spectral responsiveness of these dyes, the strongly electron-withdrawing benzothiadiazole unit (BTD) was introduced, and the spectral response of JA6 reached about 770 nm. In addition, it is very important for sensitizers to have suitable anchors, this determines the efficiency of charge injection and the degree of bonding between the sensitizer and semiconductor surface. In order to further improve the performance of DSSCs, carboxylic acid, cyanoacrylic acid and pyridine are all used as anchors in this work, and we investigated the optical properties, electrochemical properties, and photovoltaic performance of these dyes to analyze the influence of different anchors on dyes. The results show that cyanoacrylic acid is the best anchor for this series of dyes, which is very helpful to improve the light-harvesting ability of dyes; the DSSC based on JA6 with a cyanoacrylic acid anchor exhibited the highest PCE of 7.34%. In contrast, the PCE of the DSSC based on JA7 with a pyridine anchor was only 3.05%, this should be mainly due to its poor dye adsorption properties.

Purpose This paper aims to design an automatic inspection system for the characters on tire molds, which involves a vision-based inspection method for the characters on tire molds. Design/methodology/approach An automatic inspection equipment is designed according to the features of the tire mold. To implement the inspection task, the corresponding image processing methods are designed, including image preprocessing, image mosaic, image locating and character inspection. Image preprocessing mainly contains fitting the contours of the acquired tire mold images and those of the computer aided design (CAD) as the arcs of two circles and polar transformation of the acquired images and the CAD. Then, the authors propose a novel framework to locate the acquired images into the corresponding mosaicked tire mold image. Finally, a character inspection scheme is proposed by combining an support-vector-machine-based character recognition method and a string matching approach. At the stages of image locating and character inspection, image mosaic is simultaneously used to label the defects in the mosaicked tire mold image, which is based on histograms-of-gradients features. Findings The experimental results indicate that the designed automatic inspection system can inspect the characters on the tire mold with a high accuracy at a reasonable time consumption. Practical implications The designed automatic inspection system can detect the carving faults for the characters on the tire molds, which are the cases that the characters are wrongly added, deleted or modified on the tire mold. Originality/value To the best of the authors’ knowledge, this is the first automatic vision-based inspection system for the characters on tire molds. An inspection equipment is designed and many novel image processing methods are proposed to implement the inspection task. The designed system can be widely applied in the industry.

Firstly, this article introduces a two-dimensional fuzzy entropy threshold segmentation method, taking characteristics of underwater sonar image of moving target into account, the concept of time in the traditional frame difference motion detection method is used. To determine the grayscale image segmentation threshold, a two-dimensional spatio-temporal fuzzy entropy algorithm is established. The algorithm is used in forward-looking sonar image threshold segmentation to verify the applicability and good performance. Compared to other threshold algorithms, the algorithm of this article can obtain better segmentation threshold value and have more advantages in the processing of sonar image which contains more noise.

Titanium dioxide (TiO2), a widely used inorganic semiconductor owing to its superb photoelectric properties, has frequently been fabricated into composites to reduce its relatively large band gap and overcome its limited visible light absorption. In this article, a “layer-by-layer” method has been developed to prepare the composite structure of nitrogen (N)-doped graphene quantum dots (GQDs)-sensitized TiO2 nanofibers. The as-prepared structure shows considerable luminescence and exhibits excellent photoelectric properties. Various factors including the crystalline phase of TiO2, amount of N in GQDs, and irradiation wavelength were investigated to find the optimal conditions for enhanced photoelectric activity. It is demonstrated that the combination of highest N amount GQDs with TiO2 nanofibers of mixed phases (750 °C-sintered TiO2 nanofibers) possess the best photoelectric properties. The enhancement of properties using TiO2 nanofibers with mixed phases mainly contributes to the transfer of electrons between conduction bands of different phases in TiO2 and the distinctive photoluminescence (PL) property of N-GQDs. Furthermore, this enhancement can be achieved in most areas of the visible light range. The general mechanism of the electron generation and transfer of the structure is based on the normal PL and upconversion PL property of N-GQDs which serve as the sensitizer. We consider it a feasible method to improve the photoelectric conversion efficiency in photovoltaic devices.

Because of the sensitive factors, such as the larger loads, higher mass center and relatively narrow tread in comparison with the height of heavy vehicles that have the poor dynamic rollover stability. This paper set of anti-rollover LQR control algorithm based on early warning. The model-based early rollover warning algorithm utilize the TruckSim® models and early warning reference model to predict the impeding vehicle rollover time in advance and told the driver the warning signals so that drivers had enough time to take appropriate measures to prevent vehicle rollover that called time to rollover (TTR), thereby greatly improving the vehicle's active safety performance of the heavy vehicles. As to the anti-rollover LQR control algorithm, the principle was to use the optimal additional yaw moment obtained from the control algorithm and then made it reasonable impose the corresponding wheels by taking the moment distribution methods based on the differential braking for the purpose of reducing the risk of rollover. The simulation results show that the algorithm was presented in this paper can effectively reduce the lateral load transfer ratio and actively void the occurrence of rollover accidents.

M-1earning is a practice to promote learning by using mobile technology. It is also a new stage in informatization learning. Nowadays, the learning contents and learning methods of M-learning are paid no attention, and the mode of M-learning cannot be studied either. On the base of the discussion of the characteristics and normal operation schemes of M-learning, the feasibility of its application in modern education is analyzed in the paper. Meanwhile, M-learning mode in the whole process supervision of location, independent, exploration and mutual aid mobile learning is constructed. In the mode, M-learning can be supervise in the whole process to promote the information technology to develop its good effects for learning. The goal to propose the M-learning mode is to provide guidance for learners. It operates simply and has some guiding significance.

The digital image processing techniques, including gray level transformation, image sharpening, image segmentation and edge detection, is applied to the study on the meso-structure image of shale. The results show that the digital image processing technique can extract useful information from the CT images and construct a model of meso-structure of shale. The model shows the spatial distribution of the different media in the rock. The process of the damage propagation can be seen IN the meso-structure image clearly. Meanwhile, a process, including geometrical vectorization, scaling transformation, and the realization of the FEM software interface, is provided to establish a finite element grid model. The proposed approach can be a research foundation for the further study on the physical and mechanical properties of rock.