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Safety is one of the important topics in the field of civil aviation. Auxiliary Power Unit (APU) is one of important components in aircraft, which provides electrical power and compressed air for aircraft. The hazards in APU are prone to cause economic losses and even casualties. So, actively identifying the hazards in APU before an accident occurs is necessary. In this paper, a Hybrid Deep Neural Network (HDNN) based on multi-time window convolutional neural network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) neural network is proposed for active hazard identification of APU in civil aircraft. In order to identify the risks caused by different types of failures, the proposed HDNN simultaneously integrates three CNN-BiLSTM basic models with different time window sizes in parallel by using a fully connected neural network. The CNN-BiLSTM basic model can automatically extract features representing the system state from the input data and learn the time information of irregular trends in the time series data. Nine benchmark models are compared with the proposed HDNN. The comparison results show that the proposed HDNN has the highest identification accuracy. The HDNN has the most stable identification performance for data with imbalanced samples.

Multifunctional LiNbO3 material plays an important role in domain wall microelectronics and nonlinear optoelectronics. However, the material is hard and relatively inert, and hence is quite difficultly etched. A new oblique method to etch LiNbO3 memory cells at the surface of X-cut bulk crystals was proposed in this study. The process includes mask fabrication, oblique etching, and wet corrosion cleaning. The etching angle highly approaches 83° to achieve better polarization retention than others with etching angles of 0° and 70°. Meantime, the measured domain switching hysteresis loops become more symmetrical along the voltage axis than others with the presence of a strong imprint field. The horizontal electric field simulation along the polar Z axis exhibits the enhanced nucleating fields of the domains located at two edges of each memory cell in contact to Pt electrodes under the same applied voltage. The depolarization field was better screened in the memory with a higher etching angle. The new oblique method can improve the performance of the ferroelectric domain wall memory significantly.

The aircraft electrical system provides power for the normal operation of the aircraft. Its normal operation is critical to ensure the safe flight of the aircraft. Therefore, it is very important to identify the hazards in the aircraft electrical system. In this paper, a hazard identification and prediction system which can intelligently identify potential hazards in aircraft electrical system is proposed. The proposed hazard identification and prediction system mainly includes three processes: variable selection, hazard identification, and hazard prediction. In the process of variable selection, the stepwise regression analysis is used to select 8 main parameters that have the major influence on the DC bus voltage value from 18 parameters. In the process of hazard identification, support vector machine is used to identify pre-existing hazards in electrical system based on the status of all components. The identification accuracy of the support vector machine is 92.3%. When the electrical system does not have unacceptable hazards, a prediction of the variation range of the DC bus voltage value in the aircraft electrical system is performed. The average prediction relative error of support vector machine is only 0.86%. Overall, the identification accuracy and average prediction relative error show that the proposed hazard identification and prediction system can accurately and effectively identify and predict the hazards in the aircraft electrical system.

Current control of pathogenic bacteria at all biomaterial interfaces is poorly attuned to a broad range of disease-causing pathogens. Leading antimicrobial surface functionalization strategies with antimicrobial peptides (AMPs), defensins, have not shown their promised efficacy. One of the main problems is the lack of stability and swift clearance from the surface. Surface nanotopography bearing sharp protrusions is a non-chemical solution that is intrinsically stable and long-lasting. Previously, the geometrically ordered arrays of nanotipped spines repelled or rapidly ruptured bacteria that come into contact. The killing properties so far work on cocci and rod-like bacteria, but there is no validation of the efficacy of protrusional surfaces on pathogenic bacteria with different sizes and morphologies, thus broadening the utility of such surfaces to cover increasingly more disease entities. Here, we report a synthetic analogue of nanotipped spines with a pyramidal shape that show great effectiveness on species of bacteria with strongly contrasting shapes and sizes. To highlight this phenomenon in the field of dental applications where selective bacterial control is vital to the clinical success of biomaterial functions, we modified the poly(methyl)-methacrylate (PMMA) texture and tested it against Streptococcus mutans, Enterococcus faecalis, Porphyromonas gingivalis, and Fusobacterium nucleatum. These nanopyramids performed effectively at levels well above those of normal and roughened PMMA biomaterials for dentistry and a model material for general use in medicine and disease transmission in hospital environments.

Safety is an eternal issue in the civil aviation transportation. Once a civil aviation accident occurs, it will cause great casualties and economic losses. In order to ensure the civil aviation safety, the hazard identification and prediction of civil aircraft should be effectively and accurately realized. The civil aircraft uses Aircraft Communications Addressing and Reporting System (ACARS) to interact with the ground during flight. The data generated by ACARS has a simple structure and strong timeliness. In view of the advantages of ACARS data, a hazard identification and prediction method based on support vector machine optimized by particle swarm optimization (PSO-SVM) and long short term memory (LSTM) neural networks which uses ACARS report as analysis data is proposed. First, in order to reduce the identification and prediction time cost, the SVM-based recursive feature elimination method with cross-validation algorithm (SVM-RFECV) is used to select the characteristic parameters. Then, the SVM optimized by PSO is used to identify hazard based on the selected parameters. According to the identification results, the LSTM is used to predict the trend of the selected parameters to realize hazard prediction. An A13 report of APU generated by ACARS is selected as analysis data for hazard identification and predication in this paper. The analysis results show that the proposed identification method based on PSO-SVM and SVM-RFECV has high identification speed and accuracy. The proposed prediction method based on LSTM has the best prediction performance. The proposed method can effectively identify hazards and accurately predict the trend of parameters to improve the safety of aircraft.

Modular multilevel converter (MMC) has great prospects in voltage source converter-based high-voltage direct current (HVDC) transmission. Although the typical half-bridge MMC can bring huge economic benefits, it is unable to clear dc fault. An MMC based on arm transfer (AT-MMC) and corresponding timing control are presented to effectively solve this drawback. Compared with the half-bridge MMC, the AT-MMC adds the arm-transferring branch to the each phase unit of the converter and adds the current-breaking branch and the energy-absorbing branch to the dc line. Through these branches cooperating with each other, the fault current is cleared. Referring to the half-bridge MMC with the same voltage grade, the AT-MMC has a good economy and its additional device cost is low and additional conduction loss is very small. A model of 51-level MMC–HVDC system is built in RT-LAB OP5600 to verify the effectiveness of the proposed solution.

Ion-exchanged glasses are widely used in optoelectronic devices due to its high strength and optical waveguide effects. The MgF2 films deposited on the ion-exchanged glasses and the ion-exchanged glasses bombarded by argon ions (100 eV) at 220 °C were investigated in the present work. The microstructure of MgF2 films and the structural and physico-chemical properties of the ion-exchanged glasses before and after deposition and bombardment were analyzed by grazing incident X-ray diffraction (GIXRD), field emission scanning electron microscopy (FE-SEM), electron probe micro analysis (EPMA), micro-Raman spectroscopy and surface stress meter. It is shown that MgF2 films exhibit a polycrystalline and microcolumnar structure. The peak of potassium ions concentration distribution in the ion-exchanged glasses, accompanied by outward diffusion of potassium ions, decreases and moves from glass surface to the inner part to different degrees during deposition and bombardment. The potassium ions concentration in the region near the surface of argon ion-bombarded glasses is almost the same as the MgF2-coated glasses. Furthermore, the position and intensity of Raman bands in the subsurface region of the ion-exchanged glasses show obvious changes before and after deposition and bombardment. The values of compressive stress and depth of layer in the MgF2-coated ion-exchanged glasses and the argon ion-bombarded ion-exchanged glasses are far away lower than that of its values obtained in the ion-exchanged glasses. Overall results have shown that variations of chemical composition, Raman bands and surface compressive stress in the exchanged region indicate subsurface modification of the ion-exchanged glasses induced by deposition and bombardment, which provide useful information for the use of the ion-exchanged glasses in further.

In the influencing factors of reading distance of RFID tags, the tags’ geometric spatial structures have an important aspect on the corresponding reading distance. In order to achieve the goal of improving the reading distance of RFID tags, a novel three-dimensional (3D) position measurement and structure prediction method for RFID tag group based on deep belief network (DBN) is proposed. First, a 3D structure prediction system for RFID tags, which is based on stereovision, is designed. Second, an image deblurring scheme which combines the knife-edge with Wiener filtering method is adopted to restore the blur image in the 3D position measurement process. Then, RFID tags’ 3D positions are acquired by using the image matching method. Finally, the relationship model between RFID tags’ 3D positions and reading distance is built by DBN. The experimental results show that the relationship between tag group’s 3D positions and reading distance can be well built by DBN. The established DBN can forecast the reading distance accurately. The MAPE and RMSE of DBN prediction results are 1.64% and 0.168. The DBN can find the best 3D spatial distribution structure of RFID tag group corresponding to the maximum reading distance, which can guide the 3D distribution of the tag group.

Objectives The purpose of this study was to estimate the fatigue life of premolars restored with two dental ceramics, lithium disilicate (LD) and polymer infiltrated ceramic (PIC) using the numerical method and compare it with the published in vitro data. Methods A premolar restored with full-coverage crown was digitized. The volumetric shape of tooth tissues and crowns were created in Mimics®. They were transferred to IA-FEMesh for mesh generation and the model was analyzed with Abaqus. By combining the stress distribution results with fatigue stress–life (S–N) approach, the lifetime of restored premolars was predicted. Results The predicted lifetime was 1,231,318 cycles for LD with fatigue load of 1400 N, while the one for PIC was 475,063 cycles with the load of 870 N. The peak value of maximum principal stress occurred at the contact area (LD: 172 MPa and PIC: 96 MPa) and central fossa (LD: 100 MPa and PIC: 64 MPa) for both ceramics which were the most seen failure areas in the experiment. In the adhesive layer, the maximum shear stress was observed at the shoulder area (LD: 53.6 MPa and PIC: 29 MPa). Significance The fatigue life and failure modes of all-ceramic crown determined by the numerical method seem to correlate well with the previous experimental study.

We used force-field based grand-canonical Monte Carlo simulation method and density functional theory to study adsorption characteristics of carbon dioxide (CO2) molecules in a metal-organic framework (MOF) compound, [Zn(bdc)(dpds)]n. The studied MOF include a metal ion (Zn(II)), an anion organic linker (dianion of benzene dicarboxylicacid, bdc2−) and a neutral organic linker (4,4′-dipyridyldisulfide, dpds). Results from calculated adsorption isotherms and enthalpies of adsorption agree with the experimental data. The interactions between the adsorbed CO2 and the organic linkers were examined in simulations. Calculated results show available absorption sites are surrounded by two dpds ligands in which an S-S bond as an N-N′ spacer connect two pyridines. In contrast, the bdc2− ligand does not give a significant contribution to the substantial adsorption amount even though it contains the carboxylate group that provides available bonding site to CO2.

We have successfully synthesized a multilayer-structured composite based on a combination of polydopamine (PDA)-coated silver nanoparticles (Ag@PDA) and silver nanowires (AgNWs@PDA) using a flexible and rapid method. We incorporated the structure into polyvinyl alcohol (PVA) polymer to enhance thermal conductivity and dielectric properties of nanocomposite films. Compared with Ag@PDA/PVA and AgNWs@PDA/PVA films, the use of multilayer-structure facilitated increased thermal conductivity and reduced permittivity of the nanocomposite film. The maximum thermal conductivity of composite with 10 wt% filler loading was increased to 3.63 W/mK, which is an enhancement by nearly 20 times compared with the use of neat PVA matrix. The relative permittivity and dielectric loss were about 6.65 and 0.024 at 1 MHz, respectively. We discussed the underlying mechanisms in an effort to optimize the structure for polymer matrix nanocomposites.

Objective Numerous bond strength tests have been performed on dental adhesion experiments. Yet, the validity of these bond strength tests is controversial due to the name ( e.g. , “shear” or “tensile”) may not reflect to the true and complete stress situation, i.e. , assumed uniform shear or uniaxial tensile conditions. Thus, the aim of this study was to simulate and compare the stress distribution of and between shear bond strength (SBS), tensile bond strength (TBS), mold-enclosed shear bond strength (ME-SBS) and de novo lever-induced mold-enclosed shear bond strength (LIME-SBS) tests. Methods 3-Dimensional finite element method (FEM) was used on the dental resin-bonded surfaces ( i.e. , titanium alloy, dentine and porcelain) interphased with adhesive layer (thickness 5 μm) to simulate the mechanical tests. For ME-SBS, both polycarbonate and stainless steel molds were used. For LIME-SBS, stainless steel levers and molds with lengths of 3 mm, 6 mm, 12 mm, 15 mm and 18 mm were used. The applied loads on these models were 50 N, 100 N and 200 N. Results De novo LIME-SBS test was the most optimal configuration to evaluate “shear” bond strength of adhesive in regards to providing significantly high and uniform shear stress as well as eliminating tensile stress at the interface. The conventional SBS test created very high tensile stress at the load area, whereas the TBS created optimal tensile stress but shear stress indeed co-exist. The ME-SBS test could also eliminate some of the tensile stress. Similar stress distributions pattern appeared on the Ti-adhesive models, the dentine-adhesive models and porcelain-adhesive models. Significance None of the bond strength tests could give purely “shear” or “tensile” bond strength, but LIME-SBS seems to be the best model to evaluate the bond strength under true “shear” mode.

One of the most important aspects in the field of dentistry is adhesion. Adhesion can be enhanced through different mechanisms, most commonly by chemical and mechanical modifications of dental material surfaces. One example of chemical adhesion utilizes primers or bonding agents containing coupling agents. Universal primers which are (mainly organophosphate-based monomers) and silane coupling agents are the most effective and most widely used for unifying dissimilar materials. Other than improving adhesion, Silanes have many other applications in dentistry. They have been found also to enhance the mechanical properties of materials either by adhesion or by becoming a constituent of dental materials itself. Universal primers, on the other hand, are utilized in bonding between different dental materials such as ceramics, metals, alloys, as well as to resin composites. Unfortunately, these primers, coupling agents, and bonding agents have, in general, poor hydrolytic stability. They are susceptible to humid oral environment, which invariably render them potentially unstable in the humid oral environment as saliva is always present. Improving their hydrolytic stability should therefore result in stronger adhesion for dental materials and dental prosthesis with a better clinical survival rate.

Recently, the discriminant learning denoising model has deserved special attention due to its outstanding denoising property. This paper designs the RFID multi-label dynamic localization system based on visual measurement to avoid electromagnetic interference. Firstly, the potential sharp images are estimated via denoising images acquired by dual CCD. Then the labels are matched by template. Finally, Shaft Alignment Method (SAM) models the multi-label 3D coordinates in world space and image pixel. Flexible Feed-forward Denoising Convolutional Neural Network (FDnCNN) is proposed to reach the equilibrium between denoising effect and image particulars without producing artifacts in GPU effectively and flexibly. FDnCNN increases the image quality by at least 0.5 dB than WNNM and DnCNN. The 3D coordinate measurement system is evaluated by error, which proves the feasibility and effectiveness of the positioning system.

Sensitivity is an important indicator for evaluating the performance of RFID tag. Firstly, this paper gives the definition of group tags sensitivity. Furthermore, the formula of the group tags sensitivity is deduced. Secondly, Semi-physical verification platform is used to measure the reading distance of the tags. Thirdly, the sensitivity for each tag in the tags group is tested. Finally, the group tags sensitivity is obtained and calibrated. The experimental results show that the formula accords with the actual result. Therefore, this work is very useful for dynamical measurement and calibration for RFID tags group sensitivity.

In RFID multi-tag network, the image of the tag needs to be collected. Therefore, the research on the effect of LED array light source on the image capture of RFID tag is proposed. According to the optical properties of LED, the radiance illumination expression of LED array light source is deduced, and the illumination distribution of LED array is simulated. We choose the appropriate arrangement of array light sources and analyze the effect of the array on the collection of the tags. The research results show that the LED array design can effectively provide a uniform illumination of the light source. The design of LED array can obviously distinguish the tag from the background and improve the quality of the acquired RFID tag image.

The aim was to evaluate in vitro the feasibility of using three silane coupling agents/blends: (I) 1.0 vol % 3-acryloxypropyltrimethoxysilane (ACPS), (II) a novel silane system: 1.0 vol % 3-acryloxypropyl trimethoxysilane + 0.5 vol % bis-1,2-(triethoxysilyl)-ethane (ACPS + BTSE), and (III) a novel silane system: 1.0 vol % 3-acryloxypropyl trimethoxysilane + 0.5 vol % bis[3-(trimethoxysilyl)propyl]amine (ACPS + BTMA), as adhesion promoters on the repair of dental silver amalgam with a resin composite with respect to various artificial ageing conditions. Two hundred and forty flowable composite resin stubs were bonded to freshly prepared dental silver amalgam discs. The discs were silica-coated and silanized with the 3 silanes. Another 20 resin stubs were bonded to the amalgam surface without any silica-coating or treated with silica-coating only. Then, the specimens were randomly distributed into 8 sub-groups for initial (control), thermal cycling (1000, 3000 and 6000) and DI water storage (1 day, 1 week, 1 month and 3 months). Shear bond strength (SBS) test was measured using a universal testing machine with a 1 mm/min cross-head speed. Failure modes were classified and SEM images were obtained. Two-way ANOVA was used to detect statistical differences. The initial SBS of the control groups were (Mean/SD) 20.2 ± 3.3 MPa for ACPS only, 16.6 ± 3.9 MPa for ACPS + BTMA, and 19.5 ± 3.3 MPa for ACPS + BTSE. A decreasing trend of SBS was observed by using the current artificial ageing methods, and ACPS and ACPS + BTSE produced statistically higher bond strengths compared to ACPS + BTMA under thermal cycling (p

One of the important advantages of RFID technology is to identify multiple targets at the same time. However, in order to identify multi-object at the same time, it is necessary to solve the problem of improving the performance of tag reading. Among the factors affecting the performance of tag identification, the geometric distribution of multi-tag is the key one. With the advantage of GA-BP neural network in optimization analysis, we do some researches about the impacts of the multi-tag's geometric distribution to the performance of reader. By training a large number of dynamic test data under the gate entrance environment, optimal RFID tag geometric distribution can be predicted by GA-BP neural network under the maximum or minimum reading distance. Furthermore, the dynamic reading performance of multi-tag system could be effectively improved.

Three-phase rectifier voltage often use software phase-locked loop, input voltage is appropriate. However, the software phase-locked loop had previously most locks can only perform accurate voltage in order that agreement cannot be done accurately lock in reverse order (lost key). To overcome this drawback in this article combines the principle of phase locked loop voltage argued upon, examined the nature of change of voltage vector angle reverse order, analyze the causes loss of lock reverse order part of a program to design self-adaptive phase locked loop can be done in the correct key sequence voltage and voltage upon reverse order. Also in this article also offer solutions using fractional integrated controller PIX to improve software self-adaptive phase locked loop instead of using the traditional PI controller as the software was designed before. Experimental results have demonstrated the method using fractional controllers to improve software PI phase locked loop three-phase self-adaptive software makes the phase locked loop to work more flexible, done properly locked voltage law in order upon agreement or otherwise. Carry extra one further step, analysis and experiments demonstrated using software adapts phase locked loop can make PWM (pulses width modulation) rectifiers work without influenced by the phase sequence of input voltage.

We explored a novel 3D sandwich structure of fillers in the polymer matrix to enhance thermal conductivity. A variety of fillers in the polymer matrix play a significant role in the physical properties of the composite. Fillers containing particle and line structures are popular, and enhance the thermal and electrical conductivities. Therefore, filler-based matrix network improves conductivity. We propose a sandwich structure consisting of hybrid graphite nanosheets (two dimensions), and silver nanowires (AgNWs) (one dimension), to create a 3D sandwich structure of polyimide matrix with improved thermal conductivity. Surface treatment of graphite and silver nanowires were conducted to reduce the dielectric constant of the composite. We designed the filler of 20 wt% resulting in a high thermal conductivity of 3.21 W m−1 K−1 with 15% C@SiO2 and 5% AgNWs@SiO2 filler loading. The novel combination and structure markedly enhanced the thermal conductivity of the composite.