Jaw Functional Orthopedics and Cranoficial Growth
Not a member yet
968 research outputs found
Sort by
Fault diagnosis method for hydro-power plants with Bi-LSTM knowledge graph aided by attention scheme
No full textIn hydro-power systems, the fault of equipment is an important potential threat for the safe production of electricity. Therefore, the automation and intelligence of fault diagnosis becomes the popular issue in the research on hydro-power system. In this paper, a knowledge graph-based method is put forth to diagnose faults occurred in hydro-power systems, since the knowledge graph can store structured and unstructured data for better fault diagnosis and intelligently search the reasons of the faults. First, we model the knowledge graph for hydro-power plants, where the rational path for the fault reason is formulated. Then, the bi-directional long short-term memory (Bi-LSTM) with conditional random field (CRF) is used to extract the entities and relations to the given documents, which record the phenomenon and reasons for the occurred faults. Moreover, the attention scheme is employed in the Bi-LSTM to weigh the closer relationships to improve the diagnosis accuracy. An automatic diagnosis algorithm is developed to improve the diagnosing efficiency by constructing rational paths, with which directive and in-directive factors for occurring faults can be traced. Simulation results reveal that the intelligent search method with a knowledge graph can effectively find the reason, locate the position, and provide useful suggestions for the occurred faults
Production analysis of manufacturing industry in a single vacation policy under disaster
No full textThe disaster in queueing system with second optional service is considered. Arriving customer of this system will receive the essential service and optional second service if needed. When the system is interrupted by the disaster, the server initiates the repair period making all the customer leave the system immediately. The server, when idle, takes single vacation. The disaster cannot happen when server is under vacation or in repair period. The above queueing system is analysed using supplementary variable technique to obtain the probability generating function for various parameters and effects of parameters are explained graphically with numerical illustrations
DIC measurement method based on binocular stereo vision for image 3D displacement detection
No full textThe deformation detection of large machinery is usually achieved using three-dimensional displacement measurement. Binocular stereo vision measurement technology, as a commonly used digital image correlation method, has received widespread attention in the academic community. Binocular stereo vision achieves the goal of three-dimensional displacement measurement by simulating the working mode of the human eyes, but the measurement is easily affected by light refraction. Based on this, the study introduces particle swarm optimization algorithm for target displacement measurement on Canon imaging dataset, and introduces backpropagation neural network for mutation processing of particles in particle swarm algorithm to generate fusion algorithm. It combines the four coordinate systems of world, pixel, physics, and camera to establish connections. Taking into account environmental factors and lens errors, the camera parameters and deformation coefficients were revised by shooting a black and white checkerboard. Finally, the study first conducted error analysis on binocular stereo vision technology in three dimensions, and the relative error remained stable at 1 % within about 60 seconds. At the same time, three algorithms, including the spotted hyena algorithm, were introduced to conduct performance comparison experiments using particle swarm optimization and backpropagation network algorithms. The experiment shows that the three-dimensional error of the fusion algorithm gradually stabilizes within the range of [–0.5 %, 0.5 %] over time, while the two-dimensional error generally hovers around 0 value. Its performance is significantly superior to other algorithms, so the binocular stereo vision of this fusion algorithm can achieve good measurement results
A third-order shear deformation plate bending formulation for thick plates: first principles derivation and applications
No full textA third-order shear deformation plate bending formulation is presented in this study from the first principles. The derivation assumed a displacement field constructed using third-order polynomial function of the transverse (z) coordinate; and made to apriori satisfy the linear three-dimensional (3D) kinematics relations as well as the transverse shear stress free boundary conditions at the top and bottom plate surfaces. The formulation thus has no need for shear stress correction factors of the first-order shear deformation plate theories. The domain equations of equilibrium are obtained as a set of three coupled differential equations in terms of three unknown displacements. The system of coupled equations is solved for simply supported rectangular and square plates subjected to four cases of loading distributions: sinusoidal loading, uniformly distributed loading, linearly distributed loading and point load at the plate center. Navier’s double trigonometric series method is used to construct trial solutions for the three displacement functions such that the boundary conditions are satisfied identically. The integration problem is thus reduced to an algebraic problem and is solved for each considered loading. It is found that the present formulation gives exact results for the normal stresses σxx for sinusoidal and uniformly distributed loads. The study further showed that the results for deflection and stresses agreed with Krishna Murty’s higher order shear deformation plate theory results. The present formulation gave accurate results because of the inclusion of transverse normal strain effects in the formulation. The formulation gives a quadratic variation of the transverse shear stresses across the thickness in consonance with the theory of elasticity method
Application of optimized CNN algorithm in landslide boundary detection
No full textLandslide, as a natural geological phenomenon with great harm, seriously threatens human social activities and life safety. It has a variety of latent and immeasurable destructiveness, which has a significant impact on the economic losses in rural areas. Therefore, it is urgent to take measures to accurately identify landslides to reduce their negative impacts. However, traditional manual visual interpretation has been unable to meet the current needs for emergency rescue of landslides, so computer intelligent methods have been paid attention to. This study proposes a new recognition network to address the problem of low accuracy of intelligent landslide boundary recognition methods. Firstly, the experiment incorporated boundary structure information into the Full Convolutional Network (FCN) for optimization, and constructed an Improved Full Convolutional Network (IFCN) model to better achieve image reconstruction. After that, Attention Mechanism (AM) is further introduced to achieve accurate detection of landslide boundaries in images, namely the IFCN-AM model. The attention mechanism introduced include spatial attention mechanism and multi-channel attention mechanism. Both are responsible for enhancing the language representation ability of the model and aggregating the interrelated features between different channels. The experimental results show that IFCN-AM has a 3 % to 7 % improvement in accuracy, recall, F1 value, and MIoU value
CO2 geological storage prospects of Lithuania – update
No full textThe CO2 geological storage assessment in the Cambrian saline aquifers in west Lithuania is considerably improved by 3D seismic survey of the Gargždai Elevation and Syderiai Uplift. The CO2 storage capacity of the Syderiai site is assessed as large as 56.7 Mt (area 62 km2) owing to the high reservoir properties (average porosity 17 % and permeability 400 mD) of the Middle Cambrian saline aquifer of 50 m thick and 1458-1508 m deep. The tectonic uplift is controlled by the large-scale Telšiai strike-slip fault. The Syderiai site was initially considered as the potential UGS site. The acreage of the Gargždai Elevation, comprising six depleting oil fields, is assessed 133 km2 and the storage volume is evaluated 31.3 Mt. The main challenging parameter is a poor average porosity (7 %) and fractured type of reservoir (permeability about 10 mD) about 70 m thick and 2200 m deep. A residual oil zone (ROZ) assessment suggests are very high protentional for CO2 combination in west Lithuania which is the only prospective site known in the Baltic region of this kind
Vibration characteristics and seismic performance of historical buildings with brick-wood structures
No full textTo explore the vibration characteristics and seismic performance of historical buildings in Tianjin, we conducted on-site vibration testing under ambient vibration on three historical buildings with brick-wood structures in Tianjin. Specifically, we delved into the vibration characteristics and seismic performance of historic buildings with brick-wood structures by establishing the vibration analysis model, performing the theoretical calculations, and conducting seismic performance analysis. The results reveal that 1) the vibration frequencies of historical buildings with brick-wood structures are low, mainly concentrated in 2.0-8.0 Hz, which conforms to the vibration range of general buildings; 2) based on vibration analysis, it is concluded that the structural integrity of these buildings is good and there are no obvious structural defects; 3) the layout and storey height of historical buildings have great impacts on the vibration characteristics and seismic performance. 4) the comprehensive seismic capacity index of three buildings ranges from 0.91 to 1.75. The results of dynamic analysis under ambient vibration are consistent with those of theoretical analysis, providing a basis for preserving and reinforcing historical buildings
Research on road damage recognition and classification based on improved VGG-19
No full textIn recent years, methods of road damage detection, recognition and classification have achieved remarkable results, but there are still problems of efficient and accurate damage detection, recognition and classification. In order to solve this problem, this paper proposes a road damage VGG-19 model construction method that can be used for road damage detection. The road damage image is processed by digital image processing technology (DIP), and then combined with the improved VGG-19 network model to study the method of improving the recognition speed and accuracy of VGG-19 road damage model. Based on the performance evaluation index of neural network model, the feasibility of the improved VGG-19 method is verified. The results show that compared with the traditional VGG-19 model, the road damage VGG-19 road damage recognition model proposed in this paper shortens the training time by 79 % and the average test time by 68 %. In the performance evaluation of the neural network model, the comprehensive performance index is improved by 2.4 % compared with the traditional VGG-19 network model. The research is helpful to improve the model performance of VGG-19 road damage identification network model and its fit to road damages
Precision local anomaly positioning technology for large complex electromechanical systems
No full textIn recent years, Prognostics Health Management (PHM) technology has become an important reference technology in fields such as avionics and electromechanical systems due to its ability to reduce costs and achieve state based maintenance and autonomous support. However, with the operation of large and complex electromechanical systems (ES), the data generated gradually ages the status of components, and traditional PHM technology is difficult to solve the problem of electromechanical system components becoming more complex. Based on this, this study takes the hydraulic actuator cylinder as an example to construct a local component fault detection model. Firstly, fault data features are extracted using wavelet packet energy spectrum, and then a fault detection model is constructed based on support vector machine (SVM). In response to the shortcomings of SVM, a smooth support vector machine (SSVM) is proposed to replace SVM, and an improved crow search algorithm (ICSA) is used to improve SVM. Finally, an intelligent detection model for hydraulic actuator cylinder faults based on ICSA-SSVM was constructed based on the above algorithms. The experimental results show that the ICSA-SSVM model has the fastest Rate of convergence, among which, the positioning accuracy is 0.96, the fitting degree is 0.984, the fault detection accuracy is 99.16 %, the recall value is 94.52 %, and the AUC value is 0.986, all of which are better than the existing fault detection models. From this, it can be seen that the precise local anomaly localization technology for large-scale complex electromechanical systems based on the ICSA-SSVM algorithm proposed in this study can improve the efficiency and accuracy of fault detection, achieve accurate and intelligent detection of ES local anomalies, and have certain positive significance for the development of China’s industry
Public perceptions of CCUS in Central and Eastern Europe – implications for community engagement
No full textCarbon capture, utilization, and storage (CCUS) is emerging as a subject of major interest for EU climate policy due to their potential role in avoiding hard-to-abate CO2 emissions, as well as to lead to “negative emissions” through direct air capture or bioenergy with carbon capture and storage. Despite CCUS technologies being deployed since the 1970s, their widespread implementation is still challenged by a range of factors, including policy inertia, high costs, and relative novelty in the public discourse. In particular, as CCUS emerges slowly into the realm of public and political debate, opinions on these technologies and associated projects are easily changeable and affected by a range of factors, which make concerted public and community engagement extremely important for deploying them where they matter most. The Central and Eastern Europe (CEE) region is characterized by a higher-than-average economic dependence on heavy industry, old assets and infrastructure, and a high occurrence of regions where the transition to climate neutrality will have a significant impact on local economies, employment, and social welfare [1]. CCUS could play an important role in decarbonizing the heavy industry sectors of the region, particularly given the potentially significant storage capabilities of countries such as Romania and Poland, as well as emerging storage potential in the Black Sea and Eastern Mediterranean Sea. However, climate policy in these jurisdictions is sluggish, and there is a general failure to approach CCUS in a systematic way, with targeted application to sectors where it can have the highest impact, such as cement and oil refining. As a result, the public debate around CCUS is practically non-existent, and where public opinions do emerge, they may be significantly influenced by the context of a particular project and generate significant resistance based on the relationship with project developers, the amplification of perceived risks, and the lack of appropriate explanations of costs, benefits and risks. This in turn can lead to a reticence of political stakeholders to commit to deploying CCUS, causing the public debate to further stagnate and creating a vicious circle whereby opportunities to familiarize the public with these technologies (well in advance of their deployment) are missed. In order to deploy CCUS at pace and scale, as part of the catching-up climate policies of CEE countries, public perception of CCUS must be thoroughly researched and developed into appropriate guidelines for community engagement by project developers. There is experience in the region – the feasibility study for Romania’s planned Getica CCS demonstrator (subsequently abandoned) included comprehensive research into the perceptions of local communities, and a toolkit for communications around CCUS by project developers. Similarly, learnings from Poland’s failed Belchatow CCS project can serve to re-assess the state of public opinion on CCS, and how the local and national-level contexts for CCUS perceptions interact. The CEE region has significant potential for deploying CCUS, and public perception must be an integral part of planning as the region moves into the key decade of 2030-2040 for implementing large-scale projects