Semi-Automatic Modeling Technique of Torque Converter Flow Passage

The modeling technique of hydrodynamic torque converter flow passage was investigated. The semi-automatic modeling technique of torque converter flow passage was proposed. The flow passage model of each converter wheel is considered as a revolution entity sliced by two curved surfaces. In order to generate the revolution entity, a new approximation method, condition optimum arc approximation, was proposed. The method was used to approximate the meridional streamlines of the inner and outer wall. As a result, the three-dimensional revolution entity can be conveniently generated. In order to create slice surfaces, the central stream surface of flow passage was approximated with a quadric surface. The normal vector of the quadric surface and the thickness/thickness-function of bade were used to calculate the discrete point coordinates of blade surfaces. Via the rotation transformation to the coordinates, the discrete point coordinates of slice surfaces were obtained. A parameterized program code used for the hydrodynamic torque converter design and semi-automatic modeling was developed. Modeling errors were calculated and analyzed. The flow passage model was generated in several minutes with the help of the program code, Auto CAD and Solidworks software. Finally, the model was inputted into Gambit, and the pre-processing task used for the numerical simulation of torque converter flow field was successfully completed. The investigation results show that the semi-automatic modeling not only can ensure the accuracy of modeling, but also librates the research and design workers of torque converter from the time-consuming modeling work, which paves the way for the numerical simulation of the complex flow field of the hydrodynamic torque converter.

A Semi-automatic Method Based on Statistic for Mandarin Semantic Structures Extraction in Specific Domains

This paper proposed a new method of semi-automatic extraction for semantic structures from unlabelled corpora in specific domains. The approach is statistical in nature. The extracted structures can be used for shallow parsing and semantic labeling. By iteratively extracting new words and clustering words, we get an inital semantic lexicon that groups words of the same semantic meaning together as a class. After that, a bootstrapping algorithm is adopted to extract semantic structures. Then the semantic structures are used to extract new

Soil Mapping of a Small Watershed in a Brazilian Savanna Biome: A Semi-Automatic Approach

The objective of this work is to propose a semi-automatic methodology for the creation of a semidetailed to detailed watershed’s soil map. This methodological proposal is based on the traditional method (field work and photo interpretation) and on morphometric variables analysis, using data originated from the Digital Elevation Model (DEM) of the Shuttle Radar Topography Mission (SRTM) program. The steps taken through this methodological proposal were the identification of the hydromorphic areas boundaries through interpretation of aerial orthophotographs;the compartmentalization of the relief through color composition techniques using the morphometric variables—slope, altimetry and horizontal curvature;the development of a pedological database based on data gathered in the field;the elaboration of a preliminary soil map based on the compartmentalized relief and on the dataset of the soils sampled in the field;the elaboration and adjustment of the final soil map according to the interpretation of aerial photographs and also the physical and chemical analyses of the soils. This semi-automatic methodology demonstrated improved efficiency in defining the soil units, reducing operation time and subjectivity of the process, thereby contributing to the systematization of soil mapping at watershed level in scale 1:50,000, as well as, promoting better knowledge between the relation of the geomorphic and soil variables in the Brazilian Savanna Cerrado.

Integration of User-system View for Cyber Crime Analysis： A Semi-automatic Approach

There is a rapid rise in cybercrime cases. There does not exist any effective forensic methods to deal with these eybercrime cases. Investigators are required to study the details of a large amount of tedious source in order to understand the crime model and dig out the evidence. This requires a lot of effort and may result in human errors. In order to overcome these potential errors that may cause by the investigators, we propose a semi-automatic approach that integrates the user view （based on a high level study of the forensic investigator） and the system view （based on the automatic analysis of the source codes） to assist investigators in refining the scope of the investigation. The approach has been verified using a real cybercrime case and the method has been shown to be effective in assisting the investigators in refining the scope of investigation and understanding the crime model. The semi-automatic approach has improved the efficiency and reliability of the digital forensic analysis of cybercrime cases involving large volume of digital evidence from multiple sources.

The Method and Practice of Constructing 3D Geological Model from Coalfield Exploration 2D Maps

3D geological modeling is an inevitable choice for coal exploration to adapt to the transformation of coal mining for green, fine, transparent and Intelligent mining. In the traditional Coalfield exploration geological reports, the spatial expression form for the coal seams and their surrounding rocks are 2D maps. These 2D maps are excellent data sources for constructing 3D geological models of coal field exploration areas. How to construct 3D models from these 2D maps has been studying in coal exploration industry for a long time, and still no breakthrough has been achieved so far. This paper discusses the principle, method and software design idea of constructing 3D geological model of an exploration area with 2D maps made by AutoCAD/MapGIS. At first, the paper analyzes 3D geological surface expression mode in 3D geological modeling software. It is pointed out that although contour method has unique advantages in coal field exploration, TIN (Triangular Irregular Network) is still the standard configuration of 3D modeling software for coal field. Then, the paper discusses the method of 2D line features obtaining elevation and upgrading 2D curve to 3D curve. Next, the method of semi-automatic partition is introduced to build the boundary ring of the surface patch, that is, the user clicks and selects the line feature to build the outer boundary ring of the surface patch. Then, Auto-process method for fault line inside of the outer boundary ring is discussed, it including construction of fault ring, determining fault ring being normal fault ring or reverse fault ring and an algorithm of dealing with normal fault ring. An algorithm of dealing with reverse fault ring is discussed detailly, the method of expanding reverse fault ring and dividing the duplicate area in reverse fault into two portions is introduced. The paper also discusses the method of extraction ridge line/valley line, the construction of fault plane, the construction of stratum and coal body. The above ideas and methods have been initially implemented in the “3D modeling platform for coal field exploration” software, and applied to the 3D modeling practice of data from several coal field exploration areas in Ningxia, Shanxi, Qinghai, etc.

Euler Deconvolution with Improved Accuracy and Multiple Different Structural Indices

Euler deconvolution is a semi-automatic interpretation method that is frequently used with magnetic and gravity data. For a given source type, which is specified by its structural index （SI）, it provides an estimate of the source location. It is demonstrated here that by computing the solution space of individual data points and selecting common source locations the accuracy of the result can be improved. Furthermore, only a slight modification of the method is necessary to allow solutions for any number of different Sls to be obtained simultaneously. The method is applicable to both evenly and unevenly sampled geophysical data and is demonstrated on gravity and magnetic data. Source code （in Matlab format） is available from www.iamg.org.

Girth weldability evaluation for submarine pipeline laying

Based on the offshore standard DNV-OS-F101-2010,girth welding tests and girth weldability evaluations were carried out for the X65 and X70 UOE submarine pipelines of 3 wall thickness gauges,in which semi-automatic gas metal arc welding （GMAW） was adopted matched with metal powder flux cored wire. The girth welding procedure specification （WPS） was optimized to obtain good quality and cold-crack-free girth weld joint without preheating and post-weld heat treatment. At the same time, porosities, inclusions and incomplete fusion defects were avoided. Girth weld joints were evaluated for tensile strength, bending, Vickers hardness, charpy impact toughness and crack tip opening displacement （CTOD） fracture toughness. Results show that all the girth weld joints have good mechanical performances,indicating that the irth weldabilitv of Baosteel＇ s UOE submarine pipelines is excellent in the case of semi-automatic GMAW.

A Review on Extraction of Lakes from Remotely Sensed Optical Satellite Data with a Special Focus on Cryospheric Lakes

Water on the Earth’s surface is an essential part of the hydrological cycle. Water resources include surface waters, groundwater, lakes, inland waters, rivers, coastal waters, and aquifers. Monitoring lake dynamics is critical to favor sustainable management of water resources on Earth. In cryosphere, lake ice cover is a robust indicator of local climate variability and change. Therefore, it is necessary to review recent methods, technologies, and satellite sensors employed for the extraction of lakes from satellite imagery. The present review focuses on the comprehensive evaluation of existing methods for extraction of lake or water body features from remotely sensed optical data. We summarize pixel-based, object-based, hybrid, spectral index based, target and spectral matching methods employed in extracting lake features in urban and cryospheric environments. To our knowledge, almost all of the published research studies on the extraction of surface lakes in cryospheric environments have essentially used satellite remote sensing data and geospatial methods. Satellite sensors of varying spatial, temporal and spectral resolutions have been used to extract and analyze the information regarding surface water. Multispectral remote sensing has been widely utilized in cryospheric studies and has employed a variety of electro-optical satellite sensor systems for characterization and extraction of various cryospheric features, such as glaciers, sea ice, lakes and rivers, the extent of snow and ice, and icebergs. It is apparent that the most common methods for extracting water bodies use single band-based threshold methods, spectral index ratio (SIR)-based multiband methods, image segmentation methods, spectral-matching methods, and target detection methods (unsupervised, supervised and hybrid). A Synergetic fusion of various remote sensing methods is also proposed to improve water information extraction accuracies. The methods developed so far are not generic rather they are specific to either the location or satellite imagery or to the type of the feature to be extracted. Lots of factors are responsible for leading to inaccurate results of lake-feature extraction in cryospheric regions, e.g. the mountain shadow which also appears as a dark pixel is often misclassified as an open lake. The methods which are working well in the cryospheric environment for feature extraction or landcover classification does not really guarantee that they will be working in the same manner for the urban environment. Thus, in coming years, it is expected that much of the work will be done on object-based approach or hybrid approach involving both pixel as well as object-based technology. A more accurate, versatile and robust method is necessary to be developed that would work independent of geographical location (for both urban and cryosphere) and type of optical sensor.

Adaptive Multi-modal Fusion Instance Segmentation for CAEVs in Complex Conditions:Dataset,Framework and Verifications

Current works of environmental perception for connected autonomous electrified vehicles(CAEVs)mainly focus on the object detection task in good weather and illumination conditions,they often perform poorly in adverse scenarios and have a vague scene parsing ability.This paper aims to develop an end-to-end sharpening mixture of experts(SMoE)fusion framework to improve the robustness and accuracy of the perception systems for CAEVs in complex illumination and weather conditions.Three original contributions make our work distinctive from the existing relevant literature.The Complex KITTI dataset is introduced which consists of 7481 pairs of modified KITTI RGB images and the generated LiDAR dense depth maps,and this dataset is fine annotated in instance-level with the proposed semi-automatic annotation method.The SMoE fusion approach is devised to adaptively learn the robust kernels from complementary modalities.Comprehensive comparative experiments are implemented,and the results show that the proposed SMoE framework yield significant improvements over the other fusion techniques in adverse environmental conditions.This research proposes a SMoE fusion framework to improve the scene parsing ability of the perception systems for CAEVs in adverse conditions.

A New Manufacturing Method for Hourglass Worm Gear Hob Based on a Semi⁃automatic CAD

The top relief surfaces of an hourglass worm gear hob are ground manually in the traditional manufacturing process,which cannot ensure the width of the land surfaces of the hob.Moreover,each geometric feature of the hob has been produced through different manufacturing techniques and machine tools,which results in low efficiency.To solve this problem,we propose a semi-automatic computer aided design(CAD)method for hobs.The point clouds of each feature surface of a hob are calculated by combing mathematical equations of the top relief surfaces built by the proposed method with other existing equations of hob surfaces.According to the point clouds,the method can achieve the automatic modeling for the hob in three-dimensional(3D)software by classifying and extracting the parameter information of the feature-hierarchical knowledge of the hob.Based on the generated 3D model,the entire surfaces of the hob can be manufactured on a four-axis computer numerical control(CNC)milling machine through only twice clamping.Verification of the width of the land surface of the hob manufactured by semi-automatic CAD method on a measuring projector proved the precision of the designed width can be ensured.The edge of the contact area on the worm wheel in a meshing experiment is clear and distinct,which means the worm gear drive is meshed well and the hob manufactured by the proposed method has improved machinability.The method simplifies the processing technique,and improves the design efficiency and production accuracy.

A spatiotemporal data collection of viral cases for COVID-19 rapid response

Under the global health crisis of COVID-19,timely,and accurate epi-demic data are important for observation,monitoring,analyzing,modeling,predicting,and mitigating impacts.Viral case data can be jointly analyzed with relevant factors for various applications in the context of the pandemic.Current COVID-19 case data are scattered across a variety of data sources which may consist of low data quality accompanied by inconsistent data structures.To address this short-coming,a multi-scale spatiotemporal data product is proposed as a public repository platform,based on a spatiotemporal cube,and allows the integration of different data sources by adopting various data standards.Within the spatiotemporal cube,a comprehensive data processing workflow gathers disparate COVID-19 epidemic data-sets at the global,national,provincial/state,county,and city levels.This proposed framework is supported by an automatic update with a 2-h frequency and the crowdsourcing validation team to produce and update data on a daily time step.This rapid-response dataset allows the integration of other relevant socio-economic and environ-mental factors for spatiotemporal analysis.The data is available in Harvard Dataverse platform(https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/8HGECN)and GitHub open source repository(https://github.com/stccenter/COVID-19-Data).