Purpose: Patient-specific quality assurance (PSQA) requires manual operation of different workstations, which is time-consuming and error-prone. Therefore, developing automated solutions to improve efficiency and accu...Purpose: Patient-specific quality assurance (PSQA) requires manual operation of different workstations, which is time-consuming and error-prone. Therefore, developing automated solutions to improve efficiency and accuracy is a priority. The purpose of this study was to develop a general software interface with scripting on a human interactive device (HID) for improving the efficiency and accuracy of manual quality assurance (QA) procedures. Methods: As an initial application, we aimed to automate our PSQA workflow that involves Varian Eclipse treatment planning system, Elekta MOSAIQ oncology information system and PTW Verisoft application. A general platform, the AutoFrame interface with two imbedded subsystems—the AutoFlow and the PyFlow, was developed with a scripting language for automating human operations of aforementioned systems. The interface included three functional modules: GUI module, UDF script interpreter and TCP/IP communication module. All workstations in the PSQA process were connected, and most manual operations were automated by AutoFrame sequentially or in parallel. Results: More than 20 PSQA tasks were performed both manually and using the developed AutoFrame interface. On average, 175 (±12) manual operations of the PSQA procedure were eliminated and performed by the automated process. The time to complete a PSQA task was 8.23 (±0.78) minutes for the automated workflow, in comparison to 13.91 (±3.01) minutes needed for manual operations. Conclusion: We have developed the AutoFrame interface framework that successfully automated our PSQA procedure, and significantly reduced the time, human (control/clicking/typing) errors, and operators’ stress. Future work will focus on improving the system’s flexibility and stability and extending its operations to other QA procedures.展开更多
Fixture design and planning is one of the most important manufacturing activities, playing a pivotal role in deciding the lead time for product development. Fixture design, which affects the part-quality in terms of g...Fixture design and planning is one of the most important manufacturing activities, playing a pivotal role in deciding the lead time for product development. Fixture design, which affects the part-quality in terms of geometric accuracy and surface finish, can be enhanced by using the product manufacturing information(PMI) stored in the neutral standard for the exchange of product model data(STEP) file, thereby integrating design and manufacturing. The present paper proposes a unique fixture design approach, to extract the geometry information from STEP application protocol(AP) 242 files of computer aided design(CAD) models, for providing automatic suggestions of locator positions and clamping surfaces. Automatic feature extraction software "FiXplan", developed using the programming language C#, is used to extract the part feature, dimension and geometry information. The information from the STEP AP 242 file is deduced using geometric reasoning techniques, which in turn is utilized for fixture planning. The developed software is observed to be adept in identifying the primary, secondary, and tertiary locating faces and locator position configurations of prismatic components. Structural analysis of the prismatic part under different locator positions was performed using commercial finite element method software, ABAQUS, and the optimized locator position was identified on the basis of minimum deformation of the workpiece.The area-ratio(base locator enclosed area(%)/work piece base area(%)) for the ideal locator configuration was observed as 33%. Experiments were conducted on a prismatic workpiece using a specially designed fixture, for different locator configurations. The surface roughness and waviness of the machined surfaces were analysed using an Alicona non-contact optical profilometer. The best surface characteristics were obtained for the surface machined under the ideal locator positions having an area-ratio of 33%, thus validating the predicted numerical results. The efficiency, capability and applicability of the developed software is demonstrated for the finishing operation of a sensor cover – a typical prismatic component having applications in the naval industry, under different locator configurations.The best results were obtained under the proposed ideal locator configuration of area-ratio 33%.展开更多
为弥补现有开采沉陷预测程序在可视化表达中的缺陷,采用VB和SURFER的Active X Automation技术开发了基于概率积分法的开采沉陷预测分析系统。通过VB语言操纵SURFER内核程序实现开采沉陷的各种移动变形等值线、三维表面图及剖面图制作和...为弥补现有开采沉陷预测程序在可视化表达中的缺陷,采用VB和SURFER的Active X Automation技术开发了基于概率积分法的开采沉陷预测分析系统。通过VB语言操纵SURFER内核程序实现开采沉陷的各种移动变形等值线、三维表面图及剖面图制作和数据分析的自动化。以山东某煤矿多工作面、多煤层开采沉陷预计对所建立分析系统进行验证。结果表明,采用VB与SURFER结合用于开采沉陷的预测分析,能够满足工程需要,并且能极大地提高工作效率,减少程序开发的工作量,实现开采沉陷预测分析图件制作的专业化、自动化。展开更多
Sound transportation infrastructure is critical for economic development and sustainability.Pavement condition is a primary concern among agencies of the roadway infrastructure.Automation has become possible in recent...Sound transportation infrastructure is critical for economic development and sustainability.Pavement condition is a primary concern among agencies of the roadway infrastructure.Automation has become possible in recent years on collecting data and producing results for certain aspects of pavement performance,while challenges remain in several other categories,such as automated cracking survey.This paper reviews the technological advances on automated survey of pavements,and discusses the most recent breakthroughs by the team led by the author in using 3D laser imaging for capturing 1 mm surface images of pavements.展开更多
Characterization of disease models of neurodegenerative disorders requires a systematic and comprehensive phenotyping in a highly standardized manner. Therefore, automated high-resolution behavior test systems such as...Characterization of disease models of neurodegenerative disorders requires a systematic and comprehensive phenotyping in a highly standardized manner. Therefore, automated high-resolution behavior test systems such as the homecage based LabMaster system are of particular interest. We demonstrate the power of the automated LabMaster system by discovering previously unrecognized features of a recently characterized atxn3 mutant mouse model. This model provided neurological symptoms including gait ataxia, tremor, weight loss and premature death at the age of 12 months usually detectable just 2 weeks before the mice died. Moreover, using the LabMaster system we were able to detect hypoactivity in presymptomatic mutant mice in the dark as well as light phase. Additionally, we analyzed inflammation, immunological and hematological parameters, which indicated a reduced immune defense in phenotypic mice. Here we demonstrate that a detailed characterization even of organ systems that are usually not affected in SCA3 is important for further studies of pathogenesis and required for the preclinical therapeutic studies.展开更多
Since programing complex and dynamic heat source model for welding simulation is a complex job,the parametric methods are studied in this paper.Firstly,an overall flow to achieve automatically modeling welding was int...Since programing complex and dynamic heat source model for welding simulation is a complex job,the parametric methods are studied in this paper.Firstly,an overall flow to achieve automatically modeling welding was introduced.Secondly,an expert module rule for selecting welding heat source model was founded,which is based on simulation knowledge and experiences.Thirdly,a modularity routine method was investigated using writing with C++programing,which automatically creates subroutines of 3D dynamic heat source model for user.To realize the dynamic weld path,the local weld path coordinate system was moved in the global coordinate system and it is used to model the direction of weld gun,welding path and welding pose.The weld path data file was prepared by the automatic tool for the welding heat source subroutines.All above functions were integrated in the user interface and the connection with architecture was introduced.At last,a laser beam welding heat source modeling was automatically modeled and the weld pool geometry was compared with the reported literature.It demonstrated that the automated tool is valid for welding simulation.Since modeling became convenient for welding simulation using the tool proposed,it could be easy and useful for welding engineers to acquire the needed information.展开更多
Background Deep convolutional neural networks have garnered considerable attention in numerous machine learning applications,particularly in visual recognition tasks such as image and video analyses.There is a growing...Background Deep convolutional neural networks have garnered considerable attention in numerous machine learning applications,particularly in visual recognition tasks such as image and video analyses.There is a growing interest in applying this technology to diverse applications in medical image analysis.Automated three dimensional Breast Ultrasound is a vital tool for detecting breast cancer,and computer-assisted diagnosis software,developed based on deep learning,can effectively assist radiologists in diagnosis.However,the network model is prone to overfitting during training,owing to challenges such as insufficient training data.This study attempts to solve the problem caused by small datasets and improve model detection performance.Methods We propose a breast cancer detection framework based on deep learning(a transfer learning method based on cross-organ cancer detection)and a contrastive learning method based on breast imaging reporting and data systems(BI-RADS).Results When using cross organ transfer learning and BIRADS based contrastive learning,the average sensitivity of the model increased by a maximum of 16.05%.Conclusion Our experiments have demonstrated that the parameters and experiences of cross-organ cancer detection can be mutually referenced,and contrastive learning method based on BI-RADS can improve the detection performance of the model.展开更多
This paper describes the process of designing models and tools for an automated way of creating 3D city model based on a raw point cloud.Also,making and forming 3D models of buildings.Models and tools for creating too...This paper describes the process of designing models and tools for an automated way of creating 3D city model based on a raw point cloud.Also,making and forming 3D models of buildings.Models and tools for creating tools made in the model builder application within the ArcGIS Pro software.An unclassified point cloud obtained by the LiDAR system was used for the model input data.The point cloud,collected by the airborne laser scanning system(ALS),is classified into several classes:ground,high and low noise,and buildings.Based on the created DEMs,points classified as buildings and formed prints of buildings,realistic 3D city models were created.Created 3D models of cities can be used as a basis for monitoring the infrastructure of settlements and other analyzes that are important for further development and architecture of cities.展开更多
Based on observed meteorological elements,photolysis rates(J-values)and pollutant concentrations,an automated J-values predicting system by machine learning(J-ML)has been developed to reproduce and predict the J-value...Based on observed meteorological elements,photolysis rates(J-values)and pollutant concentrations,an automated J-values predicting system by machine learning(J-ML)has been developed to reproduce and predict the J-values of O^(1)D,NO_(2),HONO,H_(2)O_(2),HCHO,and NO_(3),which are the crucial values for the prediction of the atmospheric oxidation capacity(AOC)and secondary pollutant concentrations such as ozone(O_(3)),secondary organic aerosols(SOA).The J-ML can self-select the optimal“Model+Hyperparameters”without human interference.The evaluated results showed that the J-ML had a good performance to reproduce the J-values wheremost of the correlation(R)coefficients exceed 0.93 and the accuracy(P)values are in the range of 0.68-0.83,comparing with the J-values from observations and from the tropospheric ultraviolet and visible(TUV)radiation model in Beijing,Chengdu,Guangzhou and Shanghai,China.The hourly prediction was also well performed with R from 0.78 to 0.81 for next 3-days and from 0.69 to 0.71 for next 7-days,respectively.Compared with O_(3)concentrations by using J-values from the TUV model,an emission-driven observation-based model(e-OBM)by using the J-values from the J-ML showed a 4%-12%increase in R and 4%-30%decrease in ME,indicating that the J-ML could be used as an excellent supplement to traditional numerical models.The feature importance analysis concluded that the key influential parameter was the surface solar downwards radiation for all J-values,and the other dominant factors for all J-values were 2-m mean temperature,O_(3),total cloud cover,boundary layer height,relative humidity and surface pressure.展开更多
This paper proposes a novel cargo loading algorithm applicable to automated conveyor-type loading systems.The algorithm offers improvements in computational efficiency and robustness by utilizing the concept of discre...This paper proposes a novel cargo loading algorithm applicable to automated conveyor-type loading systems.The algorithm offers improvements in computational efficiency and robustness by utilizing the concept of discrete derivatives and introducing logistics-related constraints.Optional consideration of the rotation of the cargoes was made to further enhance the optimality of the solutions,if possible to be physically implemented.Evaluation metrics were developed for accurate evaluation and enhancement of the algorithm’s ability to efficiently utilize the loading space and provide a high level of dynamic stability.Experimental results demonstrate the extensive robustness of the proposed algorithm to the diversity of cargoes present in Business-to-Consumer environments.This study contributes practical advancements in both cargo loading optimization and automation of the logistics industry,with potential applications in last-mile delivery services,warehousing,and supply chain management.展开更多
文摘Purpose: Patient-specific quality assurance (PSQA) requires manual operation of different workstations, which is time-consuming and error-prone. Therefore, developing automated solutions to improve efficiency and accuracy is a priority. The purpose of this study was to develop a general software interface with scripting on a human interactive device (HID) for improving the efficiency and accuracy of manual quality assurance (QA) procedures. Methods: As an initial application, we aimed to automate our PSQA workflow that involves Varian Eclipse treatment planning system, Elekta MOSAIQ oncology information system and PTW Verisoft application. A general platform, the AutoFrame interface with two imbedded subsystems—the AutoFlow and the PyFlow, was developed with a scripting language for automating human operations of aforementioned systems. The interface included three functional modules: GUI module, UDF script interpreter and TCP/IP communication module. All workstations in the PSQA process were connected, and most manual operations were automated by AutoFrame sequentially or in parallel. Results: More than 20 PSQA tasks were performed both manually and using the developed AutoFrame interface. On average, 175 (±12) manual operations of the PSQA procedure were eliminated and performed by the automated process. The time to complete a PSQA task was 8.23 (±0.78) minutes for the automated workflow, in comparison to 13.91 (±3.01) minutes needed for manual operations. Conclusion: We have developed the AutoFrame interface framework that successfully automated our PSQA procedure, and significantly reduced the time, human (control/clicking/typing) errors, and operators’ stress. Future work will focus on improving the system’s flexibility and stability and extending its operations to other QA procedures.
基金Department of Science and Technology,Government of India for providing financial support under the scheme FIST(No.SR/FST/ETI-388/2015)。
文摘Fixture design and planning is one of the most important manufacturing activities, playing a pivotal role in deciding the lead time for product development. Fixture design, which affects the part-quality in terms of geometric accuracy and surface finish, can be enhanced by using the product manufacturing information(PMI) stored in the neutral standard for the exchange of product model data(STEP) file, thereby integrating design and manufacturing. The present paper proposes a unique fixture design approach, to extract the geometry information from STEP application protocol(AP) 242 files of computer aided design(CAD) models, for providing automatic suggestions of locator positions and clamping surfaces. Automatic feature extraction software "FiXplan", developed using the programming language C#, is used to extract the part feature, dimension and geometry information. The information from the STEP AP 242 file is deduced using geometric reasoning techniques, which in turn is utilized for fixture planning. The developed software is observed to be adept in identifying the primary, secondary, and tertiary locating faces and locator position configurations of prismatic components. Structural analysis of the prismatic part under different locator positions was performed using commercial finite element method software, ABAQUS, and the optimized locator position was identified on the basis of minimum deformation of the workpiece.The area-ratio(base locator enclosed area(%)/work piece base area(%)) for the ideal locator configuration was observed as 33%. Experiments were conducted on a prismatic workpiece using a specially designed fixture, for different locator configurations. The surface roughness and waviness of the machined surfaces were analysed using an Alicona non-contact optical profilometer. The best surface characteristics were obtained for the surface machined under the ideal locator positions having an area-ratio of 33%, thus validating the predicted numerical results. The efficiency, capability and applicability of the developed software is demonstrated for the finishing operation of a sensor cover – a typical prismatic component having applications in the naval industry, under different locator configurations.The best results were obtained under the proposed ideal locator configuration of area-ratio 33%.
文摘为弥补现有开采沉陷预测程序在可视化表达中的缺陷,采用VB和SURFER的Active X Automation技术开发了基于概率积分法的开采沉陷预测分析系统。通过VB语言操纵SURFER内核程序实现开采沉陷的各种移动变形等值线、三维表面图及剖面图制作和数据分析的自动化。以山东某煤矿多工作面、多煤层开采沉陷预计对所建立分析系统进行验证。结果表明,采用VB与SURFER结合用于开采沉陷的预测分析,能够满足工程需要,并且能极大地提高工作效率,减少程序开发的工作量,实现开采沉陷预测分析图件制作的专业化、自动化。
文摘Sound transportation infrastructure is critical for economic development and sustainability.Pavement condition is a primary concern among agencies of the roadway infrastructure.Automation has become possible in recent years on collecting data and producing results for certain aspects of pavement performance,while challenges remain in several other categories,such as automated cracking survey.This paper reviews the technological advances on automated survey of pavements,and discusses the most recent breakthroughs by the team led by the author in using 3D laser imaging for capturing 1 mm surface images of pavements.
基金supported by the European Union to OR(6th frame work programme.EuroSCA)
文摘Characterization of disease models of neurodegenerative disorders requires a systematic and comprehensive phenotyping in a highly standardized manner. Therefore, automated high-resolution behavior test systems such as the homecage based LabMaster system are of particular interest. We demonstrate the power of the automated LabMaster system by discovering previously unrecognized features of a recently characterized atxn3 mutant mouse model. This model provided neurological symptoms including gait ataxia, tremor, weight loss and premature death at the age of 12 months usually detectable just 2 weeks before the mice died. Moreover, using the LabMaster system we were able to detect hypoactivity in presymptomatic mutant mice in the dark as well as light phase. Additionally, we analyzed inflammation, immunological and hematological parameters, which indicated a reduced immune defense in phenotypic mice. Here we demonstrate that a detailed characterization even of organ systems that are usually not affected in SCA3 is important for further studies of pathogenesis and required for the preclinical therapeutic studies.
基金supported by Young Innovative Talents Training Plan of Heilongjiang(UNPYSCT-2018133).
文摘Since programing complex and dynamic heat source model for welding simulation is a complex job,the parametric methods are studied in this paper.Firstly,an overall flow to achieve automatically modeling welding was introduced.Secondly,an expert module rule for selecting welding heat source model was founded,which is based on simulation knowledge and experiences.Thirdly,a modularity routine method was investigated using writing with C++programing,which automatically creates subroutines of 3D dynamic heat source model for user.To realize the dynamic weld path,the local weld path coordinate system was moved in the global coordinate system and it is used to model the direction of weld gun,welding path and welding pose.The weld path data file was prepared by the automatic tool for the welding heat source subroutines.All above functions were integrated in the user interface and the connection with architecture was introduced.At last,a laser beam welding heat source modeling was automatically modeled and the weld pool geometry was compared with the reported literature.It demonstrated that the automated tool is valid for welding simulation.Since modeling became convenient for welding simulation using the tool proposed,it could be easy and useful for welding engineers to acquire the needed information.
基金Macao Polytechnic University Grant(RP/FCSD-01/2022RP/FCA-05/2022)Science and Technology Development Fund of Macao(0105/2022/A).
文摘Background Deep convolutional neural networks have garnered considerable attention in numerous machine learning applications,particularly in visual recognition tasks such as image and video analyses.There is a growing interest in applying this technology to diverse applications in medical image analysis.Automated three dimensional Breast Ultrasound is a vital tool for detecting breast cancer,and computer-assisted diagnosis software,developed based on deep learning,can effectively assist radiologists in diagnosis.However,the network model is prone to overfitting during training,owing to challenges such as insufficient training data.This study attempts to solve the problem caused by small datasets and improve model detection performance.Methods We propose a breast cancer detection framework based on deep learning(a transfer learning method based on cross-organ cancer detection)and a contrastive learning method based on breast imaging reporting and data systems(BI-RADS).Results When using cross organ transfer learning and BIRADS based contrastive learning,the average sensitivity of the model increased by a maximum of 16.05%.Conclusion Our experiments have demonstrated that the parameters and experiences of cross-organ cancer detection can be mutually referenced,and contrastive learning method based on BI-RADS can improve the detection performance of the model.
文摘This paper describes the process of designing models and tools for an automated way of creating 3D city model based on a raw point cloud.Also,making and forming 3D models of buildings.Models and tools for creating tools made in the model builder application within the ArcGIS Pro software.An unclassified point cloud obtained by the LiDAR system was used for the model input data.The point cloud,collected by the airborne laser scanning system(ALS),is classified into several classes:ground,high and low noise,and buildings.Based on the created DEMs,points classified as buildings and formed prints of buildings,realistic 3D city models were created.Created 3D models of cities can be used as a basis for monitoring the infrastructure of settlements and other analyzes that are important for further development and architecture of cities.
基金supported by the National Key Project of the Ministry of Science and Technology of China(No.2022YFC3701200)the National Natural Science Foundation of China(No.42090030).
文摘Based on observed meteorological elements,photolysis rates(J-values)and pollutant concentrations,an automated J-values predicting system by machine learning(J-ML)has been developed to reproduce and predict the J-values of O^(1)D,NO_(2),HONO,H_(2)O_(2),HCHO,and NO_(3),which are the crucial values for the prediction of the atmospheric oxidation capacity(AOC)and secondary pollutant concentrations such as ozone(O_(3)),secondary organic aerosols(SOA).The J-ML can self-select the optimal“Model+Hyperparameters”without human interference.The evaluated results showed that the J-ML had a good performance to reproduce the J-values wheremost of the correlation(R)coefficients exceed 0.93 and the accuracy(P)values are in the range of 0.68-0.83,comparing with the J-values from observations and from the tropospheric ultraviolet and visible(TUV)radiation model in Beijing,Chengdu,Guangzhou and Shanghai,China.The hourly prediction was also well performed with R from 0.78 to 0.81 for next 3-days and from 0.69 to 0.71 for next 7-days,respectively.Compared with O_(3)concentrations by using J-values from the TUV model,an emission-driven observation-based model(e-OBM)by using the J-values from the J-ML showed a 4%-12%increase in R and 4%-30%decrease in ME,indicating that the J-ML could be used as an excellent supplement to traditional numerical models.The feature importance analysis concluded that the key influential parameter was the surface solar downwards radiation for all J-values,and the other dominant factors for all J-values were 2-m mean temperature,O_(3),total cloud cover,boundary layer height,relative humidity and surface pressure.
基金supported by the BK21 FOUR funded by the Ministry of Education of Korea and National Research Foundation of Korea,a Korea Agency for Infrastructure Technology Advancement(KAIA)grant funded by the Ministry of Land,Infrastructure,and Transport(Grant 1615013176)IITP(Institute of Information&Coummunications Technology Planning&Evaluation)-ICAN(ICT Challenge and Advanced Network of HRD)grant funded by the Korea government(Ministry of Science and ICT)(RS-2024-00438411).
文摘This paper proposes a novel cargo loading algorithm applicable to automated conveyor-type loading systems.The algorithm offers improvements in computational efficiency and robustness by utilizing the concept of discrete derivatives and introducing logistics-related constraints.Optional consideration of the rotation of the cargoes was made to further enhance the optimality of the solutions,if possible to be physically implemented.Evaluation metrics were developed for accurate evaluation and enhancement of the algorithm’s ability to efficiently utilize the loading space and provide a high level of dynamic stability.Experimental results demonstrate the extensive robustness of the proposed algorithm to the diversity of cargoes present in Business-to-Consumer environments.This study contributes practical advancements in both cargo loading optimization and automation of the logistics industry,with potential applications in last-mile delivery services,warehousing,and supply chain management.
