Satellite design encompasses a multitude of steps from concept to flight. Mission specification to flight can typically take several years, depending on the scope, requirements and budget of the mission. The process a...
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ISBN:
(数字)9781624106996
ISBN:
(纸本)9781624106996
Satellite design encompasses a multitude of steps from concept to flight. Mission specification to flight can typically take several years, depending on the scope, requirements and budget of the mission. The process also requires a wide range of design and management tools that have limited consistency, limited data interchange capability, and a lack of coherency. Detailing the relationships between the satellite configuration inventory control systems, life cycle management, design, analysis and test data is difficult at best. QuickSAT/Designer Release 2 (Figure 1), is a tool for collaborative satellite design and modeling that assists design teams to develop, populate, and visualize very broad, multidimensional trade spaces. QuickSAT/Designer with the open source step_SATdb data architecture is a model-based satellite design, mission planning, and product lifecycle management environment. Research has focused on our Satellite Design Automation architecture QuickSAT/Designer Release 2, in conjunction with our step_SATdb open database architecture, to meet this need. step_SATdb seamlessly integrates existing detail design tools with QuickSAT, as well as databases tracking requirements, hardware and software components and payloads in inventory with the final configuration of the satellite. QuickSAT/Designer with the Configurator AI based toolset, provides for rapid design, and integration with existing design tools, and provides coherency between a range of applications and data sets. QuickSAT/Designer utilizes model-based design processes, techniques and methodologies to develop conceptual designs with expedient leveraging of the best new commercially-available and open source tools. Recent research focused developing and enhancing the rapid space mission design capability, and demonstrating the utility in several customer need areas for missions that are at different stages of conceptual maturity, including where conceptual development has not yet begun. Research
In this paper, for the reactive power optimization problem of regional power grid, combined with the characteristics of load fluctuation, a reactive power optimization strategy of power grid considering load fluctuati...
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ISBN:
(数字)9798350370805
ISBN:
(纸本)9798350370812
In this paper, for the reactive power optimization problem of regional power grid, combined with the characteristics of load fluctuation, a reactive power optimization strategy of power grid considering load fluctuation is proposed. Through the statistical analysis and modeling of load data, the reactive power optimization model of power grid considering load fluctuation is established, and the particle swarm algorithm is used for reactive power optimization. Simulation experimental results show that the proposed optimization strategy achieves good results in reducing system reactive power losses. This study has certain guiding significance and practical application value for improving the reactive power control performance of regional power grids.
Automated testing is an approach to transforming human-driven testing behavior into automated machine execution, which can perform many tests that are difficult or impossible to achieve by manual testing. Due to compl...
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ISBN:
(数字)9781665458641
ISBN:
(纸本)9781665458641
Automated testing is an approach to transforming human-driven testing behavior into automated machine execution, which can perform many tests that are difficult or impossible to achieve by manual testing. Due to complex business, tight coupling, strict timing, and a large amount of data exchange, many equipment software cannot be tested by manual testing. Developing dedicated test tools is costly, time-consuming, and requires advanced programming knowledge. That poses a significant challenge for testers with limited programming skills. To this end, we propose a hybrid-driven automated testing approach. We design and realize a test automation platform based on the ideas of data-driven and keyword-driven testing. We also design configuration rules and implement them in this platform. With this platform, testers only need to focus on business analysis and make simple configurations to conduct automated testing for complex process equipment software. There is no need to develop dedicated test tools or write test scripts, which reduces the technical threshold for automated testing. Application results indicate that our approach can perform more business scenarios than manual testing and physical testing. Compared with conventional automated testing approaches, ours does not require testers to have programming skills. It is simple to use, easy to maintain, and extensible.
data-driven predictive models for end-point quality variables are important tools in industrial processmodeling. However, establishing an effective predictive model with limited labeled data remains challenging. Tran...
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ISBN:
(数字)9798350382655
ISBN:
(纸本)9798350382662
data-driven predictive models for end-point quality variables are important tools in industrial processmodeling. However, establishing an effective predictive model with limited labeled data remains challenging. Transfer learning (TL) offers a solution by leveraging knowledge from similar but different tasks. This paper introduces a novel TL-based predictive model, domain-adaptation parallel stacked auto encoders (DA-PSAE), which can extract and accumulate knowledge from multiple similar processes. First, a parallel model structure is designed for the simultaneous extraction of static and plastic latent features. Besides, an effective TL-based training strategy is proposed, which utilizes data from multiple similar processes. The proposed model is applied to a sulfur recovery unit composed of four parallel sub-units. Experimental results verify the effectiveness of the proposed model.
The proceedings contain 36 papers. The special focus in this conference is on data Warehousing and Knowledge Discovery. The topics include: Learning Paradigms and Modelling Methodologies for Digital Twins in process I...
ISBN:
(纸本)9783031683220
The proceedings contain 36 papers. The special focus in this conference is on data Warehousing and Knowledge Discovery. The topics include: Learning Paradigms and Modelling Methodologies for Digital Twins in process Industry;multiMatch: Low-Resource Generalized Entity Matching Using Task-Conditioned Hyperadapters in Multitask Learning;embedding-Based data Matching for Disparate data Sources;subtree Similarity Search Based on Structure and Text;Towards Hybrid Embedded Feature Selection and Classification Approach with Slim-TSF;evaluation of High Sparsity Strategies for Efficient Binary Classification;Incremental SMOTE with control Coefficient for Classifiers in data Starved Medical Applications;exploring Evaluation Metrics for Binary Classification in dataanalysis: the Worthiness Benchmark Concept;exploring Causal Chain Identification: Comprehensive Insights from Text and Knowledge Graphs;towards Regional Explanations with Validity Domains for Local Explanations;analyzing a Decade of Evolution: Trends in Natural Language processing;improving Serendipity for Collaborative Metric Learning Based on Mutual Proximity;ada2vec: Adaptive Representation Learning for Large-Scale Dynamic Heterogeneous Networks;differentially-Private Neural Network Training with Private Features and Public Labels;series2Graph++: Distributed Detection of Correlation Anomalies in Multivariate Time Series;anomaly Detection from Time Series Under Uncertainty;comparison of Measures for Characterizing the Difficulty of Time Series Classification;dynamic Time Warping for Phase Recognition in Tribological Sensor data;Putting Co-Design-Supporting data Lakes to the Test: An Evaluation on AEC Case Studies;creating and Querying data Cubes in Python Using PyCube;an E-Commerce Benchmark for Evaluating Performance Trade-Offs in Document Stores;effective Reward Schemes for Tardiness Optimization;a Novel Technique for Query Plan Representation Based on Graph Neural Nets.
Nitrogen gas under pressure released from storage is expected to flow at a set pressure at the downstream end of the control valve. The dynamic changes in pressure and temperature are measured and the Proportional- In...
Nitrogen gas under pressure released from storage is expected to flow at a set pressure at the downstream end of the control valve. The dynamic changes in pressure and temperature are measured and the Proportional- Integral-Derivative (PID) error-based prediction model gives feedback signal to the control valve to attain accurate downstream pressure. It was observed that the error percentage in a PID controller is very much higher and hence the system never reaches the target pressure. As it is a highly dynamic system having few seconds as the processing time, less error percentage is desired to attain the set point in a short period of time. Statistical model of the real time data was done through regression and compared with the ANFIS modeling results. It could be found that the machine learning process with ANFIS modeling resulted in very less error when compared with the existing PID controller and the traditional statistical modeling.
For tunnel deformation analysis using traditional measurement methods to obtain tunnel section data, there are problems such as small data coverage and low efficiency. 3D laser scanning technology has the advantages o...
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In order to solve the problems of poor monitoring efficiency and untimely maintenance of traditional solar power generation system, a set of intelligent monitoring and detection system for solar energy power generatio...
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Ion-absorbed rare earth mines,leached in situ,retain a large amount of ammonium nitrogen(NH4–N)that continuously releases into the surrounding ***,quantitative descriptions and predictions of the transport of NH4–N ...
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Ion-absorbed rare earth mines,leached in situ,retain a large amount of ammonium nitrogen(NH4–N)that continuously releases into the surrounding ***,quantitative descriptions and predictions of the transport of NH4–N across mining area with hill slopes are not fully ***,laboratory column experiments were designed with an inclined slope(a sand box)to examine the spatial temporal transport of NH4–N in soils collected from the ionic rare earth elements(REE)mining *** HYDRUS-2D model simulation of the experimental data over time showed that soils had a strong adsorption capacity toward NH4–*** non-equilibrium model(CNEM)could well simulate the transport of NH4–N through the soil-packed *** simulation of the transport-adsorption processes at three flow rates of leaching agents revealed that low flow rate enabled a longer residence time and an increased NH4-N adsorption,but reduced the extraction efficiency for *** the subsequent rainwater washing process,the presence of slope resulted in the leaching of NH4–N on the surface of the slope,while the leaching of NH4–N deep inside the column was ***,the high-intensity rainfall significantly increased the leaching,highlighting the importance of considering the impact of extreme weather conditions during the leaching ***,our study advances the understanding of the transport of NH4–N in mining area with hills,the impact of flow rates of leaching agents and precipitation intensities,and presents as a feasible modeling method to evaluate the environmental risks of NH4–N pollution during and post REE in situ mining activities.
When ultra-high voltage direct current (UHVDC) is used for external wind power transmission, typical ACIDC faults in the transmission sending-end system may cause transient overvoltage problems. In severe cases, it ca...
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