A Novel Semantic Ontology Modeling Method

2014 ◽  
Vol 510 ◽  
pp. 271-277
Author(s):  
Min Hui Zhang ◽  
Jian Yang
Keyword(s):  
Semantic Representation ◽  
Modeling Method ◽  
Experimental Result ◽  
Service Ontology ◽  
System A ◽  
Ontology Modeling ◽  
Data Integration System ◽  
Core Ontology ◽  
Semantic Ontology ◽  
The Relationship

To solve the semantic representation in data integration system, a novel ontology modeling method was proposed. The relationship between deferent layers including the core ontology layer, the role ontology layer, the goal ontology layer, service ontology layer and the mediator ontology layer in the ontology framework was analyzed, and the implement specification of each layer was given based on UML specification. Experimental result shows that proposed method can improve the degree of accuracy of semantic representation, and has the advantage of good adaptability for structure instability and difference in user needs in the grid computing.

Ontology-Based Workflow Semantic Representation and Modeling Method

2010 ◽  
Vol 129-131 ◽  
pp. 50-54
Author(s):  
Wei Ping Shao ◽  
Chun Yan Wang ◽  
Yong Ping Hao ◽  
Peng Fei Zeng ◽  
Xiao Lei Xu
Keyword(s):  
Semantic Information ◽  
Semantic Representation ◽  
Modeling Method ◽  
Workflow Model ◽  
Structure Information ◽  
Ontology Modeling ◽  
Ontology Model ◽  
Representation Method

An ontology-based workflow (workflow-ontology) representation method was proposed after analyzing that not only structure information but also semantic information were needed in a workflow model. Workflow-ontology concepts were composed by class and subclass of the workflow. Concepts’ properties including their values and characteristics were redefined, and then, workflow-ontology modeling method was put forward based on the ontology expresses and definitions above. With the example of applying in products examined and approved workflows, the corresponding workflow-ontology model (WFO) was built.

scholarly journals Estimation of resistance force at steady-state sinkage for cylindrical wheel-typed lunar/planetary exploration rovers with function of push–pull locomotion

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Daisuke Fujiwara ◽  
Naoki Tsujikawa ◽  
Tetsuya Oshima ◽  
Kojiro Iizuka
Keyword(s):  
Steady State ◽  
Estimation Method ◽  
Planetary Exploration ◽  
Experimental Result ◽  
Resistance Force ◽  
Estimation Model ◽  
Loose Soil ◽  
Key Factor ◽  
The Relationship ◽  
Resistance Forces

Abstract Planetary exploration rovers have required a high traveling performance to overcome obstacles such as loose soil and rocks. Push-pull locomotion rovers is a unique scheme, like an inchworm, and it has high traveling performance on loose soil. Push-pull locomotion uses the resistance force by keeping a locked-wheel related to the ground, whereas the conventional rotational traveling uses the shear force from loose soil. The locked-wheel is a key factor for traveling in the push-pull scheme. Understanding the sinking behavior and its resistance force is useful information for estimating the rover’s performance. Previous studies have reported the soil motion under the locked-wheel, the traction, and the traveling behavior of the rover. These studies were, however, limited to the investigation of the resistance force and amount of sinkage for the particular condition depending on the rover. Additionally, the locked-wheel sinks into the soil until it obtains the required force for supporting the other wheels’ motion. How the amount of sinkage and resistance forces are generated at different wheel sizes and mass of an individual wheel has remained unclear, and its estimation method hasn’t existed. This study, therefore, addresses the relationship between the sinkage and its resistance force, and we analyze and consider this relationship via the towing experiment and theoretical consideration. The results revealed that the sinkage reached a steady-state value and depended on the contact area and mass of each wheel, and the maximum resistance force also depends on this sinkage. Additionally, the estimation model did not capture the same trend as the experimental results when the wheel width changed, whereas, the model captured a relatively the same trend as the experimental result when the wheel mass and diameter changed.

scholarly journals Scene Complexity: A New Perspective on Understanding the Scene Semantics of Remote Sensing and Designing Image-Adaptive Convolutional Neural Networks

2021 ◽  
Vol 13 (4) ◽  
pp. 742
Author(s):  
Jian Peng ◽  
Xiaoming Mei ◽  
Wenbo Li ◽  
Liang Hong ◽  
Bingyu Sun ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Fundamental Problem ◽  
Semantic Representation ◽  
Feature Learning ◽  
Essential Elements ◽  
Complex Scene ◽  
Feature Representations ◽  
The Right ◽  
The Relationship

Scene understanding of remote sensing images is of great significance in various applications. Its fundamental problem is how to construct representative features. Various convolutional neural network architectures have been proposed for automatically learning features from images. However, is the current way of configuring the same architecture to learn all the data while ignoring the differences between images the right one? It seems to be contrary to our intuition: it is clear that some images are easier to recognize, and some are harder to recognize. This problem is the gap between the characteristics of the images and the learning features corresponding to specific network structures. Unfortunately, the literature so far lacks an analysis of the two. In this paper, we explore this problem from three aspects: we first build a visual-based evaluation pipeline of scene complexity to characterize the intrinsic differences between images; then, we analyze the relationship between semantic concepts and feature representations, i.e., the scalability and hierarchy of features which the essential elements in CNNs of different architectures, for remote sensing scenes of different complexity; thirdly, we introduce CAM, a visualization method that explains feature learning within neural networks, to analyze the relationship between scenes with different complexity and semantic feature representations. The experimental results show that a complex scene would need deeper and multi-scale features, whereas a simpler scene would need lower and single-scale features. Besides, the complex scene concept is more dependent on the joint semantic representation of multiple objects. Furthermore, we propose the framework of scene complexity prediction for an image and utilize it to design a depth and scale-adaptive model. It achieves higher performance but with fewer parameters than the original model, demonstrating the potential significance of scene complexity.

scholarly journals A Method of Ore Blending Based on the Quality of Beneficiation and Its Application in a Concentrator

2021 ◽  
Vol 11 (11) ◽  
pp. 5092
Author(s):  
Bingyu Liu ◽  
Dingsen Zhang ◽  
Xianwen Gao
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Industrial Test ◽  
Modeling Method ◽  
Daily Work ◽  
Production Site ◽  
Neural Network Algorithm ◽  
The Relationship ◽  
Ore Dressing

Ore blending is an essential part of daily work in the concentrator. Qualified ore dressing products can make the ore dressing more smoothly. The existing ore blending modeling usually only considers the quality of ore blending products and ignores the effect of ore blending on ore dressing. This research proposes an ore blending modeling method based on the quality of the beneficiation concentrate. The relationship between the properties of ore blending products and the total concentrate recovery is fitted by the ABC-BP neural network algorithm, taken as the optimization goal to guarantee the quality of ore dressing products at the source. The ore blending system was developed and operated stably on the production site. The industrial test and actual production results have proved the effectiveness and reliability of this method.

scholarly journals Relations Between Safety and Security in Technical Systems

2007 ◽  
Vol 3 (1) ◽  
pp. 63-73 ◽  
Author(s):  
Zbigniew Smalko
Keyword(s):  
Decision Maker ◽  
Machine System ◽  
System A ◽  
The Subject ◽  
Technical Systems ◽  
Machine Control ◽  
The Relationship

Relations Between Safety and Security in Technical Systems The subject of this paper deals with the relationship between safety and security of the man - machine system. In the above system a man can act both as a decision - maker and operator. His desired psychophysical efficiency lies in the undertaking the correct decisions as well as in the skilful machine control and operating.

scholarly journals Evaluation of laparoscopic vs robotic partial nephrectomy using the margin, ischemia and complications score system: a retrospective single center analysis

2015 ◽  
Vol 87 (1) ◽  
pp. 49 ◽  
Author(s):  
Stefano Ricciardulli ◽  
Qiang Ding ◽  
Xu Zhang ◽  
Hongzhao Li ◽  
Yuzhe Tang ◽  
...  
Keyword(s):  
Partial Nephrectomy ◽  
Risk Groups ◽  
Matched Pair ◽  
P Value ◽  
Robotic Partial Nephrectomy ◽  
Score System ◽  
Operative Outcomes ◽  
System A ◽  
Binary Regression Analysis ◽  
The Relationship

Objective: To evaluate differences between Laparoscopic Partial Nephrectomy (LPN) and Robot-Assisted Partial Nephrectomy (RAPN) using the Margin, Ischemia and Complications (MIC) score system and to evaluate factors related with MIC success. Materials and Methods: Single centre retrospective study on 258 LPN and 58 RAPN performed between January 2012 and January 2014. Success was defined when surgical margins was negative, Warm Ischemia Time (WIT) was ≤ 20 minutes and no major complications occurred. Mann-Whitney-U and Pearson χ2 correlation were used to compare LPN and RAPN. A matched pair comparison was also performed. Spearman correlation (Rho) was used to evaluate the relationship between clinical, intra and post-operative and pathological patients characteristics with MIC score. A binary regression analysis was also performed to evaluate independent factors associated with MIC success. Results: The MIC rate in LPN and RAPN was 55% and 65.5% respectively. No differences in clinical, intra and post-operative outcomes between groups were found. Clinical tumor size (p-value: < 0.001; OR: 0.829; 95% CI: 0.697-0.987), PADUA score (p-value: < 0.001; OR: 0.843; 95% CI: 0.740-0.960), PADUA risk groups (intermediate; p-value: < 0.001; OR: 0.416; 95% CI: 0.238- 0.792; high: p-value: < 0.001; OR: 0.356; 95% CI: 0.199- 0.636), WIT (p-value: < 0.001; OR: 0.598; 95% CI: 0.530- 0.675) were independently associated with MIC. eGFR (< 60 vs ≥ 60 ml/min per 1.73 m2: p-value: < 0.001; OR: 3.356; 95% CI: 1.701-6.621) and Fuhrman nuclear grade (p-value: 0.014; OR: 1.798; 95% CI:1.129-2.865) were also independently associated with MIC. Conclusions: MIC score system is a simple and useful tool to report and to compare different surgical approach.

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