Study on Automatic Test Paper Generation Algorithm Based on the Maximum Flow of the Upper and Lower Bounds

2013 ◽  
Vol 756-759 ◽  
pp. 3065-3069
Author(s):  
Ying Zhang ◽  
Shi Hang Huang ◽  
Ying Liu ◽  
Zhen Fang ◽  
De Peng Dang
Keyword(s):  
Lower Bounds ◽  
Management System ◽  
Maximum Flow ◽  
Upper And Lower Bounds ◽  
Generation Model ◽  
Test Paper ◽  
Generation Algorithm ◽  
The Core ◽  
Flow Algorithm ◽  
Complex Constraints

The core of exam management system is how to generate test paper automatically. The existing algorithmic is difficult to simultaneously achieve the requirements with efficient, random, flexible and expansive performance. In this paper, we introduce the maximum flow algorithm of the upper and lower bounds in the graph theory, create a test paper generation model based on constraint conditions, and implement the test paper generation under complex constraints. In addition, to illustrate the success rate and efficiency, the system generated automatically test papers on proposition needs. In turn, it verifies the correctness and rationality of the model. The algorithm proposed in this paper will provide theoretical and technical support for other systems, and it further promotes the development of the exam management system.

EXACT GENERATION OF MINIMAL ACYCLIC DETERMINISTIC FINITE AUTOMATA

2008 ◽  
Vol 19 (04) ◽  
pp. 751-765 ◽  
Author(s):  
MARCO ALMEIDA ◽  
NELMA MOREIRA ◽  
ROGÉRIO REIS
Keyword(s):  
Normal Form ◽  
Lower Bounds ◽  
Finite Automata ◽  
Canonical Representation ◽  
Upper And Lower Bounds ◽  
Generation Algorithm ◽  
Deterministic Finite Automata

We give a canonical representation for minimal acyclic deterministic finite automata (MADFA) with n states over an alphabet of k symbols. Using this normal form, we present a method for the exact generation of MADFAs. This method avoids a rejection phase that would be needed if a generation algorithm for a larger class of objects that contains the MADFAs were used. We give upper and lower bounds for MADFAs enumeration and some exact formulas for small values of n.

Comparison of Bending Models of Multiwire Strand

1997 ◽  
Author(s):  
C. V. Jayakumar ◽  
S. Sathikh ◽  
C. Jebaraj
Keyword(s):  
Cross Section ◽  
Lower Bounds ◽  
Effective Area ◽  
Upper And Lower Bounds ◽  
Friction Contact ◽  
The Core ◽  
The Wire ◽  
The Difference ◽  
The Moment ◽  
Bending Response

Abstract Nine discrete thin rod models are critically compared for their merit for studying the bending response of strands. Four bench marks employed for comparison are two (upper and lower) bounds of bending stiffness and two strand curvatures at slip initiation and completion. The strand configuration chosen, the way in which the lay angle is employed to describe the effective area of cross section as well as moments of inertia of the wire and the components of forces; the inclusion or omission of the wire interaction with the core through friction contact and the moment of inertia of the wire about its own diameter; and of wire couples in part or full are identified as factors that make the difference. Numerical values of the four bench marks for these models are presented.

Tidal Flow: A Fast and Teachable Maximum Flow Algorithm

2018 ◽  
Vol 12 ◽  
pp. 25-41
Author(s):  
Matthew C. FONTAINE
Keyword(s):  
Computer Science ◽  
Tidal Flow ◽  
Maximum Flow ◽  
Efficient Algorithms ◽  
Science Students ◽  
Flow Algorithm ◽  
Maximum Flows

Among the most interesting problems in competitive programming involve maximum flows. However, efficient algorithms for solving these problems are often difficult for students to understand at an intuitive level. One reason for this difficulty may be a lack of suitable metaphors relating these algorithms to concepts that the students already understand. This paper introduces a novel maximum flow algorithm, Tidal Flow, that is designed to be intuitive to undergraduate andpre-university computer science students.

Integrating Improved U-Net and Continuous Maximum Flow Algorithm for 3D Brain Tumor Image Segmentation

2020 ◽  
Vol 64 (4) ◽  
pp. 40412-1-40412-11
Author(s):  
Kexin Bai ◽  
Qiang Li ◽  
Ching-Hsin Wang
Keyword(s):  
Brain Tumor ◽  
Data Augmentation ◽  
A Priori ◽  
Class Imbalance ◽  
Maximum Flow ◽  
Magnetic Resonance Images ◽  
Tumor Segmentation ◽  
Similarity Coefficients ◽  
Segmentation Algorithms ◽  
Flow Algorithm

Abstract To address the issues of the relatively small size of brain tumor image datasets, severe class imbalance, and low precision in existing segmentation algorithms for brain tumor images, this study proposes a two-stage segmentation algorithm integrating convolutional neural networks (CNNs) and conventional methods. Four modalities of the original magnetic resonance images were first preprocessed separately. Next, preliminary segmentation was performed using an improved U-Net CNN containing deep monitoring, residual structures, dense connection structures, and dense skip connections. The authors adopted a multiclass Dice loss function to deal with class imbalance and successfully prevented overfitting using data augmentation. The preliminary segmentation results subsequently served as the a priori knowledge for a continuous maximum flow algorithm for fine segmentation of target edges. Experiments revealed that the mean Dice similarity coefficients of the proposed algorithm in whole tumor, tumor core, and enhancing tumor segmentation were 0.9072, 0.8578, and 0.7837, respectively. The proposed algorithm presents higher accuracy and better stability in comparison with some of the more advanced segmentation algorithms for brain tumor images.

scholarly journals Mapping the MPM maximum flow algorithm on GPUs

2010 ◽  
Vol 256 ◽  
pp. 012006 ◽  
Author(s):  
Steven Solomon ◽  
Parimala Thulasiraman
Keyword(s):  
Maximum Flow ◽  
Flow Algorithm

scholarly journals On the Generalized Distance Energy of Graphs

Mathematics ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 17 ◽  
Author(s):  
Abdollah Alhevaz ◽  
Maryam Baghipur ◽  
Hilal A. Ganie ◽  
Yilun Shang
Keyword(s):  
Lower Bounds ◽  
Complete Graph ◽  
Connected Graph ◽  
Distance Matrix ◽  
Wiener Index ◽  
Diagonal Matrix ◽  
Upper And Lower Bounds ◽  
Star Graph ◽  
Extremal Graphs ◽  
Generalized Distance

The generalized distance matrix D α ( G ) of a connected graph G is defined as D α ( G ) = α T r ( G ) + ( 1 − α ) D ( G ) , where 0 ≤ α ≤ 1 , D ( G ) is the distance matrix and T r ( G ) is the diagonal matrix of the node transmissions. In this paper, we extend the concept of energy to the generalized distance matrix and define the generalized distance energy E D α ( G ) . Some new upper and lower bounds for the generalized distance energy E D α ( G ) of G are established based on parameters including the Wiener index W ( G ) and the transmission degrees. Extremal graphs attaining these bounds are identified. It is found that the complete graph has the minimum generalized distance energy among all connected graphs, while the minimum is attained by the star graph among trees of order n.

scholarly journals Hadamard k-fractional inequalities of Fejér type for GA-s-convex mappings and applications

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Hui Lei ◽  
Gou Hu ◽  
Zhi-Jie Cao ◽  
Ting-Song Du
Keyword(s):  
Lower Bounds ◽  
Hypergeometric Functions ◽  
Upper And Lower Bounds ◽  
Weighted Inequalities ◽  
Convex Mappings ◽  
Special Means

Abstract The main aim of this paper is to establish some Fejér-type inequalities involving hypergeometric functions in terms of GA-s-convexity. For this purpose, we construct a Hadamard k-fractional identity related to geometrically symmetric mappings. Moreover, we give the upper and lower bounds for the weighted inequalities via products of two different mappings. Some applications of the presented results to special means are also provided.

scholarly journals On the Second-Largest Reciprocal Distance Signless Laplacian Eigenvalue

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 512
Author(s):  
Maryam Baghipur ◽  
Modjtaba Ghorbani ◽  
Hilal A. Ganie ◽  
Yilun Shang
Keyword(s):  
Lower Bounds ◽  
Complete Graph ◽  
Distance Matrix ◽  
Upper And Lower Bounds ◽  
Laplacian Eigenvalue ◽  
Signless Laplacian ◽  
Graph Parameters ◽  
Simple Connected Graph ◽  
Second Largest Eigenvalue ◽  
Reciprocal Distance Matrix

The signless Laplacian reciprocal distance matrix for a simple connected graph G is defined as RQ(G)=diag(RH(G))+RD(G). Here, RD(G) is the Harary matrix (also called reciprocal distance matrix) while diag(RH(G)) represents the diagonal matrix of the total reciprocal distance vertices. In the present work, some upper and lower bounds for the second-largest eigenvalue of the signless Laplacian reciprocal distance matrix of graphs in terms of various graph parameters are investigated. Besides, all graphs attaining these new bounds are characterized. Additionally, it is inferred that among all connected graphs with n vertices, the complete graph Kn and the graph Kn−e obtained from Kn by deleting an edge e have the maximum second-largest signless Laplacian reciprocal distance eigenvalue.

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