Conference Program of WCICA 2012

Conference Program WCICA 2012
Intelligent Test Paper Generation System Based on Slicing Processing,
pp.506–511
Ma, Fengning
Dong, Yao
Shi, Jin
Zhang, Ying
Tianjin Univ.
Hebei Univ. of Tech.
Hebei Univ. of Tech.
Tianjin Univ.
nowadays, paperless test is very popular in the universities. The classical
test paper generating methods have not satisfied the increasing
test requirement. So, an intelligent test paper generating algorithm
based on the slicing processing is proposed, which classifies the indexes,
such as the type and amount of questions, the knowledge point,
difficulty and discrimination of the test paper, with the value type and
flexible feature. The actual application indicates that the method has a
balance between the success ratio and efficiency, and provides the theory
evidence for the efficient intelligent test paper generation system.
◁ PFrA-90
An improved Transfer Learning Algorithm for Document categorization
Based on data sets reconstruct, pp.575–578
Sun, Wei
Qian, Xu
China Univ. of Mining & Tech.(Beijing)
School of Mechanical Electronic & Information
Engineering, China Univ. of Mining & Tech.
(Beijing)
Traditional machine learning and data mining algorithms usually assume
that the training and test data have the same feature space and
data distribution, but in the real application this assumption is often d-
ifficult to establish, and always lead the existing model to outdate. As
a new learning mechanism, transfer learning can solve this problem effectively,
in this paper, we will propose an improved transfer learning
algorithm for document categorization based on data sets reconstruct,
we also describe the main idea and the step of the algorithm, then use
experiment to test the algorithm and compare it with other algorithms,
the result of experiment proves the algorithm we proposed in this paper
is better than the others in some extent.
◁ PFrA-91
A New Ant Colony Optimization with Global Exploring Capability and
Rapid Convergence, pp.579–583
Deng, Xiangyang
Yu, Wenlong
Zhang, Limin
Naval Aeronautical & Astronautical Univ.
Naval Aeronautical & Astronautical Univ.
Naval Aeronautical & Astronautical Univ.
Ant colony optimization (ACO) is a meta-heuristic algorithm, and is
widely applied in combinatorial optimization. To enhance the ACO’s
global exploiting capability and convergence, a new pheromone update
strategy is presented, which results in a gradually transition of the ant
colony’s diversity, and an improved ACO algorithm called ACO+ is
proposed. For a solution to the traveling salesman problem (TSP), a s-
tatistical model of traversed ants of sub-routes is introduced to rank the
sub-routes, and an adaptive pheromone trails update mechanism is implemented,
which integrates with the iteration-best pheromone update
strategy. The algorithm can effectively combine the global exploring capability
and convergence rate. Experiments show that the ACO+ has a
good performance and robustness.
◁ PFrA-92
Study on Control Strategy for Vehicle Braking Force on Low Adhesive
Cornering Road, pp.618–622
Song, Dandan
Yang, Tao
Henan Communication Vocational Tech.
Henan Communication Vocational Tech.
Along with reducing the centrifugal force based on decreased speed using
vehicle braking, the maximum lateral force of the wheel could bear
diminishing gradually. In this paper, the deficiency of vehicle’s anti-lock
braking system on cornering road is analyzed, a control strategy of vehicle
braking on low adhesive cornering road is presented, vehicle’s
ABS and the biggest lateral force of the wheel could bearing when vehicle
occur side slip are comprehensive considered, and the maximum
braking force applied to vehicle can be determined according to relatively
small value of both. Simulation shows the validity of the proposed
control method.
◁ PFrA-93
A Novel Prototype Architecture for Equipment Tele-control and Simulation,
pp.633–637
Wang, Yong Ming
Zhao, Guang Zhou
Yin, Hong Li
Kunming Univ. of Sci. & Tech.
Kunming Univ. of Sci. & Tech.
Yunnan Normal Univ.
Due to the working hazardous or other conditions, such as tele-medical,
tele-embodiment, operations should be executed with a fully remote
control and monitoring. So, tele-control and reality simulation are crucial
in these environments. However, there exists a lack of an effective
system architecture that integrates remote condition monitoring and
control of automated equipment; that give much consideration about
data transfer time delay via TCP/IP data package. This paper presented
a novel prototype architecture for tele-control and reality simulation,
which can guarantee the non-distortion-transfer of control information
and reduce the action time difference between local simulated virtual
equipment and remote real equipment, couple the remote control and
virtual reality together. In order to demonstrate and validate the effectiveness
of the novel architecture, a 3 DOF Fischertechnik industry
robot remote operation and monitoring system have been developed.
Experimental results are encouraging and demonstrate a promising
application in any other relevant environment.
◁ PFrA-94
Keep the Geometries: Image Segmentation by K-MSVC with Random
Region Grouping and Propagation, pp.672–679
LIN, Yining
Wei, Wei
DAI, Yuanming
Zhejiang Univ.
College of Electrical Engineering, Zhejiang Univ.
Zhejiang Univ.
We propose new techniques to address low-level image segmentation
problem under clustering theory. The goal of this paper is to provide
a compromised solution between methods that produce two different
kinds of segmentation results: one generates coherent regions but
views disjoint regions as totally different objects, and the others do not
consider the spatial relationship at all. For our approach, spatial geometries
are partially preserved and disjoint regions are also allowed
to be grouped into a single cluster. The approach is built on the feature
space clustering algorithm called K-MSVC, but constrained by the
graph to maintain the capability of partially preserving the spatial coherence.
A new type of graph called Random Grouping Graph (RGG) is
introduced then, to overcome the high computational cost on the gridgraph
based image representation. It’s fast to construct, greatly reduce
the graph size and can speedup other graph-based segmentation
algorithms. Though with less vertices, the segmentation on RGG
works better than on downsampled version of the image. Nontrivial experimental
results on the Berkeley Segmentation Dataset demonstrate
that our method outperforms the existing algorithms and yields more
satisfactory results.
◁ PFrA-95
Routing in Wireless Sensor Networks Using Swarm Intelligence,
pp.680–684
Lv, Yong
JiaXing Univ.
Wireless Sensor Networks consisting of nodes with limited power are
deployed to collect and distribute useful information from the field to
the other sensor nodes. Energy consumption is a key issue in the sensor’s
communications since many use battery power, which is limited.
The sensors also have limited memory and functionality to support
communications. Ant Colony Optimization, a swarm intelligence based
optimization technique, is widely used in network routing. This paper
describes a new routing approach for Wireless Sensor Networks consisting
of stable nodes based an Ant Colony Optimization algorithm that
explore the network and learn good routes, using a novel variation of
reinforcement learning. Simulation results show that proposed algorithm
provides promising solutions allowing node designers to efficiently
operate routing tasks.
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