Cheng-Chih Chien

Ant colony optimization for designing of PID controllers

This work presents an optimum approach to design PID controllers. The primary design goal is to obtain good load disturbance response by minimizing the integral absolute control error. At the same time, the transient response is guaranteed by minimizing the maximum overshoot, settling time, rise time of step response. This study proposes a solution algorithm based on the ant colony optimization technique to determine the parameters of the PID controller for getting a well performance for a given plant. Simulation results demonstrate that better control performance can be achieved in comparison with known methods

Ant colony optimization for best path planning

The paper presents an optimal approach to search the best path of a map considering the traffic loading conditions. The main objective of this work is to minimize the path length to get the best path planning for a given map. This study proposes a solution algorithm based on the ant colony optimization technique to search the shortest path from a desired origin to a desired destination of the map. The proposed algorithm is implemented in C++. Furthermore, the simulation program can randomly generate maps for evaluating its flexibility and performance. Simulation results demonstrate that the proposed algorithm can obtain the shortest path of a map with fast speed.

A contour based image segmentation algorithm using morphological edge detection

In this paper, a novel approach for edge-based image segmentation is proposed. Image segmentation and object extraction play an important role in supporting content-based image coding, indexing, and retrieval. However, its always a tough task to partition an object in a graph-based image. We proposed an image segmentation algorithm by integrating mathematical morphological edge detector with region growing technique. The images are first enhanced by morphological closing operations, and then detect the edge of the image by morphological dilation residue edge detector. Moreover, we deploy growing seeds into the edge image that obtained by the edge detection procedure. By cross comparing the growing result and the detected edges, the partition lines of the image are generated. In this paper, we presented the theoretical backgrounds and procedure illustrations of the proposed algorithm. Furthermore, the proposed algorithm is implemented in C++ language and evaluate on several images with promising results.

A novel optimization algorithm: space gravitational optimization

A new concept for the optimization of nonlinear functions is proposed. For most of the proposed evolutionary optimization algorithms, such as particle swarm optimization and ant colony optimization, they search the solution space by sharing known knowledge. The proposed algorithm is based on the Einsteins general theory of relativity, which we utilize the concept of gravitational field to search for the global optimal solution for a given problem. In this paper, detail procedure of the proposed algorithm is introduced. The proposed algorithm has been tested on an application that is known difficult with promising and exciting results.

Computer network load-balancing and routing by ant colony optimization

A high efficient design of computer network is an important issue for the high transmission speed requirement of today. In computer network, the data packages have to be transmitted to the destination with a minimum delay for ensuring the quality of service guarantees. This work presents an algorithm to perform a dynamic load-balancing for transmitting the data packages with near minimum delays in the interconnection networks. The proposed algorithm is based on the ant colony optimization algorithm inspired by the simple behavior of biological ants. This work utilizes a cube topology network to evaluate the performance of the proposed algorithm. From the comparing results, the proposed algorithm can achieve good network utilization by the low rate of the bandwidth blocking.

Novel dynamic structure neural network for optical character recognition

This paper presents a novel dynamic structure neural network (DSNN) and a learning algorithm for training DSNN. The performance of a neural network system depends on several factors. In that, the architecture of a neural network plays an important role. The objective of the developing DSNN is to avoid trial-and-error process for designing a neural network system. The architecture of DSNN consists of a three-dimensional set of neurons with input/output nodes and connection weights. Designers can define the maximum connection number of each neuron. Moreover, designers can manually deploy neurons in a virtual 3D space, or randomly generate the system structure by the proposed learning algorithm. This work also develops an automatic restructuring algorithm integrated in the proposed learning algorithm to improve the system performance. Due to the novel dynamic structure of DSNN and the restructuring algorithm, the design of DSNN is fast and convenient. Furthermore, DSNN is implemented in C++ with man-machine interactive procedures and tested on many cases with very promising results.

A novel GA-based algorithm approach to fast biosequence alignment

This paper presents a novel approach algorithm for bimolecular sequences alignment. Sequences comparison is the most important primitive operation in computational biology. There are many computational requirements for a alignment algorithm such as computer memory space requirement and computational complexity (computation time). To overcome the computational complexity of sequence alignment, the presented method first randomly divides the entire bimolecular sequences into several small sequences, and search for a partial near optima solution. After all of the partial near optima searching operations arc completed, the algorithm starts to search for better global optima by scan the new bimolecular sequences that are combined from the optimized small sequences. It allows pairwise alignment in each small sequence and does not apply dynamic programming at any optimization operation. The proposed algorithm also provides highly alignment efficient and very fast performance. Moreover, the proposed algorithm has been implemented in an x86 program, and used to verify the validity of the proposed algorithm and experiment on real DNA and protein datasets.

Robust Multiple Targets Tracking Using Object Segmentation and Trajectory Estimation in Video

In this paper, a novel robust unsupervised video object tracking algorithm is proposed. The proposed algorithm combines several techniques: mathematical morphology, region growing, region merging, and trajectory estimation, for tracking several predetermined video objects, simultaneously. A modified mathematical morphological edge detector was employed to sketch the contour of the video frame; and an edge-based object segmentation algorithm was applied to the contour for partitioning the predetermined objects; moreover, according to the motion of the objects, the proposed algorithm can estimate and partition the objects in following video frames, automatically. The proposed algorithm is also robustness against mobile cameras. The experimental results show that the proposed algorithm can precisely partition and track multiple video objects

A novel statistical cut-strategy for DP-based multiple biosequence alignment

In this paper, a novel cut-strategy is presented for solving the problems of multiple biosequence alignment. Sequence comparison is the most important primitive operation for analyzing of the bioinformatics data. The most fundamental method for alignment of several biosequences is the dynamic programming (DP) technique. The DP method is capable of finding optimal alignments for a set of sequences. However, when the length of the sequences increased, the DP method is impracticable due to the computational complexity is extremely high. Therefore, a new method is proposed in this paper for reducing the computational cost of the DP technique. By recursively finding the structural features of the biosequences, the proposed method can divide the biosequences into very small alignment problem, which can be directly solved by DP, or other applicable methods that can produce the results of alignment faster. By utilizing the object-oriented programming technique, the proposed method also provides low memory space consumption during execution. Moreover, the proposed algorithm has been implemented in an x86 demonstration program, and compares the effective and efficient performance with other known method.