Catálogo de publicaciones - libros

DNA computing is a novel method of solving a class of intractable computational problem, in which the computing speeds up exponentially with problem size. Up to now, many accomplishments have been made to improve its performance and increase its reliability. The positive integer linear programming is an NP-complete problem, in the paper, we solved the positive integer linear programming problem with fluorescence labeling techniques based on surface chemistry by attempted to apply DNA computing to programming problem. Our method has some significant advantages such as simple encoding, low cost, and short operating time.

DNA computing is being applied to solve problems in combinatorial optimization, logic and Boolean circuits. Breakthrough solutions in combinatorial optimization are the most impressive area of success but, in order to solve combinatorial optimization problems, problems related to the reliability of biological operators, stable DNA expressions, processing speed, expandability and the universality of evaluation criteria must be solved. This study implements a DNA sequence generation system that minimizes errors using DNA coding based on evolutionary models and performs simulation using biological experiment operators. The usefulness of this system is evaluated by applying the Hamiltonian Path Problem (HPP) in the form of a genetic algorithm. The proposed system generates sequences with minimal errors, as compared to existing systems, and identifies optimal solutions for combinatorial optimization problems in significantly reduced processing times.

To achieve quantum error-correction codes with good parameters, the recursive constructions of Hadamard matrices with even length are proposed with special characters. The generators of the stabilizer of the designed codes can be constructed by selecting some rows from these matrices, hence several codes are obtained expediently via the stabilizer quantum code’s constructions. Some of the presented codes are unsurpassed by the previously published codes.

The storage capacity of the conventional neural network is 0.14 times of the number of neurons (P=0.14N). Due to the huge difficulty in recognizing large number of images or patterns,researchers are looking for new methods at all times. Quantum Neural Network (QNN), which is a young and outlying science built upon the combination of classical neural network and quantum computing,is a candidate to solve this problem.This paper presents Quantum Probability Distribution Network (QPDN) whose elements of the storage matrix are distributed in a probabilistic way on the base of quantum linear superposition and applies QPDN on image recognition. Contrasting to the conventional neural network, the storage capacity of the QPDN is increased by a factor of 2,where N is the number of neurons. Besides,the case analysis and simulation tests have been carried out for the recognition of images in this paper, and the result indicates that QPDN can recognize the images or patterns effectively and its working process accords with quantum evolvement process.

A discrete-time version of dynamic portfolio selection model for survival is proposed in fuzzy environments. The investor gains an initial wealth every period and has a given consumption requirement. The investor survives only if his wealth is large enough to meet the requirement every period over a finite time horizon. After consumption the investor allocates the rest between a risky and a risk-free asset. This paper assumes that the gross rate of return on the risky asset is a fuzzy variable, then the functional equation of dynamic programming is established. In order to get the optimal investment policy, a hybrid intelligent algorithm to solve the optimal problem is presented. Finally, an illustrative case is given to demonstrate the effectiveness of the proposed algorithm.

This paper describes how risk-based risk control allocation model works. We begin by discussing the economic rational for allocating risk control in a diversified organization like enterprises. The direct and indirect losses caused by the simulated disasters can be estimated using the engineering and financial analysis model. Basing on the model, we can generate exceeding probability (EP) curve and then calculate how much loss will be ceased or transferred to other entities, if somehow spending budgets on risk control actions. Results from the proposed formulations are compared in case studies. The model attempts to apply risk based budget guidelines to risk reduction measurement with a portfolio-based risk framework.

Different from previous literature on credit term determination mainly applying financial marginal analysis method, this paper proposes a novel idea to model trade credit term determination as an incentive mechanism design problem under supply chain coordination in principal-agent framework. With application of Schwartz’ financing motive theory, a new form of supplier’s net cost function is derived which makes it possible to find an approximation closed-formed solution to term determination. Using approximation and integration techniques, we find the explicit close-formed approximation solutions to the optimal payment time for the retailer and credit term for the supplier.

In environments where node density is massive, placement is heterogeneous and lot of sensory traffic with redundancy is produced; waste of resources such as bandwidth and energy occurs. This waste of resources minimize the network life time. Numerous routing schemes have been proposed to address such problems. They all tend to focus on similar direction, i.e. to find minimum energy path to increase the life time of the network. In this paper, we argue that it is not always wise to use the minimum energy path. Nodes along the optimal path will be used rapidly, burn out energy aggressively and eventually die hastily creating communication holes in network. This brings rapid change in the topology resulting in increased latency, poor connectivity and production of heterogeneous subnets. Therefore, utilizing suboptimal paths is encouraged for load balancing among sensor nodes. We unmitigated our efforts to augment the node life time in sensor network by frequent use of suboptimal paths, and reducing redundant sensory network traffic. Towards this end, we propose an agent-based routing approach that incorporates static and mobile agents. Static agent is responsible for calculating and maintaining the set of optimal paths. Mobile agent accounts for performing data processing and making data aggregation decisions at nodes in the network rather than bring data back to a central processor (sink). To demonstrate the performance evaluation, a prototype of a simulator is implemented.

In this paper, the coalition formation problem is studied in an open environment where agents can arrive dynamically, i.e. the set of agents is not given in advance. In order to maximize the gross income of MAS (Multi-Agent System), task allocator may incline to discard some coalition members, and then introduce some new ones when new agents arrive; we call such problem . To address this problem, we introduce a novel description of the coalition formation problem which takes time into account, and then formally present the . What’s more, we study different kinds of measures which agents and task allocator will take because of new agents’ arriving. Finally, we develop an anytime algorithm, which is proved effective and available by the simulation. An example is also designed to make it easy to understand our algorithm.

With the rapid development of WWW, HTML documents become one of the main file formats on the Web. However, blind people find difficulty in accessing the HTML documents for their complex structure and visual reliability. The main methods for the blind to browse the web pages are through screen reader and text web browser with TTS engine. These methods can only read text on the screen without knowing the relationship among the texts. It’s very difficult and time consuming to find out some information from a bunch of texts. In this paper, a special web browser called eGuideDog is designed for the visually impaired people. This web browser can extract the structure and the content of an HTML document and represent it in the form of audio. It helps the blind finding out information they concern more quickly.