Post processing of multimedia information - concepts, problems, and techniques

Currently, most research work on multimedia information processing is focused on multimedia information storage and retrieval, especially indexing and content-based access of multimedia information. We consider multimedia information processing should include one more level-post-processing. Here &quot;post-processing&quot; means further processing of retrieved multimedia information, which includes fusion of multimedia information and reasoning with multimedia information to reach new conclusions. In this paper, the three levels of multimedia information processing storage, retrieval, and post-processing- are discussed. The concepts and problems of multimedia information post-processing are identified. Potential techniques that can be used in post-processing are suggested, By highlighting the problems in multimedia information post-processing, hopefully this paper will stimulate further research on this important but ignored topic.<br /

Agent-based hybrid framework for decision making on complex problems

Electronic commerce and the Internet have created demand for automated systems that can make complex decisions utilizing information from multiple sources. Because the information is uncertain, dynamic, distributed, and heterogeneous in nature, these systems require a great diversity of intelligent techniques including expert systems, fuzzy logic, neural networks, and genetic algorithms. However, in complex decision making, many different components or sub-tasks are involved, each of which requires different types of processing. Thus multiple such techniques are required resulting in systems called hybrid intelligent systems. That is, hybrid solutions are crucial for complex problem solving and decision making. There is a growing demand for these systems in many areas including financial investment planning, engineering design, medical diagnosis, and cognitive simulation. However, the design and development of these systems is difficult because they have a large number of parts or components that have many interactions. From a multi-agent perspective, agents in multi-agent systems (MAS) are autonomous and can engage in flexible, high-level interactions. MASs are good at complex, dynamic interactions. Thus a multi-agent perspective is suitable for modeling, design, and construction of hybrid intelligent systems. The aim of this thesis is to develop an agent-based framework for constructing hybrid intelligent systems which are mainly used for complex problem solving and decision making. Existing software development techniques (typically, object-oriented) are inadequate for modeling agent-based hybrid intelligent systems. There is a fundamental mismatch between the concepts used by object-oriented developers and the agent-oriented view. Although there are some agent-oriented methodologies such as the Gaia methodology, there is still no specifically tailored methodology available for analyzing and designing agent-based hybrid intelligent systems. To this end, a methodology is proposed, which is specifically tailored to the analysis and design of agent-based hybrid intelligent systems. The methodology consists of six models - role model, interaction model, agent model, skill model, knowledge model, and organizational model. This methodology differs from other agent-oriented methodologies in its skill and knowledge models. As good decisions and problem solutions are mainly based on adequate information, rich knowledge, and appropriate skills to use knowledge and information, these two models are of paramount importance in modeling complex problem solving and decision making. Follow the methodology, an agent-based framework for hybrid intelligent system construction used in complex problem solving and decision making was developed. The framework has several crucial characteristics that differentiate this research from others. Four important issues relating to the framework are also investigated. These cover the building of an ontology for financial investment, matchmaking in middle agents, reasoning in problem solving and decision making, and decision aggregation in MASs. The thesis demonstrates how to build a domain-specific ontology and how to access it in a MAS by building a financial ontology. It is argued that the practical performance of service provider agents has a significant impact on the matchmaking outcomes of middle agents. It is proposed to consider service provider agents\u27 track records in matchmaking. A way to provide initial values for the track records of service provider agents is also suggested. The concept of ‘reasoning with multimedia information’ is introduced, and reasoning with still image information using symbolic projection theory is proposed. How to choose suitable aggregation operations is demonstrated through financial investment application and three approaches are proposed - the stationary agent approach, the token-passing approach, and the mobile agent approach to implementing decision aggregation in MASs. Based on the framework, a prototype was built and applied to financial investment planning. This prototype consists of one serving agent, one interface agent, one decision aggregation agent, one planning agent, four decision making agents, and five service provider agents. Experiments were conducted on the prototype. The experimental results show the framework is flexible, robust, and fully workable. All agents derived from the methodology exhibit their behaviors correctly as specified

The effect of sulfur on chemical looping combustion with iron oxides

Advancing climate change poses an increasing threat to humanity, together with the higher demand for energy around the world. New ‘cleaner’ technologies for the production of energy from solid fuels (coal or biomass) via thermo-chemically viable routes are required. Chemical looping combustion (CLC) is a process concept using metal oxide (oxygen carrier) for transportation of oxygen from air for electrical power generation and inherent production of a pure stream of CO2. CLC has been generally applied to gaseous fuel; however, by integrating chemical looping and gasification, the combined process shows great potential for producing H2 and power from solid fuels. Coal and biomass contain significant quantities of sulfur. Upon gasification, the sulfur is released in the form of H2S, which will be then be introduced into the iron-based chemical looping process, followed by further gasification. Iron oxides are known to form stable sulfides under reducing conditions. The performance of chemical looping using iron-based oxygen carrier could therefore be adversely affected by the introduction of H2S in a real system. The overarching aim of this thesis is to assess the effect of H2S on chemical looping combustion using iron (III) oxide in a laboratory scale spouted bed reactor. A closed-system spouted bed reactor has been designed and constructed to study the solid looping system with gaseous fuel. A model of the bed was developed, from which the bed could reasonably be assumed to be a well-mixed bubbling fluidised bed reactor at certain conditions. The reactor was used for the kinetic study of reduction of Fe2O3 to Fe3O4 with a CO/CO2 mixture under isothermal condition at the temperature range of 723K – 973K. The oxygen carrier before and after thermal cycling was characterised using SEM, mercury porosimetry, BET surface area analysis. Using a nominal particle size of Fe2O3, the rate of reduction was controlled mainly by intrinsic chemical reaction kinetics with a high effectiveness factor. The intrinsic rate constant was estimated with an activation energy of 73 kJ mol-1, which is comparable to values reported in the literature. The reactor was modified for use in the quartz internal, together with acid-washed, calcined sand (Quartz-T) to be able to significantly reduce interactions between sulfur and the inert material used in the construction of the reactor and the spouted bed. The fate of H2S in the chemical looping cycling was determined and the effect of H2S on reduction of Fe2O3 to Fe3O4 over multiple cycles was studied. There were two major mechanisms of reaction between H2S and Fe2O3 that were found to affect the rate of reduction of Fe2O3 and main sulfur product distribution: (1) production of SO2 as a main sulfur product, (2) production of FeS as a main sulfur product. The dominating mechanism was found to depend on the thermodynamic potential of S2, the thermodynamic potential of O2 (which depends on the extent of reduction), and the temperature. The effect of the H2S on the kinetics of reduction was found to be due to the structural change of Fe2O3 particles that is governed by the reaction between H2S and Fe2O3. A mathematical simulation based on a grain model under chemical reaction control was used to satisfactorily describe the relationship between rate of reduction and the extent of the reaction in the presence of sulfur.Open Acces

Building agent-based hybrid intelligent systems : a case study

Many complex problems (e.g., financial investment planning, foreign exchange trading, data mining from large/multiple databases) require hybrid intelligent systems that integrate many intelligent techniques (e.g., fuzzy logic, neural networks, and genetic algorithms). However, hybrid intelligent systems are difficult to develop because they have a large number of parts or components that have many interactions. On the other hand, agents offer a new and often more appropriate route to the development of complex systems, especially in open and dynamic environments. Thus, this paper discusses the development of an agent-based hybrid intelligent system for financial investment planning, in which a great number of heterogeneous computing techniques/packages are easily integrated into a unifying agent framework. This shows that agent technology can indeed facilitate the development of hybrid intelligent systems.<br /

An agent-based hybrid framework for database mining

While knowledge discovery in databases (KDD) is defined as an iterative sequence of the following steps: data pre-processing, data mining, and post data mining, a significant amount of research in data mining has been done, resulting in a variety of algorithms and techniques for each step. However, a single data-mining technique has not been proven appropriate for every domain and data set. Instead, several techniques may need to be integrated into hybrid systems and used cooperatively during a particular data-mining operation. That is, hybrid solutions are crucial for the success of data mining. This paper presents a hybrid framework for identifying patterns from databases or multi-databases. The framework integrates these techniques for mining tasks from an agent point of view. Based on the experiments conducted, putting different KDD techniques together into the agent-based architecture enables them to be used cooperatively when needed. The proposed framework provides a highly flexible and robust data-mining platform and the resulting systems demonstrate emergent behaviors although it does not improve the performance of individual KDD techniques. <br /