Passivity analysis of memristor-based complex-valued neural networks with time-varying delays

In this paper, the model of memristor-based complex-valued neural networks (MCVNNs) with time-varying delays is established and the problem of passivity analysis for MCVNNs is considered and extensively investigated. The analysis in this paper employs results from the theory of differential equations with discontinuous right-hand side as introduced by Filippov. By employing the appropriate Lyapunov–Krasovskii functional, differential inclusion theory and linear matrix inequality (LMI) approach, some new sufficient conditions for the passivity of the given MCVNNs are obtained in terms of both complex-valued and real-value LMIs, which can be easily solved by using standard numerical algorithms. Numerical examples are provided to illustrate the effectiveness of our theoretical results.

Application of neural networks as an auxiliary technique in the modelling of power station

Artificial neural network (NN) is an alternative way (to conventional physical or chemical based modeling technique) to solve complex ill-defined problems. Neural networks trained from historical data are able to handle nonlinear problems and to find the relationship between input data and output data when there is no obvious one between them. Neural Networks has been successfully used in control, robotic, pattern recognition, forecasting areas. This paper presents an application of neural networks in finding some key factors eg. heat loss factor in power station modeling process. In the conventional modeling of power station, these factors such as heat loss are normally determined by experience or “rule of thumb”. To get an accurate estimation of these factors special experiment needs to be carried out and is a very time consuming process. In this paper the neural networks (technique) is used to assist this difficult conventional modeling process. The historical data from a real running brown coal power station in Victoria has been used to train the neural network model and the outcomes of the trained NN model will be used to determine the factors in the conventional energy modeling of the power stations that is under the development as a part of an on-going ARC Linkage project aiming to detail modeling the internal energy flows in the power station.

Improving the quality of die castings by using artificial neural networks for porosity defect modelling

The aim of this work is to improve the quality of castings by minimizing defects and scrap through the analysis of the data generated by High Pressure Die Casting (HPDC) Machines using computational intelligence techniques. Casting is a complex process that is affected by the interdependence of die casting process parameters on each other such that changes in one parameter results in changes in other parameters. Computational intelligence techniques have the potential to model accurately this complex relationship. The project has the potential to generate optimal configurations for HPDC Machines and explain the relationships between die casting process parameters.

What is Chinese about Chinese businesses? Locating the 'rise of China' in global production networks

There is an implicit but commonly held assumption that Chinese businesses are distinctively Chinese. Casting them in unitary and national terms, this assumption has often provided the underpinnings for the conception of the strength of Chinese businesses as signs of an emerging China threat. Drawing on a global production networks (GPN) approach, this paper aims to question the assumption by arguing that many Chinese businesses, embedded in the expanding global and regional production networks, have taken on important transnational characteristics. Given these transnational connections, Chinese business networks in both 'Greater China' and China proper are characterized more by diversity and fragmentation than by cultural coherence and homogeneity. This analysis of the transnationalization and fragmentation of contemporary Chinese businesses helps better understand and respond to the complex challenge posed by the economic dynamism in China.

A hybrid approach to selecting susceptible single nucleotide polymorphisms for complex disease analysis

An increasingly popular and promising way for complex disease diagnosis is to employ artificial neural networks (ANN). Single nucleotide polymorphisms (SNP) data from individuals is used as the inputs of ANN to find out specific SNP patterns related to certain disease. Due to the large number of SNPs, it is crucial to select optimal SNP subset and their combinations so that the inputs of ANN can be reduced. With this observation in mind, a hybrid approach - a combination of genetic algorithms (GA) and ANN (called GANN) is used to automatically determine optimal SNP set and optimize the structure of ANN. The proposed GANN algorithm is evaluated by using both a synthetic dataset and a real SNP dataset of a complex disease.

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' 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.

Mixed transfer function neural networks for knowledge acquistition

Modeling helps to understand and predict the outcome of complex systems. Inductive modeling methodologies are beneficial for modeling the systems where the uncertainties involved in the system do not permit to obtain an accurate physical model. However inductive models, like artificial neural networks (ANNs), may suffer from a few drawbacks involving over-fitting and the difficulty to easily understand the model itself. This can result in user reluctance to accept the model or even complete rejection of the modeling results. Thus, it becomes highly desirable to make such inductive models more comprehensible and to automatically determine the model complexity to avoid over-fitting. In this paper, we propose a novel type of ANN, a mixed transfer function artificial neural network (MTFANN), which aims to improve the complexity fitting and comprehensibility of the most popular type of ANN (MLP - a Multilayer Perceptron).

The client's complex decision-making environment on international mega projects

More than ever before, architectural, engineering and construction (AEC) firms are working on international mega projects. The mega project environment offers a range of opportunities for firms but is but is characterised by a high level of risk and uncertainty. International mega projects bring together networks of people with differing backgrounds and cultures to work in unfamiliar locations to integrate the social, economic, technical and political components of design and construction. Within such an intense environment there is a process of rapid relationship development at an unprecedented level. The interests and power relations on such projects are often very strong given the vast amount of money, jobs, environmental impacts, publicity and national prestige involved. Therefore in a field as costly or consequential as mega project design and construction there is an increased need to effectively manage these projects given the associated high risks of failure. Internationalisation is a relatively new field of research in the AEC sector and past research has tended to focus on explaining the attitudes and behaviour of the industry itself towards improving performance on such projects. To date there has been little research investigating the sophistication of the international client in terms of their regular business environment which is characterised by a set of social, economic and political responsibilities. The values that clients ascribe to their everyday practices and experiences inevitably condition how they act economically, which in turn impacts upon project decision-making. Clients establish the structural organisation of project teams through the procurement strategy and establish the context for effective decision-making. To a large extent they establish a unique culture that project team members need to work within and make decisions. Since clients establish the context within which firms operate the findings of past studies on the industry’s position and attitudes are more indicative than enlightening. Clients occupy a distinctly different position in the construction supply chain and therefore experience and respond to project matters based upon their environment and not the construction industry environment. Clients are confronted with uncertainties and need support to help them understand the critical role that they play in creating good decision-making environments. This theoretical paper seeks to develop a rationale for studying the client’s complex decision-making environment on international mega projects. Specifically it charts the quest for improved industry performance through client leadership as documented in various industry and government publications since the 1940s and highlights that there has been considerable attention to address industry problems through client leadership, however, with little evidence that the issues have been resolved. This paper is positioned within a PhD study, which seeks to move beyond the aspirations of policymakers and idealistic descriptions of how clients ought to behave to explain the reality of what really happens on mega project client decision-making based upon a critique of cultural political economy.

Towards a model to explain knowledge sharing in complex organizational environments

Effective knowledge sharing underpins the day-to-day work activities in knowledge-intensive organizational environments. This paper integrates key concepts from the literature towards a model to explain effective knowledge sharing in such environments. It is proposed that the effectiveness of knowledge sharing is determined by the maturity of informal and formal social networks and a shared information and knowledge-based artefact network (AN) in a particular work context. It is further proposed that facilitating mechanisms within the social and ANs, and mechanisms that link these networks, affect the overall efficiency of knowledge sharing in complex environments. Three case studies are used to illustrate the model, highlighting typical knowledge-sharing problems that result when certain model elements are absent or insufficient in a particular environment. The model is discussed in terms of diagnosing knowledge-sharing problems, organizational knowledge strategy, and the role of information and communication technology in knowledge sharing.

Knowledge sharing through virtual layers in regional sustainable development networks

Our research examines how the organisational structure facilitates knowledge sharing within the group. This case study examines a Victorian regional sustainable group using interviews and social network analysis to identify the group’s organisational structure and its effect on knowledge sharing between the members. Our findings indicate that while the mixed membership, lack of hierarchy and layered structure are complex, these elements work together to provide members with a rich body of knowledge. The diversity and differences in membership are complimentary and combined can provide a more in-depth understanding of the regional sustainable development issues.

Layered dynamic probabilistic networks for spatio-temporal modelling

In applications such as tracking and surveillance in large spatial environments, there is a need for representing dynamic and noisy data and at the same time dealing with them at different levels of detail. In the spatial domain, there has been work dealing with these two issues separately, however, there is no existing common framework for dealing with both of them. In this paper, we propose a new representation framework called the Layered Dynamic Probabilistic Network (LDPN), a special type of Dynamic Probabilistic Network (DPN), capable of handling uncertainty and representing spatial data at various levels of detail. The framework is thus particularly suited to applications in wide-area environments which are characterised by large region size, complex spatial layout and multiple sensors/cameras. For example, a building has three levels: entry/exit to the building, entry/exit between rooms and moving within rooms. To avoid the problem of a relatively large state space associated with a large spatial environment, the LDPN explicitly encodes the hierarchy of connected spatial locations, making it scalable to the size of the environment being modelled. There are three main advantages of the LDPN. First, the reduction in state space makes it suitable for dealing with wide area surveillance involving multiple sensors. Second, it offers a hierarchy of intervals for indexing temporal data. Lastly, the explicit representation of intermediate sub-goals allows for the extension of the framework to easily represent group interactions by allowing coupling between sub-goal layers of different individuals or objects. We describe an adaptation of the likelihood sampling inference scheme for the LDPN, and illustrate its use in a hypothetical surveillance scenario.

An adaptive approach to information discovery in multi-dimensional wireless sensor networks

Multidimensional WSNs are deployed in complex environments to sense and collect data relating to multiple attributes (multidimensional data). Such networks present unique challenges to data dissemination, data storage and in-network query processing (information discovery). In this paper, we investigate efficient strategies for information discovery in large-scale multidimensional WSNs and propose the Adaptive MultiDimensional Multi-Resolution Architecture (A-MDMRA) that efficiently combines “push” and “pull” strategies for information discovery and adapts to variations in the frequencies of events and queries in the network to construct optimal routing structures. We present simulation results showing the optimal routing structure depends on the frequency of events and query occurrence in the network. It also balances push and pull operations in large scale networks enabling significant QoS improvements and energy savings.

Mining mechanism of top-k influential nodes based on voting algorithm in mobile social networks

In recent years, evaluating the influence of nodes and finding top-k influential nodes in social networks, has drawn a wide attention and has become a hot-pot research issue. Considering the characteristics of social networks, we present a novel mechanism to mine the top-k influential nodes in mobile social networks. The proposed mechanism is based on the behaviors analysis of SMS/MMS (simple messaging service / multimedia messaging service) communication between mobile users. We introduce the complex network theory to build a social relation graph, which is used to reveal the relationship among people's social contacts and messages sending. Moreover, intimacy degree is also introduced to characterize social frequency among nodes. Election mechanism is hired to find the most influential node, and then a heap sorting algorithm is used to sort the voting results to find the k most influential nodes. The experimental results show that the mechanism can finds out the most influential top-k nodes efficiently and effectively. © 2013 IEEE.

Placing sensors for area coverage in a complex environment by a team of robots

Existing solutions to carrier-based sensor placement by a single robot in a bounded unknown Region of Interest (ROI) do not guarantee full area coverage or termination. We propose a novel localized algorithm, named Back-Tracking Deployment (BTD). To construct a full coverage solution over the ROI, mobile robots (carriers) carry static sensors as payloads and drop them at the visited empty vertices of a virtual square, triangular, or hexagonal grid. A single robot will move in a predefined order of directional preference until a dead end is reached. Then it back-tracks to the nearest sensor adjacent to an empty vertex (an "entrance" to an unexplored/uncovered area) and resumes regular forward movement and sensor dropping from there. To save movement steps, the back-tracking is carried out along a locally identified shortcut. We extend the algorithm to support multiple robots that move independently and asynchronously. Once a robot reaches a dead end, it will back-track, giving preference to its own path. Otherwise, it will take over the back-track path of another robot by consulting with neighboring sensors. We prove that BTD terminates within finite time and produces full coverage when no (sensor or robot) failures occur. We also describe an approach to tolerate failures and an approach to balance workload among robots. We then evaluate BTD in comparison with the only competing algorithms SLD [Chang et al. 2009a] and LRV [Batalin and Sukhatme 2004] through simulation. In a specific failure-free scenario, SLD covers only 40-50% of the ROI, whereas BTD covers it in full. BTD involves significantly (80%) less robot moves and messages than LRV.

Information discovery in multi-dimensional autonomous wireless sensor networks

 The thesis proposed four novel algorithms of information discovery for Multidimensional Autonomous Wireless Sensor Networks (WSNs) that can significantly increase network lifetime and minimize query processing latency, resulting in quality of service improvements that are of immense benefit to Multidimensional Autonomous WSNs are deployed in complex environments (e.g., mission-critical applications).