Special issue : Computational intelligence models for image processing and information reasoning

Computational Intelligence (CI) models comprise robust computing methodologies with a high level of machine learning quotient. CI models, in general, are useful for designing computerized intelligent systems/machines that possess useful characteristics mimicking human behaviors and capabilities in solving complex tasks, e.g., learning, adaptation, and evolution. Examples of some popular CI models include fuzzy systems, artificial neural networks, evolutionary algorithms, multi-agent systems, decision trees, rough set theory, knowledge-based systems, and hybrid of these models. This special issue highlights how different computational intelligence models, coupled with other complementary techniques, can be used to handle problems encountered in image processing and information reasoning.

Hybrid technique of ant colony and particle swarm optimization for short term wind energy forecasting

Wind farms are producing a considerable portion of the world renewable energy. Since the output power of any wind farm is highly dependent on the wind speed, the power extracted from a wind park is not always a constant value. In order to have a non-disruptive supply of electricity, it is important to have a good scheduling and forecasting system for the energy output of any wind park. In this paper, a new hybrid swarm technique (HAP) is used to forecast the energy output of a real wind farm located in Binaloud, Iran. The technique consists of the hybridization of the ant colony optimization (ACO) and particle swarm optimization (PSO) which are two meta-heuristic techniques under the category of swarm intelligence. The hybridization of the two algorithms to optimize the forecasting model leads to a higher quality result with a faster convergence profile. The empirical hourly wind power output of Binaloud Wind Farm for 364 days is collected and used to train and test the prepared model. The meteorological data consisting of wind speed and ambient temperature is used as the inputs to the mathematical model. The results indicate that the proposed technique can estimate the output wind power based on the wind speed and the ambient temperature with an MAPE of 3.513%.

Formation of networked mobile robots

This thesis presents a novel approach for controlling a robotic swarm to generate a geometric pattern described by a given contour, and a suitable communication scheme which enables the robots to communicate with each other as an all-to-all network.

Autonomous underwater vehicles in a hazardous non-deterministic environment

This research investigated the cooperation of multi underwater robots to perform a task. This combined engineering design, electronics and consensus control to create systems capable of achieving the task. Challenges such as underwater radio communications were researched and a simulation framework was created and tested on virtual and real systems.

A probabilistic movement model for shortest path formation in virtual ant-like agents

We propose a probabilistic movement model for controlling ant-like agents foraging between two points. Such agents are all identical, simple, autonomous and can only communicate indirectly through the environment. These agents secrete two types of pheromone, one to mark trails towards the goal and another to mark trails back to the starting point. Three pheromone perception strategies are proposed (Strategy A, B and C). Agents that use strategy A perceive the desirability of a neighbouring location as the difference between levels of attractive and repulsive pheromone in that location. With strategy B, agents perceive the desirability of a location as the quotient of levels of attractive and repulsive pheromone. Agents using strategy C determine the product of the levels of attractive pheromone with the complement of levels of repulsive pheromone. We conduct experiments to confirm directionality as emergent property of trails formed by agents that use each strategy. In addition, we compare path formation speed and the quality of the formed path under changes in the environment. We also investigate each strategy's robustness in environments that contain obstacles. Finally, we investigate how adaptive each strategy is when obstacles are eventually removed from the scene and find that the best strategy of these three is strategy A. Such a strategy provides useful guidelines to researchers in further applications of swarm intelligence metaphors for complex problem solving.

An improved Gbest guided artificial bee colony (IGGABC) algorithm for classification and prediction tasks

Artificial Neural Networks (ANN) performance depends on network topology, activation function, behaviors of data, suitable synapse's values and learning algorithms. Many existing works used different learning algorithms to train ANN for getting high performance. Artificial Bee Colony (ABC) algorithm is one of the latest successfully Swarm Intelligence based technique for training Multilayer Perceptron (MLP). Normally Gbest Guided Artificial Bee Colony (GGABC) algorithm has strong exploitation process for solving mathematical problems, however the poor exploration creates problems like slow convergence and trapping in local minima. In this paper, the Improved Gbest Guided Artificial Bee Colony (IGGABC) algorithm is proposed for finding global optima. The proposed IGGABC algorithm has strong exploitation and exploration processes. The experimental results show that IGGABC algorithm performs better than that standard GGABC, BP and ABC algorithms for Boolean data classification and time-series prediction tasks.

A new color space based on K-medoids clustering for fire detection

Pixel color has proven to be a useful and robust cue for detection of most objects of interest like fire. In this paper, a hybrid intelligent algorithm is proposed to detect fire pixels in the background of an image. The proposed algorithm is introduced by the combination of a computational search method based on a swarm intelligence technique and the Kemdoids clustering method in order to form a Fire-based Color Space (FCS), in fact, the new technique converts RGB color system to FCS through a 3*3 matrix. This algorithm consists of five main stages:(1) extracting fire and non-fire pixels manually from the original image. (2) using K-medoids clustering to find a Cost function to minimize the error value. (3) applying Particle Swarm Optimization (PSO) to search and find the best W components in order to minimize the fitness function. (4) reporting the best matrix including feature weights, and utilizing this matrix to convert the all original images in the database to the new color space. (5) using Otsu threshold technique to binarize the final images. As compared with some state-of-the-art techniques, the experimental results show the ability and efficiency of the new method to detect fire pixels in color images.

A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings

Electrical load forecasting plays a vital role in order to achieve the concept of next generation power system such as smart grid, efficient energy management and better power system planning. As a result, high forecast accuracy is required for multiple time horizons that are associated with regulation, dispatching, scheduling and unit commitment of power grid. Artificial Intelligence (AI) based techniques are being developed and deployed worldwide in on Varity of applications, because of its superior capability to handle the complex input and output relationship. This paper provides the comprehensive and systematic literature review of Artificial Intelligence based short term load forecasting techniques. The major objective of this study is to review, identify, evaluate and analyze the performance of Artificial Intelligence (AI) based load forecast models and research gaps. The accuracy of ANN based forecast model is found to be dependent on number of parameters such as forecast model architecture, input combination, activation functions and training algorithm of the network and other exogenous variables affecting on forecast model inputs. Published literature presented in this paper show the potential of AI techniques for effective load forecasting in order to achieve the concept of smart grid and buildings.

Information sharing between heterogeneous uncertain reasoning models in a multi-agent environment : a case study

The issue of information sharing and exchanging is one of the most important issues in the areas of artificial intelligence and knowledge-based systems (KBSs), or even in the broader areas of computer and information technology. This paper deals with a special case of this issue by carrying out a case study of information sharing between two well-known heterogeneous uncertain reasoning models: the certainty factor model and the subjective Bayesian method. More precisely, this paper discovers a family of exactly isomorphic transformations between these two uncertain reasoning models. More interestingly, among isomorphic transformation functions in this family, different ones can handle different degrees to which a domain expert is positive or negative when performing such a transformation task. The direct motivation of the investigation lies in a realistic consideration. In the past, expert systems exploited mainly these two models to deal with uncertainties. In other words, a lot of stand-alone expert systems which use the two uncertain reasoning models are available. If there is a reasonable transformation mechanism between these two uncertain reasoning models, we can use the Internet to couple these pre-existing expert systems together so that the integrated systems are able to exchange and share useful information with each other, thereby improving their performance through cooperation. Also, the issue of transformation between heterogeneous uncertain reasoning models is significant in the research area of multi-agent systems because different agents in a multi-agent system could employ different expert systems with heterogeneous uncertain reasonings for their action selections and the information sharing and exchanging is unavoidable between different agents. In addition, we make clear the relationship between the certainty factor model and probability theory.

Toward a synergy between simulation and knowledge management for business intelligence

The adoption of simulation as a powerful enabling method for knowledge management is hampered by the relatively high cost of model construction and maintenance. A two-step procedure, based on a divide and conquer strategy, is proposed in this paper. First, a simulation program is partitioned based on a reinterpretation of the model-view-controller architecture. Individual parts are then connected, in terms of abstraction, to guard against possible changes that resulted from shifting user requirements. We explore the applicability of these design principles through a detailed discussion of an industry case study. The knowledge-based perspective guides the design of architecture to accommodate the need of emulation without compromising the integrity of the simulation program. The synergy between simulation and a knowledge management perspective, as shown in the case study, has the potential to achieve the objectives of rapid development of models, with low maintenance cost. This could, in turn, facilitate an extension of the use of simulation in the knowledge management domain.

The factorial validity of emotional intelligence : an unresolved issue

An unresolved but pertinent issue in the field of emotional intelligence (EI) is factorial validity. Numerous studies have investigated this issue (Gignac, 2005; Mayer, Salovey, Caruso, & Sitarenios, 2003; Petrides & Furnham, 2000; Saklofske, Austin, & Minski, 2003), but most are based on correlations among subscale scores from relevant measures, making the implicit assumption that subscale scores are unidimensional, rather than questioning the structure of subscales themselves. Accordingly, the present study adopts the Anderson and Gerbing (1988) two-step strategy of first considering the structure within subscales before examining the relationship between subscales. An evaluation was undertaken using the Emotional Intelligence Scale (EIS, Schutte et al., 1998), the Work Profile Questionnaire – Emotional Intelligence Version (WQPei, Cameron, 1999) and the Mayer–Salovey–Caruso Emotional Intelligence Test (MSCEIT V.2., Mayer, Salovey, & Caruso, 1999b). Results were characterised by instability, heterogeneity and inconsistency. Specifically, the EIS was not found to form the homogenous structure postulated by authors. Similarly, support was not found for the seven factor model of the WPQei. Large discrepancies exist between the one, two and four factor models described by Mayer et al. (2003) for the MSCEIT V.2. and the 21 components revealed at the primary level in the current analyses. Additionally, reliability statistics for the MSCEIT V.2. were less than optimal. Questions remain regarding the clarity, reliability and validity of the instruments examined.

Investing in intelligence : an inquiry into educational paradigm change

In this philosophical and practical-critical inquiry, I address two significant and closely related problems - whether and how those involved in the enterprise of education conceptualise a need for educational change, and the observed resistance of school cultures to change efforts. I address the apparent lack of a clear, coherent and viable theory of learning, agency and change, capable of making explicit the need, substantive nature and means of educational change. Based on a meta-analysis of numerous theories and perspectives on human knowing, learning, intelligence, agency and change, I synthesise a 'Dynamic Paradigm of Learning and Change', characterised by fifteen Constructs. I argue that this more viable Paradigm is capable of informing both design and critique of systemic curriculum and assessment policies, school organisation and planning models, professional learning and pedagogical practice, and student learning and action. The Dynamic Paradigm of Learning and Change contrasts with the assumptions reflected in the prevailing culture of institutionalised education, and I argue that dominant views of knowledge and human agency are both theoretically and practically non-viable and unsustainable. I argue that the prevailing culture and experience of schooling contributes to the formation of assumptions, identities, dispositions and orientations to the world characterised by alienation. The Dynamic Paradigm of Learning and Change also contrasts with the assumptions reflected in some educational reform efforts recently promoted at system level in Queensland, Australia. I use the Dynamic Paradigm as the reference point for a formal critique of two influential reform programs, Authentic Pedagogy and the New Basics Project, identifying significant limitations in both the conceptualisation of educational ends and means, and the implementation of these reform agendas. Within the Dynamic Paradigm of Learning and Change, knowledge and learning serve the individual's need for more adaptive or viable functioning in the world. I argue that students' attainment of knowledge of major ways in which others in our culture organise experience (interpret the world) is a legitimate goal of schooling. However, it is more viable to think of the primary function of schooling as providing for the young inspiration, opportunities and support for purposeful doing, and for assisting them in understanding the processes of 'action scheme' change to make such doing more viable. Through the practical-critical components of the inquiry, undertaken in the context of the ferment of pedagogical and curricular discussion and exploration in Queensland between 1999 and 2003, I develop the Key Abilities Model and associated guidelines and resources relating to forms of pedagogy, curriculum organisation and assessment consistent with the Dynamic Paradigm of Learning and Change. I argue the importance of showing teachers why and how their existing visions and conceptions of learning and teaching may be inadequate, and of emphasising teachers' conceptions of learning, knowing, agency and teaching, and their identities, dispositions and orientations to the world, as things that might need to change, in order to realise the intent of educational change focused on transformational student outcomes serving both the individual and collective good. A recommendation is made for implementation and research of a school-based trial of the Key Abilities Model, informed by and reflecting the Dynamic Paradigm of Learning and Change, as an important investment in the development and expression of ‘authentic' human intelligence.

Hypothesis generation for management intelligence

Investigation of the role of hypothesis formation in complex (business) problem solving has resulted in a new approach to hypothesis generation. A prototypical hypothesis generation paradigm for management intelligence has been developed, reflecting a widespread need to support management in such areas as fraud detection and intelligent decision analysis. This dissertation presents this new paradigm and its application to goal directed problem solving methodologies, including case based reasoning. The hypothesis generation model, which is supported by a dynamic hypothesis space, consists of three components, namely, Anomaly Detection, Abductive Reasoning, and Conflict Resolution models. Anomaly detection activates the hypothesis generation model by scanning anomalous data and relations in its working environment. The respective heuristics are activated by initial indications of anomalous behaviour based on evidence from historical patterns, linkages with other cases, inconsistencies, etc. Abductive reasoning, as implemented in this paradigm, is based on joining conceptual graphs, and provides an inference process that can incorporate a new observation into a world model by determining what assumptions should be added to the world, so that it can explain new observations. Abductive inference is a weak mechanism for generating explanation and hypothesis. Although a practical conclusion cannot be guaranteed, the cues provided by the inference are very beneficial. Conflict resolution is crucial for the evaluation of explanations, especially those generated by a weak (abduction) mechanism.The measurements developed in this research for explanation and hypothesis provide an indirect way of estimating the ‘quality’ of an explanation for given evidence. Such methods are realistic for complex domains such as fraud detection, where the prevailing hypothesis may not always be relevant to the new evidence. In order to survive in rapidly changing environments, it is necessary to bridge the gap that exists between the system’s view of the world and reality.Our research has demonstrated the value of Case-Based Interaction, which utilises an hypothesis structure for the representation of relevant planning and strategic knowledge. Under, the guidance of case based interaction, users are active agents empowered by system knowledge, and the system acquires its auxiliary information/knowledge from this external source. Case studies using the new paradigm and drawn from the insurance industry have attracted wide interest. A prototypical system of fraud detection for motor vehicle insurance based on an hypothesis guided problem solving mechanism is now under commercial development. The initial feedback from claims managers is promising.