PicSOM – content-based image retrieval with self-organizing maps

We have developed a novel system for content-based image retrieval in large, unannotated databases. The system is called PicSOM, and it is based on tree structured self-organizing maps (TS-SOMs). Given a set of reference images, PicSOM is able to retrieve another set of images which are similar to the given ones. Each TS-SOM is formed with a different image feature representation like color, texture, or shape. A new technique introduced in PicSOM facilitates automatic combination of responses from multiple TS-SOMs and their hierarchical levels. This mechanism adapts to the user's preferences in selecting which images resemble each other. Thus, the mechanism implements a relevance feedback technique on content-based image retrieval. The image queries are performed through the World Wide Web and the queries are iteratively refined as the system exposes more images to the user.     

Adaptive learning algorithm of self-organizing teams

In order to improve the ability of achieving good performance in self-organizing teams, this paper presents a self-adaptive learning algorithm for team members. Members of the self-organizing teams are simulated by agents. In the virtual self-organizing team, agents adapt their knowledge according to cooperative principles. The self-adaptive learning algorithm is approached to learn from other agents with minimal costs and improve the performance of the self-organizing team. In the algorithm, agents learn how to behave (choose different game strategies) and how much to think about how to behave (choose the learning radius). The virtual team is self-adaptively improved according to the strategies’ ability of generating better quality solutions in the past generations. Six basic experiments are manipulated to prove the validity of the adaptive learning algorithm. It is found that the adaptive learning algorithm often causes agents to converge to optimal actions, based on agents’ continually updated cognitive maps of how actions influence the performance of the virtual self-organizing team. This paper considered the influence of relationships in self-organizing teams over existing works. It is illustrated that the adaptive learning algorithm is beneficial to both the development of self-organizing teams and the performance of the individual agent.     

A self-organizing neural network using ideas from the immune system to solve the traveling salesman problem

Most combinatorial optimization problems belong to the NP-complete or NP-hard classes, which means that they may require an infeasible processing time to be solved by an exhaustive search method. Thus, less expensive heuristics in respect to the processing time are commonly used. These heuristics can obtain satisfactory solutions in short running times, but there is no guarantee that the optimal solution will be found. Artificial Neural Networks (ANNs) have been widely studied to solve combinatorial problems, presenting encouraging results. This paper proposes some modifications on RABNET-TSP, an immune-inspired self-organizing neural network, for the solution of the Traveling Salesman Problem (TSP). The modified algorithm is compared with other neural methods from the literature and the results obtained suggest that the proposed method is competitive in relation to the other ones, outperforming them in many cases with regards to the quality (cost) of the solutions found, though demanding a greater time for convergence in many cases.     

Design of a grey-prediction self-organizing fuzzy controller for active suspension systems

Self-organizing fuzzy controllers (SOFCs) have excellent learning capabilities. They have been proposed for the manipulation of active suspension systems. However, it is difficult to select the parameters of an SOFC appropriately, and an SOFC may extensively modify its fuzzy rules during the control process when the parameters selected for it are inappropriate. To eliminate this problem, this study developed a grey-prediction self-organizing fuzzy controller (GPSOFC) for active suspension systems. The GPSOFC introduces a grey-prediction algorithm into an SOFC, in order to pre-correct its fuzzy rules for the control of active suspension systems. This design solves the problem of SOFCs with inappropriately chosen parameters. To evaluate the feasibility of the proposed method, this study applied the GPSOFC to the manipulation of an active hydraulic-servo suspension system, in order to determine its control performance. Experimental results demonstrated that the GPSOFC achieved better control performance than either the SOFC or the passive method of active suspension control.     

An expert system based on wavelet decomposition and neural network for modeling Chua’s circuit

This paper presents an expert system based on wavelet decomposition and neural network for modeling and simulation of Chua’s circuit which is used for chaos studies. The problems which arise in modeling Chua’s circuit by neural networks are high structural complexity and slow and difficult training. With this proposed method a new solutions is produced to solve these problems. Wavelet decomposition is used for new useful feature extracting from input signal and neural network is used for modeling. Test results of proposed wavelet decomposition and neural network model are compared with test results of neural network model. Desired performance is provided by this new model. Test results showed that the suggested method can be used efficiently for modeling nonlinear dynamical systems.     

Minimization of the effect of noisy measurements on localization of multi-agent autonomous formations

This paper considers the problem of reduction of self-localization errors in multi-agent autonomous formations when only distance measurements are available to the agents in a globally rigid formation. It is shown that there is a relationship between different selections of anchors, agents with exactly known positions, and the error induced by measurement error on localization solution. This fact is exploited to develop a mechanism to select anchors in order to minimize the effects of inter-agent distance measurement errors on localization solution. Finally, some simulation results are presented to demonstrate the optimal anchor selection for a particular general class of formations, the globally rigid formations.     

Simulation and control of distributed robot search teams

This article describes the simulation of distributed autonomous robots for search and rescue operations. The simulation system is utilized to perform experiments with various control strategies for the robot team and team organizations, evaluating the comparative performance of the strategies and organizations. The objective of the robot team is to, once deployed in an environment (floor-plan) with multiple rooms, cover as many rooms as possible. The simulated robots are capable of navigation through the environment, and can communicate using simple messages. The simulator maintains the world, provides each robot with sensory information, and carries out the actions of the robots. The simulator keeps track of the rooms visited by robots and the elapsed time, in order to evaluate the performance of the robot teams. The robot teams are composed of homogenous robots, i.e., identical control strategies are used to generate the behavior of each robot in the team. The ability to deploy autonomous robots, as opposed to humans, in hazardous search and rescue missions could provide immeasurable benefits.     

Fuzzy rule-based analysis of firm’s technology transfer in Taiwan’s machinery industry

In the face of speedy revolution of high technology, the advanced technology becomes one of the key drivers to enhance productivity in a firm or even in a country. In order to compete in the global environment, the ability and effectiveness of acquiring new technology are essential for firms, especially for the traditional machinery industry. In Taiwan, the societal structure is built on the foundation of Small-Medium Enterprises (SMEs). Thus most new technologies need and depend on the technology transfer (TT) from international firms and research institutes. Due to the complicity of influential factors of TT, such as industrial feature (IF), organizational feature (OF), personnel feature (PF) and technological feature (TF), difficulties are encountered in evaluating the effectiveness of TT. This study develops a rule-based decision support mechanism using fuzzy set theory and the method of Analytic Hierarchy Process (AHP) to evaluate the effectiveness of TT. Also, a statistic analysis (Statistics Package for Social Science, SPSS) is adopted to confirm the results of fuzzy AHP analysis. This study finds that prior successful TT experience, workers’ positive attitude, proper authorization, and more codification exist in a novel technology are highly influential. Those factors are important in concern when a firm launching TT. Furthermore, firms’ age and personnel educational background are regardless.     

Multi-agent stabilisation of the psychological dynamics of one-dimensional crowds

This work is part of an investigation aimed at applying the tools of control theory to gain a deeper understanding of the behaviour and attitudes that develop in individuals and in groups of people, with the goal of ultimately controlling the relevant psychological dynamics. The present treatment investigates an approach to stabilise one-dimensional crowds, that is, structures where people are aligned in a queue. The psychological dynamics considered here are based on the notion of suggestibility put forth by Gustave Le Bon. Control action is carried out by certain authoritative figures, termed control agents, who are interspersed throughout the crowd in some predetermined arrangement, subject to one control agent being positioned at one end of the queue. The main result of this paper is the existence of a state-feedback control law for each control agent that, possibly with some communication among control agents, stabilises the crowd. Advantages of employing multiple control agents instead of a single control agent are highlighted, and, throughout, the main stabilisation results are verified using simulations. Finally, the possibility of extending the control strategy to two-dimensional crowds is briefly explored.     

Debugging complex software systems by means of pathfinder networks

This paper introduces a new methodology based on the use of Pathfinder networks (PFNETs) for the debugging of multi-agent systems (MASs). This methodology is specifically designed to develop a forensic analysis (i.e. a debugging process performed on previously recorded data of the MAS run) of MASs showing complex tissues of relationships between agents (i.e. a high complexity in their social level). Like previous works in the field of forensic analysis of MASs, our approach is performed by considering displays of the system activity which aim to be understandable by human beings. These displays allow us to understand the social behavior of the system, discover emergent behaviors, and debug possible undesirable behaviors. However, it is well known that the visualization of information in a humanly comprehensible way becomes a complex task when large amounts of information have to be represented, as is the case of the social behavior of large-scale MASs. Our methodology tackles this problem through the use of PFNETs, which are considered to reduce the data complexity in order to obtain simple representations that show only the most important global interactions in the system. In addition, the proposed methodology is customizable thanks to the use of two thresholds allowing the user to define the desired specificity level in the display. The proposal is illustrated with a detailed case study considering a complex customer-seller MAS.     

Course ontology-based user’s knowledge requirement acquisition from behaviors within e-learning systems

User’s knowledge requirement acquisition and analysis are very important for a personalized or user-adaptive learning system. Two approaches to capture user’s knowledge requirement about course content within an e-learning system are proposed and implemented in this paper. The first approach is based on the historical data accumulated by an interactive question-answering process. The association space is proposed to record and formalize the historical interactive information which is used to compute user’s knowledge requirement. The second approach is based on user’s reading behavior logs in the process of reading e-documents. User’s reading actions including underline, highlight, circle, annotation and bookmark, are used to compute user’s knowledge requirement. Two experiments are conducted to implement the two proposed approaches and acquire the user’s knowledge requirement. The evaluation results show that the user models computed by two approaches are consistent and can reflect user’s real knowledge requirements accurately.     

Nonlinear observer design using Lyapunov’s auxiliary theorem

The present work proposes a new approach to the nonlinear observer design problem. Based on the early ideas that influenced the development of the linear Luenberger observer theory, the proposed approach develops a nonlinear analogue. The formulation of the observer design problem is realized via a system of singular first-order linear PDEs, and a rather general set of necessary and sufficient conditions for solvability is derived by using Lyapunov’s auxiliary theorem. The solution to the above system of PDEs is locally analytic and this enables the development of a series solution method, that is easily programmable with the aid of a symbolic software package.     

Selecting the right MBA schools – An application of self-organizing map networks

The self-organizing map (SOM) network, an unsupervised neural computing network, is a categorization network developed by Kohonen. The SOM network was designed for solving problems that involve tasks such as clustering, visualization, and abstraction. In this study, we apply the clustering and visualization capabilities of SOM to group and plot the top 79 MBA schools as ranked by US News and World Report (USN&WR) into a two-dimensional map with four segments. The map should assist prospective students in searching for the MBA programs that best meet their personal requirements. Comparative analysis with the outputs from two popular clustering techniques K-means analysis and a two-step Factor analysis/K-means procedure are also included.     

Controlled experiment replication in evaluation of e-learning system’s educational influence

We believe that every effectiveness evaluation should be replicated at least in order to verify the original results and to indicate evaluated e-learning system’s advantages or disadvantages. This paper presents the methodology for conducting controlled experiment replication, as well as, results of a controlled experiment and an internal replication that investigated the effectiveness of intelligent authoring shell eXtended Tutor–Expert System (xTEx-Sys). The initial and the replicated experiment were based on our approach that combines classical two-group experimental design and with factoral design. A trait that distinguishes this approach from others is the existence of arbitrary number of checkpoint-tests to determine the effectiveness in intermediate states. We call it a pre-and-post test control group experimental design with checkpoint-tests. The gained results revealed small or even negative effect sizes, which could be explained by the fact that the xTEx-Sys’s domain knowledge presentation is rather novel for students and therefore difficult to grasp and apply in earlier phases of the experiment. In order to develop and improve the xTEx-Sys, further experiments must be conducted.     

Visualizing the evolution of a web-based social network

Weblogs are dynamic websites updated via easy-to-use content management systems and organized as a set of chronologically ordered stories, frequently built around a link or including links to other weblogs. Since they are managed by individuals, their links tend to mirror or, in some cases, establish new types of social relations, thereby creating a social network. Studying the evolution of this network allows the discovery of emerging social structures and their growth trends. In this paper, we demonstrate the advantages of using the self-organizing maps (SOM) to visualize the evolution of a social network formed by a set of blogs, from their beginning to their current state. By observing the position a weblog is mapped to, it is easy to see what communities it belongs to nowadays, and how and when it became a part of those communities. The proposed procedure gives some insight on how communities are formed and have evolved. In this study, we apply this method to Blogalia, a blog-hosting site from which we have obtained a complete set of data and, by using SOM projections, we have drawn some conclusions on what drives the evolution of its implicit social network.     

Strong controllability and extendibility of discrete multidimensional behaviors

In this paper we propose definitions of strong controllability and extendibility for discrete multidimensional behaviors. After characterizing extendibility in algebraic terms, we show that strong controllability is equivalent to simultaneous controllability and extendibility, thus obtaining an algebraic characterization for that property.     

A case study of applying LRFM model in market segmentation of a children’s dental clinic

Dental services marketing has become more and more crucial in Taiwan after Taiwan’s entrance of the World Trade Organization and the implementation of National Health Insurance (NHI) program. This paper develops an extended RFM (recency, frequency, and monetary) model, namely LRFM (length, recency, frequency, and monetary) model, by adopting self-organizing maps (SOM) technique for a children’s dental clinic in Taiwan to segment its dental patients. Twelve clusters are recommended for the overall 2258 patients. The average values of LRF are computed for each cluster and the overall patients, excluding monetary covered by NHI program. The values of LRF variables for each cluster greater than those of the overall average are identified. The results show that three clusters having the above average LRF values (454 patients) can be viewed as core patients.     

Comparing the performance of traditional cluster analysis, self-organizing maps and fuzzy C-means method for strategic grouping

Strategic group analysis comprises of clustering of firms within an industry according to their similarities with respect to a set of strategic dimensions and investigating the performance implications of strategic group membership. One of the challenges of strategic group analysis is the selection of the clustering method. In this study, the results of the strategic group analysis of Turkish contractors are presented to compare the performances of traditional cluster analysis techniques, self-organizing maps (SOM) and fuzzy C-means method (FCM) for strategic grouping. Findings reveal that traditional cluster analysis methods cannot disclose the overlapping strategic group structure and position of companies within the same strategic group. It is concluded that SOM and FCM can reveal the typology of the strategic groups better than traditional cluster analysis and they are more likely to provide useful information about the real strategic group structure.     

Quadratic boundedness via dynamic output feedback for constrained nonlinear systems in Takagi–Sugeno’s form

This paper studies output feedback stabilization of Takagi–Sugeno fuzzy systems with input or state constraint and bounded noise. A dynamic output feedback controller is adopted, rather than a controller based on the state observer. The notion of quadratic boundedness specified by a common Lyapunov matrix, which is novel in fuzzy control, is invoked to handle the noise. Under the proposed controller, the state of the closed-loop system is stabilizing to an ellipsoid specified by this common Lyapunov matrix. Two numerical examples are given to show the effectiveness of the controller.