Search

Abstract: This paper investigates the feasibility of a self-organizing, completely distributed traffic information system based upon vehicle-to-vehicle communication technologies. Unlike centralized traffic information systems, the proposed system does not need public infrastructure investment as a prerequisite for implementation. Due to the complexity of the proposed system, simulation is selected as the primary approach in the feasibility studies. A simulation framework is built based on an existing microscopic traffic simulation model for the simulation studies. The critical questions for building the proposed market-driven system are examined both from communication requirements and traffic engineering points of view. Traffic information propagation both in freeway and arterial networks via information exchange among IVC-equipped vehicles is tested within the simulation framework. Results on the probability of successful IVC and traffic information propagation distance obtained from the simulation studies are generated and analyzed under incident-free and incident conditions for various roadway formats and parameter combinations. Comparisons between the speed of the incident information wave and the speed of the corresponding traffic shock wave due to the incident are analyzed for different scenarios as the most crucial aspect of the information propagation as a potential foundation for application in such a decentralized traffic information system. [Copyright &y& Elsevier]

This paper proposes a novel information-theoretic approach to self-organizing called cooperative information control. The new method aims to mediate between competition and cooperation among neurons by controlling information content in the neurons. Competition is realized by maximizing information content in neurons. In the process of information maximization, only a small number of neurons win the competition, while all the others are inactive. Cooperation is implemented by having neurons behave similarly to their neighbors. These two processes are unified and controlled in the framework of cooperative information control. We applied the new method to four problems: political, medical, linguistic data analysis and applied linguistic data analysis. In all the analyses, experimental results confirmed that competition and cooperation are flexibly controlled and that the method can yield a number of different neuron firing patterns to detect macro as well as micro features in input patterns. [Copyright &y& Elsevier]

E-learning provides an unprecedented flexibility and convenience to both learners and instructors. However, it also creates a great number of lonely learners. This paper presented a collaborative learner community self-organizing algorithm based on trust award and dynamic exchange schema, which can eventually cluster learners with similar interests into the same community and help them share learning resources together. The experiments show that this mechanism can sustainably improve the efficiency and scalability. [ABSTRACT FROM AUTHOR]

The need to simultaneously achieve operational efficiency, while accommodating a continuous morphing of alliances and network arrangements, is a key challenge in the modern enterprise and market. Inter-firm interdependence and unpredictable market shifts heighten the need to establish an architecture and governance arrangement that permits rapid adaptation. Fully integrated firms have increasingly morphed into networks of collaborators. The demand for efficient and effective inter-firm coordination is no longer a desired condition, but essential for competitive position. Historically, the cost of this efficiency has been loss of flexibility, yet volatile markets reward efficiency and flexibility. Market trends demand an ability to improvise in the marketplace — converge execution with planning — while being simultaneously efficient. In this paper, we explore this improvisation-efficiency paradox. The architecture and governance issues are considered that seek the strengths of both effects. Efficiency need not come at a high cost in attaining the ability to be adaptive and spontaneous. We explore the key considerations of both network architecture and governance structures that characterize improvisational networks. Trade communities and trading partners establish a discipline of processes and decision rights that serve the community of collaborators. [ABSTRACT FROM AUTHOR]

How to share experience and resources among learners is becoming one of the hottest topics in the field of E-Learning collaborative techniques. An intuitive way to achieve this objective is to group learners which can help each other into the same community and help them learn collaboratively. In this paper, we proposed a novel community self-organization model based on multi-agent mechanism, which can automatically group learners with similar preferences and capabilities. In particular, we proposed award and exchange schemas with evaluation and preference track records to raise the performance of this algorithm. The description of learner capability, the matchmaking process, the definition of evaluation and preference track records, the rules of award and exchange schemas and the self-organization algorithm are all discussed in this paper. Meanwhile, a prototype has been built to verify the validity and efficiency of the algorithm. Experiments based on real learner data showed that this mechanism can organize learner communities properly and efficiently; and that it has sustainable improved efficiency and scalability. [ABSTRACT FROM AUTHOR]

This paper describes an analysis of IP-network traffic in terms of the time variation of self-similarity. To get a comprehensive view of network traffic conditions in analyzing the degree of long-range dependence (LRD) of IP-network traffic, this paper used a self-organizing scheme-based topology map, which provides a way to map high-dimensional data onto a low-dimensional domain. Also, in the LRD-based analysis, this paper employed detrended fluctuation analysis (DFA), which is applicable to the analysis of long-range power-law correlations or LRD in apparently non-stationary time-series signals. Based on sequential measurements of IP-network traffic at a 100-Mbps point of interface between an access provider and the Internet, this paper derived corresponding values for the LRD-related parameter α of the traffic. In training the topology map, this paper used three parameters: the α value, average throughput, and a parameter that reflects the degree of non-stationarity for each measured data set. We visually confirmed that the traffic data could be projected onto the topology map in accordance with the traffic properties, resulting in a combined depiction of the effects of the degree of LRD and other factors. The proposed method can deal with multi-dimensional parameters, projecting its results onto a two-dimensional space in which the positions of the projected data give us with an effective depiction of network conditions at different times. [Copyright &y& Elsevier]

Living systems can adapt to injuries and even heal themselves, an ability desirable also in synthetic systems. A method is presented for dynamically adapting the construction of an electronic circuit to hardware defects by formulating the process as a series of interactions between identical but specialized structures called supercells. The circuit components, including wires, can occupy any place in the hardware that has been determined to be free of defects. The circuit specification is reduced to a connected graph, with no positional information, and provided as a code repeated in each supercell. Using the code, supercells differentiate into circuit components in a late stage of the process, with highly adaptable physical location and organization; supercells also form the wires between circuit components. The structure and function of the system at three major levels is presented, the lowest cellular level, the supercell, and the target circuit level. Adaptation of circuit construction to defective hardware was observed for this method. Results obtained from this development process on simulated and real hardware with a variety of defect types and defect patterns are presented, as well as higher level simulations of the algorithm and its response to a wider range of defect patterns, amounts of hardware, and hardware to fault ratios. [Copyright &y& Elsevier]

In the knowledge economy, the human minds are the most vital center of analysis. They are the complex adaptive systems capable of processing information, establishing knowledge structure, conceptualizing idea, and making decision. The intrinsic intelligence of the individual minds, as well as the organizational/collective intelligence, drives the dynamic of all human systems. Primarily, the local self‐enrichment processes of the interacting agents are autopoietic. In addition, global forces are also present in all human organizations. The global forces are constructive only if they support the elementary processes. The global forces originate from the orgmind of the organization. A complex relationship exists between the interacting agents and their systems. Traditionally, the decision‐making dynamic of the human thinking systems has been dealt with in economics concepts such as the “economic” man that focuses on perfect rational decision, and Herbert Simon's “administrative” man that incorporates the idea of bounded rationality. In this study, the dynamic of an “intelligent” person is introduced. An intelligent person does not concentrate on optimality at all times. Instead, such a person adopts the intelligence strategy. An intelligent person is mindful and contributes continuously towards the collective intelligence of the system. The mindset of an intelligent person encompasses continual fast learning, longer‐term survival, exploitation of the butterfly effect, and co‐evolution with his/her system. In this respect, an intelligent person is a rather dissimilar interacting agent. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a phenomenological measure of ordering in statistical systems. Using this characteristic, we construct a formal model of a system evolving according to the law of steepest ascent as the control parameter increases. [ABSTRACT FROM AUTHOR]

The explosion of biological data resulting from genomic and proteomic research has created a pressing need for data analysis techniques that work effectively on a large scale. An area of particular interest is the organization and visualization of large families of protein sequences. An increasingly popular approach is to embed the sequences into a low-dimensional Euclidean space in a way that preserves some predefined measure of sequence similarity. This method has been shown to produce maps that exhibit global order and continuity and reveal important evolutionary, structural, and functional relationships between the embedded proteins. However, protein sequences are related by evolutionary pathways that exhibit highly nonlinear geometry, which is invisible to classical embedding procedures such as multidimensional scaling (MDS) and nonlinear mapping (NLM). Here, we describe the use of stochastic proximity embedding (SPE) for producing Euclidean maps that preserve the intrinsic dimensionality and metric structure of the data. SPE extends previous approaches in two important ways: (1) It preserves only local relationships between closely related sequences, thus allowing the map to unfold and reveal its intrinsic dimension, and (2) it scales linearly with the number of sequences and therefore can be applied to very large protein families. The merits of the algorithm are illustrated using examples from the protein kinase and nuclear hormone receptor superfamilies. [ABSTRACT FROM AUTHOR]

We measured the transmission of 3 keV Ne7+ ions through capillaries of 100 nm diameter and 10 μm length produced by etching ion tracks in a polyethylene terephthalate polymer foil. The foils were tilted up to ±25° for which the incident ions are forced to interact with the capillary surface. The majority of Ne7+ ions were found to survive the transmission in their initial charge state. For tilted foils the angular distributions of the transmitted particles indicate propagation of the Ne7+ ions parallel to the capillary axis. This capillary guiding of the Ne7+ ion provides evidence that part of the ions deposit charges within the capillaries in a self-organizing process so that a considerable fraction of the ions is transmitted through the capillaries. A non-linear model is introduced to describe the essential features of the capillary guiding. [Copyright &y& Elsevier]

Expressed layered structures in the cerebral and cerebellar cortices of the brain are attributed to most animals while the human and some primate neostriatum neurons are laid out as clustered higher and lower cell density mosaics. These ordered structures are probably formed by a self-organizing mechanism which is widely discussed in the present paper. We use the notation “Self-organizing” not as a self-learning or self-mapping algorithm, widespread in a neural network learning paradigm, but as a principle studying physical nonequilibrium mechanisms. Considering the theoretical principle based on neural networks, an N-shaped current–voltage relation was included in the model and its influence on the stability and conditions of self-organization examined. The formation of ordered structures was founded in the vicinity of the equilibrium point. Based on the group and bifurcation theories, the self-organized topological structures were grounded for the visual cortex. Concomitant computational experiments with wide illustrations of the ordered structures—patterns—are presented. The experimentally registered ordered structures and computational ones have been roughly compared. [Copyright &y& Elsevier]

Many real world data processing tasks demand intelligent computational models with good efficiency and adaptability in their on-line operations. Consequently, neural algorithms with constructive network structure and incremental learning ability are of increasing interest. In this paper we present an algorithm of evolving self-organizing map (ESOM), which features an evolving network structure and fast on-line learning. Experiments have been carried out on some benchmark data sets for vector quantisation and classification tasks. Compared with other methods, ESOM achieved better or comparable performance with a much shorter learning process. Our results show that ESOM is a promising computational model for on-line pattern analysis in real world problems. [Copyright &y& Elsevier]

The dynamic response of a self-organizing fuzzy sliding-mode-controlled toggle mechanism, which is driven by a permanent magnet synchronous servo motor, is studied in this paper. First, based on the principle of fuzzy control, a self-organizing fuzzy control (SOFC) system is developed to control the position of a slider of the motor-toggle servomechanism. Moreover, to reduce the control rules in the design of the SOFC system and to strengthen the robust characteristics, a self-organizing fuzzy sliding-mode control (SOFSMC) is proposed to control the motor-toggle servomechanism. The proposed SOFC and SOFSMC systems contain two sets of fuzzy inference logic: one is the fuzzy controller and the other is the rule modifier. A new fuzzy learning method of the rule modifier is developed, where the modification value of each rule is based on the fuzzy firing weight. The proposed SOFC and SOFSMC systems can automatically tune the rules bases to achieve satisfactory performance, so that they can be applied for on-line learning, real-time control. In addition, simulated and experimental results due to periodic step and sinusoidal commands show the dynamic behaviors of the proposed SOFC and SOFSMC systems are robust with regard to parameter variations and external disturbances. Comparison between SOFC and SOFSMC also shows that the SOFSMC can reduce implementation complex while defining the sliding surface. [Copyright &y& Elsevier]

In a soccer robot game, the environment is highly competitive and dynamic. In order to work in the dynamically changing environment, the decision-making system of a soccer robot system should have the features of flexibility and on-line adaptation. This paper proposed a strategy-based decision-making system for a soccer robot system. The decision system is based on the proposed self-organizing fuzzy decision tree (SOFDT). Due to event-driven strategies, the proposed decision-making system has the advantage of flexibility. The proposed SOFDT possesses simple, apparent, and fast generation/reasoning processes. The resultant system is a self-organizing strategy-based decision-making system. According to decision results, SOFDT can on-line modify its parameters and structure for achieving better adaptability and improvement. The performance of the proposed systems was verified by applying them to the simulation and experiment of 3-to-3 robot soccer games. The results are satisfactory. [ABSTRACT FROM AUTHOR]

In this paper, a novel immunized reinforcement adaptive learning mechanism employing a behavior-based knowledge and the on-line adapting capabilities of the immune system is proposed and applied to an intelligent mobile robot. Rather than building a detailed mathematical model of immune systems, we try to explore principles in the immune system focusing on its self-organization, adaptive capability and immune memory. Two levels of the immune system, underlying the ‘micro’ level of cell interactions, and emergent ‘macro’ level of the behavior of the system are investigated.To evaluate the proposed immunized architecture, a ‘food foraging work’ simulation environment containing a mobile robot, foods, with/without obstacles is created to simulate the real world. The simulation results validate several significant characteristics of the immunized architecture: adaptability, learning, self-organizing, and stable ecological niche approaching. [Copyright &y& Elsevier]

Development of computational agent organizations or “societies” has become the domiant computing paradigm in the arena of Distributed Artificial Intelligence, and many foreseeable future applications need agent organizations, in which diversified agents cooperate in a distributed manner, forming teams. In such scenarios, the agents would need to know each other in order to facilitate the interactions. Moreover, agents in such an environment are not statically defined in advance but they can adaptively enter and leave an organization. This begs the question of how agents locate each other in order to cooperate in achieving organizational goals. Locating agents is a quite challenging task, especially in organizations that involve a large number of agents and where the resource avaiability is intermittent. The authors explore here an approach based on self-organization map (SOM) which will serve as a clustering method in the light of the knowledge gathered about various agents. The approach begins by categorizing agents using a selected set of agent properties. These categories are used to derive various ranks and a distance matrix. The SOM algorithm uses this matrix as input to obtain clusters of agents. These clusters reduce the search space, resulting in a relatively short agent search time. [ABSTRACT FROM AUTHOR]

Echo state network (ESN) refers to a popular recurrent neural network with a largely and randomly generated reservoir for its rapid learning ability. However, it is difficult to design a reservoir that matches a specific task. To solve the structure design of the reservoir, a pseudo-inverse decomposition-based self-organizing modular echo state (PDSM-ESN) is proposed. PDSM-ESN is constructed by growing–pruning method, where the error and condition number are used, respectively. Since the self-organizing process may negatively affect the learning speed, the pseudo-inverse decomposition is adopted to improve learning speed, which means the output weights are learned by an iterative incremental method. Meanwhile, to solve the ill-posed problem, the modular sub-reservoirs corresponding to the high condition number are pruned. Simulation results indicate that PDSM-ESN has better prediction performance and run-time complexity compared with the traditional ESN models. • A pseudo-inverse decomposition-based self-organizing modular echo state (PDSM-ESN) is proposed. • The output weights are learned by an iterative incremental method to improve the learning speed. • To solve the ill-posed problem, the modular sub-reservoirs corresponding to the high condition number are pruned. [ABSTRACT FROM AUTHOR]

A new self-organizing neural network model is presented, which can get rid of some fatal defects facing the Kohonen self-organizing neural network, known as the slow training speed, difficulty in designing neighboring zone, and disability to deal with area constraints directly. Based on the new neural network, a new approach for performance-driven system partitioning on MCM is presented. In the algorithm, the total routing cost between the chips and the circle time are both minimized, while satisfying area and timing constraints. The neural network has a reasonable structure and its training speed is high. The algorithm is able to deal with the large scale, circuit partitioning, and has total optimization effect. The algorithm is programmed with Visual C++ language, and experimental result shows that it is an effective method. [ABSTRACT FROM AUTHOR]

Analysis of competitive sport performance from a priori observation is frequently used to prepare an athlete for a future contest. This practice necessarily assumes that the observed athletic behaviours are invariant, in that the data from an earlier contest apply to the next. We report different analyses of championship squash matches from 1987 and 1988 in search of such invariance and thus a general signature of athletic behaviour. The results show that once the preceding condition to an athletic response (shot) becomes more detailed, by either accounting for where on court the previous shot was played to, or by pairing the previous shot with its own antecedent, the corresponding response becomes more predictable (P > 0.25). This is especially the case for a squash champion when the forehand and backhand characteristics of his shot response are accounted for. The finding that the champion is more consistent if his forehand and backhand shot responses to a preceding shot are analysed separately, shows that the level of analytic detail is an important consideration if the derived information from sport analysis is to prepare an athlete reliably for future sport competition. Finally, we consider sport competition from an ecological perspective with regard to the natural organization and stability of non-linear systems in an attempt to explain the empirical data. [ABSTRACT FROM AUTHOR]

It is well known that robotic manipulators are highly non-linear coupled dynamic systems. It is difficult to establish an appropriate mathematical model for the design of a model-based controller. Although fuzzy logic control has model-free features, it still needs time-consuming work for rule bank and fuzzy parameter adjustment. Hence the self-organizing fuzzy controller is proposed to manipulate the motion trajectory of robots with multiple degrees of freedom. This approach has learning ability for responding to the time-varying characteristic of a robot. Its control rule bank can be established and modified continuously by on-line learning with zero initial fuzzy rules. However, this control strategy has larger oscillatory behaviour during initial learning. Here a self-organizing fuzzy controller with grey prediction is proposed to improve this behaviour. The experimental results show that this intelligent controller can reduce significantly the oscillatory amplitude of the output trajectory and tracking error. [ABSTRACT FROM AUTHOR]

Some typical structural schemes of Fuzzy control have been surveyed. Besides general structure of fuzzy logic controller (FLC), the structural schemes include PID fuzzy controller, self-organizing fuzzy controller, selftuning fuzzy controller, self-learning fuzzy controller, and expect fuzzy controller, etc. This survey focuses on the control principle, and provides a basis for potential applications. Most of the structures have been used in various control fields, one of application areas is in the metallurgy industry, e.g., the temperature control of the electric furnace, the control of the aluminum smelting process, etc. According to the application requirements, one can choose a structural scheme for special use. [ABSTRACT FROM AUTHOR]

The usual “Kohonen” algorithm uses samples of points in a domain to develop a topological correspondence between a grid of “neurons” and a continuous domain. “Topological” means that near points are mapped to near points. However, for many applications there are additional constraints, which are given by sets of measure zero, which are not preserved by this method, because of insufficient sampling. In particular, boundary points do not typically map to boundary points because in general the likelihood of a sample point from a two-dimensional domain falling on the boundary is typically zero for continuous data, and extremely small for numerical data. A specific application, (assigning meshes for the finite element method), was recently solved by interweaving a two-dimensional Kohonen mapping on the entire grid with a one-dimensional Kohonen mapping on the boundary. While the precise method of interweaving was heuristic, the underlying rationale seems widely applicable. This general method is problem independent and suggests a direct generalization to higher dimensions as well. [ABSTRACT FROM AUTHOR]

In this paper, by a phenomenal analysis of general systems, adaptability and self creativity are suggested to be the most important specialties in evolution. The structure of multi‐stasis is defined. And based on these, the evolution equations of self‐organizing systems are given, the evolutionary forces are analyzed. A few examples are studied. [ABSTRACT FROM PUBLISHER]

A genetic algorithm (GA) has control parameters that must be determined before execution. We propose a self-organizing genetic algorithm (SOGA) as a multimodal function optimizer which sets GA parameters such as population size, crossover probability, and mutation probability adaptively during the execution of a genetic algorithm. In SOGA, GA parameters change according to the fitnesses of individuals. SOGA and other approaches for adapting operator probabilities in GAs are discussed. The validity of the proposed algorithm is verified in simulation examples, including system identification. [ABSTRACT FROM AUTHOR]

Developing tissues have intricate, three-dimensional (3D) organizations of cells and extracellular matrix (ECM) that provide the framework necessary to meet morphogenic and necessary demands. Migrating cells, in vivo, are exposed to numerous conflicting signals: chemokines, ECM, growth factors, and physical forces. While most of these have been studied individually in vivo or in vitro, our understanding of how cells integrate these various signals is lacking. We previously developed a novel self-organizing cellularized collagen hydrogel model that is adaptable, tunable, reproducible, and capable of mimicking the multitude of stimuli that cells experience. Our model produced self-assembled toroids of cells that were formed by 24 h. Data we present here show toroids initially form as early as 3 h after seeding. Additionally, toroids formed when cells were seeded on various collagen subtypes and were sensitive to the composition of the hydrogel. Moreover, we found differences in remodeling in toroid gels compared to gels with cells embedded in them using both a collagen binding peptide and rheology. Using scanning electron microscopy, we observed toroids forming a crater-like structure compared to whole gel contractions in mixed in gels. Finally, when multiple cells were mixed prior to seeding, heterogeneous toroids formed with some containing clusters of cells.