Self-organizing Algorithm Research Articles

Cell Mapping Modeling for Complex Systems Based on ISODATA Algorithm

This paper presents a new type of modeling framework for complex dynamical systems based on cell mapping. The systems considered are difficult in modeling by physical principle due to complex mechanism. By the method of data driven, the cell mapping model is constructed and the modeling objective is describing statistical dynamic trajectory for this kind of complex system, which overcome the difficulties found in mechanism modeling. Firstly, the clustering of operation modes is achieved by Self-Organizing Data Analysis Techniques Algorithm (ISODATA). Then, a cell division measure is obtained by the clustering result, which is different from the regular partition in other measures. Meanwhile, each class bound is treated as a cell bound, and hence the dynamic trajectory of complex systems is described by the cell mapping. Finally, a simulated experience illustrates the feasibility of the proposed approach.

An ISODATA Approach to the Estimation of Atomistic Definition for Continuum Stress

The atomic stress tensor at a given continuum point is a spatial average value of some volume near the point. Recent progresses in multiscale modeling include the dealing of the optimal number and the size of these volumes. In this paper, we motivate the application of Iterative self-organizing data analysis technique algorithm to estimate volume numbers. The size of these space averaging volumes then could be got using Gaussian mixture model. Reduced computation complexity is offered by this method. Atomistic simulations are conducted to analyze the stress of a stone-wales defect graphene sheet to validate the method. Other multiscale values could also be determined using this method.

Continuous learning route map for robot navigation using a growing-on-demand self-organizing neural network

This article proposes an experience-based route map continuous learning method and applies it into robot planning and navigation. First of all, the framework for robot route map learning and navigation is designed, which incorporates the four cyclic processes of planning, motion, perception, and extraction, enabling robot to constantly learn the information of the road experience and to obtain and improve the route map of the environment. Besides, a growing-on-demand self-organizing neural network learning algorithm is also proposed. This algorithm is based on growing neural gas algorithm, but it does not require presetting of network scale, and under the condition of dynamically growing input data, it can regulate the increase scale of network online in a self-adaptive and self-organized manner to obtain stable learning results. Finally, with robot roaming in an environment, this algorithm is used to conduct continuous learning of dynamically increasing route information, extract the topological structur...

Synthesis of quasi-optimal digital phase-locking systems under the action of additive noises

Quasi-optimal synthesis of digital phase-locking systems (PLSs) is discussed to evaluate the information parameters of a frequency-modulated signal. An adaptive Kalman filter capable of functioning without a priori data on the statistical characteristics of input and test noises is employed. The model of the process under study is identified using the linear trend modified by the self-organization algorithm. Procedures and the results of numerical calculations are presented for the digital system of phase synchronization.

Offloading in HetNet: A Coordination of Interference Mitigation, User Association, and Resource Allocation

The use of heterogeneous small cell-based networks to offload the traffic of existing cellular systems has recently attracted significant attention. One main challenge is solving the joint problems of interference mitigation, user association, and resource allocation. These problems are formulated as an optimization which is then analyzed using two different approaches: Markov approximation and log-linear learning. However, finding the optimal solutions of both approaches requires complete information of the whole network which is not scalable with the network size. Thus, an approach based on a Markov approximation with a novel Markov chain design and transition probabilities is proposed. This approach enables the Markov chain to converge to the bounded near optimal distribution without complete information. In the game-theoretic approach, the payoff-based log-linear learning is used, and it converges in probability to a mixed-strategy $\epsilon$ -Nash equilibrium. Based on the principles of these two approaches, a highly randomized self-organizing algorithm is proposed to reduce the gap between optimal and converged distributions. Simulation results show that all of the proposed algorithms effectively offload more than 90 percent of the traffic from the macrocell base station to small cell base stations. Moreover, the results also show that the algorithms converge quickly irrespective of the number of possible configurations.

Delaunay meshing of implicit domains with boundary edge sharpening and sliver elimination

In 2004 Strang and Persson suggested the Delaunay mesh generation algorithm in implicit domains with smooth boundary based on the self-organization of elastic network, where each Delaunay edge is treated as a strut. Implicit domains in the algorithm are defined by the signed distance functions. Repulsive forces were suggested which allow to distribute mesh vertices according to prescribed size function. The Delaunay property is restored after each elastic relaxation step. This algorithm was generalized in Garanzha, Kudryavtseva, 2012 for the case of domains defined as zero levels of piecewise-smooth functions using edge sharpener suggested by Belyaev, Ohtake, 2002. In this paper we present variational version of self-organization algorithm. Suggested elastic potential is combination of repulsion potential and sharpening potential which acts on boundary edges and serves to minimize deviation of cell boundary normals from direction of gradient of implicit function which allows to approximate sharp edges on the boundary as polylines made from mesh edges without their predefinition. Numerical experiments demonstrated that Belyaev–Ohtake edge sharpener based on alignment of boundary cell normals becomes unstable when implicit function strongly deviates from the signed distance function. Stable version of sharpener is suggested and verified by numerical tests. When surface near sharp edge is highly curved sharpener based on alignment of normals fails to place vertices precisely on the sharp edge. To this end we apply special sharp edge attraction procedure. While theoretical foundations are not available numerical evidence shows that Delaunay meshes can be constructed for domains with very acute and curved sharp boundary edges. As soon as domain boundary is recovered cell shape deformation terms are added to the elastic potential and subsequent iterations allow to eliminate slivers without deterioration of approximation of sharp edges.

Integrating Flexible Sensor and Virtual Self-Organizing DC Grid Model With Cloud Computing for Blood Leakage Detection During Hemodialysis.

Blood leakage and blood loss are serious complications during hemodialysis. From the hemodialysis survey reports, these life-threatening events occur to attract nephrology nurses and patients themselves. When the venous needle and blood line are disconnected, it takes only a few minutes for an adult patient to lose over 40% of his / her blood, which is a sufficient amount of blood loss to cause the patient to die. Therefore, we propose integrating a flexible sensor and self-organizing algorithm to design a cloud computing-based warning device for blood leakage detection. The flexible sensor is fabricated via a screen-printing technique using metallic materials on a soft substrate in an array configuration. The self-organizing algorithm constructs a virtual direct current grid-based alarm unit in an embedded system. This warning device is employed to identify blood leakage levels via a wireless network and cloud computing. It has been validated experimentally, and the experimental results suggest specifications for its commercial designs. The proposed model can also be implemented in an embedded system.

Novel Variable Structure Measurement System with Intelligent Components for Flight Vehicles

AbstractThe paper presents a method of developing a variable structure measurement system with intelligent components for flight vehicles. In order to find a distinguishing feature of a variable structure, a numerical criterion for selecting measuring sensors is proposed by quantifying the observability of different states of the system. Based on the Peter K. Anokhin’s theory of functional systems, a mechanism of “action acceptor” is built with intelligent components, e.g. self-organization algorithms. In this mechanism, firstly, prediction models of system states are constructed using self-organization algorithms; secondly, the predicted and measured values are compared; thirdly, an optimal structure of the measurement system is finally determined based on the results of comparison. According to the results of simulation with practical data and experiments obtained during field tests, the novel developed measurement system has the properties of high-accuracy, reliable operation and fault tolerance.

Parallel Interaction Detection Algorithms for a Particle-based Live Controlled Real-time Microtubule Gliding Simulation System Accelerated by GPGPU

Real-time simulations have been getting more attention in the field of self-organizing molecular pattern formation such as a microtubule gliding assay. When appropriate microtubule interactions are set up on gliding assay experiments, microtubules often organize and create higher-level dynamics such as ring and bundle structures. In order to reproduce such higher-level dynamics in silico, we have been focusing on making a real-time 3D microtubule simulation. This real-time 3D microtubule simulation enables us to gain more knowledge on microtubule dynamics and their swarm movements by means of adjusting simulation parameters in a real-time fashion. For the recreation of microtubule dynamics our model proposes the use of the Lennard-Jones potential for our particle-based simulation, as well as a flocking algorithm for self-organization. One of the technical challenges when creating a real-time 3D simulation is computational scalability performance, as well as balancing the 3D rendering and computing work flows. GPU programming plays an essential role in executing the millions of tasks necessary for microtubule interaction detection and makes this real-time 3D simulation possible. However, an excess number of tasks sometimes causes a memory bottleneck which prevents performance scalability when using GPGPU processing. In order to alleviate the memory bottleneck, we propose a new parallel interaction detection algorithm that uses warp level optimizations for the two memory bound interactions discussed in this paper.

Personalizing self-organizing music spaces with anchors: design and evaluation

We propose and evaluate a system for content-based visualization and exploration of music collections. The system is based on a modification of Kohonen’s Self-Organizing Map algorithm and allows users to choose the locations of clusters containing acoustically similar tracks on the music space. A user study conducted to evaluate the system shows that the possibility of personalizing the music space was perceived as difficult. Conversely, the user study and objective metrics derived from users’ interactions with the interface demonstrate that the proposed system helped individuals create playlists faster and, under some circumstances, more effectively. We believe that personalized browsing interfaces are an important area of research in Multimedia Information Retrieval, and both the system and user study contribute to the growing work in this field.

GAEを用いた自己組織化の概念に基づくアルゴリズムの実装と構造形態創生への適用

Various optimization problems have been studied for the purpose of structural morphogenesis by many researchers. Structural optimization problems can be classified into three different sub-problems, namely size, shape and topology optimization problem. Size optimization problem is consisted of small sets of design variables. The shape and topology of a structure are defined by a set of design variables, and these design variables are adjusted to achieve given objectives, such as minimum volume. Such optimization problems can be solved iteratively, using gradient-based techniques. Introducing more design variables increases the complexity of the optimization problem. Therefore, it becomes difficult to solve the optimization problem by using MP techniques with large sets of design variables. Heuristics can be improved the difficulty. There are many studies using heuristics like genetic algorithm and simulated annealing. An effective method for structural morphogenesis inspired by self-organization phenomena is presented in this paper. Self-organization is a phenomenon that an entire structure gradually emerges by interaction between elements of the structure, and the elements are affected by the entire structure. Since the self-organization algorithm consists of simple calculation iteration, it can be applied to problems with large number of design variables. Proposed method is applied to problems of uniform member length, minimization of strain energy and cross-section design for frame structures, and the effectiveness is demonstrated through some examples. On the other hand, the number of projects incorporating computational design has increased in recent year, and designs with complicated forms composed of free surfaces are also increasing. In order to design such a shape, the designer needs to consider the rationality of the structure from the initial stage. So, it seems necessary to develop simple software for the structural morphogenesis. Therefore, we developed components of Grasshopper that works within Rhinoceros, so that it will be possible to reassemble algorithms in an intuitive way for designers who have never experienced programming. Grasshopper is one of Graphical Algorithm Editor (GAE), and can be visually constructed an algorithm by connecting components that is a function of modeling. Analysis by gradient-based MP techniques often involves jumping of solutions and analytical instability peculiar to non-linear problems, and it is difficult to apply to the computer aided systems as described above. Analysis algorithms based on self-organizing algorithms are considered to be suitable for the above system due to their high robustness.

A self-organised routing algorithm for cognitive radio-based wireless sensor networks using biologically-inspired method

Wireless sensor networks area collection of a large number of unattended sensor nodes. The sensor nodes are dynamically deployed in the region. In wireless sensor networks, the unlicensed sensor nodes use their temporarily available vacant licensed channels or spectrum band in a telecommunication network. The many open challenges with issues such as adaptive in self-organisation, fault-tolerance and network scalability with cooperative spectrum sensing and sharing are identifying new challenges in wireless sensor networks. In this paper, we proposed the bio-inspired adaptive routing algorithm for cognitive radio wireless sensor networks. The utilisation of the foraging approach of ANTs and BEEs for making the decision of using the available spectrum sensing and sharing of channels over multi-hops communication, provides the method to utilise idle channel and decrease the delay for communication in wireless sensor network. In this paper, we design the algorithm to solve the issue of wireless sensor network and performance of classical routing protocol LEACH and cognitive radio sensor networks (CRSN) evaluates the parameters which proof the self-organisation routing algorithm.

Self-Organizing Algorithms for Interference Coordination in Small Cell Networks

This paper discusses novel joint (intracell and intercell) resource allocation algorithms for self-organized interference coordination in multicarrier multiple-input multiple-output (MIMO) small cell networks. The proposed algorithms enable interference coordination autonomously, over multiple degrees of freedom, such as base station transmit powers, transmit precoders, and user scheduling weights. A generic $\alpha$ -fair utility maximization framework is considered to analyze performance-fairness tradeoff and to quantify the gains achievable in interference-limited networks. The proposed scheme involves limited inter-base station signaling in the form of two step (power and precoder) pricing. Based on this decentralized coordination, autonomous power and precoder update decision rules are considered, leading to algorithms with different characteristics in terms of user data rates, signaling load, and convergence speed. Simulation results in a practical setting show that the proposed pricing-based self-organization can achieve up to $100\%$ improvement in cell-edge data rates when compared to baseline optimization strategies. Furthermore, the convergence of the proposed algorithms is also proved theoretically.

Self-organization and Routing Algorithms for the Purpose of the Sensor Network Monitoring Environmental Conditions on a Given Area

The article describes an implementation of wireless sensor network (WSN) based on the IEEE 802.15.4-2006 standard, which was designed to monitor environmental conditions (e.g. temperature, humidity, light intensity, etc.) on a given area. To carry out this task, a self-organization algorithm called KNeighbors was selected. It exhibits low computational complexity and is satisfactory with respect to energy consumption. Additionally, the authors proposed a novel routing algorithm and some modifications to the MAC layer of the IEEE 802.15.4 standard. The article discusses the selected algorithms and procedures that were implemented in the network.

A socially-based distributed self-organizing algorithm for holonic multi-agent systems: Case study in a task environment

Holonic multi-agent systems (HMASs) have recently attracted many researches in multi-agent systems community. Inspired from the multi-level and self-similar structures of social and biological system, holonic multi-agent systems have been widely used to model and solve complex real-world problems. The main concern in deploying HMASs is the problem of building the hierarchical holonic structure, called holarchy, and dynamically managing it during its lifetime. The way an HMAS is organized has a great impact on its applicability and performance. This paper proposes a self-organizing algorithm to build and manage the holoic structures in multi-agent systems. This algorithm is based on the local information of the agents about other agents they can communicate with. Using common social concepts, like skills, diversity, social exchange theory, and norms in definition of the algorithm, the outcomes of this research can be used in wide ranges of distributed applications. The proposed model is extensively tested in a task allocation problem; and its performance based on various design parameters is studied. Empirical results show that the proposed model properly increases the performance of the system in terms of effectiveness and efficiency.

Development of a Measurement Complex with Intelligent Component

A method of constructing a selective measurement complex with variable structure is investigated. A measurement complex with intelligent component consisting of algorithms for the construction of predictive models and the comparison of a prediction with the current result of measurements is developed for precision determination of the parameters of a dynamic object. The algorithmic support of the complex is constructed on the basis of the Anokhin theory of functional systems with the use of a scalar estimation algorithm, self-organization algorithm with trend back-up, and criterion expressed as the degree of observability of the state variables. Models with elevated degrees of observability of the state variables are used in the information processing algorithms.

Assistive technology using integrated flexible sensor and virtual alarm unit for blood leakage detection during dialysis therapy.

Blood leakages and blood loss are both serious complications during dialysis therapies. According to dialysis survey reports, these events are life-threatening issues for nephrology nurses, medical staff, and patients. When venous needle dislodgement occurs, it takes only <2.5 min of reaction time for blood loss in an adult patient, resulting in mortality. As an early-warning design, a wireless assistive technology using an integrated flexible sensor and virtual alarm unit was developed to detect blood leakage during dialysis therapies. The flexible sensor was designed using a screen print technique with printing electronic circuits on a plastic substrate. A self-organising algorithm was used to design a virtual alarm unit, consisting of a virtual direct current grid and a virtual alarm driver. In other words, this warning device was employed to identify the blood leakage levels via wireless fidelity wireless network in cloud computing. The feasibility was verified, and commercialisation designs can also be implemented in an embedded system.

A Firefly-Inspired Scheme for Energy-Efficient Transmission Scheduling Using a Self-Organizing Method in a Wireless Sensor Networks

Various types of natural phenomena are regarded as primary sources of information for artificial occurrences that involve spontaneous synchronization. Among the artificial occurrences that mimic natural phenomena are Wireless Sensor Networks (WSNs) and the Pulse Coupled Oscillator (PCO), which utilizes firefly synchronization for attracting mating partners. However, the PCO model was not appropriate for wireless sensor networks because sensor nodes are typically not capable to collect sensor data packets during transmission (because of packet collision and deafness). To avert these limitations, this study proposed a self-organizing time synchronization algorithm that was adapted from the traditional PCO model of fireflies flashing synchronization. Energy consumption and transmission delay will be reduced by using this method. Using the proposed model, a simulation exercise was performed and a significant improvement in energy efficiency was observed, as reflected by an improved transmission scheduling and a coordinated duty cycling and data gathering ratio. Therefore, the energy-efficient data gathering is enhanced in the proposed model than in the original PCO-based wave-traveling model. The battery lifetime of the Sensor Nodes (SNs) was also extended by using the proposed model.

Self-organizing visualization and pattern matching of vectorcardiographic QRS waveforms

QRS morphology is commonly used in the electrocardiographic diagnosis of ventricular depolarization such as left bundle branch block (LBBB) and ventricular septal infarction. We investigated whether pattern matching of QRS loops in the 3-dimensional vectorcardiogram (VCG) will improve the grouping of patients whose space-time electrical activity akin to each other, thereby assisting in clinical decision making. First, pattern dissimilarity of VCG QRS loops is qualitatively measured and characterized among patients, resulting in a 93×93 distance matrix of patient-to-patient dissimilarity. Each patient is then represented as a node in the network (or a star in the galaxy), but node locations are optimized to preserve the dissimilarity matrix. The optimization is achieved with a self-organizing algorithm that iteratively minimizes the network energy. Experimental results showed that patients’ locations converge as the representation error reaches a stable phase. The convergence is independent of initial locations of network nodes. Most importantly, 93 patients are automatically organized into 3 clusters of healthy control, LBBB, and infarction. Spatial coordinates of nodes (or patients) are evidently novel predictors that can be used in the computer-assisted detection of cardiac disorders. Self-organizing pattern matching is shown to have strong potentials for large-scale unsupervised learning of patient groups.

Self-organizing ART Algorithm used in Improving the Efficiency of NLP

Self-organizing ART Algorithm used in Improving the Efficiency of NLP