Hierarchical Network Model Research Articles

A P2P Network Model Based on Hierarchical Interest Clustering Algorithm

A hierarchical clustering P2P network model based on user interest is presented in this paper to improve search efficiency. The routers in the network are abstracted as the activated peer node, and the activated routing table is constructed to depict the character of the interest cluster. The P2P network is divided into different subnets according into their interest character, and the corresponding topological structure is build. The corresponding search strategy is proposed based on the above mention method. The simulation results show that compared with the traditional algorithm the interest cluster method proposed in this paper can form cluster more rapidly, and gain the appropriate resources faster.

A new trusted roaming protocol in wireless mesh networks

Wireless Mesh Networks (WMNs) are a wireless broadband access technology based completely on IP technologies and has thus rapidly become a means for broadband access with the characteristics of high capacity, high speed and wide coverage. For trusted roaming in WMNs, the configuration of the access platforms must be checked first before access to the network can continue, and only those platforms whose configurations meet the security requirements of the network can be allowed to access the network. It is also required that mobile nodes complete access authentication not only in a timely manner, but also in a way in which the identities of the mobile nodes are well protected. In this paper, we propose a trusted roaming protocol that are based on several technologies such as hierarchical network model, elliptic curve cryptography (ECC), trust evaluation, grey relevance analysis, etc. to ensure the security of roaming in WMNs. We also show the security properties of the proposed protocol through formal analysis and the performance by presenting some simulation results.

Assessing the Effect of Quantitative and Qualitative Predictors on Gastric Cancer Individuals Survival Using Hierarchical Artificial Neural Network Models

BackgroundThere are numerous unanswered questions in the application of artificial neural network models for analysis of survival data. In most studies, independent variables have been studied as qualitative dichotomous variables, and results of using discrete and continuous quantitative, ordinal, or multinomial categorical predictive variables in these models are not well understood in comparison to conventional models.ObjectivesThis study was designed and conducted to examine the application of these models in order to determine the survival of gastric cancer patients, in comparison to the Cox proportional hazards model.Patients and MethodsWe studied the postoperative survival of 330 gastric cancer patients who suffered surgery at a surgical unit of the Iran Cancer Institute over a five-year period. Covariates of age, gender, history of substance abuse, cancer site, type of pathology, presence of metastasis, stage, and number of complementary treatments were entered in the models, and survival probabilities were calculated at 6, 12, 18, 24, 36, 48, and 60 months using the Cox proportional hazards and neural network models. We estimated coefficients of the Cox model and the weights in the neural network (with 3, 5, and 7 nodes in the hidden layer) in the training group, and used them to derive predictions in the study group. Predictions with these two methods were compared with those of the Kaplan-Meier product limit estimator as the gold standard. Comparisons were performed with the Friedman and Kruskal-Wallis tests.ResultsSurvival probabilities at different times were determined using the Cox proportional hazards and a neural network with three nodes in the hidden layer; the ratios of standard errors with these two methods to the Kaplan-Meier method were 1.1593 and 1.0071, respectively, revealed a significant difference between Cox and Kaplan-Meier (P < 0.05) and no significant difference between Cox and the neural network, and the neural network and the standard (Kaplan-Meier), as well as better accuracy for the neural network (with 3 nodes in the hidden layer). Probabilities of survival were calculated using three neural network models with 3, 5, and 7 nodes in the hidden layer, and it has been observed that none of the predictions was significantly different from results with the Kaplan-Meier method and they appeared more comparable towards the last months (fifth year). However, we observed better accuracy using the neural network with 5 nodes in the hidden layer. Using the Cox proportional hazards and a neural network with 3 nodes in the hidden layer, we found enhanced accuracy with the neural network model.ConclusionsNeural networks can provide more accurate predictions for survival probabilities compared to the Cox proportional hazards mode, especially now that advances in computer sciences have eliminated limitations associated with complex computations. It is not recommended in order to adding too many hidden layer nodes because sample size related effects can reduce the accuracy. We recommend increasing the number of nodes to a point that increased accuracy continues (decrease in mean standard error), however increasing nodes should cease when a change in this trend is observed.

Organization network enhanced detection and transmission of phase-locking

Based on the recent observation that a neuron in the local network of the mouse primary visual cortex receives convergent input from nearby neurons (Nature, 471 (2011) 177), we present a hierarchical organization network model to stress the aspect of directional coupling in neurons and study how an external signal can be transmitted in this network model. By taking numerical simulations on the paradigmatic Rössler oscillator and Hindmarsh-Rose neuron, we show that the oscillators in the network will lock in phase and frequency over a range that is much larger than one driven oscillator would, indicating the enhanced signal detectability of the hierarchical organization network. To guarantee the successful transmission of phase-lockings, a self-tuning mechanism is introduced where the weights of those links along the signal transmission path will be adaptively increased, with the total weight of network keeping constant. Moreover, we find that the organization network is in favor of the phase-locking transmission than the star-like network.

Functional Roles of Phase Resetting in the Gait Transition of a Biped Robot From Quadrupedal to Bipedal Locomotion

Although physiological studies have shown evidence of phase resetting during fictive locomotion, the functional roles of phase resetting in actual locomotion remain largely unclear. In this paper, we have constructed a control system for a biped robot based on physiological findings and investigated the functional roles of phase resetting in the gait transition from quadrupedal to bipedal locomotion by numerical simulations and experiments. So far, although many studies have investigated methods to achieve stable locomotor behaviors for various gait patterns of legged robots, their transitions have not been thoroughly examined. Especially, the gait transition from quadrupedal to bipedal requires drastic changes in the robot posture and the reduction of the number of supporting limbs, and therefore, the stability greatly changes during the transition. Thus, this transition poses a challenging task. We constructed a locomotion control system using an oscillator network model based on a two-layer hierarchical network model of a central pattern generator while incorporating the phase resetting mechanism and created robot motions for the gait transition based on the physiological concept of synergies. Our results, which demonstrate that phase resetting increases the robustness in gait transition, will contribute to the understanding of the phase resetting mechanism in biological systems and lead to a guiding principle to design control systems for legged robots.

Relationship Network Analysis of Manufacturing System Limited by Business Lines

It is a common problem of manufacturing enterprise limited by business line, which optimized its resources allocation to increase productivity, but now the researches are very weak in relationship mapping and network analysis method in the world. Aimed to the demands, proposed a resource allocation method limited by business line of manufacturing system based on the relational mapping of production system, manufacturing model of hierarchical network based on business line and node degree are been established. Defined the data structure of node on hierarchical network, simplified the network computing by dividing it to three parts - orders assignment, accomplish feedback and pure information exchange. Analyzed the distributing of network degree, established an equation of power law which has two limited parameters of node degree and side weight to automatic compute the amounts of resources of new order. Case study shows that the network analysis method limited business line proposed in the paper can give us an important reference frame of resources assignment. DOI: http://dx.doi.org/10.11591/telkomnika.v10i6.1415 Full Text: PDF

Hierarchical network-based equilibrium model and algorithm for a mixed-traffic urban transport system

Many equilibrium models and algorithms based on homogeneous motorized traffic have been devised to model urban transport systems in developed countries, but they are inadequate when it comes to represent mixed-traffic urban transport systems, including automobiles, transit, bicycles, and pedestrians, in developing countries such as China or India. In these cases, traffic flow on a road segment is an aggregated result of travellers' combined mode/route choices and corresponding interactions. Therefore, a special assignment model and algorithm are needed for modeling these distinct behaviors. In this article, the structure of a mixed-traffic urban transport system is analyzed and then expanded and represented using a hierarchical network model based on graph theory. Based on the analysis of travelers' combined mode/route choices, generalized travel cost functions and link impedance functions for different modes are formulated, where the interferences between different modes on the same road segments are taken into account. Due to the ‘asymmetric’ nature of these functions, a variational inequality model is proposed to represent the equilibrium assignment problem in a mixed-traffic urban transport system. The corresponding solution algorithm is also presented. Finally, a numerical example is provided to illustrate the practicality of the proposed model and algorithm.

Applying Hierarchical Bayesian Neural Network in Failure Time Prediction

With the rapid technology development and improvement, the product failure time prediction becomes an even harder task because only few failures in the product life tests are recorded. The classical statistical model relies on the asymptotic theory and cannot guarantee that the estimator has the finite sample property. To solve this problem, we apply the hierarchical Bayesian neural network (HBNN) approach to predict the failure time and utilize the Gibbs sampler of Markov chain Monte Carlo (MCMC) to estimate model parameters. In this proposed method, the hierarchical structure is specified to study the heterogeneity among products. Engineers can use the heterogeneity estimates to identify the causes of the quality differences and further enhance the product quality. In order to demonstrate the effectiveness of the proposed hierarchical Bayesian neural network model, the prediction performance of the proposed model is evaluated using multiple performance measurement criteria. Sensitivity analysis of the proposed model is also conducted using different number of hidden nodes and training sample sizes. The result shows that HBNN can provide not only the predictive distribution but also the heterogeneous parameter estimates for each path.

An Approximation Algorithm for Vehicle Routing Problems with Hierarchized Traffic Network

Efficient delivery planning for freight transport is required for reducing environmental load and logistical costs. By using an actual traffic condition, a more realistic delivery plan, increased delivery work efficiency, and CO2 reduction will be possible. An algorithm for quickly obtaining a best solution is required to solve actual vehicle routing problems of large-scale networks and fluctuating delivery costs. The purpose of our research was to solve vehicle routing problems on a large-scale road network that can be applied to actual vehicle routing problems. Our proposed algorithm uses a hierarchical traffic network model composed of two layers according to the frequency of road use. The algorithm is based on tabu search, and the hierarchical traffic network is applied to a generation strategy of the neighborhood solution. Specifically, priority of movement of neighborhood solutions for generating a neighborhood solution is given to delivery points connected in frequently used roads. By using travelling salesman and simple vehicle-routing benchmark problems as a first step in our computing, we confirmed that the proposed algorithm can quickly provide a better solution than the non-application of the proposed algorithm. Furthermore, the effectiveness of the proposed algorithm is discussed by using a virtual vehicle routing problem based on an actual road network.

Learning Patterns of Motion Trajectories Using Real-Time Tracking

The understanding and description of behaviors for road vehicles is a hot topic of intelligent visual surveillance system. Trajectory analysis is one of the basic problems in behavior understanding, from which anomalies can be detected and also accidents can be predicted. In this paper, we proposed a hierarchical self-organizing neural network model to learn trajectory distribution pattern and a probability model for accident recognition. Sample data including motion trajectories are first get by real-time vehicle tracking. The self-organizing neural network algorithm is then applied to learn activity patterns from the sample trajectories. Using the learned patterns, we consider anomaly detection as well as object behavior prediction. Experiments in actual road scene show the effectiveness of the proposed algorithm.

Linkit: a CALL system for learning Chinese characters, words, and phrases

Teaching Chinese as a foreign language (TCFL) is increasingly becoming a mainstream profession and an independent academic discipline. However, there is little research in CALL targeting the Chinese language to date. This research attempts to show how a CALL system can be constructed following the unique properties of the Chinese language so it can benefit the learner maximally. First, we analyze student journals to identify problematic areas for British students learning Chinese as a foreign language, which seem to spread across levels of phonology, morphology, orthography, and vocabulary in an inextricable fashion. To address this issue, we designed a hierarchical network model linking levels of sound, syllables, characters, words, and phrases together in the form of nodes and connections. Current implementation of the model is a CALL system nicknamed Linkit, which can work alongside a textbook or a corpus, offering interconnected elements of the Chinese language such as the syllable, the character, the word, and the phrase on the same screen. The purpose of the design is for students to view a Chinese character not just as a character, but to see how it relates to other homophonic characters by appreciating the underlying phonological similarities and how the character can develop into a word or a phrase. We suggest that the emphasis on this type of continuity is a key factor in designing Chinese-specific CALL programs.

Applicability of Geo-spatial Processing Open Sources to Geographic Object-based Image Analysis (GEOBIA)

At present, GEOBIA (Geographic Object-based Image Analysis), heir of OBIA (Object-based Image Analysis), is regarded as an important methodology by object-oriented paradigm for remote sensing, dealing with geo-objects related to image segmentation and classification in the different view point of pixel-based processing. This also helps to directly link to GIS applications. Thus, GEOBIA software is on the booming. The main theme of this study is to look into the applicability of geo-spatial processing open source to GEOBIA. However, there is no few fully featured open source for GEOBIA which needs complicated schemes and algorithms, till It was carried out to implement a preliminary system for GEOBIA running an integrated and user-oriented environment. This work was performed by using various open sources such as OTB or PostgreSQL/PostGIS. Some points are different from the widely-used proprietary GEOBIA software. In this system, geo-objects are not file-based ones, but tightly linked with GIS layers in spatial database management system. The mean shift algorithm with parameters associated with spatial similarities or homogeneities is used for image segmentation. For classification process in this work, tree-based model of hierarchical network composing parent and child nodes is implemented by attribute join in the semi-automatic mode, unlike traditional image-based classification. Of course, this integrated GEOBIA system is on the progressing stage, and further works are necessary. It is expected that this approach helps to develop and to extend new applications such as urban mapping or change detection linked to GIS data sets using GEOBIA.

Routing Protocol for Heterogeneous Hierarchical Wireless Multimedia Sensor Networks

Recently, wireless multimedia sensor networks (WMSNs) have emerged as one of the most important technologies, driven by the development of powerful multimedia device such as CMOS. WMSNs require several factors such as resource constraints, specific QoS, high bandwidth and so on. In this paper, we propose a hierarchical heterogeneous network model based routing protocol for WMSNs. In our proposal, proposed network model is classified into monitoring class, delivery class and aggregation class. Also we define two kinds of the routing path in order to ensure bandwidth and QoS. In simulation results, we illustrate the performance of our proposal.

A hierarchical elastic network model for unsupervised electron density map segmentation

To highlight different levels of structural and functional organization found in cryo EM density maps, we use an unsupervised hierarchical elastic network model (hENM) based on a Markov diffusion process. The hENM segments electron density maps into meaningful protein subregions Chennubhotla & Bahar 2006, and finds a simplified representation of the map in each level of the hierarchy. When implemented on maps in the 2010 Cryo-EM Modeling Challenge, hENM segmented the molecules into individual proteins and regions without manual input. For example, hENM identified the apical and equatorial domains of the 14 groEL proteins in the 23.5A groEL-groES map (EMD: 1046). These results suggest that hENM could be applied to segment density maps which otherwise would require manual curating. As the sub-regions found in each level of the hierarchy highlight the core structural regions of the protein, they can also be used as anchor points for fitting high resolution structures into EM maps. Inference of secondary structure from characteristic patterns in the affinity maps can also help guide the fitting process. Further implementations of our method will study protein dynamics at the coarsest hierarchy level, and then project insight into dynamics onto finer, more detailed levels. This method provides a unifying framework in allowing protein segmentation concurrently with the study of information propagation through the network, with dynamical inference as an additional benefit.

Hierarchical Elastic Network Modeling of cryo-EM Data

Cryo-EM images of molecules are increasing in resolution, extending their uses from rigid-body docking to generation of atomistic density maps independent of prior structural information. Single molecule cryo-EM provides images of a molecule from many viewpoints, which are averaged and compiled to form a 3D reconstruction. This reconstruction determines the smallest possible hull enclosing the molecule, but sacrifices information about dynamic properties of the molecule intrinsically contained in the varied image set.We suggest projecting the images into 3D space, without averaging out inconsistent densities, to form a probability map for the reconstruction. This map contains flexibility information neglected by earlier analyses of dynamics from density maps. To highlight different levels of molecular flexibility found in this map, we use a hierarchical elastic network model (hENM). The hENM uses the Markov transition probabilities between the voxels in the density map to segment the map into sets of multi-resolution nodes. Each voxel is initially taken as a node in the network, after-which the network is iteratively reduced to increasingly coarser sets of important nodes, forming a hierarchy. This method provides a unifying framework in allowing the derivation of dynamics concurrently with the study of information propagation through the network, with segmentation of the protein into structural sub-regions as an additional benefit. As these nodes highlight the core structural regions of the protein, they can also be used as anchor points for fitting high-resolution structures into EM maps. Preliminary results show excellent overlap between the dynamics computed using a Gaussian Network Model of the C-alpha atoms of proteins and those computed from our hENM nodes, validating the role of hENM in predicting intrinsic protein motions. With the greater goal of inferring function from dynamics, we work here to predict dynamics directly from cryo-EM images.

Hierarchical Network Model of Safe High-Speed Rail Operation

This paper presents a preliminary hierarchical network model based on an analysis of the interaction of relationships between operating safety factors of high-speed rail (HSR). The model is based on the characteristics and factors specific to HSR operation and aims to provide a new theory and method for assessing the safety of HSR operation with the use of the relationship analysis methods of a hierarchical network model. A comprehensive theory of safety assessment in HSR operations is necessary for HSR designers and operators to consider rail safety pro-actively rather than passively. The development and the application of such a broad-ranging safety assessment theory for HSR operation could help to guarantee efficient HSR operation by protecting the safety of passengers, reducing losses, maintaining social stability, and improving economic efficiency.

Topology Optimization Control with Balanced Energy and Load in Underwater Acoustic Sensor Networks

Abstract In view of unbalanced link bandwidth and node energy consumption in Underwater Acoustic Sensor Networks (UASNs), a distributed topology optimization control algorithm (EL-BatCA) is proposed, which is based on balanced network energy and load. A hierarchical network model is designed based on minimum-hop, by which the nodes are divided into n layers. EL-BatCA is implemented through three stages-topology graph formation in initialization, an innovative transmission power adjustment in topology control, and finally topology optimization through object functions. Simulation results show that EL-BatCA not only balances energy consumption and network load, but also prolongs network lifetime and improves network throughout.

Improving the Navigability of Tagging Systems with Hierarchically Constructed Resource Lists and Tag Trails

Recent research has shown that the navigability of tagging systems leaves much to be desired. In general, it was observed that tagging systems are not navigable if the resource lists of the tagging system are limited to a certain factor k. Hence, in this paper a novel resource list generation approach is introduced that addresses this issue. The proposed approach is based on a hierarchical network model. The paper shows through a number of experiments based on a tagging dataset from a large online encyclopedia system called Austria-Forum, that the new algorithm is able to create tag network structures that are navigable in an efficient manner. Contrary to previous work, the method featured in this paper is completely generic, i.e. the introduced resource list generation approach could be used to improve the navigability of any tagging system. This work is relevant for researchers interested in navigability of emergent hypertext structures and for engineers seeking to improve the navigability of tagging systems.

2218 メカトロ製品の開発初期段階における協調設計支援ツールの開発(OS5-2 製品設計開発のためのモデリング・方法論・マネジメントII,OS5 モデル駆動型の製品システム開発)

This paper introduces a model-driven product development for complexity management. Today, the complexity of the mechatronics products e.g. automobile, electric product, and airplane, makes it more difficult that the engineers collaborate each other. The causes of the complexity can be categorized into three: (1) the requirement for complex function is increasing, (2) the scope of the design is getting more widely, and (3) the combination of the line-up product is becoming huge number because of the widely-covered product family. In order to manage and control such complexity of product and product development, this paper introduces a model-driven approach based on a product model. The product model describes three dimensions: (1) the structure of the product, (2) the function requested by the customer, and (3) the product behavior of the software. The detailed structure, elements and representation ability of the system model of the product structure, function and behavior is introduced. The model structure is represented as a hierarchical network model of product component, connections, attributes, and constraints. The model of function is represented as a network of signal/material/energy flow and its translation capability. The model of behavior is represented as a network of component state and its transitions.

Evaluating the Connection-Set Algebra for the neural simulator NEST

Event Abstract Back to Event Evaluating the Connection-Set Algebra for the neural simulator NEST Jochen Martin Eppler1*, Håkon Enger2, Thomas Heiberg2, Birgit Kriener2, Hans Ekkehard Plesser2, Markus Diesmann1 and Mikael Djurfeldt3 1 Research Center Juelich, Institute of Neuroscience and Medicine (INM-6), Germany 2 Norwegian University of Life Sciences, Mathematical Sciences and Technology, Norway 3 KTH, PDC Center for High Performance Computing, Sweden We present our ongoing work to evaluate the usability and efficiency of an implementation of the Connection Set Algebra (http://software.incf.org/software/csa) [1] for the neural simulation tool NEST (http://www.nest-initiative.org) [2]. Present day simulation languages work well for simple network structures like random or feed-forward networks. However, they either lack the required expressiveness or the required computational efficiency to specify the connectivity of complex hierarchical network models now under investigation. This increases the risk of errors in the model description or prevents the investigation of relevant model sizes. Therefore, a description language in the problem domain of the neuroscientist is required which guides the researcher to correct model specifications and avoids technical clutter. The Connection Set Algebra (CSA) is a generic framework, which provides elementary connection sets and operators for combining them. It allows one to define network connectivity in a compact and accessible format. A prototype implementation of the CSA exists in form of a Python module (libcsa). NEST is a simulator for large heterogeneous networks of point neurons or neurons with a small number of compartments. The concrete goals of this project are: Reduce the complexity of NEST's native connection routines through the use of CSA. Base the topology module of NEST on CSA in order to obtain better scaling, especially for very large networks on HPC facilities. Create a C++ implementation of CSA (libcsa) that can be used by neural simulators to describe network connectivity. The result of this study is a test implementation and a quantitative assessment of the proposed technology. Acknowledgments Partially funded by "The Next-Generation Integrated Simulation of Living Matter" project, part of the Development and Use of the Next-Generation Supercomputer Project of the Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan, the Helmholtz Alliance on Systems Biology, the Research Council of Norway and the International Neuroinformatics Coordinating Facility.