Network-based Design Research Articles

OBWD: an ontology and Bayesian network-based workflow design platform

Workflow management provides a great convenience for the cooperation between different roles in modern industry and business. The task reuse and design automation are challenges of workflow management currently. In this paper, an ontology SDWMO is constructed for workflow resources integration and task request release. An algorithm DOMDM is proposed to achieve the conversion of the data from traditional workflow data base to SDWMO ontology. In order to provide workflow templates for designers, we extract statistic-oriented cases from the workflow database. Based on these cases a Bayesian network is established for workflow template recommendation. We have designed OBWD platform to implement the above methods. The experimental data indicates that OBWD is statistically effective and saves a lot of time for workflow designers. Currently, OBWD has been used in space debris mitigation domain for workflow management. Moreover, our methodology can also be applied in many other domains in the future.

Neural Network-based Design of Index Insurance

Weather risk affects economy, agricultural production in particular. Index insurance has been proposed to hedge against severe weather risk, but large basis risk and low demand accompany current piecewise-linear index insurance contracts. We propose embedding a neural network-based optimization scheme into an expected utility maximization problem to design the index insurance. Neural networks capture the highly nonlinear relationship between the high-dimensional weather variables and production losses. We endogenously solve for the optimal premium and demand of this index insurance. We test this index insurance using corn production loss and weather data in Illinois. We show that this index insurance reduces basis risk, increases farmers' utility, is less reliant on government subsidies, and improves social welfare.

Development of Bolted Flange Design Tool Based on Artificial Neural Network

Finite element analysis (FEA) of bolted flange connections is the common methodology for the analysis of bolted flange connections. However, it requires high computational power for model preparation and nonlinear analysis due to contact definitions used between the mating parts. Design of an optimum bolted flange connection requires many costly finite element analyses to be performed to decide on the optimum bolt configuration and minimum flange and casing thicknesses. In this study, very fast responding and accurate artificial neural network-based bolted flange design tool is developed. Artificial neural network is established using the database which is generated by the results of more than 10,000 nonlinear finite element analyses of the bolted flange connection of a typical aircraft engine. The FEA database is created by taking permutations of the parametric geometric design variables of the bolted flange connection and input load parameters. In order to decrease the number of FEA points, the significance of each design variable is evaluated by performing a parameter correlation study beforehand, and the number of design points between the lower and upper and bounds of the design variables is decided accordingly. The prediction of the artificial neural network based design tool is then compared with the FEA results. The results show excellent agreement between the artificial neural network-based design tool and the nonlinear FEA results within the training limits of the artificial neural network.

Neural Network-Based Adaptive Control Design of Dual-Spin Projectile with Rotating Canards

Guided munition is composed of a highly rotating main body and a guidance/control fuze with two pairs of control canards. The guidance/control fuze rotates in an opposite direction at a slower rate than the main body. The first pair of canards, called pitch canards, is used as the pitch and yaw attitude control effector, and the second pair, called spin canards, is used to generate the rotation of the fuze. Due to the highly rotating motion of the munition, the cross coupling effect between the pitch and yaw axes is significant. To decouple the pitch and yaw axes, the neural network-based L1 adaptive control with dynamic model inversion is proposed in this paper. We also present a coordinate transformation for the controller of the rotating guided munition. The 7 DOF nonlinear simulation model was conducted to validate the results of the controller.

Cost function network-based design of protein-protein interactions: predicting changes in binding affinity.

Accurate and economic methods to predict change in protein binding free energy upon mutation are imperative to accelerate the design of proteins for a wide range of applications. Free energy is defined by enthalpic and entropic contributions. Following the recent progresses of Artificial Intelligence-based algorithms for guaranteed NP-hard energy optimization and partition function computation, it becomes possible to quickly compute minimum energy conformations and to reliably estimate the entropic contribution of side-chains in the change of free energy of large protein interfaces. Using guaranteed Cost Function Network algorithms, Rosetta energy functions and Dunbrack's rotamer library, we developed and assessed EasyE and JayZ, two methods for binding affinity estimation that ignore or include conformational entropic contributions on a large benchmark of binding affinity experimental measures. If both approaches outperform most established tools, we observe that side-chain conformational entropy brings little or no improvement on most systems but becomes crucial in some rare cases. as open-source Python/C++ code at sourcesup.renater.fr/projects/easy-jayz. Supplementary data are available at Bioinformatics online.

Garry Robins – Doing Social Network Research. Network-based Research Design for Social Scientists

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Finite-time control for networked switched linear systems with an event-driven communication approach

ABSTRACTThis paper is concerned with the network-based finite-time controller design problems for a class of switched linear systems with sampled-data control approach, the quantisation errors are also taken into account. A novel event-driven communication scheme is developed which can be seen as the extension of time-driven communication cases. By invoking an input-delay approach, the networked switched control systems are firstly modelled as a kind of switched time-delay systems with time-varying delays, upon which a set of mode-dependent controllers are then designed such that the reformulated switched system with time-varying delays is finite-time bounded and has a guaranteed noise attenuation performance in the finite-time sense. Moreover, a co-design algorithm of mode-dependent control and event-driven communication is presented to find the optimal event-driven strategy for reducing transmission loads. Finally, a numerical example and an application to F-18 aircraft are provided to demonstrate the effectiveness and the advantages of the developed results.

Enhancement of hybrid renewable energy systems control with neural networks applied to weather forecasting: the case of Olvio

In this paper, an intelligent forecasting model, a recurrent neural network (RNN) with nonlinear autoregressive architecture, for daily and hourly solar radiation and wind speed prediction is proposed for the enhancement of the power management strategies (PMSs) of hybrid renewable energy systems (HYRES). The presented model (RNN) is applicable to an autonomous HYRES, where its estimations can be used by a central control unit in order to create in real time the proper PMSs for the efficient subsystems' utilization and overall process optimization. For this purpose, a flexible network-based design of the HYRES is used and, moreover, applied to a specific system located on Olvio, near Xanthi, Greece, as part of Systems Sunlight S.A. facilities. The simulation results indicated that RNN is capable of assimilating the given information and delivering some satisfactory future estimation achieving regression coefficient from 0.93 up to 0.99 that can be used to safely calculate the available green energy. Moreover, it has some sufficient for the specific problem computational power, as it can deliver the final results in just a few seconds. As a result, the RNN framework, trained with local meteorological data, successfully manages to enhance and optimize the PMS based on the provided solar radiation and wind speed prediction and make the specific HYRES suitable for use as a stand-alone remote energy plant.

Towards real-time communication between in vivo neurophysiological data sources and simulator-based brain biomimetic models.

Development of more sophisticated implantable brain-machine interface (BMI) will require both interpretation of the neurophysiological data being measured and subsequent determination of signals to be delivered back to the brain. Computational models are the heart of the machine of BMI and therefore an essential tool in both of these processes. One approach is to utilize brain biomimetic models (BMMs) to develop and instantiate these algorithms. These then must be connected as hybrid systems in order to interface the BMM with in vivo data acquisition devices and prosthetic devices. The combined system then provides a test bed for neuroprosthetic rehabilitative solutions and medical devices for the repair and enhancement of damaged brain. We propose here a computer network-based design for this purpose, detailing its internal modules and data flows. We describe a prototype implementation of the design, enabling interaction between the Plexon Multichannel Acquisition Processor (MAP) server, a commercial tool to collect signals from microelectrodes implanted in a live subject and a BMM, a NEURON-based model of sensorimotor cortex capable of controlling a virtual arm. The prototype implementation supports an online mode for real-time simulations, as well as an offline mode for data analysis and simulations without real-time constraints, and provides binning operations to discretize continuous input to the BMM and filtering operations for dealing with noise. Evaluation demonstrated that the implementation successfully delivered monkey spiking activity to the BMM through LAN environments, respecting real-time constraints.

Endpoint-agnostic address hopping communication — a network-based design by fully exploiting IPv6 huge space superiority

Network address hopping (NAH) proposed a mechanism to enhance data protection in communications across untrusted networks. It spread the data stream of a communication session across multiple channels, which tried to obstruct information interception in the first place by obscuring the fact that communication takes place between certain end-points. However, the time-stamped packets between two peers would provide a hint for correlating the intercepted packets in case the encryption of the counter got compromised. Furthermore, due to synchronization, the Internet Protocol version 6 (IPv6) addresses pair of the channel ends would appear and disappear strictly, which would perform time-relevance character. A Network-based hopping communication mechanism (NetHop) is proposed in this paper. The address hopping function is deployed on the network side instead of endpoint, which can support secure hopping communication function for universal endpoints without any restriction of Operating System or hardware. By using IPv6 to IPv6 network address translation (NAT), NetHop fully exploits the superiority of IPv6 huge address space. The hopping addresses are generated by hash function and the hopping addresses pair can be chosen randomly. Consequently, NetHop performs better on randomness and concealment than channel-rule NAH.

Stabilisation of singularly perturbed nonlinear systems via neural network-based control and observer design

This article addresses the neural network (NN)-based control and observer design for a class of singularly perturbed nonlinear (SPN) systems with guaranteed H ∞ control performance. We consider the problem of NN-based observer design for SPN systems with guaranteed H ∞ control performance. Based on the Lyapunov stability theorem and the tool of linear matrix inequality, we solve the controller and the observer gain matrices and some common positive-definite matrices. Then, two sufficient conditions were derived to stabilise the SPN systems. The allowable perturbation bound ε* can also be determined via some algebra inequalities, such that the proposed NN-based observer and the adaptive control will stabilise the SPN systems for all . A practical system is given to illustrate the validity of the proposed scheme.

Win CE and ZigBee Wireless Sensor Network-Based Design

Building supporting technologies of wireless sensor network system, the data acquisition network scenarios, Win CE and ZigBee technology, design a typical multi-level network model to achieve the CC2430 chip integrated positioning enginewireless communications, applications, short-range multi-network node microprocessor WinCE operating system the center gateway human-computer interaction platform to connect to the public network via broadband and the public telephone network, the use of serial wireless communication terminal acquisition part and gateway platformdata transmission, to achieve environmental monitoring, centralized management of data sent and comprehensive information.

Transfinite surface interpolation with interior control

There are various techniques to design complex free-form shapes with general topology. In contrast to the approaches based on trimmed surfaces and control polyhedra, in curve network-based design feature curves can be directly created and edited in 3D. Multi-sided patches interpolate this curve network with slopes given by associated tangent ribbons. The patches are smoothly connected and yield a natural and predictable surface model. This paper focuses on special design techniques to adjust the interior of transfinite patches when further shape control is needed. While the boundary constraints are retained, additional vertices, curves and even interior control surfaces are supplemented to gain more design freedom. The main idea is to apply different distance-based blending functions with special parameterizations over non-regular, n-sided domains. This concept can be naturally extended to create one- and two-sided patches as well. Shape variations will be demonstrated by a few simple examples.

Neural network-based backstepping design for the synchronization of cross-strict feedback hyperchaotic systems with unmatched uncertainties

For a class of cross-strict feedback hyperchaotic systems with unmatched uncertainties, a multilayer neural network (MNN) based adaptive backstepping design method is proposed. An MNN is introduced to estimate the uncertainties in systems. Sliding mode and adaptive backstepping control are used to deal with the unmatched uncertainties and the MNN approximation errors. If the virtual control coefficients do not pass through zeros, the proposed method guarantees that the synchronization errors of the systems approach zeros. If the virtual control coefficients pass through zeros, the proposed method guarantees that the synchronization errors of the systems are bounded. Numerical simulations are given to demonstrate the efficiency of the proposed control scheme.

Influence of host genetic and ecological factors in complex concomitant infections – relevance to sexually transmitted infections

While there is evidence that host genetics plays a role in susceptibility and subsequent sequelae of sexually transmitted infections (STIs), association findings have been inconsistent in deciphering the causal genes or biological pathways involved in the different life cycle and pathogenesis of infectious microbes. The lack of replication and validation studies from genome-wide association studies in general and specifically with infectious diseases, including STIs, is a continuing problem that limits the utility of these studies. Cohort heterogeneity, sample size, and confounding by population stratification due to differences in genetic polymorphisms in different ethnic groups are the usual explanations. However, in the context of genetic epidemiology studies of infectious disease, apart from the involvement of at least two genomes (the host and the pathogen), local environmental factors in the host shared by concomitant infections are often not examined. Different infectious microbes contribute to the shared local microenvironment, and the immune response can be favorable or unfavorable to different microbes individually and concomitantly at various levels. The balance of each infection relative to the other concomitant infections is a major confounder that has been under-studied. Thus, host genetic studies examining only one pathogen can yield inconsistent associations. This warrants a new paradigm that uses an ecological network-based study design and analysis tools. Defining the role of genetics in concomitant infection is likely to provide insight into pathogenic and protective mechanisms and to identify interdependent molecular targets for prophylactic and therapeutic interventions to multiple co-infections.

Transfinite surface interpolation over irregular [formula omitted]-sided domains

Transfinite surface interpolation is a classic topic of computer-aided geometric design (CAGD), and many non-quadrilateral schemes are known. Surfaces defined solely by means of their boundary curves and cross-tangent functions are needed, for example, in three-dimensional curve network-based design, and to fill complex irregular holes such as in vertex blending. This paper deals with interpolating so-called tangential ribbons. Former schemes are enhanced and extended in order to minimize shape artifacts and to provide a more natural patch interior. The proposed representation is based on irregular convex domains that correspond to the lengths and orientations of the boundary curves. The mapping of the individual ribbons within the n-sided domain is calculated by focused parameterization methods that ensure a balanced orientation related to the center of the domain and avoid parametric shearing. Distance-based blending functions ensure that modifying or inserting a small edge will have only a local effect over the n-sided patch. Constructions to create one-sided or two-sided patches are also presented. Examples and open research topics conclude the paper.

Optimization of L-Asparaginase production from Isolated Aspergillus niger by using Solid State Fermentation on sesame cake via application of Genetic Algorithm, and Artificial Neural Network-based design model

Optimization of L-Asparaginase production from Isolated Aspergillus niger by using Solid State Fermentation on sesame cake via application of Genetic Algorithm, and Artificial Neural Network-based design model

Research on Concurrent Design and GM-Oriented System for Typical Mechanical Products

An implementation methodology is proposed to serve a system for the network-based design and GM (Green Manufacturing), and the detailed design process of the system is introduced, which would be universally applicable for mechanical product design for green manufacturing. To realize the remote product data management, the dreamweaver software and FTP functions are used, and some new programs are developed to building the parts library based on UG NX development platform. However, the client database management has been realized by using IIS and ODBC functions. Eventually a prototype system is developed to accomplish concurrent design for green manufacturing.

Ontology-based modeling and integration of morphological characteristics of assembly joints for network-based collaborative assembly design

AbstractThis paper presents our research on developing an ontology-based framework that can represent morphological characteristics related to assembly joints. Joints within the physical structure of an assembly are inevitable because of the limitations of component geometries and the associated, required engineering properties. Consequently, a framework is needed that can capture and propagate assembly design and joint information in a robust assembly model throughout the entire product development processes. The framework and model are based on an understanding of the morphological characteristics of an assembly and its different physical effects. The morphological characteristics are consequences of the principal physical processes and of the design intentions. Therefore, the morphological characteristics should be carefully represented while considering the geometry and topology of assembly joints. In this research, assembly joint topology is defined by a mereotopology, which is a region-based theory for the parts and associated concepts. This formal ontology can differentiate often ambiguous assembly and joining relations. Furthermore, the mereotopological definitions for assembly joints are implemented in Semantic Web Rule Language (SWRL) rules and Web Ontology Language triples. This process provides universality to the mereotopological definitions. Two geometrically and topologically similar joint pairs are presented to describe how the assembly joints can be defined in mereotopology and be transformed into SWRL rules. Web3D is also employed to support network-enabled sharing of assembly geometry. Finally, the proposed modeling framework is demonstrated using a real fixture assembly. This case study demonstrates the usability of the proposed framework for network-based design collaboration.

IT Framework for Disaster Mitigation Information Sharing

The robust, loosely coupled network-based Web-service design and standard module-integrating protocol we propose serve as a general-purpose framework for information sharing supporting post-disaster rescue and restoration. Results of experiments confirmed the feasibility of our proposals. A noteworthy feature of our work is the simplicity of its elements and implementation.