Underwater multiple target tracking decision making based on an analytic network process

Underwater multi-target tracking logic and decision （UMTLD） has difficulty resolving multi-target tracking problems for underwater vehicles. Present methods assume factors in UMTLD are uncorrelated, when these are actually in a complex, interdependent relationship. To provide this, an index set of multi-target tracking decision characteristics and an analytic network process （ANP） model of the UMTLD method was -established. This method brings the index set of multi-target tracking decision into the ANP model, and the optimization multitarket tracking decision is achieved via computation of the resulting supermatrix. The rationality and robustness of decision results increase in simulations by 13% and 47% respectively with analytic hierarchy process （AHP）. These results indicate that the ANP method should be the preferred method when UMTLD factors are interdependent.

Using analytic network process to analyze problems for implementing turn-key construction projects in Taiwan

The turn-key construction project is implemented in Taiwan not by a single company but by a make-shift group of several companies. Hence,problems to coordinate the professional construction management (PCM) and the supervising architectural company often occur for the lack of long-term experience to work together. The various factors that affect the implementation of turn-key projects currently practiced in Taiwan are analyzed using the analytic network process (ANP). The objective is to study how the twelve key factors in the four layers of "Role assignment","Signing contract","Operational procedures" and "Losing capital investment" affect the progress of implementing the turn-key project in Taiwan. The results reveal that "Delay in payment" has the most negative influence with 15.62% weighing factor; "Latent risk" comes next with 11.14% weighing factor,and "Responsibility of construction company for project quality" is the third with 10.79% weighing factor.

Concurrent Material Selection of Natural Fibre Filament for Fused Deposition Modeling Using Integration of Analytic Hierarchy Process/Analytic Network Process

The employment of natural fibres in fused deposition modeling has raised much attention from researchers in finding a suitable formulation for the natural fibre composite filaments.Moreover,selection of suitable natural fibres for fused deposition modeling should be performed before the development of the composites.It could not be performed without identifying selection criteria that comprehend both materials and fused deposition modeling process requirements.Therefore,in this study,integration of the Analytic Hierarchy Process(AHP)/Analytic Network Process(ANP)has been introduced in selecting the natural fibres based in different clusters of selection concurrently.The selection process has been performed based on the interdependency among the selection criteria.Pairwise comparison matrices are constructed based on AHP’s hierarchical model and super matrices are constructed based on the ANP’s network model.As a result,flax fibre has ranked at the top of the selection by scored 19.5%from the overall evaluation.Flax fibre has excellent material properties and been found in various natural fibre composite applications.Further investigation is needed to study the compatibility of this fibre to be reinforced with a thermoplastic polymer matrix to develop a resultant natural fibre composite filament for fused deposition modeling.

The Selection of Dry Port Location by Analytic Network Process Model: A Case Study of Dosso-Niger

The aim of this paper is to select the best location for the construction of a dry port in Niger which is a land locked country (LLC). Niger is located in the Sahel and has a land area of 1,267,000 square kilometers [1], with the closest port being port of Cotonou in Benin. The transport corridor from Niamey to Cotonou is approximately 1036 km long [2]. It is estimated that this corridor carries about 40 percent of Niger’s overseas trade traffic [3]. In this work, the Analytic Network Process (ANP) model is used to determine the optimal location of the dry port, among three major cities: Niamey (capital city), Dosso and Gaya. From the application of this selection model, Dosso was selected as the best location for the location of the dry port, while Gaya and Niamey were placed second and third respectively. The results obtained in this work strongly confirm the decision of the government of Niger to construct a dry port in Dosso, a project that commenced in 2010 and is still in progress.

FUNDAMENTALS OF THE ANALYTIC NETWORK PROCESS- DEPENDENCE AND FEEDBACK IN DECISION-MAKING WITH A SINGLE NETWORK

The Analytic Network Process (ANP) is a multicriteria theory of measurement used to derive relative priority scales of absolute numbers from individual judgments (or from actual measurements normalized to a relative form) that also belong to a fundamental scale of absolute numbers. These judgments represent the relative influence, of one of two elements over the other in a pairwise comparison process on a third element in the system, with respect to an underlying control criterion. Through its supermatrix, whose entries are themselves matrices of column priorities, the ANP synthesizes the outcome of dependence and feedback within and between clusters of elements. The Analytic Hierarchy Process (AHP) with its independence assumptions on upper levels from lower levels and the independence of the elements in a level is a special case of the ANP. The ANP is an essential tool for articulating our understanding of a decision problem. One had to overcome the limitation of linear hierarchic structures and their mathematical consequences. This part on the ANP summarizes and illustrates the basic concepts of the ANP and shows how informed intuitive judgments can lead to real life answers that are matched by actual measurements in the real world (for example, relative dollar values) as illustrated in market share examples that rely on judgments and not on numerical data.

DECISION MAKING - THE ANALYTIC HIERARCHY AND NETWORK PROCESSES (AHP/ANP)

This is the first part of an introduction to multicriteria decision making using the Analytic Hierarchy Process (AHP) and its generalization, the Analytic Network Process (ANP). The discussion involves individual and group decisions both with the independence of the criteria from the alternatives as in the AHP and also with dependence and feedback in the entire decision structure as in the ANP. This part explains the Analytic Hierarchy Process, with examples, and presents in some detail the mathematical foundations. An exposition of the Analytic Network Process and its applications will appear in later issues of this journal.

FUNDAMENTALS OF THE ANALYTIC NETWORK PROCESS - MULTIPLE NETWORKS WITH BENEFITS,COSTS,OPPORTUNITIES AND RISKS

The general theory of the ANP enables one to deal with the benefits,opportunities,costs,andrisks(the BOCR merits)of a decision,by introducing the notion of negative priorites for C and Ralong with the rating(not comparison)of the top priority alternative synthesized for each of the fourmerits in terms of strategic criteria to enable one to combine the four B,O,C,and R values of eachalternative into a single outcome.Strategic criteria are very basic criteria individuals and groups useto assess whether they should make any of the many decisions they face in their daily operations.They do not depend on any particular decision for their priorities but are assessed in terms of the goalsand values of the individual or organization.Synthesis is made with two formulas,one multiplicativeand one additive subtractive that can give rise to negative overall priorities.This paper summarizesand illustrates basic complex decisions involving several control criteria under each of the BOCRmerits.

COMPARISON OF THE ANALYTIC NETWORK PROCESS AND THE GRAPH MODEL FOR CONFLICT RESOLUTION

A comparison of two decision analysis tools for the analysis of strategic conflicts, the Analytic Network Process （ANP） and the graph model for conflict resolution, is carried out by applying them to the China-US TV dumping conflict. Firstly, the graph model is introduced along with practical procedures for modeling and analyzing conflicts using the decision support software, GMCR Ⅱ. Next, ANP is explained, emphasizing structural features and procedures for synthesizing priorities. Then a framework for employing ANP to analyze strategic conflicts is designed and used to compare ANP to the graph model. The case study of the China-US TV dumping conflict provides a basis for the graph model and ANP to be compared; different features of the approaches are highlighted. The study shows that because of different theoretical backgrounds, ANP and the graph model for conflict analysis both provide useful information which can be combined to furnish a better understanding of a strategic conflict.

Fuzzy analytic network process(FANP)approach in formulating infrastructural decisions of sustainable manufacturing strategy

This paper explores a fuzzy analytic network process(FANP)approach in identifying the content of manufacturing strategy infrastructural decisions that integrates sustainability and classical manufacturing strategy framework with firm size component.The findings are as follows:(1)firms must ensure highquality product requirements with a make-to-order production system;(2)large firms must prioritize decentralization of functional areas but they must also provide higher systems integration of all production-related information to mobilize better communication channels;(3)large firms must employ highly skilled human resources in order to provide strong support on functional areas;and(4)small and medium enterprises must focus on product introduction to the market as a strategy priority area.The main contribution of this paper is the decision-making framework under uncertainty that holistically identifies the decisions that comprise a sustainable manufacturing strategy which may serve as guidelines in improving existing sustainable manufacturing practices or in creating new ones.

Time series prediction using wavelet process neural network

In the real world, the inputs of many complicated systems are time-varying functions or processes. In order to predict the outputs of these systems with high speed and accuracy, this paper proposes a time series prediction model based on the wavelet process neural network, and develops the corresponding learning algorithm based on the expansion of the orthogonal basis functions. The effectiveness of the proposed time series prediction model and its learning algorithm is proved by the Macke-Glass time series prediction, and the comparative prediction results indicate that the proposed time series prediction model based on the wavelet process neural network seems to perform well and appears suitable for using as a good tool to predict the highly complex nonlinear time series.

Network Aggregation Process in Multilayer Air Transportation Networks

The air transportation network, one of the common multilayer complex systems, is composed of a collection of individual airlines, and each airline corresponds to a different layer. An important question is then how many airlines are really necessary to represent the optimal structure of a multilayer air transportation system. Here we take the Chinese air transportation network （CATN） as an example to explore the nature of multiplex systems through the procedure of network aggregation. Specifically, we propose a series of structural measures to characterize the CATN from the multilayered to the aggregated network level. We show how these measures evolve during the network aggregation process in which layers are gradually merged together and find that there is an evident structural transition that happened in the aggregated network with nine randomly chosen airlines merged, where the network features and construction cost of this network are almost equivalent to those of the present CATN with twenty-two airlines under this condition. These findings could shed some light on network structure optimization and management of the Chinese air transportation system.

Application of Windows Socket Technique to Communication Process of the Train Diagram Network System Based on Client/Server Structure

This paper is focused on the technique for de si gn and realization of the process communications about the computer-aided train diagram network system. The Windows Socket technique is adopted to program for the client and the server to create system applications and solve the problems o f data transfer and data sharing in the system.

A Dual Model Watermarking Framework for Copyright Protection in Image Processing Networks

Image processing networks have gained great success in many fields,and thus the issue of copyright protection for image processing networks hasbecome a focus of attention. Model watermarking techniques are widely usedin model copyright protection, but there are two challenges: (1) designinguniversal trigger sample watermarking for different network models is stilla challenge;(2) existing methods of copyright protection based on trigger swatermarking are difficult to resist forgery attacks. In this work, we propose adual model watermarking framework for copyright protection in image processingnetworks. The trigger sample watermark is embedded in the trainingprocess of the model, which can effectively verify the model copyright. And wedesign a common method for generating trigger sample watermarks based ongenerative adversarial networks, adaptively generating trigger sample watermarksaccording to different models. The spatial watermark is embedded intothe model output. When an attacker steals model copyright using a forgedtrigger sample watermark, which can be correctly extracted to distinguishbetween the piratical and the protected model. The experiments show that theproposed framework has good performance in different image segmentationnetworks of UNET, UNET++, and FCN (fully convolutional network), andeffectively resists forgery attacks.

基于Geo Processing的网络(Network)分析研究与应用

本文主要介绍了传输网络的特点和构建方法,提出了结合ArcGIS Server Geo Processing和Flex API实现路径分析功能,实现了ArcGIS Flex API的功能扩展。

Hardwired Logic and Multithread Design in Network Processors

High-performance network processors are expected to play an important role in future high-speed routers. This paper focuses on two representative techniques needed for high-performance network processors: hardwired logic design and multithread design. Using hardwired logic, this paper compares a single-thread design with a multithread design, and proposes general models and principles to analyze the clock frequency and the resource cost for these environments. Then, two IP header processing schemes, one in single-thread mode and the other in double-thread mode, are developed using these principles and the implementation results verified the theoretical calculation.

MARKOV SKELETON PROCESS IN PERT NETWORKS

In this article, we investigate Programming Evaluation and Review Technique networks with independently and generally distributed activity durations. For any path in this network, we select all the activities related to this path such that the completion time of the sub-network （only consisting of all the related activities） is equal to the completion time of this path. We use the elapsed time as the supplementary variables and model this sub-network as a Markov skeleton process, the state space is related to the subnetwork structure. Then use the backward equation to compute the distribution of the sub-network＇s completion time, which is an important rule in project management and scheduling.

Identifying influential nodes based on graph signal processing in complex networks

Identifying influential nodes in complex networks is of both theoretical and practical importance. Existing methods identify influential nodes based on their positions in the network and assume that the nodes are homogeneous. However, node heterogeneity （i.e., different attributes such as interest, energy, age, and so on ） ubiquitously exists and needs to be taken into consideration. In this paper, we conduct an investigation into node attributes and propose a graph signal pro- cessing based centrality （GSPC） method to identify influential nodes considering both the node attributes and the network topology. We first evaluate our GSPC method using two real-world datasets. The results show that our GSPC method effectively identifies influential nodes, which correspond well with the underlying ground truth. This is compatible to the previous eigenvector centrality and principal component centrality methods under circumstances where the nodes are homogeneous. In addition, spreading analysis shows that the GSPC method has a positive effect on the spreading dynamics.

Polymerization Process and Morphology of Polyurethane/Vinyl Ester Resin Interpenetrating Polymer Networks

A series of polyurethane （PU）/vinyl ester resin （VER） simultaneous IPNs （interpenetrating polymer networks） with different component ratios and comonomers types introduced to VER were synthesized and the polymerization processes were traced by Fourier transform infrared spectroscopy （FTIR） to study the kinetics of IPNs and hydrogen bonding action within multi-component. Furthermore, the relationship of polymerization process with morphology was investigated in detail for the first time by the morphological information given by chemical action between two networks besides physical entanglement, atomic force microscope （AFM） observation and dynamic mechanical analysis （DMA）. The results indicated that the degree of hydrogen bonding （Xb,UT,%）, calculated from functional group conversional rate and fine structures gained from FT-IR spectra of two networks, were affected by PU/VER weight ratios and comonomer types of VER. The relationship of formation kinetics and morphology showed that the change of Xb,UT （%） values exhibited excellent consistency with that of phase sizes observed by AFM and detected by DMA.

Batch Process Modelling and Optimal Control Based on Neural Network Model

This paper presents several neural network based modelling, reliable optimal control, and iterative learning control methods for batch processes. In order to overcome the lack of robustness of a single neural network, bootstrap aggregated neural networks are used to build reliable data based empirical models. Apart from improving the model generalisation capability, a bootstrap aggregated neural network can also provide model prediction confidence bounds. A reliable optimal control method by incorporating model prediction confidence bounds into the optimisation objective function is presented. A neural network based iterative learning control strategy is presented to overcome the problem due to unknown disturbances and model-plant mismatches. The proposed methods are demonstrated on a simulated batch polymerisation process.

Modelling and Multi-Objective Optimal Control of Batch Processes Using Recurrent Neuro-fuzzy Networks

In this paper, the modelling and multi-objective optimal control of batch processes, using a recurrent neuro-fuzzy network, are presented. The recurrent neuro-fuzzy network, forms a ＂global＂ nonlinear long-range prediction model through the fuzzy conjunction of a number of ＂local＂ linear dynamic models. Network output is fed back to network input through one or more time delay units, which ensure that predictions from the recurrent neuro-fuzzy network are long-range. In building a recurrent neural network model, process knowledge is used initially to partition the processes non-linear characteristics into several local operating regions, and to aid in the initialisation of corresponding network weights. Process operational data is then used to train the network. Membership functions of the local regimes are identified, and local models are discovered via network training. Based on a recurrent neuro-fuzzy network model, a multi-objective optimal control policy can be obtained. The proposed technique is applied to a fed-batch reactor.