Passivity‐based state synchronization of homogeneous multiagent systems via static protocol in the presence of input saturation

This paper studies semiglobal and global state synchronization of homogeneous multiagent systems with partial‐state coupling (ie, agents are coupled through part of their states) via a static protocol. We consider 2 classes of agents, ie, G‐passive and G‐passifiable via input feedforward, which are subjected to input saturation. The proposed static protocol is purely decentralized, ie, without an additional channel for the exchange of controller states. For semiglobal synchronization, a static protocol is designed for an a priori given set of network graphs with a directed spanning tree. In other words, the static protocol only needs rough information on the network graph, ie, a lower bound for the real part and an upper bound for the modulus, of the nonzero eigenvalues of the corresponding Laplacian matrix. Whereas for global synchronization, only strongly connected and detailed balanced network graphs are considered. In this case, for G‐passive agents, the static protocol does not need any network information, whereas for G‐passifiable agents via input feedforward, the static protocol only needs an upper bound for the modulus of the eigenvalues of the corresponding Laplacian matrix.     

An Integrated Artificial Neural Network Fuzzy C-Means-Normalization Algorithm for performance assessment of decision-making units: The cases of auto industry and power plant

Efficiency frontier analysis has been an important approach of evaluating firms’ performance in private and public sectors. There have been many efficiency frontier analysis methods reported in the literature. However, the assumptions made for each of these methods are restrictive. Each of these methodologies has its strength as well as major limitations. This study proposes two non-parametric efficiency frontier analysis sub-algorithms based on (1) Artificial Neural Network (ANN) technique and (2) ANN and Fuzzy C-Means for measuring efficiency as a complementary tool for the common techniques of the efficiency studies in the previous studies. Normal probability plot is used to find the outliers and select from these two methods. The proposed computational algorithms are able to find a stochastic frontier based on a set of input–output observational data and do not require explicit assumptions about the functional structure of the stochastic frontier. In these algorithms, for calculating the efficiency scores, a similar approach to econometric methods has been used. Moreover, the effect of the return to scale of decision-making unit (DMU) on its efficiency is included and the unit used for the correction is selected by notice of its scale (under constant return to scale assumption). Also in the second algorithm, for increasing DMUs’ homogeneousness, Fuzzy C-Means method is used to cluster DMUs. Two examples using real data are presented for illustrative purposes. First example which deals with power generation sector shows the superiority of Algorithm 2 while the second example dealing auto industries of various developed countries shows the superiority of Algorithm 1. Overall, we find that the proposed integrated algorithm based on ANN, Fuzzy C-Means and Normalization approach provides more robust results and identifies more efficient units than the conventional methods since better performance patterns are explored.     

An integrated artificial neural network-genetic algorithm clustering ensemble for performance assessment of decision making units

This study proposes a non-parametric efficiency frontier analysis method based on artificial neural network (ANN) and genetic algorithm clustering ensemble (GACE) for measuring efficiency as a complementary tool for the common techniques of the efficiency studies in the previous studies. The proposed ANN GA algorithm is able to find a stochastic frontier based on a set of input–output observational data and do not require explicit assumptions about the functional structure of the stochastic frontier. Furthermore, it uses a similar approach to econometric methods for calculating the efficiency scores. Moreover, the effect of the return to scale of decision making unit (DMU) on its efficiency is included and the unit used for the correction is selected based on its scale (under constant return to scale assumption). Also, in this algorithm, GA is used to cluster DMUs to increase DMUs’ homogeneousness. It should be noted that data envelopment analysis (DEA) is sensitive to the presence of the outliers and statistical noise. It is also not capable of performing prediction and forecasting. This is shown by two examples related to outlier situations. However, the proposed algorithm is capable of handling outliers and noise and DEA is used as a benchmark to show advantages of the proposed algorithm. Also, the proposed algorithm and conventional algorithm are compared in viewpoint of DEA through statistical t-test. The proposed approach is applied to a set of actual conventional power plants to show its applicability and superiority.     

A semantic-based knowledge fusion model for solution-oriented information network development: a case study in intrusion detection field

Building information networks using semantic based techniques to avoid tedious work and to achieve high efficiency has been a long-term goal in the information management world. A great volume of research has focused on developing large scale information networks for general domains to pursue the comprehensiveness and integrity of the information. However, constructing customised information networks containing subject-specific knowledge has been neglected. Such research can potentially return high value in terms of both theoretical and practical contribution. In this paper, a new type of network, solution-oriented information network, is coined that includes research problems and proposed techniques as nodes, and the relationship between them. A lightweight Semantic-based Knowledge Fusion Model (SKFM) is proposed leveraging the power of Natural Language Processing (NLP) and Crowdsourcing to construct the proposed information networks using academic papers (knowledge) from Scopus. SKFM relies on NLP in terms of automatic components while Crowdsourcing is initiated when uncertain cases arise. Applying the NLP technique assists to develop a semi-automatic knowledge fusion method for saving effort and time in extracting information from academic papers. Leveraging human power in uncertain cases is to make sure the essential concepts for developing the information networks are extracted reliably and connected correctly. SKFM shows a theoretical contribution in terms of lightweight knowledge extraction and reconstruction framework, as well as practical value by providing solutions proposed in academic papers to address corresponding research issues in subject-specific areas. Experiments have been implemented which have shown promising results. In the research field of intrusion detection, the information of attack types and proposed solutions has been extracted and integrated in a graphic manner with high accuracy and efficiency.     

Improving the quality of nanocrystalline MgAl2O4 spinel coating on graphite by a prior oxidation treatment on the graphite surface

This paper aims to report the effect of surface oxidation treatment on graphite flakes and its effect on the improvement of MgAl₂O₄ spinel coating developed by sol–gel citrate process. The graphite surface was oxidized by hydrogen peroxide and the coating was subsequently applied. The coating structure, water-wettability and oxidation resistance of coated samples were taken as criteria to evaluate the coating integrity. It was clarified that the oxidation treatment developed hydrophilic functional groups on graphite surface. This helped the formation of an even and smooth MgAl₂O₄ spinel coating texture on graphite flakes. The water-wettability and oxidation resistance were also found to be improved significantly compared to non-treated samples. The results were supported by thermogravimetric analysis, contact angle and zeta-potential measurements.     

Analysing academic paper ranking algorithms using test data and benchmarks: an investigation

Research on academic paper ranking has received great attention in recent years, and many algorithms have been proposed to automatically assess a large number of papers for this purpose. How to evaluate or analyse the performance of these ranking algorithms becomes an open research question. Theoretically, evaluation of an algorithm requires to compare its ranking result against a ground truth paper list. However, such ground truth does not exist in the field of scholarly ranking due to the fact that there does not and will not exist an absolutely unbiased, objective, and unified standard to formulate the impact of papers. Therefore, in practice researchers evaluate or analyse their proposed ranking algorithms by different methods, such as using domain expert decisions (test data) and comparing against predefined ranking benchmarks. The question is whether using different methods leads to different analysis results, and if so, how should we analyse the performance of the ranking algorithms? To answer these questions, this study compares among test data and different citation-based benchmarks by examining their relationships and assessing the effect of the method choices on their analysis results. The results of our experiments show that there does exist difference in analysis results when employing test data and different benchmarks, and relying exclusively on one benchmark or test data may bring inadequate analysis results. In addition, a guideline on how to conduct a comprehensive analysis using multiple benchmarks from different perspectives is summarised, which can help provide a systematic understanding and profile of the analysed algorithms.

     

Stabilization of linear system with input saturation and unknown constant delays

In this paper, we study the stabilization of linear critically unstable systems subject to input saturation and multiple unknown input delays. We find tight upper bounds for delays which are inversely proportional to the maximal magnitude of open-loop eigenvalues on the imaginary axis. For delays satisfying these upper bounds, linear low-gain state and finite dimensional dynamic measurement feedbacks are constructed to solve the semi-global stabilization problem. The effectiveness of the proposed design is illustrated by an example.     

Time-scale structure assignment in linear multivariable systems using high-gain feedback

A systematic design method for assigning a required time-scale structure to a given multivariable system is developed. The time-scale structure assignment is carried out using high-gain feedback of either the state or the output. The method developed decomposes a given multivariable system into several single-input single-output subsystems, each of which is designed separately. A standard method of design for single-input single-output systems is developed, and this forms a building block for the multivariable system design. The state feedback design depends only on the stage sequence of the given system, while the output feedback design depends only on the set of integers that specifies the order of infinite zeros. In this way, our design is made robust with respect to certain parameter uncertainties. High-gain parameter(s) can be adjusted on line, and this provides a tool for the fine tuning of the desired performance characteristics of a control system.