Fuzzy Modeling of Thermoplastic Composites' Melt Volume Rate

Melt volume-flow rate (MVR) is one of the most important quality indicators of composite materials, which depends on the proportion of the component materials. This paper reports the development of a low complexity fuzzy model that describes the relation between percentage amount of multiwall carbon nanotube (MWCNT), acrylonitrile-butadiene-styrene (ABS), polycarbonate (PC) and MVR of the resulting composite. The rule base was generated from a sample data set obtained from experiments by the rule base extension using default set shapes (RBE-DSS) method, and the applied fuzzy inference technique was the least squares method based fuzzy rule interpolation (LESFRI). The resulting model was validated against a separate test data set as well, and it was compared to a fuzzy model generated by a traditional commercial software tool

Fuzzy Evaluation of Student Assignment Sheets

Evaluating students" work cannot be always fully automatized. In case of narrative responses or assignment sheets only general scoring guidelines and principal aspects can be set. The teachers try to follow them; however, the final decision is mainly based on their expertise. In such situations the evaluator often feels some kind of uncertainty while categorizing an assignment. The underlying uncertainty makes the area of student work evaluation a perfect field for the development of fuzzy set based solutions. This paper introduces a fuzzy arithmetic based student assignment sheet evaluation method and a software tool that supports the easy practical application of the presented technique

Identifying Relevant Features of CSE-CIC-IDS2018 Dataset for the Development of an Intrusion Detection System

Intrusion detection systems (IDSs) are essential elements of IT systems. Their key component is a classification module that continuously evaluates some features of the network traffic and identifies possible threats. Its efficiency is greatly affected by the right selection of the features to be monitored. Therefore, the identification of a minimal set of features that are necessary to safely distinguish malicious traffic from benign traffic is indispensable in the course of the development of an IDS. This paper presents the preprocessing and feature selection workflow as well as its results in the case of the CSE-CIC-IDS2018 on AWS dataset, focusing on five attack types. To identify the relevant features, six feature selection methods were applied, and the final ranking of the features was elaborated based on their average score. Next, several subsets of the features were formed based on different ranking threshold values, and each subset was tried with five classification algorithms to determine the optimal feature set for each attack type. During the evaluation, four widely used metrics were taken into consideration.Comment: 24 page

A modified particle swarm optimization algorithm for the optimization of a fuzzy classification subsystem in a series hybrid electric vehicle

Algoritmi optimizacije temeljeni na optimizaciji roja čestica (PSO - particle swarm optimization) su jednostavne i lako primjenljive metode male računske složenosti što ih čini podesnim alatom za rješavanje opsežnih nelinearnih problema optimizacije. U radu je prikazana modificirana verzija originalne metode kombiniranjem PSO i lokalne metode pretraživanja na kraju svakog ciklusa ponavljanja. Novi se algoritam primjenjuje u zadatku optimizacije parametara podsustava fuzzy klasifikacije u serijskom hibridnom električnom vozilu u cilju smanjenja ispuštanja štetnih zagađivača. Novom se metodom uz primjenu sličnih parametara osigurava vrijednost veće prikladnosti nego bilo s originalnim PSO algoritmom ili algoritmom umjetnog imunološkog sustava zasnovanog na klonskoj selekciji (CLONALG).Particle swarm optimization (PSO) based optimization algorithms are simple and easily implementable techniques with low computational complexity, which makes them good tools for solving large-scale nonlinear optimization problems. This paper presents a modified version of the original method by combining PSO with a local search technique at the end of each iteration cycle. The new algorithm is applied for the task of parameter optimization of a fuzzy classification subsystem in a series hybrid electric vehicle (SHEV) aiming at the reduction of the harmful pollutant emission. The new method ensured a better fitness value than either the original PSO algorithm or the clonal selection based artificial immune system algorithm (CLONALG) by using similar parameters

Software implementation of automatic Fuzzy system generation and optimization

Automatic fuzzy system generation from sample data is a common task in fuzzy modeling. Here usually first an initial system is created using clustering, grid partitioning or other approaches and next, the parameters of the system are optimized based on the difference between the sample output and the output of the fuzzy system. The software being presented in this paper supports the whole process providing a wide range of parameterization opportunities. It also includes an optimization toolbox that offers five optimization algorithms, from which one represents a novel approach. The proposed new algoríthm was compared with four well-known methods using several benchmark functions and it ensured better results in case of many functions

Review on Networks Defined by Software

Heretofore, most network equipment had to be configured individually by connecting manually into it. This approach is time consuming for large networks and prone to human errors. The Software Defined Networking paradigm defines several standards and protocols in order to read the network states and act on its configuration from distant servers. These protocols authorize a reconfiguration of the network in a centralized way by the use of transactions that acts on one or more devices. In general, transactions are implemented as APIs for use by third- party programs and on software components separate from the orchestrator called controllers for more modularity. Nowadays, SDN receives a lot of interest from researchers and manufacturers aiming for the modernization of the networks especially with the emergence of the loT, 5G and WAN technologies

Fuzzy logic in automotive engineering

Nowadays, automotive industry gains more and more importance due to the innovative technology use in design and manufacturing. This branch consists of several manufacturer and supplier companies. The aim of each car manufacturer company is to provide the perfect driving experience for the customers. Fuzzy logic aids to design guality products for increasing the comfort of drivers. In our study, we present a variety of automotive applications, which use fuzzy logic