ISO/EPC Addressing Methods to Support Supply Chain in the Internet of Things

RFID systems are among the major infrastructures of the Internet of Things, which follow ISO and EPC standards. In addition, ISO standard constitutes the main layers of supply chain, and many RFID systems benefit from ISO standard for different purposes. In this paper, we tried to introduce addressing systems based on ISO standards, through which the range of things connected to the Internet of Things will grow. Our proposed methods are addressing methods which can be applied to both ISO and EPC standards. The proposed methods are simple, hierarchical, and low cost implementation. In addition, the presented methods enhance interoperability among RFIDs, and also enjoys a high scalability, since it well covers all of EPC schemes and ISO supply chain standards. Further, by benefiting from a new algorithm for long EPCs known as selection algorithm, they can significantly facilitate and accelerate the operation of address mapping.Comment: arXiv admin note: text overlap with arXiv:1807.0217

An incremental approach to genetic algorithms based classification

Incremental learning has been widely addressed in the machine learning literature to cope with learning tasks where the learning environment is ever changing or training samples become available over time. However, most research work explores incremental learning with statistical algorithms or neural networks, rather than evolutionary algorithms. The work in this paper employs genetic algorithms (GAs) as basic learning algorithms for incremental learning within one or more classifier agents in a multi-agent environment. Four new approaches with different initialization schemes are proposed. They keep the old solutions and use an “integration” operation to integrate them with new elements to accommodate new attributes, while biased mutation and crossover operations are adopted to further evolve a reinforced solution. The simulation results on benchmark classification data sets show that the proposed approaches can deal with the arrival of new input attributes and integrate them with the original input space. It is also shown that the proposed approaches can be successfully used for incremental learning and improve classification rates as compared to the retraining GA. Possible applications for continuous incremental training and feature selection are also discussed

Class decomposition for GA-based classifier agents – A Pitt approach

Incremental learning has been widely addressed in the machine learning literature to cope with learning tasks where the learning environment is ever changing or training samples become available over time. However, most research work explores incremental learning with statistical algorithms or neural networks, rather than evolutionary algorithms. The work in this paper employs genetic algorithms (GAs) as basic learning algorithms for incremental learning within one or more classifier agents in a multi-agent environment. Four new approaches with different initialization schemes are proposed. They keep the old solutions and use an “integration” operation to integrate them with new elements to accommodate new attributes, while biased mutation and crossover operations are adopted to further evolve a reinforced solution. The simulation results on benchmark classification data sets show that the proposed approaches can deal with the arrival of new input attributes and integrate them with the original input space. It is also shown that the proposed approaches can be successfully used for incremental learning and improve classification rates as compared to the retraining GA. Possible applications for continuous incremental training and feature selection are also discussed

Foam: A General-Purpose Cellular Monte Carlo Event Generator

A general purpose, self-adapting, Monte Carlo (MC) event generator (simulator) is described. The high efficiency of the MC, that is small maximum weight or variance of the MC weight is achieved by means of dividing the integration domain into small cells. The cells can be $n$-dimensional simplices, hyperrectangles or Cartesian product of them. The grid of cells, called ``foam'', is produced in the process of the binary split of the cells. The choice of the next cell to be divided and the position/direction of the division hyper-plane is driven by the algorithm which optimizes the ratio of the maximum weight to the average weight or (optionally) the total variance. The algorithm is able to deal, in principle, with an arbitrary pattern of the singularities in the distribution. As any MC generator, it can also be used for the MC integration. With the typical personal computer CPU, the program is able to perform adaptive integration/simulation at relatively small number of dimensions ($\leq 16$). With the continuing progress in the CPU power, this limit will get inevitably shifted to ever higher dimensions. {\tt Foam} is aimed (and already tested) as a component in the MC event generators for the high energy physics experiments. A few simple examples of the related applications are presented. {\tt Foam} is written in fully object-oriented style, in the C++ language. Two other versions with a slightly limited functionality, are available in the Fortran77 language. The source codes are available from http://jadach.home.cern.ch/jadach

Ontology-based semantic interpretation of cylindricity specification in the next-generation GPS

Cylindricity specification is one of the most important geometrical specifications in geometrical product development. This specification can be referenced from the rules and examples in tolerance standards and technical handbooks in practice. These rules and examples are described in the form of natural language, which may cause ambiguities since different designers may have different understandings on a rule or an example. To address the ambiguous problem, a categorical data model of cylindricity specification in the next-generation Geometrical Product Specifications (GPS) was proposed at the University of Huddersfield. The modeling language used in the categorical data model is category language. Even though category language can develop a syntactically correct data model, it is difficult to interpret the semantics of the cylindricity specification explicitly. This paper proposes an ontology-based approach to interpret the semantics of cylindricity specification on the basis of the categorical data model. A scheme for translating the category language to the OWL 2 Web Ontology Language (OWL 2) is presented in this approach. Through such a scheme, the categorical data model is translated into a semantically enriched model, i.e. an OWL 2 ontology for cylindricity specification. This ontology can interpret the semantics of cylindricity specification explicitly. As the benefits of such semantic interpretation, consistency checking, inference procedures and semantic queries can be performed on the OWL 2 ontology. The proposed approach could be easily extended to support the semantic interpretations of other kinds of geometrical specifications

Incremental multiple objective genetic algorithms

This paper presents a new genetic algorithm approach to multi-objective optimization problemsIncremental Multiple Objective Genetic Algorithms (IMOGA). Different from conventional MOGA methods, it takes each objective into consideration incrementally. The whole evolution is divided into as many phases as the number of objectives, and one more objective is considered in each phase. Each phase is composed of two stages: first, an independent population is evolved to optimize one specific objective; second, the better-performing individuals from the evolved single-objective population and the multi-objective population evolved in the last phase are joined together by the operation of integration. The resulting population then becomes an initial multi-objective population, to which a multi-objective evolution based on the incremented objective set is applied. The experiment results show that, in most problems, the performance of IMOGA is better than that of three other MOGAs, NSGA-II, SPEA and PAES. IMOGA can find more solutions during the same time span, and the quality of solutions is better