Rough set feature selection and rule induction for prediction of malignancy degree in brain glioma

X. Wang, J. Yang, R. Jensen and X. Liu, 'Rough Set Feature Selection and Rule Induction for Prediction of Malignancy Degree in Brain Glioma,' Computer Methods and Programs in Biomedicine, vol. 83, no. 2, pp. 147-156, 2006.

Model Selection in SVMs Using Differential Evolution

To improve the performance of classification using Support Vector Machines (SVMs) while reducing the model selection time, this paper introduces Differential Evolution, a heuristic method for model selection in two-class SVMs with a RBF kernel. The model selection method and related tuning algorithm are both presented. Experimental results from application to a selection of benchmark datasets for SVMs show that this method can produce an optimized classification in less time and with higher accuracy than a classical grid search. Comparison with a Particle Swarm Optimization (PSO) based alternative is also included.

Directional Modulation Transmitter Synthesis using Particle Swarm Optimization

Phased DM transmitter array synthesis using particle swarm optimization (PSO) is presented in this paper. The PSO algorithm is described in details with key parameters provided for 1-D four-element half-wavelength spaced QPSK DM array synthesis. A DM transmitter array for boresight and 30º direction secure communications are taken as examples to validate the proposed synthesis approach. The optimization process exhibits good convergence performance and solution quality.

Advanced Modelling and Optimization of Infared Oven in Injection Stretch Blow-moulding for Energy Saving

In the production process of polyethylene terephthalate (PET) bottles, the initial temperature of preforms plays a central role on the final thickness, intensity and other structural properties of the bottles. Also, the difference between inside and outside temperature profiles could make a significant impact on the final product quality. The preforms are preheated by infrared heating oven system which is often an open loop system and relies heavily on trial and error approach to adjust the lamp power settings. In this paper, a radial basis function (RBF) neural network model, optimized by a two-stage selection (TSS) algorithm combined with partial swarm optimization (PSO), is developed to model the nonlinear relations between the lamp power settings and the output temperature profile of PET bottles. Then an improved PSO method for lamp setting adjustment using the above model is presented. Simulation results based on experimental data confirm the effectiveness of the modelling and optimization method.

Multimedia Resource Allocation in mmWave 5G Networks

The 5G network infrastructure is driven by the evolution of today's most demanding applications. Already, multimedia applications such as on-demand HD video and IPTV require gigabit- per-second throughput and low delay, while future technologies include ultra HDTV and machine-to-machine communication. Mm-Wave technologies such as IEEE 802.15.3c and IEEE 802.11ad are ideal candidates to deliver high throughput to multiple users demanding differentiated QoS. Optimization is often used as a methodology to meet throughput and delay constraints. However, traditional optimization techniques are not suited to a mixed set of multimedia applications. Particle swarm optimization (PSO) is shown as a promising technique in this context. Channel-time allocation PSO (CTA-PSO) is successfully shown here to allocate resource even in scenarios where blockage of the 60 GHz signal poses significant challenges.

DFIG Machine Design for Maximizing Power Output Based on Surrogate Optimization Algorithm

This paper presents a surrogate-model based optimization of a doubly-fed induction generator (DFIG) machine winding design for maximizing power yield. Based on site-specific wind profile data and the machine’s previous operational performance, the DFIG’s stator and rotor windings are optimized to match the maximum efficiency with operating conditions for rewinding purposes. The particle swarm optimization (PSO)-based surrogate optimization techniques are used in conjunction with the finite element method (FEM) to optimize the machine design utilizing the limited available information for the site-specific wind profile and generator operating conditions. A response surface method in the surrogate model is developed to formulate the design objectives and constraints. Besides, the machine tests and efficiency calculations follow IEEE standard 112-B. Numerical and experimental results validate the effectiveness of the proposed technologies.

Building information modeling based building design optimization for sustainability

Environmental problems, especially climate change, have become a serious global issue waiting for people to solve. In the construction industry, the concept of sustainable building is developing to reduce greenhouse gas emissions. In this study, a building information modeling (BIM) based building design optimization method is proposed to facilitate designers to optimize their designs and improve buildings’ sustainability. A revised particle swarm optimization (PSO) algorithm is applied to search for the trade-off between life cycle costs (LCC) and life cycle carbon emissions (LCCE) of building designs. In order tovalidate the effectiveness and efficiency of this method, a case study of an office building is conducted in Hong Kong. The result of the case study shows that this method can enlarge the searching space for optimal design solutions and shorten the processing time for optimal design results, which is really helpful for designers to deliver an economic and environmental friendly design scheme.

Cappadocia: where Greek prepositions come to die

The Cappadocian variety of Ulaghátsh is unique among the Greek-speaking world in having lost the inherited preposition ‘se’. The innovation is found with both locative and allative uses and has af-ected both syntactic contexts in which ‘se’ was originally found, that is, as a simple preposition (1) and as the left-occurring member of circumpositions of the type ‘se’ + NP + spatial adverb (2). (1) a. tránse ci [to meidán] en ávʝa see.PST.3SG COMP ART.DEF.SG.ACC yard.SG.ACC COP.3 game.PL.NOM ‘he saw that in the yard is some game’ (Dawkins 1916: 348) b. ta erʝó da qardáʃa évɣan [to qonáq] ART.DEF.PL.NOM two ART.DEF.PL.NOM friend.PL.NOM ascend.PST.3PL ART.DEF.SG.ACC house.SG.ACC ‘the two friends went up to the house’ (Dawkins 1916: 354) (2) émi [ta qonáca mésa], kiríʃde [to ʝasdɯ́q píso] enter.PST.3SG ART.DEF.PL.NOM house.PL.ACC inside hide.PST.3SG. ART.DEF.SG.ACC cushion.SG.ACC behind ‘he went into the houses and hid behind the cushions’ (Dawkins 1916: 348) In this paper, we set out to provide (a) a diachronic account of the loss of ‘se’ in Asia Minor Greek, and (b) a synchronic analysis of its ramifications for the encoding of the semantic and grammatical functions it had prior to its loss. The diachronic development of ‘se’ is traced by comparing the Ulaghátsh data with those obtained from Cappadocian varieties that have neither lost it nor do they show signs of losing it and, crucially, also from varieties in which ‘se’ is in the process of being lost. The comparative analysis shows that the loss first became manifest in circumpositions in which ‘se’ was preposed to the complement to which in turn a wide range of adverbs expressing topological relations were postposed (émi sa qonáca mésa > émi ta qonáca mésa). This finding is accounted for in terms of Sinha and Kuteva’s (1995) distributed spatial semantics framework, which accepts that the elements involved in the constructions under investigation—the verb (émi), ‘se’ and the spatial adverb (mésa)—all contribute to the expression of the spatial relational meaning but with differences in weighting. Of the three, ‘eis’ made the most minimal contribution, the bulk of it being distributed over the verb and the adverb. This allowed for it to be optionally dropped from circumpositions, a stage attested in Phlo-tá Cappadocian and Silliot, and to be later completely abandoned, originally in allative and subsequently in locative contexts (earlier: évɣan so qonáq > évɣan to qonáq; later: so meidán en ávʝa > to meidán en ávʝa). The earlier loss in allative contexts is also dealt with in distributed semantics terms as verbs of motion such as έβγαν are semantically more loaded than vacuous verbs like the copula and therefore the preposition could be left out in the former context more easily than in the latter. The analysis also addresses the possibility that the loss of ‘se’ may ultimately originate in substandard forms of Medieval Greek, which according to Tachibana (1994) displayed SPATIAL ADVERB + NP constructions. Applying the semantic map model (Croft 2003, Haspelmath 2003), the synchronic analysis of the varieties that retain ‘se’ reveals that—like many other allative markers crosslinguistically—it displays a pattern of multifunctionality in expressing nine different functions (among others allative, locative, recipient, addressee, experiencer), which can be mapped against four domains, viz. the spatiotemporal, the social, the mental and the logicotextual (cf. Rice & Kabata 2007). In Ulaghátsh Cappadocian, none of these functions is overtly marked as such. In cases like (1), the intended spatial relational meaning is arrived at through the combination of the syntax and the inherent semantics of the verb and the zero-marked NP as well as from the context. In environments of the type exemplified by (2), the adverb contributes further to the correct interpretation. The analysis additionally shows that, despite the loss of ‘se’, Ulaghátsh patterns with all other Cappadocian varieties in one important aspect: Goal and Location are expressed similarly (by zero in Ulaghátsh, by ‘se’ in the other varieties) whereas Source is being kept distinct (expressed by ‘apó’ in all varieties). Goal-Location polysemy is very common across the world’s languages and, most crucially, prevails over other possible polysemies in the tripartite distinction Source—Location—Goal (Lestrade 2010, Nikitina 2009). Taking into account this empirical observation, our findings suggest that the reor-anisation of spatial systems can have a local effect—in our case the loss of a member of the prepositional paradigm—but will keep the original global picture intact, thus conforming to crosslinguistically robust tendencies.  References Croft, W. 2001. Radical Construction Grammar: Syntactic Theory in Typological Perspective. Oxford: Oxford University Press. Dawkins, R. M. 1916. Modern Greek in Asia Minor: A Study of the Dialects of Sílli, Cappadocia and Phárasa with Grammar, Texts, Translations and Glossary. Cambridge: Cambridge University Press. Haspelmath, M. 2003. The geometry of grammatical meaning: semantic maps and cross-linguistic comparison. In M. Tomasello (Ed.), The New Psychology of Language, Volume 2. New York: Erlbaum, 211–243. Lestrade, S. 2010. The Space of Case. Doctoral dissertation. Radboud University Nijmegen. Nikitina, T. 2009. Subcategorization pattern and lexical meaning of motion verbs: a study of the source/goal ambiguity. Linguistics 47, 1113–1141. Rice, S. & K. Kabata. 2007. Cross-linguistic grammaticalization patterns of the allative. Linguistic Typology 11, 451–514. Sinha, C. & T. Kuteva. 1995. Distributed spatial semantics. Nordic Journal of Linguistics 18:2, 167–199. Tachibana, T. 1994. Syntactic structure of spatial expressions in the “Late Byzantine Prose Alexander Romance”. Propylaia 6, 35–51.

Long-term price range forecast applied to risk management using regression models

Long-term contractual decisions are the basis of an efficient risk management. However those types of decisions have to be supported with a robust price forecast methodology. This paper reports a different approach for long-term price forecast which tries to give answers to that need. Making use of regression models, the proposed methodology has as main objective to find the maximum and a minimum Market Clearing Price (MCP) for a specific programming period, and with a desired confidence level α. Due to the problem complexity, the meta-heuristic Particle Swarm Optimization (PSO) was used to find the best regression parameters and the results compared with the obtained by using a Genetic Algorithm (GA). To validate these models, results from realistic data are presented and discussed in detail.

Particle swarm optimization based approaches to vehicle-to-grid scheduling

This paper addresses the problem of energy resources management using modern metaheuristics approaches, namely Particle Swarm Optimization (PSO), New Particle Swarm Optimization (NPSO) and Evolutionary Particle Swarm Optimization (EPSO). The addressed problem in this research paper is intended for aggregators’ use operating in a smart grid context, dealing with Distributed Generation (DG), and gridable vehicles intelligently managed on a multi-period basis according to its users’ profiles and requirements. The aggregator can also purchase additional energy from external suppliers. The paper includes a case study considering a 30 kV distribution network with one substation, 180 buses and 90 load points. The distribution network in the case study considers intense penetration of DG, including 116 units from several technologies, and one external supplier. A scenario of 6000 EVs for the given network is simulated during 24 periods, corresponding to one day. The results of the application of the PSO approaches to this case study are discussed deep in the paper.

A decision-support system based on particle swarm optimization for multiperiod hedging in electricity markets

This paper proposes a particle swarm optimization (PSO) approach to support electricity producers for multiperiod optimal contract allocation. The producer risk preference is stated by a utility function (U) expressing the tradeoff between the expectation and variance of the return. Variance estimation and expected return are based on a forecasted scenario interval determined by a price range forecasting model developed by the authors. A certain confidence level is associated to each forecasted scenario interval. The proposed model makes use of contracts with physical (spot and forward) and financial (options) settlement. PSO performance was evaluated by comparing it with a genetic algorithm-based approach. This model can be used by producers in deregulated electricity markets but can easily be adapted to load serving entities and retailers. Moreover, it can easily be adapted to the use of other type of contracts.

Distributed energy resource short-term scheduling using signaled particle swarm optimization

Distributed Energy Resources (DER) scheduling in smart grids presents a new challenge to system operators. The increase of new resources, such as storage systems and demand response programs, results in additional computational efforts for optimization problems. On the other hand, since natural resources, such as wind and sun, can only be precisely forecasted with small anticipation, short-term scheduling is especially relevant requiring a very good performance on large dimension problems. Traditional techniques such as Mixed-Integer Non-Linear Programming (MINLP) do not cope well with large scale problems. This type of problems can be appropriately addressed by metaheuristics approaches. This paper proposes a new methodology called Signaled Particle Swarm Optimization (SiPSO) to address the energy resources management problem in the scope of smart grids, with intensive use of DER. The proposed methodology’s performance is illustrated by a case study with 99 distributed generators, 208 loads, and 27 storage units. The results are compared with those obtained in other methodologies, namely MINLP, Genetic Algorithm, original Particle Swarm Optimization (PSO), Evolutionary PSO, and New PSO. SiPSO performance is superior to the other tested PSO variants, demonstrating its adequacy to solve large dimension problems which require a decision in a short period of time.

A short-term risk management tool applied to OMEL electricity market using particle swarm optimization

Short-term risk management is highly dependent on long-term contractual decisions previously established; risk aversion factor of the agent and short-term price forecast accuracy. Trying to give answers to that problem, this paper provides a different approach for short-term risk management on electricity markets. Based on long-term contractual decisions and making use of a price range forecast method developed by the authors, the short-term risk management tool presented here has as main concern to find the optimal spot market strategies that a producer should have for a specific day in function of his risk aversion factor, with the objective to maximize the profits and simultaneously to practice the hedge against price market volatility. Due to the complexity of the optimization problem, the authors make use of Particle Swarm Optimization (PSO) to find the optimal solution. Results from realistic data, namely from OMEL electricity market, are presented and discussed in detail.

A long-term risk management tool for electricity markets using swarm intelligence

This paper addresses the optimal involvement in derivatives electricity markets of a power producer to hedge against the pool price volatility. To achieve this aim, a swarm intelligence meta-heuristic optimization technique for long-term risk management tool is proposed. This tool investigates the long-term opportunities for risk hedging available for electric power producers through the use of contracts with physical (spot and forward contracts) and financial (options contracts) settlement. The producer risk preference is formulated as a utility function (U) expressing the trade-off between the expectation and the variance of the return. Variance of return and the expectation are based on a forecasted scenario interval determined by a long-term price range forecasting model. This model also makes use of particle swarm optimization (PSO) to find the best parameters allow to achieve better forecasting results. On the other hand, the price estimation depends on load forecasting. This work also presents a regressive long-term load forecast model that make use of PSO to find the best parameters as well as in price estimation. The PSO technique performance has been evaluated by comparison with a Genetic Algorithm (GA) based approach. A case study is presented and the results are discussed taking into account the real price and load historical data from mainland Spanish electricity market demonstrating the effectiveness of the methodology handling this type of problems. Finally, conclusions are dully drawn.

Application-specific modified particle swarm optimization for energy resource scheduling considering vehicle-to-grid

This paper presents a modified Particle Swarm Optimization (PSO) methodology to solve the problem of energy resources management with high penetration of distributed generation and Electric Vehicles (EVs) with gridable capability (V2G). The objective of the day-ahead scheduling problem in this work is to minimize operation costs, namely energy costs, regarding he management of these resources in the smart grid context. The modifications applied to the PSO aimed to improve its adequacy to solve the mentioned problem. The proposed Application Specific Modified Particle Swarm Optimization (ASMPSO) includes an intelligent mechanism to adjust velocity limits during the search process, as well as self-parameterization of PSO parameters making it more user-independent. It presents better robustness and convergence characteristics compared with the tested PSO variants as well as better constraint handling. This enables its use for addressing real world large-scale problems in much shorter times than the deterministic methods, providing system operators with adequate decision support and achieving efficient resource scheduling, even when a significant number of alternative scenarios should be considered. The paper includes two realistic case studies with different penetration of gridable vehicles (1000 and 2000). The proposed methodology is about 2600 times faster than Mixed-Integer Non-Linear Programming (MINLP) reference technique, reducing the time required from 25 h to 36 s for the scenario with 2000 vehicles, with about one percent of difference in the objective function cost value.