Multi-objective reactive power market clearing in competitive electricity market using HFMOEA

This paper presents an application of a hybrid fuzzy multi-objective evolutionary algorithm (HFMOEA) for solving a highly constraint, mixed integer type, complex multi-objective reactive power market clearing (RPMC) problem for the competitive electricity market environment. In HFMOEA based multi-objective optimization approach, based on the output of a fuzzy logic controller crossover and mutation probabilities are varied dynamically. It enhances stochastic search capabilities of HFMOEA. In multi-objective RPMC optimization framework, two objective functions namely the total payment function (TPF) for reactive power support from generators and synchronous condensers and the total real transmission loss (TRTL) are minimized simultaneously for clearing the reactive power market. The proposed HFMOEA based multi-objective RPMC scheme is tested on a standard IEEE 24 bus reliability test system and its performance is compared with five other multi-objective evolutionary techniques such as MOPBIL, NSGA-II, UPS-EMOA and SPEA-2 and a new extended form of NSGA (ENSGA-II). Applying all these six evolutionary techniques, a detailed statistical analysis using T-test and boxplots is carried out on three performance metrics (spacing, spread and hypervolume) data for RPMC problem. The obtained simulation results confirm the overall superiority of HFMOEA to generate better Pareto-optimal solutions with higher convergence rate as compared to above mentioned algorithms. Further, TPF and TRTL values corresponding to the best compromise solutions are obtained using said multi-objective evolutionary techniques. These values are compared with one another to take better market clearing decisions in competitive electricity environment.     

Liquid flow through a diverging microchannel

In this work, experiments and three-dimensional numerical calculations of fluid flow through diverging microchannels were carried out with the aim of bringing out differences between flow in uniform and nonuniform passages. Deionized water was used as the working fluid in the experiments where the effects of mass flow rate (8.33 × 10^?6 to 8.33 × 10^?5 kg/s), microchannel hydraulic diameter (118–177 µm), length (10–30 mm) and divergence angle (4°–16°) on pressure drop were studied. The results are analyzed in detail with the help of numerical data. The pressure drop exhibits a linear dependence on the mass flow rate, whereas it is inversely proportional to the divergence angle and square of the hydraulic diameter. The pressure drop increases anomalously at 16°, suggesting that flow reversal occurs between 12° and 16°, which agrees with the corresponding value at the conventional scale. For the purpose of predicting pressure drop using straight microchannel theory, an equivalent hydraulic diameter was defined. It is observed that the equivalent hydraulic diameter, located at one-third of the diverging microchannel length from the inlet, becomes mostly independent of the mass flow rate, microchannel hydraulic diameter, length and divergence angle. The pressure drop for a diverging microchannel becomes equal to an equivalent hydraulic diameter uniform cross-section microchannel, suggesting that conventional correlations for straight microchannels can also be applied to diverging microchannels. The data presented in this work are of fundamental importance and can help in optimization of diffuser design used for example in valveless micropumps.     

On semantic annotation of decision models

The growth of service sector in recent years has led to renewed research interests in the design and management of service systems. Decision support systems (DSS) play an important role in supporting this endeavor, through management of organizational resources such as models and data, thus forming the “back stage” of service systems. In this article, we identify the requirements for semantically annotating decision models and propose a model representation scheme, termed Semantically Annotated Structure Modeling Markup Language (SA-SMML) that extends Structure Modeling Markup Language (SMML) by incorporating mechanisms for linking semantic models such as ontologies that represent problem domain knowledge concepts. This model representation format is also amenable to a scalable Service-Oriented Architecture (SOA) for managing models in distributed environments. The proposed model representation technique leverages recent advances in the areas of semantic web, and semantic web services. Along with design considerations, we demonstrate the utility of this representation format with an illustrative usage scenarios with a particular emphasis on model discovery and composition in a distributed environment.     

Robust real-time unusual event detection using multiple fixed-location monitors

We present a novel algorithm for detection of certain types of unusual events. The algorithm is based on multiple local monitors which collect low-level statistics. Each local monitor produces an alert if its current measurement is unusual, and these alerts are integrated to a final decision regarding the existence of an unusual event. Our algorithm satisfies a set of requirements that are critical for successful deployment of any large-scale surveillance system. In particular it requires a minimal setup (taking only a few minutes) and is fully automatic afterwards. Since it is not based on objects' tracks, it is robust and works well in crowded scenes where tracking-based algorithms are likely to fail. The algorithm is effective as soon as sufficient low-level observations representing the routine activity have been collected, which usually happens after a few minutes. Our algorithm runs in realtime. It was tested on a variety of real-life crowded scenes. A ground-truth was extracted for these scenes, with respect to which detection and false-alarm rates are reported.     

Optimizing one-shot learning with binary synapses

A network of excitatory synapses trained with a conservative version of Hebbian learning is used as a model for recognizing the familiarity of thousands of once-seen stimuli from those never seen before. Such networks were initially proposed for modeling memory retrieval (selective delay activity). We show that the same framework allows the incorporation of both familiarity recognition and memory retrieval, and estimate the network's capacity. In the case of binary neurons, we extend the analysis of Amit and Fusi (1994) to obtain capacity limits based on computations of signal-to-noise ratio of the field difference between selective and non-selective neurons of learned signals. We show that with fast learning (potentiation probability approximately 1), the most recently learned patterns can be retrieved in working memory (selective delay activity). A much higher number of once-seen learned patterns elicit a realistic familiarity signal in the presence of an external field. With potentiation probability much less than 1 (slow learning), memory retrieval disappears, whereas familiarity recognition capacity is maintained at a similarly high level. This analysis is corroborated in simulations. For analog neurons, where such analysis is more difficult, we simplify the capacity analysis by studying the excess number of potentiated synapses above the steady-state distribution. In this framework, we derive the optimal constraint between potentiation and depression probabilities that maximizes the capacity.     

A methodology to incorporate product mix variations in cellular manufacturing

The identification of part families and machine groups that form the cells is a major step in the development of a cellular manufacturing system and, consequently, a large number of concepts, theories and algorithms have been proposed. One common assumption for most of these cell formation algorithms is that the product mix remains stable over a period of time. In today’s world, the market demand is being shaped by consumers resulting in a highly volatile market. This has given rise to a new class of products characterized by low volume and high variety. To incorporate product mix changes into an existing cellular manufacturing system many important issues have to be tackled. In this paper, a methodology to incorporate new parts and machines into an existing cellular manufacturing system has been presented. The objective is to fit the new parts and machines into an existing cellular manufacturing system thereby increasing machine utilization and reducing investment in new equipment.     

Temperatures in linguistics as a model of thermodynamics

Natural languages have been taken as models of systems with strong statistical correlations. In such systems the usual probabilistic limiting laws (Gauss etc.) are no more valid and the concept of higher order temperatures can be applied. It has been shown that this is the case in such languages as English, German, French, Russian and Japanese, for letter and word statistics. The corresponding temperatures (up to the 5-th order), entropies and statistical moments have been calculated and their mutual relations have been checked.... erst wenn du plötzlich Fänger wirst des Balles, den eine ewige Mitspielerin dir zuwarf, ... erst dann ist Fangen-können ein Vermögen, nicht deines, einer Welt.(... only when you suddenly catch the ball which the eternal fellow player throws to you,... only then the catching ability is an accomplishment, not of you, but of the world.)R. M. Rilke (from the motto of [3])Supported by Committee of Scientific Research, Warsaw (KBN), Project Nr. 20412 91 01.     

Investigations on mode-division multiplexed free-space optical transmission for inter-satellite communication link

Wireless Networks - Inter-satellite data transmission links are very crucial for providing global inter-connectivity. We report designing and investigations on high date rate inter-satellite...     

Modulated Triple‐Material Nano‐Heterostructures: Where Gold Influenced the Chemical Activity of Silver in Nanocrystals

For efficient charge separations, multimaterial hetero‐nanostructures are being extensively studied as photocatalysts. While materials with one heterojunction are widely established, the chemistry of formation of multijunction heterostructures is not explored. This needs a more sophisticated approach and modulations. To achieve these, a generic multistep seed mediated growth following controlled ion diffusion and ion exchange is reported which successfully leads to triple‐material hetero‐nanostructures with bimetallic‐binary alloy‐binary/ternary semiconductors arrangements. Ag₂S nanocrystals are used as primary seeds for obtaining AuAg‐AuAgS bimetallic‐binary alloyed metal–semiconductor heterostructures via partial reduction of Ag(I) using Au(III) ions. These are again explored as secondary seeds for obtaining a series of triple‐materials heterostructures, AuAg‐AuAgS‐CdS (or ZnS or AgInS₂), with introduction of different divalent and trivalent ions. Chemistry of each step of the gold ion–induced changes in the rate of diffusion and/or ion exchanges are investigated and the formation mechanism for these nearly monodisperse triple material heterostructures are proposed. Reactions without gold are also performed, and the change in the reaction chemistry and growth mechanism in presence of Au is also discussed.     

FSE–Ag complex NS: preparation and evaluation of antibacterial activity

Silver (Ag) complexes of drugs and their nanosystems have great potential as antibacterials. Recently, an Ag complex of furosemide (Ag–FSE) has shown to be a promising antimicrobial. However, poor solubility of Ag–FSE could hamper its introduction into clinics. Therefore, the authors developed a nanosuspension of Ag–FSE (Ag–FSE_NS) for its solubility and antibacterial activity enhancement. The aim of this study was to introduce a novel nanoantibiotic with enhanced antibacterial efficacy. Ag–FSE_NS was prepared by precipitation–ultrasonication technique. Size, polydispersity index (PI) and zeta potential (ZP) of prepared Ag–FSE_NS were measured by dynamic light scattering, whereas surface morphology was determined using scanning electron microscopy (SEM). In vitro antibacterial activity was evaluated against Staphylococcus aureus, Escherichia coli and Pseudomonas aeruginosa using broth microdilution method. Size, PI and ZP of optimised Ag–FSE_NS1 were 191.2 ± 19.34 nm, 0.465 ± 0.059 and −55.7 ± 8.18 mV, respectively. SEM revealed that Ag–FSE_NS1 particles were rod or needle‐like with smooth surfaces. Saturation solubility of Ag–FSE in NS increased eight‐fold than pure Ag–FSE. Ag–FSE_NS1 exhibited two‐fold and eight‐fold enhancements in activity against E. coli and S. aureus, respectively. The results obtained showed that developed Ag–FSE_NS1 holds a promise as a topical antibacterial.Inspec keywords: nanomedicine, nanofabrication, light scattering, surface morphology, silver, particle size, solubility, suspensions, scanning electron microscopy, electrokinetic effects, drugs, biomedical materials, antibacterial activity, microorganisms, nanoparticles, drug delivery systems, transmission electron microscopyOther keywords: saturation solubility, topical antibacterial, size 171.86 nm to 210.54 nm, voltage ‐47.52 mV to ‐63.88 mV, Ag, broth microdilution method, Pseudomonas aeruginosa, Escherichia coli, Staphylococcus aureus, SEM, scanning electron microscopy, surface morphology, dynamic light scattering, particle size, polydispersity index, precipitation–ultrasonication technique, nanoantibiotic, nanosuspension, furosemide, nanosystems, drugs, Ag–FSE_NS preparation, in vitro antibacterial activity, pure Ag–FSE, Ag–FSE_NS1 particles, optimised Ag–FSE_NS1, zeta potential, enhanced antibacterial efficacy, antibacterials