Uncertainty based distributed generation expansion planning in electricity markets

Decision making under uncertainty is a difficult task in most areas. The distributed generation expansion planning is certainly one of the problems where it is difficult to find an optimal solution for, due to the existence of various uncertain parameters that affect the optimal solution. This paper presents a distributed generation planning strategy based on the multi-objective decision making (MODM) approach under uncertainties. The proposed strategy achieves a Pareto set of locations, sizes, and typical types, for new DG installations in a case-study power network (as the result of a multi-objective optimization). The technical constraints of DG units are also considered in the MODM process. The effects of the uncertain parameters on choosing the best plan(s) are considered using robustness, flexibility, and risk exposure attributes. This aim is performed based on a composite index which is weighted sum of the three mentioned attributes. This paper considers four categories of uncertain parameters including the peak loading factor, electricity market price, and investment and operating costs for the DG long-term planning.     

Refractive lens exchange for presbyopia

True presbyopia correction still remains one of the main challenges amongst eye professionals. Many corrective techniques exist including bifocal spectacle correction, monovision techniques both with corrective lenses and surgery, multifocal corneal excimer laser and refractive lens exchange with either accommodating or multifocal lenses. Refractive lens exchange was first described more than 10 years ago and has become more accepted in recent years with advancements in lens technology and improvements in surgical techniques. This article reviews developments in this field and current available lens options in the treatment of presbyopia.     

MMPP models for multimedia traffic

The paper presents a study on the use of Markov‐Modulated Poisson Processes (MMPP's) for characterizing multimedia traffic with short‐term and long‐term correlation. The applicability of the 2‐state MMPP and a refined moment‐based parameter‐matching technique to model an arbitrary ATM traffic stream in evaluating its queueing performance are examined by simulation. Following a discussion on the limitation of the 2‐state MMPP model, a model using a superposition of N homogeneous 2‐state MMPP to characterize bursty multimedia traffic is presented. The proposed model requires only five parameters which can be estimated from the samples of the traffic counting process by using a pdf‐based matching technique. The introduced pdf‐based parameter‐matching procedure uses the probability density function of the arrival rate and the IDC curve of the traffic samples. An approximation for the probability of loss in MMPP/D/1 queues is also obtained. The versatility and accuracy of the proposed model to characterize bursty multimedia traffic are shown by several case studies and test results. This revised version was published online in June 2006 with corrections to the Cover Date.     

Relevance of peat-draining rivers for the riverine input of dissolved iron into the ocean

Peat bogs have the ability to produce strong chelate ligands (humic and fulvic acids) which enhance the weathering rates of iron-silicate minerals and greatly increase the solubility of the essential trace metal iron in river water. Fluvial networks link peat bogs with the ocean, and thus terrestrial-derived fulvic-iron complexes fuel the ocean's biological productivity and biological carbon pump, but understanding this role is constrained by inconsistent observations regarding the behaviour of riverine iron in the estuarine mixing zone, where precipitation reactions remove iron from the water column. We applied a characterization of the colloidal iron carriers in peatland-draining rivers in North Scotland, using field-flow fractionation (FFF), in combination with end-member mixing experiments of river water sampled near the river mouth and coastal seawater using a ⁵⁹Fe radiotracer method. According to our results, the investigated river contributed “truly dissolved” Fe concentrations of about 3300 nmol L^− 1 to the ocean which is nearly two orders of magnitude higher than the dissolved iron contribution of the “average world” river (∼ 40 nmol L^− 1). Thus we conclude that peatland-draining rivers are important sources of dissolved iron to the ocean margins. We propose highly electrostatic and sterical stabilized iron-organic matter complexes in the size range of < 2 kDa to be responsible for iron transport across the estuarine mixing zone.     

Parasitic compensation in CCI-based circuits for reduced power consumption

In this paper, the compensation advantage of the first-generation current conveyor (CCI) over the second-generation current conveyor (CCII) in tunable circuits is shown. For this purpose, a new floating frequency dependent negative resistor (FDNR) simulator using three CCIs is presented and employed in a third-order high-pass filter. The compensation feature of the CCI is shown for the proposed high-pass filter. As a second example, the presented compensation method is tested in a second-order band-pass filter constructed with two CCIs. Applying the proposed compensation technique, the CCI-based circuits can operate in lower biasing currents, which result in lower power consumption.     

A new selfish thing detection method based on Voronoi diagram for Internet of Things

The Journal of Supercomputing - Internet of Things (IoT), as an emerging technology, describes a smart world that enables objects to interact with each other and with end-users through developed...     

Deep-Learning Based DOA Estimation in the Presence of Multiplicative Noise

Wireless Personal Communications - The paper addresses the narrowband direction of arrival estimation problem in the presence of multiplicative noise, namely, the local scatterers affect the...     

Speed limit reduction in urban areas: A before–after study using Bayesian generalized mixed linear models

In fall 2009, a new speed limit of 40 km/h was introduced on local streets in Montreal (previous speed limit: 50 km/h). This paper proposes a methodology to efficiently estimate the effect of such reduction on speeding behaviors. We employ a full Bayes before–after approach, which overcomes the limitations of the empirical Bayes method. The proposed methodology allows for the analysis of speed data using hourly observations. Therefore, the entire daily profile of speed is considered. Furthermore, it accounts for the entire distribution of speed in contrast to the traditional approach of considering only a point estimate such as 85th percentile speed. Different reference speeds were used to examine variations in the treatment effectiveness in terms of speeding rate and frequency. In addition to comparing rates of vehicles exceeding reference speeds of 40 km/h and 50 km/h (speeding), we verified how the implemented treatment affected “excessive speeding” behaviors (exceeding 80 km/h). To model operating speeds, two Bayesian generalized mixed linear models were utilized. These models have the advantage of addressing the heterogeneity problem in observations and efficiently capturing potential intra-site correlations. A variety of site characteristics, temporal variables, and environmental factors were considered. The analyses indicated that variables such as lane width and night hour had an increasing effect on speeding. Conversely, roadside parking had a decreasing effect on speeding. One-way and lane width had an increasing effect on excessive speeding, whereas evening hour had a decreasing effect. This study concluded that although the treatment was effective with respect to speed references of 40 km/h and 50 km/h, its effectiveness was not significant with respect to excessive speeding-which carries a great risk to pedestrians and cyclists in urban areas. Therefore, caution must be taken in drawing conclusions about the effectiveness of speed limit reduction. This study also points out the importance of using a comparison group to capture underlying trends caused by unknown factors.     

A web-based high-performance multicriteria decision support system for medical diagnosis

Multicriteria decision making (MCDM) methods can be powerful aids for evaluating patients' medical information in medical diagnostic systems. Technique ordered preference by similarity to the ideal solution (TOPSIS) is one of the more widely used MCDM methods in decision support systems. For the purpose of this work, the TOPSIS method is modified into a more suitable form and used for the implementation of a web-based medical diagnostic system. In our modified TOPSIS method, we have utilized fuzzy logic so that users can more accurately describe their symptoms. The data given to the modified TOPSIS method are often massive in proportions and may take a considerable amount of time to generate a ranking of alternatives. TOPSIS lends itself to parallel computation because it is virtually a combination of matrix computations. Therefore, computer parallelism is implemented so that a large amount of input data can be handled simultaneously, hence decreasing overall execution time. In addition, to make our MCDM system more accessible, we have designed our system to be web based. The web-based medical diagnosis system includes a dynamically generated web-based user interface, while the parallel implementation of the modified TOPSIS component, in conjunction with the Common Gateway Interface, forms the back end of the system. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 1083–1099, 2007.