Iterative Heuristic Response Surface Method for Transmission Expansion Planning

Transmission expansion planning has become a central challenge in the design of competitive electricity markets. The underlying optimization/decision problem exhibits a high level of complexity, being nonconvex, dynamic, and nonlinear, with multiple objective functions and a wide range of uncertainties. In this paper we propose a methodology for handling the expansion problem that involves a novel adaptation of an iterative optimization method based on response surface models already used successfully in the design and manufacture of integrated circuits. It is implemented in a distributed environment using Web services, and is validated and compared with a genetic algorithm based methodology. An application of the proposed methodology to the Chilean Central Interconnected System is then presented and analyzed. The results demonstrate its potential in the fields of risk analysis and decision support systems in power markets.     

An evolutionary approach to complex schema matching

The schema matching problem can be defined as the task of finding semantic relationships between schema elements existing in different data repositories. Despite the existence of elaborated graphic tools for helping to find such matches, this task is usually manually done. In this paper, we propose a novel evolutionary approach to addressing the problem of automatically finding complex matches between schemas of semantically related data repositories. To the best of our knowledge, this is the first approach that is capable of discovering complex schema matches using only the data instances. Since we only exploit the data stored in the repositories for this task, we rely on matching strategies that are based on record deduplication (aka, entity-oriented strategy) and information retrieval (aka, value-oriented strategy) techniques to find complex schema matches during the evolutionary process. To demonstrate the effectiveness of our approach, we conducted an experimental evaluation using real-world and synthetic datasets. The results show that our approach is able to find complex matches with high accuracy, similar to that obtained by more elaborated (hybrid) approaches, despite using only evidence based on the data instances.     

Ubiquitous robotics: Recent challenges and future trends

Ambient intelligence, ubiquitous and networked robots, and cloud robotics are new research hot topics that have started to gain popularity among the robotics community. They enable robots to acquire richer functionalities and open the way for the composition of a variety of robotic services with three functions: semantic perception, reasoning and actuation. Ubiquitous robots (ubirobots) overcome the limitations of stand-alone robots by integrating them with web services and ambient intelligence technologies. The overlap that exists now between ubirobots and ambient intelligence makes their integration worthwhile. It targets to create a hybrid physical–digital space rich with a myriad of proactive intelligent services that enhance the quality and the way of our living and working. Furthermore, the emergence of cloud computing initiates the massive use of a new generation of ubirobots that enrich their cognitive capabilities and share their knowledge by connecting themselves to cloud infrastructures. The future of ubirobots will certainly be open to an unlimited space of applications such as physical and virtual companions assisting people in their daily living, ubirobots that are able to co-work alongside people and cooperate with them in the same environment, and physical and virtual autonomic guards that are able to protect people, monitor their security and safety, and rescue them in indoor and outdoor spaces. This paper introduces the recent challenges and future trends on these topics.     

Towards a journalist-based news recommendation system: The Wesomender approach

The present paper introduces a context-aware recommendation system for journalists to enable the identification of similar topics across different sources. More specifically a journalist-based recommendation system that can be automatically configured is presented to exploit news according to expert preferences. News contextual features are also taken into account due to the their special nature: time, current user interests, location or existing trends are combined with traditional recommendation techniques to provide an adaptive framework that deals with heterogeneous data providing an enhanced collaborative filtering system. Since the Wesomender approach is able to generate context-aware recommendations in the journalism field, a quantitative evaluation with the aim of comparing Wesomender results with the expectations of a team of experts is also performed to show that a context-aware adaptive recommendation engine can fulfil the needs of journalists daily work when retrieving timely and primary information is required.     

A Qualitative Study of Hard Decision Making in Managing Global Software Development Teams

We empirically explored the implication of hard decisions in the context of the management of global software development (GSD) teams. We hypothesized and empirically tested our belief that hard decision making in conventional development projects is very similar to GSD and differences arise from the nature of the relationship and the ranking of issues. Findings from a Delphi study illustrated our approach and shed some light into the management of GSD teams.     

人机界面的演进

术语．“人机界面”突然间已进入技术领域专用词汇的行列，就其最基本词义而言，该术语不仅用于描述设备外观，更可以描述人与设备之间的交互过程。虽然这不是一个新概念，但是该技术能够彻底改变我们与电子设备交互方式的可用性，以及集成该技术的紧迫性表明用户对界面设计日益重视。     

Synthesis and characterization of aniline copolymers containing carboxylic groups and their application as sensitizer and hole conductor in solar cells

Copolymers of m-aminobenzoic acid and o-anisidine doped with p-toluenesulphonic acid in different proportions were successfully synthesized by oxidative polymerization. The copolymers were characterized by FT-IR, UV–vis, ¹H NMR and EPR spectroscopies, cyclic voltammetry, conductivity and SEM. The copolymer with equivalent amounts of the monomers o-anisidine and m-aminobenzoic acid presented the highest conductivity, The EPR analyses and SEM images show that this copolymer provides more homogeneous films with particle size distribution of approximately 1–2 μm. The copolymer with a high fraction of o-anisidine gives rises to films with larger particle sizes and a more defined electrochemical process. The m-aminobenzoic acid monomer was intentionally chosen in order to promote a better electronic coupling between the conducting copolymer and the TiO₂ surface. The copolymers were tested as both sensitizers and hole conducting materials in dye-sensitized solar cells. The device assembled using the copolymer with the highest proportion of m-aminobenzoic acid units as sensitizer produced the highest photocurrent (I_sc = 0.254 mA cm^?2) and photovoltage (V_oc = 0.252 V) at 100 mW cm^?2. The energy diagram shows that although the electronic injection from the conducting polymer excited state is an allowed process the regeneration of the positive charges created after the electron transfer process is forbidden, thus explaining the low efficiency of solar energy conversion. When this copolymer was applied as a hole conducting material, an improvement in the V_oc to 0.4 V, was observed, indicating that this material is more suitable for charge transport when applied in this type of solar cells.     

Projective Splines and Estimators for Planar Curves

Recognizing shapes in multiview imaging is still a challenging task, which usually relies on geometrical invariants estimations. However, very few geometric estimators that achieve projective invariance have been devised. This paper proposes a projective length and a projective curvature estimators for plane curves, when the curves are represented by points together with their tangent directions. In this context, the estimations can be performed with only three point-tangent samples for the projective length and five samples for the projective curvature. The proposed length and curvature estimator are based on projective splines built by fitting logarithmic spirals to the point-tangent samples. They are projective invariant and convergent.     

Le codeur mpeg-2 AAC expliqué aux traiteurs de signaux

The MPEG- 2 Advanced Audio Coder is the latest issue of the MPEG audio encoders/decoders family, whose most popular version is known as MP3. It gathers many of the latest highly efficient sound compression techniques in a quite classically structured coder. The main part is based on a Discrete Cosine Transform with variable resolution. The output from this filterbank is compressed by the combination of an adaptive bit allocation module, according to frequency subbands, and a set of noiseless Huffman codebooks. Bit allocation is controlled by a psychoa-coustic model which determines an audibility threshold for signal distortion in the frequency domain. This article intends to explain the ISO standard without replacing it, and also to be a general introduction to perceptual audio coding.     

Hybrid modeling of inulinase bio‐production process

BACKGROUND: A potential application of inulinase in the food industry is the production of fructooligosaccharides (FOS) through transfructosilation of sucrose. Besides their ability to increase the shelf‐life and flavor of many products, FOS have many interesting functional properties. The use of an industrial medium may represent a good, cost‐effective alternative to produce inulinase, since the activity of the enzyme produced may be improved or at least remain the same compared with that obtained using a synthetic medium. Thus, inulinase production for use in FOS synthesis is of considerable scientific and technological appeal, as is the development of a reliable mathematical model of the process. This paper describes a hybrid neural network approach to model inulinase production in a batch bioreactor using agroindustrial residues as substrate. The hybrid modeling makes use of a series artificial neural network to estimate the kinetic parameters of the process and the mass balance as constitutive equations. RESULTS: The proposed model was shown to be capable of describing the complex behavior of inulinase production employing agroindustrial residues as substrate, so that the mathematical framework developed is a useful tool for simulation of this process. CONCLUSION: The hybrid neural network model developed was shown to be an interesting alternative to estimate model parameters since complete elucidation of the phenomena and mechanisms involved in the fermentation is not required owing to the black‐box nature of the ANN used as parameter estimator. Copyright © 2010 Society of Chemical Industry