A new wavelet-based adaptive method for solving population balance equations

A new wavelet-based adaptive framework for solving population balance equations (PBEs) is proposed in this work. The technique is general, powerful and efficient without the need for prior assumptions about the characteristics of the processes. Because there are steeply varying number densities across a size range, a new strategy is developed to select the optimal order of resolution and the collocation points based on an interpolating wavelet transform (IWT). The proposed technique has been tested for size-independent agglomeration, agglomeration with a linear summation kernel and agglomeration with a nonlinear kernel. In all cases, the predicted and analytical particle size distributions (PSDs) are in excellent agreement. Further work on the solution of the general population balance equations with nucleation, growth and agglomeration and the solution of steady-state population balance equations will be presented in this framework. (C) 2002 Elsevier Science B.V. All rights reserved.

Análise do nível de estresse baseada em sinais de eletroencefalografia e de condutância da pele

O estresse pode afetar qualquer pessoa, independente de idade, sexo ou etnia. O organismo humano o utiliza como uma resposta adaptativa frente a situações diversas, as quais requeiram alguma adaptação do organismo para que possa enfrentar tal situação. Dependendo do estímulo estressor, pode ser gerado no indivíduo desgastes físico, mental ou emocional, no entanto, o estresse não representa necessariamente algo ruim ou patológico; este é um mecanismo de adaptação vital para a sobrevivência da espécie humana. Porém, o número de pessoas que são afetadas de forma negativa pelo estresse tem crescido imensamente nas últimas décadas. Pesquisas destacam que nos Estados Unidos cerca de 60% a 90% dos atendimentos médicos estão relacionados de alguma maneira com o estresse, enquanto que no Brasil aproximadamente 80% da população sofre de estresse, sendo que desses, 30% encontram-se na fase mais crítica, a chamada fase de exaustão. Tendo em vista que a principal forma de identificação de estresse ainda é realizada através do uso de questionário de autorrelato. O presente estudo apresenta como contribuição uma metodologia de análise do nível de estresse baseada na variação da condutância galvânica da pele e de sinais de eletroencefalografia, sendo utilizados como parâmetros a assimetria do ritmo alfa, assim como a razão entre os ritmos beta e alfa no córtex frontal e pré-frontal. Para a gravação dos sinais de EEG foi utilizado um dispositivo portátil, com eletrodos especificamente situados nas posições aF3, F3, F4 e aF4, de acordo com o Sistema Internacional 10/20 de posicionamento de eletrodos. Os participantes deste estudo são Bombeiros Militares da 1ª Cia de Vitória-ES. Foram utilizadas três classes de estímulos emocionais positivos, calmos e negativos, através da utilização de imagens pertencentes ao banco de dados IAPS (International Affective Picture System). Os resultados de acurácia obtidos através de um classificador SVM (Support Vector Machine) chegam a 88,24% para classe de estímulos positivos, 84,09% para classe calma e de 92,86% para os estímulos negativos. Deste modo, esta pesquisa apresenta uma combinação de parâmetros que podem ser aferidos com equipamentos de baixo custo, e fornecem condições de diferenciar estímulos estressantes, podendo assim, ser utilizada para auxiliar no treinamento de profissionais da área de urgência e emergência.

Hand-held robotic device for laparoscopic surgery and training

Laparoscopic surgery (LS) has revolutionized traditional surgical techniques introducing minimally invasive procedures for diagnosis and local therapies. LSs have undeniable advantages, such as small patient incisions, reduced postoperative pain and faster recovery. On the other hand, restricted vision of the anatomical target, difficult handling of the surgical instruments, restricted mobility inside the human body, need of dexterity to hand-eye coordination and inadequate and non-ergonomic surgical instruments may restrict LS only to more specialized surgeons. To overcome the referred limitations, this work presents a new robotic surgical handheld system – the EndoRobot. The EndoRobot was designed to be used in clinical practice or even as a surgical simulator. It integrates an electromechanical system with 3 degrees of freedom. Each degree can be manipulated independently and combined with different levels of sensitivity allowing fast and slow movements. As other features, the EndoRobot has battery power or external power supply, enables the use of bipolar radiofrequency to prevent bleeding while cutting and allows plug-and-play of the laparoscopic forceps for rapid exchange. As a surgical simulator, the system was also instrumented to measure and transmit, in real time, its position and orientation for a training software able to monitor and assist the trainee’s surgical movements.

Assessment of Laparoscopic Skills Performance: 2D Versus 3D Vision and Classic Instrument Versus New Hand-Held Robotic Device for Laparoscopy

Introduction and Objectives. Laparoscopic surgery has undeniable advantages, such as reduced postoperative pain, smaller incisions, and faster recovery. However, to improve surgeons’ performance, ergonomic adaptations of the laparoscopic instruments and introduction of robotic technology are needed. The aim of this study was to ascertain the influence of a new hand-held robotic device for laparoscopy (HHRDL) and 3D vision on laparoscopic skills performance of 2 different groups, naïve and expert. Materials and Methods. Each participant performed 3 laparoscopic tasks—Peg transfer, Wire chaser, Knot—in 4 different ways. With random sequencing we assigned the execution order of the tasks based on the first type of visualization and laparoscopic instrument. Time to complete each laparoscopic task was recorded and analyzed with one-way analysis of variance. Results. Eleven experts and 15 naïve participants were included. Three-dimensional video helps the naïve group to get better performance in Peg transfer, Wire chaser 2 hands, and Knot; the new device improved the execution of all laparoscopic tasks (P < .05). For expert group, the 3D video system benefited them in Peg transfer and Wire chaser 1 hand, and the robotic device in Peg transfer, Wire chaser 1 hand, and Wire chaser 2 hands (P < .05). Conclusion. The HHRDL helps the execution of difficult laparoscopic tasks, such as Knot, in the naïve group. Three-dimensional vision makes the laparoscopic performance of the participants without laparoscopic experience easier, unlike those with experience in laparoscopic procedures.

Adaptive deblocking filter for transform domain Wyner-Ziv video coding

Wyner-Ziv (WZ) video coding is a particular case of distributed video coding, the recent video coding paradigm based on the Slepian-Wolf and Wyner-Ziv theorems that exploits the source correlation at the decoder and not at the encoder as in predictive video coding. Although many improvements have been done over the last years, the performance of the state-of-the-art WZ video codecs still did not reach the performance of state-of-the-art predictive video codecs, especially for high and complex motion video content. This is also true in terms of subjective image quality mainly because of a considerable amount of blocking artefacts present in the decoded WZ video frames. This paper proposes an adaptive deblocking filter to improve both the subjective and objective qualities of the WZ frames in a transform domain WZ video codec. The proposed filter is an adaptation of the advanced deblocking filter defined in the H.264/AVC (advanced video coding) standard to a WZ video codec. The results obtained confirm the subjective quality improvement and objective quality gains that can go up to 0.63 dB in the overall for sequences with high motion content when large group of pictures are used.

Optical demultiplexer device operating in the visible spectrum

In this paper, we present results on the use of multilayered a-SiC:H heterostructures as a device for wavelength-division demultiplexing of optical signals. These devices are useful in optical communications applications that use the wavelength division multiplexing technique to encode multiple signals into the same transmission medium. The device is composed of two stacked p-i-n photodiodes, both optimized for the selective collection of photo generated carriers. Band gap engineering was used to adjust the photogeneration and recombination rate profiles of the intrinsic absorber regions of each photodiode to short and long wavelength absorption in the visible spectrum. The photocurrent signal using different input optical channels was analyzed at reverse and forward bias and under steady state illumination. A demux algorithm based on the voltage controlled sensitivity of the device was proposed and tested. An electrical model of the WDM device is presented and supported by the solution of the respective circuit equations.

Organization knowledge management change from a complex adaptive systems perpective with ability for ambidexeterity

We are working on the confluence of knowledge management, organizational memory and emergent knowledge with the lens of complex adaptive systems. In order to be fundamentally sustainable organizations search for an adaptive need for managing ambidexterity of day-to-day work and innovation. An organization is an entity of a systemic nature, composed of groups of people who interact to achieve common objectives, making it necessary to capture, store and share interactions knowledge with the organization, this knowledge can be generated in intra-organizational or inter-organizational level. The organizations have organizational memory of knowledge of supported on the Information technology and systems. Each organization, especially in times of uncertainty and radical changes, to meet the demands of the environment, needs timely and sized knowledge on the basis of tacit and explicit. This sizing is a learning process resulting from the interaction that emerges from the relationship between the tacit and explicit knowledge and which we are framing within an approach of Complex Adaptive Systems. The use of complex adaptive systems for building the emerging interdependent relationship, will produce emergent knowledge that will improve the organization unique developing.

Application of adaptive neuro-fuzzy inference for wind power short-term forecasting

The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Hence, good forecasting tools play a key role in tackling these challenges. In this paper, an adaptive neuro-fuzzy inference approach is proposed for short-term wind power forecasting. Results from a real-world case study are presented. A thorough comparison is carried out, taking into account the results obtained with other approaches. Numerical results are presented and conclusions are duly drawn. (C) 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Semiconductor Device as Optical Demultiplexer for Short Range Optical Communications

In this paper we present results on the use of a multilayered a-SiC:H heterostructure as a wavelength-division demultiplexing device for the visible light spectrum. The proposed device is composed of two stacked p-i-n photodiodes with intrinsic absorber regions adjusted to short and long wavelength absorption and carrier collection. An optoelectronic characterisation of the device was performed in the visible spectrum. Demonstration of the device functionality for WDM applications was done with three different input channels covering the long, the medium and the short wavelengths in the visible range. The recovery of the input channels is explained using the photocurrent spectral dependence on the applied voltage. An electrical model of the WDM device is proposed and supported by the solution of the respective circuit equations. Short range optical communications constitute the major application field, however other applications are also foreseen.

Adaptive tool for automatic data collection of real electricity markets

The study of electricity markets operation has been gaining an increasing importance in last years, as result of the new challenges that the electricity markets restructuring produced. This restructuring increased the competitiveness of the market, but with it its complexity. The growing complexity and unpredictability of the market’s evolution consequently increases the decision making difficulty. Therefore, the intervenient entities are forced to rethink their behaviour and market strategies. Currently, lots of information concerning electricity markets is available. These data, concerning innumerous regards of electricity markets operation, is accessible free of charge, and it is essential for understanding and suitably modelling electricity markets. This paper proposes a tool which is able to handle, store and dynamically update data. The development of the proposed tool is expected to be of great importance to improve the comprehension of electricity markets and the interactions among the involved entities.

Adaptive learning in multiagent systems: a forecasting methodology based on error analysis

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simu-lator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM pro-vides several dynamic strategies for agents’ behaviour. This paper presents a method that aims to provide market players strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses an auxiliary forecasting tool, e.g. an Artificial Neural Net-work, to predict the electricity market prices, and analyses its forecasting error patterns. Through the recognition of such patterns occurrence, the method predicts the expected error for the next forecast, and uses it to adapt the actual forecast. The goal is to approximate the forecast to the real value, reducing the forecasting error.

Multiagent system for adaptive strategy formulation in electricity markets

Electricity markets are complex environments with very particular characteristics. MASCEM is a market simulator developed to allow deep studies of the interactions between the players that take part in the electricity market negotiations. This paper presents a new proposal for the definition of MASCEM players’ strategies to negotiate in the market. The proposed methodology is multiagent based, using reinforcement learning algorithms to provide players with the capabilities to perceive the changes in the environment, while adapting their bids formulation according to their needs, using a set of different techniques that are at their disposal.

Cost dependent strategy for electricity markets bidding based on adaptive reinforcement learning

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simulator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM provides several dynamic strategies for agents’ behavior. This paper presents a method that aims to provide market players with strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses a reinforcement learning algorithm to learn from experience how to choose the best from a set of possible bids. These bids are defined accordingly to the cost function that each producer presents.

Contextual intelligent load management with ANN adaptive learning module

With the current increase of energy resources prices and environmental concerns intelligent load management systems are gaining more and more importance. This paper concerns a SCADA House Intelligent Management (SHIM) system that includes an optimization module using deterministic and genetic algorithm approaches. SHIM undertakes contextual load management based on the characterization of each situation. SHIM considers available generation resources, load demand, supplier/market electricity price, and consumers’ constraints and preferences. The paper focus on the recently developed learning module which is based on artificial neural networks (ANN). The learning module allows the adjustment of users’ profiles along SHIM lifetime. A case study considering a system with fourteen discrete and four variable loads managed by a SHIM system during five consecutive similar weekends is presented.

Bid definition method for electricity markets based on an adaptive multiagent system

Electricity markets are complex environments with very particular characteristics. MASCEM is a market simulator developed to allow deep studies of the interactions between the players that take part in the electricity market negotiations. This paper presents a new proposal for the definition of MASCEM players’ strategies to negotiate in the market. The proposed methodology is multiagent based, using reinforcement learning algorithms to provide players with the capabilities to perceive the changes in the environment, while adapting their bids formulation according to their needs, using a set of different techniques that are at their disposal. Each agent has the knowledge about a different method for defining a strategy for playing in the market, the main agent chooses the best among all those, and provides it to the market player that requests, to be used in the market. This paper also presents a methodology to manage the efficiency/effectiveness balance of this method, to guarantee that the degradation of the simulator processing times takes the correct measure.