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Turning the Mind's Eye Inward: The Interplay between Selective Attention and Working Memory

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Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889197217 Year: Pages: 170 DOI: 10.3389/978-2-88919-721-7 Language: English
Publisher: Frontiers Media SA
Subject: Neurology --- Science (General)
Added to DOAB on : 2016-04-07 11:22:02
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Historically, cognitive sciences have considered selective attention and working memory as largely separated cognitive functions. That is, selective attention as a concept is typically reserved for the processes that allow for the prioritization of specific sensory input, while working memory entails more central structures for maintaining (and operating on) temporary mental representations. However, over the last decades various observations have been reported that question such sharp distinction. Most importantly, information stored in working memory has been shown to modulate selective attention processing – and vice versa. At the theoretical level, these observations are paralleled by an increasingly dominant focus on working memory as (involving) the attended part of long-term memory, with some positions considering that working memory is equivalent to selective attention turned to long-term memory representations – or internal selective attention. This questions the existence of working memory as a dedicated cognitive function and raises the need for integrative accounts of working memory and attention. The next step will be to explore the precise implications of attentional accounts of WM for the understanding of specific aspects and characteristics of WM, such as serial order processing, its modality-specificity, its capacity limitations, its relation with executive functions, as well as the nature of attentional mechanisms involved. This research topic in Frontiers in Human Neuroscience aims at bringing together the latest insights and findings about the interplay between working memory and selective attention.

Improving Working Memory in Learning and Intellectual Disabilities

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Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889198979 Year: Pages: 156 DOI: 10.3389/978-2-88919-897-9 Language: English
Publisher: Frontiers Media SA
Subject: Science (General) --- Psychology
Added to DOAB on : 2016-01-19 14:05:46
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The last forty years of research have demonstrated that working memory (WM) is a key concept for understanding higher-order cognition. To give an example, WM is involved in reading comprehension, problem solving and reasoning, but also in a number of everyday life activities. It has a clear role in the case of atypical development too. For instance, numerous studies have shown an impairment in WM in individuals with learning disabilities (LD) or intellectual disabilities (ID); and several researchers have hypothesized that this can be linked to their difficulties in learning, cognition and everyday life. The latest challenge in the field concerns the trainability of WM. If it is a construct central to our understanding of cognition in typical and atypical development, then specific intervention to sustain WM performance might also promote changes in cognitive processes associated with WM. The idea that WM can be modified is debated, however, partly because of the theoretical implications of this view, and partly due to the generally contradictory results obtained so far. In fact, most studies converge in demonstrating specific effects of WM training, i.e. improvements in the trained tasks, but few transfer effects to allied cognitive processes are generally reported. It is worth noting that any maintenance effects (when investigated) are even more meagre. In addition, a number of methodological concerns have been raised in relation to the use of: 1. single tasks to assess the effects of a training program; 2. WM tasks differing from those used in the training to assess the effects of WM training; and 3. passive control groups. These and other crucial issues have so far prevented any conclusions from being drawn on the efficacy of WM training. Bearing in mind that the opportunity to train WM could have a huge impact in the educational and clinical settings, it seems fundamentally important to shed more light on the limits and potential of this line of research. The aim of the research discussed here is to generate new evidence on the feasibility of training WM in individuals with LD and ID. There are several questions that could be raised in this field. For a start, can WM be trained in this population? Are there some aspects of WM that can be trained more easily than others? Can a WM training reduce the impact of LD and ID on learning outcomes, and on everyday living? What kind of training program is best suited to the promotion of such changes?

Applications of Computational Intelligence to Power Systems

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ISBN: 9783039217601 9783039217618 Year: Pages: 116 DOI: 10.3390/books978-3-03921-761-8 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-11-08 11:31:56
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Electric power systems around the world are changing in terms of structure, operation, management and ownership due to technical, financial, and ideological reasons. Power systems keep on expanding in terms of geographical areas, asset additions, and the penetration of new technologies in generation, transmission, and distribution. The conventional methods for solving the power system design, planning, operation, and control problems have been extensively used for different applications, but these methods suffer from several difficulties, thus providing suboptimal solutions. Computationally intelligent methods can offer better solutions for several conditions and are being widely applied in electrical engineering applications. This Special Issue represents a thorough treatment of computational intelligence from an electrical power system engineer’s perspective. Thorough, well-organised, and up-to-date, it examines in detail some of the important aspects of this very exciting and rapidly emerging technology, including machine learning, particle swarm optimization, genetic algorithms, and deep learning systems. Written in a concise and flowing manner by experts in the area of electrical power systems who have experience in the application of computational intelligence for solving many complex and difficult power system problems, this Special Issue is ideal for professional engineers and postgraduate students entering this exciting field.

Selected Papers from IEEE ICKII 2018

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ISBN: 9783039212736 9783039212743 Year: Pages: 82 DOI: 10.3390/books978-3-03921-274-3 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-12-09 11:49:15
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Electronic engineering and design innovation are both academic and practical engineering fields that involve systematic technological materialization through scientific principles and engineering designs. Technological innovation via electronic engineering includes electrical circuits and devices, computer science and engineering, communications and information processing, and electrical engineering communications. The Special Issue selected excellent papers presented at the International Conference on Knowledge Innovation and Invention 2018 (IEEE ICKII 2018) on the topic of electronics and their applications. This conference was held on Jeju Island, South Korea, 23–27 July 2018, and it provided a unified communication platform for researchers from all over the world. The main goal of this Special Issue titled “Selected papers from IEEE ICKII 2018” is to discover new scientific knowledge relevant to the topic of electronics and their applications.

Intelligent Optimization Modelling in Energy Forecasting

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ISBN: 9783039283644 9783039283651 Year: Pages: 262 DOI: 10.3390/books978-3-03928-365-1 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Computer Science
Added to DOAB on : 2020-04-07 23:07:09
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Accurate energy forecasting is important to facilitate the decision-making process in order to achieve higher efficiency and reliability in power system operation and security, economic energy use, contingency scheduling, the planning and maintenance of energy supply systems, and so on. In recent decades, many energy forecasting models have been continuously proposed to improve forecasting accuracy, including traditional statistical models (e.g., ARIMA, SARIMA, ARMAX, multi-variate regression, exponential smoothing models, Kalman filtering, Bayesian estimation models, etc.) and artificial intelligence models (e.g., artificial neural networks (ANNs), knowledge-based expert systems, evolutionary computation models, support vector regression, etc.). Recently, due to the great development of optimization modeling methods (e.g., quadratic programming method, differential empirical mode method, evolutionary algorithms, meta-heuristic algorithms, etc.) and intelligent computing mechanisms (e.g., quantum computing, chaotic mapping, cloud mapping, seasonal mechanism, etc.), many novel hybrid models or models combined with the above-mentioned intelligent-optimization-based models have also been proposed to achieve satisfactory forecasting accuracy levels. It is important to explore the tendency and development of intelligent-optimization-based modeling methodologies and to enrich their practical performances, particularly for marine renewable energy forecasting.

Keywords

short-term load forecasting --- weighted k-nearest neighbor (W-K-NN) algorithm --- comparative analysis --- empirical mode decomposition (EMD) --- particle swarm optimization (PSO) algorithm --- intrinsic mode function (IMF) --- support vector regression (SVR) --- short term load forecasting --- crude oil price forecasting --- time series forecasting --- hybrid model --- complementary ensemble empirical mode decomposition (CEEMD) --- sparse Bayesian learning (SBL) --- multi-step wind speed prediction --- Ensemble Empirical Mode Decomposition --- Long Short Term Memory --- General Regression Neural Network --- Brain Storm Optimization --- substation project cost forecasting model --- feature selection --- data inconsistency rate --- modified fruit fly optimization algorithm --- deep convolutional neural network --- multi-objective grey wolf optimizer --- long short-term memory --- fuzzy time series --- LEM2 --- combination forecasting --- wind speed --- electrical power load --- crude oil prices --- time series forecasting --- improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) --- kernel learning --- kernel ridge regression --- differential evolution (DE) --- artificial intelligence techniques --- energy forecasting --- condition-based maintenance --- asset management --- renewable energy consumption --- Gaussian processes regression --- state transition algorithm --- five-year project --- forecasting --- Markov-switching --- Markov-switching GARCH --- energy futures --- commodities --- portfolio management --- active investment --- diversification --- institutional investors --- energy price hedging --- metamodel --- ensemble --- individual --- regression --- interpolation

Deep Learning Applications with Practical Measured Results in Electronics Industries

Authors: --- --- ---
ISBN: 9783039288632 / 9783039288649 Year: Pages: 272 DOI: 10.3390/books978-3-03928-864-9 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2020-06-09 16:38:57
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This book collects 14 articles from the Special Issue entitled “Deep Learning Applications with Practical Measured Results in Electronics Industries” of Electronics. Topics covered in this Issue include four main parts: (1) environmental information analyses and predictions, (2) unmanned aerial vehicle (UAV) and object tracking applications, (3) measurement and denoising techniques, and (4) recommendation systems and education systems. These authors used and improved deep learning techniques (e.g., ResNet (deep residual network), Faster-RCNN (faster regions with convolutional neural network), LSTM (long short term memory), ConvLSTM (convolutional LSTM), GAN (generative adversarial network), etc.) to analyze and denoise measured data in a variety of applications and services (e.g., wind speed prediction, air quality prediction, underground mine applications, neural audio caption, etc.). Several practical experiments were conducted, and the results indicate that the performance of the presented deep learning methods is improved compared with the performance of conventional machine learning methods.

Keywords

computational intelligence --- offshore wind --- forecasting --- machine learning --- neural networks --- neuro-fuzzy systems --- humidity sensor --- data fusion --- nonlinear optimization --- multiple linear regression --- GSA-BP --- geometric errors correction --- kinematic modelling --- lateral stage errors --- Imaging Confocal Microscope --- K-means clustering --- data partition --- Least Squares method --- deep learning --- multivariate time series forecasting --- multivariate temporal convolutional network --- CNN --- hyperspectral image classification --- information measure --- transfer learning --- neighborhood noise reduction --- visual tracking --- update occasion --- update mechanism --- background model --- network layer contribution --- saliency information --- geometric errors --- rigid body kinematics --- lateral stage errors --- imaging confocal microscope --- MCM uncertainty evaluation --- dot grid target --- smart grid --- foreign object --- binary classification --- convolutional network --- image inpainting --- content reconstruction --- instance segmentation --- underground mines --- intelligent surveillance --- residual networks --- compressed sensing --- image compression --- image restoration --- discrete wavelet transform --- intelligent tire manufacturing --- digital shearography --- faster region-based CNN --- tire bubble defects --- tire quality assessment --- unmanned aerial vehicle --- UAV --- trajectory planning --- GA --- A* --- multiple constraints --- recommender system --- human computer interaction --- eye-tracking device --- deep learning --- oral evaluation --- generative adversarial network --- neural audio caption --- gated recurrent unit --- long short-term memory --- deep learning --- machine learning --- supervised learning --- unsupervised learning --- reinforcement learning --- optimization techniques

Intelligent Control in Energy Systems

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ISBN: 9783039214150 9783039214167 Year: Pages: 508 DOI: 10.3390/books978-3-03921-416-7 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General) --- Physics (General)
Added to DOAB on : 2019-12-09 11:49:15
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The editors of this Special Issue titled “Intelligent Control in Energy Systems” have attempted to create a book containing original technical articles addressing various elements of intelligent control in energy systems. In response to our call for papers, we received 60 submissions. Of those submissions, 27 were published and 33 were rejected. In this book, we offer the 27 accepted technical articles as well as one editorial. Authors from 15 countries (China, Netherlands, Spain, Tunisia, United Sates of America, Korea, Brazil, Egypt, Denmark, Indonesia, Oman, Canada, Algeria, Mexico, and the Czech Republic) elaborate on several aspects of intelligent control in energy systems. The book covers a broad range of topics including fuzzy PID in automotive fuel cell and MPPT tracking, neural networks for fuel cell control and dynamic optimization of energy management, adaptive control on power systems, hierarchical Petri Nets in microgrid management, model predictive control for electric vehicle battery and frequency regulation in HVAC systems, deep learning for power consumption forecasting, decision trees for wind systems, risk analysis for demand side management, finite state automata for HVAC control, robust ?-synthesis for microgrids, and neuro-fuzzy systems in energy storage.

Keywords

lithium-ion battery pack --- soft internal short circuit --- model-based fault detection --- battery safety --- internal short circuit resistance --- load frequency control --- model uncertainty --- ?-synthesis --- differential evolution --- decision tree --- preventive control --- Fault Ride Through Capability --- doubly-fed induction generator --- ancillary service --- frequency regulation --- demand response --- commercial/residential buildings --- HVAC systems --- model predictive control --- rule-based control --- position control --- static friction --- exhaust gas recirculation (EGR) valve system --- automotive application --- hybrid electric vehicle --- compound structured permanent-magnet motor --- energy management strategy --- instantaneous optimization minimum power loss --- back propagation (BP) neural network --- power transformer winding --- vibration characteristics --- multiphysical field analysis --- short-circuit experiment --- winding-fault characteristics --- occupancy model --- occupancy-based control --- model predictive control --- energy efficiency --- building climate control --- solar monitoring system --- photovoltaic array --- energy management --- demand side management --- operation limit violations --- probabilistic power flow --- network sensitivity --- neural networks --- railway --- high-speed railway --- neutral section --- medium voltage --- thyristor --- AC static switch --- adaptive backstepping --- nonlinear power systems --- sliding mode control --- error compensation --- ?-class function --- energy internet --- multi-energy complementary --- integrated energy systems --- distribution network planning --- electric power consumption --- multi-step forecasting --- long short term memory --- convolutional neural network --- system identification --- parameter estimation --- system modelling --- model reduction --- polynomial expansion --- orthogonal least square --- industrial process --- electric vehicle --- battery packs --- active balance --- model predictive control --- hierarchical Petri nets --- urban microgrids --- phase-load balancing --- fuzzy logic controller --- MPPT: maximum power point tracking --- photovoltaic system --- step-up boost converter --- proton exchange membrane fuel cell --- four phases interleaved boost converter --- neural network controller --- AC-DC converters --- bridgeless SEPIC PFC converter --- repetitive controller --- current distortion --- current controller design --- stochastic power system operating point drift --- wind integrated power system --- power oscillations --- adaptive damping control --- continuous voltage control --- multiple-point control --- interaction minimization --- pilot point --- adjacent areas --- ANFIS --- artificial neural network --- fuzzy --- small scale compressed air energy storage (SS-CAES) --- voltage controlling --- electric meter --- error estimation --- line loss --- RLS --- double forgetting factors --- hybrid power plant --- control architecture --- coordination of reserves --- frequency support --- frequency control dead band --- fast frequency response --- frequency containment reserve --- line switching --- voltage violations --- three-stage --- fractional order fuzzy PID controller --- neural network algorithm --- PEM fuel cell --- MPPT operation --- sensitivity analysis --- intelligent control --- artificial intelligence --- energy management system --- smart micro-grid --- energy systems --- intelligent buildings --- forecasting --- multi-agent control --- optimization

Intelligent Imaging and Analysis

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ISBN: 9783039219209 9783039219216 Year: Pages: 492 DOI: 10.3390/books978-3-03921-921-6 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2020-04-07 23:07:08
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Imaging and analysis are widely involved in various research fields, including biomedical applications, medical imaging and diagnosis, computer vision, autonomous driving, and robot controls. Imaging and analysis are now facing big changes regarding intelligence, due to the breakthroughs of artificial intelligence techniques, including deep learning. Many difficulties in image generation, reconstruction, de-noising skills, artifact removal, segmentation, detection, and control tasks are being overcome with the help of advanced artificial intelligence approaches. This Special Issue focuses on the latest developments of learning-based intelligent imaging techniques and subsequent analyses, which include photographic imaging, medical imaging, detection, segmentation, medical diagnosis, computer vision, and vision-based robot control. These latest technological developments will be shared through this Special Issue for the various researchers who are involved with imaging itself, or are using image data and analysis for their own specific purposes.

Keywords

image inspection --- non-referential method --- feature extraction --- fault pattern learning --- weighted kernel density estimation (WKDE) --- rail surface defect --- UAV image --- defect detection --- gray stretch maximum entropy --- image enhancement --- defect segmentation --- semi-automatic segmentation --- MR spine image --- vertebral body --- graph-based segmentation --- correlation --- surface defect of steel sheet --- image segmentation --- saliency detection --- low-rank and sparse decomposition --- intervertebral disc --- segmentation --- convolutional neural network --- fine grain segmentation --- U-net --- deep learning --- magnetic resonance image --- lumbar spine --- image adjustment --- colorfulness --- contrast --- sharpness --- high dynamic range --- local registration --- iterative closest points --- multimodal medical image registration --- machine vision --- point cloud registration --- greedy projection triangulation --- local correlation --- three-dimensional imaging --- optimization arrangement --- cavitation bubble --- water hydraulic valve --- defect inspection --- image processing --- feature extraction --- classification methods --- medical image registration --- image alignment in medical images --- misalignment correction in MRI --- midsagittal plane extraction --- symmetry detection --- PCA --- conformal mapping --- mesh parameterization --- mesh partitioning --- pixel extraction --- texture mapping --- image analysis --- image retrieval --- spatial information --- image classification --- computer vision --- image restoration --- motion deburring --- image denoising --- sparse feedback --- Image processing --- segmentation --- spline --- grey level co-occurrence matrix --- gradient detection --- threshold selection --- OpenCV --- machine learning --- transfer learning --- Inception-v3 --- geological structure images --- convolutional neural networks --- image segmentation --- active contour model --- level set --- signed pressure force function --- image segmentation --- deep learning --- synthetic aperture radar (SAR) --- oil slicks --- segnet --- pectus excavatum --- nuss procedure --- patient-specific nuss bar --- minimally invasive surgery --- computerized numerical control bending machine --- computer-aided design --- computer-aided manufacturing --- statistical body shape model --- self-intersection penalty term --- 3D pose estimation --- 3D semantic mapping --- incrementally probabilistic fusion --- CRF regularization --- road scenes --- deep learning --- medical image classification --- additional learning --- CT image --- automatic training --- GoogLeNet --- intelligent evaluation --- automated cover tests --- deviation of strabismus --- pupil localization --- shape from focus --- wear measurement --- sprocket teeth --- normal distribution operator image filtering --- adaptive evaluation window --- reverse engineering --- human parsing --- depth-estimation --- computational efficiency --- capacity optimization --- underwater visual localization method --- line segment features --- PL-SLAM --- face sketch synthesis --- face sketch recognition --- joint training model --- data imbalance --- Contrast Tomography (CT) --- pre-training strategy --- segmentation --- super-resolution --- dual-channel --- residual block --- convolutional kernel parameter --- long-term and short-term memory blocks --- n/a

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