Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

Revealing ensemble state transition patterns in multi-electrode neuronal recordings using hidden Markov models

In order to harness the computational capacity of dissociated cultured neuronal networks, it is necessary to understand neuronal dynamics and connectivity on a mesoscopic scale. To this end, this paper uncovers dynamic spatiotemporal patterns emerging from electrically stimulated neuronal cultures using hidden Markov models (HMMs) to characterize multi-channel spike trains as a progression of patterns of underlying states of neuronal activity. However, experimentation aimed at optimal choice of parameters for such models is essential and results are reported in detail. Results derived from ensemble neuronal data revealed highly repeatable patterns of state transitions in the order of milliseconds in response to probing stimuli

Online Metric-Weighted Linear Representations for Robust Visual Tracking

In this paper, we propose a visual tracker based on a metric-weighted linear representation of appearance. In order to capture the interdependence of different feature dimensions, we develop two online distance metric learning methods using proximity comparison information and structured output learning. The learned metric is then incorporated into a linear representation of appearance. We show that online distance metric learning significantly improves the robustness of the tracker, especially on those sequences exhibiting drastic appearance changes. In order to bound growth in the number of training samples, we design a time-weighted reservoir sampling method. Moreover, we enable our tracker to automatically perform object identification during the process of object tracking, by introducing a collection of static template samples belonging to several object classes of interest. Object identification results for an entire video sequence are achieved by systematically combining the tracking information and visual recognition at each frame. Experimental results on challenging video sequences demonstrate the effectiveness of the method for both inter-frame tracking and object identification.Comment: 51 pages. Appearing in IEEE Transactions on Pattern Analysis and Machine Intelligenc

Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age

Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved

A comprehensive and absolute corporate sustainability assessment and enhanced input output life cycle assessment

Stresses due to economic activity are threatening to exceed environmental and societal limits with the potential to jeopardize local communities and create global crises. This research proposes new methodologies and analytic techniques to comprehensively assess corporate sustainability and enhance the efficiency of estimating environmental and social impacts with Input Output Life Cycle Assessment (IOLCA). Sustainability assessments and management require consideration of both social and environmental impacts as outflows of economic activity. There are a number of assessment tools available to gain insight into environmental and social impacts; but in most cases, these approaches lack essential components for a comprehensive and absolute sustainability assessment. This dissertation proposes a new quantitative method for assessing sustainability across all the interrelationships within multiple domains of sustainability—economic, social, environmental, and potentially others. The comprehensive sustainability target method (CSTM) is a novel extension to an existing environmental burden sustainability technique. CSTM applies the science-based targets and concept of absolute sustainability to social burdensome and beneficial impacts, environmental beneficial impacts, and the interdependencies between the sustainability domains. CSTM is contrasted with an example of the relative assessments that appear in many sustainability disclosures. In addition to science-based targets for environmental burdens, companies should attempt to meet science-based targets for social and beneficial impacts. Another area of research is focused on IOLCA, a widely used method of estimating environmental impacts based on economic sector level data and analysis. These IOLCA models rely on sector averages and require practitioners to combine impact estimation models to describe specific companies or “custom products”. This research presents a novel extension to environmental input-output modeling that increases the usability and responsiveness of the technique to perform custom product-specific assessments. This enhancement models direct impacts from emissions (and other stressors) attributable to direct spending on commodities across the economy that cause those impacts. The proposed extension directly calculates the internal impact (II); hence, the model implemented is referred to as the IOLCA-II. The IOLCA-II extension directly produces impact estimates in the categories typically used to manage and report greenhouse gas (GHG) emissions: Scope 1, Scope 2, and Scope 3. In addition to the IOLCA-II enhancement for environmental assessment, selected social impacts are incorporated into the extended model to permit social impact estimation. IOLCA-II impacts are estimated for two scenarios: first, a solar energy application at a university; and second, driverless operation of a long-haul trucking company. The baseline and scenarios are modeled using IOLCA-II and compared to explore the impacts and consequences of the proposed scenarios. These case studies reveal the advantages of using the new methodology and the efficiency of the input-output model results compared to conventional IOLCA hybrid/custom product assessment

A Comparative Analysis of Deep Learning Convolutional Neural Network Architectures for Fault Diagnosis of Broken Rotor Bars in Induction Motors

[EN] Induction machines (IMs) play a critical role in various industrial processes but are susceptible to degenerative failures, such as broken rotor bars. Effective diagnostic techniques are essential in addressing these issues. In this study, we propose the utilization of convolutional neural networks (CNNs) for detection of broken rotor bars. To accomplish this, we generated a dataset comprising current samples versus angular position using finite element method magnetics (FEMM) software for a squirrel-cage rotor with 28 bars, including scenarios with 0 to 6 broken bars at every possible relative position. The dataset consists of a total of 16,050 samples per motor. We evaluated the performance of six different CNN architectures, namely Inception V4, NasNETMobile, ResNET152, SeNET154, VGG16, and VGG19. Our automatic classification system demonstrated an impressive 99% accuracy in detecting broken rotor bars, with VGG19 performing exceptionally well. Specifically, VGG19 exhibited high accuracy, precision, recall, and F1-Score, with values approaching 0.994 and 0.998. Notably, VGG19 exhibited crucial activations in its feature maps, particularly after domain-specific training, highlighting its effectiveness in fault detection. Comparing CNN architectures assists in selecting the most suitable one for this application based on processing time, effectiveness, and training losses. This research suggests that deep learning can detect broken bars in induction machines with accuracy comparable to that of traditional methods by analyzing current signals using CNNs.K Barrera-Llanga appreciates the financial support of the Secretary of Higher Education, Science, Technology and Innovation of Ecuador as a personal sponsor entity.Barrera-Llanga, K.; Burriel-Valencia, J.; Sapena-Bano, A.; Martinez-Roman, J. (2023). A Comparative Analysis of Deep Learning Convolutional Neural Network Architectures for Fault Diagnosis of Broken Rotor Bars in Induction Motors. Sensors. 23(19):1-20. https://doi.org/10.3390/s23198196120231

Hoping for A to Z While Rewarding Only A: Complex Organizations and Multiple Goals

This paper explores the trade-offs inherent in the pursuit and fulfillment of multiple performance goals in complex organizations. We examine two related research questions: (1) What are the organizational implications of pursuing multiple performance goals? (2) Are local and myopic (as opposed to global) goal prioritization strategies effective in dealing with multiple goals? We employ a series of computational experiments to examine these questions. Our results from these experiments both formalize the intuition behind existing wisdom and provide new insights. We show that imposing a multitude of weakly correlated performance measures on even simple organizations (i.e., an organization comprised of independent employees) leads to a performance freeze in that actors are not able to identify choices that enhance organizational performance across the full array of goals. This problem increases as the degree of interdependence of organizational action increases. We also find that goal myopia, spatial differentiation of performance goals, and temporal differentiation of performance goals help rescue organizations from this status quo trap. In addition to highlighting a new class of organizational problems, we argue that in a world of boundedly rational actors, incomplete guides to action in the sense of providing only a subset of underlying goals prove more effective at directing and coordinating behavior than more complete representations of underlying objectives. Management, in the form of the articulation of a subset of goals, provides a degree of clarity and focus in a complex world

STILN: A Novel Spatial-Temporal Information Learning Network for EEG-based Emotion Recognition

The spatial correlations and the temporal contexts are indispensable in Electroencephalogram (EEG)-based emotion recognition. However, the learning of complex spatial correlations among several channels is a challenging problem. Besides, the temporal contexts learning is beneficial to emphasize the critical EEG frames because the subjects only reach the prospective emotion during part of stimuli. Hence, we propose a novel Spatial-Temporal Information Learning Network (STILN) to extract the discriminative features by capturing the spatial correlations and temporal contexts. Specifically, the generated 2D power topographic maps capture the dependencies among electrodes, and they are fed to the CNN-based spatial feature extraction network. Furthermore, Convolutional Block Attention Module (CBAM) recalibrates the weights of power topographic maps to emphasize the crucial brain regions and frequency bands. Meanwhile, Batch Normalizations (BNs) and Instance Normalizations (INs) are appropriately combined to relieve the individual differences. In the temporal contexts learning, we adopt the Bidirectional Long Short-Term Memory Network (Bi-LSTM) network to capture the dependencies among the EEG frames. To validate the effectiveness of the proposed method, subject-independent experiments are conducted on the public DEAP dataset. The proposed method has achieved the outstanding performance, and the accuracies of arousal and valence classification have reached 0.6831 and 0.6752 respectively