A Deep Spatio-Temporal Fuzzy Neural Network for Passenger Demand Prediction

In spite of its importance, passenger demand prediction is a highly challenging problem, because the demand is simultaneously influenced by the complex interactions among many spatial and temporal factors and other external factors such as weather. To address this problem, we propose a Spatio-TEmporal Fuzzy neural Network (STEF-Net) to accurately predict passenger demands incorporating the complex interactions of all known important factors. We design an end-to-end learning framework with different neural networks modeling different factors. Specifically, we propose to capture spatio-temporal feature interactions via a convolutional long short-term memory network and model external factors via a fuzzy neural network that handles data uncertainty significantly better than deterministic methods. To keep the temporal relations when fusing two networks and emphasize discriminative spatio-temporal feature interactions, we employ a novel feature fusion method with a convolution operation and an attention layer. As far as we know, our work is the first to fuse a deep recurrent neural network and a fuzzy neural network to model complex spatial-temporal feature interactions with additional uncertain input features for predictive learning. Experiments on a large-scale real-world dataset show that our model achieves more than 10% improvement over the state-of-the-art approaches.Comment: https://epubs.siam.org/doi/abs/10.1137/1.9781611975673.1

Laparoscopy Pneumoperitoneum Fuzzy Modeling

Abstract: Gas volume to intra-peritoneal pressure fuzzy modeling for evaluating pneumoperitoneum in videolaparoscopic surgery is proposed in this paper. The proposed approach innovates in using fuzzy logic and fuzzy set theory for evaluating the accuracy of the prognosis value in order to minimize or avoid iatrogenic injuries due to the blind needle puncture. In so doing, it demonstrates the feasibility of fuzzy analysis to contribute to medicine and health care. Fuzzy systems is employed here in synergy with artificial neural network based on backpropaga tion, multilayer perceptron architecture for building up numerical functions. Experimental data employed for analysis were collected in the accomplishment of the pneumoperitoneum in a random population of patients submitted to videolaparoscopic surgeries. Numerical results indicate that the proposed fuzzy mapping for describing the relation from the intra peritoneal pressure measures as function injected gas volumes succeeded in determinining a fuzzy model for this nonlinear system when compared to the statistical model

Power Quality Enhancement in Hybrid Photovoltaic-Battery System based on three–Level Inverter associated with DC bus Voltage Control

This modest paper presents a study on the energy quality produced by a hybrid system consisting of a Photovoltaic (PV) power source connected to a battery. A three-level inverter was used in the system studied for the purpose of improving the quality of energy injected into the grid and decreasing the Total Harmonic Distortion (THD). A Maximum Power Point Tracking (MPPT) algorithm based on a Fuzzy Logic Controller (FLC) is used for the purpose of ensuring optimal production of photovoltaic energy. In addition, another FLC controller is used to ensure DC bus stabilization. The considered system was implemented in the Matlab /SimPowerSystems environment. The results show the effectiveness of the proposed inverter at three levels in improving the quality of energy injected from the system into the grid.Peer reviewedFinal Published versio

Exploring fuzzy cognitive mapping for IS evaluation: A research note

Existing IS Evaluation (ISE) techniques tend to focus on modeling individuals, teams, organization, or systems, in relation to process and environmental boundaries. Whilst such approaches are noteworthy and of merit, they do not necessarily provide insights into those causal interdependencies that are inherent within decision-making task. As has been noted by the extant literature in the field, the ISE task is dependent upon many factors – the resulting outputs of which may be tangible or intangible. The implicit level of uncertainty associated with modeling such decision-making tasks and behaviors, are therefore difficult to comprehend and impart via wholly Quantitative and / or Qualitative analyses. The authors therefore present and propose supporting and on-going research into the application of Fuzzy Logic, in the guise of Fuzzy Cognitive Mapping (FCM) simulations, as a means to model tangible/intangible aspects of the ISE decision-making task. Such a Fuzzy Information Systems Evaluation (F-ISE) is shown via the application of the FCM technique, in terms of three models of investment appraisal that are aligned to an ISE task within a UK manufacturing organization. In doing so, it is anticipated that such a technique may be a useful addition to the plethora of ISE techniques available to both researcher and practitioner alike