A systematic review of application of multi-criteria decision analysis for aging-dam management

[EN] Decisions for aging-dam management requires a transparent process to prevent the dam failure, thus to avoid severe consequences in socio-economic and environmental terms. Multiple criteria analysis arose to model complex problems like this. This paper reviews specific problems, applications and Multi-Criteria Decision Making techniques for dam management. Multi-Attribute Decision Making techniques had a major presence under the single approach, specially the Analytic Hierarchy Process, and its combination with Technique for Order of Preference by Similarity to Ideal Solution was prominent under the hybrid approach; while a high variety of complementary techniques was identified. A growing hybridization and fuzzification are the two most relevant trends observed. The integration of stakeholders within the decision making process and the inclusion of trade-offs and interactions between components within the evaluation model must receive a deeper exploration. Despite the progressive consolidation of Multi-Criteria Decision Making in dam management, further research is required to differentiate between rational and intuitive decision processes. Additionally, the need to address benefits, opportunities, costs and risks related to repair, upgrading or removal measures in aging dams suggests the Analytic Network Process, not yet explored under this approach, as an interesting path worth investigating.This research was funded by the Spanish Ministry of Economy and Competitiveness along with FEDER funding (Projects BIA201456574-R and ECO2015-66673-R).Zamarrón-Mieza, I.; Yepes, V.; Moreno-Jiménez, JM. (2017). A systematic review of application of multi-criteria decision analysis for aging-dam management. Journal of Cleaner Production. 147:217-230. https://doi.org/10.1016/j.jclepro.2017.01.092S21723014

Position and Trajectory Tracking Control for the Ball and Plate System using Mixed Sensitivity Problem

This paper presents the position and trajectory tracking control scheme for the ball and plate system (BPS) using the double feedback loop structure (a loop within a loop) for effective control of the system. The inner loop was designed using linear algebraic method by solving a set of Diophantine equations. The outer inner loop was designed using   sensitivity approach. Simulation results showed that the plate was stabilized at 0.3546 seconds, and the ball was able to settle at 1.7087 seconds, when given a circular trajectory of radius 0.4 m with an angular frequency of 1.57 rad/sec, with a trajectory tracking error of 0.0095 m, which shows that the controllers have adaptability, strong robustness and control performance for the ball and plate system.           

Machine Learning in Chronic Pain Research: A Scoping Review

Given the high prevalence and associated cost of chronic pain, it has a significant impact on individuals and society. Improvements in the treatment and management of chronic pain may increase patients’ quality of life and reduce societal costs. In this paper, we evaluate state-of-the-art machine learning approaches in chronic pain research. A literature search was conducted using the PubMed, IEEE Xplore, and the Association of Computing Machinery (ACM) Digital Library databases. Relevant studies were identified by screening titles and abstracts for keywords related to chronic pain and machine learning, followed by analysing full texts. Two hundred and eighty-seven publications were identified in the literature search. In total, fifty-three papers on chronic pain research and machine learning were reviewed. The review showed that while many studies have emphasised machine learning-based classification for the diagnosis of chronic pain, far less attention has been paid to the treatment and management of chronic pain. More research is needed on machine learning approaches to the treatment, rehabilitation, and self-management of chronic pain. As with other chronic conditions, patient involvement and self-management are crucial. In order to achieve this, patients with chronic pain need digital tools that can help them make decisions about their own treatment and care

Multistep-Ahead Neural-Network Predictors for Network Traffic Reduction in Distributed Interactive Applications

Predictive contract mechanisms such as dead reckoning are widely employed to support scalable remote entity modeling in distributed interactive applications (DIAs). By employing a form of controlled inconsistency, a reduction in network traffic is achieved. However, by relying on the distribution of instantaneous derivative information, dead reckoning trades remote extrapolation accuracy for low computational complexity and ease-of-implementation. In this article, we present a novel extension of dead reckoning, termed neuro-reckoning, that seeks to replace the use of instantaneous velocity information with predictive velocity information in order to improve the accuracy of entity position extrapolation at remote hosts. Under our proposed neuro-reckoning approach, each controlling host employs a bank of neural network predictors trained to estimate future changes in entity velocity up to and including some maximum prediction horizon. The effect of each estimated change in velocity on the current entity position is simulated to produce an estimate for the likely position of the entity over some short time-span. Upon detecting an error threshold violation, the controlling host transmits a predictive velocity vector that extrapolates through the estimated position, as opposed to transmitting the instantaneous velocity vector. Such an approach succeeds in reducing the spatial error associated with remote extrapolation of entity state. Consequently, a further reduction in network traffic can be achieved. Simulation results conducted using several human users in a highly interactive DIA indicate significant potential for improved scalability when compared to the use of IEEE DIS standard dead reckoning. Our proposed neuro-reckoning framework exhibits low computational resource overhead for real-time use and can be seamlessly integrated into many existing dead reckoning mechanisms

Multistep-Ahead Neural-Network Predictors for Network Traffic Reduction in Distributed Interactive Applications

Predictive contract mechanisms such as dead reckoning are widely employed to support scalable remote entity modeling in distributed interactive applications (DIAs). By employing a form of controlled inconsistency, a reduction in network traffic is achieved. However, by relying on the distribution of instantaneous derivative information, dead reckoning trades remote extrapolation accuracy for low computational complexity and ease-of-implementation. In this article, we present a novel extension of dead reckoning, termed neuro-reckoning, that seeks to replace the use of instantaneous velocity information with predictive velocity information in order to improve the accuracy of entity position extrapolation at remote hosts. Under our proposed neuro-reckoning approach, each controlling host employs a bank of neural network predictors trained to estimate future changes in entity velocity up to and including some maximum prediction horizon. The effect of each estimated change in velocity on the current entity position is simulated to produce an estimate for the likely position of the entity over some short time-span. Upon detecting an error threshold violation, the controlling host transmits a predictive velocity vector that extrapolates through the estimated position, as opposed to transmitting the instantaneous velocity vector. Such an approach succeeds in reducing the spatial error associated with remote extrapolation of entity state. Consequently, a further reduction in network traffic can be achieved. Simulation results conducted using several human users in a highly interactive DIA indicate significant potential for improved scalability when compared to the use of IEEE DIS standard dead reckoning. Our proposed neuro-reckoning framework exhibits low computational resource overhead for real-time use and can be seamlessly integrated into many existing dead reckoning mechanisms

A trajectory-tracking controller for improving the safety and stability of four-wheel steering autonomous vehicles

To achieve anti-crosswind, anti-sideslip, and anti-rollover in trajectory-tracking for Four-Wheel Steering (4WS) autonomous vehicles, a trajectory-tracking controller based on a four-channel Active Disturbance Rejection Control (ADRC) was used to track the desired lateral displacement, longitudinal displacement, yaw angle, and roll angle, and minimize the tracking errors between the actual output values and the desired values through static decoupling steering and braking systems. In addition, the anti-crosswind, anti-sideslip, and anti-rollover simulations were implemented with CarSim®. Finally, the simulation results showed that the 4WS autonomous vehicle with the controller still has good anti-crosswind, anti-sideslip, and anti-rollover performance in path tracking, even under a small turning radius or lowadhesion curved roads. First published online 12 March 202

Human face detection techniques: A comprehensive review and future research directions

Face detection which is an effortless task for humans are complex to perform on machines. Recent veer proliferation of computational resources are paving the way for a frantic advancement of face detection technology. Many astutely developed algorithms have been proposed to detect faces. However, there is a little heed paid in making a comprehensive survey of the available algorithms. This paper aims at providing fourfold discussions on face detection algorithms. At first, we explore a wide variety of available face detection algorithms in five steps including history, working procedure, advantages, limitations, and use in other fields alongside face detection. Secondly, we include a comparative evaluation among different algorithms in each single method. Thirdly, we provide detailed comparisons among the algorithms epitomized to have an all inclusive outlook. Lastly, we conclude this study with several promising research directions to pursue. Earlier survey papers on face detection algorithms are limited to just technical details and popularly used algorithms. In our study, however, we cover detailed technical explanations of face detection algorithms and various recent sub-branches of neural network. We present detailed comparisons among the algorithms in all-inclusive and also under sub-branches. We provide strengths and limitations of these algorithms and a novel literature survey including their use besides face detection

The virtual force feedback for torque estimation and control in a vechicle Steer by Wire (SBW) system

This study presents the method to generates and control a force feedback with torque control for a driver steering feel in a vehicle steer by wire (SBW) system. The control algorithm of force feedback was developed by simulation and validated through experimental to investigated the steering and control performance. This is done by constructed a steering wheel rig hardware in the loop (HIL) and interfaced to Matlab XPC target software. Two method are proposed to generate and control a force feedback whereby the current measurement is a main element used to estimates the steering torque . For the first control algorithm, the torque at the front axle system and self aligning are used to generate a force feedback and the PID controller with fuzzy system (PID+Fuzzy) are used to control a feedback torque. Meanwhile, the reference model was used to improves the centering steering wheel position. For a second control algorithm, the torque map and torque of steering wheel and front axle system are used to generate the force feedback. Meanwhile, the LQR control with gain scheduling (LQR+GS) are used to control the torque. Furthermore, the compensation torque is used to improves the steering feel and to stabilize the system by varying a compensation gains. The results demonstrate shown, that the torque control using a LQR+GS method is improves against a torque map and 90% similar to Electric Power Steering (EPS) system. This is because there are multiple gains varying that able to improve a steering control performance. On the others hand, the hyperbolic tangent and linear equation proposed improves a vehicle maneuverability at low and high speed

One step greener: reducing 5G and beyond networks’ carbon footprint by 2-tiering energy efficiency with CO2 offsetting

Fifth generation (5G) and Beyond-5G (B5G) will be characterized by highly dense deployments, both on network plane and user plane. Internet of Things, massive sensor deployments and base stations will drive even more energy consumption. User behavior towards mobile service usage is witnessing a paradigm shift with heavy capacity, demanding services resulting in an increase of both screen time and data transfers, which leads to additional power consumption. Mobile network operators will face additional energetic challenges, mainly related to power consumption and network sustainability, starting right in the planning phase with concepts like energy efficiency and greenness by design coming into play. The main contribution of this work is a two-tier method to address such challenges leading to positively-offset carbon dioxide emissions related to mobile networks using a novel approach. The first tier contributes to overall power reduction and optimization based on energy efficient methods applied to 5G and B5G networks. The second tier aims to offset the remaining operational power usage by completely offsetting its carbon footprint through geosequestration. This way, we show that the objective of minimizing overall networks’ carbon footprint is achievable. Conclusions are drawn and it is shown that carbon sequestration initiatives or program adherence represent a negligible cost impact on overall network cost, with the added value of greener and more environmentally friendly network operation. This can also relieve the pressure on mobile network operators in order to maximize compliance with environmentally neutral activity.info:eu-repo/semantics/publishedVersio