Vehicle positioning in urban environments using particle filtering-based global positioning system, odometry, and map data fusion

This article presents a new method for land vehicle navigation using global positioning system (GPS), dead reckoning sensor (DR), and digital road map information, particularly in urban environments where GPS failures can occur. The odometer sensors and map measure can be used to provide continuous navigation and correct the vehicle location in the presence of GPS masking. To solve this estimation problem for vehicle navigation, we propose to use particle filtering for GPS/odometer/map integration. The particle filter is a method based on the Bayesian estimation technique and the Monte Carlo method, which deals with non-linear models and is not limited to Gaussian statistics. When the GPS sensor cannot provide a location due to the number of satellites in view, the filter fuses the limited GPS pseudo-range data to enhance the vehicle positioning. The developed filter is then tested in a transportation network scenario in the presence of GPS failures, which shows the advantages of the proposed approach for vehicle location compared to the extended Kalman filter

Improvement of Assembly Line Efficiency by Using Lean Manufacturing Tools and Line Balancing Techniques

This paper presents a solid methodology for improving the efficiency and productivity of assembly lines using Lean Manufacturing tools, in particular the Define, Measure, Analyze, Improve, and Control approach (DMAIC) and line balancing techniques, followed by a concrete application in a case study of a wiring industry assembly line. The first phase of the approach ensured a clear definition of the problem using the who, what, where, when, why, and how tool (5W1H) and a description of the manufacturing process. The measurement phase allowed the calculation of the Takt time (TT) and the timing of the cycle times of the 17 stations of the line with the use of data collected on the standardized work combination table (SWCT) documents. This facilitated the analysis phase by first establishing a Yamazumi chart showing the distribution of the load between the line's stations and allowing the identification of bottleneck stations, and then analyzing the situation through the 5-Why tools and the Ishikawa diagram. Thanks to the innovation phase and the ideal balancing conditions developed in this paper, it was possible to balance the line's stations using an action plan whose effectiveness was monitored during the control phase, improving efficiency from 78% to 95% with a saving in manpower by reducing the number of operators from 17 to 14

SOPHROLOGY AS A NEW MODEL FOR ACCOMPANIMENT OF AFRICAN MANAGERS, WHAT PROSPECTS?

The role of the manager within the company is not a long quiet river, purpose rather a turbulent river in which the manager must have the capacity for adaptation to live in harmony the permanent change or the only certainty that we can have is that the company will continue to change, especially with the digital transformation and artificial intelligence. The main purpose of our article consists in the proposal of a model of accompaniment of individual and collective based on sophrology. On the one hand for the control of stress within the company which is in constant evolution, on the other hand, to learn how to manage the change with serenity, develop self- confidence, and guide the intentionality toward the actions of the future by the techniques of relaxation dynamics of Caycedo

Modular Software Architecture for Local Smart Building Servers

International audienceThis paper presented the architecture and construction of a novel smart building system that could monitor and control buildings’ use in a safe and optimal way. The system operates on a Raspberry local server, which could be connected via the cloud technology to a central platform. The local system includes nine modules that inter-communicate. The system detects sensor faults, and provides a friendly interface to occupants. The paper presented the software architecture IoT used for the building monitoring and the use of this system for the management of fifteen social housing units during a year. The system allowed the investigation of indoor comfort and both energy and hot water consumptions. Data analysis resulted in the detection of abnormal energy consumptions. The system could be easily used in buildings’ management. It works in a plug-and-play mode

Use of smart monitoring and users’ feedback for to investigate the impact of the indoor environment on learning efficiency

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Comparison of M5 Model Tree and Nonlinear Autoregressive with eXogenous inputs (NARX) Neural Network for urban stormwater discharge modelling

This paper presents a comparative study of two data-driven modelling techniques in forecasting urban drainage stormwater discharge based on rainfall prediction. Both M5T and NARX (Nonlinear Autoregressive with eXogenous inputs) Neural Network are used for 30 minutes storm water forecasting. Data are collected from watershed area of 3315 ha, located in the city of Casablanca in Morocco. The results show that both models provide good results, but however with better performances of the NARX model

Model predictive control based on artificial intelligence and EPA-SWMM model to reduce CSOs impacts in sewer systems

International audienceAbstract Urbanization and an increase in precipitation intensities due to climate change, in addition to limited urban drainage systems (UDS) capacity, are the main causes of combined sewer overflows (CSOs) that cause serious water pollution problems in many cities around the world. Model predictive control (MPC) systems offer a new approach to mitigate the impact of CSOs by generating optimal temporally and spatially varied dynamic control strategies of sewer system actuators. This paper presents a novel MPC based on neural networks for predicting flows, a stormwater management model (SWMM) for flow conveyance, and a genetic algorithm for optimizing the operation of sewer systems and defining the best control strategies. The proposed model was tested on the sewer system of the city of Casablanca in Morocco. The results have shown the efficiency of the developed MPC to reduce CSOs while considering short optimization time thanks to parallel computing

Digital Technologies’ Risks and Opportunities: Case Study of an RFID System

Smart technologies have been the subject of a growing interest for the past few years due to the growing market demand. They are believed to improve human life, existence, and companies’ performance. Considering the recent advances, X.0 concept has proven to be a mindset changing so that companies can now see that they can improve their competitiveness, ensure an innovative, sustainable and resilient environment, and smarten and develop their lean manufacturing tools. Nevertheless, if X.0 adoption is still not at its highest level, it is because of the relevant challenges and difficulties that occur during the implementation process. Within this scope, this paper aims, through a systematic literature review, to identify risks and opportunities of X.0 technologies to constitute a referential to be taken into consideration for a successful implementation. Results are validated by the modelling and simulation of an RFID system applied within the automotive industry, for which we identified risks and opportunities from one side and the system contribution in terms of smart Lean Manufacturing. From one hand, the value added of this paper, on the contrary of previous researches, is mainly regrouping risks and opportunities of most relevant digital technologies to conclude on those of X.0 revolution as a concept as described in following sections. From another hand, we were able to prove, through a real case study, that X.0 concept directly contribute in smartening and improving lean manufacturing principles

Wastewater flow forecasting model based on the nonlinear autoregressive with exogenous inputs (NARX) neural network

International audienceAbstract Wastewater flow forecasts are key components in the short- and long-term management of sewer systems. Forecasting flows in sewer networks constitutes a considerable uncertainty for operators due to the nonlinear relationship between causal variables and wastewater flows. This work aimed to fill the gaps in the wastewater flow forecasting research by proposing a novel wastewater flow forecasting model (WWFFM) based on the nonlinear autoregressive with exogenous inputs neural network, real-time, and forecasted water consumption with an application to the sewer system of Casablanca in Morocco. Furthermore, this research compared the two approaches of the forecasting model. The first approach consists of forecasting wastewater flows on the basis of real-time water consumption and infiltration flows, and the second approach considers the same input in addition to water distribution flow forecasts. The results indicate that both approaches show accurate and similar performances in predicting wastewater flows, while the forecasting horizon does not exceed the watershed lag time. For prediction horizons that exceed the lag time value, the WWFFM with water distribution forecasts provided more reliable forecasts for long-time horizons. The proposed WWFFM could benefit operators by providing valuable input data for predictive models to enhance sewer system efficiency