Demand model in an automotive supply chain company

[ES] En este artículo exponemos la implementación de un proyecto de predicción de la demanda asistido por Inteligencia Artificial en una PYME (pequeña y mediana empresa) del País Vasco. La empresa pertenece al sector del recambio del automóvil orientada principalmente al sector profesional (B2B) siendo sus principales clientes, talleres de reparación de automóviles. Cuenta con ocho oficinas que gestionan un amplísimo catálogo de primeras marcas ubicadas a nivel internacional. En este contexto, la operativa logística supone una actividad clave del negocio. Por otro lado, el flujo de mercancías entre sus almacenes, la gestión de compras y los costes de transporte son también áreas a optimizar. Consecuentemente, la gestión eficiente del stock es fundamental para los resultados del negocio. En este artículo; proponemos un modelo de predicción de la demanda asistido por un algoritmo de aprendizaje automático que ayude a la toma de decisiones empresariales. La información tratada procede de varias fuentes de datos como son el sistema ERP, la plataforma B2B y la base de datos del call center. El objetivo principal de este modelo es incidir sobre un punto débil generalizado en todas las empresas de este sector.[EN] This paper expound the implementation of a project of demand forecasting powered by Artificial Intelligence (AI) in a small and medium-sized enterprise (SME) situated in Basque Country. The company works in automotive after-market segment, whose main clients are professional Car Workshops. Eight offices handle a massive product catalogue supplied by international leadership manufacturers located all over the world. Considering this scenario, the logistic operative is evidently a key business activity. On the other hand, the flow of goods among their warehouses, the purchasing management and freight costs must be also optimized. This means that an efficient stock management is equally crucial for the result of the company. In this paper, we propose a demand prediction model assisted by machine learning algorithms which facilities crucial business decisions. The information gathering uses several sources such as the ERP; the B2B Platform and the call center Data Base. The main aim of this model is to reinforce a weak point detected not only in this company, but also in others competitor enterprise

Deep learning based deep-sea automatic image enhancement and animal species classification

The automatic classification of marine species based on images is a challenging task for which multiple solutions have been increasingly provided in the past two decades. Oceans are complex ecosystems, difficult to access, and often the images obtained are of low quality. In such cases, animal classification becomes tedious. Therefore, it is often necessary to apply enhancement or pre-processing techniques to the images, before applying classification algorithms. In this work, we propose an image enhancement and classification pipeline that allows automated processing of images from benthic moving platforms. Deep-sea (870 m depth) fauna was targeted in footage taken by the crawler “Wally” (an Internet Operated Vehicle), within the Ocean Network Canada (ONC) area of Barkley Canyon (Vancouver, BC; Canada). The image enhancement process consists mainly of a convolutional residual network, capable of generating enhanced images from a set of raw images. The images generated by the trained convolutional residual network obtained high values in metrics for underwater imagery assessment such as UIQM (~ 2.585) and UCIQE (2.406). The highest SSIM and PSNR values were also obtained when compared to the original dataset. The entire process has shown good classification results on an independent test data set, with an accuracy value of 66.44% and an Area Under the ROC Curve (AUROC) value of 82.91%, which were subsequently improved to 79.44% and 88.64% for accuracy and AUROC respectively. These results obtained with the enhanced images are quite promising and superior to those obtained with the non-enhanced datasets, paving the strategy for the on-board real-time processing of crawler imaging, and outperforming those published in previous papers.This work was developed at Deusto Seidor S.A. (01015, Vitoria-Gasteiz, Spain) within the framework of the Tecnoterra (ICM-CSIC/UPC) and the following project activities: ARIM (Autonomous Robotic sea-floor Infrastructure for benthopelagic Monitoring); MarTERA ERA-Net Cofund; Centro para el Desarrollo Tecnológico Industrial, CDTI; and RESBIO (TEC2017-87861-R; Ministerio de Ciencia, Innovación y Universidades). This work was supported by the Centro para el Desarrollo Tecnológico Industrial (CDTI) (Grant No. EXP 00108707 / SERA-20181020)

Analysis of New Strategies to Reach Nearly Zero Energy Buildings (nZEBs)

The need to reduce CO2 emissions makes it necessary to review most areas of human activity because we are the main source of these emissions. In particular, the building sector is one of its main responsible. Through a State of the Art, several papers published in various scientific journals about nearly Zero Energy Building (nZEB) studies are reviewed. After carrying out an analysis of said documentation, it is considered that a new way of study can be developed with a multidisciplinary approach. For this, another series of articles are analyzed, where different advances in the field of building construction have been developed and are summarized. It is concluded that the Model Predictive Control (MPC) can also be a useful tool to optimize energy consumption in buildings, especially for public use, in order to achieve the goal of achieving nZEB, through the use of Renewable Energy Sources (RES)

Power Control Optimization of an Underwater Piezoelectric Energy Harvester

Over the past few years, it has been established that vibration energy harvesters with intentionally designed components can be used for frequency bandwidth enhancement under excitation for sufficiently high vibration amplitudes. Pipelines are often necessary means of transporting important resources such as water, gas, and oil. A self-powered wireless sensor network could be a sustainable alternative for in-pipe monitoring applications. A new control algorithm has been developed and implemented into an underwater energy harvester. Firstly, a computational study of a piezoelectric energy harvester for underwater applications has been studied for using the kinetic energy of water flow at four different Reynolds numbers Re = 3000, 6000, 9000, and 12,000. The device consists of a piezoelectric beam assembled to an oscillating cylinder inside the water of pipes from 2 to 5 inches in diameter. Therefore, unsteady simulations have been performed to study the dynamic forces under different water speeds. Secondly, a new control law strategy based on the computational results has been developed to extract as much energy as possible from the energy harvester. The results show that the harvester can efficiently extract the power from the kinetic energy of the fluid. The maximum power output is 996.25 mu W and corresponds to the case with Re = 12,000.The funding from the Government of the Basque Country and the University of the Basque Country UPV/EHU through the SAIOTEK (S-PE11UN112) and EHU12/26 research programs, respectively, is gratefully acknowledged. The authors are very grateful to SGIker of UPV/EHU and European funding (ERDF and ESF) for providing technical and human

Editorial: Special issue CISIS 2016

Artículo editoria

Cooktop Sensing Based on a YOLO Object Detection Algorithm

Deep Learning (DL) has provided a significant breakthrough in many areas of research and industry. The development of Convolutional Neural Networks (CNNs) has enabled the improvement of computer vision-based techniques, making the information gathered from cameras more useful. For this reason, recently, studies have been carried out on the use of image-based DL in some areas of people’s daily life. In this paper, an object detection-based algorithm is proposed to modify and improve the user experience in relation to the use of cooking appliances. The algorithm can sense common kitchen objects and identify interesting situations for users. Some of these situations are the detection of utensils on lit hobs, recognition of boiling, smoking and oil in kitchenware, and determination of good cookware size adjustment, among others. In addition, the authors have achieved sensor fusion by using a cooker hob with Bluetooth connectivity, so it is possible to automatically interact with it via an external device such as a computer or a mobile phone. Our main contribution focuses on supporting people when they are cooking, controlling heaters, or alerting them with different types of alarms. To the best of our knowledge, this is the first time a YOLO algorithm has been used to control the cooktop by means of visual sensorization. Moreover, this research paper provides a comparison of the detection performance among different YOLO networks. Additionally, a dataset of more than 7500 images has been generated and multiple data augmentation techniques have been compared. The results show that YOLOv5s can successfully detect common kitchen objects with high accuracy and fast speed, and it can be employed for realistic cooking environment applications. Finally, multiple examples of the identification of interesting situations and how we act on the cooktop are presented.The current study has been sponsored by the Government of the Basque Country-ELKARTEK21/10 KK-2021/00014 (“Estudio de nuevas técnicas de inteligencia artificial basadas en Deep Learning dirigidas a la optimización de procesos industriales”) and ELKARTEK23-DEEPBASK (“Creación de nuevos algoritmos de aprendizaje profundo aplicado a la industria”) research programmes

Hybrid Modeling of Deformable Linear Objects for Their Cooperative Transportation by Teams of Quadrotors

his paper deals with the control of a team of unmanned air vehicles (UAVs), specifically quadrotors, for which their mission is the transportation of a deformable linear object (DLO), i.e., a cable, hose or similar object in quasi-stationary state, while cruising towards destination. Such missions have strong industrial applications in the transportation of hoses or power cables to specific locations, such as the emergency power or water supply in hazard situations such as fires or earthquake damaged structures. This control must be robust to withstand strong and sudden wind disturbances and remain stable after aggressive maneuvers, i.e., sharp changes of direction or acceleration. To cope with these, we have previously developed the online adaptation of the proportional derivative (PD) controllers of the quadrotors thrusters, implemented by a fuzzy logic rule system that experienced adaptation by a stochastic gradient rule. However, sagging conditions appearing when the transporting drones are too close or too far away induce singularities in the DLO catenary models, breaking apart the control system. The paper’s main contribution is the formulation of the hybrid selective model of the DLO sections as either catenaries or parabolas, which allows us to overcome these sagging conditions. We provide the specific decision rule to shift between DLO models. Simulation results demonstrate the performance of the proposed approach under stringent conditions.This work has been partially supported by spanish MICIN project PID2020-116346GB-I00, and project KK-2021/00070 of the Elkartek 2021 funding program of the Basque Government. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 777720

Battery Charge Control in Solar Photovoltaic Systems Based on Fuzzy Logic and Jellyfish Optimization Algorithm

The study focuses on the integration of a fuzzy logic-based Maximum Power Point Tracking (MPPT) system, an optimized proportional Integral-based voltage controller, and the Jellyfish Optimization Algorithm into a solar PV battery setup. This integrated approach aims to enhance energy harvesting efficiency under varying environmental conditions. The study’s innovation lies in effectively addressing challenges posed by diverse environmental factors and loads. The utilization of MATLAB 2022a Simulink for modeling and the Jellyfish Optimization Algorithm for PI-controller tuning further strengthens our findings. Testing scenarios, including constant and variable irradiation, underscore the significant enhancements achieved through the integration of fuzzy MPPT and the Jellyfish Optimization Algorithm with the PI-based voltage controller. These enhancements encompass improved power extraction, optimized voltage regulation, swift settling times, and overall efficiency gains.The authors were supported by the Vitoria-Gasteiz Mobility Lab Foundation, an organization of the government of the Provincial Council of Araba and the City Council of Vitoria-Gasteiz through the following project grant (“Generación de mapas mediante drones e Inteligencia Computacional”)

Energy and thermal modelling of an office building to develop an artificial neural networks model

[EN] Nowadays everyone should be aware of the importance of reducing CO2 emissions which produce the greenhouse effect. In the field of construction, several options are proposed to reach nearly-Zero Energy Building (nZEB) standards. Obviously, before undertaking a modification in any part of a building focused on improving the energy performance, it is generally better to carry out simulations to evaluate its effectiveness. Using Artificial Neural Networks (ANNs) allows a digital twin of the building to be obtained for specific characteristics without using very expensive software. This can simulate the effect of a single or combined intervention on a particular floor or an event on the remaining floors. In this paper, an example has been developed based on ANN. The results show a reasonable correlation between the real data of the Operative Temperature with the Energy Consumption and their estimates obtained through an ANN model, trained using an hourly basis, on each of the floors of an office building. This model confirms it is possible to obtain simulations in existing public buildings with an acceptable degree of precision and without laborious modelling, which would make it easier to achieve the nZEB target, especially in existing public office buildings

A Hybrid Control Approach for the Swing Free Transportation of a Double Pendulum with a Quadrotor

In this article, a control strategy approach is proposed for a system consisting of a quadrotor transporting a double pendulum. In our case, we attempt to achieve a swing free transportation of the pendulum, while the quadrotor closely follows a specific trajectory. This dynamic system is highly nonlinear, therefore, the fulfillment of this complex task represents a demanding challenge. Moreover, achieving dampening of the double pendulum oscillations while following a precise trajectory are conflicting goals. We apply a proportional derivative (PD) and a model predictive control (MPC) controllers for this task. Transportation of a multiple pendulum with an aerial robot is a step forward in the state of art towards the study of the transportation of loads with complex dynamics. We provide the modeling of the quadrotor and the double pendulum. For MPC we define the cost function that has to be minimized to achieve optimal control. We report encouraging positive results on a simulated environmentcomparing the performance of our MPC-PD control circuit against a PD-PD configuration, achieving a three fold reduction of the double pendulum maximum swinging angle.This work has been partially supported by FEDER funds through MINECO project TIN2017-85827-P, and project KK-202000044 of the Elkartek 2020 funding program of the Basque Government. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 777720