Chemical laboratories 4.0: A two-stage machine learning system for predicting the arrival of samples

This paper presents a two-stage Machine Learning (ML) model to predict the arrival time of In-Process Control (IPC) samples at the quality testing laboratories of a chemical company. The model was developed using three iterations of the CRoss-Industry Standard Process for Data Mining (CRISP-DM) methodology, each focusing on a different regression approach. To reduce the ML analyst effort, an Automated Machine Learning (AutoML) was adopted during the modeling stage of CRISP-DM. The AutoML was set to select the best among six distinct state-of-the-art regression algorithms. Using recent real-world data, the three main regression approaches were compared, showing that the proposed two-stage ML model is competitive and provides interesting predictions to support the laboratory management decisions (e.g., preparation of testing instruments). In particular, the proposed method can accurately predict 70% of the examples under a tolerance of 4 time units.This work has been supported by FCT – Funda ̧c ̃ao para a Ciˆencia e Tecnologiawithin the R&D Units Project Scope: UIDB/00319/2020. The authors also wishto thank the chemical company staff involved with this project for providing thedata and also the valuable domain feedback

Deep Attention Models for Human Tracking Using RGBD

Visual tracking performance has long been limited by the lack of better appearance models. These models fail either where they tend to change rapidly, like in motion-based tracking, or where accurate information of the object may not be available, like in color camouflage (where background and foreground colors are similar). This paper proposes a robust, adaptive appearance model which works accurately in situations of color camouflage, even in the presence of complex natural objects. The proposed model includes depth as an additional feature in a hierarchical modular neural framework for online object tracking. The model adapts to the confusing appearance by identifying the stable property of depth between the target and the surrounding object(s). The depth complements the existing RGB features in scenarios when RGB features fail to adapt, hence becoming unstable over a long duration of time. The parameters of the model are learned efficiently in the Deep network, which consists of three modules: (1) The spatial attention layer, which discards the majority of the background by selecting a region containing the object of interest; (2) the appearance attention layer, which extracts appearance and spatial information about the tracked object; and (3) the state estimation layer, which enables the framework to predict future object appearance and location. Three different models were trained and tested to analyze the effect of depth along with RGB information. Also, a model is proposed to utilize only depth as a standalone input for tracking purposes. The proposed models were also evaluated in real-time using KinectV2 and showed very promising results. The results of our proposed network structures and their comparison with the state-of-the-art RGB tracking model demonstrate that adding depth significantly improves the accuracy of tracking in a more challenging environment (i.e., cluttered and camouflaged environments). Furthermore, the results of depth-based models showed that depth data can provide enough information for accurate tracking, even without RGB information

Automatic Generation Control System: The Impact of Battery Energy Storage in Multi Area Network

Renewable energy sources (RES) are currently experiencing significant expansion, and the integration of these sources into power systems necessitates more complex auxiliary facilities. Battery energy storage systems (BESS) have been widely recognized in recent literature as an effective means of enhancing control capabilities. This study focuses on the implementation of an Automatic Generation Control (AGC) system with the integration of BESS in a multi-area network. Maintaining system frequency, especially during peak loads, poses challenges for AGC systems. The objective of this study is to investigate the utilization of BESS to enhance AGC for frequency control in power system networks. Additionally, the effectiveness of BESS in improving frequency control in multi-area networks is demonstrated through several case studies. The AGC and BESS simulations were conducted using MATLAB Simulink to evaluate the proposed frequency control method's effectiveness. &nbsp

Automatic Generation Control System: The Impact of Battery Energy Storage in Multi Area Network

Renewable energy sources (RES) are currently experiencing significant expansion, and the integration of these sources into power systems necessitates more complex auxiliary facilities. Battery energy storage systems (BESS) have been widely recognized in recent literature as an effective means of enhancing control capabilities. This study focuses on the implementation of an Automatic Generation Control (AGC) system with the integration of BESS in a multi-area network. Maintaining system frequency, especially during peak loads, poses challenges for AGC systems. The objective of this study is to investigate the utilization of BESS to enhance AGC for frequency control in power system networks. Additionally, the effectiveness of BESS in improving frequency control in multi-area networks is demonstrated through several case studies. The AGC and BESS simulations were conducted using MATLAB Simulink to evaluate the proposed frequency control method's effectiveness. &nbsp

Quantitative Characterization of Complex Systems—An Information Theoretic Approach

A significant increase in System-of-Systems (SoS) is currently observed in the social and technical domains. As a result of the increasing number of constituent system components, Systems of Systems are becoming larger and more complex. Recent research efforts have highlighted the importance of identifying innovative statistical and theoretical approaches for analyzing complex systems to better understand how they work. This paper portrays the use of an agnostic twostage examination structure for complex systems aimed towards developing an information theorybased approach to analyze complex technical and socio-technical systems. Towards the goal of characterizing system complexity with information entropy, work was carried out in exploring the potential application of entropy to a simulated case study to illustrate its applicability and to establish the use of information theory within the broad horizon of complex systems. Although previous efforts have been made to use entropy for understanding complexity, this paper provides a basic foundation for identifying a framework to characterize complexity, in order to analyze and assess complex systems in different operational domains

Formalized Identification Of Key Factors In Safety-Relevant Failure Scenarios

This research article presents a methodical data-based approach to systematically identify key factors in safety-related failure scenarios, with a focus on complex product-environmental systems in the era of Industry 4.0. The study addresses the uncertainty arising from the growing complexity of modern products. The method uses scenario analysis and focuses on failure analysis within technical product development. The approach involves a derivation of influencing factors based on information from failure databases. The failures described here are documented individually in failure sequence diagrams and then related to each other in a relationship matrix. This creates a network of possible failure scenarios from individual failure cases that can be used in product development. To illustrate the application of the methodology, a case study of 41 Rapex safety alerts for a hair dryer is presented. The failure sequence diagrams and influencing factor relationship matrices show 46 influencing factors that lead to safety-related failures. The predominant harm is burns and electric shocks, which are highlighted by the active and passive sum diagrams. The research demonstrates a robust method for identifying key factors in safety-related failure scenarios using information from failure databases. The methodology provides valuable insights into product development and emphasizes the frequency of influencing factors and their interconnectedness

Flying Free: A Research Overview of Deep Learning in Drone Navigation Autonomy

With the rise of Deep Learning approaches in computer vision applications, significant strides have been made towards vehicular autonomy. Research activity in autonomous drone navigation has increased rapidly in the past five years, and drones are moving fast towards the ultimate goal of near-complete autonomy. However, while much work in the area focuses on specific tasks in drone navigation, the contribution to the overall goal of autonomy is often not assessed, and a comprehensive overview is needed. In this work, a taxonomy of drone navigation autonomy is established by mapping the definitions of vehicular autonomy levels, as defined by the Society of Automotive Engineers, to specific drone tasks in order to create a clear definition of autonomy when applied to drones. A top–down examination of research work in the area is conducted, focusing on drone navigation tasks, in order to understand the extent of research activity in each area. Autonomy levels are cross-checked against the drone navigation tasks addressed in each work to provide a framework for understanding the trajectory of current research. This work serves as a guide to research in drone autonomy with a particular focus on Deep Learning-based solutions, indicating key works and areas of opportunity for development of this area in the future

Estimating fuel-efficient air plane trajectories using machine learning

Airline industry has witnessed a tremendous growth in the recent past. Percentage of people choosing air travel as first choice to commute is continuously increasing. Highly demanding and congested air routes are resulting in inadvertent delays, additional fuel consumption and high emission of greenhouse gases. Trajectory planning involves creation identification of cost-effective flight plans for optimal utilization of fuel and time. This situation warrants the need of an intelligent system for dynamic planning of optimized flight trajectories with least human intervention required. In this paper, an algorithm for dynamic planning of optimized flight trajectories has been proposed. The proposed algorithm divides the airspace into four dimensional cubes and calculate a dynamic score for each cube to cumulatively represent estimated weather, aerodynamic drag and air traffic within that virtual cube. There are several constraints like simultaneous flight separation rules, weather conditions like air temperature, pressure, humidity, wind speed and direction that pose a real challenge for calculating optimal flight trajectories. To validate the proposed methodology, a case analysis was undertaken within Indian airspace. The flight routes were simulated for four different air routes within Indian airspace. The experiment results observed a seven percent reduction in drag values on the predicted path, hence indicates reduction in carbon footprint and better fuel economy