EDI and intelligent agents integration to manage food chains

Electronic Data Interchange (EDI) is a type of inter-organizational information system, which permits the automatic and structured communication of data between organizations. Although EDI is used for internal communication, its main application is in facilitating closer collaboration between organizational entities, e.g. suppliers, credit institutions, and transportation carriers. This study illustrates how agent technology can be used to solve real food supply chain inefficiencies and optimise the logistics network. For instance, we explain how agribusiness companies can use agent technology in association with EDI to collect data from retailers, group them into meaningful categories, and then perform different functions. As a result, the distribution chain can be managed more efficiently. Intelligent agents also make available timely data to inventory management resulting in reducing stocks and tied capital. Intelligent agents are adoptive to changes so they are valuable in a dynamic environment where new products or partners have entered into the supply chain. This flexibility gives agent technology a relative advantage which, for pioneer companies, can be a competitive advantage. The study concludes with recommendations and directions for further research

Intelligent Optimisation Agents in Supply Networks

This paper describes a model of intelligent supply network that improves efficiency within the supply chain. We argue that intelligence creates efficiency and results in chain optimisation. In particular, intelligent agents technology is used to optimise performance of a beverage logistics network. Optimisation agents can help solve specific problems of supply network: reduce inventories and lessen bullwhip effect, improve communication, and enable chain coordination without adverse risk sharing. We model the beer supply network to demonstrate that products can acquire intelligence to direct themselves throughout the distribution network. Further, they gain a capability to be purchased and sold while in transit. Overviews of the supporting technologies that make intelligent supply network a reality are fully discussed. In particular, optimisation agents have the characteristics of autonomous action, being proactive, reactive, and able to communicate. We demonstrate that agents enhance the flexibility, information visibility, and efficiency of the supply chain management. Suggestions and recommendations for further research are provided

Development of a Soft Actor Critic Deep Reinforcement Learning Approach for Harnessing Energy Flexibility in a Large Office Building

This research is concerned with the novel application and investigation of `Soft Actor Critic' (SAC) based Deep Reinforcement Learning (DRL) to control the cooling setpoint (and hence cooling loads) of a large commercial building to harness energy flexibility. The research is motivated by the challenge associated with the development and application of conventional model-based control approaches at scale to the wider building stock. SAC is a model-free DRL technique that is able to handle continuous action spaces and which has seen limited application to real-life or high-fidelity simulation implementations in the context of automated and intelligent control of building energy systems. Such control techniques are seen as one possible solution to supporting the operation of a smart, sustainable and future electrical grid. This research tests the suitability of the SAC DRL technique through training and deployment of the agent on an EnergyPlus based environment of the office building. The SAC DRL was found to learn an optimal control policy that was able to minimise energy costs by 9.7% compared to the default rule-based control (RBC) scheme and was able to improve or maintain thermal comfort limits over a test period of one week. The algorithm was shown to be robust to the different hyperparameters and this optimal control policy was learnt through the use of a minimal state space consisting of readily available variables. The robustness of the algorithm was tested through investigation of the speed of learning and ability to deploy to different seasons and climates. It was found that the SAC DRL requires minimal training sample points and outperforms the RBC after three months of operation and also without disruption to thermal comfort during this period. The agent is transferable to other climates and seasons although further retraining or hyperparameter tuning is recommended.Comment: submitted to Energy and A

Forecast electricity demand in commercial building with machine learning models to enable demand response programs

Electricity load forecasting is an important part of power system dispatching. Accurately forecasting electricity load have great impact on a number of departments in power systems. Compared to electricity load simulation (white-box model), electricity load forecasting (black-box model) does not require expertise in building construction. The development cycle of the electricity load forecasting model is much shorter than the design cycle of the electricity load simulation. Recent developments in machine learning have lead to the creation of models with strong fitting and accuracy to deal with nonlinear characteristics. Based on the real load dataset, this paper evaluates and compares the two mainstream short-term load forecasting techniques. Before the experiment, this paper first enumerates the common methods of short-term load forecasting and explains the principles of Long Short-term Memory Networks (LSTMs) and Support Vector Machines (SVM) used in this paper. Secondly, based on the characteristics of the electricity load dataset, data pre-processing and feature selection takes place. This paper describes the results of a controlled experiment to study the importance of feature selection. The LSTMs model and SVM model are applied to one-hour ahead load forecasting and one-day ahead peak and valley load forecasting. The predictive accuracy of these models are calculated based on the error between the actual and predicted loads, and the runtime of the model is recorded. The results show that the LSTMs model have a higher prediction accuracy when the load data is sufficient. However, the overall performance of the SVM model is better when the load data used to train the model is insufficient and the time cost is prioritized

Current challenges and future research directions in augmented reality for education

The progression and adoption of innovative learning methodologies signify that a respective part of society is open to new technologies and ideas and thus is advancing. The latest innovation in teaching is the use of Augmented Reality (AR). Applications using this technology have been deployed successfully in STEM (Science, Technology, Engineering, and Mathematics) education for delivering the practical and creative parts of teaching. Since AR technology already has a large volume of published studies about education that reports advantages, limitations, effectiveness, and challenges, classifying these projects will allow for a review of the success in the different educational settings and discover current challenges and future research areas. Due to COVID-19, the landscape of technology-enhanced learning has shifted more toward blended learning, personalized learning spaces and user-centered approach with safety measures. The main findings of this paper include a review of the current literature, investigating the challenges, identifying future research areas, and finally, reporting on the development of two case studies that can highlight the first steps needed to address these research areas. The result of this research ultimately details the research gap required to facilitate real-time touchless hand interaction, kinesthetic learning, and machine learning agents with a remote learning pedagogy

Exploring the effect of an augmented reality literacy programme for reading and spelling difficulties for children diagnosed with ADHD

AbstractChildren diagnosed with attention deficit hyperactivity disorder (ADHD) experience a variety of difficulties related to three primary symptoms: hyperactivity, inattention and impulsivity. The most common type of ADHD has a combination of all three symptom areas. These core symptoms may negatively impact the academic and social performance of children throughout their school life. The AHA (ADHD-Augmented) project focused specifically on the impact of digital technologies' intervention on literacy skills of children that participated in the pilot study and were diagnosed with ADHD prior to the intervention. Existing research has shown that augmented reality (AR) can improve academic outcomes by stimulating pupils' attention. AHA project aimed at implementing an evidence-based intervention to improve ADHD children's reading and spelling abilities through the enhancement of an existing literacy programme with AR functionality. The present paper reports preliminary findings of the pilot study aimed at evaluating the effectiveness of the AHA system in promoting the acquisition of literacy skills in a sample of children diagnosed with ADHD compared to the literacy programme as usual. Background information on the main characteristics and difficulties related to the teaching and learning process associated with children diagnosed with ADHD are first introduced; the design and methodology of the AHA project intervention are also described. The preliminary findings have shown that AHA project succeeded in delivering an AR solution within an existing online literacy programme, which integrates a set of specific technologies and supports interactive educational content, services, assessment, and feedback

Automated Bridge Deck Evaluation through UAV Derived Point Cloud

Imagery-based, three-dimensional (3D) reconstructions from Unmanned Aerial Vehicles (UAVs) hold the potential to provide a safer, more economical, and less disruptive approach for bridge inspection. This paper describes a methodology using a low-cost UAV to generate an imagery-based, dense point cloud for bridge deck inspection. Structure from motion (SfM) is employed to create a three-dimensional (3D) point cloud. Outlier data are removed through a density-based filtering method. Next, the unsupervised learning algorithm k-means and an object-based region growing algorithm are compared for accuracy with respect to bridge deck extraction. Last, an automatic pavement evaluation method is proposed to estimate the deck’s pavement condition. The procedure is demonstrated through an actual case study, in which a 3D point cloud of 16 million valid points was generated from 212 images. With that data set, the region growing method successfully extracted the deck area with an F-score close to 95%, while the unsupervised learning approach only achieved 76%. In the last, to evaluate the surface condition of the extracted pavement, a polynomial surface fitting method was designed to evaluate and visualise the damages.This project was made possible through the generous support of the European Union’s Horizon 2020 Research and Innovation programme, Marie Skłodowska-Curie grant 642453, and UCD Seed funding grant SF1404

Data analytics for sustainable global supply chains

Based on the key metrics to monitor energy sector improvements from the International Energy Agency (IEA), transport emissions must decrease 43% by 2030. Freight logistics operations in Europe are struggling with ways to reduce their carbon footprints in order to adhere to regulations on governing logistics, while providing the increasing demand for sustainable products from the customers. This study investigates the anonymised microdata from the European Road Freight Transport Survey (2011–2014) to acquire patterns in logistic operations based on over 11 million journeys within 27 EU and EFTA countries involved. Different algorithms were implemented (Horizontal Cooperation, Pooling and Physical Internet) to analyse efficiency, in terms of vehicle utilisation, degree of vehicles’ loading during each journey and sustainability in terms of the amount of emissions per journey. This study shows that existing data can provide invaluable information on the efficiency of logistics operations and the positive effects data analytics can provide. Physical Internet algorithm has performed better in terms of reducing emissions and improving the logistics’ efficiency, especially when the sample sizes are large, but this would require a shift to an open global supply web