Making vending machines smarter with the use of Machine Learning and Artificial Intelligence: Set-up and Architecture

Machine Learning and Robust Optimization techniques can significantly improve logistics operations and improve stock quantity and maintenance intervals. Machine Learning will be used to forecast item demands for each of the vending machines, taking into account past demands and calendar effects. By performing such predictions which are forwarded to a Robust Optimization model, and whose outputs will be the cash transport that each vending machine should require. These transports guarantee that demand is fulfilled up to the desired confidence level, preventing downtime of vending machines due to unplanned maintenance and out-of-stock situations, while also satisfying additional constraints arising in this particular domain. As a result of such operations, we expect productivity improvements of vending machines from 20-40%

Graph Transformer Network for Flood Forecasting with Heterogeneous Covariates

Floods can be very destructive causing heavy damage to life, property, and livelihoods. Global climate change and the consequent sea-level rise have increased the occurrence of extreme weather events, resulting in elevated and frequent flood risk. Therefore, accurate and timely flood forecasting in coastal river systems is critical to facilitate good flood management. However, the computational tools currently used are either slow or inaccurate. In this paper, we propose a Flood prediction tool using Graph Transformer Network (FloodGTN) for river systems. More specifically, FloodGTN learns the spatio-temporal dependencies of water levels at different monitoring stations using Graph Neural Networks (GNNs) and an LSTM. It is currently implemented to consider external covariates such as rainfall, tide, and the settings of hydraulic structures (e.g., outflows of dams, gates, pumps, etc.) along the river. We use a Transformer to learn the attention given to external covariates in computing water levels. We apply the FloodGTN tool to data from the South Florida Water Management District, which manages a coastal area that is prone to frequent storms and hurricanes. Experimental results show that FloodGTN outperforms the physics-based model (HEC-RAS) by achieving higher accuracy with 50% improvement while speeding up run times by at least 500x

Pembelajaran Berdiferensiasi pada Jenjang Pendidikan Sekolah Dasar

Learning at elementary school level should be carried out interactively, fun, effectively, and able to motivate students to participate actively, independently, and provide sufficient space for the development of talents, interests and creativity in learning activities. This goes straight with the basic concept of differentiated learning, namely that differentiated learning activities are an attempt to adjust the position of the learning process in the classroom to facilitate the different learning needs of each individual student. Therefore, researchers are interested in conducting a literature review regarding the implementation of differentiated learning at the elementary school level. Based on the results of the views and analysis of the literature review, it can be concluded that (1) the differentiated approach can be combined with several learning models that support differentiated learning (2) the implementation of differentiated learning is able to increase and improve student learning outcomes; (3) the differentiated approach can and is well used at the elementary school level because it is able to accommodate all students' learning needs by taking into account students' interests, talents, profiles, abilities, learning styles

PORÓWNANIE SKUTECZNOŚCI ALGORYTMÓW UCZENIA MASZYNOWEGO DLA KONSERWACJI PREDYKCYJNEJ

The consequences of failures and unscheduled maintenance are the reasons why engineers have been trying to increase the reliability of industrial equipment for years. In modern solutions, predictive maintenance is a frequently used method. It allows to forecast failures and alert about their possibility. This paper presents a summary of the machine learning algorithms that can be used in predictive maintenance and comparison of their performance. The analysis was made on the basis of data set from Microsoft Azure AI Gallery. The paper presents a comprehensive approach to the issue including feature engineering, preprocessing, dimensionality reduction techniques, as well as tuning of model parameters in order to obtain the highest possible performance. The conducted research allowed to conclude that in the analysed case , the best algorithm achieved 99.92% accuracy out of over 122 thousand test data records. In conclusion, predictive maintenance based on machine learning represents the future of machine reliability in industry.Skutki związane z awariami oraz niezaplanowaną konserwacją to powody, dla których od lat inżynierowie próbują zwiększyć niezawodność osprzętu przemysłowego. W nowoczesnych rozwiązaniach obok tradycyjnych metod stosowana jest również tzw. konserwacja predykcyjna, która pozwala przewidywać awarie i alarmować o możliwości ich powstawania. W niniejszej pracy przedstawiono zestawienie algorytmów uczenia maszynowego, które można zastosować w konserwacji predykcyjnej oraz porównanie ich skuteczności. Analizy dokonano na podstawie zbioru danych Azure AI Gallery udostępnionych przez firmę Microsoft. Praca przedstawia kompleksowe podejście do analizowanego zagadnienia uwzględniające wydobywanie cech charakterystycznych, wstępne przygotowanie danych, zastosowanie technik redukcji wymiarowości, a także dostrajanie parametrów poszczególnych modeli w celu uzyskania najwyższej możliwej skuteczności. Przeprowadzone badania pozwoliły wskazać  najlepszy  algorytm, który uzyskał dokładność na poziomie 99,92%, spośród ponad 122 tys. rekordów danych testowych. Na podstawie tego można stwierdzić, że konserwacja predykcyjna prowadzona w oparciu o uczenie maszynowe stanowi przyszłość w zakresie podniesienia niezawodności maszyn w przemyśle

INTEGRASI DAN PENGEMBANGAN SISTEM MACHINE LEARNING PADA KEGIATAN MAINTENANCE UNIT BGMF PT. FI

The Big Gossan Mill Facility (BGMF) unit has a vital function to deliver tailing supporting Big Gossan underground mining. Plant maintenance strategies have been implemented to support its availability. This study aims to study integration of machine learning model into the plant maintenance and to formulate development of Machine Learning System in BGMF unit. The maintenance planning standards is used to integrate Machine Learning model through interview. The Industrial Internet Reference Architecture (IIRA) is applied to develop machine learning system. It uses interview method to formulate business viewpoint and usage viewpoint and observation to elaborate functional viewpoint and implementation viewpoint. The study results integration of machine learning model is done by state it as PD-200 Propelling Liquid alarm. It then should be followed up by the planning crew. The machine learning system development starts with formulation of Key Objectives and Fundamental Capabilities on the business viewpoint. The usage viewpoint defines two scenarios on machine learning system. The functional viewpoint elaborates system functionality. The implementation viewpoint designed network topology. It then emphasizes on key system characteristics. This research concludes that model integration into plant maintenance can minimize PD-200’s downtime and it’s system design can be done by IIRA. Keywords: IIRA, machine learning, maintenance improvement, predictive maintenance, predictive mode

A data-driven approach to improve control room operators' response

Digitalization has significantly improved productivity and efficiency within the chemical industry. Distributed Control Systems and extensive use of sensor networks enable advanced control strategies and increase optimization opportunities. On the other hand, chemical plants are increasingly complex, equipment is highly interlinked, and it is more difficult to describe the system dynamics through first principles. Finding the root causes of process upsets and predicting dangerous deviations in process conditions is often challenging. Advanced and dynamic tools are needed to grant safe and stable operations in such a complex and multivariate environment. In this context, Machine Learning techniques may be used to exploit and retrieve knowledge from the large amount of data that chemical plants produce and store on a daily basis. Data-driven methods may be adopted to develop predictive models and support a proactive approach to process safety. The study aims to develop Machine Learning techniques to improve the response of control room operators during critical events. Specifically, alarm data originated in an upper-tier Seveso site have been collected, cleaned, and analyzed to identify periods of intense alarm activity. Alarm behavior following operator responses has been evaluated to assess whether the actions were adequate to prevent future alarm occurrences. In doing so, alarm events that reoccur within 30 minutes after an operator acknowledgment have been identified and labeled. Subsequently, a hybrid classification algorithm was trained to predict the probability that a critical alarm reoccurs after being acknowledged by the operator. This predictive tool might be used to support the operator's decision-making process and focus his/her attention on critical alarms that are more likely to occur again in the near future

Predictive Maintenance for Aircraft Engine Using Machine Learning: Trends and Challenges

This article aims to prove that Machine Learning (ML) methods are effective for Predictive Maintenance (PdM) and to obtain other developing methods that suitable applied on PdM, especially for aircraft engine, and potential method that can apply on future research, and also compared between articles in International and Indonesia institution. Maintenance factors are important to prognostic the states of a machine. PdM is one of the factor strategies based on realtime data to diagnosis a failure of the machine through forecasting remaining useful life (RUL), especially on aircraft machine where the safety is priority due to enormous cost and human life. ML is the technique that accurately prediction through the data. Applied ML on PdM is the huge contribution for saving cost and human life guarantee of safety. This work provides the literature survey for recent research which trends and challenges on PdM of aircraft engine using ML that compared the research from international and Indonesia from 2016 to 2021. Result of this work shows that ML method, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) are the best method to calculate PdM with more than 99% on rate accuracy, and low level of Indonesia institution research which focused on PdM on aircraft engine using M

Federated Learning for Predictive Maintenance and Quality Inspection in Industrial Applications

Data-driven machine learning is playing a crucial role in the advancements of Industry 4.0, specifically in enhancing predictive maintenance and quality inspection. Federated learning (FL) enables multiple participants to develop a machine learning model without compromising the privacy and confidentiality of their data. In this paper, we evaluate the performance of different FL aggregation methods and compare them to central and local training approaches. Our study is based on four datasets with varying data distributions. The results indicate that the performance of FL is highly dependent on the data and its distribution among clients. In some scenarios, FL can be an effective alternative to traditional central or local training methods. Additionally, we introduce a new federated learning dataset from a real-world quality inspection setting