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Using Machine Learning Approaches to Localization in an Embedded System on RobotAtFactory 4.0 Competition: A Case Study

The use of machine learning in embedded systems is an interesting topic, especially with the growth in popularity of the Internet of Things (IoT). The capacity of a system, such as a robot, to self-localize, is a fundamental skill for its navigation and decision-making processes. This work focuses on the feasibility of using machine learning in a Raspberry Pi 4 Model B, solving the localization problem using images and fiducial markers (ArUco markers) in the context of the RobotAtFactory 4.0 competition. The approaches were validated using a realistically simulated scenario. Three algorithms were tested, and all were shown to be a good solution for a limited amount of data. Results also show that when the amount of data grows, only Multi-Layer Perception (MLP) is feasible for the embedded application due to the required training time and the resulting size of the model.

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Sustained changes in body composition during 6 months follow-up after combined lifestyle intervention in older adults with obesity and type 2 diabetes

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The potential of permaculture principles in the agrifood transition

The current western agrifood system is highly successful in providing for human needs. However, the dominant agricultural approach of up-scaling and specialisation is put under pressure by a number of developments in the global landscape. Global developments such as population growth, pollution, soil degradation and climate change, in which agriculture plays a crucial role, make the need for a transition towards a paradigm with a broader range of values evident. Niche initiatives often develop as a reaction to needs not fulfilled by the regime. Therefore, certain niches may have the potential of driving a necessary transition. This research aims to determine if permaculture, being a niche, has this potential. The main question for this research was formulated as follows: How can a production system based on permaculture principles contribute to the agrifood transition? To answer this question, relevant current trends and global developments were used as a basis for developing a future scenario. Empirical qualitative data on permaculture businesses in the Netherlands was gathered as well, of which the results were used for a determination of permaculture’s performance in this future scenario. This was done by comparing a standardised permaculture system with a conventional potato system. As a result of this comparison, the Unique Selling Points of permaculture were identified, which determine the future potential of permaculture.

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The potential of permaculture principles in the agrifood transition

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Decision Support System for corporate reputation based social media listening using a cross-source sentiment analysis engine

This paper presents a Decision Support System (DSS) that helps companies with corporate reputation (CR) estimates of their respective brands by collecting provided feedbacks on their products and services and deriving state-of-the-art key performance indicators. A Sentiment Analysis Engine (SAE) is at the core of the proposed DSS that enables to monitor, estimate, and classify clients’ sentiments in terms of polarity, as expressed in public comments on social media (SM) company channels. The SAE is built on machine learning (ML) text classification models that are cross-source trained and validated with real data streams from a platform like Trustpilot that specializes in user reviews and tested on unseen comments gathered from a collection of public company pages and channels on a social networking platform like Facebook. Such crosssource opinion analysis remains a challenge and is highly relevant in the disciplines of research and engineering in which a sentiment classifier for an unlabeled destination domain is assisted by a tagged source task (Singh and Jaiswal, 2022). The best performance in terms of F1 score was obtained with a multinomial naive Bayes model: 0,87 for validation and 0,74 for testing.

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Decision Support System for corporate reputation based social media listening using a cross-source sentiment analysis engine

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Building Smart Vision Blocks

Gepubliceerd in Mikroniek, nr. 6 2018 In manufacturing environments where collaborative robots are employed, conventional computer vision algorithms have trouble in the robust localisation and detection of products due to changing illumination conditions and shadows caused by a human sharing the workspace with the robotic system. In order to enhance the robustness of vision applications, machine learning with neural networks is explored. The performance of machine-learning algorithms versus conventional computer vision algorithms is studied by observing a generic user scenario for the manufacturing process: the assembly of a product by localisation, identification and manipulation of building blocks.

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Airport passenger flow prediction using simulation data farming and machine learning

Passenger flow management is an important issue at many airports around the world. There are high concentrations of passengers arriving and leaving the airport in waves of large volumes in short periods, particularly in big hubs. This might cause congestion in some locations depending on the layout of the terminal building. With a combination of real airport data, as well as synthetic data obtained through an airport simulator, a Long Short-Term Memory Recurrent Neural Network has been implemented to predict the possible trajectories that passengers may travel within the airport depending on user-defined passenger profiles. The aim of this research is to improve passenger flow predictability and situational awareness to make a more efficient use of the airport, that could also positively impact communication with public and private land transport operators.

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Airport passenger flow prediction using simulation data farming and machine learning

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Airport passenger flow prediction using LTSM Recurrent Neural Networks

Passenger flow management is an important issue at many airports around the world. There are high concentrations of passengers arriving and leaving the airport in waves of large volumes in short periods, particularly in big hubs. This might cause congestion in some locations depending on the layout of the terminal building. With a combination of real airport data, as well as synthetic data obtained through an airport simulator, a Long Short-Term Memory Recurrent Neural Network has been implemented to predict the possible trajectories that passengers may travel within the airport depending on user-defined passenger profiles. The aim of this research is to improve passenger flow predictability and situational awareness to make a more efficient use of the airport, that could also positively impact communication with public and private land transport operators.

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Airport passenger flow prediction using LTSM Recurrent Neural Networks

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Machine Learning Method and Visible Light-Based Sensors for Multiinterface Level Measurement

In this work, a feasible and low-cost approach is proposed for level measurement in multiphase systems inside tanks used for petroleum-derived oil production. The developed level sensor system consisted of light-emitting diodes (LEDs), light-dependent resistor (LDR), and a low-cost microprocessor. Two different types of oil were tested: AW460 and AW68. Linear regression (LR) was applied for 11 scenarios and showed a direct correlation between the level of oil and the sensor’s output. The measurement with AW460 oil presented a perfect linear behavior, while for AW68, a higher standard deviation was obtained justifying the occurrence of the nonlinearity in several scenarios. In order to overcome the nonlinear effect, two machine learning (ML) techniques were tested: K-nearest neighbors regression (KNNR) and multilayer perceptron (MLP) neural network regression. The highest correlation coefficient ( R2 ) and the lowest root mean squared error (RMSE) were obtained for AW68 with MLP. Therefore, MLP was used for regression (level prediction for water, oil, and emulsion) as well as classification (identify the type of oil in the reservoir) simultaneously. The suggested network exhibited a high accuracy for oil identification (99.801%) and improved linear performance in regression ( R2 = 0.9989 and RMSE = 0.065).

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Hyper-spectral frequency selection for the classification of vegetation diseases

Reducing the use of pesticides by early visual detection of diseases in precision agriculture is important. Because of the color similarity between potato-plant diseases, narrow band hyper-spectral imaging is required. Payload constraints on unmanned aerial vehicles require reduc- tion of spectral bands. Therefore, we present a methodology for per-patch classification combined with hyper-spectral band selection. In controlled experiments performed on a set of individual leaves, we measure the performance of five classifiers and three dimensionality-reduction methods with three patch sizes. With the best-performing classifier an error rate of 1.5% is achieved for distinguishing two important potato-plant diseases.

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Products 24

Using Machine Learning Approaches to Localization in an Embedded System on RobotAtFactory 4.0 Competition: A Case Study

Sustained changes in body composition during 6 months follow-up after combined lifestyle intervention in older adults with obesity and type 2 diabetes

The potential of permaculture principles in the agrifood transition

Decision Support System for corporate reputation based social media listening using a cross-source sentiment analysis engine

Building Smart Vision Blocks

Airport passenger flow prediction using simulation data farming and machine learning

Airport passenger flow prediction using LTSM Recurrent Neural Networks

Machine Learning Method and Visible Light-Based Sensors for Multiinterface Level Measurement

Hyper-spectral frequency selection for the classification of vegetation diseases

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Products 24

Using Machine Learning Approaches to Localization in an Embedded System on RobotAtFactory 4.0 Competition: A Case Study

Sustained changes in body composition during 6 months follow-up after combined lifestyle intervention in older adults with obesity and type 2 diabetes

The potential of permaculture principles in the agrifood transition

Decision Support System for corporate reputation based social media listening using a cross-source sentiment analysis engine

Building Smart Vision Blocks

Airport passenger flow prediction using simulation data farming and machine learning

Airport passenger flow prediction using LTSM Recurrent Neural Networks

Machine Learning Method and Visible Light-Based Sensors for Multiinterface Level Measurement

Hyper-spectral frequency selection for the classification of vegetation diseases