Greece

Radar sensors, unlike lidars or cameras, can measure objects&#39; instantaneous velocity and composition. However, the ability to process and retrieve spatial information from radar point clouds has not yet been fully established. In this work, we propose a key technique for improving the performance of standard machine learning point-wise processing methods on radar point clouds. We show that a network can learn to extract object-related signatures for every point using automotive radar measurements. In addition, we propose RadarPCNN, a novel architecture for performing semantic segmentation, specifically designed for radar point clouds. RadarPCNN uses PointNet++, aided with mean shift as feature extractor module and an attention mechanism to fuse information from different neighborhood levels. We show that our model outperforms state-of-the-art solutions on our dataset.

EANN 2020

Leveraging Radar Features to Improve Point Clouds Segmentation with Neural Networks

radar point clouds

semantic segmentation

neural network

ECAI2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI, including:




**An extensive programme of workshops, tutorials, and invited speakers**

**The Prestigious Applications of Intelligent Systems conference (PAIS)**

**The Frontiers in AI track sessions**

**The Lab To Market event**

**The Women in AI time**

**The EU Challenges forum**

… and many more!

ECAI 2020

ECAI 2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI.

Radar sensors, unlike lidars or cameras, can measure objects' instantaneous velocity and composition. However, the ability to process and retrieve spatial information from radar point clouds has not yet been fully established. In this work, we propose a key technique for improving the performance of standard machine learning point-wise processing methods on radar point clouds. We show that a network can learn to extract object-related signatures for every point using automotive radar measurements. In addition, we propose RadarPCNN, a novel architecture for performing semantic segmentation, specifically designed for radar point clouds. RadarPCNN uses PointNet++, aided with mean shift as feature extractor module and an attention mechanism to fuse information from different neighborhood levels. We show that our model outperforms state-of-the-art solutions on our dataset.

technical paper

21st edition of EANN (International Conference on Engineering Applications of Neural Networks) should be held in Halkidiki, Greece. Due to current Covid-19 situation the conference will be held virtually and Underline will host a repository of video talks and preserve them for the future. Videos will be released on Underline one day after the conference. 

Unbalanced electrical loads on feeders of power electrical systems can cause serious problems, including power losses, significantly lower power quality, damaging of electrical equipment, and tripping of protective devices. Nevertheless, the problem of balancing such systems ---which essentially is equivalent to the problem of three-way integer partitioning--- has proven to be NP-complete. Against this background, in this article, an algorithm based on the powerful, declarative framework of Answer Set Programming (ASP) is provided, that efficiently attacks practical instances of the phase-balancing problem. To the best of our knowledge, this is the first attempt of approaching this significant engineering problem by means of the ASP paradigm. The whole study indicates that the examined problem is of great interest from an algorithmic viewpoint, as well as an engineering application that highlights ASP's modelling methodology.

An ASP-Based Approach for Phase Balancing in Power Electrical Systems

Public Key Encryption with Keyword Search (PEKS) allows users to search encrypted files by a specific keyword without compromising
the original data security. Almost all current PEKS schemes enable users to search exact keyword only instead of imprecise keyword (such as
“latest”, “biggest”, etc.). Therefore, if the keyword is fuzzy, these PEKS schemes will be terminated and then report errors. Besides, some PEKS
schemes are not secure mainly because they are vulnerable to Off-line Keyword Guessing Attack (OKGA). This research paper incorporates
with Mamdani Fuzzy Inference System to PEKS for supporting Fuzzy Keyword Search. Secondly, the proposed scheme is proved to be semantic
secure under the random oracle models so that it is able to resist OKGA. In addition, the new scheme allows users to search multiple keywords and therefore, it could be applied to the general public networks.

Fuzzy Logic Application to Searchable Cryptography (PEKS)

Common types of artificial neural networks have been well known to suffer from the presence of outlying measurements (outliers) in the data. However, there are only a few available robust alternatives for training common form of neural networks. In this work, we investigate robust fitting of multilayer perceptrons, i.e. alternative approaches to the most common type of feedforward neural networks. Particularly, we consider robust neural networks based on the robust loss function of the least trimmed squares, for which we express formulas for 
derivatives of the loss functions. Some formulas, which are however incorrect, have been already available. Further, we consider a very recently proposed multilayer perceptron based on the loss function of the least weighted squares, which appears a promising highly robust approach. We also derive the derivatives of the loss functions, which are to the best of our knowledge a novel contribution of this paper. The derivatives may find applications in implementations of the robust neural networks, if a (gradient-based) backpropagation algorithm is used.

Robust multilayer perceptrons: Robust loss functions and their derivatives

A fully-connected Deep Neural Network (DNN) architecture, to be referred to as λ-DNN, used to predict 2D/3D scalar fields is presented. In aerodynamics, the λ-DNN is firstly trained on fields computed using a Computational Fluid Dynamics (CFD) software. Then, it can be incorporated into engineering processes in various ways. One possibility is to use them in optimization problems solved by stochastic population-based methods, in which the λ-DNN may act as the surrogate evaluation model, replacing calls to the CFD tool. Another possibility is in multi-disciplinary problems, to replicate the numerical solver for any of the disciplines. This small list of possible usages is not exhaustive and, of course, different usages can be combined. The input to each DNN contains information to identify the geometrical shape and case-related data, nodal coordinates and, in multi-disciplinary problems, interfacing data connecting solvers for different disciplines on adjacent domains. In this presentation, the λ-DNN is firstly used in the aerodynamic shape optimization of a wing using evolutionary algorithms, in which it replicates the CFD solver. Then, it is used in a conjugate heat transfer problem dealing with a solid domain in contact with a flow within a duct. In this problem, the λ-DNN acts as a surrogate to the solver of the heat conduction equation on the solid domain, by interfacing with a CFD solver of the fluid domain.

λ-DNNs and their Implementation in Aerodynamic and Conjugate Heat Transfer Optimization

The combinatorial pharmacological effects of drugs are influenced by the complex interactions among them. When the patient is allergic to some drugs, the combination of drugs has to be changed. Based on that, we put forward a linear-algebra-equation query task. In this paper, we propose a model called Drug2vec that approximates the relationship among drugs and can solve the linear-algebra-equation query task. For example, we can find drugs with the following relationship: drug A + drug B = drug C. Drug2vec applies a three-layer neural network, which firstly projects a drug into an embedded space and then retrieves another drug that interacts with it. Experimental results show that Drug2vec can approximate the relationship among drugs to linear equations, and the drugs that fit a linear equation have connections with respect to their structures. We also propose a metric called AUE (area under the enrichment curve) to evaluate the performance of our model. Drug2vec can predict drug-drug interactions with high accuracy, and the AUE can be 0.96 in the normal test.

Drug2vec: A Drug Embedding Method with Drug-Drug Interaction as the Context

We present a project to design of the simplified model of thinking located in a little-explored field between neurobiology and psychology. While neurobiology in the incredibly complex microscopic system is dedicated to the structures and signals at the molecular level and the psychology is committed at the opposite extreme with highly sophisticated, they are very abstract and, therefore, difficult to grasp these wholes. There is a vast space between these two extremes. From the perspective of the inherent observers, it is investigable without overcoming the complexity of both points of interest. The primary goal of this research is to construct a multi-layer analog configuration space, the Electronic Equivalent of Consciousness (ECC), wherein the signals have the same properties (bioelectrical) as in the human brain.

https://screencast-o-matic.com/watch/cYhTcHpfFw"

Electronic equivalent of consciousness with the elementary mental process model

In this presentation, a method is proposed in order to implement a virtual sensor which is able to estimate the evaporated water in cooling towers, considering the variations of environmental and operating conditions. A generative adversarial network (GAN) is used for training a deep learning-based generative model which is able to generalize the estimation of the evaporated water, even in situations not included in the training data. The proposed method is tested using real data from a cooling tower located at the Hospital of León.

Probabilistic estimation of evaporated water in cooling towers using a generative adversarial network

Reduction of data variables is an important issue and it is needed for the processing of higher dimensional data in the application domains and AI, in which threshold neural networks are extensively used. We develop a reduction of data variables and classification method based on the nearest neighbor relations for threshold networks. First, the nearest neighbor relations are shown to be useful for the generation of threshold functions and Chow parameters. Second, the extended application of the nearest neighbor relations is developed for the reduction of variables based on convex cones. The edges of convex cones are compared for the reduction of variables. Further, hyperplanes with reduced variables are obtained on the convex cones for data classification.

Reduction of Variables and Generation of Functions through Nearest Neighbor Relations in Threshold Networks

In the future power system called Smart Grid, power generators using renewable energies will be widely introduced to the power grid and required to balance supply and demand on the grid quickly. Therefore, fast automated demand response (FastADR) that contributes to balance the power grid from demand side through electrical facilities like building air conditioner are focused recently. When electric grid operator will pay incentive to aggregators of the demand side, it is important to estimate accurate baseline load. However, the FastADR must returns quick response by unit of seconds or minute (fine-granularity), therefore it is difficult to estimate baseline load accurately using conventional method. In this research, the baseline load estimation model for air-con time-series data is constructed using long short-term memory (LSTM) neural network, and compared with multilayer perceptron (MLP) neural network model for baseline load estimation. The training and evaluating time-series data is generated by air-con simulator (AE) carried out on the virtual building. In the estimation results using data that were simulated for a month, the average estimation error of the LSTM model is 2.7% and of the MLP model is 5.3%. Therefore, the LSTM model is more effective for baseline estimation than the MLP model. However, data in various situations are required.

LSTM Neural Network for Fine-Granularity Estimation on Baseline Load of Fast Demand Response

Engineering drawings are common across different domains such as Oil \& Gas, construction, mechanical and other domains. Automatic processing and analysis of these drawings is a challenging task. This is partly due to the complexity of these documents and also due to the lack of dataset availability in the public domain that can help push the research in this area. In this paper, we present a multiclass imbalanced dataset for the research community made of 2432 instances of engineering symbols. These symbols were extracted from a collection of complex engineering drawings known as Piping and Instrumentation Diagram (P&ID). By providing such dataset to the research community, we anticipate that this will help attract more attention to an important, yet overlooked industrial problem, and will also advance the research in such important and timely topics. We discuss the datasets characteristics in details, and we also show how Convolutional Neural Networks (CNNs) perform on such extremely imbalanced datasets. Finally, conclusions and future directions are discussed.

Leveraging Radar Features to Improve Point Clouds Segmentation with Neural Networks

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An ASP-Based Approach for Phase Balancing in Power Electrical Systems

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Leveraging Radar Features to Improve Point Clouds Segmentation with Neural Networks

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Next from EANN 2020

An ASP-Based Approach for Phase Balancing in Power Electrical Systems

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Predicting Long-Term Skeletal Motions by a Spatio-Temporal Hierarchical Recurrent Network

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