Search

Remaining useful life (RUL) of an asset or system is defined as the length from the current time and operating state to the end of the useful life. It is of paramount importance for safety-critical industries such as aviation and lies in the heart of prognostics and health management (PHM). This paper investigates the usage of automated machine learning (AutoML) for RUL estimation, based on using classical machine learning algorithms for regression. The data is pre-processed by extracting statistical features from expanding windows of the signal in order to uncover the degradation that has been accumulating from the early life of the system or after an overhaul. We evaluate our methodology on the widely-used C-MAPSS dataset and compare our approach to the state-of-the-art deep neural networks (DNNs) and classical machine learning algorithms. The experimental results show that AutoML outperforms or is comparable to traditional machine learning techniques and standard neural networks, while being outperformed by specifically designed neural networks on datasets with multiple fault mode and operating conditions. These results show that with the correct pre-processing automated machine learning is able to accurately estimate the RUL, which implies that such approaches can be industrially deployed.

Early time series classification (EarlyTSC) involves the prediction of a class label based on partial observation of a given time series. Most EarlyTSC algorithms consider the trade-off between accuracy and earliness as two competing objectives, using a single dedicated hyperparameter. To obtain insights into this trade-off requires finding a set of non-dominated (Pareto efficient) classifiers. So far, this has been approached through manual hyperparameter tuning. Since the trade-off hyperparameters only provide indirect control over the earliness-accuracy trade-off, manual tuning is tedious and tends to result in many sub-optimal hyperparameter settings. This complicates the search for optimal hyperparameter settings and forms a hurdle for the application of EarlyTSC to real-world problems. To address these issues, we propose an automated approach to hyperparameter tuning and algorithm selection for EarlyTSC, building on developments in the fast-moving research area known as automated machine learning (AutoML). To deal with the challenging task of optimising two conflicting objectives in early time series classification, we propose MultiETSC, a system for multi-objective algorithm selection and hyperparameter optimisation (MO-CASH) for EarlyTSC. MultiETSC can potentially leverage any existing or future EarlyTSC algorithm and produces a set of Pareto optimal algorithm configurations from which a user can choose a posteriori. As an additional benefit, our proposed framework can incorporate and leverage time-series classification algorithms not originally designed for EarlyTSC for improving performance on EarlyTSC; we demonstrate this property using a newly defined, “naïve” fixed-time algorithm. In an extensive empirical evaluation of our new approach on a benchmark of 115 data sets, we show that MultiETSC performs substantially better than baseline methods, ranking highest (avg. rank 1.98) compared to conceptually simpler single-algorithm (2.98) and single-objective alternatives (4.36).

This book contains a selection of the best papers of the 32nd Benelux Conference on Artificial Intelligence, BNAIC/Benelearn 2020, held in Leiden, The Netherlands, in November 2020. Due to the COVID-19 pandemic the conference was held online. The 12 papers presented in this volume were carefully reviewed and selected from 41 regular submissions. They address various aspects of artificial intelligence such as natural language processing, agent technology, game theory, problem solving, machine learning, human-agent interaction, AI and education, and data analysis.

From detecting skin cancer to translating languages and to forecasting electricity consumption, machine learning is enabling advanced capabilities of computer systems across a broad range of important real-world applications. In this work, we present machine learning models for forecasting the consumption of electricity. Short-term electric load forecasting has been a fundamental concern in power operation systems for over a century. Energy load forecasting is of even greater importance, due to applications in the planning of demand side management, smart electric vehicles and other smart grid technologies. We use two state-of-the-art automated machine learning systems (auto-sklearn and TPOT), which automate model selection and hyperparameter optimization, to achieve maximum prediction accuracy, and compare their performance for the task of load prediction using two benchmark problems. These benchmarks are derived from real world load consumption tasks, namely household consumption from the UCI data repository and consumption data from an industrial office building. Our experimental results indicate great potential for improving the accuracy of energy consumption prediction by using automated machine learning approaches.

Recently, Point of interest (POI) recommendation has gained ever-increasing importance in various Location-Based Social Networks (LBSNs). With the recent advances of neural models, much work has sought to leverage neural networks to learn neural embeddings in a pre-training phase that achieve an improved representation of POIs and consequently a better recommendation. However, previous studies fail to capture crucial information about POIs such as categorical information. In this paper, we propose a novel neural model that generates a POI embedding incorporating sequential and categorical information from POIs. Our model consists of a check-in module and a category module. The check-in module captures the geographical influence of POIs derived from the sequence of users' check-ins, while the category module captures the characteristics of POIs derived from the category information. To validate the efficacy of the model, we experimented with two large-scale LBSN datasets. Our experimental results demonstrate that our approach significantly outperforms state-of-the-art POI recommendation methods., Comment: 4 pages, 1 figures

In this paper, we address the problem of how automated situational awareness in a specifi c location can be achieved by characterizing the fingerprint of recurrent situations from ubiquitously generated mobility data. Without semantic input about the time and space (location) where situations take place, this turns out to be a fundamental challenging problem. Uncertainties in data also introduce technical challenges when data is generated in irregular time intervals, being mixed with noise, and errors. Purely relying on temporal patterns observable in mobility data, in this paper, we propose Spaceprint, a fully automated algorithm for fi nding the repetitive pattern of similar situations in spaces. We evaluate this technique by showing how the latent variables describing the actual identity of a space can be discovered from the extracted situation patterns.

Movement ecology is a field which places movement as a basis for understanding animal behavior. To realize this concept, ecologists rely on data collection technologies providing spatio-temporal data in order to analyze movement. Recently, wireless sensor networks have offered new opportunities for data collection from remote places through multi-hop communication and collaborative capability of the nodes. Several technologies can be used in such networks for sensing purposes and for collecting spatio-temporal data from animals. In this paper, we investigate and review technological solutions which can be used for collecting data for wildlife monitoring. Our aim is to provide an overview of different sensing technologies used for wildlife monitoring and to review their capabilities in terms of data they provide for modeling movement behavior of animals.

Ubiquity of portable location-aware devices and popularity of online location-based services, have recently given rise to the collection of datasets with high spatial and temporal resolution. The subject of analyzing such data has consequently gained popularity due to numerous opportunities enabled by understanding objects' (people and animals, among others) mobility patterns. In this paper, we propose a hidden semi-Markov-based model to understand the behavior of mobile entities. The hierarchical state structure in our model allows capturing spatio-temporal associations in the locational history both at stay-points and on the paths connecting them. We compare the accuracy of our model with a number of other spatio-temporal models using two real datasets. Furthermore, we perform sensitivity analysis on our model to evaluate its robustness in presence of common issues in mobility datasets such as existence of noise and missing values. Results of our experiments show superiority of the proposed scheme compared with the other models.