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Smart cities and communities (SCC) constitute a new paradigm in urban development. SCC ideate a data-centered society aimed at improving efficiency by automating and optimizing activities and utilities. Information and communication technology along with Internet of Things enables data collection and with the help of artificial intelligence (AI) situation awareness can be obtained to feed the SCC actors with enriched knowledge. This paper describes AI perspectives in SCC and gives an overview of AI-based technologies used in traffic to enable road vehicle automation and smart traffic control. Perception, smart traffic control and driver modeling are described along with open research challenges and standardization to help introduce advanced driver assistance systems and automated vehicle functionality in traffic. To fully realize the potential of SCC, to create a holistic view on a city level, availability of data from different stakeholders is necessary. Further, though AI technologies provide accurate predictions and classifications, there is an ambiguity regarding the correctness of their outputs. This can make it difficult for the human operator to trust the system. Today there are no methods that can be used to match function requirements with the level of detail in data annotation in order to train an accurate model. Another challenge related to trust is explainability: models can have difficulty explaining how they came to certain conclusions, so it is difficult for humans to trust them.
Cristofer Englund; Eren Aksoy; Fernando Alonso-Fernandez; Martin Cooney; Sepideh Pashami; Björn Åstrand. AI Perspectives in Smart Cities and Communities to Enable Road Vehicle Automation and Smart Traffic Control. Smart Cities 2021, 4, 783 -802.
AMA StyleCristofer Englund, Eren Aksoy, Fernando Alonso-Fernandez, Martin Cooney, Sepideh Pashami, Björn Åstrand. AI Perspectives in Smart Cities and Communities to Enable Road Vehicle Automation and Smart Traffic Control. Smart Cities. 2021; 4 (2):783-802.
Chicago/Turabian StyleCristofer Englund; Eren Aksoy; Fernando Alonso-Fernandez; Martin Cooney; Sepideh Pashami; Björn Åstrand. 2021. "AI Perspectives in Smart Cities and Communities to Enable Road Vehicle Automation and Smart Traffic Control." Smart Cities 4, no. 2: 783-802.
Ensemble learning methods combine multiple models to improve performance by exploiting their diversity. The success of these approaches relies heavily on the dissimilarity of the base models forming the ensemble. This diversity can be achieved in many ways, with well-known examples including bagging and boosting. It is the diversity of the models within an ensemble that allows the ensemble to correct the errors made by its members, and consequently leads to higher classification or regression performance. A mistake made by a base model can only be rectified if other members behave differently on that particular instance, and provide the aggregator with enough information to make an informed decision. On the contrary, lack of diversity not only lowers model performance, but also wastes computational resources. Nevertheless, in the current state of the art ensemble approaches, there is no guarantee on the level of diversity achieved, and no mechanism ensuring that each member will learn a different decision boundary from the others. In this paper, we propose a parallel orthogonal deep learning architecture in which diversity is enforced by design, through imposing an orthogonality constraint. Multiple deep neural networks are created, parallel to each other. At each parallel layer, the outputs of different base models are subject to Gram–Schmidt orthogonalization. We demonstrate that this approach leads to a high level of diversity from two perspectives. First, the models make different errors on different parts of feature space, and second, they exhibit different levels of uncertainty in their decisions. Experimental results confirm the benefits of the proposed method, compared to standard deep learning models and well-known ensemble methods, in terms of diversity and, as a result, classification performance.
Peyman Sheikholharam Mashhadi; Sławomir Nowaczyk; Sepideh Pashami. Parallel orthogonal deep neural network. Neural Networks 2021, 140, 167 -183.
AMA StylePeyman Sheikholharam Mashhadi, Sławomir Nowaczyk, Sepideh Pashami. Parallel orthogonal deep neural network. Neural Networks. 2021; 140 ():167-183.
Chicago/Turabian StylePeyman Sheikholharam Mashhadi; Sławomir Nowaczyk; Sepideh Pashami. 2021. "Parallel orthogonal deep neural network." Neural Networks 140, no. : 167-183.
Many industries today are struggling with early the identification of quality issues, given the shortening of product design cycles and the desire to decrease production costs, coupled with the customer requirement for high uptime. The vehicle industry is no exception, as breakdowns often lead to on-road stops and delays in delivery missions. In this paper we consider quality issues to be an unexpected increase in failure rates of a particular component; those are particularly problematic for the original equipment manufacturers (OEMs) since they lead to unplanned costs and can significantly affect brand value. We propose a new approach towards the early detection of quality issues using machine learning (ML) to forecast the failures of a given component across the large population of units. In this study, we combine the usage information of vehicles with the records of their failures. The former is continuously collected, as the usage statistics are transmitted over telematics connections. The latter is based on invoice and warranty information collected in the workshops. We compare two different ML approaches: the first is an auto-regression model of the failure ratios for vehicles based on past information, while the second is the aggregation of individual vehicle failure predictions based on their individual usage. We present experimental evaluations on the real data captured from heavy-duty trucks demonstrating how these two formulations have complementary strengths and weaknesses; in particular, they can outperform each other given different volumes of the data. The classification approach surpasses the regressor model whenever enough data is available, i.e., once the vehicles are in-service for a longer time. On the other hand, the regression shows better predictive performance with a smaller amount of data, i.e., for vehicles that have been deployed recently.
Reza Khoshkangini; Peyman Mashhadi; Peter Berck; Saeed Gholami Shahbandi; Sepideh Pashami; Sławomir Nowaczyk; Tobias Niklasson. Early Prediction of Quality Issues in Automotive Modern Industry. Information 2020, 11, 354 .
AMA StyleReza Khoshkangini, Peyman Mashhadi, Peter Berck, Saeed Gholami Shahbandi, Sepideh Pashami, Sławomir Nowaczyk, Tobias Niklasson. Early Prediction of Quality Issues in Automotive Modern Industry. Information. 2020; 11 (7):354.
Chicago/Turabian StyleReza Khoshkangini; Peyman Mashhadi; Peter Berck; Saeed Gholami Shahbandi; Sepideh Pashami; Sławomir Nowaczyk; Tobias Niklasson. 2020. "Early Prediction of Quality Issues in Automotive Modern Industry." Information 11, no. 7: 354.
The data shared over the Internet tends to originate from ubiquitous and autonomous sources such as mobile phones, fitness trackers, and IoT devices. Centralized and federated machine learning solutions represent the predominant way of providing smart services for users. However, moving data to central location for analysis causes not only many privacy concerns, but also communication overhead. Therefore, in certain situations machine learning models need to be trained in a collaborative and decentralized manner, similar to the way the data is originally generated without requiring any central authority for data or model aggregation. This paper presents a decentralized and adaptive k-means algorithm that clusters data from multiple sources organized in peer-to-peer networks. Our algorithm allows peers to reach an approximation of the global model without sharing any raw data. Most importantly, we address the challenge of decentralized clustering with skewed non-IID data and asynchronous computations by integrating HyperLogLog counters with k-means algorithm. Furthermore, our clustering algorithm allows nodes to individually determine the number of clusters that fits their local data. Results using synthetic and real-world datasets show that our algorithm outperforms state-of-the-art decentralized k-means algorithms achieving accuracy gain that is up-to 36%.
Amira Soliman; Sarunas Girdzijauskas; Mohamed-Rafik Bouguelia; Sepideh Pashami; Slawomir Nowaczyk. Decentralized and Adaptive K-Means Clustering for Non-IID Data Using HyperLogLog Counters. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 12084, 343 -355.
AMA StyleAmira Soliman, Sarunas Girdzijauskas, Mohamed-Rafik Bouguelia, Sepideh Pashami, Slawomir Nowaczyk. Decentralized and Adaptive K-Means Clustering for Non-IID Data Using HyperLogLog Counters. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; 12084 ():343-355.
Chicago/Turabian StyleAmira Soliman; Sarunas Girdzijauskas; Mohamed-Rafik Bouguelia; Sepideh Pashami; Slawomir Nowaczyk. 2020. "Decentralized and Adaptive K-Means Clustering for Non-IID Data Using HyperLogLog Counters." Transactions on Petri Nets and Other Models of Concurrency XV 12084, no. : 343-355.
Predictive Maintenance (PM) is a proactive maintenance strategy that tries to minimize a system’s downtime by predicting failures before they happen. It uses data from sensors to measure the component’s state of health and make forecasts about its future degradation. However, existing PM methods typically focus on individual measurements. While it is natural to assume that a history of measurements carries more information than a single one. This paper aims at incorporating such information into PM models. In practice, especially in the automotive domain, diagnostic models have low performance, due to a large amount of noise in the data and limited sensing capability. To address this issue, this paper proposes to use a specific type of ensemble learning known as Stacked Ensemble. The idea is to aggregate predictions of multiple models—consisting of Long Short-Term Memory (LSTM) and Convolutional-LSTM—via a meta model, in order to boost performance. Stacked Ensemble model performs well when its base models are as diverse as possible. To this end, each such model is trained using a specific combination of the following three aspects: feature subsets, past dependency horizon, and model architectures. Experimental results demonstrate benefits of the proposed approach on a case study of heavy-duty truck turbochargers.
Peyman Sheikholharam Mashhadi; Sławomir Nowaczyk; Sepideh Pashami. Stacked Ensemble of Recurrent Neural Networks for Predicting Turbocharger Remaining Useful Life. Applied Sciences 2019, 10, 69 .
AMA StylePeyman Sheikholharam Mashhadi, Sławomir Nowaczyk, Sepideh Pashami. Stacked Ensemble of Recurrent Neural Networks for Predicting Turbocharger Remaining Useful Life. Applied Sciences. 2019; 10 (1):69.
Chicago/Turabian StylePeyman Sheikholharam Mashhadi; Sławomir Nowaczyk; Sepideh Pashami. 2019. "Stacked Ensemble of Recurrent Neural Networks for Predicting Turbocharger Remaining Useful Life." Applied Sciences 10, no. 1: 69.
Mohamed-Rafik Bouguelia; Alexander Karlsson; Sepideh Pashami; Sławomir Nowaczyk; Anders Holst. Mode tracking using multiple data streams. Information Fusion 2018, 43, 33 -46.
AMA StyleMohamed-Rafik Bouguelia, Alexander Karlsson, Sepideh Pashami, Sławomir Nowaczyk, Anders Holst. Mode tracking using multiple data streams. Information Fusion. 2018; 43 ():33-46.
Chicago/Turabian StyleMohamed-Rafik Bouguelia; Alexander Karlsson; Sepideh Pashami; Sławomir Nowaczyk; Anders Holst. 2018. "Mode tracking using multiple data streams." Information Fusion 43, no. : 33-46.
Martin Cooney; Sepideh Pashami; Anita Sant'anna; Yuantao Fan; Slawomir Nowaczyk. Pitfalls of Affective Computing. Companion of the The Web Conference 2018 on The Web Conference 2018 - WWW '18 2018, 1563 -1566.
AMA StyleMartin Cooney, Sepideh Pashami, Anita Sant'anna, Yuantao Fan, Slawomir Nowaczyk. Pitfalls of Affective Computing. Companion of the The Web Conference 2018 on The Web Conference 2018 - WWW '18. 2018; ():1563-1566.
Chicago/Turabian StyleMartin Cooney; Sepideh Pashami; Anita Sant'anna; Yuantao Fan; Slawomir Nowaczyk. 2018. "Pitfalls of Affective Computing." Companion of the The Web Conference 2018 on The Web Conference 2018 - WWW '18 , no. : 1563-1566.
Tove Helldin; Maria Riveiro; Sepideh Pashami; Göran Falkman; Stefan Byttner; Slawomir Nowaczyk. Supporting Analytical Reasoning. Transactions on Petri Nets and Other Models of Concurrency XV 2016, 20 -31.
AMA StyleTove Helldin, Maria Riveiro, Sepideh Pashami, Göran Falkman, Stefan Byttner, Slawomir Nowaczyk. Supporting Analytical Reasoning. Transactions on Petri Nets and Other Models of Concurrency XV. 2016; ():20-31.
Chicago/Turabian StyleTove Helldin; Maria Riveiro; Sepideh Pashami; Göran Falkman; Stefan Byttner; Slawomir Nowaczyk. 2016. "Supporting Analytical Reasoning." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 20-31.
Many applications of metal oxide gas sensors can benefit from reliable algorithms to detect significant changes in the sensor response. Significant changes indicate a change in the emission modality of a distant gas source and occur due to a sudden change of concentration or exposure to a different compound. As a consequence of turbulent gas transport and the relatively slow response and recovery times of metal oxide sensors, their response in open sampling configuration exhibits strong fluctuations that interfere with the changes of interest. In this paper we introduce TREFEX, a novel change point detection algorithm, especially designed for metal oxide gas sensors in an open sampling system. TREFEX models the response of MOX sensors as a piecewise exponential signal and considers the junctions between consecutive exponentials as change points. We formulate non-linear trend filtering and change point detection as a parameter-free convex optimization problem for single sensors and sensor arrays. We evaluate the performance of the TREFEX algorithm experimentally for different metal oxide sensors and several gas emission profiles. A comparison with the previously proposed GLR method shows a clearly superior performance of the TREFEX algorithm both in detection performance and in estimating the change time.
Sepideh Pashami; Achim J. Lilienthal; Erik Schaffernicht; Marco Trincavelli. TREFEX: Trend Estimation and Change Detection in the Response of MOX Gas Sensors. Sensors 2013, 13, 7323 -7344.
AMA StyleSepideh Pashami, Achim J. Lilienthal, Erik Schaffernicht, Marco Trincavelli. TREFEX: Trend Estimation and Change Detection in the Response of MOX Gas Sensors. Sensors. 2013; 13 (6):7323-7344.
Chicago/Turabian StyleSepideh Pashami; Achim J. Lilienthal; Erik Schaffernicht; Marco Trincavelli. 2013. "TREFEX: Trend Estimation and Change Detection in the Response of MOX Gas Sensors." Sensors 13, no. 6: 7323-7344.
We address the problem of detecting changes in the activity of a distant gas source from the response of an array of metal oxide (MOX) gas sensors deployed in an open sampling system. The main challenge is the turbulent nature of gas dispersion and the response dynamics of the sensors. We propose a change point detection approach and evaluate it on individual gas sensors in an experimental setup where a gas source changes in intensity, compound, or mixture ratio. We also introduce an efficient sensor selection algorithm and evaluate the change point detection approach with the selected sensor array subsets.
Sepideh Pashami; Achim J. Lilienthal; Marco Trincavelli. Detecting Changes of a Distant Gas Source with an Array of MOX Gas Sensors. Sensors 2012, 12, 16404 -16419.
AMA StyleSepideh Pashami, Achim J. Lilienthal, Marco Trincavelli. Detecting Changes of a Distant Gas Source with an Array of MOX Gas Sensors. Sensors. 2012; 12 (12):16404-16419.
Chicago/Turabian StyleSepideh Pashami; Achim J. Lilienthal; Marco Trincavelli. 2012. "Detecting Changes of a Distant Gas Source with an Array of MOX Gas Sensors." Sensors 12, no. 12: 16404-16419.