Electrical Engineering and Computer Science

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Open Access
State Estimation of Li-ion Batteries Using Machine Learning Algorithms
(2023-03-28) Babaeiyazdi, Iman; Rezaei Zare, Afshin
Lithium-ion batteries are mainly utilized in electric vehicles, electric ships, etc. due to their virtue of high energy density, low self-discharge, and low costs. Electric vehicles are prone to accelerated battery degradation due to the high charging/discharging cycles and high peak power demand. Hence, efficient management of the batteries is a dire need in this regard. Battery management systems (BMS) have been developing to control, monitor, and measure the variables of the battery such as voltage, current, and temperature, to estimate the states of charge (SOC) and state of health (SOH) of the battery. This study is divided into three parts; in the first part, the SOC of the battery is estimated utilizing electrochemical impedance spectroscopy (EIS) measurements. The EIS measurements are obtained at different SOC and temperature levels. The highly correlated measurements with the SOC are then extracted to be used as input features. Gaussian process regression (GPR) and linear regression (LR) are employed to estimate the SOC of the battery. In the second part of this study, the EIS measurements at different SOC and temperature levels are employed to estimate the SOH of the battery. In this part, transfer learning (TL) along with deep neural network (DNN) is adopted to estimate the SOH of the battery at another outrange temperature level. The effect of the number of fixed layers is also investigated to compare the performance of various DNN models. The results indicate that the DNN with no fixed layer outclasses the other DNN model with one or more fixed layers. In the third part of this dissertation, the co-estimation of SOC and SOH is conducted as SOC and SOH are intertwined characteristics of the battery, and a change in one affects the other variation. First, the SOH of the battery is estimated using EIS measurements by GPR and DNN. The estimated SOH, along with online-measurable variables of the battery, i.e., voltage and current, are then utilized as input features for long-short term memory (LSTM) and DNN algorithms to estimate the SOC of the battery.
Open Access
Machine Learning and Quantitative Imaging for the Management of Brain Metastasis
(2023-03-28) Jalalifar, Seyed Ali; Sadeghi-Naini, Ali
Significantly affecting patients’ clinical course and quality of life, a growing number of cancer cases are diagnosed with brain metastasis annually. Although a considerable percentage of cancer patients survive for several years if the disease is discovered at an early stage while it is still localized, when the tumour is metastasized to the brain, the median survival decreases considerably. Early detection followed by precise and effective treatment of brain metastasis may lead to improved patient survival and quality of life. A main challenge to prescribe an effective treatment regimen is the variability of tumour response to treatments, e.g., radiotherapy as a main treatment option for brain metastasis, despite similar cancer therapy, due to many patient-related factors. Stratifying patients based on their predicted response and consequently assessing their response to therapy are challenging yet crucial tasks. While risk assessment models with standard clinical attributes have been proposed for patient stratification, the imaging data acquired for these patients as a part of the standard-of-care are not computationally analyzed or directly incorporated in these models. Further, therapy response monitoring and assessment is a cumbersome task for patients with brain metastasis that requires longitudinal tumour delineation on MRI volumes before and at multiple follow-up sessions after treatment. This is aggravated by the time-sensitive nature of the disease. In an effort to address these challenges, a number of machine learning frameworks and computational techniques in areas of automatic tumour segmentation, radiotherapy outcome assessment, and therapy outcome prediction have been introduced and investigated in this dissertation. Powered by advanced machine learning algorithms, a complex attention-guided segmentation framework is introduced and investigated for segmenting brain tumours on serial MRI. The experimental results demonstrate that the proposed framework can achieve a dice score of 91.5% and 84.1% to 87.4% on the baseline and follow-up scans, respectively. This framework is then applied in a proposed system that follows standard clinical criteria based on changes in tumour size at post-treatment to assess tumour response to radiotherapy automatically. The system demonstrates a very good agreement with expert clinicians in detecting local response, with an accuracy of over 90%. Next, innovative machine-learning-based solutions are proposed and investigated for radiotherapy outcome prediction before or early after therapy, using MRI radiomic models and novel deep learning architectures that analyze treatment-planning MRI with and without standard clinical attributes. The developed models demonstrate an accuracy of up to 82.5% in predicting radiotherapy outcome before the treatment initiation. The ground-breaking machine learning platforms presented in this dissertation along with the promising results obtained in the conducted experiments are steps forward towards realizing important decision support tools for oncologists and radiologists and, can eventually, pave the way towards the personalized therapeutics paradigm for cancer patients
Open Access
Transformer Thermal Assessment under Geomagnetically Induced Current Conditions
(2023-03-28) Akbari, Milad; Rezaei Zare, Afshin
Power transformers are one of the most critical and expensive pieces of equipment in power systems. The widespread use of the transformer in power grids and its high cost make the lifetime and reliability of this apparatus highly important. Although some factors affect the reliability of transformers, thermal and electrical stresses are the main reasons for transformer failure. As a result, transformer thermal modeling is essential in the design and operation stage to represent the thermal behavior of transformers during normal operation or transient phenomena. However, the multi-physics behavior of transformers and the nonlinear and frequency-dependent parameters make this modeling a challenging task. This thesis aims to develop a more accurate transformer model for representing the thermal behavior of transformers, especially during transient phenomena such as Geomagnetically Induced Current (GIC). To fulfill this goal, it is necessary to perform several tasks in different fields, such as geometry and material modeling, electromagnetic studies, and investigation into computational fluid dynamic (CFD) analysis of transformers. First, the GIC phenomenon and its impact on the transformer are briefly explained. Then, a comprehensive literature review of existing transformer thermal models is performed to find their drawbacks. A 3-phase, 3-leg transformer is then subjected to an electromagnetic-thermal study in both normal and GIC conditions. It is shown that the structural parts, including the tank, clamps, and tank shunts, are saturated with a small amount of GIC. However, the transformer core becomes saturated with larger currents, resulting in additional stray losses in the structural parts. The findings show that the most vulnerable part is the tank, as the hot spot temperature (HST) of the tank approaches 372.14 0C, which is double the permissible limit, under 66.6A GIC per phase. Finally, a new approach is proposed to determine the HST of OIP bushing based on the FEM-modified thermal equivalent circuit (TEC) model. The proposed model can accurately estimate the HST of the bushings under normal and GIC conditions. Furthermore, a detailed thermal analysis is performed to investigate the impact of different parameters such as load, ambient temperature, and top oil temperature on the thermal performance of bushings.
Open Access
Novel Design and Energy Management Approaches for Seamless Integration and Adoption of Plug-In Electric Vehicles
(2022-12-14) Al-Obaidi, Abdullah Azhar Abdullah; Farag, Hany E. Z.
Electric vehicles (EVs) are witnessing increased utilization throughout the world as an alternative to fossil-fueled vehicles. However, the adoption of EVs and their integration into the power grid is yet to be fully materialized due to several issues, of which two are the most salient. First, the extensive deployment of EVs can bring challenges to the grid if not properly managed. Second, access to a variety of EV supply equipment (EVSE) in different areas is still lacking. To that end, the research in this thesis aims to address these issues through the development of adaptive approaches that enhance the management of EV energy and the development of a charging strategy and a design approach that help to expand the proliferation of EV charging infrastructure. Three approaches that are adaptive to their operator/user preferences are developed to enhance energy management in EVs. The first approach allows adaptive utilization of EV batteries' distributed energy resources in an EV fleet system for concurrent services to the transportation sector and ancillary services market. The second approach is a decentralized quality of service (QoS)-based scheme for peer-peer (P2P) energy trading among EV energy providers and consumers. The proposed mechanism is designed to match energy traders based on consumers' and providers' QoS requirements and offers, respectively. The third approach is a bidirectional smart charging algorithm for EVs considering P2P energy trade, provision of ancillary services to the grid, and utilization of low electricity prices for battery charging. The algorithm incorporates user preferences into the scheduling process enabling it to adapt to various conditions. Further, to expand the proliferation of EV charging infrastructure, this thesis introduces (i) a charging control strategy that does not require a communication network, which in turn reduces additional grid upgrades, and (ii) a design approach for EV parking lots that helps private investors to participate in the growth of charging facilities. The findings of this thesis highlight the efficacy of the proposed approaches in achieving their objectives. This would provide implementable and cost-effective solutions to facilitate EVs deployment and address imminent and timely concerns that limit the wide adoption of EVs into electric distribution infrastructure.
Open Access
Bridging Data Management and Machine Learning: Case Studies on Index, Query Optimization, and Data Acquisition
(2022-12-14) Li, Yifan; Yu, Xiaohui
Data management tasks and techniques can be observed in a variety of real world scenarios, including web search, business analysis, traffic scheduling, and advertising, to name a few. While data management as a research area has been studied for decades, recent breakthroughs in Machine Learning (ML) provide new perspectives to define and tackle problems in the area, and at the same time, the wisdom integrated in data management techniques also greatly helps to accelerate the advancement of Machine Learning. In this work, we focus on the intersection area of data management and Machine Learning, and study several important, interesting, and challenging problems. More specifically, our work mainly concentrates on the following three topics: (1) leveraging the ability of ML models in capturing data distribution to design lightweight and data-adaptive indexes and search algorithms to accelerate similarity search over large-scale data; (2) designing robust and trustworthy approaches to improve the reliability of both conventional query optimizer and learned query optimizer, and boost the performance of DBMS; (3) developing data management techniques with statistical guarantees to acquire the most useful training data for ML models with a budget limitation, striving to maximize the accuracy of the model. We conduct detailed theoretical and empirical study for each topic, establishing these fundamental problems as well as developing efficient and effective approaches for the tasks.
Open Access
Methods for Voltage Monitoring, Analysis and Improvement in Active Distribution Networks
(2022-12-14) Liu, Jingyuan; Srikantha, Pirathayini
Power Distribution Networks (DNs) deliver electricity from the transmission systems to the consumers. The proliferation of diverse load components and distributed generators in active DNs is drastically changing the power demand and supply patterns in the DN, which in turn has led to significant stress and uncertainty on the voltage profiles of the DNs. Nevertheless, the communication and computation capabilities of the modern DNs have enabled cyber-enabled power components such as DG (Distributed Generator)}devices to make intelligent decisions through information exchanges. As such, in this dissertation we leverage on this novel capability to present algorithms for voltage monitoring, analysis and improvement that allow the system operator to assess the voltage profile of the DN and to take preventative actions for enhancing voltage profiles and preventing undervoltage/overvoltage incidents. In the subsequent chapters, we present performance guarantees and simulation studies on the proposed algorithms, and compare the algorithms introduced in this dissertation with the state-of-the-art.
Open Access
Exploiting Novel Deep Learning Architecture in Character Animation Pipelines
(2022-12-14) Ghorbani, Saeed; Troje, Nikolaus
This doctoral dissertation aims to show a body of work proposed for improving different blocks in the character animation pipelines resulting in less manual work and more realistic character animation. To that purpose, we describe a variety of cutting-edge deep learning approaches that have been applied to the field of human motion modelling and character animation. The recent advances in motion capture systems and processing hardware have shifted from physics-based approaches to data-driven approaches that are heavily used in the current game production frameworks. However, despite these significant successes, there are still shortcomings to address. For example, the existing production pipelines contain processing steps such as marker labelling in the motion capture pipeline or annotating motion primitives, which should be done manually. In addition, most of the current approaches for character animation used in game production are limited by the amount of stored animation data resulting in many duplicates and repeated patterns. We present our work in four main chapters. We first present a large dataset of human motion called MoVi. Secondly, we show how machine learning approaches can be used to automate proprocessing data blocks of optical motion capture pipelines. Thirdly, we show how generative models can be used to generate batches of synthetic motion sequences given only weak control signals. Finally, we show how novel generative models can be applied to real-time character control in the game production.
Open Access
A Multi-Mode Stacked-Switch Inverter/Rectifier Leg for Bidirectional Power Converters
(2022-08-08) Emamalipour Shalkouhi, Reza; Lam, John Chi Wo
The development of renewable energy systems (e.g. wind and solar) is significant to cope with an energy crisis yet, at the same time, it presents challenges to the grid for their MW-scale integration due to their volatile characteristics. Battery energy storage systems are essential in providing sustainable power and improving the overall system reliability effectively with the large deployments of renewable energy conversion systems. Bidirectional power converters are responsible for transferring power between the battery energy storage system and the grid. Selecting an efficient and cost-effective power topology along with a reliable control system is critical to ensure that the energy storage system operates safely with prolonged service life and minimized maintenance cost. In this dissertation, a multi-mode stacked-switch leg with soft-switching capability for use in bidirectional DC/DC converters is proposed for battery energy storage applications. This dissertation consists of three parts. The first part focuses on the development of a bidirectional soft-switched converter utilizing a CLLC resonant circuit and the proposed multi-mode switching legs. The presented leg is able to facilitate multiple operating modes to enable high voltage gain under different operating conditions and allow the converter to operate with a much lower output voltage ripple (50%) compared with the conventional stacked-switches-based converter topology. In the second part of this thesis, a fault-tolerant control scheme is proposed which enables seamless post fault operation of the presented multi-mode DC/DC converter if any switches in the presented leg experience an open-circuit fault. In the third part of this thesis, a comprehensive hybrid control system is proposed so that the overall voltage gain range of the converter is widely extended with a narrow switching frequency range (less than 10% of the base frequency), while at the same time, the efficiency of the converter is improved over the whole gain range (more than 1%). The operating principles and characteristics of the proposed converter and the proposed control schemes are explained in detail in this thesis. The performance of each of the presented circuit and control concepts is verified through simulation as well as experimental results on silicon-carbide (SiC)-based proof-of-concept hardware prototypes.
Open Access
Biologically-inspired Neural Networks for Shape and Color Representation
(2022-03-03) Mehrani, Paria; Tsotsos, John K.
The goal of human-level performance in artificial vision systems is yet to be achieved. With this goal, a reasonable choice is to simulate this biological system with computational models that mimic its visual processing. A complication with this approach is that the human brain, and similarly its visual system, are not fully understood. On the bright side, with remarkable findings in the field of visual neuroscience, many questions about visual processing in the primate brain have been answered in the past few decades. Nonetheless, a lag in incorporating these new discoveries into biologically-inspired systems is evident. The present work introduces novel biologically-inspired models that employ new findings of shape and color processing into analytically-defined neural networks. In contrast to most current methods that attempt to learn all aspects of behavior from data, here we propose to bootstrap such learning by building upon existing knowledge rather than learning from scratch. Put simply, the processing networks are defined analytically using current neural understanding and learned where such knowledge is not available. This is thus a hybrid strategy that hopefully combines the best of both worlds. Experiments on the artificial neurons in the proposed networks demonstrate that these neurons mimic the studied behavior of biological cells, suggesting a path forward for incorporating analytically-defined artificial neural networks into computer vision systems.
Open Access
Leveraging Dual-Pixel Sensors for Camera Depth of Field Manipulation
(2022-03-03) Abuolaim, Abdullah Ahmad Taleb; Brown, Michael S.
Capturing a photo with clear scene details is important in photography and for computer vision applications. The range of distance in the real world that makes the scene's objects appear with clear details is known to be the camera's depth of field (DoF). The DoF is controlled by either adjusting lens distance to sensor (i.e., focus distance), aperture size, and/or focal length of the cameras. At capture time, especially for video recording, DoF adjustment is often restricted to lens movements as adjusting other parameters introduces artifacts that can be visible in the recorded video. Nevertheless, the desired DoF is not always achievable at capture time due to many reasons like the physical constraints of the camera optics. This leads to another direction of adjusting DoF after effect as a post-processing step. Although pre- or post-capture DoF manipulation is essential, there are few datasets and simulation platforms that enable investigating DoF at capture time. Another limitation is the lack of real datasets for DoF extension (i.e., defocus deblurring), where the prior work relies on synthesizing defocus blur and ignores the physical formation of defocus blur in real cameras (e.g., lens aberration and radial distortion). To address this research gap, this thesis revisits DoF manipulation from two point of views: (1) adjusting DoF at capture time, a.k.a. camera autofocus (AF), within the context of dynamic scenes (i.e., video AF); (2) computationally manipulating the DoF as a post-capturing process. To this aim, we leverage a new imaging sensor technology known as the dual-pixel (DP) sensor. DP sensors are used to optimize camera AF and can provide good cues to estimate the amount of defocus blur present at each pixel location. In particular, this thesis provides the first 4D temporal focal stack dataset along with AF platform to examine video AF. It also presents insights about user preference that lead to propose two novel video AF algorithms. As for post-capture DoF manipulation, we examine the problem of reducing defocus blur (i.e., extending DoF) by introducing a new camera aperture adjustment to collect the first dataset that has images with real defocus blur and their corresponding all-in-focus ground truth. We also propose the first end-to-end learning-based defocus deblurring method. We extend image defocus deblurring to a new domain application (i.e., video defocus deblurring) by designing a data synthesis framework to generate realistic DP video data through modeling physical camera constraints, such as lens aberration and redial distortion. Finally, we build on top of a data synthesis framework to synthesize shallow DoF with other aesthetic effects, such as multi-view synthesis and image motion.
Open Access
Distributed Communication and Control Frameworks for Smart Grids using the Internet of Things and Blockchain Technology
(2021-11-15) Saxena, Shivam Kumar; Farag, Hany E. Z.
Smart distribution grids (SDGs) are power systems that harness distributed energy resources (DERs) to increase their operational efficiency and sustainability. However, the uncontrolled operation of DERs lead to operational challenges, resulting in transformer overload and voltage violations. Distribution system operators (DSOs) are responsible for preventing such issues, however, DERs are typically owned by agents such as homeowners and private enterprises, whose motivations revolve around financial incentives and maximizing operational convenience, which do not always align with the DSO's objectives. Thus, new communication and control frameworks are required to coordinate the actions of agents and DSOs to deliver mutually beneficial results. The architectures of these frameworks should be distributed to avoid unilateral authority, and auditable to alleviate any trust issues between participants. Thus, this thesis develops distributed communication and control frameworks for SDGs that are built upon modern communication technologies such as the Internet of Things (IoT), and blockchains, both of which provide architectures that are distributed. The proposed control strategies of this thesis are inspired from principles related to transactive energy systems (TES), where distributed control techniques are combined with economically oriented decision making to improve overall energy efficiency. Accordingly, this thesis proposes three new frameworks, and validates their efficacy using both simulated and real-world experiments at a microgrid in Vaughan, Ontario. First, a fully distributed communication framework (DCF) is proposed for agent messaging, which is built upon the IoT-based framework known as Data Distribution Service (DDS). The DCF provides 1000 messages/second at 36 millisecond latency, and also enhances the efficacy of agents in resolving voltage violations in real-time at the microgrid. Second, a blockchain-based TES is proposed to enable agents to bid for voltage regulation services, where smart contracts enable multiple violations to be resolved in parallel, leading to less bidding cycles. Third, a blockchain-based residential energy trading system (RETS) is proposed , which enables residential communities and DSOs to participate in peer to peer energy trading and demand response. The RETS reduces the peak demand of the community by 48 kW (62%), which leads to an average savings of $1.02 M for the DSO by avoiding transformer upgrades.
Open Access
Learned Exposure Selection for High Dynamic Range Image Synthesis
(2021-03-08) Segal, Shane Maxwell; Brown, Michael; Brubaker, Marcus
High dynamic range (HDR) imaging is a photographic technique that captures a greater range of luminance than standard imaging techniques. Traditionally accomplished by specialized sensors, HDR images are regularly created through the fusion of multiple low dynamic range (LDR) images that can now be captured by smartphones or other consumer grade hardware. Three or more images are traditionally required to generate a well-exposed HDR image. This thesis presents a novel system for the fast synthesis of HDR images by means of exposure fusion with only two images required. Experiments show that a sufficiently trained neural network can predict a suitable exposure value for the next image to be captured, when given an initial image as input. With these images fed into the exposure fusion algorithm, a high-quality HDR image can be quickly generated.
Open Access
Machine Stereo Vision for Medical Image Registration
(2021-03-08) Speers, Andrew David; Wildes, Richard
Image guided liver surgery aims to enhance the precision of resection and ablation by providing fast localization of tumours and adjacent complex vasculature to improve oncologic outcome. This dissertation presents a novel end-to-end system for fast and accurate 3D surface reconstruction and motion estimation of the liver for alignment of intraoperative imagery with a preoperative volumetric scan. The system is designed and evaluated for application to liver surgery in an open setting, where open surgery is the dominant setting. The system is comprised of three key components: initialization, 3D surface recovery, and 3D motion estimation. Initialization is performed semi-automatically using a Branch-and-Bound (BnB) strategy to generate a set of globally optimal shape-based registration candidates from which the user can select a suitable initialization. 3D surface recovery is performed using a computationally efficient adaptive Coarse-to-Fine (CTF) stereo algorithm providing data-driven dense reconstructions in a computationally-efficient manner. A robust, 3D motion estimation technique based on interframe feature matching is then used to register a time series of reconstructions back to the initial frame of the sequence. The system has been evaluated empirically with reference to novel laboratory and intraoperative datasets, with results showing that performance is within tolerances expected for integration into Surgical Navigation (SN) systems.
Open Access
Performance Modeling, Design, and Analysis of Large Scale Terahertz Networks
(2021-03-08) Sayehvand, Javad; Tabassum, Hina
Multi-band and multi-tier heterogeneous networks have been considered as a key technology to meet the requirements of the future wireless networks; that is, 5G and beyond. In this dissertation, I studied heterogeneous cellular networks consisting of two tiers, where tier 1 is composed of small base stations (SBSs) operating on the sub-6GHz spectrum, and tier 2 consists of dense deployment of Terahertz (THz) base stations (TBSs) with lower power transmission compared to the RF layer. Using stochastic geometry (SG) tools, I modeled and analyzed the downlink performance of (i) THz-only network, and (ii) two-tier (co-existing) RF and THz network in terms of the downlink interference and coverage probability of a typical user. First, I characterized the exact LT of the aggregate interference and coverage probability of a user in a {THz-only} network. Then, for a {coexisting RF/THz network}, I derive the coverage probability of a typical user considering biased received signal power association (BRSP). In addition, asymptotic approximations are presented for scenarios where the intensity of THz BSs tends to infinity or the molecular absorption coefficient in THz approaches to zero. The proposed framework is generic to capture the performance of a typical user in various network configurations such as RF-only, THz-only, opportunistic RF/THz, and hybrid RF/THz. Finally, I extend the framework to incorporate the impact of blockages and side lobe antenna gains. The derived theoretical results are validated through extensive Monte-Carlo simulations.
Open Access
Improving the Logging Practices in DevOps
(2020-11-13) Chen, Boyuan; Jiang, ZhenMing
DevOps refers to a set of practices dedicated to accelerating modern software engineering process. It breaks the barriers between software development and IT operations and aims to produce and maintain high quality software systems. Software logging is widely used in DevOps. However, there are few guidelines and tool support for composing high quality logging code and current application context of log analysis is very limited with respect to feedback for developers and correlations among other telemetry data. In this thesis, we first conduct a systematic survey on the instrumentation techniques used in software logging. Then we propose automated approaches to improving software logging practices in DevOps by leveraging various types of software repositories (e.g., historical, communication, bug, and runtime repositories). We aim to support the software development side by providing guidelines and tools on developing and maintaining high quality logging code. In particular, we study historical issues in logging code and their fixes from six popular Java-based open source projects. We found that existing state-of-the-art techniques on detecting logging code issues cannot detect a majority of the issues in logging code. We also study the use of Java logging utilities in the wild. We find the complexity of the use of logging utilities increases as the project size increases. We aim to support the IT operation side by enriching the log analysis context. In particular, we propose a technique, LogCoCo, to systematically estimate code coverage via executing logs. The results of LogCoCo are highly accurate under a variety of testing activities. Case studies show that our techniques and findings can provide useful software logging suggestions to both developers and operators in open source and commercial systems.
Open Access
A Cloud-Based Extensible Avatar For Human Robot Interaction
(2019-07-02) AlTarawneh, Enas Khaled Ahm; Jenkin, Michael
Adding an interactive avatar to a human-robot interface requires the development of tools that animate the avatar so as to simulate an intelligent conversation partner. Here we describe a toolkit that supports interactive avatar modeling for human-computer interaction. The toolkit utilizes cloud-based speech-to-text software that provides active listening, a cloud-based AI to generate appropriate textual responses to user queries, and a cloud-based text-to-speech generation engine to generate utterances for this text. This output is combined with a cloud-based 3D avatar animation synchronized to the spoken response. Generated text responses are embedded within an XML structure that allows for tuning the nature of the avatar animation to simulate different emotional states. An expression package controls the avatar's facial expressions. The introduced rendering latency is obscured through parallel processing and an idle loop process that animates the avatar between utterances. The efficiency of the approach is validated through a formal user study.
Open Access
User Acquisition and Engagement in Digital News Media
(2019-03-05) Davoudi, Heidar; An, Aijun
Generating revenue has been a major issue for the news industry and journalism over the past decade. In fact, vast availability of free online news sources causes online news media agencies to face user acquisition and engagement as pressing issues more than before. Although digital news media agencies are seeking sustainable relationships with their users, their current business models do not satisfy this demand. As a matter of fact, they need to understand and predict how much an article can engage a reader as a crucial step in attracting readers, and then maximize the engagement using some strategies. Moreover, news media companies need effective algorithmic tools to identify users who are prone to subscription. Last but not least, online news agencies need to make smarter decisions in the way that they deliver articles to users to maximize the potential benefits. In this dissertation, we take the first steps towards achieving these goals and investigate these challenges from data mining /machine learning perspectives. First, we investigate the problem of understanding and predicting article engagement in terms of dwell time as one of the most important factors in digital news media. In particular, we design data exploratory models studying the textual elements (e.g., events, emotions) involved in article stories, and find their relationships with the engagement patterns. In the prediction task, we design a framework to predict the article dwell time based on a deep neural network architecture which exploits the interactions among important elements (i.e., augmented features) in the article content as well as the neural representation of the content to achieve the better performance. In the second part of the dissertation, we address the problem of identifying valuable visitors who are likely to subscribe in the future. We suggest that the decision for subscription is not a sudden, instantaneous action, but it is the informed decision based on positive experience with the newspaper. As such, we propose effective engagement measures and show that they are effective in building the predictive model for subscription. We design a model that predicts not only the potential subscribers but also the time that a user would subscribe. In the last part of this thesis, we consider the paywall problem in online newspapers. The traditional paywall method offers a non-subscribed reader a fixed number of free articles in a period of time (e.g., a month), and then directs the user to the subscription page for further reading. We argue that there is no direct relationship between the number of paywalls presented to readers and the number of subscriptions, and that this artificial barrier, if not used well, may disengage potential subscribers and thus may not well serve its purpose of increasing revenue. We propose an adaptive paywall mechanism to balance the benefit of showing an article against that of displaying the paywall (i.e., terminating the session). We first define the notion of cost and utility that are used to define an objective function for optimal paywall decision making. Then, we model the problem as a stochastic sequential decision process. Finally, we propose an efficient policy function for paywall decision making. All the proposed models are evaluated on real datasets from The Globe and Mail which is a major newspaper in Canada. However, the proposed techniques are not limited to any particular dataset or strict requirement. Alternatively, they are designed based on the datasets and settings which are available and common to most of newspapers. Therefore, the models are general and can be applied by any online newspaper to improve user engagement and acquisition.

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