Digital Commons Network^™

Eng+ | Ethics For Sustainable Development In Engineering Programmes A Praxis Report Of Tu Berlin’S Think Tank Technology Reflection, Sabine Ammon, Mareen Derda, Tim Hildebrandt, Stefan Kühne, Nadine Klein

Practice Papers

Many technical universities alike, TU Berlin is in a future-oriented process of programme transformation to invite a holistic perspective on technology which includes critical thinking and ethical reflection. To this end, TU Berlin recently issued a general study guideline calling for an orientation of all programmes toward sustainable development. Accordingly, students should know about the historical, social and cultural contexts of science and technology and learn to reflect on the ethical consequences of their actions. Together with training in good scientific practice, this content should comprise 12 ECTS in each respective BA and MA programme. With only minor integration of …

Unlocking Deeper Insights: A Qualitative Approach To Evaluating Stem Outreach In Engineering Education, Yiduo Wang, Lauren Schrock, Jane Andrews, Robin Clark

Practice Papers

Much previous research and evaluation has been conducted of STEM Outreach activities in schools using quantitative approaches. Surveys in particular are popular as they are low-cost and time-saving. However, quantitative methods are limited in that they do not generally generate deeper insights into students’ experiences in STEM; usually lacking rich detail about the context and complexity of the data being analysed. Hence, this paper proposes a different approach to evaluating STEM outreach: a unique multi-method, qualitative approach.

Starting with the Research Question “How to qualitatively evaluate STEM outreach in Engineering Education?”, this paper is grounded in ongoing doctoral research that …

Identifying The Characteristics, Constraints, And Enablers To Creating Value In Applied Performance Analysis, Denise Martin, Peter G. O'Donoghue, Jonathan Bradley, Sam Robertson, Denise Mcgrath

Articles

While applied performance analysts (PAs) are a well-established applied sports science practitioner group, there is no clear definition of the purpose of an analyst, their key relationships, or the expertise required to execute the role successfully. This research sought to understand how PA practitioners and educators perceive their role as applied PA practitioners. Twenty-seven applied PAs and educators with applied PA experience participated in six online focus groups, completing an online survey in advance. Reflexive thematic analysis of transcripts and survey data generated an overarching theme: embedded applied PA practitioners have a value co-creation role within performance ecosystems which is …

Determining Child Sexual Abuse Posts Based On Artificial Intelligence, Susan Mckeever, Christina Thorpe, Vuong Ngo

Conference papers

The volume of child sexual abuse materials (CSAM) created and shared daily both surface web platforms such as Twitter and dark web forums is very high. Based on volume, it is not viable for human experts to intercept or identify CSAM manually. However, automatically detecting and analysing child sexual abusive language in online text is challenging and time-intensive, mostly due to the variety of data formats and privacy constraints of hosting platforms. We propose a CSAM detection intelligence algorithm based on natural language processing and machine learning techniques. Our CSAM detection model is not only used to remove CSAM on …

Work In Progress: A Virtual Educational Robotics Coding Club Framework To Improve K-6 Students Emotional Engagement In Stem, Kate Carmody, Julie Booth, Jospehine Bleach, Pramod Pathak, Paul Styles

Conference papers

The growing popularity and deployment of Internet of Things (IoT) devices has led to serious security concerns. The integration of a security operations center (SOC) becomes increasingly important in this situation to ensure the security of IoT devices. In this article, we will present a summary of IoT device security issues, their vulnerabilities, a review of current challenges to keep these devices secure, and discuss the role that SOC can bring in protecting IoT devices while considering the challenges encountered and the directions to consider when implementing a reliable SOC for IoT monitoring.

Robustness Of Image-Based Malware Classification Models Trained With Generative Adversarial Networks, Ciaran Reilly, Stephen O Shaughnessy, Christina Thorpe

Conference papers

As malware continues to evolve, deep learning models are increasingly used for malware detection and classification, including image based classification. However, adversarial attacks can be used to perturb images so as to evade detection by these models. This study investigates the effectiveness of training deep learning models with Generative Adversarial Network-generated data to improve their robustness against such attacks. Two image conversion methods, byte plot and space-filling curves, were used to represent the malware samples, and a ResNet-50 architecture was used to train models on the image datasets. The models were then tested against a projected gradient descent attack. It …

Interpretable Input-Output Hidden Markov Model-Based Deep Reinforcement Learning For The Predictive Maintenance Of Turbofan Engines, Ammar N. Abbas, Georgios C. Chasparis, John Kelleher

Conference papers

An open research question in deep reinforcement learning is how to focus the policy learning of key decisions within a sparse domain. This paper emphasizes on combining the advantages of input-output hidden Markov models and reinforcement learning. We propose a novel hierarchical modeling methodology that, at a high level, detects and interprets the root cause of a failure as well as the health degradation of the turbofan engine, while at a low level, provides the optimal replacement policy. This approach outperforms baseline deep reinforcement learning (DRL) models and has performance comparable to that of a state-of-the-art reinforcement learning system while …

Nesdeepnet: A Fusion Framework For Multi-Step Forecasting Of Near-Surface Air Pollutants, Prasanjit Dey, Soumyabrata Dev, Bianca Schoen-Phelan

Conference papers

Air pollution is a global issue that poses significant threats to human health and the environment due to industrial development. Forecasting the concentrations of major pollutants such as NO₂ and CO can provide early warnings of harmful substances, minimizing health risks and losses. Recent deep learning models have shown promise in air quality prediction, but they have limitations such as insufficient feature representation, high computational costs, and poor generalization. This paper proposes a near-surface deep network (NeSDeepNet) to overcome these limitations. The NeSDeepNet integrates multiple deep learning models and a shallow model to form a hybrid forecasting system. The …

Communety: Deep Learning-Based Face Recognition System For The Prediction Of Cohesive Communities, Syed Afaq Ali Shah, Weifeng Deng, Muhammad Aamir Cheema, Abdul Bais

Research outputs 2022 to 2026

Effective mining of social media, which consists of a large number of users is a challenging task. Traditional approaches rely on the analysis of text data related to users to accomplish this task. However, text data lacks significant information about the social users and their associated groups. In this paper, we propose CommuNety, a deep learning system for the prediction of cohesive networks using face images from photo albums. The proposed deep learning model consists of hierarchical CNN architecture to learn descriptive features related to each cohesive network. The paper also proposes a novel Face Co-occurrence Frequency algorithm to quantify …

Synthetic Heart Sound Dataset, Davoud Shariat Panah, Andrew Hines, Susan Mckeever

Datasets

The repository contains synthetic heart sound recordings. The publication related to this dataset is "Exploring the impact of noise and degradations on heart sound classification models", Biomedical Signal Processing and Control journal.

Hdd Dataset: Optimising Retrofitted Insulation For Irish Residential Building Walls, Rakshit D. Muddu, Aimee Byrne, Anthony James Robinson

Datasets

In this study, a thermal-economic analysis was conducted to determine the optimum insulation thickness of retrofitted insulation walls in different regions in Ireland. This was based on the Heating Degree Day method (HDD). This dataset contains optimum insulation thickness, payback period, cost savings and carbon emission for all 25 counties in the Republic of Ireland

Dataset For Gendered Language, Shweta Soundararajan

Datasets

Gendered language is the use of words that denote an individual’s gender. This can be explicit where the gender is evident in the actual word used, e.g. mother, she, man, but it can also be implicit where social roles or behaviours can signal an individual’s gender - for example, expectations that women display communal traits (e.g., affectionate, caring, gentle) and men display agentic traits (e.g., assertive, competitive, decisive). The use of gendered language in NLP systems can perpetuate gender stereotypes and bias. This paper proposes an approach to generating gendered language datasets using ChatGPT which will provide data for data-driven …

Towards Quantum Computing For Audio And Music Expression, Paulo Vitor Itaboraí

School of Law, Humanities and Social Sciences Theses

This research delves into the initial approaches of the use of emerging Quantum Computing technologies in music practice, anchored on preliminary studies on Quantum Representation of Audio (QRA). The approach is interdisciplinary, and attempts to explore the object - QRA - both from a technical scope and an artistic scope. A systematic review unifies diverse QRA techniques, spotlighting the need to bring accessibility to these techniques and terminologies (dense in theoretical physics formalism) for a computer music and/or artistic audiences, in a didactic manner. In this review, some additional techniques initially only discussed for representing images are translated into audio. …

Understanding Student Perceptions Of Supplemental Instruction: Student Perceptions Of Supplemental Instruction Implemented In Order To Overcome Perceived Skill Deficits, Andrew Miehe

Dissertations and Theses @ UNI

Supplemental instruction has become so prevalent that it is simply a thing educators do to students without the students’ input or their understanding of its purpose. This prevalence combined with a lack of understanding on the part of the students could create problems around programmatic success and student motivation in addition to the potential of limiting access to coursework. Due to the fact that there is not an abundance of research focused on the student perceptions of supplemental instruction there is consensus that this perspective is often absent despite its critical role (Yazzie-Mintz & McCormick, 2012).

The proliferation of supplemental …

Non-Traditional Learning In Stem: How Students Autonomy And The Impact Of Teacher Delivery Develops Deeper Conceptual Understanding At The Middle School Level, Sarah Kelly

Dissertations and Theses @ UNI

Non-traditional forms of curriculum development and delivery have shown particular promise in supporting the autonomy of students particularly in relation to science. Programs such as OPENSCIED are inquiry based- learning that promotes problem solving skills and cross curricular development supported by the Next Generation Science Standards (NGSS). This mixed method research study sought to illuminate how these types of curricular programs influence student autonomy and learning, especially with the implementation of OpenSciEd within Des Moines Public Schools. The study utilizes student state testing (ISASP)data alongside teacher surveys, interviews and observations to investigate the strengths or potential deficits of inquiry based …

Solving Electromagnetic Inverse Scattering Problems In Inhomogeneous Media By Deep Convolutional Encoder-Decoder Structure, Huan Huan Zhang, He Ming Yao, Lijun Jiang, Michael Ng

Electrical and Computer Engineering Faculty Research & Creative Works

This communication proposes a novel deep learning (DL) approach to solve electromagnetic inverse scattering (EMIS) problems in inhomogeneous media. The conventional approaches for solving inhomogeneous EMIS problems generally have to consider inhomogeneous Green’s functions or conduct approximation operations to media, which inevitably introduces various challenges, including complex mathematical derivation, high computation cost, unavoidable nonlinearity, and even strong ill-posedness. To avoid these challenges, we propose a DL approach based on the complex-valued deep convolutional neural networks (DConvNets), which comprise the deep convolutional encoder–decoder (DCED) structure. Its training data are collected based on the simple synthetic dataset. While the scattered fields received …

Towards Robust Consensus For Intelligent Decision-Making In Iot Blockchain Networks, Charles Rawlins, S. (Sarangapani) Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

Distributed consensus is the core aspect of blockchain protocol security design. Recent protocols like IOTA have improved concurrency and scalability over Proof-of-work (PoW) with Bitcoin but have core design decisions that are inefficient for limited devices and do not take advantage of previous network experience to reduce calculations. This work proposes the first blockchain consensus protocol based on active machine-learning decisions, called Proof-of-history (PoH). PoH is setup as a distributed reinforcement-learning task for monitoring classification and training of blockchain transactions with an inner deep classifier. Early theoretical analysis and simulations show that PoH is robust to uncoordinated byzantine attacks through …

Accelerated Statistical Eye Diagram Estimation Method For Efficient Signal Integrity Analysis, Junyong Park, Youngwoo Kim, Donghyun Kim

Electrical and Computer Engineering Faculty Research & Creative Works

This article proposes an accelerated statistical eye diagram estimation method for efficient signal integrity (SI) analysis. An eye diagram is a critical metric in the SI analysis, however obtaining an eye diagram is time-consuming in simulation. The required time might be a few weeks depending on the complexity. Because the eye diagram is obtained by superposition of the received waveforms. Statistical eye diagram estimation methods were proposed to make the eye diagram acquisition efficient, however, it still has a limited improvement due to a large number of convolution operations. To address this limitation, the proposed method includes two approaches: (i) …

Implementing The Fast Full-Wave Electromagnetic Forward Solver Using The Deep Convolutional Encoder-Decoder Architecture, He Ming Yao, Lijun Jiang, Michael Ng

Electrical and Computer Engineering Faculty Research & Creative Works

In this communication, a novel deep learning (DL)-based solver is proposed for the electromagnetic forward (EMF) process. It is based on the complex-valued deep convolutional neural networks (DConvNets) comprising an encoder network and a corresponding decoder network with pixel-wise regression layer. The encoder network takes the incident EM wave and the contrast (permittivity) distribution of the object as the input. It channels the processed data into the corresponding decoder network to predict the total EM field due to the scatter of the input incident EM wave. The training of the proposed DConvNets is done using the simple synthetic dataset. Due …

Bidirectional Asymmetric Clllc Resonant Dc-Dc Converter For Onboard Electric Vehicle Chargers, Angshuman Sharma, Alvaro Cardoza, Kartikeya J.P. Veeramraju, Oroghene Oboreh-Snapps, Jonathan W. Kimball

Electrical and Computer Engineering Faculty Research & Creative Works

Bidirectional CLLLC resonant dc-dc converters with an asymmetric tank can narrow the switching frequency bandwidth required to meet the asymmetric voltage gains in the two directions of power flow. Consequently, higher power density and efficiency may be feasible. in this paper, a CLLLC resonant converter with asymmetric primary and secondary capacitances is investigated for charging and discharging the next-generation 900 V traction battery of an electric vehicle. First, a new voltage gain equation is formulated using the First Harmonic Approximation. Then, the proposed gain equation is used to design the asymmetric resonant tank. the effects of asymmetric capacitances on the …

Retracted: A Robust Integrated Approach For Optimal Management Of Power Networks Encompassing Wind Power Plants, Aliasghar Baziar, Mohammad Reza Akbarizadeh, Amin Hajizadeh, Mousa Marzband, Rui Bo

Electrical and Computer Engineering Faculty Research & Creative Works

This paper basically concentrates on providing some significant steps for congestion management of the power systems based on an interval-Based robust chance constrained transmission switching (IBRCC-TS) approach for decreasing the congestion of the system while increasing the robustness of the system against uncertainties of the wind turbines. However, the utilization of TS approach in the power system is a severe challenge, since there is no limitation over the switching rate during a certain timespan in the network as well as the power switches' failure uncertainty. Besides, the frequent switching by the TS method decreases the switch maintenance and puts the …

Small Signal Stability Analysis Of Virtual Impedance Control In Islanded Microgrid, Oroghene Oboreh-Snapps, Kartikeya J.P. Veeramraju, Arnold Fernandes, Alvaro Cardoza, Angshuman Sharma, Jonathan Saelens, Jonathan W. Kimball

Electrical and Computer Engineering Faculty Research & Creative Works

This paper examines the impact of virtual impedance control in an inverter-dominated microgrid (MG) system. the goal is to provide a clear understanding of Virtual Impedance (VI) control toward MG stability. This analysis is performed by developing the system's Small-Signal Model (SSM) and studying the poles' trajectory change when the system's key control parameters are varied. the results indicate that VI will benefit system stability when considering MG with low X/R while negatively affecting MG with high X/R. Also, there is a trade-off between the coupling impedance of the inverter and the VI parameters, which needs to be considered when …

Application Of A Variable Path Length Repetitive Process Control For Direct Energy Deposition Of Thin-Walled Structures, Elias B. Snider, Douglas A. Bristow

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Direct Energy Deposition (DED) Additive Manufacturing is Well Suited to Fabricating Large Thin-Walled Metal Structures Such as Rocket Nozzles but Suffers from Layer-To-Layer Defect Propagation. Propagating Defects May Exhibit as Slumping or a Ripple in Bead Geometry. Recent Works Have Used Repetitive Process Control (RPC) Methods for Additive Manufacturing to Stabilize the Layer-Wise Defect Propagation, But These Methods Require Repetition of the Same Path. However, Typical Thin-Wall DED Applications, Sometimes Referred to as Vase Structures, Have Changing Paths with Each Layer Such as Expanding or Contracting Diameters and Changing Profiles. This Paper Presents an Extension to Optimal RPC that Uses …

Quantifying Stranded Assets Of The Coal-Fired Power In China Under The Paris Agreement Target, Weirong Zhang, Yiou Zhou, Zhen Gong, Junjie Kang, Changhong Zhao, Zhixu Meng, Jian Zhang, Tao Zhang, Jiahai Yuan

Research Collection School Of Computing and Information Systems

Coal-fired power plays a critical role in China's compliance with the Paris Agreement. This research quantifies China's stranded coal assets under different coal capacity expansion scenarios with an integrated approach and high-precision coal-fired power database. From a top-down perspective, firstly, the pathway of China's coal-fired power capacity consistent with the global 2 degrees C scenario is outlined and then those stranded coal-fired power plants are identified with a bottom-up perspective. Stranded value is estimated based upon a cash flow algorithm. Results show that if coal capacity stabilizes during 2020-2030, China will only incur a sizeable yet manageable stranded asset loss …

Just-In-Time Obsolete Comment Detection And Update, Zhongxin Liu, Xin Xia, David Lo, Meng Yan, Shangping Li

Research Collection School Of Computing and Information Systems

Comments are valuable resources for the development, comprehension and maintenance of software. However, while changing code, developers sometimes neglect the evolution of the corresponding comments, resulting in obsolete comments. Such obsolete comments can mislead developers and introduce bugs in the future, and are therefore detrimental. We notice that by detecting and updating obsolete comments in time with code changes, obsolete comments can be effectively reduced and even avoided. We refer to this task as Just-In-Time (JIT) Obsolete Comment Detection and Update. In this work, we propose a two-stage framework named CUP2 (Two-stage Comment UPdater) to automate this task. CUP2 consists …

Heterogeneous Microstructure And Mechanical Behaviour Of Al-8.3fe-1.3v-1.8si Alloy Produced By Laser Powder Bed Fusion, S. J. Yu, P. Wang, H. C. Li, R. Setchi, M. W. Wu, Z. Y. Liu, Z. W. Chen, S. Waqar, Lai-Chang Zhang

Research outputs 2022 to 2026

The relationship between processing parameters, microstructure, and mechanical properties of Al-8.3Fe-1.3V-1.8Si alloy processed by laser powder bed fusion is seldom studied. Therefore, fully dense alloys with two parameters were selected to investigate this key issue. The results show that the alloy with low power and scanning speed (S200) shows fan-shell-shaped melt pools and laser tracks while another (S350) shows a deeper and wider melt pool. Both alloys obtain a heterogeneous microstructure without a secondary phase in melt pool (MP) and a nano-sized phase in melt pool boundary (MPB). The difference between solid-solution strengthening and Orowan strengthening in MP and MPB …

Closing The Shell: Gas-Phase Solvation Of Halides By 1,3-Butadiene, Peter D. Watson, Christian T. Haakansson, Hayden T. Robinson, Timothy R. Corkish, James Brookes, Allan J. Mckinley, Duncan A. Wild

Research outputs 2022 to 2026

Gas-phase solvation of halides by 1,3-butadiene has been studied via a combination of photoelectron spectroscopy and density functional theory. Photoelectron spectra for X−⋯(C4H6)n (X=Cl, Br, I where n=1-3, 1–3 and 1–7 respectively) are presented. For all complexes, the calculated structures indicate that butadiene is bound in a bidentate fashion through hydrogen-bonding, with the chloride complex showing the greatest degree of stabilisation of the internal C−C rotation of cis-butadiene. In both Cl− and Br− complexes, the first solvation shell is shown to be at least n = 4 from the vertical detachment energies (VDEs), however for I−, increases in the …

Biofilm Formation On The Surface Of Monazite And Xenotime During Bioleaching, Arya Van Alin, Melissa K. Corbett, Homayoun Fathollahzadeh, M. Christian Tjiam, William D. A. Rickard, Xiao Sun, Andrew Putnis, Jacques Eksteen, Anna H. Kaksonen, Elizabeth Watkin

Research outputs 2022 to 2026

Microbial attachment and biofilm formation is a ubiquitous behaviour of microorganisms and is the most crucial prerequisite of contact bioleaching. Monazite and xenotime are two commercially exploitable minerals containing rare earth elements (REEs). Bioleaching using phosphate solubilizing microorganisms is a green biotechnological approach for the extraction of REEs. In this study, microbial attachment and biofilm formation of Klebsiella aerogenes ATCC 13048 on the surface of these minerals were investigated using confocal laser scanning microscopy (CLSM) and scanning electron microscopy (SEM). In a batch culture system, K. aerogenes was able to attach and form biofilms on the surface of three phosphate …

Chatgpt In Higher Education: Considerations For Academic Integrity And Student Learning, Miriam Sullivan, Andrew Kelly, Paul Mclaughlan

Research outputs 2022 to 2026

The release of ChatGPT has sparked significant academic integrity concerns in higher education. However, some commentators have pointed out that generative artificial intelligence (AI) tools such as ChatGPT can enhance student learning, and consequently, academics should adapt their teaching and assessment practices to embrace the new reality of living, working, and studying in a world where AI is freely available. Despite this important debate, there has been very little academic literature published on ChatGPT and other generative AI tools. This article uses content analysis to examine news articles (N=100) about how ChatGPT is disrupting higher education, concentrating specifically on Australia, …

Instantaneous Frequency Estimation Of Fm Signals Under Gaussian And Symmetric Alpha-Stable Noise: Deep Learning Versus Time-Frequency Analysis, Huda Saleem Razzaq, Zahir M. Hussain

Research outputs 2022 to 2026

Deep learning (DL) and machine learning (ML) are widely used in many fields but rarely used in the frequency estimation (FE) and slope estimation (SE) of signals. Frequency and slope estimation for frequency-modulated (FM) and single-tone sinusoidal signals are essential in various applications, such as wireless communications, sound navigation and ranging (SONAR), and radio detection and ranging (RADAR) measurements. This work proposed a novel frequency estimation technique for instantaneous linear FM (LFM) sinusoidal wave using deep learning. Deep neural networks (DNN) and convolutional neural networks (CNN) are classes of artificial neural networks (ANNs) used for the frequency and slope estimation …