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Conference Object Driving Conditions Leading To Thermal Runaway in Li-Ion Battery EV's(IEEE, 2024) Ertan, H. Bulent; Azuaje-Berbeci, Bernardo J.The adoption of high-energy-density lithium-ion batteries (LIB) as the energy source in electric vehicles (EV) introduces significant safety concerns. Thermal runaway (TR), a self-accelerating rise in battery temperature resulting in catastrophic failure, is a significant safety concern. Cooling system failure within the EV's thermal management system is one of several factors that can trigger TR. Typically, TR is initiated by exceeding a critical temperature threshold under abusive conditions. Understanding the operating conditions that lead to the path of TR is essential for ensuring EV and occupant safety. Recently, a detailed electrochemical-thermal model that incorporates the chemical reactions within the battery until TR is introduced. This paper aims to illustrate how this model can be used to identify the conditions leading to TR under realistic EV driving scenarios. For this purpose, an Advisor/Matlab-based model of a hybrid EV is developed and verified by tests, is used to estimate the current required from the vehicle's battery pack at a given driving condition. This is followed by the prediction of battery thermal response using the mentioned finite-element-analysis-based battery model. Several scenarios are tested in this paper to determine whether TR occurs and to identify the factors contributing to TR. This study aids in comprehending the factors that contribute to TR and the development of preventative measures for battery management system design.Conference Object Citation - WoS: 2Citation - Scopus: 2Radar Cross Section Studies of Low Signature UAVs in X-Band: Simulation, Measurement and Performance Evaluation(IEEE, 2024) Unalir, Dizdar; Gokdogan, Bengisu Yalcinkaya; Aydin, ElifIn this study, the effectiveness of a radar cross section (RCS) reduction method based on a proposed shaping technique for four-legged unmanned aerial vehicles (UAV) has been proven with simulation tools and experimental measurements in X-Band. Simulative RCS values were obtained with CST and HFSS electromagnetic calculation tools, and the advantages of these tools compared to each other were examined. Experimental measurements were carried out in a laboratory environment with a vector network analyzer (VNA) and confirmed with simulation results. The effects of frequency, polarization and aspect angle factors on RCS were examined. It has been shown that with the proposed measurement method, low-cost and easily applicable RCS analysis can be performed in X-Band, one of the frequency bands frequently used in the defense industry. With the proposed shaping method, RCS reduction in the range of 5-10 dBsm was achieved.Conference Object Citation - WoS: 7Citation - Scopus: 12Utilization of Three Software Size Measures for Effort Estimation in Agile World: A Case Study(IEEE, 2022) Unlu, Huseyin; Hacaloglu, Tuna; Buber, Fatma; Berrak, Kivilcim; Leblebici, Onur; Demirors, OnurFunctional size measurement (FSM) methods, by being systematic and repeatable, are beneficial in the early phases of the software life cycle for core project management activities such as effort, cost, and schedule estimation. However, in agile projects, requirements are kept minimal in the early phases and are detailed over time as the project progresses. This situation makes it challenging to identify measurement components of FSM methods from requirements in the early phases, hence complicates applying FSM in agile projects. In addition, the existing FSM methods are not fully compatible with today's architectural styles, which are evolving into event-driven decentralized structures. In this study, we present the results of a case study to compare the effectiveness of different size measures: functional -COSMIC Function Points (CFP)-, event-based - Event Points-, and code length-based - Line of Code (LOC)- on projects that were developed with agile methods and utilized a microservice- based architecture. For this purpose, we measured the size of the project and created effort estimation models based on three methods. It is found that the event-based method estimated effort with better accuracy than the CFP and LOC-based methods.Conference Object An Overview of Challenges To Long-Term Sustainability and Scalability of Radio Frequency Fingerprinting(IEEE, 2024) Demiroglu, Harun Senol; Awan, Maaz Ali; Kara, AliInternet of Things (IoT) technology has become ubiquitous with a broad spectrum of applications. This vast penetration entails formidable cyber-security for the stable operation of the associated systems. Most inexpensive IoT devices employ rudimentary cryptographic security mechanisms due to their resource-limited architecture. Radio frequency fingerprinting (RFF) is a physical layer security mechanism that leverages hardware impairments for authentication and device classification. To this end, its scope has been limited to academia owing to daunting challenges. In this work, an abridged overview of the state-of-the-art is provided, along with a summary of the challenges that hinder progress toward practical applications. The article culminates with a discussion on the intricacies of performance metrics in RFF and the direction for future research.Conference Object Model Enhancement for UAV Stealth in X-Band(IEEE, 2025) Unalir, Dizdar; Yalcinkaya, Bengisu; Aydin, ElifWith the rapid advancement of technology, radar detection techniques continue to evolve, challenging the effectiveness of traditional unmanned aerial vehicles (UAVs) stealth techniques. As the usage of UAVs in military applications expands, the need for effective radar cross section reduction (RCSR) methods to enhance their stealth capabilities has grown significantly. In this study, we propose an enhancement of a previously developed Low-RCS UAV model, focusing on RCSR with shaping technique in the X-band. For the identification and optimization of the UAV model's highly reflective components, a detailed simulative analysis of the RCS was performed using CST Studio Suite Environment. The modifications are applied to the body and leg components to minimize radar reflections. Simulation results demonstrated that the proposed enhancements significantly reduced RCS values compared to the original Low-RCS UAV model. A total of 13 dBsm reduction in RCS was observed compared to the traditional UAV models. Comparative analysis for different frequencies in X-Band and various aspect angles confirmed the effectiveness of the improved design, validating its potential for stealth applications. The findings can contribute to the research in UAV stealth technology and provide insights into future low-visibility UAV designs.Conference Object How To Teach Usage of Equipments in a Remote Laboratory(IEEE, 2007) Alparslan, N. Ceren; Cagiltay, Nergiz Ercil; Ozen, Mustafa; Aydin, ElifEuropean Remote Radio Laboratory (ERRL) is an e-learning project for students, teachers and technicians of the universities who will use the very important devices of this laboratory remotely. These devices are very expensive to buy and can be broken easily while they have been using by the people who does not really know how to use them professionally. As a solution we have developed an e-leaming system which aims to support the ERRL learners while studying on how to use equipments in the system. The system is developed according to the electronic performance support systems (EPSS) approach. An EPSS is a computer-based, well-structured system which improves the performance of individuals. It is an electronic infrastructure that contains, stores and distributes personal (individual) or corporate knowledge to enable people to reach necessary levels of performance in the fastest possible time and with minimum teaching support of other people. This paper discusses how the content for such a system is developed and how this content is interactively used in the EPSS platform. The technical details of the developed EPSS are also discussed in this study. We believe that this paper will help instructional system designers for designing different alternatives to improve learners' performance.Conference Object Citation - WoS: 1Citation - Scopus: 1Optimizing Radio Frequency Fingerprinting for Device Classification: a Study Towards Lightweight Dl Models(IEEE, 2024) Iyiparlakoglu, Raif; Awan, Maaz Ali; Dalveren, Yaser; Kara, AliAs the Internet of Things (IoT) permeates diverse application domains, ensuring the security of wireless networks has become increasingly critical. However, the constraints of resource-limited IoT devices render complex encryption impractical. Consequently, Radio Frequency Fingerprinting (RFF) has emerged as a promising avenue, leveraging unique device characteristics resulting from manufacturing nonlinearities. RFF enhances physical layer security by enabling device classification and authentication at IoT gateways. While deep learning (DL) aided RFF systems offer exceptional classification accuracy, their deployment on edge devices remains challenging to this end. Accordingly, there is a gap in the literature for efficient model exploration and implementation. This study proposes a lightweight Convolutional Neural Network (CNN) model using 1D convolutional filters to reduce inference latency. The model was applied to an open-source dataset comprising 30 LoRa devices. An evaluation was conducted to compare classification accuracy and inference latency using Short Time Fourier Transform (STFT) and Fast Fourier Transform (FFT) for preprocessing. Additionally, the performance of the proposed model was compared against a CNN model utilizing 2D convolutional filters. The model exhibited a significant reduction in inference latency with miniscule degradation in classification accuracy, addressing the identified gap, and propelling the academic discourse towards RFF for edge devices.Conference Object Citation - WoS: 3Citation - Scopus: 5Predicting Software Functional Size Using Natural Language Processing: an Exploratory Case Study(IEEE, 2024) Unlu, Huseyin; Tenekeci, Samet; Ciftci, Can; Oral, Ibrahim Baran; Atalay, Tunahan; Hacaloglu, Tuna; Demirors, OnurSoftware Size Measurement (SSM) plays an essential role in software project management as it enables the acquisition of software size, which is the primary input for development effort and schedule estimation. However, many small and medium-sized companies cannot perform objective SSM and Software Effort Estimation (SEE) due to the lack of resources and an expert workforce. This results in inadequate estimates and projects exceeding the planned time and budget. Therefore, organizations need to perform objective SSM and SEE using minimal resources without an expert workforce. In this research, we conducted an exploratory case study to predict the functional size of software project requirements using state-of-the-art large language models (LLMs). For this aim, we fine-tuned BERT and BERT_SE with a set of user stories and their respective functional size in COSMIC Function Points (CFP). We gathered the user stories included in different project requirement documents. In total size prediction, we achieved 72.8% accuracy with BERT and 74.4% accuracy with BERT_SE. In data movement-based size prediction, we achieved 87.5% average accuracy with BERT and 88.1% average accuracy with BERT_SE. Although we use relatively small datasets in model training, these results are promising and hold significant value as they demonstrate the practical utility of language models in SSM.
