Mühendislik Fakültesi Koleksiyonu
Bu koleksiyon için kalıcı URI
Güncel Gönderiler
Öğe Energy and exergy analysis of PV modules: Sustainable improvements through fin structure and spray cooling(Elsevier B.V., 2025-01-30) Ismael Ismael, Omar Rashid; Selimli, SelcukStudies in the literature focused on improving photovoltaic energy performance through spray or fin cooling have inspired this study to investigate the combined effect of these methods using a temperature-controlled, water-saving spray technique. The temperature-controlled spraying technique aims to improve energy efficiency through efficient cooling and reduced water loss. The effects of cooling a photovoltaic module with a fin structure and improving cooling by a spray system on energy performance were experimentally tested on three modules: PV₁ works without cooling, PV₂ uses a fin structure, and PV₃ includes both a fin structure and spray cooling. The temperatures of the PV₂ and PV₃ modules are 1.59 % and 4.59 % lower, respectively, as contrasted to the PV₁ module. PV₁, PV₂, and PV₃ modules had energy efficiency of 12.7 %, 14.10 %, and 14.93 %, respectively, and exergy efficiencies of 8.25 %, 11.25 %, and 14.86 %, respectively. The PV₂ and PV₃ modules had a higher energy efficiency of 1.40 % and 2.20 %, respectively, and a higher exergy efficiency of 3 % and 6.61 %, compared to the PV₁ module. The fin structure with spray cooling improved the availability of renewable energy. The sustainability index of PV₁, PV₂ and PV₃ modules are 1.09, 1.13 and 1.17, respectively, which shows that the cooling effect reduces irreversibility and environmental degradation.Öğe An additional value for the disposed wastes: An experimental and RSM optimization study based on the enhancement of waste plastic oil/diesel fuel blend with optimum B₂O₃ nanoparticles for cleaner emissions(Elsevier B.V., 2025-01-23) Uslu, SametIn the current study, the ability of waste cable pyrolysis oil (WCPO) and boron oxide (B2O3) nanoparticles to improve diesel engine response was evaluated. Firstly, WCPO was produced and 20 % was determined as the most suitable mixture ratio for diesel engines. Then, different amounts (20, 40, and 60 ppm) of B2O3 were added to the fuel mixture containing 20 % WCPO/80 % diesel to strengthen the negative aspects of WCPO. The addition of 20 % WCPO reduced BTHE by an average of 7.93 % and with the positive effect of the addition of 20 ppm B2O3, this reduction was increased to an average of 0.83 %. Furthermore, the addition of B2O3 nanoparticles decreased CO and HC emissions, whereas the addition of 20 % WCPO enhanced them. HC decreased by 27.18 % with 20 ppm B2O3, after increasing by an average of 5.61 % with WCPO20 compared to diesel. Likewise, for CO, there was a 67.96 % increase with WCPO20 and a 5.92 % drop with 20 ppm B2O3. However, response surface methodology (RSM) optimization was also carried out to determine the ideal concentration of B2O3 because nanoparticles are expensive. In RSM optimization, the quantity of B2O3 (QoN) and engine load were modeled as variables, and brake thermal efficiency (BTHE), brake-specific fuel consumption (BSFC), nitrogen oxide (NOx), carbon monoxide (CO), hydrocarbon (HC), and carbon dioxide (CO2) were modeled as responses. According to the model, the optimum B2O3 amount was determined as 22 ppm at 1500 W load. Under these conditions, the best results for BTHE, BSFC, NOx, CO, HC, and CO2 are 24.5755 %, 387.533 g/kWh, 523.141 ppm, 0.0413 %, 23.7139 ppm, and 5.2072 % respectively. Moreover, the composite desirability value was within acceptable limits at 0.7156. In addition, the maximum difference between the RSM and the experimental results was 4.81 %, indicating that the RSM gave successful results in this study.Öğe Role of dysprosium substitution on microscopy architecture, structural stability, and crack propagation mechanism in Bi-2212 engineering ceramics(Institute of Physics, 2025-01-28) Kurtul G.; Ulgen A.T.; Armagan O.; Turkoz M.B.; Erdem I.; Yildirim G.This study achieves a strong link between microscopy architecture and fundamental characteristics including electrical conductivity, superconducting, and key mechanical design properties of Bi2.1-xDyxSr2.0Ca1.1Cu2.0Oy (Bi-2212) ceramic structures with different dysprosium molar ratio ranges of 0.00 ≤ x ≤ 0.10. The Dy/Bi substituted Bi-2212 ceramics are characterized by scanning electron microscopy (SEM), electrical resistivity (ρ-T), Electron Dispersive x-ray (EDX) investigations, and microindentation Vickers hardness (Hv) tests. Powder x-ray diffraction (XRD) experimental inspection is also studied to support SEM and Hv results. All experimental findings show significant improvement with an increase in the Dy impurity molar ratio to x = 0.01. On this basis, the Bi2.09Dy0.01Sr2.0Ca1.1Cu2.0Oy ceramic structure exhibits the lowest resistivity of 8.95 mΩ.cm at 300 K and transition width of 4.75 K, and the highest T c o n s e t of 85.00 K and T c o f f s e t of 80.25 K. Additionally, XRD examinations show that optimum Dy ion substitution in the Bi-2212 system stabilizes the high superconducting phase by improving crystallinity, crystallite size, grain orientation distributions, texturing, and interlayer interactions. In contrast, excessive substitution severely deteriorates crystallographic properties. Further, SEM images reveal that the presence of optimum Dy impurity enhances the crystallinity, couplings between the adjacent layers, homogeneous surface appearance, and microstructure. Moreover, the key mechanical design features and stability of the durable tetragonal phase improve significantly for x = 0.01. As a result, the material exhibits superior mechanical properties, including a microhardness of 0.5556 GPa, fracture toughness of 0.5390 MPa.m1/2, elastic modulus of 45.5389 GPa, shear modulus of 18.2156 GPa, yield strength of 0.1852 GPa, and resilience of 0.3766 MPa under a 0.295 N load.Öğe Harnessing nuclear power for sustainable electricity generation and achieving zero emissions(SAGE Publications Inc., 2025-01-23) Khaleel, Mohamed; Yusupov, Ziyodulla; Rekik, Sassi; Kılıç, Heybet; Nassar, Yasser F.; El-Khozondar, Hala J.; Ahmed, Abdussalam AliNuclear power plays a pivotal role in sustainable electricity generation and global net zero emissions, contributing significantly to this secure pathway. Nuclear power capacity is expected to double, escalating from 413 gigawatts (GW) in early 2022 to 812 GW by 2050 within the net zero emissions (NZE) paradigm. The global energy landscape is undergoing significant transformation as nations strive to transition to more sustainable energy systems. Amidst this shift, nuclear power has emerged as a crucial component in the pursuit of a sustainable energy transition. This study examines nuclear power's multifaceted role in shaping sustainable energy transition. It delves into nuclear energy's contributions toward decarbonization efforts, highlighting its capacity to provide low-carbon electricity and its potential role in mitigating climate change. Furthermore, the study explores the challenges and opportunities associated with integrating nuclear power into energy transition strategies, addressing issues such as safety, waste management, and public perception. In conclusion, the global nuclear power capacity is anticipated to reach approximately 530 GW by 2050, representing a substantial shortfall of 35% compared with the trajectory outlined in the NZE pathway. Under the NZE scenario, nuclear power demonstrates exceptional expansion, nearly doubling from 413 GW in early 2022 to 812 GW by 2050. Concurrently, the trajectory highlights a transformative shift in renewable energy investments, with annual expenditures surging from an average of US$325 billion during 2016–2020 to an impressive US$1.3 trillion between 2031 and 2035. These projections underscore the critical role of nuclear and renewable energy investments in achieving global sustainability and emission reduction goals.Öğe Effects of pack boriding temperature on wear and corrosion performance of high-strength armor steel(ICE Publishing, 2025-01-28) Neccaroglu, Vahap; Karademir, Ibrahim; Unal, OkanThis study systematically investigates the effects of pack boriding on the microstructure, hardness, wear, and corrosion resistance of armor steel. A saw-like boride layer was formed as a result of the treatment at temperatures of 800, 900, and 1000 °C. The thickness of the boride layer was positively influenced by increases in temperature. At 1000 °C, the microhardness of the borided surface achieved a maximum level of 3250 HV0.02. The wear resistance of the borided specimens was improved significantly, resulting in a notable reduction in volume losses. Furthermore, the boriding process enhanced the corrosion resistance of the steel by a factor of three to four. Specimens borided at 1000 °C demonstrated the highest level of corrosion resistance.Öğe Cybersecurity Defence Mechanism Against DDoS Attack with Explainability(Mesopotamian Academic Press, 2024-12-26) Mahmood, Alaa Mohammed; Avcı, İsaApplication-layer attacks (Layer 7 attacks), a form of distributed denial-of-service (DDoS) aimed at web servers, have become a significant concern in cybersecurity because of their ability to disrupt services by overwhelming server resources. This study focuses on addressing the challenges of detecting and mitigating the impact of such attacks, which are difficult to counter due to their sophisticated nature. The primary objective of this study is to develop an effective monitoring and defence model to detect, defend, and respond to these attacks efficiently. To achieve this, SHapley Additive exPlanations (SHAP) technology was used to understand the behaviour of the model and to increase the efficiency of the detection classifiers. The defence model is designed with three states: normal, observing, and suspicious. The observing mode, which represents the detection part, is triggered when the server load exceeds a predefined threshold. The detection system incorporates five machine learning (ML) algorithms: decision trees (DTs), support vector machines (SVMs), logistic regression (LR), naive Bayes (NB), and K-nearest neighbours (KNNs). A stacked classifier (SC) was then employed to combine these models to achieve optimal performance. The algorithms were evaluated in terms of accuracy (ACC), precision (PRC), recall (REC), F1 score (F1), and time (T). The SC demonstrates superior accuracy in distinguishing between legitimate traffic and malicious traffic. If the server continues to suffer from overload, the suspicious part of the defence model will be activated, and the mitigation algorithm will be called, which, in turn, bans users responsible for the attack and prevents illegitimate users from connecting to the server. The effects of the mitigation algorithm were noticeable in the server traffic rate, transmission rate, memory utilization, and CPU utilization, confirming its ability to defend against application-layer attacks.Öğe Design and Comparative Analysis of a Microstrip Patch Antenna With Different Feed Technique at 2.4 GHz for Wireless Applications(Institute of Electrical and Electronics Engineers (IEEE), 2024-12-17) Hanashi, Salem Mohamed Al; Almohamad, Tarik Adnan; Aladwani, Azam Isam; Aziz, Ahlem; Guneser, Muhammet Tahir; Albreem, Mahmoud A.Nowadays, according to the wireless application's requirements, which grow very fast, the antenna designs must enhance their output parameters to be compatible with the new generation's demand and to have the advantages of being less weight, low cost and highly reliable. In this paper, we present the design and analysis of a Microstrip Patch Antenna (MPA) with different feeding techniques with the same parameters at 2.4 GHz for the applications of wireless communications, the CST 2020 software was used for antenna simulation, and the comparison was made among Microstrip Patch (MP) Antenna with Inset feed line and else with coaxial feeding. The results show that The MPA fed by a coaxial probe yields superior performance in the standing of return loss, bandwidth, and Voltage Standing Wave Ratio (VSWR) compared to the antenna utilizing an inset feed line.Öğe Internet of Robotic Things (IoRT) approach to lifelong learning and medical education with Internet of Medical Things (IoMT)(CEUR-WS, 2024-01-07) Hasko, Roman; Hasko, Oleksandra; Hakan, KutucuThe article describes the use of relatively new Internet of Robotic Things (IoRT) and Internet of Medical Things (IoMT) paradigms as a logical development of the Internet of Things (IoT) concept in terms of Lifelong Learning and the specifics of medical education from the point of view of the applied use of robotics and artificial intelligence. The Internet of Robotic Things (IoRT) is specifically proposed for robotics and will be important for the development of multi-purpose robotic systems. As the Internet of Things (IoT) provides a reliable framework for connecting things to the Internet and simplifies machine-to-machine communication and data transfer over core network protocols, and is developing at such a rapid pace that billions of devices are now connected, with the prospect of trillions in the coming years, it is understandable to use and the expansion of IoT concepts and technologies to other fields, in particular robotics in its various applications, such as in the military, agriculture, industry, health care, and biotechnology. One of these directions is education, especially lifelong and medical. Another branch of IoT in the medical direction should be highlighted separately, i.e. Medical Education with Internet of Medical Things (IoMT). IoRT is a symbiosis of various technologies such as cloud computing, artificial intelligence (AI), machine learning and the Internet of Things. An example of the implementation of IoRT for education is considered on the basis of the active university Laboratory of Robotics with collaborative robots (cobots) Dobot MG400 and the integration of several such cobots into a single Internet-based system based on ROS and applied applications, which allows teaching new skills and knowledge for the implementation of robotic systems on based on IoT in real-world implementations.Öğe Corrosion behaviour of biodegradable Mg-Zn-Mn-Ce, Mg-Zn-Ca-Ce, and Mg-Zn-Ca-Mn quaternary magnesium alloys in phosphate-buffered saline(Elsevier BV, 2025-01-09) Zengin, Hüseyin; Hofinger, Manuel; Silva Campos, Maria del Rosario; Blawert, Carsten; Polat, Safa; Nienaber, Maria; Bohlen, Jan; Zheludkevich, Mikhail; Hassel, Achim WalterIn this study, lean quaternary magnesium alloys with nominal compositions (wt%) of Mg-1Zn-0.5Mn-0.3Ce (Mg-Zn-Mn-Ce), Mg-1Zn-0.2Ca-0.3Ce (Mg-Zn-Ca-Ce) and Mg-1Zn-0.2Ca-0.5Mn (Mg-Zn-Ca-Mn) were produced using permanent mould direct chill casting and the corrosion behaviours up to 168 h of immersion in phosphate buffer solution (PBS) at 37 °C were investigated. Various techniques were employed to conduct corrosion tests, including weight loss, hydrogen evolution, inductively coupled plasma optical emission spectroscopy (ICP-OES) to quantify the amount of released Mg during both static immersion tests and downstream analysis using a flow cell, potentiodynamic polarization, and electrochemical impedance spectroscopy (EIS). The obtained data was analysed in detail to compare the corrosion resistance of the three magnesium alloys and the effectiveness of the various test methods. Among the studied alloys, the Mg-Zn-Mn-Ce alloy exhibited the highest dissolution rate during the initial immersion period. However, a substantial improvement in the corrosion resistance was observed for this alloy, especially after 24 h of immersion due to the formation of a dense and compact protective surface film. Additionally, the Mg-Zn-Ca-Mn alloy displayed better corrosion resistance compared to the Mg-Zn-Ca-Ce alloy for immersion durations up to 24 h, above which it significantly decreased.Öğe Co-stand: swashplateless micro aerial robot test stand(Strojarski Facultet, Sveuciliste Josipa Jurja Strossmayera u Osijeki, 2025) Karasahin, Ali Tahir; Gungor, GokhanMicro aerial robots are mainly employed for tasks in indoor environments where high maneuverability is required, particularly in navigating complex and constrained spaces with numerous closely positioned obstacles. Swashplateless mechanisms can reduce noise, increase operational efficiency, and enhance maneuverability, enabling agile and precise movements in indoor operations. This paper introduces a test stand, called Co-Stand, designed to evaluate the performance of the micro aerial robots equipped with the swashplateless mechanisms using ground-based testing, without requiring actual flight tests. The Co-Stand is constructed to collect data on operational performance to investigate the design criteria of the swashplateless mechanisms. The experiments performed on the Co-Stand are used to evaluate both the swashplateless and standard propeller design performances. The results demonstrate that the swashplateless mechanisms achieve the performance criteria of the standard propellers, showcasing their advantages in indoor environments.Öğe An innovative real-time recursive framework for techno-economical self-healing in large power microgrids against cyber–physical attacks using large change sensitivity analysis(Multidisciplinary Digital Publishing Institute (MDPI), 2025-01-04) Jahromi, Mehdi Zareian; Yaghoubi, Elnaz; Yaghoubi, Elaheh; Maghami, Mohammad RezaIn the past, providing an online and real-time response to cyber–physical attacks in large-scale power microgrids was considered a fundamental challenge by operators and managers of power distribution networks. To address this issue, an innovative framework is proposed in this paper, enabling real-time responsiveness to cyberattacks while focusing on the techno-economic energy management of large-scale power microgrids. This framework leverages the large change sensitivity (LCS) method to receive immediate updates to the system’s optimal state under disturbances, eliminating the need for the full recalculation of power flow equations. This significantly reduces computational complexity and enhances real-time adaptability compared to traditional approaches. Additionally, this framework optimizes operational points, including resource generation and network reconfiguration, by simultaneously considering technical, economic, and reliability parameters—a comprehensive integration often overlooked in recent studies. Performance evaluation on large-scale systems, such as IEEE 33-bus, 69-bus, and 118-bus networks, demonstrates that the proposed method achieves optimization in less than 2 s, ensuring superior computational efficiency, scalability, and resilience. The results highlight significant improvements over state-of-the-art methods, establishing the proposed framework as a robust solution for real-time, cost-effective, and resilient energy management in large-scale power microgrids under cyber–physical disturbances.Öğe Hydrothermal carbonization of citrus peels and their electrochemical efficacy in double-layer supercapacitors(Taylor and Francis, 2025-01-07) Durmaz, Hasan; Simsir, HamzaThis work involved the fabrication of supercapacitors with rapid charge/discharge rates using waste biomass. Initially, hydrochars were produced using the hydrothermal carbonization of four distinct citrus peels. Subsequently, physical, acidic, and basic activation was employed to enhance surface area and induce porosity. They were analyzed using elemental analysis, FTIR spectroscopy, and SEM examination. The electrochemical performance of the electrodes was assessed using galvanostatic charge-discharge and cyclic voltammetry techniques. The maximum specific capacity recorded was 65.1 mF/cm2 at a current rate of 0.50 mA for the electrode produced through potassium hydroxide activation and calcination. Consequently, it was demonstrated that supercapacitors with comparable specific capacity values may be fabricated from these waste orange peels.Öğe Enhancing fall detection accuracy: the ground-face coordinate system for 3D accelerometer data(Sakarya University, 2024-12-24) Sözer, Abdullah TalhaThe global elderly population is on the rise, leading to increased physical, sensory, and cognitive changes that heighten the risk of falls. Consequently, fall detection (FD) has emerged as a significant concern, attracting considerable attention in recent years. Utilizing 3D accelerometer sensors for FD offers advantages such as cost-effectiveness and ease of implementation; however, traditional raw 3D accelerometer signals are inherently dependent on the device's orientation and placement within the device coordinate system. Misalignment between the device's axes and the direction of movement can lead to misinterpretation of acceleration signals, potentially causing misclassification of activities and resulting in false positives or missed falls. This study introduces a novel coordinate system called "ground-face," which is designed to be independent of the device's orientation and placement. In this system, the vertical axis is aligned perpendicularly to the Earth, while the device's x-axis is aligned with the individual's direction of movement. To assess the potential of the vertical component of ground-face referenced accelerometer signals for FD, it was compared with the commonly used acceleration magnitude signal. Detailed analysis was conducted using frequently preferred features in FD studies, and fall detection was performed with various classifiers. Comprehensive experiments demonstrated that the vertical component of the ground-face signal effectively characterizes falls, yielding approximately a 2% improvement in detection accuracy. Moreover, the proposed coordinate system is not limited to FD but can also be applied to human activity recognition (HAR) systems. By mitigating orientation-related discrepancies, it reduces the likelihood of misclassification and enhances the overall HAR capabilities.Öğe Feasibility assessment of hybrid renewable energy based EV charging station in Libya(Libyan Center for Solar Energy Research and Studies, 2024-11-13) Abodwair, Abdullah; Guneser, Muhammet T.; Khaleel, Mohamed M.; Nassar, Yasser F.; El-Khozondar, Hala J.; Yusupov, Ziyodulla; Elbaz, AbdurazaqThis study presents an assessment of the feasibility of implementing a hybrid renewable energy-based electric vehicle (EV) charging station at a residential building in Tripoli, Libya. Utilizing the advanced capabilities of HOMER Grid software, the research evaluates multiple scenarios involving combinations of solar and wind energy sources integrated with energy storage and the utility grid. This analysis provides a novel approach to enhancing urban energy systems with renewable technologies in a region traditionally reliant on fossil fuels. Furthermore, the study addresses the practical implications for local energy policy, suggesting that such hybrid systems can significantly enhance energy security and support sustainable urban development. The authors studied five scenarios using HOMER. The results reveals that the annual total costs and payback periods are as follows: for Scenario 1 (wind/utility grid), the expenditure totals US$1,554,416 and payback period of 4.8/5.8 years; for Scenario 2 (solar/wind/Utility grid), the amount is US$1,554,506 and payback period of 4.8/5.8 years; and for Scenario 3(solar/wind/storage/utility grid), it escalates slightly to US$1,554,731, all predicated on the utility grid tariffs and payback period of 4.8/5.8 years. Furthermore, in Scenario 4 (solar/ utility grid), the annual total cost is significantly reduced to US$30,589 and a payback period of 8.1/14.3 years, while Scenario 5 (solar/storage/utility grid) incurs an even lower expenditure of US$28,572, again based on the utility grid tariffs and a payback period of 14.0 years. The findings contribute valuable insights into the scalability and adaptability of renewable energy solutions, providing a robust framework for policymakers and planners considering similar implementations in other regions. Overall, the research underscores the potential of integrated renewable energy systems to transform urban energy infrastructures, promoting a sustainable and resilient energy future. The HOMER Grid analysis shows that configurations with energy storage are more cost-effective in the long run, even though they require higher initial costs. It also offers important insights into the economic viability and optimization of hybrid renewable energy systems for an EV charging station in Tripoli, Libya. These results highlight the significance of making calculated investments in renewable energy infrastructure and supporting policies for the development of sustainable energy.Öğe Impact of hot rolling, Zn and Sn on the mechanical and corrosion characteristics of Mg-Zn-Ca alloys(Chulalongkorn University, 2024-12-02) Eren, Halil; Güngör, Ali; Koç, Erkan; Çuğ, HarunIn this study, it was aimed to develop a biodegradable metallic plate that is an alternative to bioinert metal plates. The main advantage of using biodegradable materials for implants is that they can be gradually replaced with the patient’s own tissue, which reduces the need for additional surgeries to remove the implant after it has served its purpose. Magnesium and its alloys can provide biocompatibility as orthopedic implant materials. Mg-Zn-Ca and Mg-Zn-Ca-Sn alloys were prepared using the gravity die casting method. Zn (1.0 wt% and 2.0 wt%) and Sn (0.0 wt%, 0.5 wt% and 1.0 wt%) ratios were used as variables, and the Ca ratio (0.3 wt%) was kept constant in all alloys. After homogenization heat treatment, alloys were hot rolled. Hot rolling resulted in grain refinement, much higher yield and tensile strength, and hardness at the expense of the lower strain. However, hot rolling had a detrimental impact on the corrosion resistance of the alloys. Among the alloys, ZX20-h alloy showed the highest yield and tensile strength before and after corrosion tests. The lowest corrosion rate was measured in ZXT200 alloy as 5.1 mm∙year‒1 after 10 day of immersion. Although ZX20-h alloy has a higher corrosion rate (13.56 mm∙year‒1) than ZXT200 alloy, it can be improved further to be used as biodegradable bone support plate material.Öğe Energy, exergy, and thermoeconomic analysis of a natural gas combined power plant(Springer Netherlands, 2024-12-27) Al-Dulaimi, Bashar Mohammed; Bayat, Mutlucan; Tekir, MutluThis paper explores an innovative power plant design integrating three organic Rankine cycle (ORC) subsystems with a Brayton cycle (BC) to enhance energy conversion efficiency by utilising various waste heat sources. The study applies advanced energy, exergy, and thermoeconomic analyses to comprehensively assess the performance of a natural gas combined cycle (NGCC) power plant, using the energy equation solver (EES) software. The model has been validated against previous research with different parameters, such as compressor efficiency, ambient temperature, and pressure ratio, confirming its accuracy and reliability. The numerical results demonstrate that increasing compressor efficiency from 70 to 88% boosts the NGCC system’s net power output by nearly 60% compared to the Brayton cycle alone. Additionally, both energy and exergy efficiencies of the NGCC improve by 6.6% from the initial state, while the annual cost rate shows a parabolic increase over this range. Furthermore, higher turbine efficiency leads to a 14% increase in overall energy efficiency and a 13% increase in exergy efficiency. An increase in pressure ratio from 6 to 15 raises energy and exergy efficiency by 4% and 3%, respectively. However, the influence of the pressure ratio is less significant compared to the other parameters. Moreover, cycle performance is inversely related to ambient and exhaust gas temperatures.Öğe Performance analysis of three-phase grid-connected inverter with model predictive control method in PIL test environment(Institute of Electrical and Electronics Engineers Inc., 2024) Demir, Ekrem; Gulbudak, OzanProcessor-in-the-loop (PIL) is a testing technique that allows designers to evaluate a microprocessor that will enable them to control a system running in an imaginary environment to emulate the system behavior. The system is realized via the PC in the fictional environment, while the control algorithm is implemented via the real-time microprocessor. In power electronics applications, PIL testing is applied to analyze the accuracy of the code to be run on the microprocessor. In this paper, using the classical Model Predictive Control (MPC) method, the PIL experiment of a three-phase grid-connected inverter system was realized in the Matlab/SIMULINK environment. According to the results of the PIL experiment, the system's stable performance confirmed the theoretical concept by using the MPC method.Öğe Sensitivity of global solar irradiance to transposition models: assessing risks associated with model discrepancies(Elsevier, 2024-12-29) Nassar, Yasser F.; El-Khozondar, Hala J.; Khaleel, Mohamed M.; Ahmed, Abdussalam A.; Alsharif, Abdulgader H.; Elmnifi, Monaem; Nyasapoh, Mark AmoahEstimating solar irradiance is essential for solar energy systems evaluations, energy audit of buildings and. Global and sky-diffuse horizontal irradiances are measured by meteorological stations and satellites. Global horizontal solar irradiance is converted into a global tilted solar irradiance using transposition models (TMs). Despite its importance, many sites—especially isolated rural areas in need of sustainable energy sources—have a conspicuous dearth of information regarding these models. Significant errors can occur when choosing the incorrect TM for feasibility assessments or determining the optimum tilt angles (β) for solar collectors. A novel theory (Risky Index theory) for determining the least risky TM is introduced in this work. To evaluate and validate the proposed theory, eight commonly used TMs from literature, database platforms and software were chosen and tested on 133 sites with various climatic and geographical conditions in the Northern Hemisphere. The study concludes that risk index (RI) is <10 % for all models when the collector is facing south with low tilt angles (β<40°). However, for 40°< β<60° the RI rises above 15 %, and it becomes significant (RI<50 %) as β becomes close to vertical plane. The least risky TM was determined for each site. The results matched satisfactorily with other researchers’ outputs without exceeding 3.5 % of error. A unique TM has been recommended for the entire world corresponding to each interval of β.Öğe Real-time nail-biting detection on a smartwatch using three CNN models pipeline(Wiley-Blackwell Publishing, 2025-02-01) Alesmaeil, Abdullah; Şehirli, EftalNail-biting (NB) or onychophagia is a compulsive disorder that affects millions of people in both children and adults. It has several health complications and negative social effects. Treatments include surgical interventions, pharmacological medications, or additionally, it can be treated using behavioral modification therapies that utilize positive reinforcement and periodical reminders. Although it is the least invasive, such therapies still depend on manual monitoring and tracking which limits their success. In this work, we propose a novel approach for automatic real-time NB detection and alert on a smartwatch that does not require surgical intervention, medications, or manual habit monitoring. It addresses two key challenges: First, NB actions generate subtle motion patterns at the wrist that lead to a high false-positives (FP) rate even when the hand is not on the face. Second, is the challenge to run power-intensive applications on a power-constrained edge device like a smartwatch. To overcome these challenges, our proposed approach implements a pipeline of three convolutional neural networks (CNN) models instead of a single model. The first two models are small and efficient, designed to detect face-touch (FT) actions and hand movement away (MA) from the face. The third model is a larger and deeper CNN model dedicated to classifying hand actions on the face and detecting NB actions. This separation of tasks addresses the key challenges: decreasing FPs by ensuring NB model is activated only when the hand on the face, and optimizing power usage by ensuring the larger NB model runs only for short periods while the efficient FT model runs most of the time. In addition, this separation of tasks gives more freedom to design, configure, and optimize the three models based on each model task. Lastly, for training the main NB model, this work presents further optimizations including developing NB dataset from start through a dedicated data collection application, applying data augmentation, and utilizing several CNN optimization techniques during training. Results show that the model pipeline approach minimizes FPs significantly compared with the single model for NB detection while improving the overall efficiency.Öğe Enhanced disc herniation classification using grey wolf optimization based on hybrid feature extraction and deep learning methods(Multidisciplinary Digital Publishing Institute (MDPI), 2024-12-26) Sarı, Yasemin; Aydın Atasoy, NesrinDue to the increasing number of people working at computers in professional settings, the incidence of lumbar disc herniation is increasing. Background/Objectives: The early diagnosis and treatment of lumbar disc herniation is much more likely to yield favorable results, allowing the hernia to be treated before it develops further. The aim of this study was to classify lumbar disc herniations in a computer-aided, fully automated manner using magnetic resonance images (MRIs). Methods: This study presents a hybrid method integrating residual network (ResNet50), grey wolf optimization (GWO), and machine learning classifiers such as multi-layer perceptron (MLP) and support vector machine (SVM) to improve classification performance. The proposed approach begins with feature extraction using ResNet50, a deep convolutional neural network known for its robust feature representation capabilities. ResNet50’s residual connections allow for effective training and high-quality feature extraction from input images. Following feature extraction, the GWO algorithm, inspired by the social hierarchy and hunting behavior of grey wolves, is employed to optimize the feature set by selecting the most relevant features. Finally, the optimized feature set is fed into machine learning classifiers (MLP and SVM) for classification. The use of various activation functions (e.g., ReLU, identity, logistic, and tanh) in MLP and various kernel functions (e.g., linear, rbf, sigmoid, and polynomial) in SVM allows for a thorough evaluation of the classifiers’ performance. Results: The proposed methodology demonstrates significant improvements in metrics such as accuracy, precision, recall, and F1 score, outperforming traditional approaches in several cases. These results highlight the effectiveness of combining deep learning-based feature extraction with optimization and machine learning classifiers. Conclusions: Compared to other methods, such as capsule networks (CapsNet), EfficientNetB6, and DenseNet169, the proposed ResNet50-GWO-SVM approach achieved superior performance across all metrics, including accuracy, precision, recall, and F1 score, demonstrating its robustness and effectiveness in classification tasks.
