FOCUS AND SCOPE Aintelia Science Notes Journal (ASNJ) is an international, open-access, double-anonymous, peer-reviewed journal established in 2022. The journal is published biannually (in June and December) with the objective of establishing a connection between theoretical engineering research and practical industrial applications. ASNJ is dedicated to the advancement of knowledge across the entire spectrum of engineering disciplines. The journal welcomes high-caliber original research articles and comprehensive review papers from all engineering fields. Its primary focus is on four foundational domains: 1. Engineering, Multidisciplinary This category functions as the journal's focal point, promoting interdisciplinary collaboration. We invite original contributions that do not conform to a single traditional category or that span multiple disciplines (e.g., Civil, Chemical, Environmental, Computer, and Industrial Engineering). Scope: Theoretical analysis, numerical investigations, laboratory experiments, and full-scale field projects. Key Topics: Sustainable engineering solutions, smart systems, emerging technologies, and applied sciences in engineering. 2. Electrical and Electronic Engineering This section serves as an international forum for the dissemination of significant research in the domain of electrical and electronic systems. The objective is to present state-of-the-art results regarding the design, development, and application of electrical technologies. Scope: Power systems, renewable energy integration, signal processing, telecommunications, control systems, and electronics design. 3. Mechanical, Materials, and Manufacturing Engineering This section provides a forum for original work contributing to the advancement of knowledge in the core disciplines of mechanical systems and material sciences. The journal places particular emphasis on the synergy between material properties and manufacturing processes. Scope: Thermodynamics, fluid mechanics, solid mechanics, advanced materials (including polymers, composites, and nanomaterials), additive manufacturing, and production technology. 4. Agricultural Engineering and Machinery It provides an international forum for the dissemination of original scholarly works pertaining to agricultural engineering and machinery, encompassing substantial findings in research, development, application, and design within these disciplines. Scope: Agricultural machinery design, precision agriculture, irrigation technologies, post-harvest technology, and smart farming solutions.
Bilimsel Araştırmalar ve Uygulamalar Derneği - Association for Scientific Research and Applications
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Wireless Communication Techniques in Agricultural Mobile Robots: An Expert Review of Architectures and Performance Trade-offs
The integration of autonomous mobile robots (Agri-Robots) is critical for addressing global food security challenges and increasing efficiency in precision agriculture (PA). The operational efficacy of these robots, which perform tasks from planting to harvesting, hinges entirely on robust, high-speed, and low-latency wireless communication. This review analyzes the performance trade-offs among short-range (Wi-Fi, Bluetooth, Zigbee) and wide-area (LoRaWAN, 4G, 5G-SA) technologies, classifying their suitability based on key metrics: Massive Machine-Type Communications (mMTC) for sensing, Ultra-Reliable Low-Latency Communication (URLLC) for control, and Enhanced Mobile Broadband (eMBB) for data offload. Findings indicate that a single standard is insufficient; LoRaWAN excels in long-range, low-power sensing, while private 5G-SA is mandatory for safety-critical, low-latency control. Operational success is inhibited by environmental factors, such as vegetation-induced signal attenuation and systemic issues, including market fragmentation and proprietary data ecosystems. The critical pathway forward involves adopting hybrid network architectures that intelligently combine Low-Earth Orbit (LEO) satellite services for ubiquitous geographic coverage with terrestrial 5G/Wi-Fi for high-throughput edge computing. Further advancements depend on the development of AI-driven autonomous network management and the adoption of lightweight application protocols like CoAP.
Copyright (c) 2024 AINTELIA Science Notes Journal https://creativecommons.org/licenses/by-nc/4.0
Comparison of K-Means, Fuzzy C-Means and Fuzzy Logic Based Clustering Algorithms for Customer Segmentation
Customer segmentation is a critical tool for businesses to optimize their marketing strategies and increase customer loyalty. This study aims to segment customers based on their buying behavior using K-Means, Fuzzy C-Means (FCM) and Fuzzy Logic based clustering algorithms. While the K-Means algorithm divides the data into a certain number of clusters and determines a center for each cluster, the FCM algorithm allows data points to belong to more than one cluster with varying degrees of membership. Fuzzy Logic based clustering provides a more nuanced segmentation by using a system of fuzzy membership functions and rules defined for each feature. In this study, the performance of all three algorithms is evaluated with Silhouette score, Calinski-Harabasz score and Davies-Bouldin score using Online Retail dataset. The results show that K-Means generally delivers higher Silhouette scores and Calinski-Harabasz index values, FCM provides flexibility to manage similarities across clusters, and the Fuzzy Logic-based approach better captures complex relationships between features. The findings provide valuable insights for customer segmentation and optimization of marketing strategies.
A Review of EEG-Based Face Recognition: Methodologies, Feature Extraction Techniques, and Classification Methods
This review explores EEG-based face recognition, focusing on methodologies, feature extraction techniques, and classification methods. Recognizing familiar and unfamiliar faces is crucial for social interaction, with EEG capturing brain activity to provide objective insights. Critical neural responses like N170, P300, and P600 are discussed in terms of their role in face recognition. The review highlights traditional machine learning approaches, alongside advanced deep learning techniques. Despite advancements, challenges such as signal noise, individual variability, and limited dataset sizes persist. Future research directions include improved noise reduction, personalized models, and data augmentation, aiming to enhance the accuracy and applicability of EEG-based face recognition systems.
Bibliometric analysis of particle swarm optimization algorithm.
Nowadays, metaheuristic algorithms are used in large-scale work environments in order to reach the optimum solution in the solution space faster by using efficient search operations and to find solutions to optimization problems. One of these algorithms, the Particle Swarm Optimization algorithm, has been popularly preferred in recent years due to its ease of coding and high performance, and its successful results in various optimization problems, especially in complex and multi-dimensional problems. Within the scope of this study, a bibliometric analysis of the researches conducted in the literature in the last five years (2020-2024) using the Particle Swarm Optimization algorithm was conducted. In the study, 29867 publications were reached by searching the keywords “Particle”, “Swarm” and “Optimization” in the Web of Science database. The data obtained from the Web of Science database was analyzed using literature analysis, network analysis and visual data discovery methods in the VOSviewer program. As a result of the analyzes, studies related to various criteria such as category distribution, distribution of publications over the years, document types of publications, index distribution of publications, distribution of publications by country were given quantitatively with all their details.
The Effect of Rotor Slot Number on the Performance of Asynchronous Motors in Light Electric Vehicle
Light electric vehicles provide significant advantages in reducing environmental pollution and increasing efficiency in urban transportation. The performance of the motor directly affects the overall performance of electric vehicles. This study investigates the performance analysis of asynchronous motors designed for light electric vehicles used in urban transportation. The initial design parameters of the motor are specified, and various motor structures designed with changes in the rotor slot number are presented. Additionally, the effects of the slot number on the motor's magnetic circuit and performance are discussed. Finite Element Analysis (FEA) is used to investigate the motor's performance. Performance parameters such as efficiency, magnetic flux density, weight, starting torque, and power factor are obtained for different slot numbers. The results indicate that the rotor slot number is a critical design parameter affecting motor performance. In particular, it is stated that maximum efficiency can be achieved with a certain slot number, but an excessive increase in the slot number may have adverse effects on performance.
Copyright (c) 2023 AINTELIA Science Notes Journal https://creativecommons.org/licenses/by-nc/4.0
PSIM simulation and analysis of LCC series-parallel resonant converter
In the control of DC-DC converters, pulse width modulation (PWM) is commonly used. In converters operating with PWM control, the switches operate under hard-switching conditions, resulting in increased switching losses, current and voltage stresses on the switching elements, and electromagnetic interference (EMI) noise as the operating frequency increases. Achieving higher power density and faster transient response in DC-DC converters is possible only by increasing the switching frequency. Therefore, reducing these losses and noises while increasing the switching frequency is only possible with soft switching techniques. Particularly in cases of variable loads, such as batteries, and when a smoother output voltage is required, the series-parallel LCC-type circuit topology from resonant converter topologies is employed. In this study, a simple algorithm has been developed to rapidly control the output voltage and switching frequency of the Full-Bridge Isolated LCC Series-Parallel Resonance circuit based on the current drawn by the changing load over time. The circuit has been successfully operated in the range of 100-130 kHz.
Effect of core material on induced voltage in electromagnetic harvester
With the increasing demand for energy today, diversification of resources and meeting this demand has become crucial. Environmentally friendly alternative solutions have been the subject of research. Especially investments in renewable energy sources have increased in this regard. The power required by low-power sensor technologies can be met by harvesters. For this purpose, the design and Finite Element Analysis (FEA) of the toroid core electromagnetic harvester have been carried out in the study. The material of the toroidal core is determined by selecting Nickel-steel, M19, and nanocrystalline, and the effect of induced voltage and mutual inductance on the core material has been identified. In addition, the line current was determined as a variable and its effect on both flux density and voltage was determined.
Utilizing Machine Learning for Enhanced Diagnosis and Management of Pediatric Appendicitis: A Multilayer Neural Network Approach
This study focuses on pediatric appendicitis, a leading cause of hospital admissions due to abdominal pain in children, characterized by a substantial risk of perforation, especially in younger patients. Traditional diagnostic methods, while effective, often lack specificity and are sup plemented by varying laboratory and imaging techniques. This research introduces a novel application of machine learning (ML), specifically a multi multi-output neural network model, to address the complexities of diagnosing appendicitis, determining its severit y, and guiding management strategies in pediatric cases. The model, with its unique architecture, has been trained and tested on a comprehensive dataset from Children’s Hospital St. Hedwig in Regensburg, Germany, which includes a wide array of clinical dat a and ultrasound images. The results demonstrate remarkable accuracy in classifying management approaches, severity levels, and diagnosis, highlighting the model's potential in supporting clinical decisiondecision-making. While not a replacement for clinical judgm ent, this model serves as a promising tool in the ongoing efforts to improve pediatric appendicitis care, offering a glimpse into the future of AI AI-enhanced medical diagnostics and treatment planning.
Enhancing Bank Marketing Strategies: The Impact of Feature Reduction Techniques on Machine Learning Model Performance
This research investigates the application of machine learning models in the banking sector, specifically focusing on the classification of bank marketing datasets. We explore the use of feature reduction techniques, including Principal Component Analysis (PCA) and SelectKBest, to enhance the performance of various models such as Multi-Layer Neural Networks (MLNN), K-Nearest Neighbors (KNN), Random Forest(RF), and Linear Discriminant Analysis (LDA). Our study reveals the pivotal role of these reduction techniques in addressing the challenges posed by high-dimensional data and imbalanced distributions in customer behavior prediction. Through comparative analysis, we demonstrate how PCA and SelectKBest, through F-Value and Chi-Squared methods, influence model accuracy and efficiency, providing insights into the effectiveness of these models in strategic bank marketing efforts.
Predicting Vehicle Fuel Efficiency: A Comparative Analysis of Machine Learning Models on the Auto MPG Dataset
This study explores the application of various machine learning models to predict vehicle fuel consumption using the Auto MPG dataset. It examines the effectiveness of algorithms such as Decision Tree Regressors, Random Forests, Support Vector Regressors, and neural network-based models like LSTM and GRU. The study aims to enhance fuel efficiency prediction by analyzing factors like engine specifications, driving habits, and vehicle design. The models' performance is evaluated using metrics such as R-squared (R2), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) to ensure accuracy and minimize error.
Biologically-Inspired Speech Emotion Recognition Using Rate Map Representations: An Application to the ShEMO Persian Speech Database
This paper presents an innovative Speech Emotion Recognition (SER) model, inspired by the human auditory system, for analyzing and interpreting emotions in speech. Our proposed model utilizes a rate map representation to encode the spectro-temporal characteristics of auditory nerve activity, closely mimicking the intricate processes of human auditory perception. This model comprises several stages: pre-emphasis of the audio signal, cochlear filtering using a Gammatone Filter bank (GTF), neuromechanical transduction modeled by the Dau inner hair cell model, and the assembly of a rate map representation through integration of responses from each frequency channel. We apply this model to the ShEMO database, an extensive collection of Persian emotional speech, to detect and classify a spectrum of emotions. Our experimental results, obtained using deep learning architectures, demonstrate the effectiveness of the proposed model. We report the highest classification metrics with the Mobilenet architecture, achieving a performance of 71.57% and an F1 score of 51.52%. Overall, this study contributes to the field of speech emotion detection by offering a biologically-inspired model, validated with a substantial dataset, and yielding promising results in emotion classification using advanced machine learning techniques.
PLC based PID control for induction motor drive using VFD
In today's rapidly developing and changing landscape, PLC technology, widely used in the field of industrial automation, offers users various solutions. In industry, management, and scientific technical jobs, the execution of tasks without human labor is achieved through automation and machines. In this context, Asynchronous motors are preferred over other electrical machines due to their lower cost and being the most widely used machine in the industry. In this study, the control of an Asynchronous motor was realized using a PLC with a VFD driver. In the established experimental system, the speed of the Asynchronous motor was controlled using a Proportional Integral Derivative (PID) controller algorithm. Different control PID values were experimented with constant speed trials to achieve the best results. The obtained results were interpreted through system graphs.
Optimization of Tractor Tire Tread Structure Using Finite Element Method
In this study, the tractor tire used for the field was modeled using the Visual Media Finite Element Analysis (FEA) technique and the tire life of this model was tried to be estimated by using the ANSYS software. Mooney-Rivlin was chosen as the tire material because it is a material with high elasticity and behaves in accordance with the mechanics and behavior of the rubber used in tires. The wheel model is modeled as solid, consisting of Solid186 elements.
Power system optimization by using combined Fast-Decoupled power flow algorithm and UPFC
Electric power systems are structures that should operate stable in all scenarios. Unified power flow controller (UPFC), a flexible alternating current transmission system (FACTS) device, is one of the main assistants to achieve this necessity. In this study a control strategy for power flow optimization that based on the combined Fast-Decoupled (FD) method and UPFC is proposed. A detailed model was designed under MATLAB/Simulink platform in association with MATLAB editor. The IEEE-30 bus system was used to validate the model, and the results were addressed in terms of power loss values and optimization. According to the obtained results, it is observed that proposed model has ability to regulate system parameters for various conditions.
Conscious Artificial Intelligence: Can Current Artificial Intelligence Achieve Consciousness?
Consciousness is said to be associated with neuronal activity in the brain, but how this relationship works is still unknown. There are different theories and opinions on whether consciousness is a biological phenomenon or a physical process. The question of whether consciousness can be created in artificial intelligence systems has also been raised. In this article, current artificial neural network models that attempt to mimic the workings of the brain have been discussed and evaluated for their adequacy in addressing the issue of consciousness.
Alüminyum İletken Sektöründe Isıl İşlem Prosesinin Makina Öğrenmesi ile Matematiksel Modellemesi
Elektrik enerjisi sektöründeki çıplak iletim hatlarında, içinde toplam %2‘den daha az uygun alaşım elementi bulunan saf alüminyumu istenen mekanik özelliklere getirebilmek için uygun ısıl işlemler uygulanmaktadır. Isıl işlemler sonucunda 6101-T6 formuna getirilen alaşım, elektrik iletiminde iletken olarak kullanılabilmektedir. Fakat alüminyum alaşımlı iletkenleri (AAAC) T6 formuna getirmek için uygulanan proses, sektördeki bir çok firma tarafından bilinmemektedir. Bu çalışmada, Al-Si-Mg alaşımları 6101 serisi alaşım malzemelerde uygun mukavemet, iletkenlik ve çekilebilirlik sağlayabilmek için ısıl işleme temel etken olan parametreler ve sürenin optimizasyonunu gerçekleştirecek bir paket program oluşturulacaktır. Çalışmamızda, belirlenen algoritma Python dili ve IDE (Geliştirme Ortamı) kullanılarak makine öğrenmesi ile tel çapına göre otomatik olarak ısıl işlem süresi ve derecesi ayarlaması yapılacaktır. Ek olarak bilimsel çalışmalara katkı sağlayarak özgün şekilde anlaşılabilir, pratik bir ara yüz tasarlanıp belirli birimin kullanıcısına sunulacaktır. Böylece ısıl işlem süreçleri için sektördeki firmaların ürün bakımından kendine ve ürüne özgün fırın reçetesini belirleyebilmesi, enerji tüketim maliyetlerini en aza indirecek şekilde sağlanacaktır. Bu makine öğrenmesi modeli ile birlikte ısıl işlem uygulamalarıyla elde edilen veriler değerlendirilerek, firmalara hem zaman hem de maliyet açısından deneme üretimleri yapılmadan daha etkin ve ekonomik çalışma koşulları sağlanacaktır. Ayrıca deneme üretimlerinden kaynaklanan iş tekrarları en aza indirilerek enerji tasarrufu, personel ve makinelerin verimli çalışması sağlanacaktır.
Copyright (c) 2022 AINTELIA Science Notes Journal https://creativecommons.org/licenses/by-nc/4.0
Use of variable structure ducts in busbars with fire barriers
The use of busbar systems, especially in large and complex structures, has facilitated the installation and use of energy distribution systems. However, the use of busbar systems composes security risks. Busbar modules mounted on the building wall in busbar systems are transferred through the holes drilled in the walls. This situation causes the fire not to be confined during a fire and to spread rapidly to the entire structure through busbar holes. After the installation of energy distribution systems, specialized companies undertake the fire insulation of the holes against fire. However, these companies provide insulation by insulating the outside of the modules passing through the wall. Such an application creates disadvantages. In our country, it is seen that such building materials are not used in most buildings due to the difficulty of installation and the high cost of fire barriers. In the study; A fire barrier system has been developed that encourages the use of fire barriers, increases the endurance time to 3 hours and above, and also reduces the module sales price. Optimum design geometry and construction has been created, durable raw materials have been developed, desired success criteria and international test norms have been provided.
Electricity transmission and distribution
Technical And Financial Risks Challenging the Adoption of Smart Contracts in Construction
A smart contract is characterized as computerized transaction procedures that meet contractual constraints such as progress payment, claims, and confidentially while minimizing the need for an intermediary. Smart contracts may be thought of as an effective and different answer to the typically lengthy, complex, and difficult-to-comprehend construction contracts. Construction businesses face several technical and financial risks when using smart contracts. Risks discovered through literature research were communicated to specialists, and their comment was obtained following the study. From a financial and technological aspect, it is recognizable that the incorporation of smart contracts into construction management processes will be risky, according to expert opinions.
Mathematical Optimization of Artificial Neural Network Regression for Mineral Composition of Different Tea Types
Tea contains numerous minerals and has effects on human nutrition. The purpose of this study was to identify the production parameters required to achieve the desired mineral content levels in teas. Four different tea varieties, black Turkish (BT), green Turkish (GT), black Ceylon (BC), and green Ceylon (GC), were used to produce tea at concentrations of 1%, 2%, and 3%. Seven different brewing times were used to produce these teas: 2, 5, 10, 20, 30, 45, and 60 min. Inductively coupled plasma-optical emission spectrometry (ICP-OES) was used to examine tea infusion samples for the minerals Al, Ca, Cd, Cr, Cu, Hg, Fe, K, Mg, Mn, Na, Pg, and Zn. For each mineral, artificial neural network (ANN) regressions were built, and the regressions were then optimized to find the production parameters needed to achieve the appropriate concentrations of minerals. The highest R2 values were for the Mg (0.9890) and Na (0.9878) regression equations, while the lowest R2 values were for the Cu (0.9076) and Al (0.9431) regression equations. According to the optimization results, the highest Fe content (0.278 mg/L) can be obtained with 3%, 60 min, and GC tea, while 1%, 2 min, and GC tea are required to obtain the lowest Al content (2.136 mg/L).
Numerical Investigation of Supersonic Combustion in Axisymmetric Scramjet Engine with Mach 4.5
Scramjets are the main vehicles for hypersonic flight. Due to its complex physics, researches are carried out to increase scramjet engine efficiency. In this study, supersonic combustion in scramjet engine was investigated. Although scramjet engines have simple design, they have complex combustion phenomena. The goal of this study is to understand this complex flow and its stability. This investigation was conducted with threedimensional geometry. Ethylene was used for fuel injection with 0.75 mm sonic holes. The investigation was conducted with global reaction mechanism. The cavity flame holder was designed to increase the efficiency of combustion. As a result, it was ensured that the inlet and isolator were designed in accordance with preventing unstart phenomena due to shock-boundary layer interaction in the shock train region. Secondly it was seen that heat addition in near wall region existed much more than the core flow region since the high-enthalpy air had higher momentum than the product species.
The Impact of Air gap of Toroid Core on Magnetic Flux Density in Electromagnetic Energy Harvester
Depending on the development of technology and industry, monitoring and inspection of energy transmission lines is important for the sustainability of energy. The information gathered from the sensors employed in power transmission lines allows for prompt intervention in potential fault situations. The amount of energy required for the sensors to work can be provided by various energy harvesting methods. One of the easiest ways to provide energy to the aforementioned sensors is the electromagnetic energy harvesting method. This method provides an important advantage as the harvesting process is done directly from the line itself. Electromagnetic energy harvesters consist of a core that can be composed of different geometric shapes and different magnetic materials, and the windings on the core. One of the biggest problems in harvesting energy from the electromagnetic field is the saturation of the magnetic material. To overcome this problem, some solution methods such as an air gap in the core and switching are used. In this study, a toroidal electromagnetic energy harvester was designed to obtain energy from the lines. In order to prevent saturation of the core material, it was analyzed by Finite Element Method (FEM) using air gap at variable distances. The toroid core is made of M15 material and has dimensions of 30x10x15 mm. Magnetic flux density distributions were obtained by adjusting the air gap in the range of 0 - 1 mm with an accuracy of 0.1 mm. The flux density value decreased as the air gap increased. After the air gap value was 0.6 mm, the magnetic flux density approached zero.
Developing Different Solutions for Collision Avoidance Algorithms in Medium Altitude Air Systems
The traffic collision avoidance system (TCAS) has a significant effect on reducing the risks of mid-altitude collisions. It is used despite the increasing air traffic and is used to prevent accidents and their destructive effects. Although TCAS offers an advantageous use with its high success rate, this success rate depends on the presence of this system in other aircraft. The solution proposal produced by the system is successful if TCAS is also present on the opposite aircraft, which poses a collision risk. If the opposite aircraft does not have this system, TCAS produces a solution proposal for the existing aircraft. However, it remains unknown whether the opposing aircraft will take any action that will increase the risk of collision during the implementation. Based on this motivation, in this article, it has been studied to bring a new capability to TCAS. In this study, it is aimed to increase the success rate of TCAS's solution proposals thanks to the newly added mode interrogators to the identification friend or foe (IFF) systems. As a result, the solution proposal produced by the aircraft with TCAS capability was transmitted to the other aircraft without TCAS capability thanks to this new add-on, and it was seen that the success of the solution was guaranteed.
Traffic Alert and Collision Avoidance System
An overview of electronic waste threats and WEEE-RoHS regulations
world and another product is designed by adding to this product in another part. These designs, on the other hand, bring with them some threats and responsibilities. If each electrical/electronic device produced is not destroyed in appropriate environment and conditions after its useful life, or if it is not repaired and reused, it poses a danger to nature. When electrical and electronic devices, which are indispensable parts of our lives, become electronic waste (e-waste), harmful substances in their structures are unconsciously released into nature. As a result, environmental pollution increases. With the WEEE (Waste from Electrical and Electronic Equipment) and RoHS (Restriction of Hazardous Substances) regulations that the European Union came into force in 2003, it aimed to minimize the mixing of these harmful substances, which are hostile to the environment and human health, into nature, and to protect the environment and human health. In this study, a literature research was conducted on the consequences of electrical and electronic devices when they become e-waste, what harmful substances are in e-waste, the aims and scopes of WEEE and RoHS, which are European Union regulations. The result of a RoHS test that the manufacturer Emas has had for its products has been shown. With this study, it is aimed to raise awareness of consumers and producers about human-environmental health.
A Review of The Development of Porous Biomedical Implants Through Additive Manufacturing
Nowadays, with the development of additive manufacturing techniques, new generation implants used in biomedical applications are designed in accordance with the bone structure in the cellular structure. The cellular structures in the implants designed with additive manufacturing methods allow bone and tissue formation, allowing the implant to hold on to the bone more tightly and shortening the healing time. In addition, the design of the implant in accordance with the bone cell structure increases the compatibility of the implant with the body and prevents extra surgeries. In addition to cell type, heat treatments and coating procedures applied to implant materials produced by additive manufacturing in order to increase mechanical and biocompatibility have an important place in the literature. Among these processes, especially heat treatment cycles in which short-term aging processes are applied and hydroxyapatite coating processes have increased osseointegration.
Induction Motor Speed Controller Interface Design Via Simulink External Mode
The use of induction motors in industrial systems has been increasing rapidly in recent years. The main reasons for this it has low cost, does not require much maintenance, and has a robust structure. The scalar control method, which has a low system cost due to the need for a single sensor (speed sensor), is preferred for speed control of induction motor in this study. The speed of induction motor is realized with PI control method using TI Delfino F28335 processor and 3-phase inverter over Simulink external mode. Thanks to the designed control interface, it is possible to turn the system on and off, change the reference speed value, and observe the actual speed value numerically and graphically. In addition, the controller coefficients Kp and Ki values can be changed without reprogramming the processor.
More Efficiency of Solar Energy System in Libya Using Artificial Intelligence (Fuzzy Logic Control)
Recently, the entire world is facing the problem of energy. One method of harnessing incident solar radiation to generate power without releasing carbon dioxide (CO2) is solar photovoltaic (PV) energy. So, it's critical to give a broad summary of Libya's current energy production situation. The purpose of this paper was to shed light on the energy issues that the Libyan state is now dealing with, as well as the potential for diagnosing and outlining a plan for developing and locating solutions that make the most of solar radiation. The majority of the nation's energy consumption—roughly 36%—comes from residential building loads. This paper focus to how solar PV is currently being used in Libya and suggests using artificial intelligence as a controller to boost the effectiveness of the solar energy system. Using Fuzzy Logic controller (FLC) to collect the largest amount of energy which can reach 95% or more, by control tracking the radiation of the solar, cleaning and control on the temperature solar board.
Investigation of Off-shore Wind Energy Potential in Türkiye: A Case Study of Bandırma Bay
In this study, the main factors in off-shore wind energy have examined and the current situation in the world and in Türkiye has presented. In addition, the pre-investment feasibility analysis for the planned offshore wind power plant facility in Türkiye and the steps to be followed have examined in detail. According to these plans, a case study has conducted for Bandırma Bay, which has high offshore wind energy potential. Hourly wind speed and direction data of the Bandırma Meteorological Station of the Meteorological Service between 2010 and 2020 were obtained. The analyzes carried out showed that the wind efficiency of the region is suitable for off-shore wind power plant installation. In addition, the most suitable location for the installation of the offshore wind power plant in Bandırma has been proposed.
Active Investment Planning in the Electricity Distribution System
The investment planning of the distribution network has evolved significantly the emergence of distributed energy resources (DEKs) with generated demand, rapid technological advanced in energy storage system, and rising consumer expectations. Nowadays, planning of active distribution systems (ADS) has been an important issue. There are numerous extensive literatures on ADS planning. This paper reviews the current state of distribution planning. Firstly, traditional investment plans made in Turkey and the approach to ADS are examined, ADS studies conducted in other countries, outputs and case studies are examined. Secondly, the effects of DECs on ADS planning are investigated. The features of ADS planning and the factors affecting ADS are outlined. Finally, it explores some aspects of ADS planning research, including planning within all the elements combined in ADS.
Active distribution system planning (ADS)
Integrated planning, İnvestment planning
Optimal Dartboard Design with Simulated Annealing
Darts game is a popular game where people compete in competition while having fun. Although most people do not know, the dartboard design used while playing the game is an optimization problem studied by different researchers. A dartboard design that will increase the competition in the game and make the game more complex can be set up as a combinatorial optimization problem. This study used the Simulated Annealing algorithm to obtain an optimal dartboard design. Contrary to the algorithm's classical structure, it aims to achieve better results by using more than one neighborhood structure. By running the designed algorithm with four different objective functions, the best designs were tried to be obtained. As a result, in addition to the existing designs in the literature, two new designs are proposed that provide good solutions for all objective functions.
COVID-19 Face Mask Detection with Pre-trained Deep Learning Models
The global pandemic known as COVID-19 puts huge pressure on researchers to use technological solutions to provide further protection mechanisms. Face masks are one of the most important protection mechanisms among other health protocols. This study detects the wearing mask classification problem using four CNN models: VGG16, ResNet50V2, IncptionV3, and MobileNetV2 based on the Transfer Learning and also makes a fair comparison among their performance. The proposed models enhance the classification of wearing masks into three classes; without the mask, the correct wearing of the mask, and not the correct wearing of the mask. The four-transfer learning models of CNN architectures were used to train, test, and validate based on the image dataset. The results reveal that the proposed models have performed the classification task to detect the condition of wearing a mask. VGG16, ResNet50V2, and MobileNetV2 models achieved the same accuracy level of 99%, while the IncptionV3 achieved a little bit lower accuracy at 97%.