Search

SRMs have high torque ripples. Modifying the stator and rotor shape of SRMs is one of their methods to remove torque ripples. In this study, new rotor and stator geometries have been proposed for SRM. Performance characteristics are obtained and dynamic analyses are carried out for both novel and classical 6/4 SRMs. FFT analyses of the torque characteristics are also performed for the both SRMs. The changes in performance and the FFT results of torque characteristics are determined for both the current-controlled and uncontrolled operations in both SRMs. 23.60% and 15.20% improvements in torque ripples are achieved for the uncontrolled and current-controlled operations in SRMs respectively. When the FFT analyses are applied to the torque characteristics, the THD values are obtained as 21.19% and 8.31% in the uncontrolled and controlled motor current operations respectively. A 122 W three-phase 6/4 SRM simulation based on the MATLAB–SIMULINK environment is performed to validate the improvement of the torque ripple of the proposed model.

Poly(ethylene oxide) (PEO)-based polymer electrolytes are a promising class of materials for use in lithium-ion batteries due to their high ionic conductivity and flexibility. In this study, the effects of polymer architecture including linear, star, and hyperbranched and salt (lithiumbis(trifluoromethanesulfonyl)imide (LiTFSI)) concentration on the glass transition (Tg), microstructure, phase diagram, free volume, and bulk viscosity, all of which play a significant role in determining the ionic conductivity of the electrolyte, have been systematically studied for PEO-based polymer electrolytes. The branching of PEO widens the liquid phase toward lower salt concentrations, suggesting decreased crystallization and improved ion coordination. At high salt loadings, ion clustering is common for all electrolytes, yet the cluster size and distribution appear to be strongly architecture-dependent. Also, the ionic conductivity is maximized at a salt concentration of [Li/EO ≈ 0.085] for all architectures, and the highly branched polymers displayed as much as three times higher ionic conductivity (with respect to the linear analogue) for the same total molar mass. The architecture-dependent ionic conductivity is attributed to the enhanced free volume measured by positron annihilation lifetime spectroscopy. Interestingly, despite the strong architecture dependence of ionic conductivity, the salt addition in the highly branched architectures results in accelerated yet similar monomeric friction coefficients for these polymers, offering significant potential toward decoupling of conductivity from segmental dynamics of polymer electrolytes, leading to outstanding battery performance.

© 2023 Informa UK Limited, trading as Taylor & Francis Group.Deep learning methods have been successfully applied to remote-sensing problems for several years. Among these methods, CNN-based models have high accuracy in solving the land classification problem using satellite or aerial images. Although these models have high accuracy, this generally comes with large memory size requirements. However, it is desirable to have small-sized models for applications, such as the ones implemented on unmanned aerial vehicles, with low memory space. Unfortunately, small-sized CNN models do not provide as high accuracy as with their large-sized versions. In this study, we propose a novel method to improve the accuracy of CNN models, especially the ones with small size, by injecting traditional features into them. To test the effectiveness of the proposed method, we applied it to the CNN models SqueezeNet, MobileNetV2, ShuffleNetV2, VGG16 and ResNet50V2 having size 0.5 MB to 528 MB. We used the sample mean, grey-level co-occurrence matrix features, Hu moments, local binary patterns, histogram of oriented gradients and colour invariants as traditional features for injection. We tested the proposed method on the EuroSAT dataset to perform land classification. Our experimental results show that the proposed method significantly improves the land classification accuracy especially when applied to small-sized CNN models.

© 2023, The Minerals, Metals & Materials Society.It is becoming crucial to design/fabricate eco-friendly, sustainable electronic and photonic devices to minimize the carbon footprint for future systems. In this study, we have demonstrated a steady photoresponse enhancement of the self-powered GaN ultraviolet photodetector (GaN-UVPD) via sequentially deposited gold nanoparticles (Au NPs) under 254, 302, and 365 nm UV light exposure. The AuNP-deposited GaN-UVPD exhibited excellent responsivity of 0.65 A/W and detectivity of 6.51 × 1012 cm.Hz1/2 W−1 under 302 nm UV light without any external power. Moreover, the sensitivity of the device increased from 1.98 × 106% to 3.32 × 106% following Au nanoparticle deposition. Additionally, the plausible mechanisms for the self-powered and Au nanoparticle-induced photoresponse enhancement have been discussed. In brief, the high-performance photoresponsivity of our self-powered GaN-UVPD could find many useful applications in sustainable energy and eco-friendly optoelectronic devices.

In this study, the average power consumption of an electrode welding machine during the welding process was estimated using the features of the sound emitted during welding. First, the instantaneous values of electrode current and voltage and the sound emitted during the welding process were recorded simultaneously. The minimum, maximum, average, root mean square (RMS), and energy values of the sound data were found and feature extraction was performed, and the instantaneous power and average power values were calculated using the instantaneous current and voltage values. Three Adaptive Neuro-Fuzzy Inference Systems (ANFIS) using the sound features as inputs and average power values as outputs were created, and their results were compared. The average power values consumed during the welding process have been successfully estimated at a rate of 87-95%.

Considering the increasing demand for cloud computing, and the financial and environmental impact of the increasing energy consumption trend of data centers, improving energy efficiency is vital for cloud service providers. In this study, an energy-aware virtual machine scheduling model is proposed which is based on the multi-unit nondiscriminatory combinatorial auction. The model includes a powerful bidding language that allows users to declare their complicated virtual machine requests using logical AND and OR relations along with the time constraints. The study also presents the formal definition of the model and the associated optimization problem for determining the optimum schedule and energy-efficient placement of VMs on physical servers. The optimization problem is formulated using integer linear programming and several heuristic solution methods including the Genetic Algorithm are proposed for this problem. The performances of the model and the proposed heuristics are assessed on a comprehensive test suite. The proposed model is estimated to provide approximately a 37% improvement in revenues, and the solution methods are estimated to provide high-quality solutions within only 5% of the optimum which enable the model to be deployed in large-scale clouds.

The assembly line balancing problem is an important issue for every manufacturing company. A balanced assembly line ensures that a product is produced in the optimum time, and as a result of this effect, less machinery, materials and labour are used during this production. In this article, theoretical information about assembly line balancing has been given, and then the data needed for assembly line balancing has been obtained by making a time study of the production of Downlight Luminaire. With these data obtained, assembly line balancing was done and compared by using Hoffman and Comsoal methods. The aim of this study is to investigate the applicability of Hoffman and Comsoal methods, which are one of the heuristic assembly line balancing methods, in the assembly lines of the companies producing in the lighting sector.

Two wheeled Self balancing robots have become more popular in recent times and used in many applications, commonly used in transportation as these balancing robots are faster and apparently stable. Balancing is achieved by using controllers tuned for the system. Inverted pendulum principle is used in these robots. This paper proposes the use of two control methods, namely Proportional-Integral-Derivative (PID) and Extended PID (EPID), for maintaining the balance of two-wheeled and self-balancing robots, even when slightly tilted, thus preventing them from falling. PID control theory generally depends up on transfer functions, this paper extends the theory using EPID. EPID provides a new perspective on the working principle of PID control and its relationship with other control theories, such as tracking control and disturbance observer. This approach is illustrated using the fundamental principle of inverted pendulum technology. The key advantage of these robots over other mobile robots is their increased stability due to zero turning radius, which allows for smoother rotations during changes in direction.

Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the project being ongoing. Copyright © 2023 by the authors. Electric distribution grids are seeing an increased penetration of photovoltaic (PV) generation. High PV generation exceeding the grid load demand results in a reverse active power flow in the grid, which raises the voltage level. This paper presents a reactive power controller to overcome the overvoltage problem in the distribution system. A sequentially coordinated and cooperative volt/var control technique is presented. The proposed controller aims to use as low reactive power as possible while mitigating the voltage issues. Accordingly, it reduces the active power loss associated with reactive power flow and reduces the probability for active power curtailment of the PV system. The controller is developed for each lateral and is replicated for all laterals. The lateral controller coordinates the operation of the smart PV inverters in a sequential manner. Cooperative control is proposed between the laterals’ controllers as well and is engaged when the individual laterals’ controllers are unable to solve their overvoltage issues. The performance of the proposed controller is evaluated by comparing it to two other volt/var controllers, and it demonstrates better performance in terms of reactive power requirement. To conduct the simulation study, a modified version of the unbalanced IEEE 13-bus system is utilized, which includes an additional 44 low-voltage bus. The study involves simulating 720 operating points across daily time series. The results indicate that the proposed controller effectively addresses overvoltage problems that occur during periods of high PV generation. Newton Fund Institutional Links grant number 623801791, under the Newton-Katip Çelebi Fund partnership. The grant was funded by the UK Department for Business, Energy and Industrial Strategy and TUBITAK grant number 120N996 and delivered by the British Council.

In 2019, the world had been attacked with a severe situation by the new version of the SARSCOV- 2 virus, which is later called COVID-19. One can use artificial intelligence techniques to reduce time consumption and find safe solutions that have the ability to handle huge amounts of data. However, in this article, we investigated the classification performance of eight deep transfer learning methodologies involved (GoogleNet, AlexNet, VGG16, MobileNet-V2, ResNet50, DenseNet201, ResNet18, and Xception). For this purpose, we applied two types of radiographs (X-ray and CT scan) datasets with two different classes: non-COVID and COVID-19. The models are assessed by using seven types of evaluation metrics, including accuracy, sensitivity, specificity, negative predictive value (NPV), F1- score, and Matthew\"s correlation coefficient (MCC). The accuracy achieved by the X-ray was 99.3%, and the evaluation metrics that were measured above were (98.8%, 99.6%, 99.6%, 99.0%, 99.2%, and 98.5%), respectively. Meanwhile, the CT scan model classified the images without error. Our results showed a remarkable achievement compared with the most recent papers published in the literature. To conclude, throughout this study, it has been shown that the perfect classification of the radiographic lung images affected by COVID- 19.

As technology scales, transistors become smaller and aggressive power optimization techniques combined with high operation frequencies and performance-enhancing microarchitectural techniques are employed to achieve increasingly higher performance and power efficiencies. Unfortunately, these developments make the modern systems more vulnerable to soft errors, which are becoming a critical issues in both hardware and software domains. Motivated by this observation, in this work, we propose, implement, and evaluate two error propagation metrics in order to characterize error propagation at both software and hardware levels. The first metric aims to measure error propagation on program data structures, whereas the second one measures the fraction of corrupted locations in the cache memory structure for a given period of time. We evaluate our proposed metrics by performing an empirical study of two application programs using both single-threaded and multi-threaded executions, and varying various experimental parameters such as thread count, error rate, location of errors, and architectural parameters. Our extensive experimental analysis reveals that error propagation over program data structures is highly dependent on application behavior.Further, depending on the cache parameters used, propagation of errors on cache can exhibit different patterns. This paper also discusses how our observed error propagation trends in program data structures and data caches are correlated with each other, focusing in particular on the differences in error propagation speeds in application data structures and data caches.

Smart retail stores started to take place in our lives. Several computer vision and sensor-based systems are working together to achieve such a complex and automated operation. Besides, the retail sector has several open and challenging problems which can be solved by embedded computer vision systems. One important problem to be tackled is shelf control or stock out detection. Here, shelves in a store should be controlled regularly such that no item is missing in the shelf. In this study, we propose an embedded computer vision system to solve this problem. To do so, we frame the shelf control operation as change detection. Due to the constraints posed by the retail sector, we formed the system by an ultra-low or low power microcontroller with an embedded camera attached to it. We provided all the implementation details of the system both from software and hardware perspectives. We also tested the proposed shelf control system from different perspectives. Hence, the reader can form such a system for retail sector or for a broad class of change detection problems in which stand-alone embedded system usage is mandatory.

This paper presents a novel optimization approach to find the optimal STATCOM installation in the power system using Mixed Integer Distributed Ant Colony optimization (MIDACO). The proposed method is mathematically formulated based on minimizing the Voltage Deviation Index (VDI) and the installation cost of STATCOM. The ability of STATCOM to provide shunt compensation to maintain bus voltage is exploited here. The proposed approach has been tested on the standard IEEE 14-bus system.

© 2023 by the authors.The important parts of a transformer, such as the core, windings, and insulation materials, are in the oil-filled tank. It is difficult to detect faults in these materials in a closed area. Dissolved Gas Analysis (DGA)-based fault diagnosis methods predict a fault that may occur in the transformer and take the necessary precautions before the fault grows. Although these fault diagnosis methods have an accuracy of over 95%, their validity is controversial since limited data are used in the studies. The success rates and reliability of fault diagnosis methods in transformers, one of the most important pieces of power systems equipment, should be increased. In this study, a hybrid fault diagnosis system is designed using DGA-based methods and Fuzzy Logic. A mathematical approach and support vector machines (SVMs) were used as decision-making methods in the hybrid fault diagnosis systems. The results of tests performed with 317 real fault data sets relating to transformers showed accuracy of 95.58% using a mathematical approach and 96.23% using SVMs.

Enerji; yaşamımızda en çok duyduğumuz ve kullandığımız kavramlardan biri olmakla beraber, günümüzde sosyal, ekonomik, teknolojik ve çevresel yönleriyle ele alınan ve değerlendirilen çok geniş bir kapsama sahiptir. Kavram olarak günlük yaşamda farklı kullanımları ve birçok disiplinde farklı tanımlamaları vardır. Günlük yaşamda kullandığımız enerji kavramı, kullanıma göre farklı büyüklüklerin oluşmasına neden olur. Bu büyüklükleri bilmek ve yaşamımızın içerisinde bilinçli olarak kullanmak, hem topluma hem de bireylere ekonomik ve çevresel faydalar sağlayacağı bir gerçektir. Bu kapsamda toplumun tüm eğitim seviyelerinde okul öncesinden başlayarak, yaşam boyu öğrenme süreci ile enerji okuryazarlığı kavramı ele alınmalı ve geliştirilmelidir. Böylece bireylerin günlük kullanımlarında farklı bakış açıları ve gelişim süreci elde edebilir.Enerji teknolojileri; enerjinin üretiminden tüketimine kadar birçok alt sistem ve bileşeni kapsamaktadır. Çok büyük etki alanına sahip olan enerjinin üretim-tüketim sürecinde sürdürebilir yaşamı desteklemek, ekonomik ve teknolojik olmak gibi birçok ölçüt öne çıkmaktadır. Bu kapsamda enerji teknolojilerinin yenilenmesi, genel olarak mevcut teknolojileri iyileştirmek veya yenilerini oluşturmak olarak ifade edilmektedir. Uzun vadeli olarak enerji teknolojileri üzerine yapılan araştırmalarda ve istihdamda uluslararası literatürdeki çalışmaların kapsamında en önemli üç başlık; enerji verimliliği, enerji depolanması ve yenilenebilir enerji kaynakları olarak ifade edilmektedir. Bu temel alanlar öğrencilerimizin yönlendirilmesi açısından önemlidir.Sunulan bu çalışmayla öğretmenler, enerji teknolojileri başlığı altında öncelikle öğrencilerin enerji kavramından başlayarak, enerji okuryazarlığı ve sürdürülebilir yaşamı destekleme süreçlerini kavramaları hedeflemelidir. Bu çerçevede çalışmada ilk olarak enerji kavramının tanımı, büyüklük olarak anlamı, dönüşümü, enerji verimliliği konuları yer almaktadır. Sonraki aşamada ise, enerji teknolojilerinin üretim süreçleri ele alınmaktadır. Böylece geleneksel ve yenilenebilir enerji kaynakları üzerine enerji teknolojileri ifade edilmektedir. Özellikle çalışmada rüzgâr ve güneş bazlı yenilenebilir enerji üretim teknolojileri detaylı olarak sunulmaktadır. Son olarak, günümüzde enerji teknolojilerinin en önemli parçası haline gelen enerji depolama sistemleri üzerine bir bölüm çalışma içinde bulunmaktadır. Bu çalışma, öğretmenlere \"Dene ve Yap\" öğrenme süreci içerisinde öğrencilerin konuları ele alması yaklaşımıyla oluşturulmuştur. Özellikle deney sürecinde öğrencinin karşılaşacağı sorunlara vereceği çözümler, değişkenlerin belirlemesi ve nasıl değişeceği gibi fikirler, yaparak ve yaşayarak öğrenme sürecini oluşturması bu çalışmanın istenen aşamalarıdır. Enerji teknolojileri başlığı altında verilen bu çalışma diğer Deneyap başlıklarından farklı olarak, konunun içeriği ve farklılığı nedeniyle öğrencilerin en yüksek düzeyde anlama ve kavrama sağlamak hedefi ile 5E öğretim yöntemi kullanılarak tasarlanmıştır. Bu çalışmanın 5E öğrenme döngüsü modeli ile öğrencilerin araştırma sorgulamaya teşvik etmesi hedeflenmektedir.Böylece \"Enerji Teknolojileri\" başlığı altında mühendisliğin temel konularına odaklanılarak, enerji kavramı üzerine açıklama ve tanımlamalar yapılması sağlanacaktır. Analitik bir çerçevede enerji kavramını açık bir şekilde öğretmek, disiplinler arası sorunlar hakkında düşünmesini sağlamak iyi donanımlı yeni nesil bilim insanları ve mühendisler yetiştirmeye yardımcı olacaktır.

Purpose The purpose of this study is to investigate the ammonia sensing performance of polyaniline/polyethylene oxide (PANI/PEO) and polyaniline/polyethylene oxide/zinc oxide (PANI/PEO-ZnO) composite nanofibers at room temperature. Design/methodology/approach Gas sensor structures were fabricated using micro-fabrication techniques. First, onto the SiO2 wafer, gold electrodes were fabricated via thermal evaporation. PANI/PEO nanofibers were produced by the electrospinning method and the ZnO layer was deposited by RF magnetron sputtering on the electrospun nanofibers as a sensing layer. Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM) were performed for characterization analysis of nanofibers. After all, gas sensing analysis of PANI/PEO and PANI/PEO/ZnO nanofibers was performed using an experimental setup at room temperature conditions. Findings FTIR analysis confirms the presence of functional groups of PANI, PEO and ZnO in nanofiber structure. SEM images demonstrate beads-free, thinner and smooth nanofibers with ZnO contribution to electrospun PANI/PEO nanofibers. Moreover, according to the gas sensing results, the PANI/PEO nanofibers exhibit 115 and 457 s response time and recovery time, respectively. However, the PANI/PEO/ZnO nanofibers exhibit 245 and 153 s response time and recovery time, respectively. Originality/value In this study, ZnO was deposited via RF magnetron sputtering techniques on PANI/PEO nanofibers as a different approach instead of in situ polymerization, to investigate and enhance the sensor response and recovery time of the PANI/PEO/ZnO and PANI/PEO composite nanofibers to ammonia. These results indicated that ZnO can enhance the sensing properties of conductive polymer based resistive sensors.

This paper presents a deployment method of various test maneuver scenarios for 2 degree of freedom (2 DoF) vehicle simulator by using feature extraction and neural networks (NN). A prototype version has been set up for the 2 DoF vehicle simulator. Then, a hardware in the loop (HIL) model with 2 inputs (torque, τ1-τ2) and 3 outputs (acceleration, ax-ay-az) is created. System identification is performed to obtain the training data of NNs to be used for the deployment of test maneuvers. In the system identification process, 2 arbitrary sinusoidal torque signals (τ1-τ2) are generated by using the actuator specs of the 2 DoF vehicle simulator. By applying the generated torque signals to the actuators, acceleration (ax-ay-az) data are collected from the inertial measurement sensor (IMU) on the 2 DoF vehicle simulator. It is determined to create 3 different NN models for the obtained data. The 1st NN model is trained with 3 inputs (ax-ay-az) and 2 targets (τ1-τ2) training data. The 2nd NN model is trained with 6 inputs (amplitudes and phases of ax-ay-az) and 2 targets (τ1-τ2) training data. The input data features for the 2nd NN model is extracted by using the Fast Fourier Transform (FFT). The 3rd NN model is trained with 6 inputs (amplitudes and phases of ax-ay-az) and 4 targets (amplitudes and phases of τ1-τ2) training data. For the 3rd NN model, the features of input and target data are extracted by using the FFT. The NN training process continues until acceptable performance criteria are reached. Then, 3 NN models are run and analyzed under various test scenarios such as Double Lane Change, Constant Radius, Increase Steer, Fish Hook, Sine with Dwell and Swept Sine. Only for the 3rd NN, the actuator signals (τ1-τ2) are recomposed by applying an inverse FFT process to the 4 targets (amplitudes and phases of τ1-τ2). Finally, the reference trajectory tracking performances are evaluated by comparing the NN models that are run under the test scenarios.

Enerji; yaşamımızda en çok duyduğumuz ve kullandığımız kavramlardan biri olmakla beraber, günümüzde sosyal, ekonomik, teknolojik ve çevresel yönleriyle ele alınan ve değerlendirilen çok geniş bir kapsama sahiptir. Kavram olarak günlük yaşamda farklı kullanımları ve birçok disiplinde farklı tanımlamaları vardır. Günlük yaşamda kullandığımız enerji kavramı, kullanıma göre farklı büyüklüklerin oluşmasına neden olur. Bu büyüklükleri bilmek ve yaşamımızın içerisinde bilinçli olarak kullanmak, hem topluma hem de bireylere ekonomik ve çevresel faydalar sağlayacağı bir gerçektir. Bu kapsamda toplumun tüm eğitim seviyelerinde okul öncesinden başlayarak, yaşam boyu öğrenme süreci ile enerji okuryazarlığı kavramı ele alınmalı ve geliştirilmelidir. Böylece bireylerin günlük kullanımlarında farklı bakış açıları ve gelişim süreci elde edebilir.Enerji teknolojileri; enerjinin üretiminden tüketimine kadar birçok alt sistem ve bileşeni kapsamaktadır. Çok büyük etki alanına sahip olan enerjinin üretim-tüketim sürecinde sürdürebilir yaşamı desteklemek, ekonomik ve teknolojik olmak gibi birçok ölçüt öne çıkmaktadır. Bu kapsamda enerji teknolojilerinin yenilenmesi, genel olarak mevcut teknolojileri iyileştirmek veya yenilerini oluşturmak olarak ifade edilmektedir. Uzun vadeli olarak enerji teknolojileri üzerine yapılan araştırmalarda ve istihdamda uluslararası literatürdeki çalışmaların kapsamında en önemli üç başlık; enerji verimliliği, enerji depolanması ve yenilenebilir enerji kaynakları olarak ifade edilmektedir. Bu temel alanlar öğrencilerimizin yönlendirilmesi açısından önemlidir.Sunulan bu çalışmayla öğretmenler, enerji teknolojileri başlığı altında öncelikle öğrencilerin enerji kavramından başlayarak, enerji okuryazarlığı ve sürdürülebilir yaşamı destekleme süreçlerini kavramaları hedeflemelidir. Bu çerçevede çalışmada ilk olarak enerji kavramının tanımı, büyüklük olarak anlamı, dönüşümü, enerji verimliliği konuları yer almaktadır. Sonraki aşamada ise, enerji teknolojilerinin üretim süreçleri ele alınmaktadır. Böylece geleneksel ve yenilenebilir enerji kaynakları üzerine enerji teknolojileri ifade edilmektedir. Özellikle çalışmada rüzgâr ve güneş bazlı yenilenebilir enerji üretim teknolojileri detaylı olarak sunulmaktadır. Son olarak, günümüzde enerji teknolojilerinin en önemli parçası haline gelen enerji depolama sistemleri üzerine bir bölüm çalışma içinde bulunmaktadır. Bu çalışma, öğretmenlere \"Dene ve Yap\" öğrenme süreci içerisinde öğrencilerin konuları ele alması yaklaşımıyla oluşturulmuştur. Özellikle deney sürecinde öğrencinin karşılaşacağı sorunlara vereceği çözümler, değişkenlerin belirlemesi ve nasıl değişeceği gibi fikirler, yaparak ve yaşayarak öğrenme sürecini oluşturması bu çalışmanın istenen aşamalarıdır. Enerji teknolojileri başlığı altında verilen bu çalışma diğer Deneyap başlıklarından farklı olarak, konunun içeriği ve farklılığı nedeniyle öğrencilerin en yüksek düzeyde anlama ve kavrama sağlamak hedefi ile 5E öğretim yöntemi kullanılarak tasarlanmıştır. Bu çalışmanın 5E öğrenme döngüsü modeli ile öğrencilerin araştırma sorgulamaya teşvik etmesi hedeflenmektedir.Böylece \"Enerji Teknolojileri\" başlığı altında mühendisliğin temel konularına odaklanılarak, enerji kavramı üzerine açıklama ve tanımlamalar yapılması sağlanacaktır. Analitik bir çerçevede enerji kavramını açık bir şekilde öğretmek, disiplinler arası sorunlar hakkında düşünmesini sağlamak iyi donanımlı yeni nesil bilim insanları ve mühendisler yetiştirmeye yardımcı olacaktır.

We report the magnetic properties of LaFe2As2, La0.8Ba0.2Fe2As2, and La1-xBaxPt0.1Fe1.9As2 (0

A gel card incubator is a significant clinical interface device that brings the gel card’s temperature up, and maintains it at body temperature, and completes the initial step in determining the blood grouping. Achieving a much faster closed-loop response to shorten the warm-up period and maintaining a uniform temperature inside the incubator are crucial requirements for medical incubation devices. To accomplish the incubator control requirements and deal with dead time, this study proposes a novel nonlinear plus integral control (NPI) scheme coupled with a time-delay compensator using an incubator dynamics model. The NPI successfully shaped the control structure by utilizing a few nonlinear parameters. The control strategy, which promises significant performance by combining linear and nonlinear control actions, is easy to realize, simple to tune, and allows smooth transition between control actions. Experiments were performed in a microprocessor-controlled gel card incubator by using a computer interface to demonstrate the superiority of the proposed control technique. The comparative results prove that the proposed control scheme exhibits superior responses for transient and steady states. It also achieves successful disturbance rejection and less energy consumption than the Smith predictor-based proportional integral (PI) control.

This work was supported in part by IHS Kurumsal Teknoloji A.S., and in part by the Tuerkiye Bilimsel ve Teknik Arastirma Kurumu(TUBITAK) under Grant 115E389 User Provided Network (UPN) is a promising solution for sharing the limited network resources by utilizing user capabilities as a part of the communication infrastructure. In UPNs, it is an important problem to decide how to share the resources among multiple clients in decentralized manner. Motivated by this problem, we introduce a new class of games termed distribution games that can be used to distribute efficiently and fairly the bandwidth capacity among users. We show that every distribution game has at least one pure strategy Nash equilibrium (NE) and any best response dynamics always converges to such an equilibrium. We consider social welfare functions that are weighted sums of bandwidths allocated to clients. We present tight upper bounds for the price of anarchy and price of stability of these games provided that they satisfy some reasonable assumptions. We define two specific practical instances of distribution games that fit these assumptions. We conduct experiments on one of these instances and demonstrate that in most of the settings the social welfare obtained by the best response dynamics is very close to the optimum. Simulations show that this game also leads to a fair distribution of the bandwidth. IHS Kurumsal Teknoloji A.Ş Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (TÜBİTAK) Publisher's Version Q2 WOS:000892934100001

Learning from noisy data is a challenging task especially when the system under consideration has a non-stationary nature. The source of the noise is often assumed to be stationary, however the severity or characteristics of noise may also be time-varying, which causes multiple sources of drift in the collected data. This study introduces a novel adaptive learning rate approach to improve learning when the observations from a non-stationary system is altered by an also non-stationary source of noise. As an example to this approach, we propose Persistence Aware Robust Learner (PeARL), an online learning method that utilizes a novel concept called persistence, which is a local noisiness estimation metric to measure the correspondence of a signal to discrete white noise. Making use of this metric, PeARL is able to adaptively adjust the learning rate for each observation during learning to reduce the effect of noise. With this level of control on the learning rate, noisy instances have less disruptive effect on the maintained estimate. We experimentally evaluate PeARL on (a) systematically generated synthetic data and (b) real-world data, including accelerometer readings collected from people (HASC2010corpus) and current measurements from electric motors collected within the scope of EU-funded research project iRel40. The experiments reveal a favorable region of noise rate, in which the proposed method achieves up to 40% reduction in mean absolute error (MAE).

© 2022 The Authors. Published by American Chemical Society.Catalysts based on metal-free conjugated porous polymers (CPPs) are still rare for electrochemical oxygen reduction reactions (ORR). In this study, a conjugated porous polymer, TT-TPB, based on thieno[3,2-b]thiophene (TT) and triphenylbenzene (TPB), was synthesized applying palladium(0) catalyzed Suzuki coupling reaction and its ORR activity was investigated in alkaline condition. It demonstrated comparable electrocatalytic performance of ≈0.89-0.9 V Eonset vs RHE with the commercially available Pt/C. Density-functional theory (DFT) calculations revealed that TT-TPB featured efficient electrocatalytic active sites derived from volumetric, areal, and O2 adsorbing calculations, which were in line with the experimental results. Moreover, semiconducting and surface properties of TT-TPB were investigated in detail using electrochemical and spectrophotometric techniques. This work shows the potential application of thienothiophene-based metal-free CPP in the electrochemical conversion process.

Today, the use of smart transportation systems has become widespread due to the change in the structure of cities, technological developments, and the increase in the number of vehicles. It aims at targets such as reducing the damage to people and the environment by increasing traffic safety with smart transportation systems. In addition, one of the big problems of long-distance drivers is falling asleep while driving. This is an event that puts both the driver’s life, the passengers in the vehicle, and the vehicles on the road in danger, and puts the company in a difficult situation if it works for a company. In this direction, the driver sleep alarm project has been developed. This project is a control system based on monitoring the user, measuring, and analyzing their movements. Literature research, which is the first stage of the project, was conducted and research continued throughout the project process. Within the scope of this project, a headset was designed for the driver’s sleep alarm and a mobile application was created. The first prototype of the project was built. First, in the project, the angle values made by the driver's head were produced with the IMU connected to the microcontroller. 10° to 20° margins identified as sleeping state for roll, pitch, and yaw angles. Later, a wearable headset was designed for the driver. Based on these angles produced in the microcontroller, an algorithm has been created that detects whether the driver is asleep. These generated angle values and status information were transferred from the headset to the mobile application via Bluetooth. This data transferred in the mobile application is reflected on the screen. In addition, an algorithm has been created in the mobile application, which sends a notification to the driver when sleep status is detected and sends an informative SMS to the headquarters if the driver is unresponsive to this notification. For the second prototype, it was aimed to produce a PCB for the headset and thus make a new mechanical design. Data mining applications are planned with the collected data from drivers for future works.

© 2022 IEEE.This paper proposes an optimal coordination strategy for electric vehicles and energy storage devices in distribution grids besides the optimal allocation problem of renewable distributed generation (RDGs) and energy storage devices (ESDs). By finding the optimal number, size, and site of the RDGs and ESDs, together with the operation strategy of the ESDs and smart charging of a large number of EVs, the performance of the distribution grids will be improved. An advanced grey wolf optimization (AGWO) algorithm is used to minimize energy losses and voltage violations simultaneously in the test systems. Simulations are tested on IEEE 33 and 69 bus networks to find near-optimal solutions for the optimization problem. Based on the simulation results, the proposed optimization framework reduced the systems\" losses while minimizing the voltage violations by finding the optimal control parameters of the devices.

Gemilerin çoğunda elektrik enerjisi ihtiyacı dizel jeneratör grupları ile karşılanmaktadır. Gemilerin türüne bağlı olarak gemilerde farklı sayılarda ve kapasitelerde jeneratör grupları bulunabilir. Gemilerde elektrik enerjisi ihtiyacının birden fazla jeneratör ile karşılanması durumunda diğer jeneratörün senkronize bir şekilde devreye alınması gerekmektedir. Bu süreç hem otomatik hem de manuel olarak yürütülebilmektedir. Bu çalışmada, bir gemide iki jeneratörün senkronizasyon süreci Matlab&Simulink programında bir benzetim çalışması yapılarak incelenmiştir. Sonrasında, senkronizasyon sürecinde jeneratör baralarındaki elektriksel parametrelerin değişimleri yorumlanmıştır. Bu çalışma ile özellikle gemi personelinin senkronizasyon süreci hakkında teorik bilgi edinmesi ve literatüre gemi elektriği alanında katkı sunulması amaçlanmıştır. The electrical energy needs of ships are generally met by diesel generator sets. Depending on the type of ships, there may be different numbers and capacities of generator sets on the ships. If the electrical energy needs of ships are met by more than one generator, the other generator should be activated in a synchronized manner. This process can be carried out both automatically and manually. In this study, the synchronization process of two generators on a ship was performed by making a simulation study in Matlab&Simulink. Then, the changes in the electrical parameters in the generator busbars during the synchronization process were interpreted. With this study, it is aimed to obtain theoretical knowledge about the synchronization process of ship personnel and to contribute to the literature in the field of ship electricity.

Bu çalışmada EMG sinyalleri üzerinde öznitelikler hesaplanmıştır. Bu öznitelikler ile el hareketlerini sınıflandırmak için derin öğrenme algoritmaları kullanılmıştır. Bir zaman serisi olarak toplanan EMG sinyalleri üzerinde zaman alanında hesaplanan öznitelik vektörleri belirli boyutlarda simetrik matrisler olarak kaydedilmiştir. Yeniden oluşturulan ve resim dosyası formatında kaydedilen veri seti ile Evrişimsel Sinir Ağı eğitilmiştir. Bu eğitim sonucunda tüm veriler ile %93, test verileri ile %79 başarı ile hareket sınıflandırması gerçekleştirilmiştir. In this study, the statistical features are calculated with EMG signals. Deep learning algorithms are used to classify hand gestures with these features. the feature vectors calculated in the time domain using the EMG signals recorded as a time series are stored as symmetric matrices with specific sizes. The data set that is rebuilt and saved as image file format is used to train the convolutional neural network. Thereafter the training, motion classification is carried out with a success rate of 93% for all data and 79% for test data

Most studies in the literature aim to discuss and examine the technologies in smart cities. Much less is known about the dynamics of their acceptance by citizens. Therefore, we conducted a systematic literature review of studies examining the acceptance of smart city technologies (SCTs), focussing particularly on Technology User Acceptance Models (TAM). Using a novel framework of SCTs based on the technology visibility and citizen interaction, we categorise them as individual-based explicit SCTs, collective-purpose explicit SCTs, digitising SCTs, and infrastructure SCTs. We assess that studies about citizen acceptation of SCTs have been mainly focussed on individual technologies. Also, the review shows that the field is nascent and fragmented, and that a wide variety of variables have been used to predict the adoption of individual-based explicit SCTs, with the classic ones of TAM (perceived use and perceived usefulness) unmistakably being relevant in the smart city context as well. As a result of the fragmentation of the field, our review was not yet able to identify generalisable knowledge about the acceptance of the smart city and its technologies by citizens. We end with some suggestions for future research approaches and designs.

© 2022 IEEE.This paper solves the optimal operation problem of distribution systems using renewable energy sources and voltage regulators. The developed model makes use of Whale Optimization Algorithm based optimization. We apply the proposed optimization model to a modified low voltage distribution system of Almaty, Kazakhstan. Three different cases were simulated. The cases considered are medium load level with medium PV power output, high load level with medium PV output, and high load level and low PV power outputs. The simulation results show that the proposed algorithm is able lower the active power losses.

© 2022 IEEE.Increased photovoltaic (PV) penetration in distribution systems helps improve the system\"s performance by reducing the active power loss, in addition to the environmental benefits. However, high PV generation during light loading periods results in reverse power flows in the system. In turn, the voltage increases at the nodes located further from the main substation. This paper proposes a reactive power control method to coordinate the participation of different PV smart inverters in solving the overvoltage problem. The proposed control method operates at the lateral level based on the measured voltage at the lateral\"s end-node (or most downstream PV system). The lateral controller sequentially dispatches power factor commands to the smart inverters. This method maintains the coordination between the PV systems at different operating conditions keeping the reactive power requirement as low as possible. The proposed control method is compared to the volt/var control method from the IEEE standard 1547 and shows an improved performance in terms of reactive power requirement and active power loss.

Ship type recognition has gained serious interest in applications required in the maritime sector. A large amount of the studies in literature focused on the use of images taken by shore cameras, radar images, and audio features. In the case of image-based recognition, a very large number and variety of ship images must be collected. In the case of audio-based recognition, systems may suffer from the background noise. In this study, we present a method, which uses the frequency domain characteristics with an image-based deep learning network. The method computes the fast Fourier transform of sound records of ships and generates the frequency vs magnitude graphs as images. Next, the images are given into the ResNet50 network for classification. A public dataset with nine different ship types is used to test the performance of the proposed method. According to the results, we obtained a 99% accuracy rate., Gemi tiplerinin tanınmasi, denizcilik sektöründe ihtiyaç duyulan ve ilgi ceken bir uygulamadir. Literatürdeki çalışmaların büyük bir kısmı kıyı kameraları tarafından çekilen görüntülerin, radar görüntülerinin veya ses özelliklerinin kullanımına odaklanmıştır. Görüntü tabanlı tanıma durumunda, çok sayıda ve çeşitlilikte gemi görüntüsünün toplanması gerekir. Ses tabanlı tanıma durumunda ise, sistemlerin performansi arka plan gürültüsünden olumsuz etkilenebilir. Bu çalışma, görüntü tabanlı bir derin öğrenme ağı ile ses-frekans alanı özelliklerini kullanan bir yöntem sunmaktadir. Yöntem, gemilerin ses kayıtlarının hızlı Fourier dönüşümünü hesaplar ve elde edilen spektrumlari resim formatinda kaydeder. Bu resimler sınıflandırma için ResNet50 ağına verilir. Önerilen yöntemin performansını test etmek için dokuz farklı gemi tipine sahip erisime acik bir veri seti kullanılmıştır. Elde edilen sonuçlara göre onerilen metot %99 siniflandirma basarisi saglamistir.

Rigid-body industrial robotic manipulators and pneumatic compression systems are widely used in mechano-therapeutic applications. However, rigid and bulky components reduce the wearability of the system in mobile environments. The current trend in wearable robotics tends to build such devices using textiles/silicone materials thanks to their lightness, compliancy, and comfort properties. In this study, thermally powered soft fluidic actuators that utilize liquid/gas phase transition property of low boiling point liquids are investigated. Proposed actuators generate desired pressure level and have great potential to free mechano-therapy devices from such bulky components. The developed structure incorporates textile-based heaters, thin film actuator shells, and low boiling point liquids. Here, two low boiling liquids with different boiling temperature are mixed successfully in order to obtain desired boiling point. Results a revealed that actuators constructed with PE/PA membrane did not show any deterioration even after 500 cycle actuation. In order to control the system, instantaneous temperature of the textile-based heater on the actuator device is used as feedback for the on-off controller.

The effect of pH on the performance of undoped silicon carbide nanowire field-effect transistors (SiCNW-FETs) was systematically studied using various solutions with pH ranging from pH 2 to pH 13 and important transport parameters such as transconductance, mobility, and resistivity were reported. Interestingly, at 2 V, alkaline solutions with high pH value (pH 13) revealed a higher transconductance of 7.13 nS and lower resistivity of 40 omega cm as compared to acidic solutions with 0.01 nS and 2.1x10(4) omega cm at pH 2, respectively. A model describing the pH-dependent conductance of the SiCNW-FETs was proposed. Moreover, a comprehensive comparison of the pH effects on the transport properties of the undoped SiCNW-FETs and nitrogen-doped SiCNW-FET was presented and the measurements clearly revealed opposite trends for a wide range of pH solutions. In short, our SiCNW-FETs with high sensitivity, high stability, and minuscule sample volume can provide solutions for the development of harsh environment compatible nanosensors for chemical, biochemical, and environmental sensing applications.

The goal of multi-criteria decision making (MCDM) is to select the most appropriate of the alternatives by evaluating many conflicting criteria together. MCDM methods are widely available in the literature and have been used in various energy problems. The key problems studied in electrical power systems in recent years have included voltage instability and voltage collapse. Different flexible alternating current transmission systems (FACTS) equipment has been used for this purpose for decades, increasing voltage stability while enhancing system efficiency, reliability and quality of supply, and offering environmental benefits. Finding the best locations for these devices in terms of voltage stability in actual electrical networks poses a serious problem. Many criteria should be considered when determining the most suitable location for the controller. The aim of this paper is to provide a comparative analysis of MCDM techniques to be used for optimal location of a static VAR compensator (SVC) device in terms of voltage stability. The ideal location can be determined by means of sorting according to priority criteria. The proposed approach was carried out using the Power System Analysis Toolbox (PSAT) in MATLAB in the IEEE 14-bus test system. Using ten different MCDM methods, the most appropriate locations were compared among themselves and a single ranking list was obtained, integrated with the Borda count method, which is a data fusion technique. The application results showed that the methods used are consistent among themselves. It was revealed that the integrated model was an appropriate method that could be used for optimal location selection, providing reliable and satisfactory results to power system planners.

© 2022 Elsevier B.V.BESS are now preferred to provide ancillary services due to their advantages of cost reductions and rapid response times. Based on the criteria in the regulation for the electricity grid, an advanced power management control algorithm with the technical features to provide the grid frequency support specified in the regulation on the use of BESSs in ancillary services has been developed in this study and evaluated in various scenarios. A 2 MW/1 MWh installed power BESS is considered for frequency support control, which is one of the grid ancillary services. The algorithm is tested for providing the appropriate power by optimally using the state of charge of the battery within the limits specified in the regulation. Since the maximum and minimum set limits for the state of charge cannot be exactly defined in the literature, the fuzzy logic method was chosen as the decision-maker for active power output. In the algorithm, the amount of power that must be supplied by the battery energy storage system will change according to the dynamically varied state of charge. The results show that the fuzzy logic control algorithm successfully provides the frequency response in the regulation zone with optimum SOC level and contributes the extending the battery life.

© 2022 Elsevier B.V.This research describes a simple, sensitive, and disposable modified glassy carbon electrode constructed using platinum nanoparticles anchored on reduced graphene oxide nanocomposite as a conductive modifier (Pt@rGO/GCE) to detect an anti-coronavirus drug, Favipiravir (FAV). The as-synthesized nanocomposite was characterized by Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), field emission scanning electron microscopy (FE-SEM), energy-dispersive X-ray spectroscopy (EDX), and atomic force microscopy (AFM). Under optimized conditions, the square wave voltammetry (SWV) method was used to determine trace amounts of FAV in real samples. The proposed electrode demonstrated a wide linear concentration range of 3.16 to 100.0 μM with a low detection limit (LOD) of 2.46 μM. Moreover, the developed electrode showed outstanding selectivity in the presence of several interferences with high repeatability and reproducibility. Finally, the developed electrode was applied to detect FAV in human plasma and pharmaceutical samples.