: Eccentricity fault in double-sided axial flux permanent magnet generator is very difficult to be detected as the fault generated variations in terminal electrical parameters are very weak and chaotic, especially at the initial stages of the fault occurrence. In addition, one of the most important problems in any fault diagnosis approach is the investigation of load and speed variation on the proposed indices. To overcome the aforementioned difficulty and problems, this paper adopts a novelty detection algorithm based on Hilbert–Huang transform (HHT) which is a time-frequency signal analysis approach based on empirical mode decomposition and the Hilbert transform. It is well suited for reliable fault diagnosis since it is unaffected by transient conditions which make the diagnosis process incur into false alarms. The HHT-based methods has been demonstrated in recent years for rotor and bearing faults detection of induction machine and also for stator faults identification in PM synchronous machines with radial flux structure. This study explores the possibility of applying the technique to the detection of dynamic eccentricity faults in double-rotor double-sided stator structure axial flux permanent magnet generator under variable load and speed conditions. This approach relies on two steps: estimating the intrinsic mode functions (IMFs) by the empirical mode decomposition (EMD) and computing the instantaneous amplitude (IA) and instantaneous frequency (IF) of IMFs using the Hilbert transform. The more significant IMFs are determined by means of Hilbert spectrum, which is applied for accurate eccentricity fault diagnosis. The eccentricity severity can be evaluated based on the IMFs energy value. The theoretical basis of the proposed method is presented. The effectiveness of the proposed method is verified by a series of simulation and experimental tests under different conditions. The results show that the presented approach in this paper is robust against
{"title":"An effective Hilbert-Huang transform-based approach for dynamic eccentricity fault diagnosis in double-rotor double-sided stator structure axial flux permanent magnet generator under various load and speed conditions","authors":"Makan Torabi, Y. Alinejad‐Beromi","doi":"10.55730/1300-0632.3971","DOIUrl":"https://doi.org/10.55730/1300-0632.3971","url":null,"abstract":": Eccentricity fault in double-sided axial flux permanent magnet generator is very difficult to be detected as the fault generated variations in terminal electrical parameters are very weak and chaotic, especially at the initial stages of the fault occurrence. In addition, one of the most important problems in any fault diagnosis approach is the investigation of load and speed variation on the proposed indices. To overcome the aforementioned difficulty and problems, this paper adopts a novelty detection algorithm based on Hilbert–Huang transform (HHT) which is a time-frequency signal analysis approach based on empirical mode decomposition and the Hilbert transform. It is well suited for reliable fault diagnosis since it is unaffected by transient conditions which make the diagnosis process incur into false alarms. The HHT-based methods has been demonstrated in recent years for rotor and bearing faults detection of induction machine and also for stator faults identification in PM synchronous machines with radial flux structure. This study explores the possibility of applying the technique to the detection of dynamic eccentricity faults in double-rotor double-sided stator structure axial flux permanent magnet generator under variable load and speed conditions. This approach relies on two steps: estimating the intrinsic mode functions (IMFs) by the empirical mode decomposition (EMD) and computing the instantaneous amplitude (IA) and instantaneous frequency (IF) of IMFs using the Hilbert transform. The more significant IMFs are determined by means of Hilbert spectrum, which is applied for accurate eccentricity fault diagnosis. The eccentricity severity can be evaluated based on the IMFs energy value. The theoretical basis of the proposed method is presented. The effectiveness of the proposed method is verified by a series of simulation and experimental tests under different conditions. The results show that the presented approach in this paper is robust against","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77487301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mona Naghdehforoushha, M. Dehghan, Mohammad Hossein Rezvani, M. Sadeghi
: The spot market is one of the most common cloud markets where cloud providers, such as Amazon EC2, rent their surplus computing resources at lower prices in the form of spot virtual machines (SVMs). In this market, which is often managed through an auction mechanism, users seek optimal bidding strategies for renting SVMs to minimize cost and risk. Uncertainty in the price of SVMs and their low availability/reliability is a challenging issue to bid on the user side. In this paper, we present a robust model for minimizing the cost of executing tasks by considering the uncertainty of the price of SVMs based on the Information Gap Decision Theory (IGDT). It evaluates the risk-aversion and the risk-seeker nature of the user’s bidding strategy and measures the cost of risk/immunity. The main advantage of this method is that it formulates user decisions without the need for any presumption about the price distribution of SVMs. With this decision-support system, users can rely on the predicted confidence intervals to make the optimal bid for future time slots according to the selected risk level. The results are compared with Monte Carlo simulations and a scenario-based approach to evaluate the effectiveness of the proposed IGDT-based model. Evaluation results based on historical Amazon EC2 prices confirm the efficiency of the proposed method to handle the uncertain nature of the price of SVMs in terms of significant criteria such as robustness cost, opportunity cost, uncertainty budget, and execution time.
现货市场是最常见的云市场之一，云提供商(如Amazon EC2)以现货虚拟机(svm)的形式以较低的价格租用其剩余的计算资源。在这个通常通过拍卖机制管理的市场中，用户寻求租用svm的最佳竞标策略，以最小化成本和风险。支持向量机价格的不确定性及其低可用性/可靠性是用户方竞标的一个具有挑战性的问题。本文基于信息差距决策理论(Information Gap Decision Theory, IGDT)，考虑支持向量机价格的不确定性，提出了一种鲁棒的任务执行成本最小化模型。它评估用户投标策略的风险规避和寻求风险的性质，并衡量风险/豁免的成本。这种方法的主要优点是，它在不需要对支持向量机的价格分布进行任何假设的情况下制定用户决策。利用该决策支持系统，用户可以根据所选择的风险水平，依靠预测的置信区间对未来时段进行最优出价。结果与蒙特卡罗模拟和基于场景的方法进行了比较，以评估所提出的基于igdt的模型的有效性。基于历史Amazon EC2价格的评估结果证实了所提方法在处理支持向量机价格的不确定性方面的有效性，这些不确定性包括鲁棒性成本、机会成本、不确定性预算和执行时间等重要标准。
{"title":"A robust model for spot virtual machine bidding in the cloud market using information gap decision theory (IGDT)","authors":"Mona Naghdehforoushha, M. Dehghan, Mohammad Hossein Rezvani, M. Sadeghi","doi":"10.55730/1300-0632.3979","DOIUrl":"https://doi.org/10.55730/1300-0632.3979","url":null,"abstract":": The spot market is one of the most common cloud markets where cloud providers, such as Amazon EC2, rent their surplus computing resources at lower prices in the form of spot virtual machines (SVMs). In this market, which is often managed through an auction mechanism, users seek optimal bidding strategies for renting SVMs to minimize cost and risk. Uncertainty in the price of SVMs and their low availability/reliability is a challenging issue to bid on the user side. In this paper, we present a robust model for minimizing the cost of executing tasks by considering the uncertainty of the price of SVMs based on the Information Gap Decision Theory (IGDT). It evaluates the risk-aversion and the risk-seeker nature of the user’s bidding strategy and measures the cost of risk/immunity. The main advantage of this method is that it formulates user decisions without the need for any presumption about the price distribution of SVMs. With this decision-support system, users can rely on the predicted confidence intervals to make the optimal bid for future time slots according to the selected risk level. The results are compared with Monte Carlo simulations and a scenario-based approach to evaluate the effectiveness of the proposed IGDT-based model. Evaluation results based on historical Amazon EC2 prices confirm the efficiency of the proposed method to handle the uncertain nature of the price of SVMs in terms of significant criteria such as robustness cost, opportunity cost, uncertainty budget, and execution time.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80161430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
: This study performed a deep learning-based classification of chaotic systems over their phase portraits. To the best of the authors’ knowledge, such classification studies over phase portraits have not been conducted in the literature. To that end, a dataset consisting of the phase portraits of the most known two chaotic systems, namely Lorenz and Chen, is generated for different values of the parameters, initial conditions, step size, and time length. Then, a classification with high accuracy is carried out employing transfer learning methods. The transfer learning methods used in the study are SqueezeNet, VGG-19, AlexNet, ResNet50, ResNet101, DenseNet201, ShuffleNet, and GoogLeNet deep learning models. As a result of the study, classification accuracy between 97.4% and 100% for 2-ways classifier and between 83.68% and 99.82% for 3-ways classifier is achieved depending on the problem. Thanks to this, random signals obtained in real life can be associated with a mathematical model.
{"title":"Deep learning-based classification of chaotic systems over phase portraits","authors":"S. Kaçar, Süleyman Uzun, B. Arıcıoğlu","doi":"10.55730/1300-0632.3969","DOIUrl":"https://doi.org/10.55730/1300-0632.3969","url":null,"abstract":": This study performed a deep learning-based classification of chaotic systems over their phase portraits. To the best of the authors’ knowledge, such classification studies over phase portraits have not been conducted in the literature. To that end, a dataset consisting of the phase portraits of the most known two chaotic systems, namely Lorenz and Chen, is generated for different values of the parameters, initial conditions, step size, and time length. Then, a classification with high accuracy is carried out employing transfer learning methods. The transfer learning methods used in the study are SqueezeNet, VGG-19, AlexNet, ResNet50, ResNet101, DenseNet201, ShuffleNet, and GoogLeNet deep learning models. As a result of the study, classification accuracy between 97.4% and 100% for 2-ways classifier and between 83.68% and 99.82% for 3-ways classifier is achieved depending on the problem. Thanks to this, random signals obtained in real life can be associated with a mathematical model.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90371305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
: Object reidentification (ReID) in cluttered rigid scenes is a challenging problem especially when same-looking objects coexist in the scene. ReID is accepted to be one of the most powerful tools for matching the correct identities to each individual object when issues such as occlusion, missed detections, multiple same-looking objects coexisting in the same scene, and disappearance of objects from the view and/or revisiting the same region arise. We propose a novel framework towards more efficient object ReID, improved object reidentification (IO-ReID), to perform object ReID in challenging scenes with real-time processing in mind. The proposed approach achieves distinctive and efficient object embedding via training with the triplet loss, with input from both the foreground/background split by bounding box, and the full input image. With extensive experiments on two datasets serving for Object ReID, we demonstrate that the proposed method, IO-ReID, obtains a higher ReID accuracy and runs faster compared to the state-of-the-art methods on object ReID.
{"title":"Improved Object Re-Identification via More Efficient Embeddings","authors":"Ertugrul Bayraktar","doi":"10.55730/1300-0632.3984","DOIUrl":"https://doi.org/10.55730/1300-0632.3984","url":null,"abstract":": Object reidentification (ReID) in cluttered rigid scenes is a challenging problem especially when same-looking objects coexist in the scene. ReID is accepted to be one of the most powerful tools for matching the correct identities to each individual object when issues such as occlusion, missed detections, multiple same-looking objects coexisting in the same scene, and disappearance of objects from the view and/or revisiting the same region arise. We propose a novel framework towards more efficient object ReID, improved object reidentification (IO-ReID), to perform object ReID in challenging scenes with real-time processing in mind. The proposed approach achieves distinctive and efficient object embedding via training with the triplet loss, with input from both the foreground/background split by bounding box, and the full input image. With extensive experiments on two datasets serving for Object ReID, we demonstrate that the proposed method, IO-ReID, obtains a higher ReID accuracy and runs faster compared to the state-of-the-art methods on object ReID.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90715321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cahit Berkay Kazangirler, Caner Özcan, Buse Yaren Tekin
: User Interface (UI) is a basic concept in which individuals interact with any computer program or technological device to create a graphical design. In the initial stages of app development, UI prototype is a must. An automatic analysis system for the basic execution of UI designs will considerably speed up the development of designs according to old-fashioned methods. In this approach, it is aimed at saving cost and time by automating the process. For the aforesaid objective, we present a new approach rather than the traditional methods. For this reason, a high amount of elements in wireframes are detected and segmented. Furthermore, with the state-of-the-art methods, one of the machine learning classifiers is expected to give lower performance than deep learning for comparison purposes. In this study, the detection and segmentation of elements, which is the first stage which will eliminate time loss, redundant time, cost, and labor in the communication between designers and front-end developers. To test the classification task of the Mask R-CNN, was designed using transfer learning supported neural networks to compare with other algorithms. As a result, the precision reached 93.15% and the mAP (@IOU > 0.5) reached 96.50%. Then, we improved the algorithm by replacing the convolution blocks in the graphs, adding them, and changing the input units, and the accuracy increased to 98.49%.
{"title":"UIBee: An improved deep instance segmentation and classification of UI elements in wireframes","authors":"Cahit Berkay Kazangirler, Caner Özcan, Buse Yaren Tekin","doi":"10.55730/1300-0632.3999","DOIUrl":"https://doi.org/10.55730/1300-0632.3999","url":null,"abstract":": User Interface (UI) is a basic concept in which individuals interact with any computer program or technological device to create a graphical design. In the initial stages of app development, UI prototype is a must. An automatic analysis system for the basic execution of UI designs will considerably speed up the development of designs according to old-fashioned methods. In this approach, it is aimed at saving cost and time by automating the process. For the aforesaid objective, we present a new approach rather than the traditional methods. For this reason, a high amount of elements in wireframes are detected and segmented. Furthermore, with the state-of-the-art methods, one of the machine learning classifiers is expected to give lower performance than deep learning for comparison purposes. In this study, the detection and segmentation of elements, which is the first stage which will eliminate time loss, redundant time, cost, and labor in the communication between designers and front-end developers. To test the classification task of the Mask R-CNN, was designed using transfer learning supported neural networks to compare with other algorithms. As a result, the precision reached 93.15% and the mAP (@IOU > 0.5) reached 96.50%. Then, we improved the algorithm by replacing the convolution blocks in the graphs, adding them, and changing the input units, and the accuracy increased to 98.49%.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90774841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sajad Ghabeli Sani, M. Banaei, Seyed Hossein Hosseini
: This paper proposes an innovative structure for DC-DC converters with high buck gain by using a lower number of elements. The converter provides highly efficient output power and an extended output voltage range. In addition, the distribution of output current between two inductors and the soft-switching capability of the power switches have made the converter suitable for applications that require high output current. All power switches accomplish the ZVZCS (zero-voltage and zero-current switching) condition with the aid of a small auxiliary inductor (Lx), which charges and discharges parallel capacitors of main switches to provide soft-switching conditions. Thus, the switching losses associated with power switches are considerably reduced. Additionally, the output voltage ratio of the proposed converter can be changed by varying the switching frequency and duty cycle. In addition, the variation range of output voltage has been expanded compared to other topologies, allowing for a wider output voltage range. A coupled inductor is utilized to establish a relationship between the output gain and the turn ratios, resulting in a wider output voltage gain range. Eventually, a theoretical analysis is conducted and a 200-watt experimental prototype has been implemented to illustrate the proposed converter’s efficacy. It converts a voltage input (300 V) to a voltage output (10 V).
{"title":"Analysis and implementation of a new high-buck DC-DC converter with interleaved output inductors and soft switching capability","authors":"Sajad Ghabeli Sani, M. Banaei, Seyed Hossein Hosseini","doi":"10.55730/1300-0632.4000","DOIUrl":"https://doi.org/10.55730/1300-0632.4000","url":null,"abstract":": This paper proposes an innovative structure for DC-DC converters with high buck gain by using a lower number of elements. The converter provides highly efficient output power and an extended output voltage range. In addition, the distribution of output current between two inductors and the soft-switching capability of the power switches have made the converter suitable for applications that require high output current. All power switches accomplish the ZVZCS (zero-voltage and zero-current switching) condition with the aid of a small auxiliary inductor (Lx), which charges and discharges parallel capacitors of main switches to provide soft-switching conditions. Thus, the switching losses associated with power switches are considerably reduced. Additionally, the output voltage ratio of the proposed converter can be changed by varying the switching frequency and duty cycle. In addition, the variation range of output voltage has been expanded compared to other topologies, allowing for a wider output voltage range. A coupled inductor is utilized to establish a relationship between the output gain and the turn ratios, resulting in a wider output voltage gain range. Eventually, a theoretical analysis is conducted and a 200-watt experimental prototype has been implemented to illustrate the proposed converter’s efficacy. It converts a voltage input (300 V) to a voltage output (10 V).","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78906096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
: Latent fingerprints are ubiquitously used as forensic evidence by law enforcement agencies in solving crimes. However, due to deformations and artifacts within latent fingerprint images, performance of the automated latent recognition systems are far from desired levels. A basic matcher specifically designed for clean fingerprints using a minutiae-based matching algorithm can have high speed and accuracy in a sensor-to-sensor matching task, but low accuracy in matching latent prints, due to scale, rotation and quality differences between latent and sensor images. In this study, we propose a unique multistep fusion matcher (FM) on top of a base matcher that would utilize scale, rotation, and quality attributes of minutiae with speed, memory, and accuracy trade options in the latent recognition process. FM match characteristics are analyzed by using a private dataset consisting of 5560 latent and 1M slap/rolled fingerprint images. In addition, 292 domain expert selected latents are used to compare the nationwide performance of the proposed method. FM’s with multiresolution fusion (MRF) option have achieved competitive accuracy rates when searching 292 latent against 1 million background and projecting predictions for 69 million background. On the NIST SD302 public dataset, FM6 (FM option prioritizing accuracy for latent-to-sensor search) with MRF correctly recognizes 911 latent in rank-1, while the COTS system referenced in the NIST SD302 documentation recognizes only 790 from a gallery composed of 5950 latent and 100K rolled background database. FM6 MRF rank-1 count for 10K latent of NIST SD302 is 1415, whereas NIST’s referenced matcher rank-1 count is 880 for the same dataset. In addition, NIST SD302 rank-1 latents are used to construct 722 latent pairs to evaluate latent-to-latent matching performance. FM8 (FM option prioritizing accuracy for latent-to-latent search) with MRF has 46.1% rank-1 identification rate for latent-to-latent search against 10K latent background. Moreover, on a private 1457 latent palmprint versus 2296 sensor palmprint background, a palm matcher designed by dividing latent and palm images into 512x512 pixel segments produces 85.45% rank-1 accuracy by using FM6.
{"title":"A Multistep Fusion Matcher Approach for Large Scale Latent Fingerprint/Palmprint Recognition","authors":"Ismail Kilinç, Y. Artan, E. Baseski","doi":"10.55730/1300-0632.3992","DOIUrl":"https://doi.org/10.55730/1300-0632.3992","url":null,"abstract":": Latent fingerprints are ubiquitously used as forensic evidence by law enforcement agencies in solving crimes. However, due to deformations and artifacts within latent fingerprint images, performance of the automated latent recognition systems are far from desired levels. A basic matcher specifically designed for clean fingerprints using a minutiae-based matching algorithm can have high speed and accuracy in a sensor-to-sensor matching task, but low accuracy in matching latent prints, due to scale, rotation and quality differences between latent and sensor images. In this study, we propose a unique multistep fusion matcher (FM) on top of a base matcher that would utilize scale, rotation, and quality attributes of minutiae with speed, memory, and accuracy trade options in the latent recognition process. FM match characteristics are analyzed by using a private dataset consisting of 5560 latent and 1M slap/rolled fingerprint images. In addition, 292 domain expert selected latents are used to compare the nationwide performance of the proposed method. FM’s with multiresolution fusion (MRF) option have achieved competitive accuracy rates when searching 292 latent against 1 million background and projecting predictions for 69 million background. On the NIST SD302 public dataset, FM6 (FM option prioritizing accuracy for latent-to-sensor search) with MRF correctly recognizes 911 latent in rank-1, while the COTS system referenced in the NIST SD302 documentation recognizes only 790 from a gallery composed of 5950 latent and 100K rolled background database. FM6 MRF rank-1 count for 10K latent of NIST SD302 is 1415, whereas NIST’s referenced matcher rank-1 count is 880 for the same dataset. In addition, NIST SD302 rank-1 latents are used to construct 722 latent pairs to evaluate latent-to-latent matching performance. FM8 (FM option prioritizing accuracy for latent-to-latent search) with MRF has 46.1% rank-1 identification rate for latent-to-latent search against 10K latent background. Moreover, on a private 1457 latent palmprint versus 2296 sensor palmprint background, a palm matcher designed by dividing latent and palm images into 512x512 pixel segments produces 85.45% rank-1 accuracy by using FM6.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87027054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Melih Aslan, Kaan Şik, İzzet Güzelkara, İ. Özdür, V. Kılıç
: In this paper a study of a helical antenna resonating at high-frequency (HF) band with a very compact structure is reported. The designed antenna’s S11 parameter magnitude change with frequency was calculated for different geometrical parameters. For each case, first, only a single parameter was changed. Then for a fair comparison, multiple parameters were changed simultaneously while the total wire length was set to be constant. Also, shifts in resonance frequencies and variations in –10 dB bandwidths were investigated. Our results show that resonance behaviour changes distinctively with the geometrical parameters and it allows shortening of the antenna wire length. For the designed antenna, the resonances shift to lower frequencies and –10 dB bandwidths around the resonances decrease as the winding wire thickness, number of turns, and turn radius increase. Whereas as the turn spacing increases the resonances shift to higher frequencies and –10 dB bandwidths widen, although the total wire length of the antenna increases. To verify the simulation results, the designed antenna was fabricated with an exclusive manufacturing process and characterized. The measurement results are in good agreement with the simulation results. It demonstrates the feasibility of the proposed manufacturing technique, which is new in the literature and enables accurate and rigid antenna fabrication with simple and low-cost steps.
{"title":"Study of Helical Antenna Endowing Short Wire Length and Compact Structure for High-Frequency Operations and Its Exclusive Manufacturing Process","authors":"Melih Aslan, Kaan Şik, İzzet Güzelkara, İ. Özdür, V. Kılıç","doi":"10.55730/1300-0632.3991","DOIUrl":"https://doi.org/10.55730/1300-0632.3991","url":null,"abstract":": In this paper a study of a helical antenna resonating at high-frequency (HF) band with a very compact structure is reported. The designed antenna’s S11 parameter magnitude change with frequency was calculated for different geometrical parameters. For each case, first, only a single parameter was changed. Then for a fair comparison, multiple parameters were changed simultaneously while the total wire length was set to be constant. Also, shifts in resonance frequencies and variations in –10 dB bandwidths were investigated. Our results show that resonance behaviour changes distinctively with the geometrical parameters and it allows shortening of the antenna wire length. For the designed antenna, the resonances shift to lower frequencies and –10 dB bandwidths around the resonances decrease as the winding wire thickness, number of turns, and turn radius increase. Whereas as the turn spacing increases the resonances shift to higher frequencies and –10 dB bandwidths widen, although the total wire length of the antenna increases. To verify the simulation results, the designed antenna was fabricated with an exclusive manufacturing process and characterized. The measurement results are in good agreement with the simulation results. It demonstrates the feasibility of the proposed manufacturing technique, which is new in the literature and enables accurate and rigid antenna fabrication with simple and low-cost steps.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80979796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Shaheen, Ragab. A. Elsehiemy, M. Kharrich, S. Kamel
: Transmission network planning problem (TNPP) is one of the pertinent issues of the planning activities in power systems. It aims to optimally pick out the routs, types, and number of the new installed lines to confront the expected future loading conditions. In this line, this study proposes a new economic model to the TNPP. The aim of the model is to find the optimal transmission routes at least investment and operating costs. Three recent algorithms called grey wolf optimization algorithm (GWOA), spotted hyena optimization algorithm (SHOA) and whale optimization algorithm (WOA) are developed to solve the TNPP. The concept of these algorithms is based on encircling prey operation. The competitive methods are investigated to find the optimal TNPP solution for two realistic Egyptian networks. The first tested network is the 66 kV West Delta Region (WDR) system while the second one is the extra high voltage (EHV) 500 kV system. Their demand forecasting is extracted forward to 2030 dependent upon the adaptive neuro-fuzzy inference system (ANFIS). Tremendous technical and economic advantages through application of the encircling prey-based algorithms to handle the TNPP.
{"title":"Transmission network planning for realistic Egyptian systems via encircling prey based algorithms","authors":"A. Shaheen, Ragab. A. Elsehiemy, M. Kharrich, S. Kamel","doi":"10.3906/elk-2010-82","DOIUrl":"https://doi.org/10.3906/elk-2010-82","url":null,"abstract":": Transmission network planning problem (TNPP) is one of the pertinent issues of the planning activities in power systems. It aims to optimally pick out the routs, types, and number of the new installed lines to confront the expected future loading conditions. In this line, this study proposes a new economic model to the TNPP. The aim of the model is to find the optimal transmission routes at least investment and operating costs. Three recent algorithms called grey wolf optimization algorithm (GWOA), spotted hyena optimization algorithm (SHOA) and whale optimization algorithm (WOA) are developed to solve the TNPP. The concept of these algorithms is based on encircling prey operation. The competitive methods are investigated to find the optimal TNPP solution for two realistic Egyptian networks. The first tested network is the 66 kV West Delta Region (WDR) system while the second one is the extra high voltage (EHV) 500 kV system. Their demand forecasting is extracted forward to 2030 dependent upon the adaptive neuro-fuzzy inference system (ANFIS). Tremendous technical and economic advantages through application of the encircling prey-based algorithms to handle the TNPP.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79628432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
: It is well known that classifiers trained using imbalanced datasets usually have a bias toward the majority class. In this context, classification models can present a high classification performance overall and for the majority class, even when the performance for the minority class is significantly lower. This paper presents a genetic programming (GP) model with a crossover-based oversampling technique for oversampling the imbalanced dataset for binary text classification. The aim of this study is to apply an oversampling technique to solve the imbalanced issue and improve the performance of the GP model that employed the proposed technique. The proposed technique employs a crossover operator for generating new samples for the minority class in an imbalanced text dataset. By using a combination of this crossover-based oversampling technique with GP, the performance was improved. It is shown that the proposed combination outperforms all GP applications that use the original dataset without resampling. Moreover, the performance of the proposed system surpassed GP approaches using the synthetic minority oversampling technique (SMOTE) and random oversampling. Further comparison with the state-of-the-art on five imbalanced text datasets in terms of F1-score shows the superior performance of the proposed approach.
{"title":"Binary text classification using genetic programming with crossover-based oversampling for imbalanced datasets","authors":"Mona Khalifa A. Aljero, Nazife Dimililer","doi":"10.55730/1300-0632.3978","DOIUrl":"https://doi.org/10.55730/1300-0632.3978","url":null,"abstract":": It is well known that classifiers trained using imbalanced datasets usually have a bias toward the majority class. In this context, classification models can present a high classification performance overall and for the majority class, even when the performance for the minority class is significantly lower. This paper presents a genetic programming (GP) model with a crossover-based oversampling technique for oversampling the imbalanced dataset for binary text classification. The aim of this study is to apply an oversampling technique to solve the imbalanced issue and improve the performance of the GP model that employed the proposed technique. The proposed technique employs a crossover operator for generating new samples for the minority class in an imbalanced text dataset. By using a combination of this crossover-based oversampling technique with GP, the performance was improved. It is shown that the proposed combination outperforms all GP applications that use the original dataset without resampling. Moreover, the performance of the proposed system surpassed GP approaches using the synthetic minority oversampling technique (SMOTE) and random oversampling. Further comparison with the state-of-the-art on five imbalanced text datasets in terms of F1-score shows the superior performance of the proposed approach.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82916276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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