Decision making is a mental process in which one of the different options is chosen. All decision -mak ng processes end with a decision. In this thesis, site selection for a new project was examined. Investments requiring very high budgets, such as the cons truction sector, need to be made more meticulously. AHP (Analytic Hierarchy Process) and TOPSIS methods were used in decision making process. Expert Choice program was used for AHP analysis. The main criteria were determined during the selection process. T hese criteria were determined by the construction industry experts and individuals (potential customers) by taking into consideration the marital status of the people, their children, their financial situation and their way of liv ing. Criteria have differe nt degrees of importance for people. Therefore, each criterion was compared with the other criteria by weight method. In comparison, we worked very meticulously. Each comparison matrix was examined individually. During the implementation, attention was pai d to the innovations around the candidate construction sites. In this study, site selection was made according to site selection, ra sportation cost, title deeds, cadastre and municipal operations, and preference suggestions were given.
{"title":"Analytic Hierarchy Process and Topsis Methods of Construction Site Selection","authors":"S. Arslankaya, Ö. Baştürk","doi":"10.36287/setsci.4.6.067","DOIUrl":"https://doi.org/10.36287/setsci.4.6.067","url":null,"abstract":"Decision making is a mental process in which one of the different options is chosen. All decision -mak ng processes end with a decision. In this thesis, site selection for a new project was examined. Investments requiring very high budgets, such as the cons truction sector, need to be made more meticulously. AHP (Analytic Hierarchy Process) and TOPSIS methods were used in decision making process. Expert Choice program was used for AHP analysis. The main criteria were determined during the selection process. T hese criteria were determined by the construction industry experts and individuals (potential customers) by taking into consideration the marital status of the people, their children, their financial situation and their way of liv ing. Criteria have differe nt degrees of importance for people. Therefore, each criterion was compared with the other criteria by weight method. In comparison, we worked very meticulously. Each comparison matrix was examined individually. During the implementation, attention was pai d to the innovations around the candidate construction sites. In this study, site selection was made according to site selection, ra sportation cost, title deeds, cadastre and municipal operations, and preference suggestions were given.","PeriodicalId":6817,"journal":{"name":"4th International Symposium on Innovative Approaches in Engineering and Natural Sciences Proceedings","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74979867","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}
Traffic flow forecasting has an important place in designing a successful intelligent transportation system. The success of the forecasting is related to the accuracy and timely acquisition of the traffic flow data. The inadequacy in the number of data has led to the use of shallow architectures in the traffic forecasting models realized so far or to design models with generated data. These models failed to occur forecast results with sufficient success. Nowadays, in the age of big data, in parallel with the increase in traffic density, there has been a significant increase in the diversity and size of the collected traffic flow data. This result constitutes the main motivation in our study. Our study aims to forecast traffic density at the exit of a motorway that have linked roads. The forecasting models proposed in our study were designed using generally accepted, Deep Learning techniques, which can occur meaningful prediction results with big data. The techniques used in our study are Recurrent Neural Network (RNN), Long-Short Time Memory (LSTM), Stacked Long Short-Term Memory (S-LSTM), Bidirectional Long Short-Term Memory (B-LSTM) and Gated Recurrent Unit (GRU) neural networks. The dataset used in the study consists of 929 thousand 640 measurement data collected by loop sensors placed at 6 different points. Three different training data sets were created, split 90%, 80% and 70% of all data and the remainder of the data used as the test dataset. Forecast achievements of the designed models on the test dataset were recorded by calculating the Mean Square Error (MSE) values. In addition, all models are run with different number of epochs and the effect of the training set size and iterations on learning was investigated. The results show that Deep Learning techniques in traffic flow forecasting with low MSE values occur successful results and can be used in traffic flow prediction models. When the results of selected Deep Learning techniques and designed models are compared, it is observed that B-LSTM has the best forecast performance with the lowest MSE value of 36,60.
{"title":"Analysis of Highway Traffic Using Deep Learning Techniques / Derin Öğrenme Teknikleri Kullanılarak Anayol Trafik Analizi","authors":"Muhammet Esad Özdağ, N. Atasoy","doi":"10.36287/setsci.4.6.098","DOIUrl":"https://doi.org/10.36287/setsci.4.6.098","url":null,"abstract":"Traffic flow forecasting has an important place in designing a successful intelligent transportation system. The success of the forecasting is related to the accuracy and timely acquisition of the traffic flow data. The inadequacy in the number of data has led to the use of shallow architectures in the traffic forecasting models realized so far or to design models with generated data. These models failed to occur forecast results with sufficient success. Nowadays, in the age of big data, in parallel with the increase in traffic density, there has been a significant increase in the diversity and size of the collected traffic flow data. This result constitutes the main motivation in our study. Our study aims to forecast traffic density at the exit of a motorway that have linked roads. The forecasting models proposed in our study were designed using generally accepted, Deep Learning techniques, which can occur meaningful prediction results with big data. The techniques used in our study are Recurrent Neural Network (RNN), Long-Short Time Memory (LSTM), Stacked Long Short-Term Memory (S-LSTM), Bidirectional Long Short-Term Memory (B-LSTM) and Gated Recurrent Unit (GRU) neural networks. The dataset used in the study consists of 929 thousand 640 measurement data collected by loop sensors placed at 6 different points. Three different training data sets were created, split 90%, 80% and 70% of all data and the remainder of the data used as the test dataset. Forecast achievements of the designed models on the test dataset were recorded by calculating the Mean Square Error (MSE) values. In addition, all models are run with different number of epochs and the effect of the training set size and iterations on learning was investigated. The results show that Deep Learning techniques in traffic flow forecasting with low MSE values occur successful results and can be used in traffic flow prediction models. When the results of selected Deep Learning techniques and designed models are compared, it is observed that B-LSTM has the best forecast performance with the lowest MSE value of 36,60.","PeriodicalId":6817,"journal":{"name":"4th International Symposium on Innovative Approaches in Engineering and Natural Sciences Proceedings","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76332128","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}
Environmental contamination with heavy metals is a serious growing problem throughout the world. In today’s industrial society, there is no way to avoid the exposure to toxic chemicals and metals. Agricultural lands, which are an important part of the environment, are a part of this phenomenon. Toxic industrial wastes can be mixed with liquid fertilizers and spread to agricultural lands. Various methods are used in order to eliminate environmental disturbances as a result of industrial activities. Most of these methods use advanced technologies, which seem to be quite expensive and difficult to apply to large areas. On the other hand, phytoremediation method is one of the most preferred options in combating metal pollution problem and it is an ideal candidate to use medicinal and aromatic plants as a sustainable, aesthetic and environment friendly technique. When it is investigated, it can be seen that the flora of our country consists of 38 hyperaccumulator plants from different families that are also mentioned in international literature. Hyperacumulator plants such as Thyme (Thymus vulgaris L.), Sage (Salvia officinalis), Dandelion (Taraxacum officinale), Centaury (Hypericum perforatum) are known to absorb heavy metals, releasing them into the atmosphere in the form of gas. This review briefly describes benefits of using medicinal and aromatic plants for the recovery of soils contaminated with heavy metals.
{"title":"Ağır Metallerle Kirlenmiş Toprakların İyileştirilmesinde Fitoremediasyon Yöntemi: Tıbbi ve Aromatik Bitkilerin Uygunluğu","authors":"Mahmut Yildizteki̇n, H. Ulusoy, A. Tuna","doi":"10.36287/setsci.4.6.133","DOIUrl":"https://doi.org/10.36287/setsci.4.6.133","url":null,"abstract":"Environmental contamination with heavy metals is a serious growing problem throughout the world. In today’s industrial society, there is no way to avoid the exposure to toxic chemicals and metals. Agricultural lands, which are an important part of the environment, are a part of this phenomenon. Toxic industrial wastes can be mixed with liquid fertilizers and spread to agricultural lands. Various methods are used in order to eliminate environmental disturbances as a result of industrial activities. Most of these methods use advanced technologies, which seem to be quite expensive and difficult to apply to large areas. On the other hand, phytoremediation method is one of the most preferred options in combating metal pollution problem and it is an ideal candidate to use medicinal and aromatic plants as a sustainable, aesthetic and environment friendly technique. When it is investigated, it can be seen that the flora of our country consists of 38 hyperaccumulator plants from different families that are also mentioned in international literature. Hyperacumulator plants such as Thyme (Thymus vulgaris L.), Sage (Salvia officinalis), Dandelion (Taraxacum officinale), Centaury (Hypericum perforatum) are known to absorb heavy metals, releasing them into the atmosphere in the form of gas. This review briefly describes benefits of using medicinal and aromatic plants for the recovery of soils contaminated with heavy metals.","PeriodicalId":6817,"journal":{"name":"4th International Symposium on Innovative Approaches in Engineering and Natural Sciences Proceedings","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79726441","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}
Korovkin type approximation theorems have very important role in the approximation theory. Many mathematicians investigate and improve these type of approximation theorems for various operators defined on different spaces via several new convergence methods. The convergence of a sequence of positive linear operators defined on weighted space was first studied by Gadjiev [Theorems of Korovkin type, Math. Zametki 20(1976), 781-786]. Then, these results were improved by many authors for different type of convergence methods. Recently, some authors study Korovkin type theorems for two variables functions by means of single and double sequences on weighted spaces. In this paper, we prove a Korovkin type approximation theorem for the notion of statistical equal convergence for double sequences on two dimensional weighted spaces. Then, we construct an example such that our new approximation result works but its classical and statistical cases do not work. Also, we compute the rate of statistical equal convergence for double sequences on two dimensional weighted spaces.
{"title":"A-Statistical Equal Approximation on Two Dimensional Weighted Spaces","authors":"S. Yildiz, F. Dirik, K. Demirci","doi":"10.36287/setsci.4.6.055","DOIUrl":"https://doi.org/10.36287/setsci.4.6.055","url":null,"abstract":"Korovkin type approximation theorems have very important role in the approximation theory. Many mathematicians investigate and improve these type of approximation theorems for various operators defined on different spaces via several new convergence methods. The convergence of a sequence of positive linear operators defined on weighted space was first studied by Gadjiev [Theorems of Korovkin type, Math. Zametki 20(1976), 781-786]. Then, these results were improved by many authors for different type of convergence methods. Recently, some authors study Korovkin type theorems for two variables functions by means of single and double sequences on weighted spaces. In this paper, we prove a Korovkin type approximation theorem for the notion of statistical equal convergence for double sequences on two dimensional weighted spaces. Then, we construct an example such that our new approximation result works but its classical and statistical cases do not work. Also, we compute the rate of statistical equal convergence for double sequences on two dimensional weighted spaces.","PeriodicalId":6817,"journal":{"name":"4th International Symposium on Innovative Approaches in Engineering and Natural Sciences Proceedings","volume":"22 3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87231859","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}
High temperature superconductors (HTSs) have been widely used in magnetic bearing systems, magnetically levitated transportation systems (Maglev), superconducting motors, etc. due to their stable levitation properties. Although the studies on Maglev systems have been increasing in recent years, both the vertical levitation and lateral guidance forces are not at desired level for technological applicability of these systems. Furthermore, the studies have been mostly focused on enhancing the levitation force rather than the guidance force. One of the ways to improve the levitation and guidance forces of Maglev systems is improving the superconducting properties of HTSs and/or producing HTSs in larger single domains and in large geometries. The most effective method to produce HTSs in larger single domain within a reasonable production time is the multi‒seeded melt growth (MSMG) method. However, it can be seen from the studies in literature that the increasing seed number on HTSs corrupts the superconducting properties of MSMG samples. One can overcome this negation by changing the number, orientation and distance of the seeds. In this study, we have produced cylindrical YBCO superconducting samples with different distance of seeds by MSMG method and investigated the effect of seed distance on the lateral guidance force both in zero field cooling (ZFC) and field cooling (FC) regimes at different measurement temperatures of 77 K, 80 K and 83 K. The results showed that the movement stability of Maglev systems can be increased by changing the distance of the seeds in HTSs.
{"title":"The Effect of Seed Distance on the Lateral Guidance Force of Multi-Seeded YBCO Superconductors","authors":"S. B. Guner, M. Abdioğlu","doi":"10.36287/setsci.4.6.040","DOIUrl":"https://doi.org/10.36287/setsci.4.6.040","url":null,"abstract":"High temperature superconductors (HTSs) have been widely used in magnetic bearing systems, magnetically levitated transportation systems (Maglev), superconducting motors, etc. due to their stable levitation properties. Although the studies on Maglev systems have been increasing in recent years, both the vertical levitation and lateral guidance forces are not at desired level for technological applicability of these systems. Furthermore, the studies have been mostly focused on enhancing the levitation force rather than the guidance force. One of the ways to improve the levitation and guidance forces of Maglev systems is improving the superconducting properties of HTSs and/or producing HTSs in larger single domains and in large geometries. The most effective method to produce HTSs in larger single domain within a reasonable production time is the multi‒seeded melt growth (MSMG) method. However, it can be seen from the studies in literature that the increasing seed number on HTSs corrupts the superconducting properties of MSMG samples. One can overcome this negation by changing the number, orientation and distance of the seeds. In this study, we have produced cylindrical YBCO superconducting samples with different distance of seeds by MSMG method and investigated the effect of seed distance on the lateral guidance force both in zero field cooling (ZFC) and field cooling (FC) regimes at different measurement temperatures of 77 K, 80 K and 83 K. The results showed that the movement stability of Maglev systems can be increased by changing the distance of the seeds in HTSs.","PeriodicalId":6817,"journal":{"name":"4th International Symposium on Innovative Approaches in Engineering and Natural Sciences Proceedings","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87429832","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}
{"title":"Karadeniz Bölgesinin İstilacı Böcek Türlerine Genel Bir Bakış","authors":"Temel Göktürk","doi":"10.36287/setsci.4.6.028","DOIUrl":"https://doi.org/10.36287/setsci.4.6.028","url":null,"abstract":"","PeriodicalId":6817,"journal":{"name":"4th International Symposium on Innovative Approaches in Engineering and Natural Sciences Proceedings","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89502598","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}
{"title":"Determination Of Sediment Yield By GeoWEPP Erosion Estimation Model","authors":"Cengizhan Yıldırım, Mustafa Tüfekçioğlu","doi":"10.36287/setsci.4.6.127","DOIUrl":"https://doi.org/10.36287/setsci.4.6.127","url":null,"abstract":"","PeriodicalId":6817,"journal":{"name":"4th International Symposium on Innovative Approaches in Engineering and Natural Sciences Proceedings","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78117928","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}
{"title":"Statistical Equi-Equal Convergence of Double Sequences and Korovkin Type Approximation Theorems","authors":"F. Dirik","doi":"10.36287/setsci.4.6.053","DOIUrl":"https://doi.org/10.36287/setsci.4.6.053","url":null,"abstract":"","PeriodicalId":6817,"journal":{"name":"4th International Symposium on Innovative Approaches in Engineering and Natural Sciences Proceedings","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74471294","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}
{"title":"ISO/IEC 27037, ISO/IEC 27041, ISO/IEC 27042 ve ISO/IEC 27043 Standartlarına Göre Sayısal Kanıtlar","authors":"Nursel Yalçin, Berker Kiliç","doi":"10.36287/setsci.4.6.118","DOIUrl":"https://doi.org/10.36287/setsci.4.6.118","url":null,"abstract":"","PeriodicalId":6817,"journal":{"name":"4th International Symposium on Innovative Approaches in Engineering and Natural Sciences Proceedings","volume":"134 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73840719","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 important to determine the initial level of failures of induction motors used in many industrial applications. The sudden stops of the system can be prevented with the pre-detection of the fault. The experiment mechanism was established to detect mechanical unbalance and short circuit faults in the induction motors. Current values were measured and saved at fault time. As a result, 9.000 data were obtained consisting of 3 phase currents. In this study, a Long-Short Term Memory (LSTM) deep neural network has been developed that classification of induction motors by fault type. In the training of the neural network, 3 input parameters and 3 classification types of 1 output parameter are used. It was reserved for training 60% of data and 40% for testing the model in the dataset. As a result of the fault type classification with the LSTM model, 98.5% accuracy and 1.12 average absolute error value were obtained. It has been shown that the proposed bi-LSTM network can be used for fault detection of asynchronous motors.
{"title":"Classification of Induction Motors by Fault Type with bidirectional Long-Short Term Memory Method","authors":"Ahmet Ali Süzen, K. Kayaalp","doi":"10.36287/setsci.4.6.074","DOIUrl":"https://doi.org/10.36287/setsci.4.6.074","url":null,"abstract":"It is important to determine the initial level of failures of induction motors used in many industrial applications. The sudden stops of the system can be prevented with the pre-detection of the fault. The experiment mechanism was established to detect mechanical unbalance and short circuit faults in the induction motors. Current values were measured and saved at fault time. As a result, 9.000 data were obtained consisting of 3 phase currents. In this study, a Long-Short Term Memory (LSTM) deep neural network has been developed that classification of induction motors by fault type. In the training of the neural network, 3 input parameters and 3 classification types of 1 output parameter are used. It was reserved for training 60% of data and 40% for testing the model in the dataset. As a result of the fault type classification with the LSTM model, 98.5% accuracy and 1.12 average absolute error value were obtained. It has been shown that the proposed bi-LSTM network can be used for fault detection of asynchronous motors.","PeriodicalId":6817,"journal":{"name":"4th International Symposium on Innovative Approaches in Engineering and Natural Sciences Proceedings","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73843898","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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