{"title":"Estimation of the Moisture Content, Volatile Matter, Ash Content, Fixed Carbon and Calorific Values of Saw Dust Briquettes","authors":"Francis Inegbedion","doi":"10.51354/mjen.940760","DOIUrl":"https://doi.org/10.51354/mjen.940760","url":null,"abstract":"","PeriodicalId":102219,"journal":{"name":"MANAS Journal of Engineering","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123195445","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":"Launching to an Orbit with a Chemical Propellant Staged Rocket Systems","authors":"Erk Inger","doi":"10.51354/mjen.1000248","DOIUrl":"https://doi.org/10.51354/mjen.1000248","url":null,"abstract":"","PeriodicalId":102219,"journal":{"name":"MANAS Journal of Engineering","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123662333","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}
In this paper, we define modified Tribonacci-Lucas matrix sequence and investigate its properties.
本文定义了修正tribonaci - lucas矩阵序列，并研究了它的性质。
{"title":"On Matrix Sequence of modified Tribonacci-Lucas Numbers","authors":"Y. Soykan, E. Taşdemir, Vedat Irge","doi":"10.51354/mjen.894514","DOIUrl":"https://doi.org/10.51354/mjen.894514","url":null,"abstract":"In this paper, we define modified Tribonacci-Lucas matrix sequence and investigate its properties.","PeriodicalId":102219,"journal":{"name":"MANAS Journal of Engineering","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130321607","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}
In crowded city centers, drivers looking for available parking space generate extra traffic and in addition, the resulting excessive exhaust gases cause air pollution. Therefore, directing the drivers to a parking spot in an intelligent way is an important task for smart city applications. This task requires the prediction of occupancy states of parking lots which involves appropriate processing of the historical parking data. In this work, Long-Short Term Memory (LSTM) and Autoregressive Integrated Moving Average (ARIMA) methods were applied to parking data collected from curbside parking spots of Adana, Turkey for predicting the parking lot occupancy rates of future values. The experiments were performed for making predictions with different prediction horizons that are 1 minute, 5 minutes, and 15 minutes. The performances of the methods were compared by calculating root mean squared error (RMSE) and mean absolute error (MAE) values. The experiments were performed on data from five different days. According to the results, when the prediction horizon is set to 1 minute, LSTM achieved RMSE and MAE values of 0.98 and 0.72, respectively. For the same prediction horizon, ARIMA achieved RMSE and MAE values of 0.62 and 0.35, respectively. On the other hand, LSTM achieved smaller error values for larger prediction horizons. In conclusion, it was shown that LSTM is more suitable for larger prediction horizons, however, ARIMA is better at predicting near-future values.
{"title":"Parking Lot Occupancy Prediction Using Long Short-Term Memory and Statistical Methods","authors":"Yusuf Can Anar, E. Avşar, Abdurrahman Özgür Polat","doi":"10.51354/mjen.986631","DOIUrl":"https://doi.org/10.51354/mjen.986631","url":null,"abstract":"In crowded city centers, drivers looking for available parking space generate extra traffic and in addition, the resulting excessive exhaust gases cause air pollution. Therefore, directing the drivers to a parking spot in an intelligent way is an important task for smart city applications. This task requires the prediction of occupancy states of parking lots which involves appropriate processing of the historical parking data. In this work, Long-Short Term Memory (LSTM) and Autoregressive Integrated Moving Average (ARIMA) methods were applied to parking data collected from curbside parking spots of Adana, Turkey for predicting the parking lot occupancy rates of future values. The experiments were performed for making predictions with different prediction horizons that are 1 minute, 5 minutes, and 15 minutes. The performances of the methods were compared by calculating root mean squared error (RMSE) and mean absolute error (MAE) values. The experiments were performed on data from five different days. According to the results, when the prediction horizon is set to 1 minute, LSTM achieved RMSE and MAE values of 0.98 and 0.72, respectively. For the same prediction horizon, ARIMA achieved RMSE and MAE values of 0.62 and 0.35, respectively. On the other hand, LSTM achieved smaller error values for larger prediction horizons. In conclusion, it was shown that LSTM is more suitable for larger prediction horizons, however, ARIMA is better at predicting near-future values.","PeriodicalId":102219,"journal":{"name":"MANAS Journal of Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114211793","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}
Sahin Demirci, B. Arı, Sultan Bütün Şengel, Erk Inger, N. Sahiner
{"title":"Boric acid versus boron trioxide as catalysts for green energy source H2 production from sodium borohydride methanolysis","authors":"Sahin Demirci, B. Arı, Sultan Bütün Şengel, Erk Inger, N. Sahiner","doi":"10.51354/mjen.980286","DOIUrl":"https://doi.org/10.51354/mjen.980286","url":null,"abstract":"","PeriodicalId":102219,"journal":{"name":"MANAS Journal of Engineering","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126357826","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":"Study of the Activity of a Novel Green Catalyst Used in the Production of Hydrogen from Methanolysis of Sodium Borohydride","authors":"Tulin Avci Hansu","doi":"10.51354/mjen.934839","DOIUrl":"https://doi.org/10.51354/mjen.934839","url":null,"abstract":"","PeriodicalId":102219,"journal":{"name":"MANAS Journal of Engineering","volume":"3 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132683962","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":"A CLEANER APPLICATION ON HYDROGEN SULFIDE","authors":"Merve Aksu, M. Morcalı","doi":"10.51354/mjen.953547","DOIUrl":"https://doi.org/10.51354/mjen.953547","url":null,"abstract":"","PeriodicalId":102219,"journal":{"name":"MANAS Journal of Engineering","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125575951","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}
Feature extraction layers like Local Binary Patterns (LBP) transform can be very useful for improving the accuracy of machine learning and deep learning models depending on the problem type. Direct implementations of such layers in Python may result in long running times, and training a computer vision model may be delayed significantly. For this purpose, TensorFlow framework enables developing accelerated custom operations based on the existing operations which already have support for accelerated hardware such as multicore CPU and GPU. In this study, LBP transform which is used for feature extraction in various applications, was implemented based on TensorFlow operations. The evaluations were done using both standard Python operations and TensorFlow library for performance comparisons. The experiments were realized using images in various dimensions and various batch sizes. Numerical results show that algorithm based on TensorFlow operations provides good acceleration rates over Python runs. The implementation of LBP can be used for the accelerated computing for various feature extraction purposes including machine learning as well as in deep learning applications.
{"title":"A TensorFlow implementation of Local Binary Patterns Transform","authors":"D. Akgün","doi":"10.51354/mjen.822630","DOIUrl":"https://doi.org/10.51354/mjen.822630","url":null,"abstract":"Feature extraction layers like Local Binary Patterns (LBP) transform can be very useful for improving the accuracy of machine learning and deep learning models depending on the problem type. Direct implementations of such layers in Python may result in long running times, and training a computer vision model may be delayed significantly. For this purpose, TensorFlow framework enables developing accelerated custom operations based on the existing operations which already have support for accelerated hardware such as multicore CPU and GPU. In this study, LBP transform which is used for feature extraction in various applications, was implemented based on TensorFlow operations. The evaluations were done using both standard Python operations and TensorFlow library for performance comparisons. The experiments were realized using images in various dimensions and various batch sizes. Numerical results show that algorithm based on TensorFlow operations provides good acceleration rates over Python runs. The implementation of LBP can be used for the accelerated computing for various feature extraction purposes including machine learning as well as in deep learning applications.","PeriodicalId":102219,"journal":{"name":"MANAS Journal of Engineering","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124935537","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}
Recently, scientists are paying more attention to the Organic Light Emitting Diode (OLED) technology as it is being used in devices and displays to play videos and show photos with high resolution. This technology is used in products such as mobile phones, televisions, laptops, etc. To make the energy consumed less, new methods were shown up to prevent high energy consumption while presenting videos and photos on OLED devices and displays without losing their details and quality, one of the methods is a deep learning-based technique which is related to artificial intelligence. In this review paper, the last methods were discussed as well as their results. Saturation, brightness, contrast, and luminance are factors that impacting energy consumption. In terms of OLED mobile phones, there were a few studies that concentrated on turning off the unnecessary pixels which will be black as default, and as a result, the lifetime of batteries will be extended. Also, for OLED mobile phones, a web browser called Chameleon was presented as it has some modes to save the energy consumed while surfing the internet by remapping the displayed colors of the website.
{"title":"Technologies based on energy savings for OLED devices","authors":"Mohammed Albaba, Meryem Sena Akkus","doi":"10.51354/mjen.872956","DOIUrl":"https://doi.org/10.51354/mjen.872956","url":null,"abstract":"Recently, scientists are paying more attention to the Organic Light Emitting Diode (OLED) technology as it is being used in devices and displays to play videos and show photos with high resolution. This technology is used in products such as mobile phones, televisions, laptops, etc. To make the energy consumed less, new methods were shown up to prevent high energy consumption while presenting videos and photos on OLED devices and displays without losing their details and quality, one of the methods is a deep learning-based technique which is related to artificial intelligence. In this review paper, the last methods were discussed as well as their results. Saturation, brightness, contrast, and luminance are factors that impacting energy consumption. In terms of OLED mobile phones, there were a few studies that concentrated on turning off the unnecessary pixels which will be black as default, and as a result, the lifetime of batteries will be extended. Also, for OLED mobile phones, a web browser called Chameleon was presented as it has some modes to save the energy consumed while surfing the internet by remapping the displayed colors of the website.","PeriodicalId":102219,"journal":{"name":"MANAS Journal of Engineering","volume":"14 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114085478","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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