IEEE EUROCON 2021 - 19th International Conference on Smart Technologies最新文献_第7页

The Land Degradation Estimation Remote Sensing Methods Using RUE-adjusted NDVI 基于rue调整NDVI的土地退化遥感估算方法

IEEE EUROCON 2021 - 19th International Conference on Smart Technologies Pub Date : 2021-07-06 DOI: 10.1109/EUROCON52738.2021.9535610

A. Shelestov, L. Shumilo, Y. Bilokonska, A. Lavreniuk

{"title":"The Land Degradation Estimation Remote Sensing Methods Using RUE-adjusted NDVI","authors":"A. Shelestov, L. Shumilo, Y. Bilokonska, A. Lavreniuk","doi":"10.1109/EUROCON52738.2021.9535610","DOIUrl":"https://doi.org/10.1109/EUROCON52738.2021.9535610","url":null,"abstract":"State of the art methodologies for land degradations assessment accepted by United Nations, Food and Agriculture Organization and other official organizations that work on food security problems are based on the use of satellite data. In this case, the basis for the land degradation maps are vegetation indices, calculated using combinations of multispectral channels of satellite images. Evaluation of the land degradation state and trends is grounded on the analysis of land productivity maps changes over time (land productivity trend), land cover changes and carbon stocks changes.The most common methodology for the land degradation assessment is used for the UN Sustainable Development Goal 15.3.1 \"Proportion of land that is degraded over total land area\" calculation. This study considers the improvement for the calculation of land productivity / degradation based on the use of means of net primary productivity (NPP). For the NPP calculation we used open databases of satellite products of MODIS with spatial resolution 500 m and Landsat-8 with 30 m spatial resolution in the Google Earth Engine cloud platform. The satellite data for 2015 to 2019 years were used to build land productivity map and determine the areas of land degradation, productive and sustainable land for the territory of Ukraine. The use of NPP improve the land productivity assessment by consideration of agroclimatic conditions. The results were compared with product of Trends.Earth (official QGIS built-in plugin) which calculate land degradation maps by the UN methodology. The total areas of productive, degraded and sustainable land were calculated for the territory of Ukraine for 2015-2019 period.","PeriodicalId":328338,"journal":{"name":"IEEE EUROCON 2021 - 19th International Conference on Smart Technologies","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124248251","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}

引用次数: 0

Validation and Data Processing in JSON Format JSON格式的验证和数据处理

IEEE EUROCON 2021 - 19th International Conference on Smart Technologies Pub Date : 2021-07-06 DOI: 10.1109/EUROCON52738.2021.9535582

I. Spivak, S. Krepych, Mykola Litvynchuk, S. Spivak

引用次数: 0

RSO: A Novel Reinforced Swarm Optimization Algorithm for Feature Selection RSO:一种新的特征选择强化群优化算法

IEEE EUROCON 2021 - 19th International Conference on Smart Technologies Pub Date : 2021-07-06 DOI: 10.1109/EUROCON52738.2021.9535639

Hritam Basak, Mayukhmali Das, Susmita Modak

{"title":"RSO: A Novel Reinforced Swarm Optimization Algorithm for Feature Selection","authors":"Hritam Basak, Mayukhmali Das, Susmita Modak","doi":"10.1109/EUROCON52738.2021.9535639","DOIUrl":"https://doi.org/10.1109/EUROCON52738.2021.9535639","url":null,"abstract":"Swarm optimization algorithms are widely used for feature selection before data mining and machine learning applications. The metaheuristic nature-inspired feature selection approaches are used for single-objective optimization task, though the major problem is their frequent premature convergence, leading to weak contribution to data mining. In this paper, we propose a novel feature selection algorithm named Reinforced Swarm Optimization (RSO) leveraging some of the existing problems in feature selection. This algorithm embeds the widely used Bee Swarm Optimization (BSO) algorithm along with Reinforcement Learning (RL) to maximize the reward of a superior search agent and punish the inferior ones. This hybrid optimization algorithm is more adaptive and robust with a good balance between exploitation and exploration of the search space. The proposed method is evaluated on 25 widely known UCI dataset containing a perfect blend of balanced and imbalanced data. The obtained results are compared with several other popular and recent feature selection algorithms with similar classifier configuration. The experimental outcome shows that our proposed model outperforms BSO in 22 out of 25 instances (88%). Moreover, experimental results also show that RSO performs the best among all the methods compared in this paper in 19 out of 25 cases (76%), establishing the superiority of our proposed method.","PeriodicalId":328338,"journal":{"name":"IEEE EUROCON 2021 - 19th International Conference on Smart Technologies","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117262708","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}

引用次数: 3

Modern Trends and Skill Gaps of Cyber Security in Smart Grid : Invited Paper 智能电网网络安全的现代趋势与技术差距:特邀论文

IEEE EUROCON 2021 - 19th International Conference on Smart Technologies Pub Date : 2021-07-06 DOI: 10.1109/EUROCON52738.2021.9535632

B. Siemers, S. Attarha, Jirapa Kamsamrong, Michael Brand, Maria Valliou, R. Pirta-Dreimane, J. Grabis, N. Kunicina, M. Mekkanen, Tero Vartiainen, S. Lehnhoff

引用次数: 5

Analytics and Optimization Techniques on Feeder Identification in Smart Grids 智能电网馈线识别的分析与优化技术

IEEE EUROCON 2021 - 19th International Conference on Smart Technologies Pub Date : 2021-07-06 DOI: 10.1109/EUROCON52738.2021.9535580

L. Aranburu, A. Unzueta, M. Garín, Juan I. Modroño, Aitor Amezua

{"title":"Analytics and Optimization Techniques on Feeder Identification in Smart Grids","authors":"L. Aranburu, A. Unzueta, M. Garín, Juan I. Modroño, Aitor Amezua","doi":"10.1109/EUROCON52738.2021.9535580","DOIUrl":"https://doi.org/10.1109/EUROCON52738.2021.9535580","url":null,"abstract":"One of the problems faced by electric power distribution system operators is to know with certainty the actual location of all their assets in order to manage properly the grid and provide the best service to their customers. In this work, we present a procedure for the identification of low voltage feeders or distribution lines in smart grids that is based on the mathematical formulation of the problem as an optimization model. In particular, we define the model with 0-1 variables (as many as meters to be identified in the different feeders) and with as many restrictions as the number of points in time that are considered. Given the large size of the problem in practice, the use of conventional optimization software becomes unfeasible. Based on this approach, and making use of the linear relaxation of the problem, some analytics over the coefficients (i.e., meter loads) and the special structure of the problem itself, we have developed an iterative procedure that allows us to recover the entire solution of the initial model in an efficient way. We have carried out a computational experience on a set of anonymized real data, obtaining results that support the efficiency of the proposed procedure.","PeriodicalId":328338,"journal":{"name":"IEEE EUROCON 2021 - 19th International Conference on Smart Technologies","volume":"246 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123025681","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}

引用次数: 0

Dynamic and Reliable Multichannel Interfacing System for FPGAs 动态可靠的fpga多通道接口系统

IEEE EUROCON 2021 - 19th International Conference on Smart Technologies Pub Date : 2021-07-06 DOI: 10.1109/EUROCON52738.2021.9535622

Salma K. Elsokkary, Salma M. Soliman, Nada Badawy, Cherif R. Salama, H. Amer, G. Alkady, I. Adly

引用次数: 0

Power Conversion Equipment in Ukraine: Experience and Prospects 乌克兰电力转换设备:经验与展望

IEEE EUROCON 2021 - 19th International Conference on Smart Technologies Pub Date : 2021-07-06 DOI: 10.1109/EUROCON52738.2021.9535638

E. Tverytnykova, M. Gutnyk, Yulianna A. Demidova, H. Salata

{"title":"Power Conversion Equipment in Ukraine: Experience and Prospects","authors":"E. Tverytnykova, M. Gutnyk, Yulianna A. Demidova, H. Salata","doi":"10.1109/EUROCON52738.2021.9535638","DOIUrl":"https://doi.org/10.1109/EUROCON52738.2021.9535638","url":null,"abstract":"The stages of the formation of scientific research in the field of converting equipment in Ukraine were investigated. It turned out that the initial research was started by Academician V. M. Khrushchev at the Kharkov Electrotechnical Institute in the early 1930s. The creation of the Institute of Energy (later – Institute of Electrical Engineering, Institute of Electrodynamics) within the system of the Academy of Sciences of Ukraine gave impetus to the development of research in this area, both in academic institutes and in polytechnic ones. An in-depth study of reports on the scientific research work of the Institute of Electrodynamics of the National Academy of Sciences of Ukraine made it possible to reveal that the research teams of the Institute made a significant contribution to the development of theoretical research and the creation of highly efficient electromagnetic and semiconductor converters for various purposes. O. M. Miliakh, together with his students, created new areas of research: a unique Kyiv school of transistor converting equipment under the leadership of Yu. I. Drabovich; research of thyristor converters for power supply systems (V. Yu. Tonkal); development of the theory, methods and technical methods of the parameters of electricity stabilizing and electromagnetic compatibility in electrical systems and networks (A. K. Shidlovsky). In addition, great contribution for the formation of the direction of converting technology in Ukraine made the fundamental developments of Academician I. M. Chizhenko and the achievements of the scientific school of converting equipment of the National Technical University \"Kharkov Polytechnic Institute\" (O. O. Mayevsky, V. T. Dolbnya, E. I. Sokol).","PeriodicalId":328338,"journal":{"name":"IEEE EUROCON 2021 - 19th International Conference on Smart Technologies","volume":"384 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121762752","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}

引用次数: 1

Vessel Trajectory Prediction Using Radial Basis Function Neural Networks 基于径向基函数神经网络的船舶轨迹预测

IEEE EUROCON 2021 - 19th International Conference on Smart Technologies Pub Date : 2021-07-06 DOI: 10.1109/EUROCON52738.2021.9535562

M. Stogiannos, Myron Papadimitrakis, H. Sarimveis, A. Alexandridis

引用次数: 2

Concept for Automated Energy Trading in MV and LV Electrical Distribution Grids Based on Approximated Supply Function Equilibrium 基于近似供给函数均衡的中低压配电网自动能源交易概念

IEEE EUROCON 2021 - 19th International Conference on Smart Technologies Pub Date : 2021-07-06 DOI: 10.1109/EUROCON52738.2021.9535567

Perica Ilak, L. Herenčić, Helena Benković, I. Rajšl

引用次数: 0

Next Item Prediction Using Neural Networks with Embedding Initialized Weights 基于嵌入初始化权值的神经网络下一项预测

IEEE EUROCON 2021 - 19th International Conference on Smart Technologies Pub Date : 2021-07-06 DOI: 10.1109/EUROCON52738.2021.9535591

Ç. Yildiz, M. Aker, Y. Yaslan

{"title":"Next Item Prediction Using Neural Networks with Embedding Initialized Weights","authors":"Ç. Yildiz, M. Aker, Y. Yaslan","doi":"10.1109/EUROCON52738.2021.9535591","DOIUrl":"https://doi.org/10.1109/EUROCON52738.2021.9535591","url":null,"abstract":"Session-based recommendation systems became a very part of humankind’s daily life, as a result of the increasing transaction volume of e-commerce and e-marketing fields. Companies that can analyze the trails left by their current users on their systems and know their customers better than other firms, can become one step ahead of their rivals. In this concept, representations of items become a key point when related deep learning models are investigated closer. Since the relationship between each item within a session will have a direct effect on the task that involves predicting the next item in that session, extracting these relationships among each item needs to be handled in an effective manner. Using auto encoder systems to achieve the task of revealing hidden features between items and representing these relationships in a more meaningful way, will result in both boosting current state-of-art models’ performance and offering new session-based methods that can overperform the current state-of-art models. In this paper, state-of-the-art graph neural networks SR-GNN’s and TA-GNN’s weights are initialized with item embeddings that are obtained from autoencoder with RBM layers, and the performance of the models are compared with random weight initialization. According to the proposed weight initialization, using pre-trained item embeddings will increase the performance of the recommender model. Thanks to the pre-trained item embeddings, the hidden relationship between items modeled in a better way, and the introduced model has overperformed the current state-of-art techniques.","PeriodicalId":328338,"journal":{"name":"IEEE EUROCON 2021 - 19th International Conference on Smart Technologies","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122363675","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}

引用次数: 0