Pub Date : 2021-09-01DOI: 10.1109/ISSSR53171.2021.00018
Shuwen Zheng, Jie Liu
Wind power is an important source of renewable energy. Owing to the randomness of wind speed, wind power forecasting has always been a challenging issue and is of paramount significance to the operation safety of power systems. In this paper, we proposed a hybrid method for multi-steps wind power forecasting, which combines the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Long Short-Term Memory (LSTM) neural network with modified Genetic Algorithm optimization. The unknown parameters of LSTM and component aggregation weights in result reconstruction are optimized to improve the forecasting performance. A case study concerning the real wind power datasets from ELIA is carried out to validate the effectiveness of the proposed method.
{"title":"Automatic Multi-steps Prediction Modelling for Wind Power Forecasting","authors":"Shuwen Zheng, Jie Liu","doi":"10.1109/ISSSR53171.2021.00018","DOIUrl":"https://doi.org/10.1109/ISSSR53171.2021.00018","url":null,"abstract":"Wind power is an important source of renewable energy. Owing to the randomness of wind speed, wind power forecasting has always been a challenging issue and is of paramount significance to the operation safety of power systems. In this paper, we proposed a hybrid method for multi-steps wind power forecasting, which combines the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Long Short-Term Memory (LSTM) neural network with modified Genetic Algorithm optimization. The unknown parameters of LSTM and component aggregation weights in result reconstruction are optimized to improve the forecasting performance. A case study concerning the real wind power datasets from ELIA is carried out to validate the effectiveness of the proposed method.","PeriodicalId":211012,"journal":{"name":"2021 7th International Symposium on System and Software Reliability (ISSSR)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128793034","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}
The mutation testing method of Deep Learning (DL) system proposes a series of DL mutation operators, but the same as traditional software mutation testing methods, a large number of mutants will be generated during the testing process, which will cause huge costs. The traditional mutation operator reduction method is based on source program business logic. Owe to the fundamental difference between traditional system and DL system, traditional reduction methods cannot be directly applied to the DL mutation operators. In this paper, we propose the mutation operator reduction method for DL system, which can be divided into three steps. It firstly classifies all mutation operators by the scope of action of them. Then, it combines different classes of mutation operators. Finally, it analyzes the mutation score of different mutation operators combinations to obtain a sufficient mutation operators subset. This method has been tested on the MNIST datasets and the LENET-5 model. The experimental results shows that the number of mutants reduced by 41.67%, which effectively proved that our reduction method can effectively reduce the number of mutants generated, reduce the testing cost, and improve the accuracy of the mutation score.
{"title":"Mutation Operator Reduction for Deep Learning System","authors":"Shiyu Zhang, Xingya Wang, Lichao Feng, Zhihong Zhao","doi":"10.1109/ISSSR53171.2021.00014","DOIUrl":"https://doi.org/10.1109/ISSSR53171.2021.00014","url":null,"abstract":"The mutation testing method of Deep Learning (DL) system proposes a series of DL mutation operators, but the same as traditional software mutation testing methods, a large number of mutants will be generated during the testing process, which will cause huge costs. The traditional mutation operator reduction method is based on source program business logic. Owe to the fundamental difference between traditional system and DL system, traditional reduction methods cannot be directly applied to the DL mutation operators. In this paper, we propose the mutation operator reduction method for DL system, which can be divided into three steps. It firstly classifies all mutation operators by the scope of action of them. Then, it combines different classes of mutation operators. Finally, it analyzes the mutation score of different mutation operators combinations to obtain a sufficient mutation operators subset. This method has been tested on the MNIST datasets and the LENET-5 model. The experimental results shows that the number of mutants reduced by 41.67%, which effectively proved that our reduction method can effectively reduce the number of mutants generated, reduce the testing cost, and improve the accuracy of the mutation score.","PeriodicalId":211012,"journal":{"name":"2021 7th International Symposium on System and Software Reliability (ISSSR)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132341527","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}
Pub Date : 2021-09-01DOI: 10.1109/ISSSR53171.2021.00023
Liu Mengxu
The system architecture of the software is one of the decisive factors. In this article, we propose a solution based on a microservices architecture that greatly improves the reliability and stability of the system and used it to solve the problems encountered by the Credit Information sharing platform of Henan Province. It is the first case of upgrading a provinciallevel similar platform based on the new technical architecture in China. After more than one year of formal operation, it has achieved the expected goal. At present, the total amount of credit information of the platform is more than 5 billion, and the number of queries is more than 350 million. The reliability and stability of the system are excellent. We can be sure that the appropriate application of microservices architecture can greatly improve the reliability and stability of the system.
{"title":"Practical Application of Improving the System Reliability and Stability via Microservices Architecture","authors":"Liu Mengxu","doi":"10.1109/ISSSR53171.2021.00023","DOIUrl":"https://doi.org/10.1109/ISSSR53171.2021.00023","url":null,"abstract":"The system architecture of the software is one of the decisive factors. In this article, we propose a solution based on a microservices architecture that greatly improves the reliability and stability of the system and used it to solve the problems encountered by the Credit Information sharing platform of Henan Province. It is the first case of upgrading a provinciallevel similar platform based on the new technical architecture in China. After more than one year of formal operation, it has achieved the expected goal. At present, the total amount of credit information of the platform is more than 5 billion, and the number of queries is more than 350 million. The reliability and stability of the system are excellent. We can be sure that the appropriate application of microservices architecture can greatly improve the reliability and stability of the system.","PeriodicalId":211012,"journal":{"name":"2021 7th International Symposium on System and Software Reliability (ISSSR)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128252158","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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