Current LTE networks are experiencing significant growth in the number of users worldwide. The use of data services for online browsing, e-learning, online meetings and initiatives such as smart cities means that subs...Current LTE networks are experiencing significant growth in the number of users worldwide. The use of data services for online browsing, e-learning, online meetings and initiatives such as smart cities means that subscribers stay connected for long periods, thereby saturating a number of signalling resources. One of such resources is the Radio Resource Connected (RRC) parameter, which is allocated to eNodeBs with the aim of limiting the number of connected simultaneously in the network. The fixed allocation of this parameter means that, depending on the traffic at different times of the day and the geographical position, some eNodeBs are saturated with RRC resources (overused) while others have unused RRC resources. However, as these resources are limited, there is the problem of their underutilization (non-optimal utilization of resources at the eNodeB level) due to static allocation (manual configuration of resources). The objective of this paper is to design an efficient machine learning model that will take as input some key performance indices (KPIs) like traffic data, RRC, simultaneous users, etc., for each eNodeB per hour and per day and accurately predict the number of needed RRC resources that will be dynamically allocated to them in order to avoid traffic and financial losses to the mobile network operator. To reach this target, three machine learning algorithms have been studied namely: linear regression, convolutional neural networks and long short-term memory (LSTM) to train three models and evaluate them. The model trained with the LSTM algorithm gave the best performance with 97% accuracy and was therefore implemented in the proposed solution for RRC resource allocation. An interconnection architecture is also proposed to embed the proposed solution into the Operation and maintenance network of a mobile network operator. In this way, the proposed solution can contribute to developing and expanding the concept of Self Organizing Network (SON) used in 4G and 5G networks.展开更多
Predicting the direction of the stock market has always been a huge challenge.Also,the way of forecasting the stock market reduces the risk in the financial market,thus ensuring that brokers can make normal returns.De...Predicting the direction of the stock market has always been a huge challenge.Also,the way of forecasting the stock market reduces the risk in the financial market,thus ensuring that brokers can make normal returns.Despite the complexities of the stock market,the challenge has been increasingly addressed by experts in a variety of disciplines,including economics,statistics,and computer science.The introduction of machine learning,in-depth understanding of the prospects of the financial market,thus doing many experiments to predict the future so that the stock price trend has different degrees of success.In this paper,we propose a method to predict stocks from different industries and markets,as well as trend prediction using traditional machine learning algorithms such as linear regression,polynomial regression and learning techniques in time series prediction using two forms of special types of recursive neural networks:long and short time memory(LSTM)and spoken short-term memory.展开更多
精确的短期光伏功率区间概率预测可以有效量化光伏功率预测的不确定性,对于新型电力系统运行调度避险至关重要。为了提高模型预测性能,基于气象变量的数据特征提出模糊C均值(fuzzy C-means,FCM)聚类方法,将历史数据集聚类为晴天、晴转...精确的短期光伏功率区间概率预测可以有效量化光伏功率预测的不确定性,对于新型电力系统运行调度避险至关重要。为了提高模型预测性能,基于气象变量的数据特征提出模糊C均值(fuzzy C-means,FCM)聚类方法,将历史数据集聚类为晴天、晴转多云和阴雨天,采用与测试集具有相似天气类型的历史数据作为训练样本训练模型;集合卷积神经网络(convolutional neural network,CNN)模型出色的特征提取优势,双向长短期记忆(bidirectional long short term memory,BiLSTM)神经网络模型擅长双向捕捉长时间序列中长期依赖关系的优势,以及可生成区间预测结果的分位数回归(quantile regression,QR)模型,提出QR-CNN-Bi LSTM深度学习融合模型,计及筛选得到的多种气象因素,对光伏功率进行以5min为间隔的精细时间粒度分类区间预测,最后采用交叉验证和网格搜索方法的核密度估计给出概率密度预测结果。选取多种评价指标对提出的模型进行评价,并与QR-LSTM、QR-BiLSTM模型预测结果做对比分析,结果表明:1)FCM算法能有效实现光伏历史数据集的聚类;2)QR-CNN-BiLSTM融合模型能够生成以5min为间隔的高质量区间预测结果,95%置信预测区间综合评价指标平均值由QR-LSTM、QR-BiLSTM的0.1371、0.1288减小到0.0971;3)基于交叉验证和网格搜索方法的核密度估计能够实现可靠的光伏功率概率密度预测结果生成。展开更多
文摘Current LTE networks are experiencing significant growth in the number of users worldwide. The use of data services for online browsing, e-learning, online meetings and initiatives such as smart cities means that subscribers stay connected for long periods, thereby saturating a number of signalling resources. One of such resources is the Radio Resource Connected (RRC) parameter, which is allocated to eNodeBs with the aim of limiting the number of connected simultaneously in the network. The fixed allocation of this parameter means that, depending on the traffic at different times of the day and the geographical position, some eNodeBs are saturated with RRC resources (overused) while others have unused RRC resources. However, as these resources are limited, there is the problem of their underutilization (non-optimal utilization of resources at the eNodeB level) due to static allocation (manual configuration of resources). The objective of this paper is to design an efficient machine learning model that will take as input some key performance indices (KPIs) like traffic data, RRC, simultaneous users, etc., for each eNodeB per hour and per day and accurately predict the number of needed RRC resources that will be dynamically allocated to them in order to avoid traffic and financial losses to the mobile network operator. To reach this target, three machine learning algorithms have been studied namely: linear regression, convolutional neural networks and long short-term memory (LSTM) to train three models and evaluate them. The model trained with the LSTM algorithm gave the best performance with 97% accuracy and was therefore implemented in the proposed solution for RRC resource allocation. An interconnection architecture is also proposed to embed the proposed solution into the Operation and maintenance network of a mobile network operator. In this way, the proposed solution can contribute to developing and expanding the concept of Self Organizing Network (SON) used in 4G and 5G networks.
文摘Predicting the direction of the stock market has always been a huge challenge.Also,the way of forecasting the stock market reduces the risk in the financial market,thus ensuring that brokers can make normal returns.Despite the complexities of the stock market,the challenge has been increasingly addressed by experts in a variety of disciplines,including economics,statistics,and computer science.The introduction of machine learning,in-depth understanding of the prospects of the financial market,thus doing many experiments to predict the future so that the stock price trend has different degrees of success.In this paper,we propose a method to predict stocks from different industries and markets,as well as trend prediction using traditional machine learning algorithms such as linear regression,polynomial regression and learning techniques in time series prediction using two forms of special types of recursive neural networks:long and short time memory(LSTM)and spoken short-term memory.
文摘精确的短期光伏功率区间概率预测可以有效量化光伏功率预测的不确定性,对于新型电力系统运行调度避险至关重要。为了提高模型预测性能,基于气象变量的数据特征提出模糊C均值(fuzzy C-means,FCM)聚类方法,将历史数据集聚类为晴天、晴转多云和阴雨天,采用与测试集具有相似天气类型的历史数据作为训练样本训练模型;集合卷积神经网络(convolutional neural network,CNN)模型出色的特征提取优势,双向长短期记忆(bidirectional long short term memory,BiLSTM)神经网络模型擅长双向捕捉长时间序列中长期依赖关系的优势,以及可生成区间预测结果的分位数回归(quantile regression,QR)模型,提出QR-CNN-Bi LSTM深度学习融合模型,计及筛选得到的多种气象因素,对光伏功率进行以5min为间隔的精细时间粒度分类区间预测,最后采用交叉验证和网格搜索方法的核密度估计给出概率密度预测结果。选取多种评价指标对提出的模型进行评价,并与QR-LSTM、QR-BiLSTM模型预测结果做对比分析,结果表明:1)FCM算法能有效实现光伏历史数据集的聚类;2)QR-CNN-BiLSTM融合模型能够生成以5min为间隔的高质量区间预测结果,95%置信预测区间综合评价指标平均值由QR-LSTM、QR-BiLSTM的0.1371、0.1288减小到0.0971;3)基于交叉验证和网格搜索方法的核密度估计能够实现可靠的光伏功率概率密度预测结果生成。
