检索结果-内蒙古大学图书馆

An automated pre-term prediction system using EHG signal with the aid of deep learning technique

MULTIMEDIA TOOLS AND APPLICATIONS 2023年第2期83卷 4093-4113页

作者： Veena, A. Gowrishankar, S. Dr Ambedkar Inst Technol Dept Comp Sci & Engn Bengaluru 560056 Karnataka India

Prematurity is the leading cause of infant morbidity and mortality around the world. Surface uterine electromyogram (sEMG) is a non-invasive uterine electromyogram. One of most hopeful biophysical tests is the electro hysterogram (EHG). As a result, EHG prove to be a biomarker for detecting preterm birth may allow us to detect a preterm birth before labour begins. The goal of this article is to use a deep learning technique called Spiking Neural Network with seagull optimization Technique to predict preterm labour using EHG signals (SNN-SOA). This work pre-processes the EHG signal and extracts metrics such as sample frequency peak, median frequency, RMSE, and entropy. In this study, signal is pre-processed with filter Wiener for improving the quality of signal. The features stripped from the pre-processed signal is to define the separate-class. Furthermore, Ant Lion optimization based on opposition is utilised to choose the training methods and multi-kernel-support-vector-machine as forecasting preterm delivery. The suggested method is tested for classification accuracy using MATLAB software. As per the experimental study results, the research framework enhanced accuracy of classification by 3-19percent when matched to earlier task.

关键词： Pre-processing optimization Spiking neural network seagull optimization algorithm Deep learning

来源：评论

学校读者我要写书评

暂无评论

Short-Term Photovoltaic Power Prediction Based on Similar Days and Improved SOA-DBN Model

引用

IEEE ACCESS 2021年 9卷 1958-1971页

作者： Hu, Wei Zhang, Xinyan Zhu, Lijuan Li, Zhenen Xinjiang Univ Sch Elect Engn Urumqi 830047 Peoples R China Xinjiang Inst Technol Sch Informat Engn Aksu 843015 Peoples R China

Existing methods in predicting short-term photovoltaic (PV) power have low accuracy and cannot satisfy actual demand. Thus, a prediction model based on similar days and seagull optimization algorithm (SOA) is proposed to optimize a deep belief network (DBN). Fast correlation-based filter (FCBF) method is used to select a meteorological feature set with the best correlation with PV output and avoid redundancy among meteorological factors affecting PV output. In addition, a comprehensive similarity index combining European distance and gray correlation degree is proposed to select the similar day. Then, SOA is used to optimize the number of neurons and the learning rate parameters in DBN. Based on the nonuniform mutation and opposition-based learning method, an improved seagull optimization algorithm (ISOA) with higher optimization accuracy is proposed. Finally, the ISOA-DBN prediction model is established, and the experimental analysis is conducted using the actual data of PV power stations in Australia. Results show that compared with DBN, support vector machine (SVM), extreme learning machine (ELM), radial basis function (RBF), Elman, and back propagation (BP), the mean absolute percentage error indicator of ISOA-DBN is only 1.512% on a sunny day, 5.975 on a rainy day, 3.359 on a cloudy to sunny day, and 1.911% on a sunny to cloudy day. Therefore, the good accuracy of the proposed model is verified.

关键词： PV power generation prediction model similar day seagull optimization algorithm deep belief network

来源：评论

学校读者我要写书评

暂无评论

Energy Consumption Prediction and optimization of Electric Vehicles Based on RLS and Improved SOA

引用

IEEE ACCESS 2024年 12卷 38180-38191页

作者： Liu, Chunling Changchun Univ Technol Inst Automot Engn Coll Humanities & Informat Changchun 130000 Peoples R China

The new energy vehicle industry is facing new challenges. To predict and optimize the energy consumption of electric vehicles, this study predicts energy consumption based on the energy consumption characteristics of the electric vehicle power system and air conditioning system, and combines path optimization algorithms for energy-saving path planning. The study first improves the recursive least squares algorithm by combining the forgetting factor, and constructs a vehicle energy consumption identification model based on the improved recursive least squares algorithm and neural network. Then, a path optimization model based on improved seagull optimization is established using chaotic mapping strategy and t-distribution to improve the seagull optimization algorithm. The results showed that the predicted final energy consumption of the model constructed in the study was 2.81kW.h, with an error rate of 5.1%. The improved seagull optimization algorithm obtained an optimal solution of 30.88m for burma14 and 423.74m for oliver30, which were consistent with the published optimal solutions. When the air conditioning was turned on, the energy consumption of the path selected by the algorithm was reduced by about 5.6%. Under the condition of not turning on the air conditioning, the energy consumption of the path selected by the algorithm was reduced by about 4.98%. In summary, the model constructed through research has good application effects in predicting and optimizing vehicle energy consumption. The contribution of the research lies in it helps to reveal the laws of energy utilization in electric vehicles, improve the economy, safety, and environmental friendliness of electric vehicles during operation, and promote the overall management of new energy vehicles.

关键词： Recursive least squares algorithm seagull optimization algorithm vehicle energy consumption neural network path planning

来源：评论

学校读者我要写书评

暂无评论

Optimal location determination for an EVPL and capacitors in distribution network considering power loss and voltage profile: SGOS2A technique

引用

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第3期44卷 4853-4868页

作者： Swapna, S. Premila, T. R. Janaki, N. Kirubakaran, D. Anna Univ Chennai Dept Elect & Elect Engn GRT Inst Engn & Technol Chennai Tamil Nadu India Vels Inst Sci Technol & Adv Studies Chennai Dept Elect & Elect Engn Chennai Tamil Nadu India St Josephs Inst Technol Dept Elect & Elect Engn Chennai Tamil Nadu India

This paper proposes a hybrid optimization system depending on optimal location for electric vehicles parking lot (PL) and capacitors on distribution system to maintain voltage profile with electricity loss. The proposed system is the consolidation of seagull optimization algorithm (SGO) and salp swarm algorithm (SSA). The migration and attacking behaviors of seagull is empowered through SSA method. By this manner, the proposed hybrid optimization scheme is known as SGOS(2)A method. Here, parking zone allocation with capacitor is considered to congestion management in conjunction through the compensation of reactive energy. So, one can optimally decide the size of automobile parking space, SGOS(2)A method is followed. Moreover, parking lot with capacitor allocation is considered to congestion control at the side of reactive power compensation. By this proper manipulate, the capacitors exact location, automobile parking space of electric vehicles on the grid, lessening of active with reactive power loss, voltage profile conversion is selected optimally. Besides, the proposed SGOS(2)Ascheme is activated onMATLAB/Simulink site, then the efficiency is examined with different techniques. The mean, median and standard deviation of the proposed approach achieves 1.0593, 1.0915 and 0.1050.

关键词： Electric vehicle voltage and power loss parking lot seagull optimization algorithm salp swarm algorithm

来源：评论

学校读者我要写书评

暂无评论

A neural network-based prediction model in water monitoring networks

引用

WATER SUPPLY 2021年第5期21卷 2347-2356页

作者： Ji, Xiaohong Pan, Ying Jia, Guoqing Fang, Weidong Qinghai Nationalities Univ Xining 810007 Peoples R China Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Sci & Technol Microsyst Lab Shanghai 201800 Peoples R China

To improve the prediction accuracy of ammonia nitrogen in water monitoring networks, the combination of a bio-inspired algorithm and back propagation neural network (BPNN) has often been deployed. However, due to the limitations of the bio-inspired algorithm, it would also fall into the local optimal. In this paper, the seagull optimization algorithm (SOA) was used to optimize the structure of BPNN to obtain a better prediction model. Then, an improved SOA (ISOA) was proposed, and the common functional validation method was used to verify its optimization performance. Finally, the ISOA was applied to improve BPNN, which is known as the improved seagull optimization algorithm-back propagation (ISOA-BP) model. The simulation results showed that the prediction accuracy of ammonia nitrogen was greatly improved and the proposed model can be better applied to the prediction of complex water quality parameters in water monitoring networks.

关键词： ammonia nitrogen prediction back propagation neural network improved seagull optimization algorithm neural network seagull optimization algorithm water monitoring

来源：评论

学校读者我要写书评

暂无评论

Offshore wind power ramp prediction based on optimal combination model

引用

ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2022年第2期44卷 4334-4348页

作者： Zheng, Xiaoxia Yang, Shuangmian Ye, Yuhan Wang, Jing Shanghai Univ Elect Power Coll Automat Engn Shanghai Peoples R China

Wind power ramp event is a small probability and large hazard event, for effectively avoiding its impact on the grid, an offshore wind power ramp prediction method based on optimal combination model is proposed. First, on the basis of the output power data of existing offshore wind turbines, the data preprocessing of the wind power series is carried out by using the Variational Modal Decomposition method optimized by the novel seagull optimization algorithm, which is decomposed into several Intrinsic Mode Function components and one residual component to extract the salient features of the power signal and reduce the data fluctuation. Second, the prediction models of Extreme Learning Machine and Bayesian optimized Long Short Term Memory network are built, respectively, while the optimal combined prediction method is adopted to combine the two single models for obtaining the optimal combined model. Individual components are treated as the inputs of the model, then the final offshore wind power is obtained by superimposing and integrating the prediction results of each component. Eventually, adopting the definition method to predict and characterize the offshore wind power ramp. The research shows that, compared with BP, RNN, LSTM and single model, the wind power prediction error of the combined model proposed in this paper is smaller, with RMSE of 71.10 kW, MAE of 50.26 kW and SMAPE of 0.01%, and it can completely predict 7 ramp events with a higher accuracy. Meanwhile, the applicability of the proposed method is verified by varying the time resolution. The final results show that the proposed method in this paper has a high accuracy in offshore wind power ramp prediction.

关键词： Offshore wind power ramp prediction optimal combination forecast seagull optimization algorithm Variational Modal Decomposition ramp characteristic

来源：评论

学校读者我要写书评

暂无评论

Short-term wind speed forecasts through hybrid model based on improved variational mode decomposition

引用

INTERNATIONAL JOURNAL OF GREEN ENERGY 2024年第10期21卷 2281-2298页

作者： Dai, Yiyan Zhang, Mingjin Xin, Xu Chen, Xiaohu Li, Yongle Liu, Maoyi Southwest Jiaotong Univ Dept Bridge Engn Chengdu Peoples R China Southwest Jiaotong Univ State Key Lab Bridge Intelligent & Green Construct Chengdu Peoples R China TY Lin Int Engn Consulting Chongqing Co Ltd Dept Bridge Business Chongqing Peoples R China Chongqing Construct Investment Grp Co Ltd Dept Sci & Technol Chongqing Peoples R China Southwest Jiaotong Univ Dept Bridge Engn Chengdu 610031 Peoples R China

Scientific and accurate wind predictions are the basis for exploiting and utilizing wind energy. Combining the VMD and KELM, this research proposes a new hybrid model to capture the pattern of wind speed change, the parameters of which are optimized by the Spotted Hyena Optimizer(SHO) and seagull optimization algorithm(SOA), respectively. The VMD is optimized by the SHO to achieve the purpose of adaptive decomposition of historical wind speed time series. After that, the parameters of the KELM are optimized by the SOA. Then, the decomposed wind speed series is input into the optimized KELM. The output prediction Intrinsic Mode Functions(IMFs) is added up to obtain the short-term wind speed prediction results. The measured wind speeds for four seasons were selected as the case study for the proposed model. The prediction results are compared with the measured wind speed series to verify the accuracy and reliability of the model. Besides, the prediction accuracy and stability of the proposed model are better than traditional prediction models, with stronger performance and lower computational cost. The results of case studies indicate that the proposed model can satisfy the need for actual applications.

关键词： Wind speed forecasting intelligent algorithms Variational mode decomposition Spotted Hyena Optimizer Kernel extreme learning machine seagull optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Optimum PV reconfiguration approach based on SOA for improving the harvest power under PS situations

引用

ELECTRICAL ENGINEERING 2024年第1期106卷 869-889页

作者： Mohanapriya, V. Sharmila, B. Bannari Amman Inst Technol Dept EEE Erode Tamil Nadu India Sri Ramakrishna Engn Coll Dept EIE Coimbatore Tamil Nadu India

Photovoltaic (PV) systems-based harvest energy becomes huge support for the electric grids in recent days and also becomes the sustainable replacement for conventional energy sources. But the efficacy of PV-based electricity is degraded due to the effect of partial shading conditions. The reconfiguration technique is one effective strategy to diminish the impact of shadow on the photovoltaic (PV) array. This entails rearranging the PV array's switching matrix by the temperature and shading levels. A seagull optimization algorithm (SOA) is proposed in this work to identify the optimal pattern for rearranging the matrix and optimising power output from the PV array. A 9x9 PV array operating under four common shadow patterns-Short Wide (SW), Long Wide (LW), Short Narrow (SN), and Long Narrow (LN)-is subjected to the algorithm. Mismatch power, fill factor, percentage power loss, and percentage power enhancement are used to assess the algorithm's effectiveness. Comparisons are made between the results from the proposed SOA and those from the Total-Cross-Tied (TCT), Competence Square, Butterfly Optimisation algorithm, and Harris Hawks optimization. The highest increase in Global Maximum Power has been attained by the proposed SOA corresponding to the TCT array is 36% and 30% in the SW and LW shadow arrangement, while the least increase of 7% occurs in the SN and LN arrangements. The evaluated results show the proposed SOA's competence and effective in optimising the shaded array reconfiguration.

关键词： Photovoltaic system Reconfiguration seagull optimization algorithm Partial shading conditions Global maximum power

来源：评论

学校读者我要写书评

暂无评论

Segmentation of ovarian cyst using improved U-NET and hybrid deep learning model

引用

MULTIMEDIA TOOLS AND APPLICATIONS 2023年第14期83卷 42645页

作者： Kamala, C. Shivaram, Joshi Manisha Dr Ambedkar Inst Technol Dept Med Elect Bengaluru 560056 Karnataka India BMS Coll Engn Dept Med Elect Bengaluru 560019 Karnataka India

The female reproductive system relies on the ovaries to produce eggs, but ovarian cysts can lead to complications such as torsion, infertility, and cancer, making it essential to diagnose them quickly. Ultrasound images are commonly used to detect ovarian cysts, but segmenting cyst regions from the surrounding tissue poses a challenge due to complex patterns and similar intensities. Few methods use the background's texture information to facilitate foreground segmentation. Ultrasound images include characters like speckle noise, low contrast appearance, and blurry boundaries that further complicate the task. Lesion shape and position variations exacerbate these challenges. This study proposes an improved deep learning-based segmentation technique using a database of ovarian ultrasound cyst images to overcome these issues. At the outset, the input has undergone pre-processing using non-sub-sampled contourlet domain-based cross-guided bilateral filtering (CGBF) and improved U-Net (IU-NET) for image segmentation. The presented architecture involved reducing the intricacy of U-Net through the alleviation of certain parameters. This resulted in a substantial acceleration of the learning process, by a factor of 100. To optimize the improved U-Net model, the seagull optimization algorithm (SOA) was used. The algorithm helped to fine-tune the hyper-parameters of the U-Net architecture, including the batch size, learning rate, and epoch count, to achieve optimal performance. The optimization was performed by solving an objective function, which involved determining the dice loss coefficient (DLC) and weight cross-entropy (WCE). A numerical analysis was conducted, which demonstrated that the proposed methodology outperforms existing methods in terms of segmentation accuracy. The proposed model achieved a pixel accuracy of 99.36%, which was significantly higher than that achieved by existing methods.

关键词： Ovarian cancer Ultrasound detection Deep learning model Image enhancement Cystic ovary Segmentation U-Net model seagull optimization algorithm Bilateral filter

来源：评论

学校读者我要写书评

暂无评论

An SOA-RBFNN approach for the system modelling of optimal energy management in grid-connected smart grid system

引用

ARTIFICIAL INTELLIGENCE REVIEW 2023年第5期56卷 4171-4196页

作者： Kuppusamy, Karthikumar Vairakannu, Senthil Kumar Marimuthu, Karuppiah Natarajan, Udhayaraj Sekar, Krishnakumar Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Dept Elect & Elect Engn Chennai Tamil Nadu India Anna Univ Coll Engn Dept Elect & Elect Engn Chennai Tamil Nadu India

A hybrid method for energy management on grid-connected MG system is proposed under this manuscript. Grid-connected MG system takes photovoltaic (PV), wind turbine (WT), battery. The proposed system is an integration of seagull optimization algorithm (SOA) and the radial basic functional neural network (RBFNN), thus it is named SOA-RBFNN. Here, in the grid-connected microgrid configuration, the necessary load demand is always monitored with RBFNN methodology. SOA optimizes the perfect match of the MG taking into account the predictable load requirement. The fuel cost, together with the power variation per hour of the electric grid, the operation and maintenance cost of microgrid system linked with grid, is described. The proposed model runs on the MATLAB/Simulink workstation and efficiency is investigated using existing techniques as AGO-RNN and MBFA-ANN. Statistical analysis, elapsed time, modeling metrics, and determination of optimal sample size for adjustment and validation of proposed and existing technique are evaluated. The efficiency values on the 100, 200, 500, and 1000 trails are 99.7673%, 99.7609%, 99.9099%, and 99.9373%.

关键词： Energy management system Grid connected SG system Predicted load demand seagull optimization algorithm Radial basis functional neural network

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：