检索结果-内蒙古大学图书馆

deep learning based method for false data injection attack detection in AC smart islands

IET GENERATION TRANSMISSION & DISTRIBUTION 2020年第24期14卷 5756-5765页

作者： Dehghani, Moslem Kavousi-Fard, Abdollah Dabbaghjamanesh, Morteza Avatefipour, Omid Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran Univ Texas Dallas Dept Elect & Elect Engn Richardson TX 75083 USA Univ Michigan Dept Elect & Elect Engn Dearborn MI 48128 USA

This paper investigates the false data injection attacks (FDIA) in an AC smart island and the detection solution of the attack on distributed energy resources in a smart island. In this study, a new scheme of FDIA detection is proposed based on wavelet singular values as input index of deep learning algorithm. In the proposed method, switching surface based on sliding mode control breaks down for adjusting accurate factors of wavelet transform and then features of wavelet coefficients are extracted by singular value decomposition. Indexes are determined according to the wavelet singular values in switching surface of voltage and current which defines the input indexes of deep machine learning and detecting FDIA. This cyber-protection plan has been put forward for cyber diagnostic and examined in different types of attacks happening in voltage and current signals derivation of measuring sensors as well as sending and receiving data from communication and control systems. The main priority of the suggested detection plan is the high capability to detect FDIA with a high accuracy. To show the effectiveness of the proposed method, simulation studies are performed on AC smart island in MATLAB/Simulink environment.

关键词： wavelet transforms power distribution faults security of data variable structure systems learning (artificial intelligence) smart power grids singular value decomposition distributed power generation power engineering computing power system security fault diagnosis suggested detection plan deep machine learning singular value decomposition wavelet coefficients deep learning algorithm input index wavelet singular values FDIA detection distributed energy resources detection solution false data injection attacks AC smart island false data injection attack detection

来源：评论

学校读者我要写书评

暂无评论

deep neural network based m-learning model for predicting mobile learners' performance

引用

TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2020年第3期28卷 1422-1441页

作者： Adnan, Muhammad Habib, Asad Ashraf, Jawad Mussadiq, Shafaq Raza, Arsalan Ali Kohat Univ Sci & Technol Fac Numer & Phys Sci Inst Comp Kohat Pakistan COMSATS Univ Islamabad Fac Sci Dept Comp Sci Vehari Campus Vehari Punjab Pakistan

The use of deep learning (DL) techniques for mobile learning is an emerging field aimed at developing methods for finding mobile learners' learning behavior and exploring important learning features. The learning features (learning time, learning location, repetition rate, content types, learning performance, learning time duration, and so on) act as fuel to DL algorithms based on which DL algorithms can classify mobile learners into different learning groups. In this study, a powerful and efficient m-learning model is proposed based on DL techniques to model the learning process of m-learners. The proposed m-learning model determines the impact of independent learning features on the dependent feature i.e. learners' performance. The m-learning model dynamically and intuitively explores the weights of optimum learning features on learning performance for different learners in their learning environment. Then it split learners into different groups based on features differences, weights, and interrelationships. Because of the high accuracy of the DL technique, it was used to classify learners into five different groups whereas random forest (RF) ensemble method was used in determining each feature importance in making adaptive m-learning model. Our experimental study also revealed that the m-learning model was successful in helping m-learners in increasing their performance and taking the right decision during the learning flow.

关键词： M-learning model artificial neural network deep learning algorithm features importance machine learning mobile learning

来源：评论

学校读者我要写书评

暂无评论

A short-Term photovoltaic power prediction model using cyclical encoding and STL decomposition based on LSTM 3

A short-Term photovoltaic power prediction model using cycli...

引用

3rd International Conference on Electronic Information Engineering and Computer Communication, EIECC 2023

作者： Bin, Feng He, Junwen School of Physics and Electronic Sciences Changsha University of Science and Technology Changsha China Changsha University of Science and Technology Hunan Provincial Key Laboratory of Flexible Electronic Materials Genome Engineering Changsha China

ISBN: (纸本)9798350359961

Photovoltaic (PV) power generation is greatly influenced by time series, and the accurate short-Term prediction of PV power is helpful to the planning and scheduling of power grid. Traditional forecasting methods have a poor ability to capture periodic and seasonal information and temporal dependency. To address this problem, a short-Term PV power prediction model based on cyclical encoding (CE), seasonal and trend decomposition using loess (STL), and long short-Term memory (LSTM) is proposed. Firstly, the input sequence is decomposed into three components, including seasonal, trend, and remainder components, using STL decomposition. Moreover, the timestamps are encoded using a cyclical encoding method to represent the positional feature of input sequence. The positional features are combined with the three components, respectively, to form three new feature sets. Subsequently, the three feature sets are separately inputted into three independent LSTM models to obtain three corresponding predicted components, which are summed up as the final PV power prediction results. Lastly, two real datasets from the PV power stations in Ningxia and Xinjiang Provinces, China, are used to verify the proposed model. The results indicate it has better performance than existing models and achieves the smallest prediction error on the two datasets, i.e., mean absolute errors of 1.32 MW and 0.99 MW, respectively. © 2023 IEEE.

关键词： component cyclical encoding deep learning algorithm LSTM PV power prediction STL decomposition

来源：评论

学校读者我要写书评

暂无评论

Sustainable flood risk assessment using deep learning-based algorithms with a blockchain technology

引用

GEOCARTO INTERNATIONAL 2022年第1期38卷 2112982-2112982页

作者： Mia, Md Uzzal Rahman, Mahfuzur Elbeltagi, Ahmed Abdullah-Al-Mahbub, Md Sharma, Gitika Islam, H. M. Touhidul Pal, Subodh Chandra Costache, Romulus Islam, Abu Reza Md Towfiqul Islam, Md Monirul Chen, Ningsheng Alam, Edris Washakh, Rana Muhammad Ali Begum Rokeya Univ Dept Disaster Management Rangpur Bangladesh Int Univ Business Agr & Technol IUBAT Dept Civil Engn Dhaka Bangladesh Mansoura Univ Fac Agr Agr Engn Dept Mansoura Egypt Thapar Inst Engn & Technol Dept Comp Sci & Engn Patiala Punjab India Univ Burdwan Dept Geog Bardhaman W Bengal India Transilvania Univ Brasov Dept Civil Engn Brasov Romania Danube Delta Natl Inst Res & Dev Tulcea Romania Chinese Acad Sci Inst Mt Hazards & Environm Key Lab Mt Hazards & Earth Surface Proc Chengdu Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Acad Plateau Sci & Sustainabil Xining Peoples R China Fac Resilience Abu Dhabi U Arab Emirates Univ Chittagong Dept Geog & Environm Studies Chittagong Bangladesh Neijiang Normal Univ Sch Architecture Neijiang Sichuan Peoples R China

The couplings of convolutional neural networks (CNN) with random forest (RF), support vector machine (SVM), long short-term memory (LSTM), and extreme gradient boosting (XGBoost) ensemble algorithms were used to construct novel ensemble computational models (CNN-LSTM, CNN-XG, CNN-SVM, and CNN-RF) for flood hazard mapping in the monsoon-dominated catchment, Bangladesh. The results revealed that geology, elevation, the normalized difference vegetation index (NDVI), and rainfall are the most significant parameters in flash floods based on the Pearson correlation technique. Statistical method such as the area under the curve (AUC) was used to evaluate model performance. The CNN-RF model could be a promising tool for precisely predicting and mapping flash floods as it is outperformed the other models (AUC = 1.0). Furthermore, to meet sustainable development goals (SDGs), a blockchain-based technology is proposed to create a decentralized flood management tool for help seekers and help providers during and post floods. The suggested tool accelerates emergency rescue operations during flood events.

关键词： Blockchain relief sustainable development flash floods deep learning algorithm

来源：评论

学校读者我要写书评

暂无评论

Analysis of multi-level capital market linkage driven by artificial intelligence and deep learning methods

引用

SOFT COMPUTING 2020年第11期24卷 8011-8019页

作者： Jing, Xinxin Peng, Pin Huang, Zhe Zhongnan Univ Econ & Law Wuhan 430073 Peoples R China Shanghai Univ Finance & Econ Jinhua 321000 Zhejiang Peoples R China

Artificial intelligence has a very wide application in modern society. deep learning algorithms promote the development of artificial intelligence industry. The development of artificial intelligence industry needs the support of capital market. As far as the relationship between the three is concerned, the artificial intelligence industry is in the middle position. In short, the computer deep learning algorithm is the technical basis for the development of artificial intelligence, and the capital market is an important guarantee for the development of the artificial intelligence industry. However, under the current development background of China, the capital market is far from meeting the improvement and development of high-tech industries. Therefore, in order to further clarify the important role of deep learning algorithms in the development of artificial intelligence and capital markets, this paper systematically analyzes these three relationships and proposes measures from the perspective of supporting theoretical research and development and improving capital markets.

关键词： deep learning algorithm Artificial intelligence Capital market

来源：评论

学校读者我要写书评

暂无评论

An IGA Model for Resource Allocation in Heterogeneous Networks for the Internet of Things 1

An IGA Model for Resource Allocation in Heterogeneous Networ...

引用

1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Du, Yansong College of Design and Engineering National University of Singapore Singapore

ISBN: (纸本)9798350316469

Dynamic obstructions such as trees in the road environment can affect the quality of images captured by industrial cameras. In order to improve the quality of image acquisition in high dynamic scenes, this study proposes a new imaging algorithm based on convolutional neural networks and combined with single exposure high dynamic range imaging methods. This algorithm is mainly divided into a color camera image signal processing link module and a single exposure high dynamic range image enhancement module. The above two modules can improve the resolution of high dynamic range images. The experimental results show that the algorithm proposed in this study can achieve a response time of 63ms at the fastest, with PSNR, SSIM, and perceptual loss scores reaching 38.67, 0.976, and 0.087, respectively. Research has found that this algorithm has good application value in the field of high dynamic range image imaging. © 2023 IEEE.

关键词： bad auto-exposure deep learning algorithm high dynamic image imaging single-exposure HDRI method

来源：评论

学校读者我要写书评

暂无评论

A deep learning image-based intrinsic molecular subtype classifier of breast tumors reveals tumor heterogeneity that may affect survival

引用

BREAST CANCER RESEARCH 2020年第1期22卷 12-12页

作者： Jaber, Mustafa I. Song, Bing Taylor, Clive Vaske, Charles J. Benz, Stephen C. Rabizadeh, Shahrooz Soon-Shiong, Patrick Szeto, Christopher W. NantOmics LLC 9920 Jefferson Blvd Culver City CA 90232 USA ImmunityBio 9920 Jefferson Blvd Culver City CA 90232 USA Univ Southern Calif Keck Sch Med Dept Pathol HMR 2011 Zonal AveHlth Sci Campus Los Angeles CA 90033 USA ImmunityBio 2901 Mission St Ext Santa Cruz CA 95066 USA

Background Breast cancer intrinsic molecular subtype (IMS) as classified by the expression-based PAM50 assay is considered a strong prognostic feature, even when controlled for by standard clinicopathological features such as age, grade, and nodal status, yet the molecular testing required to elucidate these subtypes is not routinely performed. Furthermore, when such bulk assays as RNA sequencing are performed, intratumoral heterogeneity that may affect prognosis and therapeutic decision-making can be missed. Methods As a more facile and readily available method for determining IMS in breast cancer, we developed a deep learning approach for approximating PAM50 intrinsic subtyping using only whole-slide images of H&E-stained breast biopsy tissue sections. This algorithm was trained on images from 443 tumors that had previously undergone PAM50 subtyping to classify small patches of the images into four major molecular subtypes-Basal-like, HER2-enriched, Luminal A, and Luminal B-as well as Basal vs. non-Basal. The algorithm was subsequently used for subtype classification of a held-out set of 222 tumors. Results This deep learning image-based classifier correctly subtyped the majority of samples in the held-out set of tumors. However, in many cases, significant heterogeneity was observed in assigned subtypes across patches from within a single whole-slide image. We performed further analysis of heterogeneity, focusing on contrasting Luminal A and Basal-like subtypes because classifications from our deep learning algorithm-similar to PAM50-are associated with significant differences in survival between these two subtypes. Patients with tumors classified as heterogeneous were found to have survival intermediate between Luminal A and Basal patients, as well as more varied levels of hormone receptor expression patterns. Conclusions Here, we present a method for minimizing manual work required to identify cancer-rich patches among all multiscale patches in H&E-stained WSIs

关键词： Breast cancer Intrinsic molecular subtype (IMS) Whole-slide imaging (WSI) deep learning algorithm

来源：评论

学校读者我要写书评

暂无评论

Surrogate model-based multi-objective optimization algorithm for train-ballast turnout system: Balancing safety and vibration damping in parameter design

引用

ENGINEERING STRUCTURES 2024年 304卷

作者： Cheng, Fang Liu, Hui Cent South Univ Sch Traff & Transportat Engn Inst Artificial Intelligence & Robot IAIR Key Lab Traff Safety TrackMinist Educ Changsha 410075 Hunan Peoples R China

In rail transportation structural parameter design, multi-objective optimal algorithms (MOOAs) have advanced significantly in safety, comfort, and aerodynamics domain. However, the vital factor of environment vibration remains neglected, impeding depot cover technology advancements. To address this challenge, with a focus on both safety and vibration damping. Firstly, the mechanistic models are constructed to establish a surrogate modeling database. Subsequently, a surrogate model is proposed, integrating stacked denoising autoencoders (SdAEs), self-attention (SA), and the gated recurrent unit (GRU). In addition, A novel MOOA, Dynamic Population-Multi-Objective Harris Hawks Optimization-Crowding Distance-Elitist learning Strategy (D-MOHHOCD-ELS), is proposed and its efficacy is validated against five existing MOOAs using the CTP1 similar to CTP7 test functions. Moreover, the train-ballast No.12 turnout system is considered as an engineering case. Importantly, the accuracy of both the mechanistic model and surrogate model is thoroughly validated. Among them, the performance of the data preprocessing and prediction algorithms is substantiated through a comparison of the proposed SdAEs-SA-GRU model against simulation results and eight other algorithms. Furthermore, the surrogate-based D-MOHHO-CD-ELS method, applied to tackle a multi-objective optimization problem with nine optimization parameters and four optimization objectives, demonstrates an efficiency improvement of 1 to 2 orders of magnitude compared to traditional methods. Consequently, following optimization, the wheel load reduction rate, derailment coefficient, capsizing coefficient and vibration damping effect are improved by 15.5%, 11.1%, 5.5% and 24.8% respectively, which reveals the effectiveness of enhancements in both the safety and damping efffect. the study offers a novel perspective for parameter design of TBT.

关键词： Train-turnout system Train-induced vibration Safety performance Stacked denoising auto-encoders deep learning algorithm Multi -objective optimization

来源：评论

学校读者我要写书评

暂无评论

Time Convolutional Network-Based Maneuvering Target Tracking with Azimuth-Doppler Measurement

引用

SENSORS 2024年第1期24卷 263页

作者： Huang, Jianjun Hu, Haoqiang Kang, Li Shenzhen Univ Sch Intelligent Informat Proc Shenzhen 518060 Peoples R China

In the field of maneuvering target tracking, the combined observations of azimuth and Doppler may cause weak observation or non-observation in the application of traditional target-tracking algorithms. Additionally, traditional target tracking algorithms require pre-defined multiple mathematical models to accurately capture the complex motion states of targets, while model mismatch and unavoidable measurement noise lead to significant errors in target state prediction. To address those above challenges, in recent years, the target tracking algorithms based on neural networks, such as recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and transformer architectures, have been widely used for their unique advantages to achieve accurate predictions. To better model the nonlinear relationship between the observation time series and the target state time series, as well as the contextual relationship among time series points, we present a deep learning algorithm called recursive downsample-convolve-interact neural network (RDCINN) based on convolutional neural network (CNN) that downsamples time series into subsequences and extracts multi-resolution features to enable the modeling of complex relationships between time series, which overcomes the shortcomings of traditional target tracking algorithms in using observation information inefficiently due to weak observation or non-observation. The experimental results show that our algorithm outperforms other existing algorithms in the scenario of strong maneuvering target tracking with the combined observations of azimuth and Doppler.

关键词： azimuth and Doppler convolutional neural network deep learning algorithm maneuvering targets tracking

来源：评论

学校读者我要写书评

暂无评论

Identify IoT Devices from Backbone Networks Using Lightweight Neural Networks 47

Identify IoT Devices from Backbone Networks Using Lightweigh...

引用

47th IEEE Conference on Local Computer Networks (LCN)

作者： Wu, Hua Fan, Xingmeng Cheng, Guang Hu, Xiaoyan Southeast Univ Sch Cyber Sci & Engn Nanjing Peoples R China Purple Mt Labs Network & Commun Secur Nanjing Peoples R China Southeast Univ Key Lab Comp Network & Informat Integrat Nanjing Peoples R China Jiangsu Prov Engn Res Ctr Secur Ubiquitous Networ Nanjing Peoples R China

ISBN: (数字)9781665480017

ISBN: (纸本)9781665480017

Due to the heterogeneity, fragmentation, and lack of visibility, Internet of Things has become the new target for attacks. Therefore, it is necessary for Internet Service Providers to identify IoT devices to prevent attacks and protect the entire network in time. In this paper, we propose an IoT device identification approach based on lightweight deep learning models using a single feature. Specifically, we analyze the traffic pattern specific to IoT devices and use one feature to characterize this pattern, reducing the time consumption. Moreover, we select multiple time scales to extract this feature for different IoT devices, achieving an accurate characterization and improving the accuracy. Furthermore, we use unidirectional flows as analysis objects, suitable for backbone networks. The evaluation results on real-world datasets show that our approach achieves an accuracy of over 99%, with one-seventeenth of the time consumption of the state-of-the-art approach, realizing the lightweight and real-time requirements.

关键词： Internet of Things devices identification specific traffic pattern deep learning algorithm

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：