检索结果-内蒙古大学图书馆

Physical topology optimization of infrastructure health monitoring sensor network for high-speed rail

MEASUREMENT 2016年 79卷 83-93页

作者： Li, Haijian Yao, Tingting Ren, Moyu Rong, Jian Liu, Chengkun Jia, Limin Beijing Univ Technol Beijing Key Lab Traff Engn Beijing 100124 Peoples R China Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China Hatch Mott MacDonald Los Angeles CA 91608 USA Qingdao Sifang Rolling Stock Res Inst Co Ltd Qingdao 266000 Peoples R China

With the rapid development of high-speed rail (HSR) systems, the security and safety of these huge systems are becoming the primary concerns for passengers. HSR infrastructure plays an important role in HSR systems, making the maintenance of security and safety of the HSR infrastructure especially important. Meanwhile, sensor network technologies allow the realization of real-time and all-weather monitoring of HSR infrastructure. This paper analyzes the application requirements and characteristics of infrastructure health monitoring sensor network (IHMSN) through construction of a three-layer IHMSN which is composed of end devices, repeater points, and access points. The physical topology optimization goal of IHMSN is to set the optimal number of network nodes (namely, minimum cost) as well as the best physical connections. Given types and amount of the end devices, a multiple knapsack model is established which converts the physical topology optimization problems into multiple knapsack problems. Based on the different needs of practical application, three different cases (basis case, adding devices case and weight-based case) are proposed, and the corresponding models are built. Some artificial intelligence algorithms and a traditional dynamic programming algorithm are presented to solve the problems. In addition, a general algorithmic finite state machine is proposed to describe the solving process. After comparing these algorithms in execution time, memory, and optimal results, the genetic algorithm and particle swarm optimization algorithm stand out when used to solve the basic case as well as the extended cases. The numerical results show that these proposed models and algorithms can effectively solve the physical topology optimization problem of IHMSN for HSR systems. Moreover, these methods can effectively reduce network costs and provide a theoretical basis for network communication link optimization. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： High-speed rail Infrastructure health monitoring sensor network Multi-knapsack problem artificial intelligence algorithm Physical topology optimization

来源：评论

学校读者我要写书评

暂无评论

Health diagnosis of bus operation based on multi-source data

引用

IET INTELLIGENT TRANSPORT SYSTEMS 2022年第6期16卷 754-768页

作者： Zhou, Xuemei Guan, Zhen Pang, Yusong Ji, Xiangfeng Lin, Xiaodan Tongji Univ Coll Transportat Engn Key Lab Rd & Traff Engn State Minist Educ Shanghai Peoples R China Delft Univ Technol Sect Transport Engn & Logist Mekelweg 2 NL-2628 CD Delft Netherlands Qingdao Univ Sch Business Dept Management Sci & Engn 62 Keda Branch Rd Qingdao 266000 Shandong Peoples R China

Based on the multi-source data available for bus operations, this paper proposes a health diagnosis system for single-line bus operation systems from two aspects: The operation efficiency and stability. Firstly, the index weight has been defined and calculated based on the Entropy Method. The composite index of bus operation has been obtained, and the health classification standards that pertain to efficient and effective bus operations have also been constructed (Very Healthy, Healthy, Sub-Healthy, and Unhealthy). Secondly, the more efficient machine learning method has been used in order to establish the classification algorithm training set. The effect of the k-Nearest Neighbour and Decision Tree Classification Model has also been compared and analysed in this particular study. Finally, a bus line in Foshan is taken as a case study to verify the effectiveness of the method. This paper can effectively improve the diagnosis efficiency and accuracy by introducing the artificial intelligence algorithm into bus operation diagnosis. It provides a foundation for the development of bus operation health diagnosis decision support system with the function of "bus disease" prevention and treatment.

关键词： health classification standards pattern classification decision trees bus operation health diagnosis decision support system multisource data mechanical engineering computing k-nearest neighbour bus disease prevention efficient machine learning method fault diagnosis single-line bus operation systems decision tree classification model decision support systems nearest neighbour methods entropy artificial intelligence algorithm

来源：评论

学校读者我要写书评

暂无评论

Life prediction of lithium-ion battery based on a hybrid model

引用

ENERGY EXPLORATION & EXPLOITATION 2020年第5期38卷 1854-1878页

作者： Chen, Xu-Dong Yang, Hai-Yue Wun, Jhang-Shang Wang, Ching-Hsin Li, Ling-Ling Hebei Univ Technol State Key Lab Reliabil & Intelligence Elect Equip Tianjin Peoples R China Rizhao Polytech Sch Elect Informat Engn Rizhao Peoples R China China Grid Hebei Power Co Ltd Hengshui Power Supply Branch Hengshui Peoples R China Changhua Christian Hosp Dept Surg Div Neurosurg Changhua Taiwan Natl Chin Yi Univ Technol Dept Leisure Ind Management Taichung Taiwan Asia Univ Inst Innovat & Circular Econ Taichung Taiwan Hebei Univ Technol Key Lab Electromagnet Field & Elect Apparat Relia Tianjin Peoples R China

Lithium battery is a new energy equipment. Because of its long service life and high energy density, it is widely used in various industries. However, as the number of uses increases, the life of the energy battery gradually decreases. Aging of battery will bring security risks to energy storage system. Through the life prediction of energy lithium battery, the health status of energy battery is assessed, so as to improve the safety of energy storage system. Therefore, a hybrid model is proposed to predict the life of the energy lithium battery. The lithium-ion battery capacity data are always divided into two scales, which are predicted by extreme learning machine and support vector machine model. The energy lithium-ion battery capacity attenuation data were obtained through experiments. The original signal is decomposed into five layers by using the wavelet basis function to denoise the signal. Finally, the denoised signal is synthesized. The noise reduction effect of each wavelet was analyzed. The analysis results show that the mean square error value of the Haar wavelet is 5.31e-28, which indicates that the Haar wavelet has the best noise reduction effect. Finally, the combined model was tested by using two sets of experiments. The prediction results of the combined model are compared with those of the single model. The test results show that the prediction results of the combined model are better than the single model for either experiment 1 or experiment 2. Experiment 1 indicated the root mean square error values are 29.58 and 79.68% smaller than the root mean square error values of extreme learning machine and support vector machine. The model proposed in this study has positive significance for the safety improvement of energy storage system and can promote the development and utilization of energy resources.

关键词： Energy lithium-ion battery life prediction energy storage system artificial intelligence algorithm hybrid model

来源：评论

学校读者我要写书评

暂无评论

Option pricing under sub-mixed fractional Brownian motion based on time-varying implied volatility using intelligent algorithms

引用

SOFT COMPUTING 2023年第20期27卷 15225-15246页

作者： Guo, Jingjun Kang, Weiyi Wang, Yubing Lanzhou Univ Finance & Econ Sch Stat Lanzhou 730020 Peoples R China

Against the background of the current complex international geopolitical situation and more intense trade frictions, the volatility of financial assets has important research significance as a basis for risk analysis and option pricing. First, considering the characteristics of financial assets-such as "long dependence"-the pricing model can become complicated, making it difficult to calculate the implied volatility directly. Establishing the loss function between the trading data and modeled value, the implied volatility at different moments solved using the global optimal double annealing algorithm was found to differ from the generalized autoregressive conditional heteroskedasticity (GARCH) volatility and historical volatility. Second, the implied volatility considering people's future expectations of financial assets was predicted using the previously known implied volatility via deep learning methods. The empirical results showed that the implied volatilities predicted using the long short-term memory (LSTM) and one-dimensional convolutional neural network (1D-CNN) methods performed well for option pricing. Moreover, the fractal option-pricing models outperformed the traditional Black-Scholes (B-S) pricing model. Finally, based on the accumulated local effect (ALE) algorithm-which can quantify the impact analysis of different volatilities on pricing models-it was found that the predicted implied volatility using artificial intelligence algorithms was more relevant to the truth. A combination of traditional mathematical models and emerging intelligent algorithms are promoted in this study, providing a reference for investors and risk managers and contributing to the continued development of financial markets.

关键词： Sub-mixed fractional Brownian motion Option pricing Implied volatility Deep learning artificial intelligence algorithm

来源：评论

学校读者我要写书评

暂无评论

Long time series of ocean wave prediction based on PatchTST model

引用

OCEAN ENGINEERING 2024年 301卷

作者： Huang, Xinyu Tang, Jun Shen, Yongming Dalian Univ Technol State Key Lab Coastal & Offshore Engn Dalian 116024 Peoples R China Guangdong Univ Technol Sch Ecol Environm & Resources Guangdong Prov Key Lab Water Qual Improvement & Ec Guangzhou 510006 Peoples R China Southern Marine Sci & Engn Guangdong Lab Guangzhou Guangzhou 511458 Peoples R China

In recent years, AI (artificial intelligence) algorithms based on big data have gradually emerged and been applied to short time predictions of ocean waves (e.g. 1 h, 3 h, ... , 48 h). However, limited by the lack of a better method and time-consuming computation, long time (e.g. 720 h) predictions have not been investigated yet. Based on the problems with study of long time series wave prediction, by using the hourly wave reanalysis data near Bering Sea from 2016 to 2020, this study establishes a long time series AI prediction model for waves with a forecast length of one month (720 h) based on PatchTST model. The time series prediction results of significant wave height evolution in 2021 show that compared with Informer, LSTM (Long Short Term Memory) and NeuralProphet, PatchTST ' s is significantly superior with COR (Correlation) averages at 0.90, RMSE (Root Mean Square Error) averages at 0.65 and MAE (Mean Absolute Error) averages at 0.56 on 12 months of 2021, which can be effectively applied to long time significant wave height prediction.

关键词： Wave prediction Time series PatchTST model artificial intelligence algorithm

来源：评论

学校读者我要写书评

暂无评论

Application of machine learning models for predicting acute kidney injury following donation after cardiac death liver transplantation

引用

Hepatobiliary & Pancreatic Diseases International 2021年第3期20卷 222-231页

作者： Zeng-Lei He Jun-Bin Zhou Zhi-Kun Liu Si-Yi Dong Yun-Tao Zhang Tian Shen Shu-Sen Zheng Xiao Xu Division of Hepatobiliary and Pancreatic Surgery the First Affiliated HospitalZhejiang University School of MedicineHangzhou 310003China Key Laboratory of Combined Multi-organ Transplantation Ministry of Public Healththe First Affiliated HospitalZhejiang University School of MedicineHangzhou 310003China

Background: Acute kidney injury(AKI) is a common complication after liver transplantation(LT) and is an indicator of poor prognosis. The establishment of a more accurate preoperative prediction model of AKI could help to improve the prognosis of LT. Machine learning algorithms provide a potentially effective approach. Methods: A total of 493 patients with donation after cardiac death LT(DCDLT) were enrolled. AKI was defined according to the clinical practice guidelines of kidney disease: improving global outcomes(KDIGO). The clinical data of patients with AKI(AKI group) and without AKI(non-AKI group) were compared. With logistic regression analysis as a conventional model, four predictive machine learning models were developed using the following algorithms: random forest, support vector machine, classical decision tree, and conditional inference tree. The predictive power of these models was then evaluated using the area under the receiver operating characteristic curve(AUC). Results: The incidence of AKI was 35.7%(176/493) during the follow-up period. Compared with the nonAKI group, the AKI group showed a remarkably lower survival rate( P<0.001). The random forest model demonstrated the highest prediction accuracy of 0.79 with AUC of 0.850 [95% confidence interval(CI): 0.794–0.905], which was significantly higher than the AUCs of the other machine learning algorithms and logistic regression models( P<0.001). Conclusions: The random forest model based on machine learning algorithms for predicting AKI occurring after DCDLT demonstrated stronger predictive power than other models in our study. This suggests that machine learning methods may provide feasible tools for forecasting AKI after DCDLT.

关键词： artificial intelligence algorithm Random forest Acute kidney injury Liver transplantation

来源：评论

学校读者我要写书评

暂无评论

Prediction of mechanical behavior of reinforced concrete bridges following a fire based on the fire dynamics simulator-kriging model

引用

STRUCTURAL CONCRETE 2023年第5期24卷 5855-5871页

作者： Lu, Pengzhen Shi, Qingtian Ye, Kai Wu, Ying Lu, Libo Zhou, Yutao Zhejiang Univ Technol Sch Civil Engn Hangzhou Zhejiang Peoples R China Jiaxing Nanhu Univ Sch Informat Jiaxing Zhejiang Peoples R China Huazhong Agr Univ Sch Informat Wuhan Hubei Peoples R China Jiaxing Nanhu Univ Sch Informat Jiaxing 314001 Zhejiang Peoples R China

The performance degradation and degree of damage of bridge materials are more extensive at the fire source compared to other bridge locations. The traditional finite-element model (FEM) modification method leads to a large error and is unable to achieve a good correction effect. In addition, as the traditional static load test method is used to evaluate the bearing capacities of bridges postfire, the risk of bridge damage, or collapse-induced by secondary loading-remains unaddressed. To solve these problems, this paper proposes a modified FEM of a bridge following a fire based on the fire dynamics simulator (FDS)-artificial intelligence algorithm. The static behavior of a bridge is predicted using the modified FEM. First, a numerical simulation of a bridge fire is conducted using the FDS to obtain the temperature field distribution and determine the influence ranges of local design parameters of the bridge at the fire source. Second, a response surface model with a high fitting accuracy is constructed to modify the initial FEM by using the artificial intelligence algorithm proxy model together with dynamic load test results for the bridge postfire. Finally, the static behavior is predicted using the updated model. In this study, an actual hollow girder bridge postfire is taken as the research object, and the static behavior prediction results obtained via the proposed method are compared with the static loading test results. The results indicate that the response-predicted value of the modified FEM is close to the measured value of the bridge postfire.

关键词： artificial intelligence algorithm bridge fire FDS FEM modification static behavior prediction

来源：评论

学校读者我要写书评

暂无评论

Stable and accurate methods for identification of water bodies from Landsat series imagery using meta-heuristic algorithms

引用

JOURNAL OF APPLIED REMOTE SENSING 2017年第4期11卷

作者： Gamshadzaei, Mohammad Hossein Rahimzadegan, Majid KN Toosi Univ Technol Fac Civil Engn Dept Water Resources Tehran Iran

Identification of water extents in Landsat images is challenging due to surfaces with similar reflectance Landsat images were selected from various environmental regions in Iran. Training of the algorithms was performed using 40 water pixels and 40 nonwater pixels in operational land imager images of Chitgar Lake (one of the study regions). Moreover, high-resolution images from Google Earth were digitized to evaluate the results. Two approaches were considered: index-based and artificial intelligence (AI) algorithms. In the first approach, nine common water spectral indices were investigated. AI algorithms were utilized to acquire coefficients of optimal band combinations to extract water extents. Among the AI algorithms, the artificial neural network algorithm and also the ant colony optimization, genetic algorithm, and particle swarm optimization (PSO) meta-heuristic algorithms were implemented. Index-based methods represented different performances in various regions. Among AI methods, PSO had the best performance with average overall accuracy and kappa coefficient of 93% and 98%, respectively. The results indicated the applicability of acquired band combinations to extract accurately and stably water extents in Landsat imagery. (C) 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： Landsat imagery index-based approach artificial intelligence algorithm meta-heuristic algorithms water extents

来源：评论

学校读者我要写书评

暂无评论

Cyber Security and 5G-assisted Industrial Internet of Things using Novel artificial Adaption based Evolutionary algorithm

引用

MOBILE NETWORKS & APPLICATIONS 2023年 1-17页

作者： Singh, Shailendra Pratap Piras, Giuseppe Viriyasitavat, Wattana Kariri, Elham Yadav, Kusum Dhiman, Gaurav Vimal, S. Khan, Surbhi B. Bennett Univ SCSE Greater Noida India Sapienza Univ Rome Univ & Ric Minist Istruz Rome Italy Chulalongkorn Business Sch Fac Commerce & Accountancy Dept Stat Bangkok Thailand Prince Sattam Bin Adbulaziz Univ Coll Comp Sci & Engn Al Kharj Saudi Arabia Univ Hail Coll Comp Sci & Engn Hail Saudi Arabia Lebanese Amer Univ Dept Elect & Comp Engn Byblos Lebanon Chandigarh Univ Univ Ctr Res & Dev Dept Comp Sci & Engn Mohali 140413 India Graph Era Dept Comp Sci & Engn Dehra Dun 248002 India Lovely Profess Univ Div Res & Dev Phagwara India Govt Bikram Coll Commerce Dept Comp Sci Patiala India Ramco Inst Technol Dept Artificial Intelligence & Data Sci Rajapalayam Tamilnadu India Univ Salford Sch Sci Engn & Environm Salford England

The Industrial Internet of Things (IIoT) evolved quickly at the start of the twenty-first century. Various services, such as quality of service (QoS) for smart cyber security management from the industrial domain, are complicated for us. It is challenging to select the optimal malicious nodes by taking into account QoS criteria, including information communication, and network coverage regions. Numerous constrained evolutionary optimization strategies are known to address these problems. This study proposes a broader definition of differential evolution (DE) that uses a quick adaptation strategy and an optimization-based design. It combines DE with a unique mutation approach to broaden the range of viable answers. This research also suggests a novel fitness function for energy harvesting in IoT-based applications. Both on the IIoT-service architecture and in IIoT-based applications, the suggested method is assessed. The outcomes are then contrasted using state-of-the-art algorithms. It is discovered that the proposed approach produces better results in terms of cyber security of QoS, fitness cost, and detection of IIoT nodes from the IIoT service network.

关键词： artificial intelligence algorithm Optimization Industrial internet of things Quality of services Security

来源：评论

学校读者我要写书评

暂无评论

Using imperialist competition algorithm for independent task scheduling in grid computing

引用

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2014年第1期27卷 187-199页

作者： Pooranian, Zahra Shojafar, Mohammad Javadi, Bahman Abraham, Ajith Islamic Azad Univ Andimeshk Branch Dept Comp Engn Dezful Iran Univ Roma La Sapienza Dept Informat Engn Elect DIET I-00184 Rome Italy Univ Western Sydney Sch Comp Engn & Math Sydney NSW Australia Machine Intelligence Res Labs MIR Labs Auburn WA USA VSB Tech Univ Ostrava Ctr Excellence IT4Innovat Ostrava Czech Republic

A grid computing environment provides a type of distributed computation that is unique because it is not centrally managed and it has the capability to connect heterogeneous resources. A grid system provides location-independent access to the resources and services of geographically distributed machines. An essential ingredient for supporting location-independent computations is the ability to discover resources that have been requested by the users. Because the number of grid users can increase and the grid environment is continuously changing, a scheduler that can discover decentralized resources is needed. Grid resource scheduling is considered to be a complicated, NP-hard problem because of the distribution of resources, the changing conditions of resources, and the unreliability of infrastructure communication. Various artificial intelligence algorithms have been proposed for scheduling tasks in a computational grid. This paper uses the imperialist competition algorithm (ICA) to address the problem of independent task scheduling in a grid environment, with the aim of reducing the makespan. Experimental results compare ICA with other algorithms and illustrate that ICA finds a shorter makespan relative to the others. Moreover, it converges quickly, finding its optimum solution in less time than the other algorithms.

关键词： Grid computing scheduling artificial intelligence algorithm imperialist competition algorithm (ICA) independent task scheduling

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：