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A ranking algorithm for the Thermal Interruption Ability of Potential SF6 Alternative Gases

IEEE TRANSACTIONS ON POWER DELIVERY 2025年第2期40卷 718-727页

作者： Yan, Jiu Dun Zhang, Kaiyuan Cao, Rui Wu, Yi Univ Liverpool Dept Elect Engn & Elect Liverpool L69 3GJ England Xi An Jiao Tong Univ Xian 710049 Shaanxi Peoples R China Xian High Voltage Apparat Res Inst Co Ltd Xian 710077 Shaanxi Peoples R China Xi An Jiao Tong Univ State Key Lab Elect Insulat & Power Equipment Xian 710049 Shaanxi Peoples R China

A ranking algorithm is proposed to aid the screening or initial assessment of different gases for their thermal interruption capability in high voltage gas-blast circuit breakers. The algorithm is directly based on the gas material properties without involving any complicated arc simulation. Despite that it does not explicitly consider the influence of product design, the ranking results based on the proposed algorithm show good alignment with laboratory obtained thermal interruption data of five gases and commercial-scale test data for three promising SF6 alternative gases. An in-depth analysis of the influencing factors and mechanisms responsible for the differing thermal interruption abilities of gases has been carried out, showing that the dominance of the arc column size near current zero in the arc cooling process is a natural consequence of the gas properties, especially the product of density and specific heat at constant pressure and electrical conductivity over the temperature range of 500K to 15,000K.

关键词： Gases Cooling Temperature distribution Power system reliability Integrated circuit modeling Circuit breakers Temperature measurement Material properties Analytical models Thermal stability ranking algorithm arc cooling ability thermal interruption SF6 alternative gases switching arc material properties

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Incremental C-Rank: An effective and efficient ranking algorithm for dynamic Web environments

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KNOWLEDGE-BASED SYSTEMS 2019年 176卷 147-158页

作者： Koo, Jangwan Chae, Dong-Kyu Kim, Dong-Jin Kim, Sang-Wook Hanyang Univ Seoul South Korea Brainsoft Inc Seongnam South Korea

Web page ranking is one of the core components of search engines. Given a user query, ranking aims to provide a ranked list of Web pages that the user is likely to prefer the most. By and large, the ranking algorithms can be categorized into content-based approaches, link-based approaches, and hybrid approaches. Hybrid ranking algorithms, which exploit both the content and link information, are the most popular and extensively studied techniques. Among the hybrid algorithms, C-Rank combines content and link information in a very effective way using the concept of contribution. This algorithm is known to provide high performance in terms of both accurate and prompt responses to user queries. However, C-Rank suffers from very high costs to reflect the highly dynamic and extremely frequent changes in the World Wide Web, because it re-computes all of the C-Rank scores used for ranking from scratch to reflect the changes. As a result, C-Rank may be considered inappropriate to provide users with accurate and up-to-date search results. This paper aims to remedy this limitation of C-Rank. We propose incremental C-Rank, which is designed to update the C-Rank scores of only a carefully chosen portion of the Web pages rather than those of all of the Web pages without any accuracy loss. Our experimental results on a real-world dataset confirm both the effectiveness and efficiency of our proposed method. (C) 2019 Elsevier B.V. All rights reserved. Web page ranking is one of the core components of search engines. Given a user query, ranking aims to provide a ranked list of Web pages that the user is likely to prefer the most. By and large, the ranking algorithms can be categorized into content-based approaches, link-based approaches, and hybrid approaches. Hybrid ranking algorithms, which exploit both the content and link information, are the most popular and extensively studied techniques. Among the hybrid algorithms, C-Rank combines content and link information in a very effect

关键词： Information retrieval ranking algorithm Dynamic ranking

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An evolutionary non-linear ranking algorithm for ranking scientific collaborations

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APPLIED INTELLIGENCE 2018年第2期48卷 465-481页

作者： Ghasemian, Fahimeh Zamanifar, Kamran Ghasem-Aghaee, Nasser Univ Isfahan Fac Comp Engn Dept Software Engn Esfahan Iran

The social capital theory motivates some researchers to apply link-based ranking algorithms (e.g. PageRank) to compute the fitness level of a scholar for collaborating with other scholars on a set of skills. These algorithms are executed on the collaboration network of scholars and assign a score to each scholar based on the scores of his/her neighbors by solving a linear system in an iterative way. In this paper, we propose a new ranking algorithm by focusing on link-aggregation function and transition matrix. The evolution strategy technique is applied to find the best aggregation function and transition matrix for computing the score of a scholar in the collaboration network which is modeled by a hypergraph. Experiments conducted on two datasets gathered from ScivalExpert and VIVO show that the new non-linear ranking algorithm acts better than the other iterative ranking approaches for ranking scientific collaborations.

关键词： Scientific collaboration Hypergraph ranking algorithm Evolution strategy Collaboration network

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A new ranking algorithm and an evolutionary model based on comprehensive weighted clique degree for complex network

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APPLIED MATHEMATICAL MODELLING 2017年 47卷 756-764页

作者： Xu, Jie Liu, Zhen Xu, Jun Univ Elect Sci & Technol China Sch Commun & Informat Engn Minist Educ Ctr Cyber SecurKey Lab Opt Fiber Sensing & Commu 2006 Xiyuan Ave Chengdu 611731 Peoples R China

This paper proposes a new ranking algorithm based on comprehensive weighted clique degree (CWCDR) for ranking importance of nodes in complex network. Simulation results show that CWCDR algorithm can overcome the limitation of degree ranking algorithm and find important nodes in complex networks more precisely and effectively. To the shortage of small-world model and BA model, this paper proposes an evolutionary model of complex network based on CWCDR algorithms, named CWCDR model. Simulation results show that the CWCDR model accords with power-law distribution. Compared with BA model, this model has better average shortest path length and clustering coefficient. Therefore, the CWCDR model is more consistent with the real network. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Complex network ranking algorithm Comprehensive weighted clique degree Evolutionary model

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Learning rates for regularized least squares ranking algorithm

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ANALYSIS AND APPLICATIONS 2017年第6期15卷 815-836页

作者： Zhao, Yulong Fan, Jun Shi, Lei City Univ Hong Kong Dept Appl Math Hong Kong Hong Kong Peoples R China Univ Wisconsin Dept Stat 1300 Univ Ave Madison WI 53706 USA Fudan Univ Sch Math Sci Shanghai Key Lab Contemporary Appl Math Shanghai 200433 Peoples R China

The ranking problem aims at learning real-valued functions to order instances, which has attracted great interest in statistical learning theory. In this paper, we consider the regularized least squares ranking algorithm within the framework of reproducing kernel Hilbert space. In particular, we focus on analysis of the generalization error for this ranking algorithm, and improve the existing learning rates by virtue of an error decomposition technique from regression and Hoeffding's decomposition for U-statistics.

关键词： ranking algorithm covering number generalization bound U-statistics approximation error

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Faster algorithms for ranking/Unranking Bordered and Unbordered Words 31st

Faster Algorithms for Ranking/Unranking Bordered and Unborde...

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31st International Symposium on String Processing and Information Retrieval (SPIRE)

作者： Radoszewski, Jakub Rytter, Wojciech Walen, Tomasz Univ Warsaw Warsaw Poland

ISBN: (纸本)9783031721991;9783031722004

We show how the arithmetic structure of the set of borders (periods) of a word can be used to substantially reduce complexity of an interesting problem in combinatorics on words. A word w is a bordered word if it has a non-empty proper border (a prefix which is a suffix);equivalently, it has a period smaller than |w|. Words which are not bordered are called unbordered. The problem of ranking/unranking such words of a given length n over an alphabet of size k was considered by Gabric (Inf. Process. Lett., 2024). We improve his results as follows: complexity of ranking is reduced by a factor nk/ log n and complexity of unranking by n(2) k/log n (for large alphabets these improvement factors are n(2)/log n and n(3)/log n, respectively). We use the unit-cost RAM model (the same model was used by Gabric).

关键词： string border unbordered string bordered string ranking algorithm unranking algorithm

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WEDA: A Weak Emission-Line Detection algorithm Based on the Weighted ranking

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IEEE ACCESS 2020年 8卷 97986-98000页

作者： Zhou, Yongxiang Yang, Haifeng Cai, Jianghui Zhao, Xujun Xun, Yaling Qu, Caixia Taiyuan Univ Sci & Technol Sch Comp Sci & Technol Taiyuan 030024 Peoples R China Shanxi Key Lab Adv Control & Equipment Intelligen Taiyuan 030024 Peoples R China

The emission line in rest wavelength frame of optical spectra is valuable characteristics for nebulae detection. Searching and recognizing the spectra with emission line from massive data are necessary for the further study, while the most of methods existed currently do not adapt to such spectral data, especially for the spectra with weak emission line. To address this issue, a new algorithm (named WEDA) for detection of spectra with emission line is provided in this paper. Firstly, the difference factor between the line characteristics of the specific data is defined as its weight in recognizing of the whole lines table. Secondly, a tuning function based on the momentum formula is defined to update the weights during the process. In this step, the spectra with emission line are analysed and classified as 3 different situations. The amount of spectra with emission line is different in 3 different situations, so the speed of weight of update is different in 3 different situations. The weight of update helps us detect the data containing weak emission line in the 3 situations. Based on this, a new integrated algorithm especially for the detection of the spectra with is provided. In the end, by using several spectral datasets from the DR5 of LAMOST survey, experiments results indicate that the WEDA shows higher accuracy basically unaffected by the dataset size and the signal to noise ratio(SNR) than the other similar algorithms.

关键词： Data mining Training Clustering algorithms Task analysis Tuning Telescopes Data models ranking algorithm H? emission line binary classification imbalance classification

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Post-fall Detection Using ANN Based on ranking algorithms

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INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING 2020年第10期21卷 1985-1995页

作者： Koo, Bummo Kim, Jongman Kim, Taehee Jung, Haneul Nam, Yejin Kim, Youngho Yonsei Univ Dept Biomed Engn Wonju 26493 South Korea

The purpose of this study was to develop an accurate and efficient algorithm for post-fall detection. Thirty healthy male subjects were recruited and asked to perform 23 movements, comprising 14 activities of daily living and nine fall motions. The algorithm was developed using the ANN toolbox provided with MATLAB and inertial measurement unit (IMU) data were used to distinguish between fall and non-fall cases. An IMU sensor was located at the center between the left and right anterior superior iliac spines. A total of 32 feature vectors were extracted from 3-axis acceleration and angular velocity signals. Based on the five different ranking algorithms (relief-F, T-score, correlation, Fisher score, and minimum redundancy maximum relevance) used, feature vector subsets comprising the feature vectors were created and subsequently evaluated. Accuracy was compared according to the number of feature vectors constituting the subset, which were based on rank-lists. The results showed that the subset comprising all the feature vectors showed the best accuracy (99.86%), but a similar accuracy could be obtained with a subset comprising fewer feature vectors. The T-score was found to be the most optimal among the five ranking algorithms. Furthermore, T-score with two feature vectors achieved an accuracy of 99.17%. The results of this study are expected to assist in the construction of subsets of feature vectors based on ranking algorithms for post-fall detection with high accuracy and less computational cost.

关键词： Post-fall detection IMU Machine learning ANN ranking algorithm

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Accumulative Time Based ranking Method to Reputation Evaluation in Information Networks

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Journal of Computer Science & Technology 2022年第4期37卷 960-974页

作者： Hao Liao Qi-Xin Liu Ze-Cheng Huang Ke-Zhong Lu Chi Ho Yeung Yi-Cheng Zhang National Engineering Laboratory on Big Data System Computing Technology College of Computer Science and SoftwareEngineeringShenzhen UniversityShenzhen 518060China Guangdong Province Key Laboratory of Popular High Performance Computers Shenzhen UniversityShenzhen 518060China Guangdong Province Engineering Center of China-Made High Performance Data Computing System College of ComputerScience and Software EngineeringShenzhen UniversityShenzhen 518060China Institute of Big Data Intelligent Management and Decision Shenzhen UniversityShenzhen 518060China Department of Science and Environmental Studies The Education University of Hong KongHong Kong 999077China Department of Physics University of FribourgFribourg 1700Switzerland

Due to over-abundant information on the Web, information filtering becomes a key task for online users to obtain relevant suggestions and how to extract the most related item is always a key topic for researchers in various fields. In this paper, we adopt tools used to analyze complex networks to evaluate user reputation and item quality. In our proposed Accumulative Time Based ranking (ATR) algorithm, we take into account the growth record of the network to identify the evolution of the reputation of users and the quality of items, by incorporating two behavior weighting factors which can capture the hidden facts on reputation and quality dynamics for each user and item respectively. Our proposed ATR algorithm mainly combines the iterative approach to rank user reputation and item quality with temporal dependence compared with other reputation evaluation methods. We show that our algorithm outperforms other benchmark ranking algorithms in terms of precision and robustness on empirical datasets from various online retailers and the citation datasets among research publications. Therefore, our proposed method has the capability to effectively evaluate user reputation and item quality.

关键词： temporal network behavior dynamics reputation evaluation ranking algorithm

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Scientific Research Paper ranking algorithm PTRA: A Tradeoff between Time and Citation Network

Scientific Research Paper Ranking Algorithm PTRA: A Tradeoff...

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International Conference on Design, Manufacturing and Mechatronics (ICDMM)

作者： Hasson, Mushtaq A. Lu, Songfeng Hassoon, Basheer A. Basra Univ Coll Educ Pure Sci Basra Iraq Huazhong Univ Sci & Technol Wuhan 430074 Peoples R China

ISBN: (纸本)9783038351016

Most of the scientific research engines utilize the same way to rank scientific research papers (SRP), this way is highly depend on the citation network. The retrieved results of these engines for any keyword contain old papers with the highest citation value. In this paper, we proposed a new and easy to implement ranking algorithm for scientific research papers known as Paper Time ranking algorithm (PTRA). PTRA is a new ranking algorithm that depends on three factors to rank its results;paper age, citation index and publication venue. To construct and prove our ranking algorithm we created a web crawler that crawl different scientific search papers databases in the world to collect the information that PTRA needs. Unfortunately, some of this information was missing, such as, the impact factor of the journals. To collect this information, we created another crawler that search the Internet for these journals impact factors. However, to prove our ranking algorithm results, we have compared PTRA with Google scholar ranking algorithm. We used Google scholar, since it has more than 50 million papers and its own system can gather and collect papers in a faster way. Our comparison results show that, citation index has the highest impact on Google scholars ranking algorithm results. Unlike PTRA that gains the paper age a higher impact on the ranked results. However, PTRA depends on the citation index and publication venue to rank the results but with less impact than the paper age.

关键词： Paper Time ranking algorithm (PTRA) ranking algorithm Scientific research papers (SRP) Citation index Age of publication

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