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An Efficient graph-based algorithm for Time-Varying Narrowband Interference Suppression on SAR System

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2021年第10期59卷 8418-8432页

作者： Huang, Yan Zhang, Lei Yang, Xi Chen, Zhanye Liu, Jun Li, Jie Hong, Wei Southeast Univ State Key Lab Millimeter Waves Sch Informat Sci & Engn Nanjing 210096 Peoples R China Purple Mt Lab Nanjing 211100 Peoples R China Sun Yat Sen Univ Sch Elect & Commun Engn Guangzhou 510275 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Sch Telecommun Engn Xian 710071 Peoples R China Chongqing Univ Sch Microelect & Commun Engn Chongqing 400044 Peoples R China Chongqing Univ Chongqing Key Lab Space Informat Network & Intell Chongqing 400044 Peoples R China PLA 63768 Unit Xian 710600 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing 210016 Peoples R China Minist Ind & Informat Technol Key Lab Dynam Cognit Syst Electromagnet Spectrum Nanjing 211106 Peoples R China

Synthetic aperture radar (SAR) as a wideband radar system is subject to complicated interferences, such as radio frequency interference or other narrowband interferences (NBIs). In order to suppress the NBI, voluminous literature focused on its signal models and characteristics, such as the sinusoidal model and relatively constant frequencies. However, in practice, the interference environment is commonly complicated. It is hard to model the interferences accurately and mitigate them clearly in an easy way, especially for the time-varying interferences. In this article, a novel graph-based algorithm is proposed to mitigate the time-varying NBIs by using graph theory, which constructs the connections between different azimuth samples of NBIs. As a result, the locally time-varying interferences can be clustered in a nonlinear low-dimensional manifold and effectively removed by the proposed algorithm. In addition, the case of the globally time-varying interference is also analyzed in detail with strict derivations to demonstrate its low-rank property. Furthermore, the matrix factorization scheme is introduced to improve the efficiency of the proposed algorithm, and the closed-form solutions are derived for each iteration. The real SAR data with measured NBIs are provided to demonstrate the effectiveness and efficiency of the proposed algorithm.

关键词： graph-based algorithm matrix factorization synthetic aperture radar (SAR) time-varying narrowband interference (NBI) suppression

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Job Shop Scheduling Problem Optimization by Means of graph-based algorithm

Job Shop Scheduling Problem Optimization by Means of Graph-B...

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作者： Šťastný, Jiří Brno University of Technology

In this paper we introduce the draft of a new graph-based algorithm for optimization of scheduling problems. Our algorithm is based on the Generalized Lifelong Planning A* algorithm, which is usually used for path planning for mobile robots. It was tested on the Job Shop Scheduling Problem against a genetic algorithm’s classic implementation. The acquired results of these experiments were compared by each algorithm’s required time (to find the best solution) as well as makespan. The comparison of these results showed that the proposed algorithm exhibited a promising convergence rate toward an optimal solution. Job shop scheduling (or the job shop problem) is an optimization problem in informatics and operations research in which jobs are assigned to resources at particular times. The makespan is the total length of the schedule (when all jobs have finished processing). In most of the tested cases, our proposed algorithm managed to find a solution faster than the genetic algorithm; in five cases, the graph-based algorithm found a solution at the same time as the genetic algorithm. Our results also showed that the manner of priority calculation had a non-negligible impact on solutions, and that an appropriately chosen priority calculation could improve them.

关键词： Genetic algorithms graph-based algorithm Job Shop Scheduling Problem optimization

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Traveling Salesman Problem Optimization by Means of graph-based algorithm 39

Traveling Salesman Problem Optimization by Means of Graph-ba...

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39th International Conference on Telecommunications and Signal Processing (TSP)

作者： Stastny, Jiri Skorpil, Vladislav Cizek, Lubomir Brno Univ Technol Dept Automat & Comp Sci Brno Czech Republic Brno Univ Technol Dept Telecommun Brno Czech Republic

ISBN: (纸本)9781509012886

There are many different algorithms for optimization of logistic and scheduling problems and one of the most known is Genetic algorithm. In this paper we take a deeper look at a draft of new graph-based algorithm for optimization of scheduling problems based on Generalized Lifelong Planning A* algorithm which is usually used for path planning of mobile robots. And then we test it on Traveling Salesman Problem (TSP) against classic implementation of genetic algorithm. The results of these tests are then compared according to the time of finding the best path, its travel distance, an average distance of travel paths found and average time of finding these paths. A comparison of the results shows that the proposed algorithm has very fast convergence rate towards an optimal solution. Thanks to this it reaches not only better solutions than genetic algorithm, but in many instances it also reaches them faster.

关键词： Genetic algorithm graph-based algorithm Traveling Salesman Problem GLPA NP-Hard problem logistics optimization task scheduling

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An Efficient algorithm for Approximate Polyline-Sourced Offset Computation on Triangulated Surfaces

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TSINGHUA SCIENCE AND TECHNOLOGY 2025年第4期30卷 1744-1761页

作者： Meng, Wenlong Yu, Hang Geng, Yixuan Bo, Pengbo Harbin Inst Technol Sch Comp Sci & Technol Weihai 264209 Peoples R China

The computation of polyline-sourced geodesic offset holds significant importance in a variety of applications, including but not limited to solid modeling, tool path generation for computer numerical control (CNC) machining, and parametrization. The traditional approaches for geodesic offsets have typically relied on the availability of an exact geodesic metric. Nevertheless, the computation of exact geodesics is characterized by its time-consuming nature and substantial memory usage. To tackle the limitation, our study puts forward a novel approach that seeks to circumvent the reliance on exact geodesic metrics. The proposed method entails a reformulated graph method that incorporates Steiner point insertion, serving as an effective solution for obtaining geodesic distances. By leveraging the aforementioned strategies, we present an efficient and robust algorithm designed for the computation of polyline-sourced geodesic offsets. The experimental evaluation, conducted on a diverse set of three-dimensional models, demonstrates significant improvements in computational speed and memory requirements compared to established state-of-the-art methods.

关键词： Measurement Steiner trees Solid modeling Memory management Machining Approximation algorithms Path planning Computational efficiency Numerical models Computer numerical control geodesic offsets graph-based algorithm computer numerical control (CNC) milling tool path planning

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Unsupervised Entity Resolution With Blocking and graph algorithms

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2022年第3期34卷 1501-1515页

作者： Zhang, Dongxiang Li, Dongsheng Guo, Long Tan, Kian-Lee Zhejiang Univ Coll Comp Sci & Technol Hangzhou 310027 Zhejiang Peoples R China Natl Univ Def Technol Sch Comp Sci Changsha 410073 Hunan Peoples R China Alibaba Grp Hangzhou 311121 Peoples R China Natl Univ Singapore Sch Comp Singapore 119077 Singapore

Entity resolution identifies all records in a database that refer to the same entity. In this paper, we propose an unsupervised framework for entity resolution using blocking and graph algorithms. The records are partitioned into blocks with no redundancy for efficiency improvement. For intra-block data processing, we propose a graph-theoretic fusion framework with two components, namely ITER and CliqueRank. Specifically, ITER constructs a weighted bipartite graph between terms and record-record pairs and iteratively propagates the node salience until convergence. Subsequently, CliqueRank constructs a record graph to estimate the likelihood of two records resident in the same clique. The derived likelihood from CliqueRank is fed back to ITER to rectify the edge weight until a joint optimum can be reached. Experimental evaluation was conducted with 4 real datasets. Results show that our unsupervised framework is comparable or even superior to state-of-the-art deep learning approaches.

关键词： Unsupervised entity resolution blocking graph-based algorithm

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Feature based Automatic Text Summarization Methods: A Comprehensive State-of-the-Art Survey

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IEEE ACCESS 2022年 10卷 133981-134003页

作者： Yadav, Divakar Katna, Rishabh Yadav, Arun Kumar Morato, Jorge Indira Gandhi Natl Open Univ IGNOU Sch Comp & Informat Sci SOCIS New Delhi India Natl Inst Technol Hamirpur NIT Hamirpur Dept Comp Sci & Engn Hamirpur Himachal Prades India Univ Carlos III Madrid Dept Comp Sci Legans Spain

With the advent of the World Wide Web, there are numerous online platforms that generate huge amounts of textual material, including social networks, online blogs, magazines, etc. This textual content contains useful information that can be used to advance humanity. Text summarization has been a significant area of research in natural language processing (NLP). With the expansion of the internet, the amount of data in the world has exploded. Large volumes of data make locating the required and best information time-consuming. It is impractical to manually summarize petabytes of data;hence, computerized text summarization is rising in popularity. This study presents a comprehensive overview of the current status of text summarizing approaches, techniques, standard datasets, assessment criteria, and future research directions. The summarizing approaches are assessed based on several characteristics, including approach-based, document-number-based, Summarization domain-based, document-language-based, output summary nature, etc. This study concludes with a discussion of many obstacles and research opportunities linked to text summarizing research that may be relevant for future researchers in this field.

关键词： Abstractive summarization cosine-similarity deep learning extractive summarization graph-based algorithm neural networks

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graph-based recommendation by trust

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INTERNATIONAL JOURNAL OF INTERNET PROTOCOL TECHNOLOGY 2021年第1期14卷 33-40页

作者： Wang, Liejun Pan, Long Qin, Jiwei Xinjiang Univ Sch Informat Sci & Engn Urumqi 830046 Peoples R China

The recommendation system as an effective tool is used to alleviate the information overload problem, and is being applied to personalised services. In recommendation, user's ratings as explicit feedback data can clearly express user's preference, however explicit feedback data has a natural defect that user's interest for an item would varies in context such as emotions, time etc. and the ratings could not reflect the changing. In this paper, a novel graph recommendation algorithm is presented based on user's trust relation that is regarded as implicit feedback data to calculate similarity to enhance the performance for Top-K recommendations. By evaluating the presented algorithm and compared to four competitive algorithms on the four real world datasets, the results show that the presented algorithm performs better than other algorithms in precision, recall and converge.

关键词： recommendation graph-based algorithm implicit feedback social networks

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A generalized ride-matching approach for sustainable shared mobility

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SUSTAINABLE CITIES AND SOCIETY 2022年 76卷 103383-103383页

作者： Meshkani, Seyed Mehdi Farooq, Bilal Ryerson Univ Lab Innovat Transportat LiTrans Toronto ONTARIO Canada

On-demand shared mobility is a promising and sustainable transportation approach that can mitigate vehicle externalities, such as traffic congestion and emission. On-demand shared mobility systems require matching of one (one-to-one) or multiple riders (many-to-one) to a vehicle based on real-time information. We propose a novel graph-based Many-to-One ride-Matching (GMOMatch) algorithm for the dynamic many-to-one matching problem in the presence of traffic congestion. GMOMatch, which is an iterative two-step method, provides high service quality and is efficient in terms of computational complexity. It starts with a one-to-one matching in Step 1 and is followed by solving a maximum weight matching problem in Step 2 to combine the travel requests. To evaluate the performance, it is compared with a ride-matching algorithm developed by Simonetto et al. (2019). Both algorithms are implemented in a micro-traffic simulator to assess their performance and their impact on traffic congestion in Downtown,Toronto road network. In comparison to the Simonetto, GMOMatch improved the service rate, vehicle kilometer traveled and traffic travel time by 32%, 16.07%, and 4%, respectively. The sensitivity analysis indicated that utilizing vehicles with a capacity of 10 can achieve 25% service rate improvement compared to a capacity of 4.

关键词： Shared on-demand mobility Ride-matching graph-based algorithm Congested network

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TEDAR: Temporal dynamic signal detection of adverse reactions

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2021年 122卷 102212-102212页

作者： Aparo, Antonino Sala, Pietro Bonnici, Vincenzo Giugno, Rosalba Univ Verona Dept Comp Sci Str Grazie 15 I-37134 Verona Italy

Computational approaches to detect the signals of adverse drug reactions are powerful tools to monitor the unattended effects that users experience and report, also preventing death and serious injury. They apply statistical indices to affirm the validity of adverse reactions reported by users. The methodologies that scan fixed duration intervals in the lifetime of drugs are among the most used. Here we present a method, called TEDAR, in which ranges of varying length are taken into account. TEDAR has the advantage to detect a greater number of true signals without significantly increasing the number of false positives, which are a major concern for this type of tools. Furthermore, early detection of signals is a key feature of methods to prevent the safety of the population. The results show that TEDAR detects adverse reactions many months earlier than methodologies based on a fixed interval length.

关键词： Pharmacovigilance datasets Adverse drug reactions Signal detection Early signal detection Dynamic temporal intervals graph-based algorithm

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Anomaly detection in multidimensional time series-a graph-based approach

JOURNAL OF PHYSICS-COMPLEXITY

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JOURNAL OF PHYSICS-COMPLEXITY 2021年第4期2卷

作者： Erz, Marcus Kielman, Jeremy Floyd Uzun, Bahar Selvi Guehring, Gabriele Stefanie Hsch Esslingen Fak Informat & Informat Tech Esslingen Germany

As the digital transformation is taking place, more and more data is being generated and collected. To generate meaningful information and knowledge researchers use various data mining techniques. In addition to classification, clustering, and forecasting, outlier or anomaly detection is one of the most important research areas in time series analysis. In this paper we present a method for detecting anomalies in multidimensional time series using a graph-based algorithm. We transform time series data to graphs prior to calculating the outlier since it offers a wide range of graph-based methods for anomaly detection. Furthermore the dynamic of the data is taken into consideration by implementing a window of a certain size that leads to multiple graphs in different time frames. We use feature extraction and aggregation to finally compare distance measures of two time-dependent graphs. The effectiveness of our algorithm is demonstrated on the numenta anomaly benchmark with various anomaly types as well as the KPI-anomaly-detection data set of 2018 AIOps competition.

关键词： anomaly detection graph-based algorithm time series transformation dynamic graphs

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