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Maximum Lyapunov Exponent Based nearest neighbor algorithm For Real-Time Transient Stability Assessment

ELECTRIC POWER SYSTEMS RESEARCH 2024年 234卷

作者： Sayyeda, Umbereen Bano Ghandhari, Mehrdad Eriksson, Robert KTH Royal Inst Technol Div Elect Power & Energy Syst Stockholm Sweden Swedish Natl Grid Syst Dev Sundbyberg Sweden

In power systems, ensuring transient stability is paramount to prevent unforeseen blackouts and power failures. Transient stability assessment is crucial for the early detection and mitigation of instabilities, providing a rapid response to severe fault situations. The concept of the maximum Lyapunov exponent facilitates fast predictions for transient stability assessment after severe disturbances. This paper introduces an efficient maximum Lyapunov exponent algorithm for online transient stability assessment, representing the primary contribution of this work. This approach uses the time series data from the rotor angles of generators or the phase angles of generator terminal buses. Case studies are conducted on the Nordic Power System, with simulations performed in DigSilent PowerFactory. This study contributes by offering insights into the performance and adaptability of the proposed algorithm.

关键词： Maximal Lyapunov exponent Nordic power system Time domain simulation Transient stability assessment Phasor measurement unit nearest neighbor algorithm

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A nearest neighbor algorithm for Imbalanced Classification

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INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 2021年第3期30卷 2150013-2150013页

作者： Viola, Remi Emonet, Remi Habrard, Amaury Metzler, Guillaume Riou, Sebastien Sebban, Marc Univ Lyon UJM St Etienne Grad Sch Hubert Curien LabInst OptCNRSUMR 5516 St Etienne France Univ Lyon Lyon2 ERIC UR 3083 5 Ave Pierre Mendes France F-69676 Bron France French Minist Econ & Finances Direct Gen Finances Publ Paris France

Due to the inability of the accuracy-driven methods to address the challenging problem of learning from imbalanced data, several alternative measures have been proposed in the literature, like the Area Under the ROC Curve (AUC), the Average Precision (AP), the F-measure, the G-Mean, etc. However, these latter measures are neither smooth, convex nor separable, making their direct optimization hard in practice. In this paper, we tackle the challenging problem of imbalanced learning from a nearest-neighbor (NN) classification perspective, where the minority examples typically belong to the class of interest. Based on simple geometrical ideas, we introduce an algorithm that rescales the distance between a query sample and any positive training example. This leads to a modification of the Voronoi regions and thus of the decision boundaries of the NN classifier. We provide a theoretical justification about this scaling scheme which inherently aims at reducing the False Negative rate while controlling the number of False Positives. We further formally establish a link between the proposed method and cost-sensitive learning. An extensive experimental study is conducted on many public imbalanced datasets showing that our method is very effective with respect to popular nearest-neighbor algorithms, comparable to state-of-the-art sampling methods and even yields the best performance when combined with them.

关键词： Machine learning nearest neighbor algorithm imbalanced classification

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Solving traveling salesman problem using parallel repetitive nearest neighbor algorithm on OTIS-Hypercube and OTIS-Mesh optoelectronic architectures

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JOURNAL OF SUPERCOMPUTING 2018年第1期74卷 1-36页

作者： Al-Adwan, Aryaf Mahafzah, Basel A. Sharieh, Ahmad Univ Jordan King Abdullah II Sch Informat Technol Dept Comp Sci Amman 11942 Jordan

Over the past years, researchers drew their attention to propose optoelectronic architectures, including optical transpose interconnection system (OTIS) networks. On the other hand, there are limited attempts devoted to design parallel algorithms for applications that could be mapped on such optoelectronic architectures. Thus, exploiting the attractive features of OTIS networks and investigating their performance in solving combinatorial optimization problems become a great necessity. In this paper, a parallel repetitive nearest neighbor algorithm for solving the symmetric traveling salesman problem on OTIS-Hypercube and OTIS-Mesh optoelectronic architectures is presented. This algorithm has been evaluated analytically and by simulation on both optoelectronic architectures in terms of number of communication steps, parallel run time, speedup, efficiency, cost and communication cost. The simulation results attained almost near-linear speedup and high efficiency among the two selected optoelectronic architectures, where OTIS-Hypercube gained better results in comparison with OTIS-Mesh.

关键词： Parallel heuristics algorithm nearest neighbor algorithm Traveling salesman problem Optoelectronic architecture Interconnection network OTIS

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A Local Integral Hash nearest neighbor algorithm 3

A Local Integral Hash Nearest Neighbor Algorithm

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3rd International Conference on Mechanical, Control and Computer Engineering (ICMCCE)

作者： Wang, Xi Li, Chen Yao, Jun Ji, Hongxia Zhang, Ze Ma, Beizhi Army Armored Mil Acad Informat & Commun Dept Beijing Peoples R China Beijing 4 High Sch Beijing Peoples R China PLA Mil Unit 95894 Beijing Peoples R China Beijing Aerosp Xinfeng Machinery Equipment Co LTD Beijing Peoples R China

ISBN: (纸本)9781538684818

nearest neighbor search algorithm plays a very important role in computer image algorithm. When the search data is large, we need to use fast search algorithm. The current fast retrieval algorithms are tree based algorithms. The efficiency of the tree algorithm decreases sharply with the increase of the data dimension. In this paper, a local integral hash nearest neighbor algorithm of the spatial space is proposed to construct the tree structure by changing the way of the node of the access tree. It is able to express data distribution characteristics. After experimental testing, this paper achieves more efficient performance in high dimensional data.

关键词： nearest neighbor algorithm Local integral hash High dimensional data

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A New Hybrid Method Based on nearest neighbor algorithm and 2-Opt algorithm for Traveling Salesman Problem 4

A New Hybrid Method Based on Nearest Neighbor Algorithm and ...

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4th International Conference on Wireless and Telematics (ICWT)

作者： Nuraiman, Dian Dewi, Yushinta Ilahi, Fadilah Hamidi, Eki Ahmad Zaki UIN Sunan Gunung Djati Dept Math Bandung Indonesia UIN Sunan Gunung Djati Dept Elect Engn Bandung Indonesia

ISBN: (纸本)9781538661635

Traveling salesman problem has been one of the mostly studied problems in combinatorial optimization. This paper proposes a new hybrid method combining the nearest neighbor algorithm and the 2-opt algorithm. The performance of this method has been tested to several benchmark problems from TSPLIB involving 11 instances of symmetric problems and three instances of asymmetric problems with various number of nodes from 17 to 783 nodes. The results show that the hybrid method can reduce the relative error significantly compared to the nearest neighbor algorithm only.

关键词： traveling salesman problem nearest neighbor algorithm 2-opt algorithm hybrid method

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A k-nearest neighbor algorithm Based on Homomorphic Encryption

A k-Nearest Neighbor Algorithm Based on Homomorphic Encrypti...

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IEEE International Symposium on Parallel and Distributed Processing with Applications and IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Zhenzhou Guo Shan Wang Weifeng Jin Changqing Gong Na Lin School of Computer Science Shenyang Aerospace University Shenyang China

ISBN: (纸本)9781728152103

Protection of privacy has become an essential problem in cloud platform security. In many cases, data is shared with the third party for the analysis purpose. However, the sharing of data for analysis is not safe. Fully homomorphic encryption (FHE) is very promising to deal with ciphertext without decryption, FHE has become one of the key technologies to improve the security of user sensitive information. In this paper, we solve the problem of privacy preserving k-nearest neighbor classification (K-NN), which forms the basis of many data analysis applications. We propose a scheme FK-NN, which is based on homomorphic encryption and numerical comparator. In our scheme, the homomorphic subtraction operation is designed and implemented firstly. Then, the cloud calculates the nearest neighbors of a given data point while the data point as well as the data points in the training set are in encrypted form. We can obtain classification results which are in encrypted form. The correctness of the scheme has been shown over cardiac disease dataset. The results show the efficiency of the proposed scheme satisfy the requirements of the system and accurately classify data which is in encrypted form.

关键词： cloud computing cryptography data analysis data privacy learning (artificial intelligence) pattern classification Data privacy Homomorphic encryption Pattern recognition Data analysis cryptography Cloud Computing nearest neighbor algorithm nearest neighbor K-nearest neighbor algorithm Statistical analysis Ciphertext Privacy Key technology Third party

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Development of an Interface for Detection of Slices with Ground-Glass Opacity Nodule by Using the nearest neighbor algorithm 10

Development of an Interface for Detection of Slices with Gro...

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10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Yuan, Dandan Du, Weiwei Wang, Jianming Duan, Xiaojie Ma, Yanhe Zhang, Hong Tianjin Polytech Univ Sch Elect & Informat Engn Tianjin 300387 Peoples R China Kyoto Inst Technol Informat & Human Sci Sakyo Ku Hachigami Cho Kyoto 6068585 Japan Tianjin Chest Hosp Tianjin 300051 Peoples R China

ISBN: (纸本)9781538619377

Calculating features of the GGO nodule can help to detect the benign or malignant of lung tumors, but it took a lot of time for radiologists to acquire all slices including the GGO nodules in CT volume data. This paper proposed the nearest neighbor algorithm without any parameters and developed an interface to assist the radiologist to detect all the GGO slices in the CT volume data. An interface to assist the radiologist to find slices including a GGO nodule was designed as follows. First, the region of interest (ROI) was obtained as preprocesses of the interface preprocesses from the original CT. Secondly, a slice including the GGO nodule was designated by a radiologist. The designated slice was called the labeled datum. Finally, all slices including the GGO nodules were detected based on the nearest neighbor algorithm and the labeled datum. Thus, the radiologist can quickly find all slices including GGO nodules in CT volume data without drawing the GGO nodules of all slices manually. The proposal is compared with detection of all slices containing GGO nodules manually. The experimental result showed that the proposal was quicker to find all slices including GGO nodules than the manual method.

关键词： A Ground-Glass Opacity Nodule the interface nearest neighbor algorithm

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A combined image processing and nearest neighbor algorithm tool for classification of incipient faults in induction motor drives

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COMPUTERS & ELECTRICAL ENGINEERING 2016年第0期54卷 296-312页

作者： Bandyopadhyay, Indrayudh Purkait, Prithwiraj Koley, Chiranjib Haldia Inst Technol Dept Elect Engn Haldia W Bengal India Natl Inst Technol Dept Elect Engn Durgapur W Bengal India

Switching Devices such as IGBT used in Pulse Width Modulation (PWM) Inverter feeding an induction motor often suffer from different types of incipient faults like improper contact points, poor connections and problematic solder joints. These are due to ageing or prolonged operation in unfriendly environments. These faults need to be detected at their initial stages to prevent subsequent spreading of faults. In the present work, different variations of the above mentioned faulty cases in a PWM-Inverter have been studied by recording three phase inverter output current profiles and converting them to Concordia patterns. It has been observed that the Concordia patterns are quite different in shapes for different types of faults. A suitable image based shape descriptor has been applied to extract relevant information from these Concordia patterns. Finally, nearest neighbor algorithm is employed on this information to identify the nature and location of faults. Performance of the algorithm is found to be quite satisfactory when its results are compared with two more related algorithms. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： PWM induction motor drive IGBT Concordia pattern Shape context Incipient fault nearest neighbor algorithm

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Context Awareness System on Ubiquitous Learning with Case Based Reasoning and nearest neighbor algorithm 4

Context Awareness System on Ubiquitous Learning with Case Ba...

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4th International Conference on Information and Communication Technology (ICoICT)

作者： Selviandro, Nungki Sabariah, Mira Kania Saputra, Surya Telkom Univ Sch Comp Bandung Indonesia

ISBN: (纸本)9781467398794

Several learning technologies have been developed over the years in order to help distance learning process. However, those technologies are still not enough to give the proper interaction between their users and the technologies themselves. It is caused by the absence of the technologies ability to give context based on their users conditions. When human interacts with each other, they understand each other and use information that is situational or in the form of context that can enhance the quality of the interaction. This human capability is not present in computers to interact with human. Those learning technologies can be developed to give context that are self-adjusting to the users conditions based on ubiquitous computing accompanied by context awareness system. This kind of technology is called ubiquitous learning. Context awareness system acts as the system to control the context passing to the user. This paper will discuss the design and test plans of the context awareness system that is developed by using Case Based Reasoning and nearest neighbor algorithms to build or develop a system that is capable of giving appropriate context to learners in a learning technology.

关键词： Context Awareness System Ubiquitous Learning Case Based Reasoning nearest neighbor algorithm

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Analysis and Identification of Aptamer-Compound Interactions with a Maximum Relevance Minimum Redundancy and nearest neighbor algorithm

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BIOMED RESEARCH INTERNATIONAL 2016年第0期2016卷 8351204-8351204页

作者： Wang, ShaoPeng Zhang, Yu-Hang Lu, Jing Cui, Weiren Hu, Jerry Cai, Yu-Dong Shanghai Univ Sch Life Sci Shanghai 200444 Peoples R China Chinese Acad Sci Shanghai Inst Biol Sci Inst Hlth Sci Shanghai 200031 Peoples R China Yantai Univ Collaborat Innovat Ctr Adv Drug Delivery Syst & B Key Lab Mol Pharmacol & Drug Evaluat Minist EducSch Pharm Yantai 264005 Shandong Peoples R China Chinese Acad Sci Shanghai Inst Biol Sci CAS MPG Partner Inst Computat Biol Shanghai 200031 Peoples R China Univ Houston Victoria Sch Arts & Sci Dept Math & Comp Sci Victoria TX 77901 USA

The development of biochemistry and molecular biology has revealed an increasingly important role of compounds in several biological processes. Like the aptamer-protein interaction, aptamer-compound interaction attracts increasing attention. However, it is time-consuming to select proper aptamers against compounds using traditional methods, such as exponential enrichment. Thus, there is an urgent need to design effective computational methods for searching effective aptamers against compounds. This study attempted to extract important features for aptamer-compound interactions using feature selection methods, such as Maximum Relevance Minimum Redundancy, as well as incremental feature selection. Each aptamer-compound pair was represented by properties derived fromthe aptamer and compound, including frequencies of single nucleotides and dinucleotides for the aptamer, as well as the constitutional, electrostatic, quantum-chemical, and space conformational descriptors of the compounds. As a result, some important features were obtained. To confirm the importance of the obtained features, we further discussed the associations between them and aptamer-compound interactions. Simultaneously, an optimal prediction model based on the nearest neighbor algorithm was built to identify aptamer-compound interactions, which has the potential to be a useful tool for the identification of novel aptamer-compound interactions. The program is available upon the request.

关键词： aptamer-compound interaction maximum relevance minimum redundancy incremental feature selection nearest neighbor algorithm Weka

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