The objects classification problem with application of SVM algorithm is considered. The ways of training set formation for the SVM-algorithm, realizing various versions of classification decisions accounting, received...
详细信息
ISBN:
(纸本)9781467376983
The objects classification problem with application of SVM algorithm is considered. The ways of training set formation for the SVM-algorithm, realizing various versions of classification decisions accounting, received with application of fuzzy clustering algorithms, are analyzed. Use possibility of fuzzy clustering algorithms ensemble on the base of the cluster tags vectors similarity matrices for the training set forming is shown.
Energy consumption affects Wireless Sensor Networks (WSNs) lifetime and may cause network degradation. Potential work has been focused on consumed energy reduction techniques. The consumed energy during communication ...
详细信息
ISBN:
(纸本)9781479985470
Energy consumption affects Wireless Sensor Networks (WSNs) lifetime and may cause network degradation. Potential work has been focused on consumed energy reduction techniques. The consumed energy during communication is affected exponentially by the distance between the communicating nodes;the more communication distance between two nodes the more energy consumed. clustering was used to help in reducing the energy consumed in the wireless data transmission. clustering gathers the nodes into groups called clusters. One node from each cluster is elected to be the cluster head (CH). Deciding the optimal number of clusters and which sensors should be CHs is a challenge problem. We presented two hybrid clustering algorithms called K-Means Particle Swarm Optimization (KPSO) and K-Means Genetic algorithms (KGAs) in [1], [2] with significant improvement over traditional Low Energy Adaptive clustering Hierarchy protocol (LEACH). Considering the various antenna patterns for WSN we were able to improve the clustering algorithm performance in energy saving. In this article, we shall review our presented algorithms and present in details the new antenna pattern design based PSO and GAs.
In this paper, we study the individual preference (IP) stability, which is an notion capturing individual fairness and stability in clustering. Within this setting, a clustering is a-IP stable when each data point'...
详细信息
In this paper, we study the individual preference (IP) stability, which is an notion capturing individual fairness and stability in clustering. Within this setting, a clustering is a-IP stable when each data point's average distance to its cluster is no more than a times its average distance to any other cluster. In this paper, we study the natural local search algorithm for IP stable clustering. Our analysis confirms a O (log n) - IP stability guarantee for this algorithm, where n denotes the number of points in the input. Furthermore, by refining the local search approach, we show it runs in an almost linear time, (O) over tilde (nk).
Minimum spanning tree (MST) based clustering algorithms have been employed successfully to detect clusters of heterogeneous nature. Given a dataset of n random points, most of the MST-based clustering algorithms first...
详细信息
An optical modulation format identification technique is proposed based on signal amplitude features and clustering algorithms. Successful classification among five different polarization-multiplexed modulation signal...
详细信息
The English Translation of the Quran Tafsir (ETQT) is essential to understanding and interpreting Allah's words. clustering is a common text mining technique for eliciting meaningful knowledge from a text collecti...
详细信息
The English Translation of the Quran Tafsir (ETQT) is essential to understanding and interpreting Allah's words. clustering is a common text mining technique for eliciting meaningful knowledge from a text collection. It is commonly used when the selected datasets lack typical ground truths. To the best of our knowledge, no study has evaluated and benchmarked ETQTs to select the most comprehensive and appropriate one. The process of evaluating and benchmarking ETQTs falls under the multicriteria decision-making (MCDM) problem because of different issues, namely, multiple internal clustering validation criteria, data variation, and trade-offs between different criteria. The fuzzy decision by opinion score method (FDOSM) is one of the most recommended MCDM ranking methods in the literature to address the said issues. FDOSM has been extended under different fuzzy set (FS) environments to address issues of uncertainty and vagueness caused by expert feedback subjectivity. Although prior versions of FDOSM improved the uncertainty and vagueness issues, they remain open issues. Therefore, this paper extended FDOSM into the complex Pythagorean fuzzy decision by opinion score method (CPFDOSM) to evaluate and benchmark ETQTs. The proposed method consists of two main phases. The first phase formulates decision-matrix-based cluster algorithms and internal cluster validation criteria. The second phase (CPFDOSM development) prioritizes ETQTs and selects the optimum one. Data generation is performed on five different cluster algorithms and six internal cluster validation criteria using 16 ETQTs based on three decision-makers (DMs). Results show the following: (1) 6.25% of the individual decision-making results are identical among the three DMs, whereas 93.75% (n = 15/16) are different when I = 0.5 and I = 2. When I = 0.5. In addition, T7 has consistent ranks (Rank=16) across all DMs, whereas T14 has consistent ranks (Rank=1) across all DMs when I = 1. (2) The results of the group de
This paper proposes practical weapon target assignment (WTA) algorithms for a defense system to counter multiple targets concentrated within a narrow area, such as low-altitude rocket threats or drone swarms. Since th...
详细信息
This paper proposes practical weapon target assignment (WTA) algorithms for a defense system to counter multiple targets concentrated within a narrow area, such as low-altitude rocket threats or drone swarms. Since the probability of kill (PK) is greatly affected by heading errors between launchers and targets in this type of engagement, WTA problems must first be formulated, considering heading error, to reflect more realistic engagement situations. Two WTA algorithms-a rotation-fixed strategy and a rotation strategy- are proposed based on this formulation. Moreover, we propose a method for determining launchers' initial orientation angles, based on a clustering approach, to further improve the engagement performance of the two algorithms. Numerical simulations were performed to demonstrate the effectiveness of the proposed methods.
The clustering problem has many applications in machine learning, operations research and statistics. We propose three algorithms to create starting solutions for improvement algorithms for the minimum sum of squares ...
详细信息
The clustering problem has many applications in machine learning, operations research and statistics. We propose three algorithms to create starting solutions for improvement algorithms for the minimum sum of squares clustering problem. We test the algorithms on 72 instances that were investigated in the literature. We found five new best known solutions and matched the best known solution for 66 of the remaining 67 instances. Thus, we are able to demonstrate that good starting solutions combined with a simple local search get results comparable with, and sometimes even better than, more sophisticated algorithms used in the literature.
Some recent researches have shown that the energy consumption problem caused by data collection in a wireless sensor network (WSN) based on a static data collector is a main threat to the network lifetime. However, wi...
详细信息
Some recent researches have shown that the energy consumption problem caused by data collection in a wireless sensor network (WSN) based on a static data collector is a main threat to the network lifetime. However, with the progress of the mobile terminal technology, the implementation of mobile data collectors (MDCs) has become more popular in large-scale WSNs, but it remains a big problem to improve the Quality of Service (QoS) criteria and minimize the energy consumption at the same time. However, most existing systems based on MDCs do not successfully strike a balance between routing energy consumption and QoS. In addition, most WSN protocols fail to maintain their impact when the network topology changes. Thus, for a dynamic WSN, it is important to support an intelligent MDC to continue data propagation despite the inevitable changes in the WSN topology. Considering all the above challenges, we propose a new intelligent MDC based on the traveling salesman problem (TSP) to determine the optimal path traveled by the MDC for energy efficiency and latency. Specifically, our proposed Mobile Data Collectors-Traveling Salesman Problem-Low Energy Adaptive clustering Hierarchy-K-Means (MDC-TSP-LEACH-K) protocol uses K-Means and Grid clustering algorithm to decrease energy consumption in the cluster head (CH) election phase. Additionally, MDC is utilized as an intermediate between CH and the sink to further enhance the QoS of WSNs, to reduce delays while collecting data, and improve the transmission phase of the LEACH protocol.
We address rule-based algorithms for multi-agent path finding (MAPF). MAPF is a task of finding non-conflicting paths connecting agents’ initial and goal positions in a shared environment specified via an undirected ...
详细信息
暂无评论