Data Clustering using a Hybrid of Fuzzy C-Means and Quantum-behaved Particle Swarm Optimization

Sengupta, Saptarshi; Basak, Sanchita; Peters, Richard Alan

doi:10.1109/ccwc.2018.8301693

Cited by 23 publications

(12 citation statements)

References 21 publications

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“…Aussel et al, [6] used the same dataset to perform hard drive failure prediction with SVM, RF and GBT and discussed their performances based on precision and recall. Prediction of remaining useful lives using quantum particle swarm optimization [11] [12] of lithium-ion battery has been discussed in [13] and a host of recent swarm intelligence algorithms [14] can be effectively applied in prediction of RUL of various devices in conjuction with other ML approaches. We present a comparative analysis with some of these works in Section VI.…”

Section: B Related Workmentioning

confidence: 99%

Mechanisms for Integrated Feature Normalization and Remaining Useful Life Estimation Using LSTMs Applied to Hard-Disks

Basak

Sengupta

Dubey

2019

2019 IEEE International Conference on Smart Computing (SMARTCOMP)

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In this paper we focus on application of data-driven methods for remaining useful life estimation in components where past failure data is not uniform across devices, i.e. there is a high variance in the minimum and maximum value of the key parameters. The system under study is the hard disks used in computing cluster. The data used for analysis is provided by Backblaze as discussed later. In the article, we discuss the architecture of of the long short term neural network used and describe the mechanisms to choose the various hyper-parameters. Further, we describe the challenges faced in extracting effective training sets from highly unorganized and class-imbalanced big data and establish methods for online predictions with extensive data pre-processing, feature extraction and validation through online simulation sets with unknown remaining useful lives of the hard disks. Our algorithm performs especially well in predicting RUL near the critical zone of a device approaching failure. With the proposed approach we are able to predict whether a disk is going to fail in next ten days with an average precision of 0.8435. We also show that the architecture trained on a particular model is generalizable and transferable as it can be used to predict RUL for devices in other models from same manufacturer.

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Section: B Related Workmentioning

confidence: 99%

Mechanisms for Integrated Feature Normalization and Remaining Useful Life Estimation Using LSTMs Applied to Hard-Disks

Basak

Sengupta

Dubey

2019

2019 IEEE International Conference on Smart Computing (SMARTCOMP)

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“…where for each data point with index i in the cluster, a i is the average distance between data i and the rest of data points in the same cluster, b i is the smallest average distance between data i and every other cluster. Some other clustering techniques that can be applied to these kind of problem can be found in [20]. Example.…”

Section: Month Grouping By Clustering Analysismentioning

confidence: 99%

Data-Driven Optimization of Public Transit Schedule

et al. 2019

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Bus transit systems are the backbone of public transportation in the United States. An important indicator of the quality of service in such infrastructures is on-time performance at stops, with published transit schedules playing an integral role governing the level of success of the service. However there are relatively few optimization architectures leveraging stochastic search that focus on optimizing bus timetables with the objective of maximizing probability of bus arrivals at timepoints with delays within desired on-time ranges. In addition to this, there is a lack of substantial research considering monthly and seasonal variations of delay patterns integrated with such optimization strategies. To address these, this paper makes the following contributions to the corpus of studies on transit on-time performance optimization: (a) an unsupervised clustering mechanism is presented which groups months with similar seasonal delay patterns, (b) the problem is formulated as a single-objective optimization task and a greedy algorithm, a genetic algorithm (GA) as well as a particle swarm optimization (PSO) algorithm are employed to solve it, (c) a detailed discussion on empirical results comparing the algorithms are provided and sensitivity analysis on hyper-parameters of the heuristics are presented along with execution times, which will help practitioners looking at similar problems. The analyses conducted are insightful in the local context of improving public transit scheduling in the Nashville metro region as well as informative from a global perspective as an elaborate case study which builds upon the growing corpus of empirical studies using nature-inspired approaches to transit schedule optimization.

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“…Numerous updates to the canonical PSO put forward by Clerc and Kennedy [25] have been made possible by factoring in different initialization conditions, position and velocity updates and hybridization [22] [25-27] [31]. Of these, Quantum-behaved Particle Swarm Optimization (QPSO) [26][27][28][29][30] is a particularly attractive choice as its convergence to optima is theoretically guaranteed [31]. Promising results using QPSO-inspired Particle Filters in several tracking datasets have been reported by Sun et al (2015) [7] and by Hu, Fang and Ding (2016) [8].…”

Section: Sample Impoverishment In Particle Filters and Related Workmentioning

confidence: 99%

Learning to Track On-The-Fly Using a Particle Filter with Annealed-Weighted QPSO Modeled after a Singular Dirac Delta Potential

Sengupta

Peters

2018

2018 IEEE Symposium Series on Computational Intelligence (SSCI)

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This paper proposes an evolutionary Particle Filter with a memory guided proposal step size update and an improved, fullyconnected Quantum-behaved Particle Swarm Optimization (QPSO) resampling scheme for visual tracking applications. The proposal update step uses importance weights proportional to velocities encountered in recent memory to limit the swarm movement within probable regions of interest. The QPSO resampling scheme uses a fitness weighted mean best update to bias the swarm towards the fittest section of particles while also employing a simulated annealing operator to avoid subpar fine tune during latter course of iterations. By moving particles closer to high likelihood landscapes of the posterior distribution using such constructs, the sample impoverishment problem that plagues the Particle Filter is mitigated to a great extent. Experimental results using benchmark sequences imply that the proposed method outperforms competitive candidate trackers such as the Particle Filter and the traditional Particle Swarm Optimization based Particle Filter on a suite of tracker performance indices.

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Data Clustering using a Hybrid of Fuzzy C-Means and Quantum-behaved Particle Swarm Optimization

Cited by 23 publications

References 21 publications

Mechanisms for Integrated Feature Normalization and Remaining Useful Life Estimation Using LSTMs Applied to Hard-Disks

Mechanisms for Integrated Feature Normalization and Remaining Useful Life Estimation Using LSTMs Applied to Hard-Disks

Data-Driven Optimization of Public Transit Schedule

Learning to Track On-The-Fly Using a Particle Filter with Annealed-Weighted QPSO Modeled after a Singular Dirac Delta Potential

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