- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE 2019 21st International Conference on Transparent Optical Networks (ICTON) - Angers, France (2019.7.9-2019.7.13)] 2019 21st International Conference on Transparent Optical Networks (ICTON) - Machine Learning Based Laser Failure Mode Detection
摘要： Laser degradation analysis is a crucial process for the enhancement of laser reliability. Here, we propose a data-driven fault detection approach based on Long Short-Term Memory (LSTM) recurrent neural networks to detect the different laser degradation modes based on synthetic historical failure data. In comparison to typical threshold-based systems, attaining 24.41% classification accuracy, the LSTM-based model achieves 95.52% accuracy, and also outperforms classical machine learning (ML) models namely Random Forest (RF), K-Nearest Neighbours (KNN) and Logistic Regression (LR).
关键词： degradation,laser,reliability,fault detection,machine learning,recurrent neural networks
更新于2025-09-16 10:30:52
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Rapid classification of group B Streptococcus serotypes based on matrix-assisted laser desorption ionization-time of flight mass spectrometry and machine learning techniques
摘要： Background: Group B streptococcus (GBS) is an important pathogen that is responsible for invasive infections, including sepsis and meningitis. GBS serotyping is an essential means for the investigation of possible infection outbreaks and can identify possible sources of infection. Although it is possible to determine GBS serotypes by either immuno-serotyping or geno-serotyping, both traditional methods are time-consuming and labor-intensive. In recent years, the matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) has been reported as an effective tool for the determination of GBS serotypes in a more rapid and accurate manner. Thus, this work aims to investigate GBS serotypes by incorporating machine learning techniques with MALDI-TOF MS to carry out the identification. Results: In this study, a total of 787 GBS isolates, obtained from three research and teaching hospitals, were analyzed by MALDI-TOF MS, and the serotype of the GBS was determined by a geno-serotyping experiment. The peaks of mass-to-charge ratios were regarded as the attributes to characterize the various serotypes of GBS. Machine learning algorithms, such as support vector machine (SVM) and random forest (RF), were then used to construct predictive models for the five different serotypes (Types Ia, Ib, III, V, and VI). After optimization of feature selection and model generation based on training datasets, the accuracies of the selected models attained 54.9–87.1% for various serotypes based on independent testing data. Specifically, for the major serotypes, namely type III and type VI, the accuracies were 73.9 and 70.4%, respectively. Conclusion: The proposed models have been adopted to implement a web-based tool (GBSTyper), which is now freely accessible at http://csb.cse.yzu.edu.tw/GBSTyper/, for providing efficient and effective detection of GBS serotypes based on a MALDI-TOF MS spectrum. Overall, this work has demonstrated that the combination of MALDI-TOF MS and machine intelligence could provide a practical means of clinical pathogen testing.
关键词： GBS,Machine learning,Serotypes,Group B streptococcus,MALDI-TOF-MS
更新于2025-09-16 10:30:52
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[IEEE 2019 IEEE PES Innovative Smart Grid Technologies Conference - Latin America (ISGT Latin America) - Gramado, Brazil (2019.9.15-2019.9.18)] 2019 IEEE PES Innovative Smart Grid Technologies Conference - Latin America (ISGT Latin America) - A Comparison of Machine Learning-Based Methods for Fault Classification in Photovoltaic Systems
摘要： Photovoltaic (PV) energy use has been increasing lately and, being highly dependent on environmental variables, its efficiency becomes a major factor for concern. Additionally, these systems can be affected by several kinds of faults, which can lead to a severe energy loss. In this sense, this work compares different machine-learning-based methods, such as K-Nearest Neighbors (k-NN), Decision Trees (DT), Support Vector Machines (SVM), and Artificial Neural Networks (ANN), for detecting the following faults that can occur in Photovoltaic (PV) systems: Module short circuit, MPPT fault, Open Circuit, Partial Shading, and Degradation. The accuracy and computational time taken for training each classifier were compared. ANN achieved the best accuracy, with 99.65%, while being the slowest to train. The SVM achieved a similar result, with significant less training time. There is lack of discussion on the analysis and comparison of PV fault classification methods in the literature, specially with the focus on further practical applications and computational complexity. This way, those points are the main contributions of this work, along with making all simulations and codes publicly available.
关键词： Fault Detection,Fault Diagnostic,PV Systems,Fault Classification,Machine Learning
更新于2025-09-16 10:30:52
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[IEEE 2019 18th International Conference on Optical Communications and Networks (ICOCN) - Huangshan, China (2019.8.5-2019.8.8)] 2019 18th International Conference on Optical Communications and Networks (ICOCN) - Baseband Unit Aggregation Based on Deep Reinforcement Learning in Cloud Radio Access Networks
摘要： We propose a deep reinforcement learning based baseband unit aggregation policy. The proposed policy is able to guarantee users’ quality of service while keeping BBU pool energy-efficient. Simulation results show that up to 80% less migration traffic can be achieved compared with benchmark heuristics with only 11% higher power consumption.
关键词： Machine Learning,Cloud RAN,BBU Aggregation
更新于2025-09-16 10:30:52
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Machine learning based temperature prediction of poly( <i>N</i> -isopropylacrylamide)-capped plasmonic nanoparticle solutions
摘要： The temperature-dependent optical properties of gold nanoparticles that are capped with the thermo-sensitive polymer: ‘poly(N-isopropylacrylamide)’ (PNIPAM), have been studied extensively for several years. Also, their suitability to function as nanoscopic thermometers for bio-sensing applications has been suggested numerous times. In an attempt to establish this, many have studied the temperature-dependent optical resonance characteristics of these particles; however, developing a simple mathematical relationship between the optical measurements and the solution temperature remains an open challenge. In this paper, we attempt to systematically address this problem using machine learning techniques to quickly and accurately predict the solution-temperature, based on spectroscopic data. Our emphasis is on establishing a simple and practically useful solution to this problem. Our dataset comprises spectroscopic absorption data from both nanorods and nanobipyramids capped with PNIPAM, measured at discretely varied and pre-set temperature states. Specific regions of the spectroscopic data are selected as features for prediction using random forest (RF), gradient boosting (GB) and adaptive boosting (AB) regression techniques. Our prediction results indicate that RF and GB techniques can be used successfully to predict solution temperatures instantly to within 1 1C of accuracy.
关键词： PNIPAM,spectroscopic data,temperature prediction,adaptive boosting,machine learning,random forest,gradient boosting,gold nanoparticles
更新于2025-09-16 10:30:52
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Synthesis and characterization of Mono-disperse Carbon Quantum Dots from Fennel Seeds: Photoluminescence analysis using Machine Learning
摘要： Herein, we present the synthesis of mono-dispersed c-QDs via single-step thermal decomposition process using the fennel seeds (Foeniculum vulgare). As synthesized c-QDs have excellent colloidal, photo-stability, environmental stability (pH) and do not require any additional surface passivation step to improve the fluorescence. The C-QDs show excellent PL activity and excitation-independent emission. Synthesis of excitation-independent c-QDs, to the best of our knowledge, using natural carbon source via pyrolysis process has never been achieved before. The effect of reaction time and temperature on pyrolysis provides insight into the synthesis of c-QDs. We used Machine-learning techniques (ML) such as pcA, McR-ALS, and nMf-ARD-So in order to provide a plausible explanation for the origin of the pL mechanism of as-synthesized c-QDs. ML techniques are capable of handling and analyzing the large pL data-set, and institutively recommend the best excitation wavelength for pL analysis. Mono-disperse c-QDs are highly desirable and have a range of potential applications in bio-sensing, cellular imaging, LeD, solar cell, supercapacitor, printing, and sensors.
关键词： Machine learning,Fennel seeds,Thermal decomposition,Photoluminescence,Carbon quantum dots
更新于2025-09-16 10:30:52
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Incorporating Statistical Test and Machine Intelligence Into Strain Typing of Staphylococcus haemolyticus Based on Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry
摘要： Staphylococcus haemolyticus is one of the most significant coagulase-negative staphylococci, and it often causes severe infections. Rapid strain typing of pathogenic S. haemolyticus is indispensable in modern public health infectious disease control, infections to prevent further infectious facilitating the identification of the origin of outbreak. Rapid identification enables the effective control of pathogenic infections, which is tremendously beneficial to critically ill patients. However, the existing strain typing methods, such as multi-locus sequencing, are of relatively high cost and comparatively time-consuming. A practical method for the rapid strain typing of pathogens, suitable for routine use in clinics and hospitals, is still not available. Matrix-assisted laser desorption ionization-time of flight mass spectrometry combined with machine learning approaches is a promising method to carry out rapid strain typing. In this study, we developed a statistical test-based method to determine the reference spectrum when dealing with alignment of mass spectra datasets, and constructed machine learning-based classifiers for categorizing different strains of S. haemolyticus. The area under the receiver operating characteristic curve and accuracy of multi-class predictions were 0.848 and 0.866, respectively. Additionally, we employed a variety of statistical tests and feature-selection strategies to identify the discriminative peaks that can substantially contribute to strain typing. This study not only incorporates statistical test-based methods to manage the alignment of mass spectra datasets but also provides a practical means to accomplish rapid strain typing of S. haemolyticus.
关键词： MALDI-TOF MS,strain typing,Fisher’s exact test,Staphylococcus haemolyticus,machine learning
更新于2025-09-16 10:30:52
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[IEEE 2019 18th International Conference on Optical Communications and Networks (ICOCN) - Huangshan, China (2019.8.5-2019.8.8)] 2019 18th International Conference on Optical Communications and Networks (ICOCN) - An Equalization Method based on W-KNN for PON with PAM4
摘要： We proposed a machine learning equalization technique based on weighted KNN for 50Gbps PAM4 PON systems. The simulation results show that 35dB loss budget is achieved by Weighted-KNN equalization technique.
关键词： KNN,PON,PAM4,Machine learning
更新于2025-09-16 10:30:52
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Machine-learning assisted prediction of spectral power distribution for full-spectrum white light-emitting diode
摘要： The full-spectrum white light-emitting diode (LED) emits light with a broad wavelength range by mixing all lights from multiple LED chips and phosphors. Thus, it has great potentials to be used in healthy lighting, high resolution displays, plant lighting with higher color rendering index close to sunlight and higher color fidelity index. The spectral power distribution (SPD) of light source, representing its light quality, is always dynamically controlled by complex electrical and thermal loadings when the light source operates under usage conditions. Therefore, a dynamic prediction of SPD for the full-spectrum white LED has become a hot but challenging research topic in the high quality lighting design and application. This paper proposes a dynamic SPD prediction method for the full-spectrum white LED by integrating the SPD decomposition approach with the artificial neural network (ANN) based machine learning method. Firstly, the continuous SPDs of a full-spectrum white LED driven by an electrical-thermal loading matrix are discretized by the multi-peak fitting with Gaussian model as the relevant spectral characteristic parameters. Then, the Back Propagation (BP) and Genetic Algorithm-Back Propagation (GA-BP) NNs are proposed to predict the spectral characteristic parameters of LEDs operated under any usage conditions. Finally, the dynamically predicted spectral characteristic parameters are used to reconstruct the SPDs. The results show that: (1) The spectral characteristic parameters obtained by fitting with the Gaussian model can be used to represent the emission lights from multiple chips and phosphors in a full-spectrum white LED; (2) The prediction errors of both BP NN and GA-BP NN can be controlled at low level, that is to say, our proposed method can achieve a highly accurate SPD dynamic prediction for the full-spectrum white LED when it operates under different operation mission profiles.
关键词： Machine learning,Spectral power distribution,Genetic algorithm,Full-spectrum white LED,BP neural network
更新于2025-09-16 10:30:52
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[IEEE 2019 18th International Conference on Optical Communications and Networks (ICOCN) - Huangshan, China (2019.8.5-2019.8.8)] 2019 18th International Conference on Optical Communications and Networks (ICOCN) - Object Wedge Angle and Direction Identification Using Machine Learning Algorithms
摘要： We demonstrate identification of object wedge angle and direction using machine learning algorithms based on received beam intensity profiles. CNN outperforms other algorithms with 100% accuracy. Proposed technique reduces the complexity of hardware implementation.
关键词： Image recognition,Machine learning,Object detection,Remote sensing
更新于2025-09-16 10:30:52