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International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)

作者： Priyaradhikadevi, T. Vijayakumari, P. Balaji, M. Chinnammal, V. Vijay, Sreeja Mailam Engn Coll Dept Comp Sci & Engn Mailam India Sairam Engn Coll Chennai India Mohan Babu Univ Dept ECE Erstwhile Sree Vidyanikethan Engn Coll Tirupati India Rajalakshmi Inst Technol Dept ECE Chennai India SA Engn Coll Dept ECE Chennai India

ISBN: (纸本)9798350389432;9798350389449;9798350389456

Wireless sensor networks (WSNs) can benefit from improved network lifetime and energy efficiency when routing algorithms are designed using clustering of sensor nodes. The main nodes in clustered WSNs, known as cluster heads, need more power since they have more work to do. As a result, finding the best cluster heads is a challenging issue. One way to reduce power consumption in WSNs is by using multi-hop routing protocols. But, because all nodes consume energy in an uneven manner, the network lifespan is still degraded. Consequently, in order to assess the efficacy of the suggested approach, we cross-validate it with the conventional model known as Artificial Bee Colony (ABC) and present an enhanced bee colony optimization model referred to as the Improved Bee Colony clustering model (IBCCM). The suggested IBCCM routing scheme aims to optimize energy consumption in wireless sensor networks. This is crucial because wireless network and IoT devices have limited energy reserves. Another goal is to make the network more adaptable so that it can smoothly adapt to unexpected changes, reducing disruptions and optimizing overall network performance. Algorithms take into account associated goals with informative-shaped rewards to speed up learning. By lowering data delivery delay and improving the packet delivery ratio compared to existing routing protocols, we showed via the different simulations that our approach enhanced energy efficiency and allowed for quick adaptability to unanticipated changes in the network. Using priority path selection, the nearest nodes with the lowest energy consumption are located instantly. Priority two is given to nodes with the smallest link costs if the minimum energy between their neighbours is equal. In various instances, the suggested technique is tested to see how well it performs. The simulation results demonstrate that the suggested protocol outperforms other comparable protocols that have been developed recently.

关键词： WSN Energy Efficiency Communication Protocol Improved Bee Colony clustering model IBCCM Artificial Bee Colony ABC Wireless Networks

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Statistical Basis and Physical Evidence for clustering model in FinFET Degradation

Statistical Basis and Physical Evidence for Clustering Model...

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IEEE International Reliability Physics Symposium (IRPS)

作者： Mei, S. Raghavan, N. Bosman, M. Pey, K. L. Singapore Univ Technol & Design Engn Prod Dev EPD Pillar Singapore 487372 Singapore ASTAR Inst Mat Res & Engn Singapore 138634 Singapore

ISBN: (纸本)9781509066414

In this study, we present physical and statistical evidence to highlight the similarities and differences in the kinetics of the dielectric breakdown mechanism in FinFET devices, in comparison to their planar analogues. From a physical perspective, the mechanism of breakdown in FinFET devices still involves epitaxial silicon protrusion into the oxide during the progressive and hard breakdown stages, while the location of breakdown appears more confined to the bottom fin corners in almost all cases examined. From a statistical perspective, the Weibull distribution is no longer suitable for TDDB and residual time analysis. Instead, the clustering model becomes essential not only for wafer-level TDDB studies (due to process variations) but also for device-level studies, considering the field inhomogeneity and oxide thickness non-uniformity in the non-planar fin architecture. Bimodal distribution is observed in all stacks indicating the presence of two different failure mechanisms, possibly extrinsic and intrinsic. However, STEM-EELS and -EDX based examinations on several samples do not show any evidence for more than one physical process driving the degradation.

关键词： clustering model FinFET Reliability Percolation model Process Variation

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A new clustering-function-based formulation of temporal and spatial clustering model involving area scaling and its application to parameter extraction

A new clustering-function-based formulation of temporal and ...

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International Reliability Physics Symposium (IRPS)

作者： Wu, Ernest McGowan, Brian Bolam, Ronald Huang, Huai Zhou, Huimei Wang, Miaomiao IBM Res Div Essex Jct VT 05452 USA IBM Res Div Albany NY USA

ISBN: (纸本)9798350369762;9798350369779

In this work, we develop a new clustering-function-based formulation of the temporal and spatial clustering model. This new formulation unifies the Weibull function and the clustering function into a common framework. By properly defining this clustering function, we show both the Weibull function and the clustering function have the same linear dependence on logarithmic time. More importantly, the area transformation of the clustering function is shown to be identical to that of the Weibull function. These two important properties allow us to apply the clustering function analysis methodology for the parameter extraction of experimental data, particularly with multiple areas. We demonstrate the validity of this methodology with its application to several experimental datasets with a new analysis of both T-BD and Q(BD) data. We also discuss future improvements and limitations of the clustering model in excessive variability.

关键词： TDDB variability clustering function analysis methodology clustering model dielectric breakdown failure BEOL MOL FEOL ReRAM

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Wetland Extraction in Sanjiang Plain Based on Self-Organized Feature Map Neural Network clustering model

Wetland Extraction in Sanjiang Plain Based on Self-Organized...

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1st WRI Global Congress on Intelligent Systems (GCIS 2009)

作者： Liu Hanli Pei Tao Zhou Chenghu E China Normal Univ Minist Educ Key Lab Geog Informat Sci Shanghai 200062 Peoples R China Chinese Acad Sci Inst Geog Sci & Natl Resources Res State Key Lab Resources & Environm Informat Syst Beijing 100101 Peoples R China

ISBN: (纸本)9780769535715

By analyzing spectral characteristics of MODIS remote sensing data in Sanjiang Plain, we extract the wetland in this area based on a set of multi-temporal and multi-spectral MODIS data We first transform the selected data by a Minimum Noise Fraction (MNF) rotation and take the first two components of the transformed data as the experimental data. To improve the accuracy of classification of wetland, we use a Self-organization Feature Map (SOM) neural network model. SOM has a good performance in resisting noise and can be implemented with parallel processing technique. It is capable of keeping the topological structure of the original data. As a result, it may achieve a better classification compared with other clustering models After clustering, we perform a discrete wavelet transform (DWT) to smooth the data and eliminate noise from the data. The result shows that the SOM model is effective and the clustering result has been improved.

关键词： SOM neural network clustering model Remote sensing data MODIS Wetland extraction MNF rotation Discrete wavelet transform

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Poisson limits for a hard-core clustering model

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Stochastic Processes and their Applications 1981年第1期12卷 97-106页

作者： Saunders, Roy Kryscio, Richard J. Funk, Gerald M. Department of Mathematical Sciences Northern Illinois University De Kalb IL 60115 United States Department of Mathematics Loyola University of Chicago Chicago IL 60611 United States

Let X1n,...,X>nn denote the locations of n points in a bounded, γ-dimensional, Euclidean region Dn which has positive γ-dimensional Lebesgue measure μ(Dn). Let {Yn(r): r > 0} be the interpoint distance proces... 详细信息

Let X_1n,...,X_>nn denote the locations of n points in a bounded, γ-dimensional, Euclidean region D_n which has positive γ-dimensional Lebesgue measure μ(D_n). Let {Y_n(r): r > 0} be the interpoint distance process for these points where Y_n(r) is the number of pairs of points(X_in, X_in) which with i < j have Euclidean distance {norm of matrix}X_in - X_>in{norm of matrix} < r. In this article we study the limiting distribution of Y_n(r) when n → ∞ and μ(D_n) → ∞, and the joint density of X_1n,...,X_nnis of the form f{hook}(x₁...x₁)=^C_{0 ify} where r₀ is a positive constant and C_n is a normalizing constant. These joint densities modify the Strauss [11] clustering model densities by introducing a hard-core component (no two points can have {norm of matrix}X_in - X_in{norm of matrix} < r₀) found in the Matérn [4] models. In our main result we show that the interpoint distance process converges to a non-homogeneous Poisson process for r values in a bounded interval 0 < r₀ < r < r₀₀ provided sparseness conditions discussed by Saunders and Funk [9] hold. The sparseness conditions which require μ(D_n) n² converges to a positive constant and the boundary of D_n is negligible are essentially equivalent to requiring that although the number of points n is large the region is large enough so that the points are sparse in this region. That is, it is rare for a point to have another point close to it. These results extend results for v ≤ 0 given by Saunders and Funk [9] where it is shown that without the hard core component such results do not hold for v > 0. Statistical applications are discussed. © 1981.

关键词： clustering model hard-core Poisson process raduis of influence sparseness weak convergence

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SVFR: A novel slice-to-volume feature representation framework using deep neural networks and a clustering model for the diagnosis of Alzheimer's disease

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HELIYON 2024年第1期10卷 e23008页

作者： Wang, Rubing Gao, Linlin Zhang, Xiaoling Han, Jinming China Univ Min & Technol Sch Comp Sci & Technol Xuzhou Jiangsu Peoples R China Ningbo Univ Fac Elect Engn & Comp Sci Zhejiang Engn Res Ctr Adv Mass Spectrometry & Cli Ningbo Peoples R China Lihuili Hosp Ningbo Med Ctr Ningbo Peoples R China Capital Med Univ Xuanwu Hosp Dept Neurol Beijing Peoples R China

Deep neural networks (DNNs) have been effective in classifying structural magnetic resonance imaging (sMRI) images for Alzheimer's disease (AD) diagnosis. In this study, we propose a novel two-phase slice-to-volume feature representation (SVFR) framework for AD diagnosis. Specifically, we design a slice-level feature extractor to automatically select informative slice images and extract their slice-level features, by combining DNN and clustering models. Furthermore, we propose a joint volume-level feature generator and classifier to hierarchically aggregate the slice-level features into volume-level features and to classify images, by devising a spatial pyramid set pooling module and a fusion module. Experimental results demonstrate the superior performance of the proposed SVFR, surpassing the majority of the state-of-the-art methods and achieving comparable results to the best-performing approach. Experimental results also showcase the ef-ficacy of the slice-level feature extractor in the selection of informative slice images, as well as the effectiveness of the volume-level feature generator and classifier in the integration of slice-level features for image classification. The source code for this study is publicly available at https:// ***/gll89/SVFR.

关键词： Slice-to-volume feature representation Deep neural networks clustering model Informative slice images Spatial pyramid set pooling module Diagnosis of Alzheimer 's disease

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An Hybrid clustering and BERT model for Chinese Threads in Social Media

An Hybrid Clustering and BERT Model for Chinese Threads in S...

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International Joint Conference on Neural Networks (IJCNN)

作者： Wu, Yu-Hsuan Wu, Jheng-Long Soochow Univ Dept Data Sci Taipei Taiwan

ISBN: (纸本)9798350359329;9798350359312

Communicating in online social media has become the main way of socializing today. Different from faceto-face conversations in the real world, the meaning of online conversations can only be interpreted through text, and it is difficult to observe the hidden meaning. In addition, users often use pronouns or omit subject words in online social media. In this way, the topic of the discussion thread may be led in a direction unrelated to the article content due to wrong interpretation. Most previous studies have explored the topics contained in long texts, such as the entire article content, but have not conducted research on topic clustering of discussion thread data. Therefore, this paper uses the data from social media platforms to identify five entity categories, such as people, events, times, locations, and things, and cluster the discussion thread data into the topic. Finally establish a Chinese discussion thread topic dataset. In addition, this paper uses two methods to obtain response representation and six existing topic clustering models to conduct preliminary topic hybrid clustering experiments on the Chinese discussion thread topic dataset.

关键词： topic model clustering model social media

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A clustering Poisson model for characterizing the interarrival times of sferics

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RADIO SCIENCE 2003年第4期38卷 17-1-17-14页

作者： Chrissan, DA Fraser-Smith, AC Stanford Univ Space Telecommun & Radiosci Lab Stanford CA 94305 USA

The noise waveform of atmospheric radio noise below 100 MHz is typically impulsive in nature. The impulses are caused by atmospheric events, mainly lightning strokes, that create electromagnetic emissions known as sferics. Sferic impulses in the noise waveform are seen to cluster in groups, indicating an underlying clustering process related to the physical characteristics of the lightning mechanism. The objective of this work is the statistical modeling of the clustering of noise impulses in atmospheric radio noise in the range 10 Hz to 60 kHz (denoted low-frequency noise). Based on hundreds of hours of impulse interarrival time measurements made by the Stanford Radio Noise Survey System on such noise, a new clustering Poisson atmospheric noise model is developed to describe the clustering process. This new statistical model is based on several previously known statistical-physical models of atmospheric radio noise, but in addition to these models it takes into account the clustering of sferic impulses. It is shown that the clustering model accurately characterizes the impulse interarrival time distributions found in low-frequency radio noise data.

关键词： atmospheric radio noise clustering model electromagnetic impulses

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An online clustering algorithm predicting model for prostate cancer based on PHI-related variables and PI-RADS in different PSA populations

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CANCER CELL INTERNATIONAL 2025年第1期25卷 1-11页

作者： Hu, Jiyuan Miao, Qi Ren, Jiayi Su, Hongbo Zhang, Xianlu Bi, Jianbin Zhang, Gejun China Med Univ Affiliated Hosp 1 Dept Urol Shenyang 110002 Liaoning Peoples R China China Med Univ Inst Urol Shenyang Peoples R China China Med Univ Affiliated Hosp 1 Dept Radiol Shenyang 110002 Liaoning Peoples R China Shandong Univ Inst Women Children & Reprod Hlth Jinan 250012 Shandong Peoples R China China Med Univ Affiliated Hosp 1 Dept Pathol Shenyang 110002 Liaoning Peoples R China

Background and aim Prostate cancer is the most common male malignancy. Current diagnostic methods using single TPSA and PHI lack specificity. Some researches have created nomograms for predicting risk, but these are not easily visualized. Our study aims to find the best negative predictive value (NPV) for PHI, then build a clustering model to display prostate cancer risk categories, particularly useful for patients with PSA > 20 and be actually applied in clinical work. Method We collected 708 patients in the training cohort and 143 in the validation cohort, divided into three groups based on their PSA levels. Next, we determined optimal and customized PHI cut-off values, calculated NPV and PPV, and selected logistic regression as the best method among several machine-learning algorithms. Subsequently, the significant variables were identified, and then a clustering algorithm was constructed. Finally, the model was validated and made available online for further clinical application. Results The Optimal PHI cut-off lower limits for PSA > 4, PSA4-20, PSA > 20 subgroups were 23.85, 24.35, and 40.75, with upper limits of 142.9, 143, and 135.6, respectively. The clustering model of the optimal cohort for PSA > 4 and PSA 4-20 sub-groups showed a superior Silhouette coefficients of 0.433 and 0.526 than that of the customized PHI cohort (0.432, 0.452). The PSA > 20 subgroup owned the highest Silhouette coefficient of 0.572. The validation cohort showed AUC values of 0.761, 0.823, 0.833 for these 3 sub-groups, with accuracy rates of 88.81%, 90.38%, and 82.05%. Conclusion In conclusion, our clustering model effectively categorizes patients into distinct risk groups with clear visualization and has demonstrated stability and reliability in the validation cohort, potentially aiding in early diagnosis of prostate cancer in clinical practice.

关键词： Prostate cancer PHI clustering model Clinical application Biopsy

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clustering investigation of scramjet combustion processes based on contrastive learning

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ACTA ASTRONAUTICA 2025年 233卷 55-65页

作者： Liu, Hedong Chen, Yuqian Huang, Yue Song, Ting Shen, Wubingyi Tian, Ye You, Yancheng Xiamen Univ Inst Artificial Intelligence Xiamen 361005 Peoples R China Xiamen Univ Sch Aerosp Engn Xiamen 361005 Peoples R China Zhuhai Fudan Innovat Inst Zhuhai 519031 Peoples R China China Aerodynam Res & Dev Ctr Mianyang 621000 Peoples R China

The transition between combustion processes or modes of scramjet engines is high-speed and dynamic, which may lead to unstable or even runaway. It is crucial to identify the combustion state of scramjet engine for controlling and maintaining stable operation. Based on the methods of contrastive learning, a clustering network model of combustion processes is developed to deal with the schlieren image of a scramjet combustor without labels. Firstly, manual classification is performed to define and classify the combustion processes of scramjet combustor under different experimental conditions. The datasets of scramjet combustion processes are constructed by using schlieren images. Meanwhile, the unsupervised clustering of combustion processes is realized to learn its change rule upon the contrastive learning framework of examples. Subsequently, the results of manual classification and model clustering are compared and analyzed to evaluate the identification performance. The results suggest that the clustering results of the model are basically the same as those of manual classification. All evaluation indicators show a good performance using the clustering model, and the Adjusted Rand Index and Accuracy are over 0.94 and 0.89, respectively. This work indicates that the application of an unsupervised contrastive learning method is impressively efficient in the combustion state identification of scramjet.

关键词： Scramjet combustion clustering model Contrastive learning Neural network

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