Membership Information Research Articles

LiDAR point cloud simplification strategy utilizing probabilistic membership

With the continuous progress of information acquisition technology, the volume of LiDAR point cloud data is also expanding rapidly, which greatly hinders the subsequent point cloud processing and engineering applications. In this study, we propose a point cloud simplification strategy utilizing probabilistic membership to address this challenge. The methodology initially develops a feature extraction scheme based on curvature to identify the set of feature points. Subsequently, a combination of k-means clustering and Possibilistic C-Means is employed to partition the point cloud into subsets, and to simultaneously acquire the probabilistic membership information of the point cloud. This information is then utilized to establish a rational and efficient simplification scheme. Finally, the simplification results of the feature point set and the remaining point set are merged to obtain the ultimate simplification outcome. This simplification method not only effectively preserves the features of the point cloud while maintaining uniformity in the simplified results but also offers flexibility in balancing feature retention and the degree of simplification. Through comprehensive comparative analysis across multiple point cloud models and benchmarking against various simplification methods, the proposed approach demonstrates superior performance. Finally, the proposed algorithm was critically discussed in light of the experimental results.

Integrated Fault-Tolerant Control Design With Sampled-Output Measurements for Interval Type-2 Takagi-Sugeno Fuzzy Systems.

This article is concerned with the integrated design of fault estimation (FE) and fault-tolerant control (FTC) for uncertain nonlinear systems suffering from actuator faults and external disturbance. The uncertain nonlinear systems are characterized as the interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy model, and IT2 membership functions are employed to effectively handle uncertainties. A fuzzy observer, utilizing only sampled-output measurements, is applied to simultaneously estimate actuator faults and system states. Based on the estimation, the fault-tolerant controller is designed to ensure the system stability under a predefined H∞ performance. The sampling behavior complicates the system dynamics and makes the integrated FTC design more challenging. To confront this issue, the discontinuous Lyapunov functional technique is exploited to enhance stability results by considering the sampling characteristic, upon which FE and FTC units are co-designed in the linear matrix inequality (LMI) framework. To further relax stability criteria, the analysis process incorporates the bound information of membership functions through the membership-function-dependent (MFD) method. Additionally, the relationship of mismatched premise variables resulting from the sampling scheme is also taken into account. Moreover, considering the imperfect premise matching (IPM) framework, the proposed fault-tolerant controller provides greater flexibility in selecting the shapes of membership functions and number of fuzzy rules that can vary from the counterpart of the fuzzy system. Finally, the efficacy of the proposed FTC technique is validated through a detailed numerical example.

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Comparison of Gender Diversity Among Spine Surgeons in the Japanese Society for Spine Surgery and Related Research and the Neurospinal Society of Japan: A Descriptive Study Through Secondary Analysis of Aggregated Data.

This was a descriptive study through secondary analysis of aggregated data. This study aimed to describe changes in women's membership in the Japanese Society for Spine Surgery and Related Research (JSSR) for orthopedic surgery and the Neurospinal Society of Japan (NSJ) for neurosurgery over the past decade and make predictions for the future. Although the ratio of women physicians in the field of spine surgery is known to be low worldwide, there is a lack of detailed surveys in Japan. We sent emails to the JSSR and NSJ secretariats to verify membership information (gender and age) from 2013 to 2022. Using ordinary least squares, we projected the years it would take for the JSSR and NSJ to achieve a gender diversity ratio of 30%. In 2013, the percentage of women in JSSR and NSJ was 2.3% and 2.7%, respectively. However, after 2018, the percentage of women in NSJ will be higher than in JSSR, rising to 2.7% in JSSR and 4.7% in NSJ by 2022. It would require 101 years for the NSJ and more than 1,000 years for the JSSR to realize 30% gender diversity. JSSR and NSJ have low percentages of women. Improving gender diversity is an important issue for both societies, and they may collaborate on finding a good solution. Both the JSSR and NSJ societies need to actively address gender diversity and become more attractively represented in society for the next generation of spine surgeons.

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Similarity measure on intuitionistic fuzzy sets based on Benchmark Line and it’s diverse applications

An intuitionistic fuzzy set (IFS) is an extension of fuzzy sets that can deal with inconsistent and uncertain information more accurately. In this paper, a novel similarity measure is presented that might be applied to several application issues in decision-making, pattern recognition, and medical diagnostics. IFSs are sources of information that contain both the membership and non-membership degrees of the elements in the set. As such, similarity measures based on geometric concepts might occasionally be misleading. Hence, five parameters are specified to develop the similarity measure. These parameters are membership information similarity, non-membership information similarity, maximum cross-information similarity, minimum cross-information similarity, and product cross-information similarity. Most of the existing similarity measures cannot reasonably determine the appropriate similarity degree when the IFS points lie on the benchmark line. On the other hand, product cross-information similarity is a new parameter introduced in this study that has a significant role in handling benchmark problems more effectively and reasonably. The proposed similarity measure compensates for several counterintuitive problems related to decision-making and pattern recognition. Complexity analysis illustrates the complexity level of the proposed measure, which is reliable. Finally, various challenges with applications in decision-making, pattern recognition, and medical diagnosis difficulties are used to validate the suggested similarity measure.

Meaning in life as a moderator of the relationship between bullying victimization profiles and life satisfaction in young adolescents

AbstractLatent profiles of bullying victimization were examined to determine membership information and distinctive buffering roles of meaning in life between bullying victimization groups and life satisfaction. The PISA data set (n = 4695) of 15‐year‐old young adolescents was analyzed. Static predictors of membership consist of gender, immigrant status, parental education, and grade repetition. Latent profile analysis, analysis of variance, multiple logistic regression, and multivariable regression were utilized for a comprehensive understanding of bullying victimization memberships and related outcomes. Three distinctive latent profiles emerged: (a) Stable & Resilient (SR) (75.6%); (b) Mild & Internal (MI) (18.4%); and (c) Severe & External (SE) (6.0%). Immigration, gender, grade repetition, and parental education were found to be predictors of membership in the bullying victimization groups. Males, and immigrants, with higher parental education, and grade repetition experience significantly predicted increases in possibilities of membership in the MI or SE latent groups. Meaning in life has significant buffering roles for bullying victimization on life satisfaction. The moderating effect of meaning in life was strongest for the SE group. Implications and future directions are discussed.

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ROBUST DETECTION OF LINK COMMUNITIES WITH SUMMARY DESCRIPTION IN SOCIAL NETWORKS

Community detection has been extensively studied for various applications. Recent research has started to explore node contents to identify semantically meaningful communities. However, links in real networks typically have semantic descriptions and communities of links can better characterize community behaviors than communities of nodes. The second issue in community finding is that the most existing methods assume network topologies and descriptive contents carry the same or compatible information of node group membership, restricting them to one topic per community, which is generally violated in real networks. The third issue is that the existing methods use top ranked words or phrases to label topics when interpreting communities, which is often inadequate for comprehension. To address these issues altogether, we propose a new Bayesian probabilistic approach for modeling real networks and developing an efficient variational algorithm for model inference. Our new method explores the intrinsic correlation between communities and topics to discover link communities and extract semantically meaningful community summaries at the same time. If desired, it is able to derive more than one topical summary per community to provide rich explanations. We present experimental results to show the effectiveness of our new approach and evaluate the method by a case study.

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Shared Latent Membership Enables Joint Shape Abstraction and Segmentation with Deformable Superquadrics.

Part-level 3D shape representations are crucial to shape reasoning and understanding. Two key sub-tasks are (i) shape abstraction, creating primitive-based object parts, and (ii) shape segmentation, finding partition-based object parts. However, for 3D object point clouds, most advanced methods produce parts relying on task-specific priors, such as similarity metrics and primitive geometries, resulting in misleading parts that deviate from semantics. To address prior limitations, we establish a foundation for joint shape abstraction and shape segmentation as formal linear transformations within a shared latent space, encapsulating essential dual-purpose membership information linking points and object parts for mutual reinforcement. We demonstrate that the transformations are underpinned by a derivation based on k-means, Non-negative Matrix Factorization (NMF), and the attention mechanism. As a result, we introduce Latent Membership Pursuit (LMP) for joint optimization of shape abstraction and segmentation. LMP utilizes a shared latent representation of object part membership to autonomously identify common object parts in both tasks without any supervision and priors. Furthermore, we adapt deformable superquadrics (DSQs) for primitives to capture variable part-level geometric and semantic information. Experiments on benchmark datasets validate that our approach enables mutual learning of shape abstraction and segmentation, and promotes consistent interpretations of 3D object shapes across instances and even categories in a fully unsupervised manner.

Fuzzy neuron modeling of incomplete data for missing value imputation

Missing values are a common problem found in many real-world datasets, and cannot be avoided. It is a challenging task to model incomplete data and reasonably impute missing values. This paper focuses on regression imputation and uses a tracking-removed autoencoder (TRAE) to construct the mutual fitting correlation on incomplete data. Considering the differences in regression relationships across different sample categories, we introduce Takagi-Sugeno (TS) fuzzy architecture and propose a category-based tracking-removed autoencoder (TS-TRAE) to model incomplete data for missing value imputation. The TS-TRAE model partitions the incomplete dataset into several subclusters using membership information obtained from fuzzy clustering, then establishes a TRAE-based submodel to mine relationships within each subcluster for precise modeling of incomplete data. During model training, in order to fully utilize all existing values, we treat missing values as variables and propose an iterative learning method that optimizes missing variables and network parameters collaboratively. This method allows incomplete samples to participate in model training while also enabling the imputation of missing values. The TS-TRAE model integrates the inner category structure of incomplete data and the attribute association features effectively. The experimental results verify the effectiveness of the proposed method.

Exploration of faint X-ray and radio sources in the massive globular cluster M14: a UV-bright counterpart to Nova Ophiuchus 1938

ABSTRACT Using a 12 ks archival Chandra X-ray Observatory ACIS-S observation on the massive globular cluster (GC) M14, we detect a total of 7 faint X-ray sources within its half-light radius at a $0.5{\small --}7\, \mathrm{keV}$ depth of $2.5\times 10^{31}\, \mathrm{erg s^{-1}}$. We cross-match the X-ray source positions with a catalogue of the Very Large Array radio point sources and a Hubble Space Telescope (HST) UV/optical/near-IR photometry catalogue, revealing radio counterparts to 2 and HST counterparts to 6 of the X-ray sources. In addition, we also identify a radio source with the recently discovered millisecond pulsar PSR 1737−0314A. The brightest X-ray source, CX1, appears to be consistent with the nominal position of the classic nova Ophiuchi 1938 (Oph 1938), and both Oph 1938 and CX1 are consistent with a UV-bright variable HST counterpart, which we argue to be the source of the nova eruption in 1938. This makes Oph 1938 the second classic nova recovered in a Galactic GC since Nova T Scorpii in M80. CX2 is consistent with the steep-spectrum radio source VLA8, which unambiguously matches a faint blue source; the steepness of VLA8 is suggestive of a pulsar nature, possibly a transitional millisecond pulsar with a late K dwarf companion, though an active galactic nucleus (AGN) cannot be ruled out. The other counterparts to the X-ray sources are all suggestive of chromospherically active binaries or background AGNs, so their nature requires further membership information.

Research on the optimal model for the evaluation of new power system investment projects based on the cloud model–DS evidence theory–TOPSIS method

AbstractConsidering that the traditional investment project evaluation system can no longer fully adapt to the characteristics of the new power system under the investment environment of the new power system, this study constructed a new power system investment project evaluation and optimization method based on the combination of the cloud model, evidence theory and the TOPSIS method. First, 16 evaluation indexes were considered based on four dimensions, namely green and low‐carbon, safe and reliable, flexible and intelligent, and economical and efficient, and the optimal evaluation index system of new power system investment projects was designed. Second, the normal cloud model was used to obtain the evaluation membership distribution of each index and turn it into evidence. Considering the conflicts between indicators and the combinational contradictions of traditional evidence theories, an improved DS evidence theory based on a game weight coefficient discount to reduce conflicting evidence was used to fuse the membership information of multiple indicators. On the basis of the evaluation results of each scheme, this study used the TOPSIS method to sort the advantages and disadvantages of each scheme. Finally, the project of planning and putting this model into operation in the 14th Five‐Year project database of a power grid enterprise was selected as an example to carry out empirical analysis, and the effectiveness and superiority of the proposed model were verified.

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