The entropy-based fault complexity characterization method has garnered significant attention in recent times, owing to its effectiveness and superiority in monitoring the health status of rotating machinery. Due to i...
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The entropy-based fault complexity characterization method has garnered significant attention in recent times, owing to its effectiveness and superiority in monitoring the health status of rotating machinery. Due to its high consistency, diversity Entropy (DE) can effectively quantify the irregularity of data and has been widely used in complexity analysis and fault diagnosis. However, the rigorous classification boundary leads to the absence of cosine similarity diversity during DE calculation, which will cause the inaccurate complexity estimation of time series collected from rotating machineries, unable to fully capture subtle changes in the signal, and affecting the accurate representation of fault features. In this paper, fuzzy diversity entropy (FDE) is proposed to solve this problem by incorporating the concept of fuzzy sets during the calculation diversity entropy. FDE employs fuzzy membership degrees as a replacement for the probability of cosine similarity falling into each interval, effectively distinguishing the cosine similarity of the same class that is considered equivalent by DE, and enhancing sensitivity to subtle signal variations. FDE effectively preserves the diversity information in the signal, and entropy estimation is more comprehensive and accurate, reflecting the complex dynamic characteristics of rotating machinery more realistically. Performance of the proposed FDE algorithm is verified by both numerically simulated signals and experimental signals collected from rotating machinery in comparison to original DE algorithm along with state-of-the-art fuzzy entropy (FE) and permutation entropy (PE). Results show that FDE can not only effectively quantify the complexity of rotating machinery time series but also possess low parameter sensitivity and computational cost. Furthermore, the experimental results have verified that FDE can be effectively applied in vibration signal feature extraction and fault diagnosis.
Physiological signals, manifested as time series, reflect the internal transitions of physiological systems. Analyzing their complexity provides insights into the system's core characteristics. However, traditiona...
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Physiological signals, manifested as time series, reflect the internal transitions of physiological systems. Analyzing their complexity provides insights into the system's core characteristics. However, traditional techniques based on one-dimensional time series waveforms are limited, especially in the presence of noise. We introduce the Periodic Distribution Entropy (PDEn) as a solution. PDEn employs a high -dimensional strategy to model low -dimensional signals, aiming to measure complexity through the variability of attractor return map orbits. Our simulations indicate that PDEn is more resilient against different types and intensities of observational noise than other methods. In practical tests with real -world signal, where dynamical noise introduces more complexity, PDEn adeptly identifies atrial fibrillation from ECG data, exceeding other techniques in both effectiveness and consistency. Furthermore, PDEn accurately identifies epileptic seizures within larger EEG datasets and efficiently distinguishes between healthy individuals and Parkinson's patients from the more intricate integrated multi -sensor gait signals. Following an analysis of physical mechanisms, we highlight PDEn's inherent advantage over alternative methods and, based on this, elucidate the connection between orbit heterogeneity and physiological pathological changes. Emphasizing its quantification strategy, PDEn stands out in precision and robustness, especially in detecting disease states from signals with noise, captured by medical devices in the real world.
Morphological complexity reflects the biological structure of an organism and is closely linked to its associated functions and phylogenetics. In animals with shells, ornamentation is an important characteristic of mo...
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Morphological complexity reflects the biological structure of an organism and is closely linked to its associated functions and phylogenetics. In animals with shells, ornamentation is an important characteristic of morphological complexity, and it has various functions. However, because of the variations in type, shape, density, and strength of ornamentation, a universal quantitative measure of morphological complexity for shelled animals is lacking. We propose an ornamentation index (OI) derived from 3D scanning technology and a virtual model for quantifying ornamentation complexity. This index is designed to measure the extent of folding associated with ornamentation, regardless of shape and size. Ornamentation indices were measured for 15 ammonite specimens from the Permian to Cretaceous, 2 modern bivalves, 2 gastropods from the Pliocene to the present, and a modern echinoid. Compared with other measurements, such as the fractal dimension, rugosity, and surface-volume ratio, the OI displayed superiority in quantifying ornamentational complexity. The present study demonstrates that the OI is suitable for accurately characterizing and quantifying ornamentation complexity, regardless of shape and size. Therefore, the OI is potentially useful for comparing the ornamentational complexity of various organisms and can be exploited to provide further insight into the evolution of conchs. Ultimately, the OI can enhance our understanding of morphological evolution of shelled organisms, for example, whether shell ornaments simplify under ocean acidification or extinction, and how predation pressure is reflected in ornamentation complexity.
complexity is a term applied throughout the project management field, and project complexity typically presents additional management challenges to achieving project objectives. Without an appropriate approach to asse...
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Power electronics are becoming increasingly important in modern ship board power systems. They are used for development and implementation of controls and to interface different electrical modules such as loads, batte...
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Power electronics are becoming increasingly important in modern ship board power systems. They are used for development and implementation of controls and to interface different electrical modules such as loads, batteries and generators that produce, store or consume energy. Power electronic systems are suitable for control because they provide fast operation in the range of microseconds. However, fast control operations can also add complexity to the system. Unintended complex dynamics can arise if the system (inertia) is unable to adapt to the control actions. This article studies the complexity and emergent phenomena that may develop in a ship board power system because of the power electronic components, coupling between these components, as well as feedback mechanisms such as control loops and those of human and environmental origin. A statistical complexity measure, referred to as structural complexity, is used to quantify the degree of complexity that arises during the system evolution. This metric of complexity is computed using permutation entropy and ordinal patterns. A modified procedure for structural complexity called multivariable structural complexity is developed to compute the system wide complexity. This multivariable structural complexity is also used to assist the modelling process and the decision of the best model candidate that captures observed aspects of complexity from the system data history. Various case studies on complexity quantification are conducted on simulated data from a noise coupled buck converter, two parallel connected buck converters and an electric ship board power system.
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