Order Entropy Research Articles

Living system as the core of material circulation between nature and humans

Due to the critical study of Kohei Saito on Marx’s notes written after 1868, the image of Marx as ecologist is focused on today. According to Saito, Marx tried to rewrite Capital from the ecological viewpoint. Prior to Saito, however, a heterodox Marxian political economist Yoshiro Tamanoi has referred to the existence of Marx’s notes during 1970s, even when the contents of the notes had not been disclosed. Tamanoi emphasized that Marx had shifted his major interest from the logic of capital to traditional communities which had conducted the material circulation between nature and humans. According to Tamanoi, Marx studied various types of non-western communities, such as Russian Mir, in order to find out the solution to retrieve the material circulation destroyed by the capitalist mode of production. In this paper, the light is shed on the concept of living system, i.e., the concept which, Tamanoi believed, must be placed at the center of the material circulation between nature and humans. Living system is a system which takes in the heat and matter in the state of low entropy and gets rid of those in the state of high entropy in order to sustain life. According to Tamanoi, late Marx’s study on communities could be extended to the search for the socioecological system which keeps its entropy in terms of heat and matter as low as possible in order to sustain the metabolic movement of the system.

Preface

The 14th Five-Year Plan clearly points out that we should adhere to the core position of innovation in the overall situation of modernization, regard scientific and technological self-reliance and self-improvement as the strategic support for national development. At present, from a global perspective, science and technology have increasingly become the main force to promote economic and social development, and innovation is the trend of the times. In the fierce international competition, only innovators advance, only innovators are strong, and only innovators win.However, the foundation of China’s scientific and technological innovation is not yet solid, there is still a significant gap in the level of innovation, and in some fields the gap has not narrowed, but has a tendency to expand. Therefore, improving innovation capabilities and creating an innovation ecology are the top priorities for building an innovative country and a world science and technology power.The International Conference on Mechanical Engineering and Automation (MEA 2022), as a sub-venue of the 4th International Conference on Scientific and Technological Innovation, was held in Chongqing, China on December 9th to 11th, 2022 via virtual form.MEA 2022 focused on the latest research and technology of Mechanical Engineering and Automation to discuss the innovation and development in these fields, and to create a forum for students, scholars, engineers, participants to give reports, share ideas as well as get new advice and insights from each other. It brought together about 150 participants. Many renowned professors delivered dramatic keynote speeches during the conference. One of them, Chunbo Xiu, a Professor from Tiangong University, China, shared a report on Design and Circuit Implementations of Memristor Hyperchaotic System. To enhance the order and Kolmogorov entropy of the memristive hyperchaotic system and improve the realizability of the circuit, a memristive hyperchaotic system that consists of two flux-controlled memristors and one charge-controlled memristor was designed. Combined with Lyapunov exponential spectrum, phase trajectory diagram and bifurcation diagram, dynamic characteristics of the system were analyzed, and the effects of system control parameters and initial state on the system dynamic behavior were explored.This volume covers topics on all fields of research related to Mechanical Engineering and Automation, including Mechatronics, Intelligent Control, Vehicle Engineering, Detection and Sensing Technology, Intelligent Optimization Algorithms, etc., increasing the number of scientific publications for science and technology innovation in related fields.We would like to thank all the participants in the International Conference on Mechanical Engineering and Automation. We would also like to thank the Conference Chairs, all the Technical Program Committee and Organizing Committee members, for the work and preparation they put in to organize this event. Finally, our special acknowledgements go to the Journal of Physics: Conference Series, for the endeavor of all its colleagues in publishing this paper volume.The Committee of MEA 2022List of Committee Member is available in this pdf.

Periodic orbits in deterministic discrete-time evolutionary game dynamics: An information-theoretic perspective.

Even though the existence of nonconvergent evolution of the states of populations in ecological and evolutionary contexts is an undeniable fact, insightful game-theoretic interpretations of such outcomes are scarce in the literature of evolutionary game theory. As a proof-of-concept, we tap into the information-theoretic concept of relative entropy in order to construct a game-theoretic interpretation for periodic orbits in a wide class of deterministic discrete-time evolutionary game dynamics, primarily investigating the two-player two-strategy case. Effectively, we present a consistent generalization of the evolutionarily stable strategy-the cornerstone of the evolutionary game theory-and aptly term the generalized concept "information stable orbit." The information stable orbit captures the essence of the evolutionarily stable strategy in that it compares the total payoff obtained against an evolving mutant with the total payoff that the mutant gets while playing against itself. Furthermore, we discuss the connection of the information stable orbit with the dynamical stability of the corresponding periodic orbit.

Currents Analysis of a Brushless Motor with Inverter Faults—Part II: Diagnostic Method for Open-Circuit Fault Isolation

In this paper, a brushless motor with a three-phase inverter is investigated under healthy and multiple open-circuit faults. The occurrence of faults in an inverter will lead to atypical characteristics in the current measurements. This is why many usual entropies and multiscale entropies have been proposed to evaluate the complexity of the output currents by quantifying such dynamic changes. Among this multitude of entropies, only some are able to differentiate between healthy and faulty open-circuit conditions. In addition, another selection is made between these entropies in order to improve diagnostic speed. After the fault detection based on the mean values, the open-circuit faults are localized based on the fault diagnostic method. The simulation results ensure the ability of these entropies to detect and locate open-circuit faults. Moreover, they are able to achieve fault diagnostics for a single switch, double switches, three switches, and even four switches. The diagnostic time to detect and to isolate faults is between 10.85 ms and 13.67 ms. Then, in order to prove the ability of the fault diagnostic method, a load variation is performed under the rated speed conditions of the brushless motor. The validity of the method is analyzed under different speed values for a constant torque. Finally, the fault diagnostic method is independent from power levels.

Rényi Entropy and Variance Comparison for Symmetric Log-Concave Random Variables

We show that for any α > 0 the Rényi entropy of order α is minimized, among all symmetric log-concave random variables with fixed variance, either for a uniform distribution or for a two sided exponential distribution. The first case occurs for α ∈ (0, α*] and the second case for α ∈ [α*,∞), where α* satisfies the equation 2 log α* = (α* – 1) log 6, that is α* ≈ 1.241. We deduce that the one-sided exponential distribution minimizes Rényi entropy of order α ≥ 2 among all log-concave random variables with fixed variance.zed, under fixed variance, when the weights are equal.

Hybrid multi-objective optimization and thermo-economic analysis of a multi-effect desalination unit integrated with a fuel cell-based trigeneration system

Due to fossil fuel depletion and global warming, biomass plays a significant role in supplying the required energy in the industrial and residential sectors. In order to develop clean and efficient conversion technology, an innovative combined cooling, heating and power (CCHP) system based on biomass is introduced in this paper. The proposed system comprises a gasification process, solid oxide fuel cell, gas turbine cycle, Rankine and organic Rankine cycles, absorption chiller, and multi-effect desalination unit simulated in ASPEN. In this system, a new configuration is proposed in order to regenerate the waste heat efficiently at every level of temperature and increase energy and exergy efficiencies. Also, thermodynamics, economic, and sensitivity analyses are performed in order to investigate the energy and exergy efficiencies, the overall cost of the system, and effective parameters. The novel optimization approach (RSM-GA-TOPSIS) is introduced by combining the response surface methodology, genetic algorithm, and Shannon entropy and TOPSIS in order to model and optimize the objective functions in DESIGN EXPERT and MATLAB software, respectively. The objectives of the optimization are maximizing energy and exergy efficiencies and minimizing the overall cost of the system. As a result, based on the initial conditions, the electricity production, overall energy and exergy efficiencies, overall cost of the system, and overall exergy destruction can be acquired at 25.235 MW, 76.69%, 57.07%, 6.1063 × 106 $, and 26.03 MW, respectively. After performing the optimization method, the optimum solution (point 3) is obtained through the TOPSIS approach. In terms of thermodynamics analysis, the electricity production, energy and exergy efficiencies are acquired at 31 MW, 88.15%, and 67.30%, which are increased by 22.85%, 14.94%, and 17.93%, respectively, compared with the initial conditions of the proposed system. In terms of economic analysis of the optimum solution, the annualized total cost, COE, and COTP can be obtained at 6.594 × 106 $, 7.3857 $/GJ, and 6.7624 $/GJ, respectively. By comparing this point to the initial conditions, although the annualized total cost at this point is increased by 7.99%, the COE and COTP are decreased by 12.1% and 10.4% due to a further increase in electricity production. Therefore, for producing energy with high efficiency and low COE, the system should be operated according to the working conditions of this point (point 3).

Two new parameters for the ordinal analysis of images.

Local patterns play an important role in statistical physics as well as in image processing. Two-dimensional ordinal patterns were studied by Ribeiro et al. who determined permutation entropy and complexity in order to classify paintings and images of liquid crystals. Here, we find that the 2 × 2 patterns of neighboring pixels come in three types. The statistics of these types, expressed by two parameters, contains the relevant information to describe and distinguish textures. The parameters are most stable and informative for isotropic structures.

Fuzzy Evaluation of Inland Ship Lock Service Condition Based on Combination Weighting and Matter-Element Extension Cloud Model

Ship lock as a typical hydraulic structure has become an important node in waterway transportation. Due to the long operating life and high demand of throughput, many locks are under the overloaded operation situation. However, the service condition assessment of ship locks has rarely been directly studied, and there is a lack of an efficient and standardized method owing to the complex structure of the ship lock system. In this paper, a multi-level hierarchical system including 36 indexes was constructed based on the engineering breakdown structure theory. The synthetic weights of indexes were determined by the order relation method and entropy weight method combining subjectivity and objectivity. The extension cloud model combining the extension theory and cloud model was put forward, aiming to deal with the uncertainty of fuzziness and randomness in the evaluation process. Then, two typical locks were investigated, and the numerical scores indicated that their states belong to Level III and Level IV, respectively. The proposed method reveals the structural condition and provides theoretical reference for the maintenance of ship locks, which can be applied with generalizability and operability.

Thermodynamic Properties and Critical Behavior of Spin-Polarized Atomic Hydrogen (H↓) Using the Quantum Second-Virial Coefficient

The quantum–mechanical formulations for the second-virial, and the acoustic virial, coefficients are derived for spin-polarized atomic hydrogen (H↓). The dependence of these coefficients on the temperature T as well as the nuclear polarization $$\zeta \;$$ is analyzed. This is done for the first time. The main inputs in these computations are the scattering phase shifts, which are obtained using the Lippmann–Schwinger equation, with the Silvera triplet-state potential. Starting with these phase shifts, comprehensive calculations of the thermophysical properties for this system are performed for T ranging from 1 µK to 100 K, and $$\zeta \;$$ varying from 0 to 1. These properties include, in addition to the quantum second-virial coefficient and the acoustic virial coefficient: the pressure–polarization–temperature $$\left( {P {-} \zeta {-} T} \right)$$ behavior, the entropy (per atom), the speed of sound, the second-virial correction (to the total internal energy per atom and unit density), and the specific heat capacity (per atom and unit density). The Boyle temperature and the Joule inversion temperature are determined. The T-dependence of these thermophysical properties, and related quantities, is explored. As expected, this dependence is most evident in the low-T limit, where quantum effects predominate. The corresponding $$\zeta$$ -dependence becomes noticeable at 80 K and below. It is observed that the virial coefficients tend to decrease with increasing $$\zeta$$ . Comparison of the present results to previous results are included whenever possible. The overall agreement is very good. As T is lowered, the disruptive effects of thermal energy are weakened relative to the attractive interactions between the atoms. Consequently, the H↓ gas makes a transition to a state of higher order and lower entropy—Bose–Einstein condensation (BEC). This is explored carefully.

Multiocular disease detection using a generic framework based on handcrafted and deep learned feature analysis

Diabetic retinopathy (DR) and Glaucoma are two major ocular diseases that lead to vision impairment if not detected and treated promptly. Manual detection and diagnosis of these diseases is a laborious and time-consuming process. Computer aided diagnosis (CAD) systems can serve to assist physicians in manual diagnosis. This work aims to develop a generic multiocular CAD system for DR and glaucoma diagnosis, which could serve as a boon in a large scale screening scenario by reducing the time and manpower involved. To this end, a segmentation-independent approach is employed that eliminates the need for individual diagnostic systems each involving a set of disease-specific algorithms for localisation and analysis of regions of interest (ROIs). Further, the proposed approach alleviates the segmentation inaccuracies attributed to image quality and anatomical factors, which have a cascaded effect on the classification performance. Specifically, a machine learning (ML) model based on random forest (RF) classifier and a pool of non-linear features including higher order spectra (HOS), entropy and fractal features, was developed. An ensemble of this ML model and convolutional neural network (CNN)-based deep learning (DL) model was further constructed using the ‘sum rule’ for decision fusion. The proposed ensemble model resulted in accuracy, sensitivity and specificity as high as 98.08%, 98.37% and 99.07% respectively, for three-class classification to categorize samples as normal, DR or glaucoma.

Generalized LRS Estimator for Min-Entropy Estimation

The min-entropy is a widely used metric to quantify the randomness of generated random numbers, which measures the difficulty of guessing the most likely output. It is difficult to accurately estimate the min-entropy of a non-independent and identically distributed (non-IID) source. Hence, NIST Special Publication (SP) 800-90B adopts ten different min-entropy estimators and then conservatively selects the minimum value among ten min-entropy estimates. Among these estimators, the longest repeated substring (LRS) estimator estimates the collision entropy instead of the min-entropy by counting the number of repeated substrings. Since the collision entropy is an upper bound on the min-entropy, the LRS estimator inherently provides <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">overestimated</i> outputs. In this paper, we propose two techniques to estimate the min-entropy of a non-IID source accurately. The first technique resolves the overestimation problem by translating the collision entropy into the min-entropy. Next, we generalize the LRS estimator by adopting the general Rényi entropy instead of the collision entropy (i.e., Rényi entropy of order two). We show that adopting a higher order can reduce the variance of min-entropy estimates. By integrating these techniques, we propose a generalized LRS estimator that effectively resolves the overestimation problem and provides stable min-entropy estimates. Theoretical analysis and empirical results support that the proposed generalized LRS estimator improves the estimation accuracy significantly, which makes it an appealing alternative to the LRS estimator.

The Topp-Leone-Harris-G family of distributions with applications

In this paper, we present the Topp-Leone-Harris-G (TL-Harris-G) family of distributions. Some special cases, such as Topp-Leone-Harris-log-logistic (TL-Harris-LLoG), Topp-Leone-Harris-uniform (TL-Harris-U) and Topp-Leone-Harris-Weibull (TL-Harris-W) distributions are given. We examine some structural properties, such as moments, generating function, distribution of order statistics, entropy, and probability weighted moments (PWMs) of the new family of distributions. The maximum likelihood estimation method is used to estimate model parameters. To illustrate the consistency and efficiency of parameter estimations process, simulations are run, and real-world data sets are used to demonstrate the applicability of the proposed model. The proposed model proves to be a better alternative to some well known models.

Fault identification method of micro turbine blade based on the order spectral entropy of bearing vibration

In view of the difficulty in identifying the state of the micro turbine blade, this paper uses the order spectral entropy analysis method to extract the characteristic information of the blade fault based on the measured micro turbine bearing vibration data. Firstly, a micro-turbine test bench was established. The normal temperature stable inlet flow was produced by a blower, the high temperature and pressure unstable airflow was generated by turbojet combustion chamber, and the working inlet mode of a micro-turbine with a wide range of speed changes was simulated. The vibration signal of the bearing was collected by shell drilling, a variety of time-frequency domain feature analysis methods based on vibration signal are difficult to effectively identify blade faults under the combined action of unstable airflow and frequent variable speed. In this paper, the bearing vibration data in time domain is converted to the vibration data in angle domain, and then the order amplitude and entropy were compared and analyzed. The results show that the proposed method can effectively identify the blade fracture and fouling faults under the driving of stable and unstable airflow in the speed range of 0–20 000 r min−1. This method provides a new method for micro turbine blade condition monitoring through bearing vibration data.

CTMFSO algorithm-based efficient color image segmentation by fuzzy order entropy

CTMFSO algorithm-based efficient color image segmentation by fuzzy order entropy

Uncertainty measurement of a configurable business process

AbstractToday, the economic crisis is putting enormous pressure on most organizations. It evolves in a crucial competitive environment with a multiplication of production cycles and implementation on the market. Due to this dynamism, organizations must be scalable and agile by adopting an approach that aims to make the process model of a given organization reusable. Indeed, according to the new paradigm which is called “Design by reuse” the models of configurable processes, gain recently in importance since these models have the capacity to represent explicitly the common parts and variables of identical processes in a customizable model. These configurable process which group several execution choices through a variation point called configurable connector will present an uncertainty related to the execution (or not) of the conditional tasks in a process model. In this paper, we propose a measure based on Shannon's entropy in order to model the uncertainty of the process at design time to guide configurable business process designers and analysts in developing and improving processes to be more predictable, less complex, and more understandable.

Hydrodynamic entropy and emergence of order in two-dimensional Euler turbulence

Using numerical simulations, we show that the asymptotic states of two-dimensional (2D) Euler turbulence exhibit large-scale flow structures due to nonzero energy transfers among small wavenumber modes. These asymptotic states, which depend on the initial conditions, are out of equilibrium, and they are different from the predictions of Onsager and Kraichnan. We propose ``hydrodynamic entropy'' to quantify order in 2D Euler turbulence; we show that this entropy decreases with time, even though the system is isolated with no dissipation and no contact with a heat bath.

Enhanced grasshopper optimization algorithm with extreme learning machines for motor‐imagery classification

AbstractIn Brain Computer Interface (BCI), achieving a reliable motor‐imagery classification is a challenging task. The set of discriminative and relevant feature vectors plays a crucial role in classification. In this article, an enhanced optimization technique is implemented for selecting active feature vectors to enhance motor‐imagery classification using Electroencephalography (EEG) signals. After collecting the input EEG signals from BCI competition III‐4a and IV‐2a databases, the 6th‐order butter‐worth filter is employed for eliminating base‐line wander noise from the raw EEG signals. Further, the Variational Mode Decomposition technique is applied for separating the important signal components from the composite EEG signals, and then, the Higher Order Statistic, kurtosis, skewness, standard deviation, and entropy are utilized for feature extraction. The high‐dimensional feature values are given to the Enhanced Grasshopper Optimization Algorithm for optimum feature selection, which are given to the Extreme Learning Machines (ELM) classifier for motor‐imagery classification. Finally, in the resulting section, the optimized ELM model achieved 99.48% and 99.12% of accuracy on the BCI competition III‐4a and IV‐2a databases, where the achieved results are maximum compared to the traditional deep learning models.

Multi-UAV Cooperative Path Planning with Monitoring Privacy Preservation

UAVs have shown great potential application in persistent monitoring, but still have problems such as difficulty in ensuring monitoring frequency and easy leakage of monitoring path information. Therefore, under the premise of covering all monitoring targets by UAVs, it is necessary to improve the monitoring frequency of the target and the privacy protection of the monitoring intention as much as possible. In response to the above problems, this research proposes monitoring overdue time to evaluate the monitoring frequency and monitoring period entropy in order to evaluate the ability to ensure monitoring privacy protection. It then establishes a multi-UAV cooperative persistent monitoring path planning model. In addition, the multi-group ant colony optimization algorithm, called overdue-aware multiple ant colony optimization (OMACO), is improved based on the monitoring overdue time. Finally, an optimal flight path for multi-UAV monitoring with high monitoring frequency and strong privacy preservation of monitoring intention is obtained. The simulation results show that the method proposed in this paper can effectively improve the monitoring frequency of each monitoring node and the privacy preservation of the UAV monitoring path and has great significance for enhancing security monitoring and preventing intrusion.

High order entropy preserving ADER-DG schemes

In this paper, we develop a fully discrete entropy preserving ADER-Discontinuous Galerkin (ADER-DG) method. To obtain this desired result, we equip the space part of the method with entropy correction terms that balance the entropy production in space, inspired by the work of Abgrall. Whereas for the time-discretization we apply the relaxation approach introduced by Ketcheson that allows to modify the timestep to preserve the entropy to machine precision. Up to our knowledge, it is the first time that a provable fully discrete entropy preserving ADER-DG scheme is constructed. We verify our theoretical results with various numerical simulations.

Prostate Magnetic Resonance Imaging Delta-Radiomics during Image-Guided Conventional Fractionation and Stereotactic Ablative Radiotherapy

<h3>Purpose/Objective(s)</h3> The ability to detect changes during radiotherapy to the prostate with radiomics techniques may allow prediction of clinical outcomes and influence subsequent treatment choices. In this feasibility study, we aimed to assess changes in features (delta-radiomics) across a course of treatment in men undergoing MRI-guided radiotherapy for prostate cancer. We compared changes between conventional fractionation and ultrahypofractionated SABR treatment. <h3>Materials/Methods</h3> MRI images from men receiving radiotherapy only to the prostate on an MR Linac developed by a precision radiation medicine company in a large tertiary cancer center were analyzed. Treatment localization scans were transferred from one treatment planning system, (from a precision radiation medicine company, Crawley UK) to a different treatment planning system. The whole prostate was re-contoured from T2 sequences obtained at fraction 1 and at 4 evenly spread time points. For SABR this was at fractions 2 to 5 and for conventional fractionation at fraction 5, 10, 15 and 20 respectively. Radiomics data was extracted with an open-source software platform. Data for energy, entropy, kurtosis and shape parameters were analyzed using a linear mixed-effects model to explore changes over time within a patient (within subject variability) as well as between subject variability between men treated with conventional fractionation v SABR. <h3>Results</h3> 40 men were analyzed, 20 receiving conventional fractionation (60Gy in 20 fractions over four weeks) and 20 receiving SABR (36.25Gy in 5 fractions over two weeks) respectively. Mean age was 69.3years (range 53-82) and mean PSA at diagnosis 9.37 (range 2.18 – 32). Gleason score was 6 in 1 patient, 7 in 38 patients and 8 in 1 patient. Five men did not receive any androgen deprivation therapy prior to radiotherapy (3 patients receiving conventional fractionation, 2 receiving SABR). The only radiomics variable that showed a time-trend was first order entropy in the conventional fractionation group, not in the SABR group (p<0.001). Treatment type was not a significant covariate for any of the other metrics. I.e., in long fractionation a reduction in entropy is observed during treatment, showing the prostate gray values become more homogeneous over time. <h3>Conclusion</h3> Our analyses showed a reduction in entropy for conventional fractionation rather than ultrahypofractionated SABR suggesting that image changes only become apparent over longer treatment courses. During SABR, no radiomics features showed a significant trend. These may still develop on later imaging as changes in cell appearance from DNA damage may only be apparent over time. Further analyses are planned to explore the relationship between delta radiomics and clinical outcomes and whether entropy can be used as an imaging biomarker.