Entropy-based Approach Research Articles

A Relation-Specific Entropy-Based Ensemble Approach for Knowledge Graph Embedding

A Relation-Specific Entropy-Based Ensemble Approach for Knowledge Graph Embedding

Assessment of Environmental Impact by Entropy-Based Approach for Cryogenic Cooling Processes

Assessment of Environmental Impact by Entropy-Based Approach for Cryogenic Cooling Processes

Assessment of environmental impact by entropy-based approach for cryogenic cooling processes

Assessment of environmental impact by entropy-based approach for cryogenic cooling processes

Assessment of entropy-based approach for the environmental impact of the cooling process in clinker production

Assessment of entropy-based approach for the environmental impact of the cooling process in clinker production

From the detection of monitoring anomalies to the probabilistic forecast of the evolution of volcanic unrest: an entropy-based approach

Owing to the current lack of plausible and exhaustive physical pre-eruptive models, often volcanologists rely on the observation of monitoring anomalies to track the evolution of volcanic unrest episodes. Taking advantage from the work made in the development of Bayesian Event Trees (BET), here we formalize an entropy-based model to translate the observation of anomalies into probability of a specific volcanic event of interest. The model is quite general and it could be used as a stand-alone eruption forecasting tool or to set up conditional probabilities for methodologies like the BET and of the Bayesian Belief Network (BBN). The proposed model has some important features worth noting: (i) it is rooted in a coherent logic, which gives a physical sense to the heuristic information of volcanologists in terms of entropy; (ii) it is fully transparent and can be established in advance of a crisis, making the results reproducible and revisable, providing a transparent audit trail that reduces the overall degree of subjectivity in communication with civil authorities; (iii) it can be embedded in a unified probabilistic framework, which provides an univocal taxonomy of different kinds of uncertainty affecting the forecast and handles these uncertainties in a formal way. Finally, for the sake of example, we apply the procedure to track the evolution of the 1982–1984 phase of unrest at Campi Flegrei.

A Fuzzy Entropy-Based Group Consensus Measure for Financial Investments

This study presents a novel, fuzzy entropy-based approach to the measurement of consensus in group decision making. Here, the basic assumption is that the decision inputs are the ‘yes’ or ‘no’ votes of group members on a financial investment that has a particular expected rate of return. In this paper, using a class of fuzzy entropies, a novel consensus measure satisfying reasonable requirements is introduced for a case where the decision inputs are dichotomous variables. It is also shown here that some existing consensus measures are just special cases of the proposed fuzzy entropy-based consensus measure when the input variables are dichotomous. Next, the so-called group consensus map for financial investments is presented. It is demonstrated that this construction can be used to characterize the level of consensus among the members of a group concerning financial investments as a function of the expected rate of return. Moreover, it is described how a consensus map can be constructed from empirical data and how this map is connected with behavioral economics.

Entropy-based damage model for assessing the remaining useful fatigue life

A reliable approach based on an entropy-damage model for assessing remaining useful fatigue life is presented. Two damage models are presented and evaluated to assess their effectiveness in predicting remaining useful life. The first model focuses on reduced toughness caused by fatigue degradation, while the second is based on accumulating entropy during fatigue loading. The entropy-based approach employs infrared thermography to anticipate entropy accumulation and damage status. Outcomes reveal that the entropy-driven technique offers enhanced precision. Moreover, its damage growth rate remains consistent, regardless of the number of cycles leading to failure, ensuring a more stable tracking of damage evolution. It successfully predicts the remaining useful life and can treat variable load sequencing without knowing the loading history. An extensive set of experimental results with carbon steel 1018 are presented to illustrate the utility of the approach.

Social and Economic Impact Assessment of Coal Power Phase-Down at the Provincial Level: An Entropy-Based TOPSIS Approach

With the rollout of the carbon peak and neutrality targets, conventional coal-fired power plants will gradually be phased down in China’s power system in an orderly manner. The economic and social impact of the energy transition is a vital topic that requires scientific measurements and evaluation. In this paper, we establish a comprehensive approach to assess the impact of provincial coal power phase-down with 11 indicators covering dimensions of economy, society, and industry. An entropy-based Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) approach is adopted to calculate entropy weight, relative closeness, and other evaluation benchmark data. Then, the influence degree in 30 provinces is ranked based on the assessment. The results show that there is a significant regional imbalance in the process of coal power phase-down, among which Shanxi, Inner Mongolia, and other coal base provinces are the most vulnerable regions bound by their huge raw coal production and coal industry employment. Although the coal power industry is less affected than the coal industry, it will face pressure from the optimization of coal power units, followed by the dual impact of taxation and employment issues. Finally, the potential impacts of coal power phase-down and policy implications are proposed.

An optimized entropy-based model for estimating river confluence hydrodynamics: Accounting for the effects of velocity dip

The entropy model has been successfully used to calculate the flow velocity profile of rivers according to the measured surface-flow velocity. However, it has limitations in the large river where the obvious secondary flow and flow dip (i.e., the location of maximum flow velocity moves down from the surface) occur. The confluence of rivers is usually characterized by strong secondary circulations, posing great challenges for the accurate flow velocity and discharge estimation. This paper aims to assess the effectiveness of the previous entropy-based approach in velocity estimation of a large confluence and then propose an optimized method. The flow velocity data of the large confluence between the Yangtze River and the Poyang Lake under different discharge ratios were used. The performance of the previous entropy model got worse for the stronger secondary circulation survey. To better estimate the confluence hydrodynamics, a moving average method was introduced with its advantage of smoothing data and integrated into the framework of the entropic method for the better assessment of dip parameter, and hence the better estimation of flow velocity profiles. Furthermore, the maximum velocity was found to locate at one third of the total depth below the water surface. The range of entropic function 0.48 ∼ 0.68 could provide a basis for the determination under various flow conditions in rivers. The value of entropic function and the dip parameter got larger as the discharge ratio decreased. This research has expanded the application for estimating hydrodynamics in large rivers.

Information Entropy-Based Cooperative Surveillance in Areas with Obstacles

This paper solves the problem of searching a surveillance region and tracking possible radio emitters by multiple unmanned aerial vehicles cooperating through communication links in a decentralized manner. An innovative entropy-based approach is utilized to obtain the routes and tasks (search/track) for the vehicles. Another significant contribution of this research is the assumption of each vehicle being equipped with only one simple and cost-effective direction finding (DF) sensor. The routes and tasks are determined by minimizing a well-defined entropy-based cost function. The proposed surveillance scheme allows one to find a feasible and near-optimal solution. The algorithm’s efficiency is verified through various simulations.

An entropy-based membership approach on type-II fuzzy set (EMT2FCM) for biomedical image segmentation

Medical image segmentation is a challenging task owing to its aliasing artifacts, existence of mixed pixels, interference of noise and blurring effect, etc. The brain and the spinal cord together constitute the Central Nervous System (CNS), with the brain being the most complex component. Brain segmentation is crucial for surgical planning, disease detection, and therapeutic interventions. A unique entropy based soft clustering approach (EMT2FCM) for the brain MRI imaging on both real and simulated datasets is proposed in this paper. This paper presents a refined version of the type-2 Fuzzy C-Means (FCM) technique (EMT2FCM) for isolating diverse tissues in Magnetic Resonance (MR) brain images, achieved through an enhanced entropy-based membership function. Difficulty in edge detection occurs due to the vagueness caused by mixed pixels. To overcome the problem with mixed pixels a new membership function proposal has been given. EMT2FCM, i.e., fuzzy membership function with Shannon's entropy-based variation, is able to handle uncertainty and vagueness in images offered by mixed pixels, by implementing new distance criteria modification for the calculation of optimum cluster. The approach has been validated using datasets from BRAINWEB (simulated) and IBSR (real-life). Following the deployment of our new algorithm, we assessed its performance using six observation criteria. The results demonstrated significant improvements in clustering performance, as evidenced by enhanced Mean Squared Error (MSE), Dice Index, and Jaccard Index, Accuracy, Sensitivity and Partition Indices validation measures. The evaluation of EMT2FCM in comparison to other state-of-the-art methods is established to reduce segmentation errors significantly.

An entropy-based group decision-making approach for software quality evaluation

Software quality evaluation (SQE) is a multi-dimensional decision-making problem. The increasingly complex socioeconomic environment has made group decision making (GDM) more important. Determination of the relative contribution (weight) of individual decision makers is an important part of the GDM process. Calculation of the similarity between decision matrices, potentially using a comprehensive projection measure, is also critical. This paper presents: (1) a new entropy-based approach to determine the weight of decision makers; (2) a novel normalized projection approach to measuring the similarity between two decision matrices; and (3) new group regret and satisfaction reference matrices that are modeled in a GDM setting. A comprehensive GDM method is proposed and adopted for SQE. An experimental analysis is conducted to investigate the practicability, feasibility, and validity of the proposed GDM method. The experimental results confirm the proposed GDM method as practicable, producing stable rankings of alternatives, with the same ranking of alternatives being produced in almost all of the dynamic experimental cases.

Influence of the channel bed slope on Shannon, Tsallis, and Renyi entropy parameters

Abstract Velocity distribution plays a fundamental role in understanding the hydrodynamics of open-channel flow. Among a multitude of approaches, the entropy-based approach holds great promise in achieving a reasonable characterisation of the velocity distribution. In entropy-based methods, the distribution depends on a key parameter, known as the entropy parameter (a function of the time-averaged mean velocity and maximum velocity), that relates to channel characteristics, such as channel roughness and channel bed slopes. The entropy parameter was regarded as constant for lack of experimental evidence, which would otherwise demonstrate if it had any correlation with channel properties. A series of experiments were conducted to collect velocity data in the laboratory flume for seven different values of the channel bed slope. The experimental data analysis revealed dissimilar fluctuations in entropy parameter values with varying bed slopes, with the lowest coefficient of variation in Renyi's (∼0.5%) and the highest in Shannon's case (∼10%). Performance evaluation of the predicted results substantiated good accuracy for all three entropies with the best results of Renyi entropy and lent strong support for using a constant (overall average) value of the entropy parameter for a specific channel cross-section rather than separate values for each channel bed slope.

Entropy-Based Waspas approach for financial performance in the COVID-19 era: Evidence from Islamic countries

Abstract The purpose of this study is to compare financial performance of the Islamic banks in the COVID-19 Era. The performance evaluation was conducted on seven Islamic countries between 2017 and 2021. Entropy Method was utilized to choose the best performance evaluation criteria affecting the Islamic banks’ performance and Waspas Method was used to determine the best performing Islamic country. The results indicate that ‘liquid assets to short-term liabilities’ is the best performance criteria for Islamic banks during the ‘whole period’, ‘no pandemic period’ and ‘pandemic period’. Additionally, while the Islamic country with the best performance was Bangladesh during ‘no pandemic period’, Turkey was the leading Islamic country for both ‘whole period’ and ‘pandemic period’.

An Entropy-Based Approach to Measuring Diet Adherence.

The aim of this study is to put forward an approach designed to calculate and sum up discrepancies between the actual food acquisition or intake and any standard or norm. Based on secondary analysis of cross-sectional data from the Mexican National Survey of Household Income and Expenditure, our proposed method to produce classes of entropy-based Diet Adherence Indices generates a Food Basket Adherence Index (FBAI) for Mexico City (2129 households). Findings suggest that it is possible to measure and decompose diet adherence using a cross entropy measure. Using food expenditure data and a normative food basket for Mexico City results, we show households' deviations from the suggested norm for different food groups. The average FBAI was 0.44, far above the minimum score of 0 which would indicate full adherence to the normative food basket. Our measure has a distribution wide enough to detect meaningful changes and distinguish between groups with known differences, providing important new insights on the linkages between home food environments and income distribution, and food insecurity and household distribution.

On-board vibration-based robust and unsupervised degradation detection in railway suspensions under various travelling speeds via a Multiple Model framework

A robust data-driven method for on-board vibration-based degradation detection in railway suspensions of running vehicles is introduced. The method employs two lateral vibration acceleration sensors per vehicle half, one on the bogie and one on the vehicle body. It is based on Transmittance Function data-driven models of the AutoRegressive with eXogenous excitation type within an unsupervised Multiple Model framework and aims at effective detection of early-stage component degradation while achieving robustness to varying Operating Conditions. The method is validated via thousands of Monte Carlo simulation experiments under three distinct travelling speeds. Through them, perfect detection performance is demonstrated for ‘small’ level degradation, characterised by 20 % reduction in the properties of suspension components, while even ‘minor’ degradation, characterised by 10 % reduction, is detectable but somewhat less effectively. The very good performance characteristics of the method are confirmed via field tests as well, while its superiority over alternative schemes is demonstrated via comparisons with a state-of-the-art entropy-based approach.

Detection and Mitigation of (D)DoS Attacks in SDN Environment Using Entropy

Software Defined Networking (SDN) is a paradigm for the networks, where the control planes and data planes are separated. It provides centralized network control by separating the network’s control logic from the underlying hardware devices. However, like traditional networks SDN is also susceptible to Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks. This paper aims to detect and mitigate DoS and DDoS attacks in an SDN environment using an entropy-based approach. The proposed mechanism calculates the entropy of the network over the collected traffic, and derives a dynamic threshold according to the network traffic conditions to determine whether the environment is subject to DoS or DDoS attacks. In the event of the attack, the proposed mechanism installs a drop flow rule into underlying forwarding devices, discarding the traffic sent from attacking host to victim host

On the Anti-Aliasing Properties of Entropy Filtering for Discontinuous Spectral Element Approximations of Under-Resolved Turbulent Flows

For large Reynolds number flows, it is typically necessary to perform simulations that are under-resolved with respect to the underlying flow physics. For nodal discontinuous spectral element approximations of these under-resolved flows, the collocation projection of the nonlinear flux can introduce aliasing errors which can result in numerical instabilities. In Dzanic and Witherden (J. Comput. Phys., 468, 2022), an entropy-based adaptive filtering approach was introduced as a robust, parameter-free shock-capturing method for discontinuous spectral element methods. This work explores the ability of entropy filtering for mitigating aliasing-driven instabilities in the simulation of under-resolved turbulent flows through high-order implicit large eddy simulations of a NACA0021 airfoil in deep stall at a Reynolds number of 270,000. It was observed that entropy filtering can adequately mitigate aliasing-driven instabilities without degrading the accuracy of the underlying high-order scheme on par with standard anti-aliasing methods such as over-integration, albeit with marginally worse performance at higher approximation orders.

Exploring the heterogeneities of factors affecting e-bicyclist severities in delivery e-scooter crashes

Delivery e-bicyclists are vulnerable traffic participants, and delivery e-scooter crashes annually cause great losses. There are limited empirical analyses performed to characterize the endogenous and exogenous influences of potential factors on delivery e-scooter crash severities. This study focused on identifying the heterogeneous impacts of contributing factors. Based on 3064 police-reported delivery e-scooter crashes, different potential factors selected from seven perspectives were examined. Combining the latent class clustering analysis and the partial proportional odds model, an unsupervised learning approach was developed to modeling the heterogeneities as well as the corresponding marginal effects. According to the AIC, BIC, and entropy-based approach, the most appropriate number of clusters was identified as three. The model results revealed that the top six factors significantly influencing the injury severities were running red light, reverse riding, speeding, bike lane, changing lane, and e-bicyclist age. Their maximum absolute values of the marginal effects were more than 29%. The sub-models owned a better goodness of fit in characterizing heterogeneities and avoiding the model bias. It was easy to find the remarkable heterogeneities within each factor and across different clusters. The findings could provide insightful guidance and effective recommendations for alleviating the crash losses and promoting rider safety.

Entropy-based grid approach for handling outliers: a case study to environmental monitoring data.

Grid-based approaches render an efficient framework for data clustering in the presence of incomplete, inexplicit, and uncertain data. This paper proposes an entropy-based grid approach (EGO) for outlier detection in clustered data. The given hard clusters obtained from a hard clustering algorithm, EGO uses entropy on the dataset as a whole or on an individual cluster to detect outliers. EGO works in two steps: explicit outlier detection and implicit outlier detection. Explicit outlier detection is concerned with those data points that are isolated in the grid cells. They are either far from the dense region or maybe a nearby isolated data point and therefore declared as an explicit outlier. Implicit outlier detection is associated with the detection of outliers that are perplexedly deviated from the normal pattern. The determination of such outliers is achieved using entropy change of the dataset or a specific cluster for each deviation. The elbow based on the trade-off between entropy and object geometries optimizes the outlier detection process. Experimental results on CHAMELEON datasets and other similar datasets suggested that the proposed approach(es) detect the outliers more precisely and extend the capability of outliers detection to an additional 4.5% to 8.6%. Moreover, the resultant clusters became more precise and compact when the entropy-based gridding approach is applied on top of hard clustering algorithms. The performance of the proposed algorithms is compared with well-known outlier detection algorithms, including DBSCAN, HDBSCAN, RE3WC, LOF, LoOP, ABOD, CBLOF and HBOS. Finally, a case study for detecting outliers in environmental data has been carried out using the proposed approach and results are generated on our synthetically prepared datasets. The performance shows that the proposed approach may be an industrial-oriented solution to outlier detection in environmental monitoring data.