Fusion Entropy Research Articles

The EEG-Based Fusion Entropy-Featured Identification of Isometric Contraction Forces under the Same Action.

This study explores the important role of assessing force levels in accurately controlling upper limb movements in human-computer interfaces. It uses a new method that combines entropy to improve the recognition of force levels. This research aims to differentiate between different levels of isometric contraction forces using electroencephalogram (EEG) signal analysis. It integrates eight different entropy measures: power spectrum entropy (PSE), singular spectrum entropy (SSE), logarithmic energy entropy (LEE), approximation entropy (AE), sample entropy (SE), fuzzy entropy (FE), alignment entropy (PE), and envelope entropy (EE). The findings emphasize two important advances: first, including a wide range of entropy features significantly improves classification efficiency; second, the fusion entropy method shows exceptional accuracy in classifying isometric contraction forces. It achieves an accuracy rate of 91.73% in distinguishing between 15% and 60% maximum voluntary contraction (MVC) forces, along with 69.59% accuracy in identifying variations across 15%, 30%, 45%, and 60% MVC. These results illuminate the efficacy of employing fusion entropy in EEG signal analysis for isometric contraction detection, heralding new opportunities for advancing motor control and facilitating fine motor movements through sophisticated human-computer interface technologies.

Plastic phase transitions in tris(hydroxymethyl)aminomethane perchlorate.

Organic crystal materials with metal-free feature and intrinsically low molecular mass are highly desirable for applications in flexible smart devices. Here, we reported a plastic crystal, tris(hydroxymethyl)aminomethane perchlorate (Tris-HClO4), which crystallizes in the R3̄ space group at room temperature and undergoes plastic phase transition at 369 K, showing a large entropy gain of 70.5 J mol-1 K-1, much higher than its fusion entropy gain (12.9 J mol-1 K-1). PXRD measurement indicates that it has cubic lattice symmetry in the high-temperature phase. Moreover, it exhibits excellent dielectric permittivity switching properties and robust cyclic stability. This work could be the pathway for chemical designing multifunctional switchable materials with the motive of combining the idea of symmetry breaking and plastic phase transition.

Role of conformational entropy in low melting point of ionic liquids: a molecular dynamics simulation study

Abstract Ionic liquids (ILs) are liquid salts which have very low melting points among the salts. To understand the contribution of conformational entropy (Sconf) to the low melting points of ILs, molecular dynamics (MD) simulations of 1-alkyl-3-methylimidazolium bis(trifluoromethylsulfonyl)amide ([Cnmim][NTf2] where n = 2, 4, 6, 8, and 10) were performed in liquid and gas states. The increase in Sconf with increasing alkyl chain length corresponded to an increase in fusion entropy (ΔfusS), suggesting that the other entropic contributions such as kinetic and configurational entropies depend negligibly on the alkyl chain length. Comparing the same cation, Sconf in the liquid state with a long alkyl chain was slightly smaller than that in the gas state because the trans conformers of the cation were favored in the liquid state. The trans conformers of the cations in the liquid state were stabilized by the van der Waals and Coulomb interactions. Meanwhile, populations of the trans conformer for the anion in the gas and liquid state were almost the same.

SegForest: A Segmentation Model for Remote Sensing Images

The accurate estimation of forest area is of paramount importance for carbon sequestration projects, ecotourism and ecological safety. Forest segmentation using remote sensing images is a crucial technique for estimating forest area. However, due to the complex features, such as the size, shape and color of forest plots, traditional segmentation algorithms struggle to achieve accurate segmentation. Therefore, this study proposes a remote sensing image forest segmentation model named SegForest. To enhance the model, we introduce three new modules: multi-feature fusion (MFF), multi-scale multi-decoder (MSMD) and weight-based cross entropy loss function (WBCE) in the decoder. In addition, we propose two new forest remote sensing image segmentation binary datasets: DeepGlobe-Forest and Loveda-Forest. SegForest is compared with multiple advanced segmentation algorithms on these two datasets. On the DeepGlobe-Forest dataset, SegForest achieves a mean intersection over union (mIoU) of 83.39% and a mean accuracy (mAcc) of 91.00%. On the Loveda-Forest dataset, SegForest achieves a mIoU of 73.71% and a mAcc of 85.06%. These metrics outperform other algorithms in the comparative experiments. The experimental results of this paper demonstrate that by incorporating the three proposed modules, the SegForest model has strong performance and generalization ability in forest remote sensing image segmentation tasks.

Detection of Respiratory Events during Sleep Based on Fusion Analysis and Entropy Features of Cardiopulmonary Signals.

Sleep apnea hypopnea syndrome (SAHS) is a common sleep disorder with a high prevalence. The apnea hypopnea index (AHI) is an important indicator used to diagnose the severity of SAHS disorders. The calculation of the AHI is based on the accurate identification of various types of sleep respiratory events. In this paper, we proposed an automatic detection algorithm for respiratory events during sleep. In addition to the accurate recognition of normal breathing, hypopnea and apnea events using heart rate variability (HRV), entropy and other manual features, we also presented a fusion of ribcage and abdomen movement data combined with the long short-term memory (LSTM) framework to achieve the distinction between obstructive and central apnea events. While only using electrocardiogram (ECG) features, the accuracy, precision, sensitivity, and F1 score of the XGBoost model are 0.877, 0.877, 0.876, and 0.876, respectively, demonstrating that it performs better than other models. Moreover, the accuracy, sensitivity, and F1 score of the LSTM model for detecting obstructive and central apnea events were 0.866, 0.867, and 0.866, respectively. The research results of this paper can be used for the automatic recognition of sleep respiratory events as well as AHI calculation of polysomnography (PSG), which provide a theoretical basis and algorithm references for out-of-hospital sleep monitoring.

An Evaluation Method of Crack Variation on Structural Performance of Concrete Dams with Fusion Entropy Based on Observation and Simulation

This study proposes a method for evaluating crack variations on structural performance of concrete dams using observational and computational output. The structural performance is represented by the system entropy in concrete dams, and the stress-strain field is established by incorporating observations and simulations for entropy calculations. Considering the characterization ability of the sample points, the weighted entropy sequence is constructed with a selected index system. Thereafter, the weighted fusion entropy is successively separated and analyzed with rescaled range (R/S) analysis, and the Hurst exponent difference and corresponding warning indicators are calculated to quantify the destructiveness of the crack variation. The application of the presented method is elaborated in a concrete arch dam, and the behavior of J01-crack exhibits no significant destructiveness on its structural condition. The analysis results verified the feasibility of the proposed method in destructiveness evaluation of crack behavior on the structural performance of concrete dams.

New organic ionic plastic crystals based on pyrrolidinium dication for a solid‐phase electrolyte

AbstractA series of 1,4‐bis(N‐alkylpyrrolidinium)butane salts combined with Br−, I−, PF6−, and Tf2N− anions were newly designed and synthesized. In nonpolar CDCl3 solution, the axial and equatorial proton peaks of the bis‐pyrrolidinium cation ring are distinguished in 1H NMR. Also, the strengths of ion paring could be estimated from the 1H NMR chemical shift changes of the bridge and pyrrolidinium proton peaks. 1,4‐Bis(N‐hepthylpyrrolidinium)butane I− is an organic ionic plastic crystal showing the lowest fusion entropy value (ΔSf = 3.4 J K·mol−1). Softer crystalline morphologies were found by polarized optical microscope images at plastic crystal phase of each sample and the results were corresponding to differential scanning calorimetry results. 1,4‐Bis(N‐ethylpyrrolidinium)butane Tf2N− showed the softest crystal phase. Ionic conductivity changes and electrochemical stabilities of the bis‐pyrrolidinium PF6− and Tf2N− salts were also measured, and they were stable up to 4.3 V (vs. Li/Li+) and expected to be applied as an electrolyte component.

Online Chatter Identification for Thin-Walled Parts Machining Based on Improved Multisensor Signal Fusion and Multiscale Entropy

Thin-walled workpieces frequently appear to chatter during milling, causing the creation of vibration patterns on the surface of the workpiece, which affects the machining quality and workpiece performance. Identification of online chatter is important for thin-walled parts. The vibration signal associated with chatter changes dynamically throughout the milling process due to changes in tool position, removal of workpiece material, and transmission-related vibration signal attenuation characteristics, making reliable chatter identification difficult. This research proposes a unique online chatter identification approach for the machining of thin-walled parts based on improved multi-sensor signal fusion and multi-scale entropy. In order to acquire the fused vibration signals, the vibration signals produced during the machining of thin-walled parts are first acquired by sensors at various test sites and adaptively given weight coefficients by the improved Hausdorff distance and distance factor. Second, using the multi-scale sample entropy (MSSE) and multi-scale weighted permutation entropy (MSWPE), the eigenvalues of the fused vibration signals are obtained. Finally, the trained Logistic regression (LR) classification model is used to determine the vibration status of thin-walled parts. The analysis’s results demonstrated that the technique can properly identify the vibration state of thin-walled parts, and that the fusion signal can reflect the vibration state at the tool contact site more accurately.

Structure Damage Identification Based on Information Entropy and Bayesian Fusion

When processing signals, information entropy theory and data fusion theory have their own advantages. The former can improve the sensitivity of signals, while the latter can superimpose multisource information to correct system deviations and obtain the best identification results. Therefore, we introduce two theories into structural damage identification to improve the reliability of damage identification. First, based on the modal strain energy damage identification index, combined with information entropy and data fusion theory, a fusion entropy index (FE) and an entropy weight fusion index (EWF) are constructed. Then, the simply supported beam and truss structure model are established for damage simulation, which verified that the FE index and EWF index can accurately locate the damage. The polynomial fitting method is used to identify the damage degree of the structure, and the identification results obtained are more accurate. Finally, a simple-supported steel beam model is established in the laboratory for verification and analysis. The results show that the proposed FE index and EWF index have high damage sensitivity, noise resistance, and robustness, and relatively speaking, EWF index damage recognition ability is better. The method proposed in this paper provides an empirical method for practical engineering application.

Detection of DDoS Attacks in Software Defined Networking Using Entropy

Software Defined Networking (SDN) is one of the most commonly used network architectures in recent years. With the substantial increase in the number of Internet users, network security threats appear more frequently, which brings more concerns to SDN. Distributed denial of Service (DDoS) attacks are one of the most dangerous and frequent attacks in software defined networks. The traditional attack detection method using entropy has some defects such as slow attack detection and poor detection effect. In order to solve this problem, this paper proposed a method of fusion entropy, which detects attacks by measuring the randomness of network events. This method has the advantages of fast attack detection speed and obvious decrease in entropy value. The complementarity of information entropy and log energy entropy is effectively utilized. The experimental results show that the entropy value of the attack scenarios 91.25% lower than normal scenarios, which has greater advantages and significance compared with other attack detection methods.

Multi-source heterogeneous fusion entropy ratio distance feature of bearing performance degradation based on DTW

The key to the evaluation of bearing performance degradation is to extract sensitive characteristic indicators that can reflect the degradation process. In order to solve the problem of insufficient sensitivity of the bearing multi-source signals feature, a fusion entropy ratio feature based on dynamic time warping is proposed. First, the segmented approximate entropy, sample entropy and fuzzy entropy of the bearing performance degradation process are extracted, and then the mean clustering is used to obtain the standard entropy vector of the health state. In order to eliminate the influence of working environment and entropy value on state evaluation, a concept of entropy ratio is defined by the ratio of the entropy vector of each state to the standard entropy vector of the health state. Finally, using dynamic time warping has the advantage of accurately describing the similarity between vectors from a global perspective, and the multi-source heterogeneous fusion entropy ratio distance feature is constructed to describe the degraded state of bearing performance. Experiments show that this feature is more sensitive to the bearing performance degradation than other features, such as fusion entropy ratio feature based on Euclidean distance, single entropy ratio feature based on dynamic time bending.

An effective multi-channel fault diagnosis approach for rotating machinery based on multivariate generalized refined composite multi-scale sample entropy

Fault diagnosis of critical rotating machinery components is necessary to ensure safe operation. However, the commonly used rotating machinery fault diagnosis methods are generally based on the single-channel signal processing method, which is not suitable for processing multi-channel signals. Thus, to extract features and carry out the intelligent diagnosis of multi-channel signals, a novel method for rotating machinery fault diagnosis is proposed. Firstly, a novel nonlinear dynamics technique named the multivariate generalized refined composite multi-scale sample entropy was presented and applied to extract fusion entropy features of multi-channel signals. Secondly, a practical manifold learning known as supervised isometric mapping was introduced to map the high-dimensional fusion entropy features in a low-dimensional space. In a third step, the Harris hawks optimization-based support vector machine was applied to carry out the intelligent fault recognition. Finally, aiming to verify the effectiveness of the proposed method and present its advantages, it was applied to analyze the rotating machinery system bearing and gear data. The experimental results have shown that the method at hand can accurately identify various faults in both the bearings and gears. Furthermore, in addition to being suitable for multi-channel signal fault diagnosis, it had higher recognition accuracy compared to other multi-channel or single-channel methods.

Solubility determination, model evaluation and solution thermodynamics of isovanillin in 15 pure solvents and 4 binary solvents

Researches on the basic solubility data and the physicochemical parameters of isovanillin are scarce in literature. In this paper, the solubility data, dissolution thermodynamic properties of isovanillin in 15 organic pure solvents (methanol, ethanol, n-propanol, iso-propanol, n-butanol, iso-butanol, methyl acetate, ethyl acetate, isobutyl acetate, formic acid, acetic acid, propionic acid, acetone, xylene and diethyl ether) and 4 binary solvents (ethanol + water, iso-propanol + water acetic acid + water, propionic acid + water) were determined at T = 278.15 to 318.15 K and T = 283.15 to 315.15 K, respectively, and reported for the first time. Three classic models were employed to correlate the experimental solubility, including an empirical equation and two thermodynamics-based models of Apelblat model and λh model. The models used were further evaluated by root mean square deviation (RMSD). Results show that the solubility of isovanillin presents a strong dependency on solution temperature and the polarity of solvents used. It shows high solubility in strong polar solvents such as formic acid, methanol and acetone, while low in less polar solvents such as xylene, diethyl ether. In the investigated binary solvents, a significant co-solvency phenomenon is observed, and the increase in solubility is found to do with the polarity of good solvent used in this study. RMSD value suggests that all the models used perform good correlation. Furthermore, the single crystal structure of isovanillin was solved and reported for the first time. Besides, the melting point and the fusion entropy of isovanillin were determined by DSC, and the thermodynamic properties of isovanillin dissolution process including ΔsolG, ΔsolH and ΔsolS were calculated by Van’t Hoff equation. Results indicate that the dissolution process of isovanillin is an endothermic, nonspontaneous entropy-driven process.

Investigating the Working Efficiency of Typical Work in High‐Altitude Alpine Metal Mining Areas Based on a SeqGAN‐GABP Mixed Algorithm

Man‐machine efficacy evaluations of typical work in the safe mining of high‐altitude alpine metal mines are associated with fuzziness, multiple indexes, and large subjective components. This results in difficulties in the prediction of the typical work efficiency in high‐altitude alpine metal mining areas. In this study, ergonomic theory was applied to establish the evaluation index system of typical work efficiency in high‐altitude alpine metal mining areas by studying the cooperative relationship between operators, working machines, working environment, and design variables. First, we investigated the collaborative relationship between workers, operating machinery, operating environment, and design variables in order to establish the evaluation index system of typical work efficiency in high‐altitude alpine metal mining areas. Second, principal component analysis (PCA) was integrated with the fusion entropy weight method to (i) analyze the coupling correlation and overlapping effects between the factors influencing efficiency at different altitudes and (ii) to determine the key influencing factors. Third, a model based on the sequence generative adversarial network genetic algorithm backpropagation (SeqGAN‐GABP) hybrid algorithm was established to predict the trends in the operating efficiency of typical work types in high‐altitude alpine metal mining areas. Finally, three high‐altitude alpine metal mines in Xinjiang were selected as representative examples to verify the proposed framework by comparing it with other state‐of the art models (multiple linear regression prediction model, backpropagation (BP) neural network model, and genetic algorithm back propagation (GA‐BP) neural network model). Results determine the average relative error of each model as 2.74%, 1.97%, 1.29%, and 1.02%, respectively, indicating the greater accuracy of our proposed method in predicting the efficiency of typical work types in high‐altitude alpine mining areas. This study can provide a scientific basis for the establishment of mining safety judgment standards in high‐altitude alpine areas.

Heterogeneous iris certification method with universal sensors and safety output based on multisource fusion features and entropy labels of lightweight samples

Current iris recognition technology faces practical difficulties. For example, the unsteady morphology of a heterogeneous iris is generated by a variety of different devices and environments. The existing iris data sets have difficulty meeting the requirements of learning methods under a single deep learning framework. Research on iris recognition security has limited response to results stealing. Therefore, we propose a one-to-one heterogeneous iris certification method with universal sensors and safety output based on multisource fusion features and entropy labels of lightweight samples. This method is based on the classic convolutional neural network structure, and a lightweight neural network certification structure is designed. At first, convert the constrained multistate iris image into the digital features based on statistical learning and multisource feature fusion mechanism. The information entropy of the iris feature label is used to set the iris entropy feature category label and design a certification function that meets the requirements of different acquisition sensors according to the category label to obtain the certification result. Through the result encryption output module, the security output is achieved between the user and the certification result, and measures can be taken in time to confirm the stealing attack to improve the security of the output of certification result. As the requirement for the number and quality of irises changes, the category labels in the certification function are dynamically adjusted using a feedback learning mechanism. Three different acquisition sensors in the JLU iris library are used to do the experiments. The results prove that, for lightweight constrained multistate irises, the above mentioned problems are ameliorated to a certain extent by this method.

Melt density, equilibrium melting temperature, and crystallization characteristics of highly pure cyclic poly(ε-Caprolactone)s

A series of cyclic and linear poly (ε-caprolactone)s (c-PCLs: c-PCLn, n = 30–200, average degree of polymerization; l-PCLs: l-PCLn, n = 30–200) in extremely high purity have been quantitatively investigated in terms of melt density, equilibrium melting temperature crystallization nature in connection to molecular topology effects. Synchrotron X-ray reflectivity analysis determined higher melt densities for c-PCLs than those of the linear counterparts. c-PCL was determined to exhibit 8.4 °C higher equilibrium melting temperature than that of the linear counterpart. Such higher equilibrium melting temperature may originate from a cooperative effort of thermodynamic factors (larger equilibrium heat of fusion and smaller equilibrium entropy) and morphological factors (thicker crystalline layer and higher fold surface energy). Comparing with l-PCL, c-PCL revealed apparently faster crystallization behavior at a chosen Tc but slower crystallization behavior at a same degree of supercooling because of the higher equilibrium melting temperature. Both c-PCL and linear counterpart were observed to follow heterogeneous nucleation and growth mechanism in crystallizations. Overall, this study achieved comprehensive insights into cyclic topology effects on the melt density, equilibrium temperature, and crystallization behavior of PCL.

Human Behavior Analysis Based on Multi-Types Features Fusion and Von Nauman Entropy Based Features Reduction

Human Behavior Analysis Based on Multi-Types Features Fusion and Von Nauman Entropy Based Features Reduction

Driving Fatigue Classification Based on Fusion Entropy Analysis Combining EOG and EEG

The rising number of traffic accidents has become a major issue in our daily life, which has attracted the concern of society and governments. To deal with this issue, in our previous study, we have designed a real-time driving fatigue detection system using power spectrum density and sample entropy. By using the wireless technology and dry electrodes for EEG collection, we further integrated virtual reality simulated driving environment, which made our study more applicable to realistic settings. However, the high accuracy of classification for driving fatigue has not been obtained. To measure the time series complexity of the EEG signal, we proposed a fusion entropy (sample entropy, approximate entropy, and spectral entropy) analysis method of EEG and EOG. First, a sample entropy was applied for feature extraction from the horizontal and vertical EOG. Second, an approximate entropy, sample entropy, and spectral entropy features of each sub-band of EEG are extracted. Third, feature fusion for sub-band is performed by canonical correlation analysis (CCA). Finally, the features of EOG and EEG are classified using a relevant vector machine (RVM). Twenty-two subjects participated in the driving fatigue experiments for a duration of 90 min. The results demonstrated that the fusion entropy analysis combining EOG and EEG could provide an alternative method for driving fatigue detection, and the average accuracy rate was up to 99.1 ± 1.2%. The authors further analyzed the effect of feature fusion in four sub-bands (δ, α, β and θ) and compared with every single sub-band on classification performance, it is proved that the former is superior to the latter presenting the proposed method can provide effective indicators for driving fatigue detection.

Melting of nucleobases. Getting the cutting edge of "Walden's Rule".

Walden's Rule is an empirical observation of an invariant fusion entropy during fusion of non-associated organic compounds. For the five nucleobases, adenine, thymine, cytosine, guanine, and uracil, surprisingly high fusion temperatures and enthalpies have been measured using a specially developed fast scanning calorimetry method that prevents decomposition. Even when nucleobases admittedly possess very high fusion temperatures, e.g. the value of 862 K measured for guanine really exceeds all expectations of the feasible dimension of the fusion temperature for such a relatively small and simple organic molecule. Hirshfeld surface analysis has been applied in order to find out an explanation for such extremely unusual thermal behavior of nucleobases. We rationalized the observed trends in terms of fusion entropy (Walden's constant = 56.5 J K-1 mol-1) as the entropic penalty of fusion not only for "non-associated", as proposed by Walden in 1908, but also for "ideal associated" systems like nucleobases.

Affinity prediction computations and mechanosynthesis of carbamazepine based cocrystals

A combination of the excess enthalpy with the fusion entropy of the pure coformer is suggested to be of interest for coformers screening in order to form a multicomponent system with a given API (cocrystal/co-amorphous).