Entropy Algorithm Research Articles

Urban Ecological Planning Model Based on Information Entropy and Meteorological Suitability Evaluation Algorithm

In order to improve the effect of regional urban ecological planning and ecological restoration, aiming at the problem of low accuracy of ecological planning and poor matching with the current situation, taking Yucheng County, Shangqiu City, Henan Province as an example, an urban ecological planning model based on information entropy and meteorological suitability evaluation algorithm is designed. The geographical environment of Yucheng County, Shangqiu City, Henan Province is analyzed. Based on the current situation of regional ecological environment, the meteorological suitability evaluation algorithm is introduced to establish the urban ecological planning model. Comprehensively considering land planning factors and meteorological factors, calculate the information entropy of each influencing factor, determine the weight, and optimize the ecological planning model. The experimental results show that this method has achieved good results in the accuracy of ecological planning and the matching degree of the current situation in the urban ecological planning of Yucheng County, Shangqiu City, Henan Province.

Seismic and Economic Optimization of Water Distribution Networks Using Entropy and Ant Colony Algorithm

Seismic and Economic Optimization of Water Distribution Networks Using Entropy and Ant Colony Algorithm

Automatic detection method of epileptic seizures based on IRCMDE and PSO-SVM.

Multi-scale dispersion entropy (MDE) has been widely used to extract nonlinear features of electroencephalography (EEG) signals and realize automatic detection of epileptic seizures. However, information loss and poor robustness will exist when MDE is used to measure the nonlinear complexity of the time sequence. To solve the above problems, an automatic detection method for epilepsy was proposed, based on improved refined composite multi-scale dispersion entropy (IRCMDE) and particle swarm algorithm optimization support vector machine (PSO-SVM). First, the refined composite multi-scale dispersion entropy (RCMDE) is introduced, and then the segmented average calculation of coarse-grained sequence is replaced by local maximum calculation to solve the problem of information loss. Finally, the entropy value is normalized to improve the robustness of characteristic parameters, and IRCMDE is formed. The simulated results show that when examining the complexity of the simulated signal, IRCMDE can eliminate the issue of information loss compared with MDE and RCMDE and weaken the entropy change caused by different parameter selections. In addition, IRCMDE is used as the feature parameter of the epileptic EEG signal, and PSO-SVM is used to identify the feature parameters. Compared with MDE-PSO-SVM, and RCMDE-PSO-SVM methods, IRCMDE-PSO-SVM can obtain more accurate recognition results.

Sparse Aperture Autofocusing and Imaging Based on Fast Sparse Bayesian Learning From Gapped Data

Sparse aperture (SA) autofocusing and imaging is a hot research problem in the signal processing field and has been widely used. Under SA, the absence of echoes destroys the coherence between the pulses, which then affects the autofocusing accuracy of the imaging, leading to defocus of the image. In this paper, a novel SA autofocusing and imaging algorithm based on Sparse Bayesian Learning (SBL) is proposed, which uses a fast SBL algorithm to achieve SA high resolution imaging, and uses the minimum Tsallis entropy algorithm to realize autofocusing. As is known to all, SBL has strong robustness and high precision. Unfortunately, the direct calculation of the inversion and multiplication operations involved in each iteration of SBL results in significant computational costs. In the proposed fast SBL algorithm, the matrix required to be inverted has a special structure. The inverse matrix can then be represented by Gohberg-Semencul (G-S) factorization. And almost all operations except for G-S factorization during each iteration can be completed by fast Fourier transform (FFT) or inverse FFT (IFFT), which greatly reduces the amount of computation by several orders of magnitude. In each SBL iteration, the minimum Tsallis entropy algorithm is used for estimating the phase error, which has better noise sensitivity and obtains the images with the best focused degree. Finally, the effectiveness and high efficiency of the proposed fast algorithm are verified by experimental results obtained by simulation and measured data.

Drivers’ Workload Electroencephalogram Characteristics in Cognitive Tasks Based on Improved Multiscale Sample Entropy

Cognitive workload is an internal factor that can be influenced by external factors. When drivers are distracted by these internal and external influences, their ability to drive safely can be compromised. Electroencephalogram (EEG) is a non-invasive neuroimaging technique that measures the electrical activity of the brain. and it can provide information about brain activity related to various cognitive and motor functions. In this study, on-road driving experiments were conducted to investigate the EEG characteristics of drivers under different levels of cognitive workload generated through mathematical calculations with varying difficulties. The collected EEG signals were processed using continuous wavelet transform to decompose them into delta, theta, alpha, beta, and gamma waveforms. Numerical values and power spectral characteristics of each waveband in the left and right frontal regions of the driver were then analyzed. Furthermore, the study utilized an improved multiscale sample entropy algorithm to analyze the EEG entropy characteristics of drivers in different states. The results indicate that under different degrees of cognitive workload, the amplitude of delta, theta, and alpha waves decreased, while beta and gamma waves showed significant changes. The improved multiscale sample entropy algorithm provided a more objective assessment of changes in entropy across different wavebands and stages, with smaller entropy fluctuation ranges and more accurate complexity changes. Overall, this research provides valuable insights into the characteristics of drivers under cognitive workload, contributing to the analysis of human factors in driving safety. These findings can inform the development of strategies to mitigate the adverse effects of cognitive workload on driving performance and enhance overall road safety.

Speed of extirpation of the huemul in the history of human occupation in Patagonia

Context The Patagonian huemul, an endangered Odocoilinedeer, has an estimated 350–500 individuals remaining in Argentina. Today’s population size, representing a numerical reduction of >99% of original estimates, is fragmented into small groups along ~2000 km of Andean mountains. The species’ numbers were heavily reduced by past overexploitation and they disappeared in areas of high anthropogenic activity, predominantly the fertile valley bottoms. Aims This research delineates the current potential distribution of Patagonian huemul by using climatic indicators, topographic and vegetational proxies, and anthropogenic pressure, to determine the relevance of the climatic envelope on current distribution. Methods Occurrence records (latitude and longitude) were compiled (n = 159) by consulting the literature. Twenty environmental variables were used (WorldClim database) and two other representative environmental variables (normalised difference vegetation index (NDVI) and enhanced vegetation index (EVI)) were added to test their predictive power. We added the human footprint index (HFP) as a variable to control for model bias. Using the maximum entropy algorithm (MaxEnt), we modelled the species’ potential distribution. We designated the historical distribution as area M. Additionally, we calculated three areas of distribution: current, historical and potential. Finally, we calculated distributional retraction of the species and area lost per year. Key results The model showed good predictive power (AUCTest = 0.764 ± 0.091). However, low values were obtained for AUCtrain and AUCprom for the different predictor scenarios. Although the model shows the interaction among several climatic, environmental, and topographic variables, the human footprint index (39.9%) was the variable that most influenced the current potential distribution of this species. Conclusion Our model shows that most of Patagonia’s surface is climatically suitable for huemul. This suggests that the causes of distributional retraction are not related to limitations imposed by the climate envelope, but rather concur with recent research showing impact owing to the species’ behavioural response to anthropogenic activity. Implications Current populations are small, fragmented, and confined to poor-quality sites. Although the species is currently found mainly within protected areas, management actions must be initiated that promote innovative strategies in unprotected areas, as well as high-value habitats, particularly as protected areas contain limited fertile lower-valley habitats.

Предиктивное археологическое моделирование методом максимальной энтропии (на примере поселений позднеримского времени в бассейне Верхнего Дона)

The paper is devoted to an archaeological predictive modeling technique with GIS and machine learning based on maximal entropy algorithm (MaxEnt). Archaeological sites of the late roman time presented by settlements of Kashirka-Sedelok type (mid-3rd — early 4th century AD) in the Upper Don basin were the modeling objects. We found out that there are 169 known sites of such type in the study region, and collected all available information on their locations into a geodatabase including our own field survey data. The coordinates of these sites split into training and testing subsets served for developing a predictive model with MaxEnt. Environmental GIS-layers based on a digital elevation model and thematic maps were the model predictors. We made such predictors to reflect the paleogeography of the Upper Don basin in the study time period by choosing relatively stable landscape features of relief, bedrock, climate, hydrography and soils of the area. The trained model analyzed through more than 7 million cells of 90x90 m with overall area of 59 400 sq. km to identify potentially suitable areas for the Kashirka-Sedelok settlements and found 29 860 ha the most suitable areas (less than 1 % of the study area) as the result. The efficiency of the conducted modeling, evaluated on the testing sites, is high based on metrics of receiver operating characteristic (AUC = 0,915) and Kvamme’s Gain (KG = 0,97). The results of modeling serve as an agenda for the upcoming archaeological surveys in the region.

A Divide-and-Conquer Information Entropy Algorithm for Dependency Matrix Processing

A Divide-and-Conquer Information Entropy Algorithm for Dependency Matrix Processing

Clinical application of acupuncture-moxibustion for the treatment of spirit based on approximate entropy of electrooculogram signal

According to the theory of acupuncture-moxibustion for the treatment of spirit, starting from the relationship between eye movement and spirit, the application of electrooculogram (EOG) signal acquisition and analysis technology for the clinical treatment of spirit by acupuncture-moxibustion is discussed. Based on the nonlinear dynamic characteristics of EOG signals, it is proposed to apply the approximate entropy algorithm to extract the EOG signal characteristics in autism spectrum disorder children under different behavior states, which could realize the preliminary exploration of the correlation between EOG signals and cognitive activities. This could provide a possibility to objectively reflect the patient' s current mental state, and could be used as a potential method to grasp spirit in clinical acupuncture- moxibustion treatment. Furthermore, considering the characteristics of acupoint stimulation on the body surface, the EOG signal acquisition and analysis technology could further be combined with biofeedback technology, and a new idea for clinical acupuncture-moxibustion to treat spirit guided by biofeedback of EOG is proposed.

Threat assessment of aerial targets based on improved GRA-TOPSIS method and three-way decisions.

Target threat assessment is a critical aspect of information warfare and can offer valuable auxiliary support to combat command decision-making. Aiming to address the shortcomings of three decision-making methods in air combat target assessment, such as the inability to effectively handle uncertain situation information and quantitatively rank the decision-making targets according to their importance, a dynamic intuitionistic fuzzy decision model based on the improved GRA-TOPSIS method and three-way decisions is proposed. First, the target attribute weight is obtained by cosine intuitionistic fuzzy entropy algorithm; then, a novel intuitionistic fuzzy distance measure is introduced, and grey incidence analysis and TOPSIS are used to build the conditional probability for three-way decisions that fully utilize the existing information and reflect the consistency of dynamic change trend; finally, the comprehensive loss function matrix is constructed and the threat classification results are obtained using the decision rules. The example analysis shows that the proposed method can not only effectively handle complex battlefield situations and dynamic uncertain information, but it can also classify targets, improving the effectiveness and rationality of decision-making and providing a reference basis for scientific command decision-making.

Entropy Based Method for Malicious File Detection

Ransomware is by no means a recent invention, having existed as far back as 1989, yet it still poses a real threat in the 21st century. Given the increasing number of computer users in recent years, this threat will only continue to grow, affecting more victims as well as increasing the losses incurred towards the people and organizations impacted in a successful attack. In most cases, the only remaining courses of action open to victims of such attacks were the following: either pay the ransom or lose their data. One commonly shared behavior by all crypto ransomware strains is that there will be attempts to encrypt the victims’ files at a certain point during the ransomware execution. This paper demonstrates a technique that can identify when these encrypted files are being generated and is independent of the strain of the ransomware. Previous research has highlighted the difficulty in differentiating between compressed and encrypted files using Shannon entropy, as both file types exhibit similar values. Among the experiments described in this study, one showed a unique characteristic for the Shannon entropy of encrypted file header fragments, which was used to differentiate between encrypted files and other high entropy files such as archives. The Shannon entropy of encrypted file header fragments has a unique characteristic in one of the tests discussed in this study. This property was used to distinguish encrypted files from other files with high entropy, such as archives. To overcome this drawback, this study proposed an approach for test case generation by enhancing the entropy-based threat tree model, which would improve malicious file identification. The file identification was enhanced by combining three entropy algorithms, and the test case was generated based on the threat tree model. This approach was then evaluated using accuracy measurements: True Positive, True Negative, False Positive, False Negative. A promising result is expected. This method solves the challenge of leveraging file entropy to distinguish compressed and archived files from ransomware-encrypted files in a timely manner.

EEG-induced Fear-type Emotion Classification Through Wavelet Packet Decomposition, Wavelet Entropy, and SVM

Among the most significant characteristics of human beings is their ability to feel emotions. In recent years, human-machine interface (HM) research has centered on ways to empower the classification of emotions. Mainly, human-computer interaction (HCI) research concentrates on methods that enable computers to reveal the emotional states of humans. In this research, an emotion detection system based on visual IAPPS pictures through EMOTIV EPOC EEG signals was proposed. We employed EEG signals acquired from channels (AF3, F7, F3, FC5, T7, P7, O1, O2, P8, T8, FC6, F4, F8, AF4) for individuals in a visual induced setting (IAPS fear and neutral aroused pictures). The wavelet packet transform (WPT) combined with the wavelet entropy algorithm was applied to the EEG signals. The entropy values were extracted for every two classes. Finally, these feature matrices were fed into the SVM (Support Vector Machine) type classifier to generate the classification model. Also, we evaluated the proposed algorithm as area under the ROC (Receiver Operating Characteristic) curve, or simply AUC (Area under the curve) was utilized as an alternative single-number measure. Overall classification accuracy was obtained at 91.0%. For classification, the AUC value given for SVM was 0.97. The calculations confirmed that the proposed approaches are successful for the detection of the emotion of fear stimuli via EMOTIV EPOC EEG signals and that the accuracy of the classification is acceptable.

Analysis of the Dispersion Havrda–Charvat Entropy Plane in Financial Time Series

This paper introduces a new statistical tool: dispersion Havrda–Charvat entropy plane, which is used to analyze the complexity characteristics of time series. The Havrda–Charvat entropy with one parameter can provide flexibility in applications and provide more information about time series. The dispersion entropy algorithm is a fast and powerful algorithm for evaluating time series, which has been proposed in recent years. The statistical complexity measure defined by Jensen–Shannon divergence reflects the chaotic degrees of complex systems. The dispersion Havrda–Charvat entropy plane is constructed using the above conceptions. The performance of the dispersion entropy plane is evaluated by simulated chaotic processes and fractional Brownian motions, and then we apply the method to stock data. This demonstrates that dispersion Havrda–Charvat entropy plane can distinguish the intensive properties of time series well and is a powerful method to classify stock markets. In addition, the multiscale measure is experimented, the results show that it can eliminate the noise contained in the data and effectively extract the information contained in time series with different time scales.

Entropy Algorithms Using Deep Learning for Signal Processing

Image and video processing operatons are significant in our life as most electronic devices, such as PCs and mobiles, are all developed by signal processing [...].

Monthly precipitation prediction in Luoyang city based on EEMD-LSTM-ARIMA model.

At present, the method of using coupled models to model different frequency subseries of precipitation series separately for prediction is still lacking in the research of precipitation prediction, thus in this paper, a coupled model based on Ensemble Empirical Mode Decomposition (EEMD), Long Short-Term Memory neural network (LSTM) and Autoregressive Integrated Moving Average (ARIMA) is proposed for month-by-month precipitation prediction. The monthly historical precipitation data of Luoyang City from 1973 to 2021 were used to build the model, and the modal components of different frequencies obtained by EEMD decomposition were divided into high-frequency series part and low-frequency series part using the Permutation Entropy (PE) algorithm, the LSTM model is used to predict the high-frequency sequence part, while the ARIMA model is used to predict the low-frequency sequence part. Monthly precipitation forecasts are obtained by superimposing the results of the two models. Finally, the predictive performance is evaluated using several assessment metrics. The indicators show that the model predictive performance outperforms the EMD-LSTM (Empirical Mode Decomposition), EEMD-LSTM, EEMD-ARIMA combined models and the single models, and the model has high confidence in the prediction results of future precipitation.

Delta-radiomics Entropy Based on Tumor Heterogeneity Concept – Response Predictor to Irradiation for Unresectable/recurrent Glioblastoma

Although adjuvant radiotherapy in combination with Temozolomide administration has clearly demonstrated the benefit in improving the prognosis of patients with multiforme glioblastoma, radiotherapy as only treatment or in combination with systemic treatment is one of the best supportive in unresectable cases. For recurrent cases, the salvage radiotherapy option (re-irradiation) can be chosen in carefully selected cases so that the benefit is greater than the toxicities. Radiomics, a new subdomain of artificial intelligence (AI), relies on advanced analysis in high-resolution medical imaging to establish diagnostic, prognostic and predictive models for clinical medicine. The variation of the delta-radiomics parameters analyzed within a tumor volume may be via tumor heterogeneity indirectly correlated with the response to treatment. The aim of the study is to propose a delta-radiomic based on entropy algorithm to allow the non-invasive pre-therapeutic identification of patients with unresectable or recurrent multiform glioblastoma who will benefit from irradiation and/or salvage re-irradiation.

Fault diagnosis of bearing based on refined piecewise composite multivariate multiscale fuzzy entropy

As one of the key components of the train, the condition of the bearing is related to the train's safe operation. The vibration signal of the bearing is usually nonlinear and nonstationary, which makes it difficult to extract fault features and leads to low diagnostic accuracy. Entropy theory can effectively measure the change of nonlinearity and complexity of the vibration signal. However, the conventional entropy algorithm cannot extract the characteristics of a multi-channel signal simultaneously. A bearing fault diagnosis method based on refined piecewise composite multivariate multiscale fuzzy entropy (RPCMMFE) and convolutional neural network (CNN) is proposed. The proposed method can fully extract fault features and improve fault diagnosis accuracy. Firstly, based on multivariate multiscale fuzzy entropy (MMFE), RPCMMFE is proposed by introducing refined theory and piecewise composite theory. Then, the RPCMMFE of different fault signals is calculated, and RPCMMFE is considered to be a feature vector that acts as an input into the CNN for fault diagnosis. Finally, it is verified by two groups of experiments. The experimental results show that the features extracted by this method have better stability and discrimination ability and can identify bearing faults more accurately. The average accuracy rates are 99% and 99.17%, respectively.

Noise-Robust ISAR Translational Motion Compensation via HLPT-GSCFT

Translational motion compensation is a prerequisite of inverse synthetic aperture radar (ISAR) imaging. Translational motion compensation for datasets with low signal-to-noise ratio (SNR) is important but challenging. In this work, we proposed a noise-robust translational motion compensation method based on high-order local polynomial transform–generalized scaled Fourier transform (HLPT-GSCFT). We first model the translational motion as a fourth-order polynomial according to order-of-magnitude analysis, and then design HLPT-GSCFT for translation parameter estimation and parametric translational motion compensation. Specifically, HLPT is designed to estimate the acceleration and third-order acceleration of the translational motion and GSCFT is introduced to estimate the second-order acceleration. Both HLPT and GSCFT have a strong ability for cross-term suppression. In addition, we use a minimum weighted entropy algorithm to estimate the velocity of the translational motion, which can improve the noise robustness of the parameter estimation. Experimental results based on a measured dataset prove that the proposed method is effective and noise-robust.

A new 5D fractional-order conservative hyperchaos system

At present, most of the encryption algorithms based on chaotic systems use dissipative chaotic systems. However, the dissipative chaotic systems have attractors and are easy to reconstruct, which leads to potential security risks in the process of data transmission. Therefore, a novel five-dimensional conservative hyperchaotic system is proposed in this paper, and the integer order system is transformed into a fractional-order system based on the Adomian decomposition method(ADM). The dynamic characteristics of the system are discussed by using classical analysis methods such as Lyapunov exponent spectrum(LEs), bifurcation diagram, phase diagram, and timing diagram. By changing the system parameters and the differential order q, we found a wealth of dynamic phenomena, such as quasi-periodic flow, chaotic flow, and hyperchaotic flow. When the initial value is used as a variable, it is found that the system has initial offset boosting behavior, multiple stability, and special transient behavior. In addition, we use the spectral entropy algorithm to analyze the complexity of the system. Finally, hardware experiments are also carried out using digital signal processor (DSP) to verify the correctness of the numerical simulation, and also to prove the physical realizability of the system, to create conditions for its subsequent engineering applications.

Minimal climate change impacts on the geographic distribution of Nepeta glomerulosa, medicinal species endemic to southwestern and central Asia

Medicinal plants are valuable species, but their geographic distributions may be limited or exposed to extinction by climate change. Therefore, research on medicinal plants in the face of climate change is fundamental for developing conservation strategies. Distributional patterns for a semi-endemic medicinal plant species, Nepeta glomerulosa, distributed in southwestern and central Asia was determined based on a maximum–entropy algorithm. We evaluated potential geographic shifts in suitability patterns for this species under two Shared Socioeconomic Pathways scenarios (SSP2-4.5 and SSP5-8.5) of climate change for 2060. Our models based on climatic features indicate that the species occupies montane areas under current conditions; transfer of the model to future climate scenarios indicated that suitable areas for the species will increase in general, and the species will likely track its favored set of climate conditions. But the types and degrees of these changes differ among areas. Our findings can be used to inform conservation management programs for medicinal, endemic, and endangered species that probably respond similarly to climate change in southwestern and central Asia.