Use Of Information Entropy Research Articles

Optimising the acquisition conditions of high information quality low-field NMR signals based on a cutting-edge approach applying information theory and Taguchi's experimental designs – Virgin olive oil as an application example

BackgroundDeveloping a new spectrometric analytical method based on a fingerprinting approach requires optimization of the experimental stage, particularly with novel instruments like benchtop low-field NMR spectrometers. To ensure high-quality LF-NMR spectra before developing the multivariate model, an experimental design to optimize instrument conditions is essential. However, difficult-to-control factors may be critical for optimization. Taguchi methodology addresses these factors to obtain a system robust to variation. This study uses the Taguchi methodology to optimize instrument settings for acquiring high-quality 1H and 13C LF-NMR signals in a short time from virgin olive oil (VOO). ResultsTwo experimental trials (for 1H and 13C signals, respectively) were carried out and analysed to find an optimal and robust combination of instrument settings against changes in two difficult-to-control factors: ambient temperature and small deviations of the NMR tube volume (700 ± 50 μL). The responses to be optimised, run time and spectral information quality, were analysed separately and jointly, as some factors showed opposite behaviour in the effect on the responses. Multiple response analysis based on suitable desirability functions yielded a combination of factors resulting in desirability values above 0.8 for 1H LF-NMR signals and almost 1.0 for 13C LF-NMR signals.In addition, a novel approach to assess the information quality of an analytical signal was proposed, addressing a major challenge in analytical chemistry. By applying information theory and calculating information entropy, this approach demonstrated its potential for selecting the highest quality (i.e. most informative) analytical signals. SignificanceThe acquisition instrument conditions of LF-NMR were successfully optimised using Taguchi methodology to acquire highly informative 1H and 13C spectra in a minimum run time. The importance lies in the future development of non-targeted analytical applications for VOO quality control. In addition, the innovative use of information entropy to a priori assess the signal quality represents a significant advance and proposes a solution to a long-standing challenge in analytical chemistry.

Robust fault recovery strategy for multi-source flexibly interconnected distribution networks in extreme disaster scenarios

To enhance the resilience of power distribution networks against extreme natural disasters, this article introduces a robust fault recovery strategy for multi-source, flexible interconnected power distribution networks, particularly under scenarios of extreme disasters. Initially, the comprehensive risk of system failure due to ice load on distribution lines and poles is fully considered, and a model for the overall failure rate of lines is constructed. This model addresses the diverse failure scenarios triggered by various meteorological conditions. Through the use of information entropy, typical extreme disaster failure scenarios are identified, and lines with high failure rates under these scenarios are determined. Subsequently, a box-type interval model is developed to represent the uncertainty in the output of distributed generation (DG), and on this basis, a robust fault recovery model for multi-source power distribution networks interconnected through soft open points (SOPs) is established, and use the Column and Constraint Generation (C&CG) algorithm to solve the problem. Finally, the fault recovery model and strategy proposed are validated through an illustrative example based on a modified IEEE 33-node interconnected system.

The Use of Information Entropy and Expert Opinion in Maximizing the Discriminating Power of Composite Indicators.

This research offers a solution to a highly recognized and controversial problem within the composite indicator literature: sub-indicators weighting. The research proposes a novel hybrid weighting method that maximizes the discriminating power of the composite indicator with objectively defined weights. It considers the experts' uncertainty concerning the conceptual importance of sub-indicators in the multidimensional phenomenon, setting maximum and minimum weights (constraints) in the optimization function. The hybrid weighting scheme, known as the SAW-Max-Entropy method, avoids attributing weights that are incompatible with the multidimensional phenomenon's theoretical framework. At the same time, it reduces the influence of assessment errors and judgment biases on composite indicator scores. The research results show that the SAW-Max-Entropy weighting scheme achieves greater discriminating power than weighting schemes based on the Entropy Index, Expert Opinion, and Equal Weights. The SAW-Max-Entropy method has high application potential due to the increasing use of composite indicators across diverse areas of knowledge. Additionally, the method represents a robust response to the challenge of constructing composite indicators with superior discriminating power.

GAs with escape from stagnation of search for multiset iteration permutations problem – consecutive meals planning

AbstractIn this paper, consecutive meals planning is formulated as a multiset iteration permutation problem that determines the optimal meals plan on a period consisting of consecutive days. In this problem, a meal is characterized by some characteristics such as food style, ingredient, cooking method, and so on. The evaluation function is defined by use of information entropy for measuring the appearance order of meal's characteristics on the meals plan. For optimizing this problem, Genetic Algorithms (GAs) with escape from stagnation of search are proposed. It is empirically shown that the proposed GAs with escape operation work better than the conventional permutation GA without escape operation for small and large size optimization problems.

Information in morphological characters.

The construction of morphological character matrices is central to paleontological systematic study, which extracts paleontological information from fossils. Although the word information has been repeatedly mentioned in a wide array of paleontological systematic studies, its meaning has rarely been clarified nor specifically defined. It is important, however, to establish a standard to measure paleontological information because fossils are hardly complete, rendering the recognition of homologous and homoplastic structures difficult. Here, based on information theory, we show the deep connections between paleontological systematic study and communication system engineering. Information is defined as the decrease of uncertainty and it is the information in morphological characters that allows distinguishing operational taxonomic units (OTUs) and reconstructing evolutionary history. We propose that concepts in communication system engineering such as source coding and channel coding, correspond to the construction of diagnostic features and the entire character matrices in paleontological studies. The two coding strategies should be distinguished following typical communication system engineering, because they serve dual purposes. With character matrices from six different vertebrate groups, we analyzed their information properties including source entropy, mutual information, and channel capacity. Estimation of channel capacity shows character saturation of all matrices in transmitting paleontological information, indicating that, due to the presence of noise, oversampling characters not only increases the burden in character scoring, but also may decrease quality of matrices. We further test the use of information entropy, which measures how informative a variable is, as a character weighting criterion in parsimony‐based systematic studies. The results show high consistency with existing knowledge with both good resolution and interpretability.

The Use of Information Entropy for Evaluation on Visual Quality of Urban Skylines

Urban skylines are seen as catchy images of cities and as a viewpoint where the change of the city can be observed. The visual change of cities has a wide variety of political, cultural, sociological, economic, ecological, and physical dimensions.

Information Entropy as a Reliable Measure of Nanoparticle Dispersity.

Nanoparticle size impacts properties vital to applications ranging from drug delivery to diagnostics and catalysis. As such, evaluating nanoparticle size dispersity is of fundamental importance. Conventional approaches, such as standard deviation, usually require the nanoparticle population to follow a known distribution and are ill-equipped to deal with highly poly- or heterodisperse populations. Herein, we propose the use of information entropy as an alternative and assumption-free method for describing nanoparticle size distributions. This measure works equally well for mono-, poly-, and heterodisperse populations and represents an unbiased route to evaluation and optimization of nanoparticle synthesis. We provide intuitive software tools for analysis and supply guidelines for interpretation with respect to known standards.

Multiscale Tsallis permutation entropy analysis for complex physiological time series

Discussing the complexity of time series has been a long-standing problem, including the use of information entropy to determine the complexity of the sequence. Permutation entropy (PE) has been regarded as a learning process to investigate the complexity of time series, such as financial time series and physiological time series. The permutation entropy, which is based on the Shannon entropy (SE), has undeniable shortcomings in dealing with some specific problems. Hence in this paper, we propose the multiscale Tsallis permutation entropy (MTPE) as an improved measuring tool for assessing the hidden temporal correlations in time series. The modified method not only presents a different way showing clear characteristics but also provides more significant results compared with the SE. Robustness of the hypothesis is proved by the correlation between Tsallis permutation entropy (TPE) and HURST exponent obtained from the sequences which are generated from fractional brownian motion stochastic model. Experimental results of autoregressive sequences (AR) and electroencephalograph time series (EEG) make the advantages of Tsallis permutation entropy more obvious, which also well validate the efficiency and integrity of principle of maximum entropy (PME). Multiscale analysis provides us with another perspective to analyze permutation entropy, which also allows us to better analyze the complexity of the sequence.

Autonomous Path Planning Scheme Research for Mobile Robot

AbstractAn autonomous path-planning strategy based on Skinner operant conditioning principle and reinforcement learning principle is developed in this paper. The core strategies are the use of tendency cell and cognitive learning cell, which simulate bionic orientation and asymptotic learning ability. Cognitive learning cell is designed on the base of Boltzmann machine and improved Q-Learning algorithm, which executes operant action learning function to approximate the operative part of robot system. The tendency cell adjusts network weights by the use of information entropy to evaluate the function of operate action. The results of the simulation experiment in mobile robot showed that the designed autonomous path-planning strategy lets the robot realize autonomous navigation path planning. The robot learns to select autonomously according to the bionic orientate action and have fast convergence rate and higher adaptability.

Miner*: A Weighted Distance Sum based Outlier Mining System of Star Spectrum Data

Existing distance-based outlier mining methods do not consider the impact of each attribute's importance degree, thereby resulting in poor mining accuracies. To address this problem, we propose a new outlier mining algorithm – Miner* – that makes use of information entropy and Weighted Distance Sum to substantially improve mining accuracies. Miner* employs information entropy to determine weight values indicating the importance degrees of data attributes. An input dataset is reduced by Miner* through the neighbour-radius-based pruning technologies. Thus, Miner* obtains a candidate outlier set by removing any data objects that are unlikely to be outliers. Miner* calculates the weighted distance sum value Wkof each object in the candidate outlier set; Wkvalue ranks the top n to be regarded as outliers. Due to the sum of distance, which takes full advantage of the clustering characteristics of the dataset, edge distribution data objects and local outliers can be effectively mined out. To demonstrate the effectiveness of the Miner* algorithm, we implement Miner* in a prototype system to detect star spectrum data objects with abnormal characteristic lines. Our experimental results show that the algorithm in Miner* achieves high accuracy, high scalability, and low man-made influence by utilizing UCI and star spectrum dataset. Our results also confirm that Miner* is feasible and effective in mining spectrum data with abnormal characteristic lines from massive star spectrum dataset.

An Information-Entropy-based Risk Measurement Method of Software Development Project

Risk is the major reason for the failure of software development projects. The risk measurement can provide the decision-making data for the risk management and control. The traditional risk measurement method usually assesses in terms of the occurrence probability and the loss of the risk factors, which usually are assessed by experts subjectively. Therefore, they are greatly influenced by artificial factors. In this way, it is difficult to realize the objective and effective measurement. This paper proposes one risk measurement method of software development project based on information entropy, which makes up for the shortcomings of the former studies. This method makes use of information entropy to measure the amount of information so as to measure the software development project risk. In this paper, a new risk checklist is given. First of all, it obtains the risk information through the checklist. Then it obtains the information amount of risk factors by aid of information entropy. This paper concludes that the larger the information amount of the risk factors is, the larger the removed uncertainty is, the smaller the information entropy is and the smaller the risk is. In short, there is a positive correlation between risk and entropy. This paper demonstrates the scientificity and rationality of the method theoretically. Compared with other risk measurement methods, this method manifests certain advantage. The case analysis prove the rationality and feasibility of this method.

Study on a Classification Model of Data Stream based on Concept Drift

In the data stream classification process, in addition to the solution of massive and realtime data stream, the dynamic changes of the need to focus and study. From the angle of detecting concept drift, according to the dynamic characteristics of the data stream. This paper proposes a new classification method for data stream based on the combined use of concept drift detection and classification model. The data stream classification model can’t adapt to concept drift problem to solve. Before the model classification, the use of information entropy to judge the data block concept drift, the concept of history to have appeared, the use of a classifier pool mechanism to save it, to makes the classification model has stronger resistance to concept drift.

Autonomous Navigation Strategy in Mobile Robot

To solve the navigation problem of mobile robot in unknown environment, a navigation scheme based on bionic strategy was proposed, which simulates operant conditioning mechanism. In this scheme, the tendency cell was designed by use of information entropy, which represents the tendency degree for state. The improved Q learning algorithm used as learning core to direct the learning direction. The Boltzmann machine was used to process annealing calculation, which can randomly selected navigation action. The selected strategy of action will tend to optimal with the learning process. Simulation analyses were carried out in mobile robot; results showed that the proposed method had quick learning velocity and accurate navigation ability, and robot could successfully evade obstacles and arrived at goal point with optimal path.

Privacy Metric for User’s Trajectory in Location-Based Services

针对一种流行的用户轨迹隐私保护方法——Silent Cascade,提出一种新的轨迹隐私度量方法.该度量方法将用户运动轨迹用带权无向图描述,并从信息熵的角度计算用户的轨迹隐私水平.已有文献指出,当攻击者拥有新的背景知识时,任何一种隐私保护方法都会受到隐私威胁.因此,将攻击者的背景知识分级融入到度量方法中,隐私度量的结果由对背景知识的假设和相应的轨迹隐私水平值组成,并提出(K_UL(K_i+,K_i-),K_L(K_i+,K_i-))联系规则的方法来描述对背景知识的假设.模拟实验结果表明,此度量方法为移动用户和轨迹隐私保护方法的设计者提供了一个有价值的工具,能够准确地评估在攻击者具有可变背景知识情况下,用户的轨迹隐私水平.;This paper proposes a trajectory privacy measure for Silent Cascade, which is a prevalent trajectory privacy preserving method in LBS (location-based services). In this measure, the user’s trajectory is modeled as a weighted undirected graph, and the user’s trajectory privacy level is calculated through the use of information entropy. It is pointed out in literatures that any privacy preserving methods will be subject to privacy threats once the attacker has new background knowledge. Therefore, adversarial background knowledge is hierarchically integrated into this measure. The privacy metric result composes of the assumptive background knowledge and the corresponding trajectory privacy level. (K_UL(K_i+,K_i-),K_L(K_i+,K_i-)) association rules is also proposed to describe the assumptive background knowledge. Simulation results show that this metric is an effective and valuable tool for mobile users and the designers of trajectory privacy preserving methods to measure the user’s trajectory privacy level correctly, even the attacker has variable background knowledge.

An adaptive region growing algorithm for breast masses in mammograms

This study attempted to accurately segment the mammographic masses and distinguish malignant from benign tumors. An adaptive region growing algorithm with hybrid assessment function combined with maximum likelihood analysis and maximum gradient analysis was developed in this paper. In order to accommodate different situations of masses, the likelihood and the edge gradients of segmented masses were weighted adaptively by the use of information entropy. 106 benign and 110 malignant tumors were included in this study. We found that the proposed algorithm obtained segmentation contour more accurately and delineated the tumor body as well as tumor peripheral regions covering typical mass boundaries and some spiculation patterns. Then the segmented results were evaluated by the classification accuracy. 42 features including age, intensity, shape and texture were extracted from each segmented mass and support vector machine (SVM) was used as a classifier. The classification accuracy was evaluated using the area (Az) under the receiver operating characteristic (ROC) curve. It was found that the maximum likelihood analysis achieved an Az value of 0.835, the maximum gradient analysis got an Az value of 0.932 and the hybrid assessment function performed the best classification result where the value of Az was 0.948. In addition, compared with traditional region growing algorithm, our proposed algorithm is more adaptive and provides a better performance for future works.

Information entropy in fragmenting systems

The possibility of facing critical phenomena in nuclear fragmentation is a topic of great interest. Different observables have been proposed to identify such a behavior, in particular, some related to the use of information entropy as a possible signal of critical behavior. In this work we critically examine some of the most widespread used ones comparing its performance in bond percolation and in the analysis of fragmenting Lennard Jones Drops.

Entropy and Granular Materials: Experiments

Experiments on circular disks supported on a horizontal air table and enclosed in an initially square frame that provides simple shear, constant area, and constant pressure external conditions are reported. Each test was performed on disks of one size; disks of 2.54, 1.27, and 0.64 cm (1, 1/2, and 1/4 in.) diameter were used. The excitation of the system was caused by changing the angle of the frame either monotonically or side to side. The arrangements of the disks were photographed from above at various stages in each experiment. Twenty-one experiments were conducted in which the initial state of the disks, the disk size, the friction between the table and disks, and the external conditions were altered. The number of contacts for each disk was counted, and the frequency of occurrence of states with the same number of contacts was obtained. Similarly, the number of disks about each void was counted, and the frequency of each number was determined. These two counts allowed the contact and void entropies to be calculated. The presentation of these entropies showed that they approached limiting values as excitation increased and that the values depended on the external conditions and not on the friction condition. The behavior of the arrays was random and not chaotic. These results supported the use of information entropy in the geometric description of granular materials and the validity of Jaynes's maximum entropy procedures.