Mutual Redundancy Research Articles

Power Quality Disturbances Classification Through Optimal Feature Selective Mechanism

In the Detection and classification of Power Quality Disturbances, feature selection is very important because the more number of features causes more complexity and less number of feature impacts the accuracy. Hence, this paper proposed a new feature selection mechanism called as Flexible Mutual Information based Feature Selection (FMIFS) to represent the Power Quality Disturbances with an effective set of features. FMIFS computes the Mutual Redundancy between PQDs and removes redundant features between them. Once the optimal features are obtained for each PQD, then they are fed to Multi-class Support Vector Machine (MC-SVM) for classification. MC-SVM classifies each PQD based on the feature trained to the system. At experimental analysis, we applied our method on totally 11 types of PQDs for classification and the performance is measured through recall, precision, F1-score, and False Alarm Rate (FAR).

INCREASING THE CAPACITY OF ELECTRICAL TRANSMISSION LINES IN THE POST-EMERGENCY MODE

Automatic switching on of the reserve (AVR) points are used to increase the reliability of power supply to consumers, which are advisable to install in distribution power networks and consumer networks for the power supply of production processes, the technology of which does not allow even short-term interruptions in power supply. The purpose of the research: substantiating the location of the AVR point for mutual redundancy of power transmission lines; determination of the effect of longitudinal capacitive compensation on the transmission capacity in the post-emergency mode; determining the impact of reactive power compensation on the economy of the post-emergency power transmission mode. The article considers a method of increasing the capacity of power transmission in the post-emergency mode, as a result of which an adjacent (reserved) line is connected to one of the working power transmission lines with a voltage of 10 kV using the point of automatic switching on of the reserve (AVR). However, the effectiveness of such redundancy in most cases turns out to be low due to the limited capacity of trunk sections of mutual-reserved lines with smaller cross-sections of wires at the end sections of both mutually-reserved lines, which were built as radials. As a result of such redundancy, there are increased losses of active power and voltage, and, as a result, unacceptable deviation of the voltage in remote nodes of the load of the redundant power transmission line. In order to improve the parameters of the operation mode of power transmission, it is proposed to use devices of longitudinal capacitive compensation and reactive power compensation in the scheme of the AVR point.

Multi-Class Classifier in Parkinson’s Disease Using an Evolutionary Multi-Objective Optimization Algorithm

In this contribution, a novel methodology for multi-class classification in the field of Parkinson’s disease is proposed. The methodology is structured in two phases. In a first phase, the most relevant volumes of interest (VOI) of the brain are selected by means of an evolutionary multi-objective optimization (MOE) algorithm. Each of these VOIs are subjected to volumetric feature extraction using the Three-Dimensional Discrete Wavelet Transform (3D-DWT). When applying 3D-DWT, a high number of coefficients is obtained, requiring the use of feature selection/reduction algorithms to find the most relevant features. The method used in this contribution is based on Mutual Redundancy (MI) and Minimum Maximum Relevance (mRMR) and PCA. To optimize the VOI selection, a first group of 550 MRI was used for the 5 classes: PD, SWEDD, Prodromal, GeneCohort and Normal. Once the Pareto Front of the solutions is obtained (with varying degrees of complexity, reflected in the number of selected VOIs), these solutions are tested in a second phase. In order to analyze the SVM classifier accuracy, a test set of 367 MRI was used. The methodology obtains relevant results in multi-class classification, presenting several solutions with different levels of complexity and precision (Pareto Front solutions), reaching a result of 97% as the highest precision in the test data.

A study on the improvement of the future branch system

This study aims to suggest an improvement plan for the army branch system considering the development trend of weapon systems through a case analysis of the vision and major weapon systems for each army branch system in R.O.K army. In the future, with the development of science and technology, hyper-connected networks based on artificial satellites would be built, and mosaic warfare, which integrates multiple domains simultaneously, and weapon systems capable of performing all-weather multifunctional battles across land, sea, and air would emerge. As a result, the common areas of the Army, Navy, and Air Force would be expanded, and the division of each army or branch itself would become ambiguous. Hence, it will be inevitable to move away from the branch operation concepts that have been operational until now to seek the concept of jointness or integration. To study this phenomenon, based on the Korean Army Vision 2050 published by the Army, the transition process of the current Army branch system and the cases of vision and major weapon systems for each branch were analyzed. The results of the analysis confirmed that although new advanced complex weapon systems are being developed for each branch, relatively little change has been made to the system. In particular, with the advent of hybrid drones and intelligent autonomous combat robots that can simultaneously perform ‘Surveillance, Reconnaissance - Decision – Strike’, it is expected that the area of expansion and mutual redundancy of combat functions will be further deepened. Therefore, in connection with the development of the weapon system, we will seek a solution to improve the Army branch system in the future and clarify the implications for the Navy and Air Force in the future.

Preliminary Design of the Fusion Power Shutdown System Unit for ITER

As an important part of the ITER gas injection system, the fusion power shutdown system delivers large quantities of gas into the vacuum vessel to stop the fusion power in an emergency case. Two identical but independent units are designed for mutual redundancy. Each unit includes an injection pipeline and a unit that contains a gas reservoir, solenoid valve, pneumatic isolation valve, and pressure switches. Among these components, the working gas reservoir parameters are investigated by experiments based on the required total gas quantity of at least 3000 Pa m3 neon or mixtures of neon and hydrogen injected within 3 s. The working gas is released utilizing a pneumatically actuated valve that is not affected by the strong stray magnetic field of about 0.205 T. The associated solenoid valve is equipped with magnetic shielding that is designed by a magnetostatic analysis. These components lie on the same plane in the unit to maximize the maintainability. Furthermore, the structure integrity of the unit and its support frame is validated by a preliminary structural analysis.

Enhanced representation of natural sound sequences in the ventral auditory midbrain

The auditory midbrain (inferior colliculus, IC) plays an important role in sound processing, acting as hub for acoustic information extraction and for the implementation of fast audio-motor behaviors. IC neurons are topographically organized according to their sound frequency preference: dorsal IC regions encode low frequencies while ventral areas respond best to high frequencies, a type of sensory map defined as tonotopy. Tonotopic maps have been studied extensively using artificial stimuli (pure tones) but our knowledge of how these maps represent information about sequences of natural, spectro-temporally rich sounds is sparse. We studied this question by conducting simultaneous extracellular recordings across IC depths in awake bats (Carollia perspicillata) that listened to sequences of natural communication and echolocation sounds. The hypothesis was that information about these two types of sound streams is represented at different IC depths since they exhibit large differences in spectral composition, i.e., echolocation covers the high-frequency portion of the bat soundscape (> 45 kHz), while communication sounds are broadband and carry most power at low frequencies (20–25 kHz). Our results showed that mutual information between neuronal responses and acoustic stimuli, as well as response redundancy in pairs of neurons recorded simultaneously, increase exponentially with IC depth. The latter occurs regardless of the sound type presented to the bats (echolocation or communication). Taken together, our results indicate the existence of mutual information and redundancy maps at the midbrain level whose response cannot be predicted based on the frequency composition of natural sounds and classic neuronal tuning curves.

Two-stage adaptive integration of multi-source heterogeneous data based on an improved random subspace and prediction of default risk of microcredit

Some scholars have shown that the machine learning methods based on a single-source data can successfully monitor the risks of formal financial activities, but not those of informal financial activities. This is because the data generated by formal financial activities, whether it is the structured or unstructured data, are of high quality and quantity, while the data generated by informal financial activities are not. Therefore, multi-source data are the key to monitor the risks of informal financial activities through machine learning. Although a few studies attempted to use multi-source data for financial risk prediction, they simply stack the obtained multi-source data, but ignore the original sources, heterogeneity, mutual redundancy and other characteristics of the data, so that the improvement of the prediction effect is not obvious. Therefore, TSAIB_RS method based on the two-stage adaptive integration of multi-source heterogeneous data was constructed in the paper, in which the data with different sources and different distributions were adaptively integrated. In order to test the reliability of TSAIB_RS method, the paper takes the default risk of microcredit in China as the test target and compares the prediction results of various test methods. It concludes that TSAIB_RS method can significantly improve the prediction effects.

Causes detection of unqualified bendability of hot rolled strip via improved RankBoost with multiple feature ranking algorithms

ABSTRACT In hot rolling process, mechanical properties of steel materials are important to steel quality. The bendability is one of the key parameters to evaluate the formability of the strip. When the bendability is unqualified, how to detect causes becomes a big challenge. In this paper, a model to find the causes of bendability of hot rolled strip based on improved RankBoost with multiple feature selection algorithms using historical data is built. Firstly, the related process variables and bendability results are collected. And then, seven feature ranking methods including Fisher score, Relief, Gini index, T-test, Kruskal–Wallis, mutual information entropy and minimum redundancy maximum relevance (MRMR), are used to rank the significance of features individually. Finally, to summarize the results of the seven methods, the total importance of every feature can be obtained using the improved RankBoost method to select the most important features as the major causes. The real field data set from hot rolling strip steel process is used to validate the model. The results demonstrate that the RankBoost method can give a more credible result.

Collapse of the hepatic gene regulatory network in the absence of FoxA factors.

The FoxA transcription factors are critical for liver development through their pioneering activity, which initiates a highly complex regulatory network thought to become progressively resistant to the loss of any individual hepatic transcription factor via mutual redundancy. To investigate the dispensability of FoxA factors for maintaining this regulatory network, we ablated all FoxA genes in the adult mouse liver. Remarkably, loss of FoxA caused rapid and massive reduction in the expression of critical liver genes. Activity of these genes was reduced back to the low levels of the fetal prehepatic endoderm stage, leading to necrosis and lethality within days. Mechanistically, we found FoxA proteins to be required for maintaining enhancer activity, chromatin accessibility, nucleosome positioning, and binding of HNF4α. Thus, the FoxA factors act continuously, guarding hepatic enhancer activity throughout adult life.

Ensuring the Reliability and Efficiency of the Power Industry in the Agricultural Sector of the Republic of Belarus in Modern Conditions

The necessity of improving the power supply system in the agricultural sector is substantiated by the growing share of first-category loads that require targeted reliability. Improving values of the technical and economic indicators of centralized energy generation and its transmission through main and distribution grids cannot ensure high reliability of consumers of the first category, the number of which is constantly growing in agriculture. It is noted that if the power system in outage of the generating source of the high power supply is provided by inputting the emergency reserve, then at emergency switching-off phase of the section of the mains, power supply of the consumers powered by the schemes for both radial and looped distribution network may be absent for a long period of time (several hours). The ways and methods of improving the power supply system, such as integration of a single power system with distributed generation sources, ensuring their parallel operation, load management using complex tariffs for electric energy that take into account the form of load schedules, modernization of technological processes taking into account their automation and improvement of technical and economic indicators, are considered. Taking into account the increase in electricity consumption for heating and hot water supply, we consider the option of eliminating cross-subsidization, which stimulates the interest of consumers in saving electricity and reducing the fee for electricity consumption by changing the operating modes of equipment and devices. The analysis of promising energy-saving measures in agricultural power engineering demonstrated that routine measures are being supplemented by other measures induced by the fact that power electronics and microprocessor technology have significantly advanced in their development in the agricultural energy sector over the past decades, for example, for frequency control of asynchronous motors equipped by a short-circuited rotor that has high values of energy and dynamic indicators. Since there are a number of technologies in agriculture where electric energy is used without the electric drive units application and each of the technological processes has its own requirements for the reliability and quality of electric energy, the scheme of mutual redundancy from adjacent substations using distributed generation sources and technical means of automated energy accounting, control and management of electrical loads, is considered.

Combining Rough Set-based Relevance and Redundancy for the Ranking and Selection of Nominal Features

In this paper, we propose a new method for features ranking and selection. Our approach is based on ranking nominal features in terms of their relevance to the assigned class and mutual redundancy with the other features. To calculate the relevance and redundancy, we propose to use a rough-set based approach. After performing the ranking, features filtering is carried out in a supervised way enabling the user to decide on the number of the retained features. The experiments revealed that thanks to our method, it is possible to filter out numerous features describing data while still maintaining satisfactory classification accuracy achieved by the classifier trained using the reduced dataset. The comparative experiments performed with the use of publicly available datasets proved the high efficiency and competitiveness of our approach.

Практика и перспективы применения семейства эмуляторов архитектур мейнфреймов IBM

This article describes the family of emulators for IBM mainframe architectures, their development history, functional features and capability, as well as the experience of many years (since 1994) of emulators development and their implementation area. There was sold the relatively simple task (for modern standards) of creating a virtual machine in the VSE/ESA operating system for transferring legacy platform-dependent applications to this target environment. The problem was solved at first for EU computers in Russia, and then for IBM 9221 in Germany and in the other western countries. The transfer was made to the OS/390 environment, and to IBM AIX, quite modern at that time. The virtual execution of any existing IBM mainframe operating systems in the main server OS environments: Linux, Windows, AIX, Z/OS, ZLinux had been provided. There was developed the solution for combining any types of formed virtual computing nodes into heterogeneous geographically distributed computing networks that provide, in particular, multiple mutual redundancy of nodes in the network.

Mutual Information Based Feature Selection for Fingerprint Identification

In the field of fingerprint identification, local histograms coding is one of the most popular techniques used for fingerprint representation, due to its simplicity. This technique is based on the concatenation of the local histograms resulting in a high dimension histogram, which causes two problems. First, long computing time and big memory capacities are required with databases growing. Second, the recognition rate may be degraded due to the curse of dimensionality phenomenon. In order to resolve these problems, we propose to reduce the dimensionality of histograms by choosing only the pertinent bins from them using a feature selection approach based on the mutual information computation. For fingerprint features extraction we use four descriptors: Local Binary Patterns (LBP), Histogram of Gradients (HoG), Local Phase Quantization (LPQ) and Binarized Statistical Image Features (BSIF). As mutual information based selection methods, we use four strategies: Maximization of Mutual Information (MIFS), minimum Redundancy and Maximal Relevance (mRMR), Conditional Info max Feature Extraction (CIFE) and Joint Mutual Information (JMI). We compare results in terms of recognition rates and number of selected features for the investigated descriptors and selection strategies. Our results are conducted on the four FVC 2002 datasets which present different image qualities. We show that the combination of mRMR or CIFE feature selection methods with HoG features gives the best results. We also show that the selection of useful fingerprint features can surely improve the recognition rate and reduce the complexity of the system in terms of computation cost. The feature selection algorithms may reach 98% of time reduction by considering only 20% of the total number of features while also improving the recognition rate of about 2% by avoiding the curse of dimensionality phenomena.

A Correlation-Based Feature Weighting Filter for Naive Bayes

Due to its simplicity, efficiency, and efficacy, naive Bayes (NB) has continued to be one of the top 10 algorithms in the data mining and machine learning community. Of numerous approaches to alleviating its conditional independence assumption, feature weighting has placed more emphasis on highly predictive features than those that are less predictive. In this paper, we argue that for NB highly predictive features should be highly correlated with the class (maximum mutual relevance), yet uncorrelated with other features (minimum mutual redundancy). Based on this premise, we propose a correlation-based feature weighting (CFW) filter for NB. In CFW, the weight for a feature is a sigmoid transformation of the difference between the feature-class correlation (mutual relevance) and the average feature-feature intercorrelation (average mutual redundancy). Experimental results show that NB with CFW significantly outperforms NB and all the other existing state-of-the-art feature weighting filters used to compare. Compared to feature weighting wrappers for improving NB, the main advantages of CFW are its low computational complexity (no search involved) and the fact that it maintains the simplicity of the final model. Besides, we apply CFW to text classification and have achieved remarkable improvements.

Overview of the Mechanisms that May Contribute to the Non-Redundant Activities of Interferon-Inducible CXC Chemokine Receptor 3 Ligands.

The inflammatory chemokines CXCL9, CXCL10, and CXCL11 are predominantly induced by interferon (IFN)-γ and share an exclusive chemokine receptor named CXC chemokine receptor 3 (CXCR3). With a prototype function of directing temporal and spatial migration of activated T cells and natural killer cells, and inhibitory effects on angiogenesis, these CXCR3 ligands have been implicated in infection, acute inflammation, autoinflammation and autoimmunity, as well as in cancer. Intense former research efforts led to recent and ongoing clinical trials using CXCR3 and CXCR3 ligand targeting molecules. Scientific evidence has claimed mutual redundancy, ligand dominance, collaboration or even antagonism, depending on the (patho)physiological context. Most research on their in vivo activity, however, illustrates that CXCL9, CXCL10, and CXCL11 each contribute to the activation and trafficking of CXCR3 expressing cells in a non-redundant manner. When looking into detail, one can unravel a multistep machinery behind final CXCR3 ligand functions. Not only can specific cell types secrete individual CXCR3 interacting chemokines in response to certain stimuli, but also the receptor and glycosaminoglycan interactions, major associated intracellular pathways and susceptibility to processing by particular enzymes, among others, seem ligand-specific. Here, we overview major aspects of the molecular properties and regulatory mechanisms of IFN-induced CXCR3 ligands, and propose that their in vivo non-redundancy is a reflection of the unprecedented degree of versatility that seems inherent to the IFN-related CXCR3 chemokine system.

Genre Classification for Brazilian Music using Independent and Discriminant Features

Digital music files are largely available both online and in private local collections. These databases may comprise hundreds or thousands of files, which in some cases may not carry tagged information about their content, making the search for the desired audio files very time consuming. An important task in this context is to organize the available database according to the prevailing musical genre. The purpose of this work is to develop an automatic music genre classification system able to identify international music genres (i.e. pop, rock, classic, soul, funk) and also typical Brazilian rhythms such as Samba, Forr\'o and Brazilian Popular Music. The proposed signal processing chain comprises two stages. Initially, audio signal features are computed and their relevance for music genre identification estimated. Independent component analysis is applied to reduce mutual redundancy among the audio attributes. In the following, different classifiers based on neural networks and support vector machines are applied for music genre identification. The proposed system efficiency is evaluated using an experimental dataset.

Differential evolution for filter feature selection based on information theory and feature ranking

Feature selection is an essential step in various tasks, where filter feature selection algorithms are increasingly attractive due to their simplicity and fast speed. A common filter is to use mutual information to estimate the relationships between each feature and the class labels (mutual relevancy), and between each pair of features (mutual redundancy). This strategy has gained popularity resulting a variety of criteria based on mutual information. Other well-known strategies are to order each feature based on the nearest neighbor distance as in ReliefF, and based on the between-class variance and the within-class variance as in Fisher Score. However, each strategy comes with its own advantages and disadvantages. This paper proposes a new filter criterion inspired by the concepts of mutual information, ReliefF and Fisher Score. Instead of using mutual redundancy, the proposed criterion tries to choose the highest ranked features determined by ReliefF and Fisher Score while providing the mutual relevance between features and the class labels. Based on the proposed criterion, two new differential evolution (DE) based filter approaches are developed. While the former uses the proposed criterion as a single objective problem in a weighted manner, the latter considers the proposed criterion in a multi-objective design. Moreover, a well known mutual information feature selection approach (MIFS) based on maximum-relevance and minimum-redundancy is also adopted in single-objective and multi-objective DE algorithms for feature selection. The results show that the proposed criterion outperforms MIFS in both single objective and multi-objective DE frameworks. The results also indicate that considering feature selection as a multi-objective problem can generally provide better performance in terms of the feature subset size and the classification accuracy.

An Improved Combination of Spectral and Spatial Features for Vegetation Classification in Hyperspectral Images

Due to the advances in hyperspectral sensor technology, hyperspectral images have gained great attention in precision agriculture. In practical applications, vegetation classification is usually required to be conducted first and then the vegetation of interest is discriminated from the others. This study proposes an integrated scheme (SpeSpaVS_ClassPair_ScatterMatrix) for vegetation classification by simultaneously exploiting image spectral and spatial information to improve vegetation classification accuracy. In the scheme, spectral features are selected by the proposed scatter-matrix-based feature selection method (ClassPair_ScatterMatrix). In this method, the scatter-matrix-based class separability measure is calculated for each pair of classes and then averaged as final selection criterion to alleviate the problem of mutual redundancy among the selected features, based on the conventional scatter-matrix-based class separability measure (AllClass_ScatterMatrix). The feature subset search is performed by the sequential floating forward search method. Considering the high spectral similarity among different green vegetation types, Gabor features are extracted from the top two principal components to provide complementary spatial features for spectral features. The spectral features and Gabor features are stacked into a feature vector and then the ClassPair_ScatterMatrix method is used on the formed vector to overcome the over-dimensionality problem and select discriminative features for vegetation classification. The final features are fed into support vector machine classifier for classification. To verify whether the ClassPair_ScatterMatrix method could well avoid selecting mutually redundant features, the mean square correlation coefficients were calculated for the ClassPair_ScatterMatrix method and AllClass_ScatterMatrix method. The experiments were conducted on a widely used agricultural hyperspectral image. The experimental results showed that (1) the The proposed ClassPair_ScatterMatrix method could better alleviate the problem of selecting mutually redundant features, compared to the AllClass_ScatterMatrix method; (2) compared with the representative mutual information-based feature selection methods, the scatter-matrix-based feature selection methods generally achieved higher classification accuracies, and the ClassPair_ScatterMatrix method especially, produced the highest classification accuracies with respect to both data sets (87.2% and 90.1%); and (3) the proposed integrated scheme produced higher classification accuracy, compared with the decision fusion of spectral and spatial features and the methods only involving spectral features or spatial features. The comparative experiments demonstrate the effectiveness of the proposed scheme.

The cyclin D1 carboxyl regulatory domain controls the division and differentiation of hematopoietic cells.

BackgroundThe family of D cyclins has a fundamental role in cell cycle progression, but its members (D1, D2, D3) are believed to have redundant functions. However, there is some evidence that contradicts the notion of mutual redundancy and therefore this concept is still a matter of debate.ResultsOur data show that the cyclin D1 is indispensable for normal hematopoiesis. Indeed, in the absence of D1, either in genetic deficient mice, or after acute ablation by RNA interference, cyclins D2 and D3 are also not expressed preventing hematopoietic cell division and differentiation at its earliest stage. This role does not depend on the cyclin box, but on the carboxyl regulatory domain of D1 coded by exons 4–5, since hematopoietic differentiation is also blocked by the conditional ablation of this region.ConclusionThese results demonstrate that not all functions of individual D cyclins are redundant and highlight a master role of cyclin D1 in hematopoiesis.Electronic supplementary materialThe online version of this article (doi:10.1186/s13062-016-0122-9) contains supplementary material, which is available to authorized users.

Mutual redundancies in interhuman communication systems: Steps toward a calculus of processing meaning

The study of interhuman communication requires a more complex framework than Claude E. Shannon's (1948) mathematical theory of communication because “information” is defined in the latter case as meaningless uncertainty. Assuming that meaning cannot be communicated, we extend Shannon's theory by defining mutual redundancy as a positional counterpart of the relational communication of information. Mutual redundancy indicates the surplus of meanings that can be provided to the exchanges in reflexive communications. The information is redundant because it is based on “pure sets” (i.e., without subtraction of mutual information in the overlaps). We show that in the three‐dimensional case (e.g., of a triple helix of university–industry–government relations), mutual redundancy is equal to mutual information (Rxyz = Txyz); but when the dimensionality is even, the sign is different. We generalize to the measurement in N dimensions and proceed to the interpretation. Using Niklas Luhmann's (1984–1995) social systems theory and/or Anthony Giddens's (1979, 1984) structuration theory, mutual redundancy can be provided with an interpretation in the sociological case: Different meaning‐processing structures code and decode with other algorithms. A surplus of (“absent”) options can then be generated that add to the redundancy. Luhmann's “functional (sub)systems” of expectations or Giddens's “rule‐resource sets” are positioned mutually, but coupled operationally in events or “instantiated” in actions. Shannon‐type information is generated by the mediation, but the “structures” are (re‐)positioned toward one another as sets of (potentially counterfactual) expectations. The structural differences among the coding and decoding algorithms provide a source of additional options in reflexive and anticipatory communications.