Modification Of Algorithm Research Articles

Time-varying multi-objective smart home appliances scheduling using fuzzy adaptive dynamic SPEA2 algorithm

The smart city is an idea of upcoming revolution in cities, which includes smart home, smart mobility, smart living, and smart environment that can be operated using a smartphone connected to the internet. By adequately scheduling appliances, residential consumers can reduce their electricity expenses while increasing their comfort. Electricity cost and user comfort being conflicting in nature, can be formulated as a dynamic multi-objective optimization problem with varying user priority to use different home appliances at different times. Further to solve this problem, a Fuzzy Adaptive Dynamic SPEA2 with Borda Ranking Method (FDSPEA2-BR) is proposed based on the dynamic modification of popular SPEA2 algorithm. The algorithm includes an improved Borda count method along with Mamdani fuzzy rules to select the best solutions. The changes in crossover and mutation rate in the SPEA2 algorithm is now being controlled with fuzzy rules. Proposed FDSPEA2-BR is validated on fourteen benchmark dynamic multi-objective functions taken from the FDA, JY, dMOP, and DF test suites, results have validated the optimization model’s efficacy. Simulation study is performed on the scheduling of 11 smart home appliances varying over 10 time slots. The results in the form of time varying Pareto Fronts is demonstrated and the schedules corresponding to 5 diversified points on each Pareto Front are reported. The end user can make use of the optimized schedules that were thus obtained to modify his or her patterns of demand and energy use.

ИНТЕЛЛЕКТУАЛЬНОЕ ПЛАНИРОВАНИЕ СТРАТЕГИЙ И УПРАВЛЕНИЕ ГРУППОЙ МОБИЛЬНЫХ РОБОТОВ В УСЛОВИЯХ НЕПОЛНОТЫ ИНФОРМАЦИИ

Different problems of strategy planning and control of a mobile robot group under complex dynamicconditions with incomplete information about the external environment are considered. Approachesto solving problems of effective work scheduling under conditions of inconstant active groupcomposition, searching for the source of a nonstationary concentration field, supervisory control ofdiscrete-event systems are presented. An original mathematical model formulated in terms of work-shiftscheduling problems and a problem-oriented modification of evolutionary algorithms with a specializedset of heuristics for its efficient solution are developed for the problem of scheduling top-level groupwork. Searching and monitoring the source of the nonstationary concentration field is carried out usinga decentralized multi-agent control strategy that combines elements of bionic and gradient approaches,as well as a method for generating artificial potential fields. The considered control strategy has lowcomputational complexity, high variability with respect to the types of fields surveyed, and is easily scalableto control any available number of mobile robots. The latter is of special importance, in particularwhen considering the problem of parallel and independent monitoring of multiple sources. It is proposedto use the means of logical inference, namely automatic theorem proving in the calculus of positivelyconstructed formulas, to solve various problems of the supervised control theory of discrete-eventsystems used at different levels of the robotic complex hierarchical control system. Features of the calculusallows solving complex problems of dynamic systems control, as well as processing and controllingevents based on environmental data in real time in the process of logical inference efficiently. Theapproach based on positively constructed formulas allows studying the properties of automata-baseddiscrete-event systems, as well as to synthesize and model finite automata for the construction and realizationof monolithic and modular supervisors. A general scheme combining the considered approachesfor controlling a group of mobile robots at different levels and time scales within a single hierarchicalcontrol system is proposed.

The Entropy of Digital Texts-The Mathematical Background of Correctness.

Based on Shannon's communication theory, in the present paper, we provide the theoretical background to finding an objective measurement-the text-entropy-that can describe the quality of digital natural language documents handled with word processors. The text-entropy can be calculated from the formatting, correction, and modification entropy, and based on these values, we are able to tell how correct or how erroneous digital text-based documents are. To present how the theory can be applied to real-world texts, for the present study, three erroneous MS Word documents were selected. With these examples, we can demonstrate how to build their correcting, formatting, and modification algorithms, to calculate the time spent on modification and the entropy of the completed tasks, in both the original erroneous and the corrected documents. In general, it was found that using and modifying properly edited and formatted digital texts requires less or an equal number of knowledge-items. In information theory, it means that less data must be put on the communication channel than in the case of erroneous documents. The analysis also revealed that in the corrected documents not only the quantity of the data is less, but the quality of the data (knowledge pieces) is higher. As the consequence of these two findings, it is proven that the modification time of erroneous documents is severalfold of the correct ones, even in the case of minimal first level actions. It is also proven that to avoid the repetition of the time- and resource-consuming actions, we must correct the documents before their modification.

Novel Neural Network Optimized by Electrostatic Discharge Algorithm for Modification of Buildings Energy Performance

Proper analysis of building energy performance requires selecting appropriate models for handling complicated calculations. Machine learning has recently emerged as a promising effective solution for solving this problem. The present study proposes a novel integrative machine learning model for predicting two energy parameters of residential buildings, namely annual thermal energy demand (DThE) and annual weighted average discomfort degree-hours (HDD). The model is a feed-forward neural network (FFNN) that is optimized via the electrostatic discharge algorithm (ESDA) for analyzing the building characteristics and finding their optimal contribution to the DThE and HDD. According to the results, the proposed algorithm is an effective double-target model that can predict the required parameters with superior accuracy. Moreover, to further verify the efficiency of the ESDA, this algorithm was compared with three similar optimization techniques, namely atom search optimization (ASO), future search algorithm (FSA), and satin bowerbird optimization (SBO). Considering the Pearson correlation indices 0.995 and 0.997 (for the DThE and HDD, respectively) obtained for the ESDA-FFNN versus 0.992 and 0.938 for ASO-FFNN, 0.926 and 0.895 for FSA-FFNN, and 0.994 and 0.995 for SBO-FFNN, the ESDA provided higher accuracy of training. Subsequently, by collecting the weights and biases of the optimized FFNN, two formulas were developed for easier computation of the DThE and HDD in new cases. It is posited that building engineers and energy experts could consider the use of ESDA-FFNN along with the proposed new formulas for investigating the energy performance in residential buildings.

A novel method of deinterleaving radar pulse sequences based on a modified DBSCAN algorithm

A modified DBSCAN algorithm is presented for deinterleaving of radar pulses in modern EW environments. A main characteristic of the proposed method is that using only time of arrival of pulses, the method can sort the pulses efficiently. Other PDW information such as rise time, carrier frequency, pulse width, modulation on pulse, fall time and direction of arrival are not required. To identify the valid PRIs in a set of interleaved pulses, an innovative modification of the DBSCAN algorithm is introduced which is accurate and easy to implement. The proposed method determines valid PRIs more accurately and neglects the spurious ones more efficiently as compared to the classical histogram based algorithms such as SDIF. Furthermore, without specifying any input parameter, the proposed method can deinterleave radar pulses while up to 30% jitter is present in the associated PRI. The accuracy and efficiency of the proposed method are verified by computer simulations and real data results. Experimental simulations are based on different real and operational scenarios where the presence of missing and spurious pulses are also considered. So, the simulation results can be of practical significance.

A comprehensive survey of whale optimization algorithm: modifications and classification

Whale optimization algorithm (WOA) is an emerging nature-inspired, swarm-intelligence based algorithm to solve optimization problems more efficiently. This algorithm is based on the bubble-net hunting strategy of the humpback whales. It has gained immense popularity among researchers, typically, due to its simple nature, fast convergence, and having minimum parameters. In the recent past, it has been widely adopted in various fields including data mining, machine learning, wireless sensor networks, cloud computing, civil engineering, and power systems due to its optimal performance. The WOA has given competitive results in comparison to the state-of-the-art optimization algorithms. In this study, we aim to present a comprehensive survey of WOA consisting of more than eighty existing variants of WOA. More specifically, we intend to put forward key aspects of WOA variants with reference to modifications and applications. Further, we classify the most dominant variants of WOA in distinct categories based on modification area such as equation modification, parameter tuning or the problem space for which an algorithm has been specifically altered. We believe that this study will be beneficial for the community working on optimization problems and it can serve as a basis for understanding the modification and improvement process of an optimization algorithm.

Improved Parkinsonian tremor quantification based on automatic label modification and SVM with RBF kernel

Objective. The quantitative assessment of Parkinsonian tremor, e.g. (0, 1, 2, 3, 4) according to the Movement Disorder Society-Unified Parkinson’s Disease Rating Scale, is crucial for treating Parkinson’s disease. However, the tremor amplitude constantly fluctuates due to environmental and psychological effects on the patient. In clinical practice, clinicians assess the tremor severity for a short duration, whereas manual tremor labeling relies on the clinician’s physician experience. Therefore, automatic tremor quantification based on wearable inertial sensors and machine learning algorithms is affected by the manual labels of clinicians. In this study, an automatic modification method for the labels judged by clinicians is presented to improve Parkinsonian tremor quantitation. Approach. For the severe overlapping of dynamic feature range between different severities, an outlier modification algorithm (PCA-IQR) based on the combination of principal component analysis and interquartile range statistic rule is proposed to learn the blurred borders between different severity scores, thereby optimizing the labels. Afterward, according to the modified feature vectors, a support vector machine (SVM) with a radial basis function (RBF) kernel is proposed to classify the tremor severity. The classifier models of SVM with RBF kernel, k-nearest neighbors, and SVM with the linear kernel are compared. Main results. Experimental results show that the proposed method has high classification performance and excellent model generalization ability for tremor quantitation (accuracy: 97.93%, precision: 97.96%, sensitivity: 97.93%, F1-score: 97.94%). Significance. The proposed method may not only provide valuable assistance for clinicians to assess the tremor severity accurately, but also provides self-monitoring for patients at home and improve the assessment skills of clinicians.

A Mixed Hardware-Software Implementation of a High-Performance PMSM Controller

Implementation of the permanent magnet synchronous motor vector control implies strong time dependencies. The control process requires precise measurement of motor shaft position and winding currents to establish correct driving. The tight time dependencies are difficult to achieve using a programmatic approach. Specific controller architecture is proposed for programmable systems on chip architectures enabling operations precise timing and improved processing performance. The controller is decomposed into a dedicated hardware interface system and programmatic part for easy implementation and modification of the control algorithm. The proposed architecture offers precise and repeatable input-output operations timing and assures meeting tight time dependencies. The control algorithm is executed as an interrupt service requested by the interface system in a constant processing period with relatively weak time dependencies. Additionally, the interface system preprocesses input and output signals reducing the computation effort and saving time for algorithm computations. The specific implementation enabled improved measurement of the motor’s windings current with suppression of disturbances caused by inverter operation. There is shown an efficient implementation of Parke’s and Clarke’s transformations using specific resources of modern programmable logic devices. In opposite to the software-managed implementation presented implementation assures completing processing faster, using a minimal number of hardware resources of the FPGA platform and offering the highest flexibility of software part implementation.

Enumerating Stable Nanopores in Graphene and Their Geometrical Properties Using the Combinatorics of Hexagonal Lattices

Nanopores in two-dimensional (2D) materials, including graphene, can be used for a variety of applications, such as gas separations, water desalination, and DNA sequencing. So far, however, all plausible isomeric shapes of graphene nanopores have not been enumerated. Instead, a probabilistic approach has been followed to predict nanopore shapes in 2D materials, due to the exponential increase in the number of nanopores as the size of the vacancy increases. For example, there are 12 possible isomers when N = 6 atoms are removed, a number that theoretically increases to 11.7 million when N = 20 atoms are removed from the graphene lattice. In this regard, the development of a smaller, exhaustive data set of stable nanopore shapes can help future experimental and theoretical studies focused on using nanoporous 2D materials in various applications. In this work, we use the theory of 2D triangular "lattice animals" to create a library of all stable graphene nanopore shapes based on a modification of a well-known algorithm in the mathematical combinatorics of polyforms known as Redelmeier's algorithm. We show that there exists a correspondence between graphene nanopores and triangular polyforms (called polyiamonds) as well as hexagonal polyforms (called polyhexes). We develop the concept of a polyiamond ID to identify unique nanopore isomers. We also use concepts from polyiamond and polyhex geometries to eliminate unstable nanopores containing dangling atoms, bonds, and moieties. We verify using density functional theory calculations that such pores are indeed unstable. The exclusion of these unstable nanopores leads to a remarkable reduction in the possible nanopores from 11.7 million for N = 20 to only 0.184 million nanopores, thereby indicating that the number of stable nanopores is almost 2 orders of magnitude lower and is much more tractable. Not only that, by extracting the polyhex outline, our algorithm allows searching for nanopores with dimensions and shape factors in a specified range, thus aiding the design of the geometrical properties of nanopores for specific applications. We also provide the coordinate files of the stable nanopores as a library to facilitate future theoretical studies of these nanopores.

Improved estimation of rail–wheel contact forces from instrumented wheel-set data through higher harmonic cancellation and a back-propagation neural network scheme

Instrumented wheelsets, equipped with numbers of strain gauges, are widely used to estimate the rail–wheel contact parameters. The accuracy in estimation of rail–wheel contact parameters from the measured strain signals depends on the layout of the strain gauges and correlation techniques adopted for processing measured signals. Harmonic components estimation is the most widely used conventional correlation technique. However, its accuracy in estimation of rail–wheel contact parameters is limited to the order of 80%–90%. This study proposes a signal modification algorithm, initially utilizing cancellation of next significant higher harmonics than those considered in the existing algorithms and then further, a novel Artificial Neural Network architecture for the same purpose.The signal modification approach is based on the realization that the seventh harmonic has significant contribution on the strain signals. The proposed algorithm is aimed at estimating and cancelling the effects of the seventh harmonic in strain signals. The accuracy thus improves to 91% in vertical contact force estimates, while the accuracy in estimation of lateral contact force and lateral contact position are improved to 97% and 95%, respectively. Due to the highly non-linear nature of the rail–wheel contact parameters, an optimized nonlinear mapping is required for further improvement in the accuracy.The viability of using artificial neural networks in estimation of rail–wheel contact parameters have been explored earlier. The proposed architectures are complicated and require extensive manual optimization of tuning parameters. A simple back-propagation artificial neural network architecture, using Bayesian regularization training function with Levenberg–Marquardt optimization technique is proposed in this study. The proposed neural network consists of 18 measured strain signals as input, three hidden layers with 20, 6 and 3 neurons, respectively, and vertical force, lateral force and lateral contact position as three outputs. Accuracies of the order of 99%, 99% and 96% are achieved in the estimation of vertical contact force, lateral contact force and lateral contact position respectively. The input and training data is generated from the multi-body dynamics and finite element simulations.

Fuzzy Gaussian Decision Tree

The Decision Tree algorithm is one of the first machine learning algorithms developed. It is used both as a standalone model and as an ensemble of many cooperating trees like Random Forest, AdaBoost, Gradient Boosted Trees, or XGBoost. In this work, a new version of the Decision Tree was developed for classifying real-world signals using Gaussian distribution functions and a fuzzy decision process. The research was carried out on Power Quality Classification Dataset hosted on the platform kaggle.com for accessing the algorithm’s classification. The proposed algorithm modification produces a sparse tree of multidimensional Gaussian kernels performing fuzzy, proximity-based division of solution space instead of a typical Decision Tree performing definite space restrictions. The machine learning model can cluster samples based on the common pattern and evaluate the input’s similarity in a fuzzified fashion. The studies were conducted for the prediction of 6 classes of current. The proposed modification achieved the best accuracy and F1 score compared to the default Decision Tree and other machine learning algorithms.

Transmission Network Loss Reduction and Voltage Profile Improvement Using Network Restructuring and Optimal DG Placement

This paper introduced a method using hybrid combination of network restructuring and optimal placement of optimally sized distributed generators (DG) to reduce loss and improve voltage profile in a practical transmission network for scenario of high load demand for a period of ten years. A study is performed for four study cases which includes the test transmission network without considering optimal DG placement and network restructuring, considering network restructuring, optimal placement of DG units using proposed grid parameter oriented harmony search algorithm (GPOHSA) and considering hybrid combination of network restructuring and DG placement using GPOHSA. Network restructuring is achieved by addition of a new 400 kV Grid-substation (GSS) and a 220 kV GSS along with associated transmission system. GPOHSA is obtained by a modification in the conventional harmony search algorithm (HSA) where grid coordinates are used for locating the individuals in an objective space. Performance Improvement Indicators such as real power loss reduction indicator (SPLRI), reactive power loss reduction indicator (SQLRI) and summation of node voltage deviation reduction indicator (SNVDRI) are proposed to evaluate performance of each case of study. The period of investment return is assessed to evaluate the pay back period of the investments incurred in network restructuring and DG units. It is established that hybrid combination of network restructuring and DG units placement using GPOHSA is effective to meet the increased load demand for time period of ten years with reduced losses and improved voltage profile. Investment incurred on the network restructuring and DG units placement will be recovered in a time period of 4 years. Effectiveness of the GPOHSA is better relative to the conventional genetic algorithm (GA) for DG unit placement. The study is performed using the MATLAB software on a practical transmission network in India.

Histopathological subtyping of high-grade serous ovarian cancer using whole slide imaging.

We have established 4 histopathologic subtyping of high-grade serous ovarian cancer (HGSOC) and reported that the mesenchymal transition (MT) type has a worse prognosis than the other subtypes. In this study, we modified the histopathologic subtyping algorithm to achieve high interobserver agreement in whole slide imaging (WSI) and to characterize the tumor biology of MT type for treatment individualization. Four observers performed histopathological subtyping using WSI of HGSOC in The Cancer Genome Atlas data. As a validation set, cases from Kindai and Kyoto Universities were independently evaluated by the 4 observers to determine concordance rates. In addition, genes highly expressed in MT type were examined by gene ontology term analysis. Immunohistochemistry was also performed to validate the pathway analysis. After algorithm modification, the kappa coefficient, which indicates interobserver agreement, was greater than 0.5 (moderate agreement) for the 4 classifications and greater than 0.7 (substantial agreement) for the 2 classifications (MT vs. non-MT). Gene expression analysis showed that gene ontology terms related to angiogenesis and immune response were enriched in the genes highly expressed in the MT type. CD31 positive microvessel density was higher in the MT type compared to the non-MT type, and tumor groups with high infiltration of CD8/CD103 positive immune cells were observed in the MT type. We developed an algorithm for reproducible histopathologic subtyping classification of HGSOC using WSI. The results of this study may be useful for treatment individualization of HGSOC, including angiogenesis inhibitors and immunotherapy.

Split-n-Swap: A New Modification of the Twofish Block Cipher Algorithm

Securing digital data from unauthorized access throughout its entire lifecycle has been always a critical concern. A robust data security system should protect the information assets of any organization against cybercriminal activities. The Twofish algorithm is one of the well-known symmetric key block cipher cryptographic algorithms and has been known for its rapid convergence. But when it comes to security, it is not the preferred cryptographic algorithm to use compared to other algorithms that have shown better security. Many applications and social platforms have adopted other symmetric key block cipher cryptographic algorithms such as the Advanced Encryption Standard (AES) algorithm to construct their main security wall. In this paper, a new modification for the original Twofish algorithm is proposed to strengthen its security and to take advantage of its fast convergence. The new algorithm has been named Split-n-Swap (SnS). Performance analysis of the new modification algorithm has been performed using different measurement metrics. The experimental results show that the complexity of the SnS algorithm exceeds that of the original Twofish algorithm while maintaining reasonable values for encryption and decryption times as well as memory utilization. A detailed analysis is given with the strength and limitation aspects of the proposed algorithm.

Application of lasso for identification of functional groups with significant contributions to antioxidant activities of Centella asiatica

High-dimensional data has more variables than observations (p>>n). In this case, modeling with regression analysis becomes ineffective because it will violate the multicollinearity assumption. The least absolute shrinkage and selection operator (LASSO) can handle high-dimensional data and multicollinearity because LASSO works by reducing the parameters of variables with significant effects and selecting variables with minor effects. In its application, several variables have the same characteristics. Reducing and selecting variables in the form of groups can solve the problem so that the group LASSO can be used as a solution. This study used data on antioxidant activity in C. asiatica. It is a plant that contains antioxidants. The spectroscopic technique can find important information about antioxidants, namely the Fourier transformed infrared spectrophotometer (FTIR). FTIR is a spectroscopic technique based on molecular vibrations subjected to infrared so that it can characterize molecules with functional groups. FTIR data has large dimensions and multicollinearity. This study has 1866 explanatory variables (p) and 15 predictor variables (n). So, this study aimed to implement LASSO and the group LASSO to identify functional groups that major affect the antioxidant. This study concluded that group LASSO was better than the LASSO with modification of the LAR algorithm in identifying functional groups that had a major contribution to antioxidant activity. The results showed that the functional groups that had a major effect on the antioxidant activity of C. asiatica were –NH –OH, and C –O. In general, the functional groups that had a major effect on the antioxidant activity of C. asiatica came from phenolic compounds.

Novel Neural Network Based on New Modification of BFGS Update Algorithm for Solving Partial Differential Equations

Novel Neural Network Based on New Modification of BFGS Update Algorithm for Solving Partial Differential Equations

Багатокласовий класифікатор на основі бінарних логістичних регресій, одержаних за принципами МГУА

Introduction. The issue of accuracy improvement in classification tasks is always topical, and various approaches have been developed, applied in accordance with the peculiarities of the problem formulation and properties of the feature space. Among the most effective models, classifiers based on multiple logistic regressions have proved themselves. Purpose. The aim of the paper is to develop an algorithm for solving multiclassification problems on the basis of binary logistic models built by the stepwise multiple logistic regression algorithm of the Stepwise type, improved according to the principles of the method of group accounting of arguments. Methods. The paper proposes a modification of the stepwise algorithm for creating binary multivariate logistic regressions Stepwise, where it is proposed to optimize the algorithm parameters in accordance with the principles of the method of group consideration of arguments: significance levels by the logarithmic likelihood ratio test for inclusion and exclusion of model arguments. The choice of optimal parameters is realized in accordance with an external criterion that takes into account the balance of classification accuracy of training and test samples and the balance of class classification accuracy. Subsequently, the binary class models obtained by the one-versus-all principle are combined into a multiclass classifier that returns the answer according to the maximum likelihood of the class. The comparison of classification models obtained by the classical Stepwise algorithm and the one proposed in the robot is carried out on the medical data of the publicly available Internet resource Kaggle. Conclusion. The paper substantiates and demonstrates the advantages of classifiers based on logistic multivariate regressions optimized according to the principles of the method of group consideration of arguments relative to the classical version of the Stepwise algorithm. The effective application of the algorithm in solving multiclass classification problem is shown.

Fusion Strategy for Improving Medical Image Segmentation

In this paper, we combine decision fusion methods with four meta-heuristic algorithms (Particle Swarm Optimization (PSO) algorithm, Cuckoo search algorithm, modification of Cuckoo Search (CS McCulloch) algorithm and Genetic algorithm) in order to improve the image segmentation. The proposed technique based on fusing the data from Particle Swarm Optimization (PSO), Cuckoo search, modification of Cuckoo Search (CS McCulloch) and Genetic algorithms are obtained for improving magnetic resonance images (MRIs) segmentation. Four algorithms are used to compute the accuracy of each method while the outputs are passed to fusion methods. In order to obtain parts of the points that determine similar membership values, we apply the different rules of incorporation for these groups. The proposed approach is applied to challenging applications: MRI images, gray matter/white matter of brain segmentations and original black/white images Behavior of the proposed algorithm is provided by applying to different medical images. It is shown that the proposed method gives accurate results; due to the decision fusion produces the greatest improvement in classification accuracy.

A mathematical modification of the WNTM associative algorithm for cognitive analysis and efficient detection of dualisms in text and video streaming data

We report on a modification of the WNTM (Word Network Topic Model) algorithm for efficient modelling of bitermes – pairs of words that frequently occur together in texts of different topics. The modified algorithm is an extension of the classical topic model and allows efficient detection and extraction of semantic relations between pairs of words. The paper presents formalized mathematical equations describing the process of modelling biterms, and also presents the results of experiments on real text data, confirming the effectiveness of the proposed approach.

FaDebrisFOAM validation using field data surveyed in Crucecita (Chile) alluvial fan for the event of 13th May 2017

The openFOAM solver faDebrisFOAM was recently developed to simulate debris flows with the finite area method. This new solver includes, among other capabilities, a variable density for the solid-liquid mixture, erosion and deposition processes, and a terrain modification algorithm. To test the performance of this new solver, we simulate the debris flow event that occurred in the Crucecita alluvial fan on the 13th May 2017 in the Atacama Desert, Chile. Thanks to pre- and post-event topographies, we have an accurate measure of the morphological changes that took place. Comparing the field data and our numerical model results shows that faDebrisFOAM can reproduce the main flooded, eroded, and deposited areas. Once the model is calibrated, we study the behavior of the terrain modification algorithm. Finally, we discuss the advantages of using this model to reproduce erosion and deposition processes together with changing rheologies for a debris flow event.