In this paper, we introduce an approach via regularization and Homotopy way for resolving the inverse Cauchy problem of the Laplace of system partial differential equation which appears in the wave propagation for com...
In this paper, we introduce an approach via regularization and Homotopy way for resolving the inverse Cauchy problem of the Laplace of system partial differential equation which appears in the wave propagation for communication networks. We considered the method of Homotopy Perturbation Metheod (HPM) for solving the integral equations of the first kind named Fredholm. In order to formulate the Laplace equation into the first-kind integral equation (Fredholm) the Fourier series used. Then the discretization method used to reduce the integral equation into a linear operator equation for the first kind. It is clear that this kind of problem is callsified as an ill-posed and the direct way to solve it unacceptably. Tikhonov’s regularization method with Homotopy Perturbation algorithm used for obtaing the approximation solution for the Laplace differential equation. Finally, the numerical example is proposed.
Experience in designing and building cyber physical interactive distributed monitoring systems for industrial facilities and reserve landscapes is analyzed. Advantages of the existing interactive and dialogue computer...
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An article presents an approach for cyberattack detection based on genetic algorithms is presented. The method allows detecting both known and unknown cyberattacks. The method has the heuristic nature and is based on ...
ISBN:
(数字)9781728199573
ISBN:
(纸本)9781728199580
An article presents an approach for cyberattack detection based on genetic algorithms is presented. The method allows detecting both known and unknown cyberattacks. The method has the heuristic nature and is based on the collected data about the cyberattacks. It makes it possible to give an answer about the cyberattacks' existence in the computer networks and its hosts. Developed attack detection approach consists of training and detection stages. The mechanism of attack detection system is based on the cyberattacks' features gathering from network or hosts, extracting the subset of acquired set and generation the attacks' detection rules. Genetic algorithms are used for the minimization of the feature set, which allows effective using of the system resources for attacks detection. In order to detect the attacks, the proposed technique involves the rule generation. The attacks' features are described by the set of sub-rules. It is suggested to use the feature with the smallest domain for generating the minimal set for rules. It is possible to select the optimal feature after all selected features which were discovered while applying the genetic algorithm. The sub-rule set is used with the aim to reduce false positive rate.
time series Due to better algorithms, more accessible data, and higher computing power over the past ten years, forecasting has become more popular. It is used in a variety of industries, including as financial time s...
time series Due to better algorithms, more accessible data, and higher computing power over the past ten years, forecasting has become more popular. It is used in a variety of industries, including as financial time series, weather forecasting, and medical diagnostics. In this study, we provide a model of the mechanism governing attention, which enables attended input to be provided to the model in place of actual input. In order for the model to produce more precise predictions, it seeks to demonstrate a fresh perspective on the data. The experiments were conducted with the (encoder-decoder) LSTM model as well to demonstrate the usefulness and superiority of the suggested strategy. The obtained results demonstrate that, when compared to the (encoder-decoder) LSTM base model, the proposed approach could reduce the mean square error (RMSE=9819.05), relative root mean square error (RRMSE=99.09), and coefficient of determination (R Square=0.96). The obtained results support the suggested approach’s efficacy, superiority, and importance in predicting SARS-CoV-2 infection cases.
A developed adaptive forecasting model for cloud resource allocation is presented. It employs principal component analysis on a sequence of virtual machine (VM) requests. Requests are processed to detect anomalies, an...
A developed adaptive forecasting model for cloud resource allocation is presented. It employs principal component analysis on a sequence of virtual machine (VM) requests. Requests are processed to detect anomalies, and adaptive predictions are computed using EEMD-ARIMA or EEMD-RT-ARIMA methods. The choice between EEMD-ARIMA and EEMD-RT-ARIMA methods is determined by comparing the execution time values ${\mathrm {R}}_{\mathrm {{i}}}$ (sequential series test) with the threshold value ${\mathrm {R}}_{\mathrm {{t d}}}$. If ${\mathrm {R}}_{\mathrm {{i}}} \gt {\mathrm {Rtd}}$, EEMD-ARIMA is used; if ${\mathrm {R}}_{\mathrm {{i}}} \leq {\mathrm {R}}_{\mathrm {{t d}}}$, EEMD-RT-ARIMA is applied. This adaptive approach enables the selection of a prediction method based on data characteristics and resource demands. To optimize the selection of the ${\mathrm {R}}_{\mathrm {{t d}}}$ threshold, the impact on accuracy and time costs is examined. A quartile method is utilized to detect dynamic spikes, and cubic spline interpolation is employed to smooth data. EEMDRT-ARIMA-based forecasting enhances accuracy through preprocessing of dynamic spikes and adaptive method selection. Calculations of time costs indicate that this method reduces forecasting time by 1.5 times by extracting core component sequences.
A solution for the problem of controlling the unmanned quadrotor vehicles group flight in presence of obstacles of complex form is proposed. A traditional approach to construction of the target function does not solve...
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The Hamming neural network is an effective tool for solving the problems of recognition and classification of discrete objects whose components are encoded with the binary bipolar alphabet, and the difference between ...
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ISBN:
(数字)9781728193526
ISBN:
(纸本)9781728193533
The Hamming neural network is an effective tool for solving the problems of recognition and classification of discrete objects whose components are encoded with the binary bipolar alphabet, and the difference between the number of identical bipolar components of the compared objects (vectors images) and the Hamming distance between them (Hamming distance is the number of mismatched bits in the binary vectors being compared) is used as the objects proximity measures. However, the Hamming neural network cannot be used to solve these problems in case the components of the compared objects (vectors) are encoded with the binary alphabet. It also cannot be used to evaluate the affinity (proximity) of objects (binary vectors) with Jaccard, Sokal and Michener, Kulzinsky functions, etc. In this regard, a generalized Hamming neural network architecture has been developed. It consists of two main blocks, which can vary being relatively independent on each other. The first block, consisting of one layer of neurons, calculates the proximity measures of the input image and the reference ones stored in the neuron relations weights of this block. Unlike the Hamming neural network, this block can calculate various proximity measures and signals about the magnitude of these proximity measures from the output of the first block neurons which are followed to the inputs of the second block elements. In the Hamming neural network, the Maxnet neural network is used as the second block, which gives out one maximum signal from the outputs of the first block neurons. If the inputs of the Maxnet network receive not only one but several identical maximum signals, then the second block, and, consequently, the Hamming network, cannot recognize the input vector, which is at the same minimum Hamming distance from two or more reference images stored in the first block. The proposed generalized architecture of the Hamming neural network allows using neural networks instead of the Maxnet network, whi
When developing software, it is vitally important to keep the level of technical debt down since it is well established from several studies that technical debt can, e.g., lower the development productivity, decrease ...
When developing software, it is vitally important to keep the level of technical debt down since it is well established from several studies that technical debt can, e.g., lower the development productivity, decrease the developers’ morale, and compromise the overall quality of the software. However, even if researchers and practitioners working in today’s software development industry are quite familiar with the concept of technical debt and its related negative consequences, there has been no empirical research focusing specifically on how software managers actively communicate and manage the need to keep the level of technical debt as low as *** paper aims to explore how software companies encourage and reward practitioners for actively keeping the level of technical debt down and also whether the companies use any forcing or penalizing initiatives when managing technical *** paper reports the results of both an online web-survey provided quantitative data from 258 participants and follow-up interviews with 32 industrial software practitioners. The findings show that having a TD management strategy can significantly impact the amount of TD in the software. When surveying how commonly used different TD management strategies are, we found that only the encouraging strategy is, to some extent, adopted in today’s’ software industry. This study also provides a model describing the four assessed strategies by presenting its strategies and tactics, together with recommendations on how they could be operationalized in today’s software companies.
In recent decades, global climate change has become one of the most critical environmental issues, leading to increased environmental and social concerns about the sustainability of logistics networks. This study prop...
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the application of a distributed intelligent control system for a group of unmanned aerial vehicles is substantiated, a method for coordinating their interaction to maximize the target indicator is proposed and substa...
the application of a distributed intelligent control system for a group of unmanned aerial vehicles is substantiated, a method for coordinating their interaction to maximize the target indicator is proposed and substantiated on the example of servicing several unequally important targets in an autonomous mode.
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