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One of the problems solved by developers of autonomously controlled unmanned aerial vehicles is the task of determining by the drone its exact position over the terrain without the help of global satellite navigation systems. The existing mass-dimensional and energy limitations for small-sized drones lead to the necessity of using relatively simple algorithms in drone computing devices. The paper considers methods of navigation of unmanned aerial vehicles using computer vision implemented by on-board optical and computing devices. Machine vision implemented by on-board computing devices provides autonomy of small-sized aircraft in the absence or unstable communication channel with the control center and/or satellite navigation system. The proposed algorithm solves the problem of identifying an area of terrain observed from a drone with a terrain image stored in the memory of the drone control system. The drone location is determined by the minimum (maximum) value of the discrepancy between the observed current image and the image of the terrain area stored in the drone memory device. The solution of the identification problem is based on the concept of immunocomputing using singular value decomposition of the feature matrix of the identified objects. This approach allows providing high quality indicators of identification due to decomposition of the feature matrix into three simple transformations for transition to a new feature space which is not identifiable, but whose components are statistically significant. The quality indicators of the developed algorithm were evaluated in comparison with the known method of image identification by calculating the correlation function between two arrays of features. A series of tests were carried out in which the probability of correct location determination and the speed of the algorithms were evaluated for the same initial data. It is shown that when pre-preparing a “reference” image stored in the drone memory device, the speed of the developed method exceeds the speed of the method based on the calculation of the correlation function of the compared images by an order of magnitude. The mean absolute error of correct positioning using the proposed method ranges from 0.109 to 0.153. The proposed algorithm can be used by developers of navigation systems for small-sized unmanned aerial vehicles due to its low resource requirements while maintaining a level of accuracy sufficient in the context of solving problems of orientation on the terrain. Devices realizing the proposed orientation algorithm have better energy and mass-size characteristics.

Modern techniques for managing multidimensional stochastic processes that reflect the dynamics of unstable environments are proactive, which refers to decision making based on forecasting the system’s state vector evolution. At the same time, the dynamics of open nonlinear systems are largely determined by their chaotic nature, which leads to a violation of stationarity and ergodicity of the series of observations and, as a result, to a catastrophic decrease in the efficiency of forecasting algorithms based on traditional methods of multivariate statistical data analysis. In this article, we make an attempt to reduce the instability influence by employing singular spectrum analysis (SSA) algorithms. This technique has been employed in a wide class of applied data analysis problems formulated in terms of singular decomposition of data matrices: technologies of immunocomputing and SSA.

This paper aims to automatically design optimal LSTM topologies using the clonal selection algorithm (CSA) to solve text classification tasks such as sentiment analysis and SMS spam classification. Designing optimal topologies involves determining the best configuration of hyperparameters that will give the best performance. The current state-of-the-art LSTM topologies are often designed using trial and error approaches which are incredibly time-consuming and require domain experts. Our proposed method, referred to as CSA-LSTM, is evaluated using the Large Movie Review Dataset (IMDB). Furthermore, to verify the robustness of the hyperparameters discovered by CSA for the IMDB dataset, we have used them for the other datasets, viz. the Twitter US Airline Sentiment and the SMS Spam Collection. Additionally, the discovered hyperparameters for the LSTM are combined with pre-determined convolutional neural network (CNN) layers to achieve the same or better results to fast the training time and fewer trainable parameters. For further verification and evaluation of the generalization ability and effectiveness of the proposed approach, it is compared with four machine learning algorithms widely used for text classification tasks: (1) random forest (RF), (2) logistic regression (LR), (3) support vector machine (SVM), and (4) multinomial naive Bayes (NB). The results of our experiments show that the LSTM topologies automatically designed by our CSA method are less expensive, reusable and outperform the machine learning algorithms and other models in the literature evaluated on the same three datasets. Through our proposed method, LSTM’s best topology can be self-determined without any human intervention, making CSA-based algorithms a promising approach to automatically design optimal LSTM topologies that provide the best performance for a given task.

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Ultrasonic wire bonding is one of the most frequently used techniques in semiconductor production to establish electrical interconnections. Improper bonding process parameters, wire or substrate contamination or low substrate quality are some of the causes of failed bonds. Process integrated wire-bond quality control techniques compare process feedback signals to a reference for monitoring online the quality of a bond. The feedback signals sampled at high frequencies, constitute high dimensional vectors representing the bonding process characteristics. In the area of online bond failure detection, dimensionality reduction of the input signals and feature extraction of the characteristics of the process are very demanding. Cytokine-Formal Immune Network (cFIN) is a procedure for pattern recognition which presents a low recognition failure rate and a fast recognition due to the reduction of dimensions and feature extraction of the training pattern data set produced in the learning phase. We use cytokine-Formal Immune Networks for recognizing faults present during the wire bonding process. The recognition methodology is intended to be applied into a process integrated quality control system. Further an automated optimization procedure has been developed to find optimal cFIN training parameters. Very promising results for two wire bonding process setups are shown in this paper. [ABSTRACT FROM AUTHOR]

A new approach to web server attacks detection based on the statistical analysis of HTTP requests and principles of immunocomputing is proposed. We use a set of legitimate HTTP requests to the server as the training data. Each request is represented as its byte frequency distribution. Immunocomputing is used to calculate the binding energy between the training data and sampled HTTP requests. Our system gives the fuzzy output which allows us to give different kinds of response. The proposed approach has been tested with use of the DARPA data set and the data set collected from the vulnerable web server. It is shown that the given approach detects various attacks with a high degree of accuracy. [ABSTRACT FROM AUTHOR]

Based on immunocomputing (IC), this paper describes an approach to intelligent signal processing. The approach includes both low-level feature extraction and high-level (intelligent) pattern recognition. The key model is the formal immune network (FIN), which includes apoptosis (programmed cell death) and immunization and controls them by cytokines (messenger proteins). Such FIN can be formed from the signal or combination of signals using discrete tree transform, singular value decomposition and the proposed index of inseparability as a measure of quality of FIN. Several comparisons with neural computing and support vector machines suggest that IC may outperform state of the art approaches to intelligent signal processing. [ABSTRACT FROM AUTHOR]

Based on immunocomputing, this paper describes an approach to intrusion detection. The approach includes both low-level signal processing (feature extraction) and high-level (intelligent) pattern recognition. The key model is the formal immune network (FIN) including apoptosis (programmed cell death) and immunization, both controlled by cytokines (messenger proteins). Such FIN can be formed from the network traffic signals using discrete tree transforms, singular value decomposition, and the proposed index of inseparability as a measure of quality of FIN. Recent results suggest that the approach outperforms (by training time and accuracy) state-of-the-art approaches of computational intelligence. [ABSTRACT FROM PUBLISHER]

This review paper attempts to position the area of Artificial Immune Systems (AIS) in a broader context of interdisciplinary research. We review AIS based on an established conceptual framework that encapsulates mathematical and computational modelling of immunology, abstraction and then development of engineered systems. We argue that AIS are much more than engineered systems inspired by the immune system and that there is a great deal for both immunology and engineering to learn from each other through working in an interdisciplinary manner. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach to calculation of hydroacoustic fields based on immunocomputing. The essence of the approach is the reconstruction of scalar field by a formal immune network which is trained using known values of the field in some points of multidimensional space. For this purpose, we modify previously developed algorithm of (discrete) pattern recognition to obtain real-valued (continuous) output. We demonstrate that our approach provides better accuracy than neurocomputing and more conventional methods of interpolation. [ABSTRACT FROM AUTHOR]

This paper proposes a new method of identification of cellular automata, using immunocomputing. The essence of the approach is the representation of states and transitions of the automaton by using a formal immune network and the faultless reducing of number of the transitions by apoptosis and immunization. Numerical examples utilize excitable media and real-world atlas of the sea surface temperature and demonstrate the advantage of our approach compared to neurocomputing and conventional interpolation. [ABSTRACT FROM AUTHOR]