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Journal articles on the topic 'Automatic Aircraft Recognition System'
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Jia, Jiaqi, and Haibin Duan. "Automatic target recognition system for unmanned aerial vehicle via backpropagation artificial neural network." Aircraft Engineering and Aerospace Technology 89, no. 1 (January 3, 2017): 145–54. http://dx.doi.org/10.1108/aeat-07-2015-0171.
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Purpose The purpose of this paper is to propose a novel target automatic recognition method for unmanned aerial vehicle (UAV), which is based on backpropagation – artificial neural network (BP-ANN) algorithm, with the objective of optimizing the structure of backpropagation network, to increase the efficiency and decrease the recognition time. A hardware-in-the-loop system for UAV target automatic recognition is also developed. Design/methodology/approach The hybrid model of BP-ANN structure is established for aircraft automatic target recognition. This proposed method identifies controller parameters and reduces the computational complexity. Approaching speed of the network is faster and recognition accuracy is higher. This kind of network combines or better fuses the advantages of backpropagation artificial neural algorithm and Hu moment. with advantages of two networks and improves the speed and accuracy of identification. Finally, a hardware-in-the-loop system for UAV target automatic recognition is also developed. Findings The double hidden level backpropagation artificial neural can easily increase the speed of recognition process and get a good performance for recognition accuracy. Research limitations/implications The proposed backpropagation artificial neural algorithm can be ANN easily applied to practice and can help the design of the aircraft automatic target recognition system. The standard backpropagation algorithm has some obvious drawbacks, namely, converging slowly and falling into the local minimum point easily. In this paper, an improved algorithm based on the standard backpropagation algorithm is constructed to make the aircraft target recognition more practicable. Originality/value A double hidden levels backpropagation artificial neural algorithm is presented for automatic target recognition system of UAV.
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Bobin, A. V., V. A. Azarov, S. A. Bulgakov, and D. A. Savin. "Technique for recognition of aircrafts and radar traps in the control circuit of airspace control system based on neural network technology." Izvestiya MGTU MAMI 7, no. 1-4 (July 10, 2013): 124–30. http://dx.doi.org/10.17816/2074-0530-67843.
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The paper proposes a method for building of automatic recognizers of aircrafts on a set of radar measurements based on the cascade of multilayer feedforward neural networks. The practical application of this technique in recognizing of three types of aircraft is presented as well.
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Silva Filho, P., E. H. Shiguemori, and O. Saotome. "UAV VISUAL AUTOLOCALIZATON BASED ON AUTOMATIC LANDMARK RECOGNITION." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W3 (August 18, 2017): 89–94. http://dx.doi.org/10.5194/isprs-annals-iv-2-w3-89-2017.
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Deploying an autonomous unmanned aerial vehicle in GPS-denied areas is a highly discussed problem in the scientific community. There are several approaches being developed, but the main strategies yet considered are computer vision based navigation systems. This work presents a new real-time computer-vision position estimator for UAV navigation. The estimator uses images captured during flight to recognize specific, well-known, landmarks in order to estimate the latitude and longitude of the aircraft. The method was tested in a simulated environment, using a dataset of real aerial images obtained in previous flights, with synchronized images, GPS and IMU data. The estimated position in each landmark recognition was compatible with the GPS data, stating that the developed method can be used as an alternative navigation system.
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Roopa, K., T. V. Rama Murthy, and P. Cyril Prasanna Raj. "Neural Network Classifier for Fighter Aircraft Model Recognition." Journal of Intelligent Systems 27, no. 3 (July 26, 2018): 447–63. http://dx.doi.org/10.1515/jisys-2016-0087.
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Abstract Fighter aircraft recognition is important in military applications to make strategic decisions. The complexity lies in correctly identifying the unknown aircraft irrespective of its orientations. The work reported here is a research initiative in this regard. The database used here was obtained by using rapid prototyped physical models of four classes of fighter aircraft: P51 Mustang, G1-Fokker, MiG25-F, and Mirage 2000. The image database was divided into the training set and test set. Two feature sets, Feature Set1 (FS1) and FS2, were extracted for the images. FS1 consisted of 15 general features and FS2 consisted of 14 invariant moment features. Four multilayered feedforward backpropagation neural networks were designed and trained optimally with the normalized feature sets. The neural networks were configured to classify the test aircraft image. An overall accuracy of recognition of 91% and a response time of 3 s were achieved for the developed automatic fighter aircraft model image recognition system.
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Сафтли, Ф. Х. А., and С. Т. Баланян. "Methodology for assessing the control system of aircraft weapons in the process of aiming controlled aircraft weapons equipped with optelectronic homing heads." Vestnik of Russian New University. Series «Complex systems: models, analysis, management», no. 1 (March 23, 2022): 64–75. http://dx.doi.org/10.18137/rnu.v9187.22.01.p.064.
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Рассматривается повышение эффективности боевого применения управляемой авиационной ракеты класса «воздух – поверхность» по полученным целеуказаниям с беспилотного летательного аппарата, позволяющее на больших расстояниях автоматически решать задачи поиска, обнаружения и распознавания целей в условиях реального масштаба времени при сложной фоноцелевой обстановке в зоне боевых действий, тем самым уменьшая риск попадания самолета-носителя в зону действия ПВО противника. Разработана оптимизированная архитектура сверточной нейронной сети для сегментации изображений и распознавания наземных целей в оптико-электронной системе беспилотного летательного аппарата, а также разработанный алгоритм автоматического распознавания наземной цели искусственной нейронной сетью в телевизионной головке самонаведения управляемых авиационных средств поражения класса «воздух – поверхность». Проведено аналитическое сравнительное исследование по вероятности поражения наземной цели типа танка между разработанными алгоритмами автоматического распознавания наземной цели и использованием визуального (оптического) обнаружения и распознавания наземной цели летчиком (штурманом) при разных значениях средней интенсивности потока огневого воздействия ракет противовоздушной обороны противника. Осуществлена программная реализация алгоритмов автоматического распознавания наземной цели и обучения оптимизированной нейронной сети с использованием объектно ориентированного языка программирования Matlab. This article discusses the increase in the effectiveness of the combat use of an air-to-surface guided missile based on the received target designations from an unmanned aerial vehicle, which makes it possible to automatically solve the tasks of searching, detecting and recognizing targets in real time conditions at large distances in a complex background-target situation in the combat zone actions, thereby reducing the risk of the carrier aircraft falling into the enemy air defense coverage area. An optimized architecture of a convolutional neural network has been developed for image segmentation and ground target recognition in the optoelectronic system of an unmanned aerial vehicle, as well as an algorithm for automatic recognition of a ground target by an artificial neural network in a television homing head of controlled air-to-surface weapons. An analytical comparative study was carried out on the probability of hitting a ground target such as a tank between the developed algorithms for automatic recognition of a ground target and the use of visual (optical) detection and recognition of a ground target by a pilot (navigator) at different values of the average intensity of the flow of fire from enemy air defense missiles. The software implementation of algorithms for automatic recognition of a ground target and training of an optimized neural network using the object-oriented programming language Matlab has been implemented.
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Zhang, Li Ping, Chao Wang, Hong Zhang, and Bo Zhang. "Aircraft Type Recognition in High-Resolution SAR Images Using Multi-Scale Autoconvolution." Key Engineering Materials 439-440 (June 2010): 1475–80. http://dx.doi.org/10.4028/www.scientific.net/kem.439-440.1475.
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Automatic target recognition is the key stage of SAR image interpretation system and has been taking a great interest to the researchers in recent years. Aiming at the issue of aircraft type recognition in high-resolution SAR images, a novel method based on multi-scale autoconvolution (MSA) affine invariant moment is proposed. First, the texture analysis and clustering method are used to segment the SAR images and then the denoising algorithm and morphological processing are applied to segmented results. Second, 29 MSA features are extracted and form a feature vector to represent the target, then the vector components are standardized by gauss normalization. In the final, the vectors are classified by using the nearest neighbor classifier and template library constructed previously. Experimental results show that the proposed method can obtain high accuracy rate with high processing speed, in which the accuracy rate of two type aircrafts with real data arrives at 85.17% and the accuracy rate of four type aircrafts with simulated data arrives at 87.85%.
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Bohouta, Gamal. "Automatic speech recognition for unmanned aerial vehicles." Journal of the Acoustical Society of America 152, no. 4 (October 2022): A98. http://dx.doi.org/10.1121/10.0015671.
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Unmanned aerial vehicles (UAVs), also known as unmanned aerial systems (UASs), are quickly becoming a ubiquitous technology, poised to enter some key large-scale markets in the very near future. Fleets of such vehicles will be required in these large-scale deployments for commercial, industrial, and emergency response, along with the ability to efficiently control these fleets. Voice control and communication between human operators and these fleets will become imperative. This paper explores the framework for building an automatic speech recognition (ASR) use to the control of unmanned aerial vehicles (UAVs). The ARS system will be used by Aeronyde Corporation to fully-autonomous fleets with minimal human intervention. Aeronyde Corporation is working to shift and advance the current industry thinking of unmanned platforms from Remotely Piloted Aircraft (RPA) to fully-autonomous fleets with minimal human intervention. The Aeronyde Avionics package enables a single operator to control and monitor missions of many drones in real time anywhere in the world. The “1 Pilot – Many Drones” approach to aerial data collection is revolutionary for Big Data aggregation and analytics of the 4th Industrial Revolution. Multi-UAV autonomous aerial systems will transform data acquisition for many commercial applications, including: agriculture and forestry, railroad inspections, pipeline inspections, powerline inspections, windmill inspections, terrain mapping, search and rescue, firefighting and police work, and border patrol.
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Shabelnik, Tetyana, Serhii Krivenko, and Olena Koneva. "AUTOMATIC PILOT SYSTEM FOR UNMANNED OF AIRCRAFT IN THE ABSENCE OF RADIO COMMUNICATION." Cybersecurity: Education, Science, Technique 1, no. 9 (2020): 93–103. http://dx.doi.org/10.28925/2663-4023.2020.9.93103.
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One of the most pressing problems of piloting unmanned aerial vehicles (UAV) in the absence of radio communication is considered in the article. Therefore, the aim of the article is to develop an algorithm and method of automatic piloting of UAV in terms of loss of radio control signal using the methods of technical vision. The most effective methods of tracking, identification and detection of landmarks are based on the comparison of reference information (database of known navigation objects) with the observation scene in real time.Working system of automatic piloting of UAVs in the conditions of loss of radio control signal or GPS-navigation developed. The hardware and software of the UAV provides full automatic control. The programming of the system consists of two stages: planning the flight task and calculating the trajectory of the UAV in flight. The planning of the flight task is carried out by setting the topographic landmarks and flight parameters in relation to them. At this stage, the criteria for the generalization of the various components of the landscape are formed and their division by gradations. This work is combined with the recognition of points with altitude marks, and fixing the heights of horizontal surfaces available in the area. All horizontal surfaces are tied with the shortest shooting strokes to at least of three points with elevations. The process of topography-based object selection is directly related to its segmentation, the results of which significantly affect the further process of image analysis and UAV control. The calibration of the starting point of the route occurs during the launch of the UAV. The control system automatically monitors the location of the UAV throughout the trajectory of the movement on a topographic basis relative to the prespecified landmarks. Structured shots of the terrain and topographic bases are compared during the flight. The algorithm is based on the comparison of geometric parameters of landmarks. The parameters of the geometric center O(x, y) and the area S are taken into account by such parameters. The control signal in the three axes OX, OY and OZ is determined for the first time by the method of least squares depending on the values of the calculated coefficients of the original equations.
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Kniaz, V. V. "A Fast Recognition Algorithm for Detection of Foreign 3D Objects on a Runway." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3 (August 11, 2014): 151–56. http://dx.doi.org/10.5194/isprsarchives-xl-3-151-2014.
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!The systems for detection of foreign objects on a runway during the landing of an aircraft are highly demanded. Such systems could be installed in the airport or could be mounted on the board of an aircraft. This work is focused on a fast foreign object recognition algorithm for an onboard foreign object detection system. <br><br> The algorithm is based on 3D object minimal boundary extraction. The boundary is estimated through an iterative process of minimization of a difference between a pair of orthophotos. During the landing an onboard camera produces a sequence of images from which a number of stereo pair could be extracted. For each frame the runway lines are automatically detected and the external orientation of the camera relative to the runway is estimated. Using external orientation parameters the runway region is projected on an orthophoto to the runway plane. The difference of orthophotos shows the objects that doesn't coincide with the runway plane. After that the position of the foreign object relative to the runway plane and its minimal 3D boundary could be calculated. The minimal 3D boundary for each object is estimated by projection of a runway region on a modified model of the runway. The extracted boundary is used for an automatic recognition of a foreign object from the predefined bank of 3D models.
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Sun, Yuchuang, Wen Jiang, Jiyao Yang, and Wangzhe Li. "SAR Target Recognition Using cGAN-Based SAR-to-Optical Image Translation." Remote Sensing 14, no. 8 (April 8, 2022): 1793. http://dx.doi.org/10.3390/rs14081793.
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Target recognition in synthetic aperture radar (SAR) imagery suffers from speckle noise and geometric distortion brought by the range-based coherent imaging mechanism. A new SAR target recognition system is proposed, using a SAR-to-optical translation network as pre-processing to enhance both automatic and manual target recognition. In the system, SAR images of targets are translated into optical by a modified conditional generative adversarial network (cGAN) whose generator with a symmetric architecture and inhomogeneous convolution kernels is designed to reduce the background clutter and edge blur of the output. After the translation, a typical convolutional neural network (CNN) classifier is exploited to recognize the target types in translated optical images automatically. For training and testing the system, a new multi-view SAR-optical dataset of aircraft targets is created. Evaluations of the translation results based on human vision and image quality assessment (IQA) methods verify the improvement of image interpretability and quality, and translated images obtain higher average accuracy than original SAR data in manual and CNN classification experiments. The good expansibility and robustness of the system shown in extending experiments indicate the promising potential for practical applications of SAR target recognition.
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Li, Kai-Yun, Niall G. Burnside, Raul Sampaio de Lima, Miguel Villoslada Peciña, Karli Sepp, Victor Henrique Cabral Pinheiro, Bruno Rucy Carneiro Alves de Lima, Ming-Der Yang, Ants Vain, and Kalev Sepp. "An Automated Machine Learning Framework in Unmanned Aircraft Systems: New Insights into Agricultural Management Practices Recognition Approaches." Remote Sensing 13, no. 16 (August 12, 2021): 3190. http://dx.doi.org/10.3390/rs13163190.
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The recent trend of automated machine learning (AutoML) has been driving further significant technological innovation in the application of artificial intelligence from its automated algorithm selection and hyperparameter optimization of the deployable pipeline model for unraveling substance problems. However, a current knowledge gap lies in the integration of AutoML technology and unmanned aircraft systems (UAS) within image-based data classification tasks. Therefore, we employed a state-of-the-art (SOTA) and completely open-source AutoML framework, Auto-sklearn, which was constructed based on one of the most widely used ML systems: Scikit-learn. It was combined with two novel AutoML visualization tools to focus particularly on the recognition and adoption of UAS-derived multispectral vegetation indices (VI) data across a diverse range of agricultural management practices (AMP). These include soil tillage methods (STM), cultivation methods (CM), and manure application (MA), and are under the four-crop combination fields (i.e., red clover-grass mixture, spring wheat, pea-oat mixture, and spring barley). Furthermore, they have currently not been efficiently examined and accessible parameters in UAS applications are absent for them. We conducted the comparison of AutoML performance using three other common machine learning classifiers, namely Random Forest (RF), support vector machine (SVM), and artificial neural network (ANN). The results showed AutoML achieved the highest overall classification accuracy numbers after 1200 s of calculation. RF yielded the second-best classification accuracy, and SVM and ANN were revealed to be less capable among some of the given datasets. Regarding the classification of AMPs, the best recognized period for data capture occurred in the crop vegetative growth stage (in May). The results demonstrated that CM yielded the best performance in terms of classification, followed by MA and STM. Our framework presents new insights into plant–environment interactions with capable classification capabilities. It further illustrated the automatic system would become an important tool in furthering the understanding for future sustainable smart farming and field-based crop phenotyping research across a diverse range of agricultural environmental assessment and management applications.
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Valero, Xavier, and Francesc Alías. "Hierarchical Classification of Environmental Noise Sources Considering the Acoustic Signature of Vehicle Pass-Bys." Archives of Acoustics 37, no. 4 (December 1, 2012): 423–34. http://dx.doi.org/10.2478/v10168-012-0054-z.
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Abstract This work is focused on the automatic recognition of environmental noise sources that affect humans’ health and quality of life, namely industrial, aircraft, railway and road traffic. However, the recognition of the latter, which have the largest influence on citizens’ daily lives, is still an open issue. Therefore, although considering all the aforementioned noise sources, this paper especially focuses on improving the recognition of road noise events by taking advantage of the perceived noise differences along the road vehicle pass-by (which may be divided into different phases: approaching, passing and receding). To that effect, a hierarchical classification scheme that considers these phases independently has been implemented. The proposed classification scheme yields an averaged classification accuracy of 92.5%, which is, in absolute terms, 3% higher than the baseline (a traditional flat classification scheme without hierarchical structure). In particular, it outperforms the baseline in the classification of light and heavy vehicles, yielding a classification accuracy 7% and 4% higher, respectively. Finally, listening tests are performed to compare the system performance with human recognition ability. The results reveal that, although an expert human listener can achieve higher recognition accuracy than the proposed system, the latter outperforms the non-trained listener in 10% in average.
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DWIJESH H P, JAYANTH, SANDEEP S. V, and RASHMI S. "Computerized or Automated Object Recognition and Analysis of Pattern Matching in Runways Using Surface Track Data." Journal of University of Shanghai for Science and Technology 23, no. 11 (November 6, 2021): 159–65. http://dx.doi.org/10.51201/jusst/21/10867.
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In today’s world, accurate and fast information is vital for safe aircraft landings. The purpose of an EMAS (Engineered Materials Arresting System) is to prevent an aeroplane from overrunning with no human injury and minimal damage to the aircraft. Although various algorithms for object detection analysis have been developed, only a few researchers have examined image analysis as a landing assist. Image intensity edges are employed in one system to detect the sides of a runway in an image sequence, allowing the runway’s 3-dimensional position and orientation to be approximated. A fuzzy network system is used to improve object detection and extraction from aerial images. In another system, multi-scale, multiplatform imagery is used to combine physiologically and geometrically inspired algorithms for recognizing objects from hyper spectral and/or multispectral (HS/MS) imagery. However, the similarity in the top view of runways, buildings, highways, and other objects is a disadvantage of these methods. We propose a new method for detecting and tracking the runway based on pattern matching and texture analysis of digital images captured by aircraft cameras. Edge detection techniques are used to recognize runways from aerial images. The edge detection algorithms employed in this paper are the Hough Transform, Canny Filter, and Sobel Filter algorithms, which result in efficient detection.
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Li, Zhuoyi, Deshan Chen, Tsz Leung Yip, and Jinfen Zhang. "Sparsity Regularization-Based Real-Time Target Recognition for Side Scan Sonar with Embedded GPU." Journal of Marine Science and Engineering 11, no. 3 (February 24, 2023): 487. http://dx.doi.org/10.3390/jmse11030487.
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Side Scan Sonar (SSS) is widely used to search for seabed objects such as ships and wrecked aircraft due to its high-imaging-resolution and large planar scans. SSS requires an automatic real-time target recognition system to enhance search and rescue efficiency. In this paper, a novel target recognition method for SSS images in varied underwater environment, you look only once (YOLO)-slimming, based on convolutional a neural network (CNN) is proposed. The method introduces efficient feature encoders that strengthen the representation of feature maps. Channel-level sparsity regularization in model training is performed to speed up the inference performance. To overcome the scarcity of SSS images, a sonar image simulation method is proposed based on deep style transfer (ST). The performance on the SSS image dataset shows that it can reduce calculations and improves the inference speed with a mean average precision (mAP) of 95.3 and at least 45 frames per second (FPS) on an embedded Graphics Processing Unit (GPU). This proves its feasibility in practical application and has the potential to formulate an image-based real-time underwater target recognition system.
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Ming, John, and Bir Bhanu. "ORACLE: An Integrated Learning Approach for Object Recognition." International Journal of Pattern Recognition and Artificial Intelligence 11, no. 06 (September 1997): 961–90. http://dx.doi.org/10.1142/s0218001497000445.
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Model-based object recognition has become a popular paradigm in computer vision research. In most of the current model-based vision systems, the object models used for recognition are generally a priori given (e.g. obtained using a CAD model). For many object recognition applications, it is not realistic to utilize a fixed object model database with static model features. Rather, it is desirable to have a recognition system capable of performing automated object model acquisition and refinement. In order to achieve these capabilities, we have developed a system called ORACLE: Object Recognition Accomplished through Consolidated Learning Expertise. It uses two machine learning techniques known as Explanation-Based Learning (EBL) and Structured Conceptual Clustering (SCC) combined in a synergistic manner. As compared to systems which learn from numerous positive and negative examples, EBL allows the generalization of object model descriptions from a single example. Using these generalized descriptions, SCC constructs an efficient classification tree which is incremently built and modified over time. Learning from experience is used to dynamically update the specific feature values of each object. These capabilities provide a dynamic object model database which allows the system to exhibit improved performance over time. We provide an overview of the ORACLE system and present experimental results using a database of thirty aircraft models.
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Ukhanov, Evgenii V. "Statistical characteristics of the signal at the output of the optimal radar system for identifying mobile air objects." T-Comm 17, no. 4 (2023): 26–31. http://dx.doi.org/10.36724/2072-8735-2023-17-4-26-31.
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This work can be considered as a continuation of the article, or as separate materials devoted to the recognition of moving air objects based on the theory of testing statistical hypotheses. In the future, when presenting the materials of this article, the terminology proposed in [1] will be used. The relevance of this article is due to the events taking place on the European continent, indicating the need for further development of radar systems for various purposes and the transition from radar to radio vision, which will significantly improve the effectiveness of air defense and airspace control in general. The construction of a radar portrait and further automatic recognition of moving air objects, as an element of artificial intelligence, will eliminate human error, as well as significantly reduce the time it takes to make a decision on the necessary measures to influence the detected aircraft. Studying the freely available scientific works of various scientists devoted to such a direction of research and development of artificial intelligence as the statistical theory of pattern recognition, one can note the absence of such an important element as the function of the dependence of the probability of correct recognition on the quality and noise of the image. Within the framework of this article, a variant of solving this problem in the form of a mathematical model will be proposed. Since in [1] a recognition method is considered that uses the correlation coefficient between the current image and the reference one, and the probability of correct recognition is estimated using the Neumann-Pearson criterion, it is proposed to plot the dependence of the probability of correct recognition on the ratio of the image energy EI to the spectral noise density (R = 2EI/N0R), for a given false recognition probability . In the future, in the presented work, this dependence will be called recognition characteristics. Graphs will also be considered, in this paper referred to as the distribution density of the recognition probability.
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Sahith, Jai Krishna. "RECOGNITION OF AIRCRAFT IN REMOTE SENSING IMAGES USING CONVOLUTIONAL NEURAL NETWORK." Journal of Airline Operations and Aviation Management 1, no. 1 (July 25, 2022): 63–70. http://dx.doi.org/10.56801/jaoam.v1i1.8.
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Picture processing is a stand-alone, non-human image comprehension system that is one of the major breakthroughs (IUS). When one tries to explain what comprehension is, the effort of comprehending images becomes massive. In addition to classical signal processing, pattern recognition and artificial intelligence are applied. The early phases of the picture comprehension process might be called scene analysis methods that use edge and texture segmentation. As a result, putting a man in a signal processing loop at particular sensors, such as remotely piloted vehicles, satellites, and spacecraft, is not acceptable. Smart sensors and semi- automated procedures are being created as a result. Another major use of image processing is land remote sensing. With the debut of shows like Star Wars, this application has taken on a new level of relevance in the military's eyes. This study gives an overview of digital image processing and investigates the reach of remote sensing and IUS technology from the military's perspective. To demonstrate the significance of IUSs, a detailed description of a current autonomous car project in the United States is provided.
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Petrova, Teodora. "RESEARCH ON ALGORITHMS FOR FILTRATION OF AERIAL AND RADAR IMAGES." KNOWLEDGE INTERNATIONAL JOURNAL 31, no. 6 (June 5, 2019): 1923–36. http://dx.doi.org/10.35120/kij31061923p.
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In the 1960s a new special science of imagery began to develop – „imagery“, which deals with the study of the general image properties, the goals and tasks of their transformation, the processing and reproduction as well as the recognition of graphic images. Image forming, increasing image quality and the automated processing of aerial images including images captured from satellites, unmanned aircraft, radars equipped with synthesizing equipment and so on, are a subject of a number of researches and developments. The automatic analysis is widely applied in modern monitoring systems as the ones used in overwatch of areas, forests, asserting damage done on crops, reconnaissance and in the fire department services. In this article researches are conducted on the use of different image filtering methods from radar with synthesized aperture and aerial photography. The results indicate that filters can used for image pre-treatment under different scenarios, but the appropriate filter and its parameters need to be carefully selected.
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Wick, Frank. "Trail Blazer into Space." Mechanical Engineering 122, no. 10 (October 1, 2000): 71–74. http://dx.doi.org/10.1115/1.2000-oct-3.
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This article reviews that from the Cold War to Voyager, the work of Robert Goddard has received much recognition. Independently, Goddard started conceiving and designing a variety of air and space vehicles, and analyzing methods for propulsion and control. In 1903, Wilbur and Orville Wright had achieved powered flight with the three-axis control they had invented, but the flying machine was extremely difficult to manage. In 1907, while he was still an undergraduate, Goddard studied the dynamics of the Wright Flyer, and designed a gyroscope-based stabilizer for automatic control. His attempts to procure government funding were rejected by a United States military that did not recognize any value of rockets beyond the possibility of assistance at takeoff for aircraft. Rockets increasingly are supporting the marvels of our post-Cold War information revolution. The satellite-based Global Positioning System has brought the most sophisticated navigation system into the personal automobile. Within the century, Robert Goddard’s vision and life’s work begat far more than he could have imagined.
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Feary, Michael, and Lance Sherry. "Evaluation of a Formal Methodology for Developing Aircraft Vertical Flight Guidance Training Material." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 42, no. 1 (October 1998): 72–76. http://dx.doi.org/10.1177/154193129804200117.
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Aircraft automation, particularly the automation surrounding vertical navigation, has been cited as an area of training difficulty and a source of confusion during operation. A number of incidents have been attributed to a lack of crew understanding of what the automation is doing. This paper describes the translation of information from a formal methodology used in design of an automated vertical guidance system to a training package, and an experiment that tested the new training. This study is part of a larger project to improve the recognition and understanding of the “objectives and behaviors” of automated systems through a formal methodology. The formal method, referred to as the operational procedures methodology, integrates the design of the system with the design of the training and display information requirements for that system (Sherry, 1995). The study utilized a training package designed to teach the vertical guidance portion of the Flight Mode Annunciator (FMA), as seen in normal operations of the Boeing MD-11. The results of the study showed that this type of training can be successfully delivered via a computer based training device. Additionally, a study in a full cockpit simulator showed that the training, coupled with the new display, provided significantly less errors on a simulated flight, although the training alone did not provide significantly better performance.
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Zaugg, Serge, Gilbert Saporta, Emiel van Loon, Heiko Schmaljohann, and Felix Liechti. "Automatic identification of bird targets with radar via patterns produced by wing flapping." Journal of The Royal Society Interface 5, no. 26 (March 10, 2008): 1041–53. http://dx.doi.org/10.1098/rsif.2007.1349.
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Bird identification with radar is important for bird migration research, environmental impact assessments (e.g. wind farms), aircraft security and radar meteorology. In a study on bird migration, radar signals from birds, insects and ground clutter were recorded. Signals from birds show a typical pattern due to wing flapping. The data were labelled by experts into the four classes BIRD, INSECT, CLUTTER and UFO (unidentifiable signals). We present a classification algorithm aimed at automatic recognition of bird targets. Variables related to signal intensity and wing flapping pattern were extracted (via continuous wavelet transform). We used support vector classifiers to build predictive models. We estimated classification performance via cross validation on four datasets. When data from the same dataset were used for training and testing the classifier, the classification performance was extremely to moderately high. When data from one dataset were used for training and the three remaining datasets were used as test sets, the performance was lower but still extremely to moderately high. This shows that the method generalizes well across different locations or times. Our method provides a substantial gain of time when birds must be identified in large collections of radar signals and it represents the first substantial step in developing a real time bird identification radar system. We provide some guidelines and ideas for future research.
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LEE, RAYMOND S. T., and JAMES N. K. LIU. "AN AUTOMATIC SATELLITE INTERPRETATION OF TROPICAL CYCLONE PATTERNS USING ELASTIC GRAPH DYNAMIC LINK MODEL." International Journal of Pattern Recognition and Artificial Intelligence 13, no. 08 (December 1999): 1251–70. http://dx.doi.org/10.1142/s0218001499000719.
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In the past decades, satellite interpretation was one of the vital methods for the determination of weather patterns all over the world, especially for the identification of severe weather patterns such as tropical cyclones (TC). The method is based on Dvorak Technique8 which provides a means of the identification of the cyclone and its intensity. This is a kind of pattern-matching techniques and is based on some well-known TC templates for reference. Due to the high variation and complexity of cloud activities for the tropical cyclone patterns, meteorological analysts all over the world so far are still relying on subjective human justification for TC identification purposes. In this paper, an Elastic Graph Dynamic Link Model (EGDLM) is proposed to automate the satellite interpretation process and provides an objective analysis for tropical cyclones. The method integrates Dynamic Link Architecture (DLA) for neural dynamics and Active Contour Model (ACM) for contour extraction of TC patterns. Over 120 satellite pictures provided by National Oceanic and Atmospheric Administration (NOAA) were used to evaluate the system, and 145 tropical cyclone cases that appeared in the period between 1990 to 1998 were extracted for the study. An overall correct rate for TC classification was found to be above 95%. For hurricanes with distinct "eye" formation, the model reported a deviation within 3 km from the "actual eye" location, which was obtained from the reconnaissance aircraft measurements of minimum surface pressure.
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Dixon, Michael, Robert Glaubius, Philip Freeman, Robert Pless, Michael P. Gleason, Matthew M. Thomas, and William D. Smart. "Measuring optical distortion in aircraft transparencies: a fully automated system for quantitative evaluation." Machine Vision and Applications 22, no. 5 (April 16, 2010): 791–804. http://dx.doi.org/10.1007/s00138-010-0258-z.
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Slyusar, Vadym, Mykhailo Protsenko, Anton Chernukha, Stella Gornostal, Sergey Rudakov, Serhii Shevchenko, Oleksandr Chernikov, Nadiia Kolpachenko, Volodymyr Timofeyev, and Roman Artiukh. "Construction of an advanced method for recognizing monitored objects by a convolutional neural network using a discrete wavelet transform." Eastern-European Journal of Enterprise Technologies 4, no. 9(112) (August 31, 2021): 65–77. http://dx.doi.org/10.15587/1729-4061.2021.238601.
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The tasks that unmanned aircraft systems solve include the detection of objects and determining their state. This paper reports an analysis of image recognition methods in order to automate the specified process. Based on the analysis, an improved method for recognizing images of monitored objects by a convolutional neural network using a discrete wavelet transform has been devised. Underlying the method is the task of automating image processing in unmanned aircraft systems. The operability of the proposed method was tested using an example of processing an image (aircraft, tanks, helicopters) acquired by the optical system of an unmanned aerial vehicle. A discrete wavelet transform has been used to build a database of objects' wavelet images and train a convolutional neural network based on them. That has made it possible to improve the efficiency of recognition of monitored objects and automate a given process. The effectiveness of the improved method is achieved by preliminary decomposition and approximation of the digital image of the monitored object by a discrete wavelet transform. The stages of a given method include the construction of a database of the wavelet images of images and training a convolutional neural network. The effectiveness of recognizing the monitored objects' images by the improved method was tested on a convolutional neural network, which was trained with images of 300 monitored objects. In this case, the time to make a decision, based on the proposed method, decreased on average from 0.7 to 0.84 s compared with the artificial neural networks ResNet and ConvNets. The method could be used in the information processing systems in unmanned aerial vehicles that monitor objects; in robotic complexes for various purposes; in the video surveillance systems of important objects
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Becker, Denise, and Jörg Klonowski. "Object Recognition of a GCP Design in UAS Imagery Using Deep Learning and Image Processing—Proof of Concept Study." Drones 7, no. 2 (January 30, 2023): 94. http://dx.doi.org/10.3390/drones7020094.
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Image-based unmanned aircraft systems (UASs) are used in a variety of geodetic applications. Precise 3D terrain surface mapping requires ground control points (GCPs) for scaling and (indirect) georeferencing. In image analysis software (e.g., Agisoft Metashape), the images can be generated to a 3D point cloud using Structure-from-Motion (SfM). In general, the conventional GCP design for UAS flights is a checkerboard pattern, which is provided in the software and used for automatic marker detection in each image. When changing the pattern, manual work would be required by picking the GCP individually by hand. To increase the level of automation in the evaluation, this article aims to present a workflow that automatically detects a new edge-based GCP design pattern in the images, calculates their center points, and provides this information to the SfM software. Using the proposed workflow based on deep learning (DL) and image processing, the quality of the resulting 3D model can be equated to the result with GCP center points picked by human evaluator. Consequently, the workload can be accelerated with this approach.
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Wang, Liang, Jianliang Ai, Li Zhang, and Zhenlin Xing. "Design of Airport Obstacle-Free Zone Monitoring UAV System Based on Computer Vision." Sensors 20, no. 9 (April 27, 2020): 2475. http://dx.doi.org/10.3390/s20092475.
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In recent years, a rising number of incidents between Unmanned Aerial Vehicles (UAVs) and planes have been reported at airports and airfields. A design scheme for an airport obstacle-free zone monitoring UAV system based on computer vision is proposed. The system integrates the functions of identification, tracking, and expelling and is mainly used for low-cost control of balloon airborne objects and small aircrafts. First, a quadcopter dynamic model and 2-Degrees of Freedom (2-DOF) Pan/Tilt/Zoom (PTZ) model are analyzed, and an attitude back-stepping controller based on disturbance compensation is designed. Second, a low and slow small-target self-identification and tracking technology is constructed against a complex environment. Based on the You Only Look Once (YOLO) and Kernel Correlation Filter (KCF) algorithms, an autonomous target recognition and high-speed tracking plan with great robustness and high reliability is designed. Third, a PTZ controller and automatic aiming strategy based on Anti-Windup Proportional Integral Derivative (PID) algorithm is designed, and a simplified, automatic-aiming expelling device, the environmentally friendly gel ball blaster, which features high speed and high accuracy, is built. The feasibility and stability of the project can be verified through prototype experiments.
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Kozlova, A. E., M. Yu Narkevich, O. S. Logunova, and K. E. Shakhmayeva. "Visualization Elements in the Examination of Dangerous Production Facilities using an Unmanned Aircraft." Scientific Visualization 15, no. 2 (June 2023): 113–24. http://dx.doi.org/10.26583/sv.15.2.10.
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The paper considers the possibility of using visualization when preparing a task for frontal surface examination of hazardous industrial facilities using unmanned aerial vehicles. The research is based on the results of an experimental survey of buildings and structures of a large metallurgical enterprise in Russia. As initial data, photographs of buildings and structures obtained using a camera of an unmanned aerial vehicle and their drawings were used. When building a 3D model, the capabilities of Autodesk AutoCAD and Autodesk Revit were used. When building a 2D information model, a combination of a 3D model and a photograph of the object was used. The resulting 3D and 2D models are included in the structure of the flight chart as part of an applied digital platform in an automated decision-making system for expert assessment of the technical condition compliance of a hazardous production facility with regulatory requirements. Violation of regulatory requirements entails the occurrence of emergencies and incidents at an industrial enterprise. Visualization elements allow increasing the reliability of information entering the database of expert information.
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Aleksandrovskaya, L. N., A. E. Ardalionova, and A. V. Kirillin. "Ultra-low risk assessment under the confirmed compliance of automatic aircraft landing characteristics with airworthiness requirements." Journal of Computer and Systems Sciences International 55, no. 2 (March 2016): 232–41. http://dx.doi.org/10.1134/s1064230716010032.
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Ponomarov, Alexander Nikolaevich. "GROUND-BASED EXPERIMENTAL TESTING OF ELEMENTS OF AUTOMATION OF PNEUMATIC-HYDRAULIC SYSTEMS OF ROCKET AND SPACE TECHNOLOGY." Journal of Rocket-Space Technology 27, no. 4 (December 30, 2019): 58–61. http://dx.doi.org/10.15421/451909.
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The design and production of sophisticated technical systems, which include modern rockets and other aircraft, requires their reliability and trouble-free operation. To achieve the required level of reliability of aerospace products, a wide variety of test methods are applied at all stages of the life cycle. One of the most important systems of the launch vehicle is the pneumatic hydraulic power system of the liquid rocket propulsion system. Development of new and improvement of existing methods of control and diagnostics is one way of increasing the design and technological reliability of products of aviation and space technology. The use of functional diagnostics systems for bench and flight tests significantly increases the reliability and efficiency of space rocket technology. Researches are directed on increase of a level of reliability of products of aerospace branch. Application of systems of functional diagnostics is described at bench tests. The results of experimental researches of elements of automatics of pneumatic hydraulic power supply systems of liquid rocket engines are considered. The technique of processing of experimental data of a pulsing-acoustic method of diagnostics with use of the mathematical technology of recognition of images is presented. Deciding rules of recognition of a technical condition of object of diagnosing by results of tests are resulted. The developed method with a high degree of accuracy allows to determine the technical condition of the object of diagnosis as defective or to detect the presence of characteristic defects. Experimental testing and the proposed method of processing the results showed the efficiency of the method.
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Apud Baca, Javier Gibran, Thomas Jantos, Mario Theuermann, Mohamed Amin Hamdad, Jan Steinbrener, Stephan Weiss, Alexander Almer, and Roland Perko. "Automated Data Annotation for 6-DoF AI-Based Navigation Algorithm Development." Journal of Imaging 7, no. 11 (November 10, 2021): 236. http://dx.doi.org/10.3390/jimaging7110236.
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Accurately estimating the six degree of freedom (6-DoF) pose of objects in images is essential for a variety of applications such as robotics, autonomous driving, and autonomous, AI, and vision-based navigation for unmanned aircraft systems (UAS). Developing such algorithms requires large datasets; however, generating those is tedious as it requires annotating the 6-DoF relative pose of each object of interest present in the image w.r.t. to the camera. Therefore, this work presents a novel approach that automates the data acquisition and annotation process and thus minimizes the annotation effort to the duration of the recording. To maximize the quality of the resulting annotations, we employ an optimization-based approach for determining the extrinsic calibration parameters of the camera. Our approach can handle multiple objects in the scene, automatically providing ground-truth labeling for each object and taking into account occlusion effects between different objects. Moreover, our approach can not only be used to generate data for 6-DoF pose estimation and corresponding 3D-models but can be also extended to automatic dataset generation for object detection, instance segmentation, or volume estimation for any kind of object.
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Zhyrnov, V., S. Solonskaya, and I. Shubin. "Evaluation of radar image processing efficiency based on intelligent analysis of processes." Radiotekhnika, no. 207 (December 24, 2021): 83–88. http://dx.doi.org/10.30837/rt.2021.4.207.09.
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The paper presents results of development of the method and experimental studies of the system for automatic detection of radar signals of aerial objects and their recognition with the processing of real records in surveillance radars. The relevance of this work consists in creation of algorithms for automatic information processing to ensure effective detection of useful signals due to accumulation of signal (energy) and semantic information. The method is based on the definition of semantic components at the stage of formation and analysis of the symbolic model of signals from point and extended air objects. Signal information is described by the predicate function of process knowledge of the formation and analysis of a symbolic model of a burst of impulse signals from point-like mobile aircraft such as an airplane, a helicopter, a UAV, and from extended atmospheric formations such as angel-echoes, clouds. As a result of semantic analysis of symbolic images of signal marks, classification distinctive features of air objects were obtained. The semantic components of the decision-making algorithm, similar to the decision-making algorithms used by the operator, have been investigated. In the developed algorithm, signal information is described by a predicate function on the set of signal mark pulse amplitudes that have exceeded a certain threshold value. Recognizing of aerial objects is carried out by solving the developed equations of predicate operations. The verification of the developed method was carried out on real data obtained on a survey radar of the centimeter range (pulse duration was 1 μs, probing frequency wass 365 Hz, survey period was 10 s). Based on these data, the types of characteristic marks of radar signals are modeled. According to the results of the experiments, they were all correctly identified.
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Sulub, Yusuf, and Gary W. Small. "Simulated Radiance Profiles for Automating the Interpretation of Airborne Passive Multi-Spectral Infrared Images." Applied Spectroscopy 62, no. 10 (October 2008): 1049–59. http://dx.doi.org/10.1366/000370208786049150.
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Methodology is developed for simulating the radiance profiles acquired from airborne passive multispectral infrared imaging measurements of ground sources of volatile organic compounds (VOCs). The simulation model allows the superposition of pure-component laboratory spectra of VOCs onto spectral backgrounds that simulate those acquired during field measurements conducted with a downward-looking infrared line scanner mounted on an aircraft flying at an altitude of 2000–3000 ft (approximately 600–900 m). Wavelength selectivity in the line scanner is accomplished through the use of a multichannel Hg:Cd:Te detector with up to 16 integrated optical filters. These filters allow the detection of absorption and emission signatures of VOCs superimposed on the upwelling infrared background radiance within the instrumental field of view (FOV). By combining simulated radiance profiles containing analyte signatures with field-collected background signatures, supervised pattern recognition methods can be employed to train automated classifiers for use in detecting the signatures of VOCs during field measurements. The targeted application for this methodology is the use of the imaging system to detect releases of VOCs during emergency response scenarios. In the work described here, the simulation model is combined with piecewise linear discriminant analysis to build automated classifiers for detecting ethanol and methanol. Field data collected during controlled releases of ethanol, as well as during a methanol release from an industrial facility, are used to evaluate the methodology.
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Ding, Jian Li, and Yong Yang. "Automatic Recognition of Aircraft Noise with PLP Method." Applied Mechanics and Materials 160 (March 2012): 145–49. http://dx.doi.org/10.4028/www.scientific.net/amm.160.145.
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This paper proposes a modified auditory feature extraction algorithm based on perceptual linear predictive analysis which is more suitable for automatic recognition of aircraft noise. In this algorithm, a different distribution of filter-bank is introduced in order to fit the physical characteristic of aircraft noise and the result shows that the modified method indeed performs better. The effect of Gammatone filter in improving the robustness of recognition algorithm is also demonstrated in the experiment.
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Lu, Jing, Hongjun Chai, and Ruchun Jia. "A General Framework for Flight Maneuvers Automatic Recognition." Mathematics 10, no. 7 (April 6, 2022): 1196. http://dx.doi.org/10.3390/math10071196.
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Flight Maneuver Recognition (FMR) refers to the automatic recognition of a series of aircraft flight patterns and is a key technology in many fields. The chaotic nature of its input data and the professional complexity of the identification process make it difficult and expensive to identify, and none of the existing models have general generalization capabilities. A general framework is proposed in this paper, which can be used for all kinds of flight tasks, independent of the aircraft type. We first preprocessed the raw data with unsupervised clustering method, segmented it into maneuver sequences, then reconstructed the sequences in phase space, calculated their approximate entropy, quantitatively characterized the sequence complexity, and distinguished the flight maneuvers. Experiments on a real flight training dataset have shown that the framework can quickly and correctly identify various flight maneuvers for multiple aircraft types with minimal human intervention.
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Krawczyk, Mariusz, Cezary Szczepański, and Albert Zajdel. "Automatic Taxiing Direction Control System for Carrier-Based Aircraft." Transactions on Maritime Science 8, no. 2 (October 21, 2019): 171–79. http://dx.doi.org/10.7225/toms.v08.n02.002.
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This paper solves the problem of automatic taxiing direction control of carrier-based aircraft. On modern aircraft carriers, taxiing aircraft either propel themselves using their own engines or are towed by specialised tugs, which requires dedicated personnel and assets. The automatization of this process would simultaneously increase aircraft flow and decrease the number of personnel and assets required. The key challenge in the automatization of this type of process is the development of an automatic control system capable of performing the requisite tasks, which our researchers managed to do. First, the specific conditions of taxiing on-board carriers were analysed and modelled. The model of a fixed-wing aircraft best suited to this purpose was identified and the proper method of automatic control – ADRC – chosen. The algorithm used in the methodto facilitate effective direction control of a taxiing aircraft was formulated and extensively tested. The results of automatic taxiing simulation for F/A-18 aircraft have been presented. The conclusion is that the ADRC type control algorithm can ensure effective automatic control of taxiing aircraft.
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Crassidis, John L., D. Joseph Mook, and James M. McGrath. "Automatic carrier landing system utilizing aircraft sensors." Journal of Guidance, Control, and Dynamics 16, no. 5 (September 1993): 914–21. http://dx.doi.org/10.2514/3.21101.
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Khaparde, Devesh, Heet Detroja, Jainam Shah, Rushikesh Dikey, and Bhushan Thakare. "Automatic Number Plate Recognition System." International Journal of Computer Applications 179, no. 49 (June 15, 2018): 26–29. http://dx.doi.org/10.5120/ijca2018917277.
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Li, Bo, Zhi Yuan Zeng, Hua Li Dong, and Xiao Ming Zeng. "Automatic License Plate Recognition System." Applied Mechanics and Materials 20-23 (January 2010): 438–44. http://dx.doi.org/10.4028/www.scientific.net/amm.20-23.438.
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This paper proposed an algorithm for license plate recognition system(LPRS). The vertical edge was first detected by sobel color edge detector. Then, the invalid edge was removed regarding edge density. Next, the license plate(LP) image was converted into HSV color model, and by edge density template and fuzzy color information judgement, the LP region was located. Then, color-reversing judgement and tilt correction was conducted. Afterward, characters were segmented by means of vertical projection and convolution, by which character width and position can be exactly confirmed, and character recognition was conducted based on radial basis function (RBF) neural network. With a lot of samples verified in night hours and daytime under real conditions, the experimental results show that the proposed method can achieve accuracy and effectiveness in LPRS.
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Gomez-Donoso, Francisco, Miguel Cazorla, Alberto Garcia-Garcia, and Jose Garcia-Rodriguez. "Automatic Schaeffer's gestures recognition system." Expert Systems 33, no. 5 (July 13, 2016): 480–88. http://dx.doi.org/10.1111/exsy.12160.
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Kukla, Radovan, Jiří Šťastný, and Jan Kolomazník. "System for automatic crate recognition." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 60, no. 2 (2012): 151–56. http://dx.doi.org/10.11118/actaun201260020151.
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This contribution describes usage of computer vision and artificial intelligence methods for application. The method solves abuse of reverse vending machine. This topic has been solved as innovation voucher for the South Moravian Region. It was developed by Mendel university in Brno (Department of informatics – Faculty of Business and Economics and Department of Agricultural, Food and Environmental Engineering – Faculty of Agronomy) together with the Czech subsidiary of Tomra. The project is focused on a possibility of integration industrial cameras and computers to process recognition of crates in the verse vending machine. The aim was the effective security system that will be able to save hundreds-thousands financial loss. As suitable development and runtime platform there was chosen product ControlWeb and VisionLab developed by Moravian Instruments Inc.
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Szczepanski, Cezary, Mariusz Krawczyk, and Albert Zajdel. "The airplane trim system – new functionalities." Aircraft Engineering and Aerospace Technology 92, no. 9 (June 4, 2020): 1401–6. http://dx.doi.org/10.1108/aeat-12-2019-0241.
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Purpose A standard automatic flight control system – autopilot – will become required equipment of the future aircraft, operating in the common sky. For a specific group of aircraft, they are too expensive and too energy-consuming solutions. This paper aims to present the concept of an automatic flight control system that overcomes those limitations. Design/methodology/approach The proposed automatic flight control system uses the trim tabs in all prime flight controlling surfaces: elevator, ailerons and rudder, for stabilizing and controlling the steady flights of an aircraft. Findings The results of an aeroplane flight controlled with the use of trim tabs simulation tests and remarks have been presented and discussed. The simulation was conducted in real-time hardware in the loop environment. The stabilization of the flight was achieved in performed test scenarios. Originality/value The possibility to control an aircraft with coordinated deflections of the trimming surfaces is a beneficial alternate to those currently used and can be recommended for use in the next-generation aircraft.
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Rogalski, Tomasz, Mariusz Dojka, Kamila Jakubik, and Lukasz Walek. "Automatic take-off control system." Aeronautics and Aerospace Open Access Journal 7, no. 2 (June 16, 2023): 93–97. http://dx.doi.org/10.15406/aaoaj.2023.07.00175.
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The purpose of the work is to present a solution that will significantly contribute to the technological development of the European defense sector. The proposed solution raises the issue of controlling autonomous vehicles. This paper presents the concept of an algorithm that allows for automatic take-off of unmanned aircraft. Arguments are presented which justify the need for developing and applying such a system. The socioeconomic and environmental aspects of the project are also discussed. The automatic take-off algorithm complements existing aircraft flight control systems. The considered solution takes into account take-off as a maneuver made of three phases. The control on a runway is possible due to data fusion from the INS and GNSS systems. Data fusion may be supported by using the runway image processing system. The concept of an automatic take-off algorithm is presented along with the most appropriate testing methods, including software-in-the-loop and hardware-in-the-loop simulations.
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Rawat, Prince, Mohit Jha, Bhavay Chawla, and Dr Sunil Mathur. "Automatic attendance system using facial recognition system." International Journal of Engineering in Computer Science 4, no. 2 (July 1, 2022): 01–07. http://dx.doi.org/10.33545/26633582.2022.v4.i2a.70.
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Roopa, K., and T. V. Rama Murthy. "Aircraft Image Recognition System Using Phase Correlation Method." Journal of Intelligent Systems 22, no. 3 (September 1, 2013): 283–97. http://dx.doi.org/10.1515/jisys-2013-0035.
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AbstractThis article describes the aircraft image recognition system implemented using the phase correlation technique in Matlab environment. The phase correlation is computed by using the normalized cross-power spectrum between the database and the template test image. The main objective of this article is to develop methods for static analysis of aircraft images. An unknown fighter aircraft is recognized by comparing its static image with those from a database of images of aircraft. This work is a research initiative involving the use of image processing techniques to detect three-dimensional (3D) aircraft object based on their 2D images, providing feedback information for strategic purposes. The phase correlation technique is found to give a better recognition result for the set of database and test images considered, compared with the invariant moments. The phase correlation method is also used in other areas such as image registration. Aircraft images used include those from AeroIndia 2011 held at Bangalore, India.
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Bi, Yun Bo, Yi Hang Jiang, Yong Chao Li, Wei Wang, Mian Gao, and Shuo Li. "A New Flexible Track Automatic Drilling System." Applied Mechanics and Materials 433-435 (October 2013): 2178–83. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.2178.
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The Flexible track automatic drilling equipment is widely used in aircraft assembly because of the low cost, high efficiency and high quality of holes. This paper constructs a new flexible track drilling system for large-size aircraft assembly. The system structure is introduced, and the transformation algorithm between product/device coordinate system and axes position parameters is proposed. The experimental results show that the transformation algorithm has the merits of high computational efficiency and high stability, and can meet the requirement of precision drilling.
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Somaie, A., A. Badr, and T. Salah. "FROM NEURAL NETWORK TO AIRCRAFT RECOGNITION SYSTEM." International Conference on Electrical Engineering 2, no. 2 (November 1, 1999): 305–11. http://dx.doi.org/10.21608/iceeng.1999.62514.
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Zaitseva, Alina, Nikolai Dudayev, and Konstantin Shcherbakov. "MICROPROCESSOR SYSTEM FOR AUTOMATIC CONTROL OF AIRCRAFT FIRE PROTECTION MEANS." Electrical and data processing facilities and systems 18, no. 1 (2022): 131–42. http://dx.doi.org/10.17122/1999-5458-2022-18-1-131-142.
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Relevance The ever-increasing requirements for the safety of the use of aviation technology are inextricably linked with the problem of providing fire protection for aircraft, both military and civilian. The complexity of the problem of ensuring the fire safety of flights is associated with an increase in the intensity of the use of aviation equipment and the expansion of the range of functional tasks performed by it. The resulting complication of on-board equipment and an increase in the number of energy-intensive devices creates the prerequisites for the occurrence of fires on board the aircraft. At the same time, the remoteness of places where fires are possible, the variety of causes leading to fires, as well as the ambiguity of the conditions for the onset and spread, increase the likelihood of equipment failures, but also complicate the crew's activities. The purpose of the study is to implement a prospective aircraft fire protection system that will increase the effectiveness of existing fire extinguishing equipment. The relevance of this research project lies in the creation of an aircraft fire-fighting system that will provide timely detection of overheating/fire in the nacelles of the main power unit, in the compartments of the auxiliary power unit, baggage and cargo compartments and aircraft toilets; reliability of information from fire detection and elimination systems; increase the effectiveness of existing firefighting equipment. Aim of research The aim of the study is to develop a future aircraft fire protection system that will increase the effectiveness of existing fire extinguishing equipment. The objectives of the research project are: 1. Choosing a hardware complex for electronic indication and signaling of the aircraft fire-fighting system; 2. Integration of the fire protection complex into the general aircraft equipment control system. Research methods Analysis of modern high-performance aircraft fire protection systems and creation of a promising fire protection system based on the data obtained Results In the course of this research project, a hardware complex for electronic indication and signaling of the aircraft fire system was selected, and the integration of the fire protection complex into the control system of general aircraft equipment was carried out.
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Smith, G. Allan, and George Meyer. "Aircraft automatic flight control system with model inversion." Journal of Guidance, Control, and Dynamics 10, no. 3 (May 1987): 269–75. http://dx.doi.org/10.2514/3.20213.
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Juang *, Jih-Gau, and Jern-Zuin Chio. "Fuzzy modelling control for aircraft automatic landing system." International Journal of Systems Science 36, no. 2 (February 10, 2005): 77–87. http://dx.doi.org/10.1080/0020772042000325961.
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Šlihta, Mareks, Vladimirs Šestakovs, and Ramachandran Karunanidhi. "Aircraft Automatic Control System Failure and Flight Safety." Transport and Aerospace Engineering 3, no. 1 (December 1, 2016): 15–23. http://dx.doi.org/10.1515/tae-2016-0002.
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Abstract This article presents a mathematical model estimating the probability of successful completion of the aircraft’s flight in case of aviation equipment failure in flight. This paper shows the relationship between the aircraft’s automatic control system and flight safety. The calculations of probability are made for the successful completion of the flight on Boeing 737 aircraft when the automatic control system has failed.