The existing camera tracking and perception methods mainly rely on sparse SLAM, which limits the dense perception ability of the scene and affects the reliability of auxiliary decision-making. Different from this, this work proposes a real-time tracking and unsupervised dense sensing framework. Firstly, the dense depth value of the scene is predicted by unsupervised multi-view stereo to remove the dependence on labeled data. Then, the quality of synthetic pseudo-reference image is quantified according to the predicted depth map and used as a weighted guidance to train the unsupervised model, thus reducing the ambiguity of feature matching in areas such as specular reflection. Moreover, the sparse optical flow of the keyframes is solved by real-time and robust ORB feature matching operator, which assists the high-precision training of unsupervised depth inference model. To increase the prediction accuracy of occluded area, a novel rendering consistency loss via neural radiance fields is designed to constrain the geometric characteristics of object surface. Finally, dense direct image alignment is performed from a global model to improve the tracking robustness, which is incrementally constructed from dense depth prediction. Extensive experiments on synthetic datasets and real datasets validate the effectiveness and practicability of the proposed work, which is an effective supplement to the existing SLAM work.

The application of aerial refueling technology to UAVs can reduce the dependence on the pilot’s operation, which has unique advantages in carrying out battlefield reconnaissance, monitoring suspicious targets and collecting intelligence through all-weather work. The existing vision-based drogue detection methods are assumed to be carried out under daily lighting conditions, but special weather, such as fog, makes it difficult to identify the characteristics of the drogue, which will greatly degrade the model performance or even fail. Moreover, the traditional computing architecture is difficult to be directly applied to real airborne equipment, so it is necessary to adopt AI processor module with faster and better computing power and supporting parallel computing to meet the requirements of low delay and high security in aerial refueling. Therefore, this work proposes a robust detection network based on image domain adaption. Firstly, an end-to-end image defogging module is designed to deal with foggy image enhancement under weak supervision. Then, knowledge distillation with the teacher-student network is applied to guide the student model to obtain the instance-level features of the unlabeled target domain. In addition, the detection model is compiled and transplanted on the system-on-chip chip of JFMQL100TAI. The comprehensive experimental results on public datasets and real refueling datasets validate the effectiveness and feasibility of the proposed work, which effectively complements the drogue detection of special autonomous aerial refueling tasks. Note to Practitioners—As a widely used refueling technology in the field of national defense, probe-and-drogue refueling has developed from manual control docking to monitoring auxiliary docking. However, it is difficult for pilots to accurately and quickly obtain the relative position of the refueling drogue through visual perception. In this work, a semi-supervised drogue detection network for special aerial refueling task is designed. The proposed work has good application potential in refueling scenes, which can provide fast and accurate drogue positioning under foggy conditions.

In future battlefields, missiles are expected to become highly precise and efficient strike weapons, with missile intelligence emerging as a critical development trend. To address the problem of optimizing 3-D missile interception guidance laws, this article introduces the deep Q-network (DQN) algorithm on the foundation of proportional navigation guidance (PNG) and proposes an adaptive proportional guidance algorithm based on deep reinforcement learning (DRL). The proposed algorithm uses air combat situational information as the state space and incorporates parameters such as the missile-target relative distance and line-of-sight (LOS) angle into the reward function design. The optimal proportional navigation coefficient$K^{*}$for low-overload maneuvering targets is determined through network search, and the longitudinal and lateral control commands of the missile are decoupled by designing the proportional coefficient increment$\Delta K$, constructing a discretized action space. Simulation results show that, compared to the PNG with a constant$K^{*}$, the proposed method significantly improves the hit probability of high-overload maneuvering targets while maintaining the hit rate for low-overload maneuvering targets. As an exploration of future intelligent combat scenarios, this guidance law design method holds both theoretical significance and practical application value.

A prototyped data mining system, DBLearn, has been developed, which efficiently and effectively extracts different kinds of knowledge rules from relational databases. It has the following features: high level learning interfaces, tightly integrated with commercial relational database systems, automatic refinement of concept hierarchies, efficient discovery algorithms and good performance. Substantial extensions of its knowledge discovery power towards knowledge mining in object-oriented, deductive and spatial databases are under research and development.

A quantitative rule is a rule associated with quantitative information which assesses the representativeness of the rule in the database. An efficient induction method is developed for learning quantitative rules in relational databases. With the assistance of knowledge about concept hierarchies, data relevance, and expected rule forms, attribute-oriented induction can be performed on the database, which integrates database operations with the learning process and provides a simple, efficient way of learning quantitative rules from large databases. The method involves the learning of both characteristic rules and classification rules. Quantitative information facilitates quantitative reasoning, incremental learning, and learning in the presence of noise. Moreover, learning qualitative rules can be treated as a special case of learning quantitative rules. It is shown that attribute-oriented induction provides an efficient and effective mechanism for learning various kinds of knowledge rules from relational databases.>

The development of efficient algorithms for learning from large relational databases is an important task in applicative machine learning. In this paper, we study knowledge discovery in relational databases and develop an attribute‐oriented learning method which extracts generalization rules from relational databases. The method adopts the artificial intelligence “learning‐from‐examples” paradigm and applies in the learning process an attribute‐oriented concept tree ascending technique which integrates database operations with the learning process and provides a simple and efficient way of learning from databases. The method learns both characteristic rules and classification rules of a learning concept, where a characteristic rule characterizes the properties shared by all the facts of the class being learned; while a classification rule characterizes the properties that distinguish the class being learned from other classes. The learning result could be a conjunctive rule or a rule with a small number of disjuncts. Moreover, learning can be performed with databases containing noisy data and exceptional cases using database statistics. Our analysis of the algorithms shows that attribute‐oriented induction substantially reduces the computational complexity of the database learning process. Le développement d'algorithmes efficaces permettant l'apprentissage à partir de bases de donnees relationnelles est une fonction importante de l'apprentissage automatique applicatif. Dans cet article, les auteurs examinent la découverte des connaissances dans les bases de données relationnelles et élaborent une méthode d'apprentissage orientée sur l'attribut qui extrait des bases de données relationnelles les règies de généralisation. La méthode adopte le paradigme d'apprentissage à partir d'exemples et applique au processus d'apprentissage la technique de l'arbre des concepts orientés sur l'attribut qui incorpore les opérations de base de données au processus d'apprentissage, ce qui permet d'obtenir une méthode simple et efficace d'apprentissage à partir des bases de données. La méthode fait l'apprentissage des règies caractéristiques et des règies de classification d'un concept d'apprentissage; la règie caractéristique qualifie les pro‐priétés communes à tous les faits d'une categorie faisant l'objet d'un apprentissage alors que la règie de classification caractérise les propriétés qui distinguent la catégorie faisant l'objet d'un apprentissage des autres catégories. Le résultat peut ětre une règie conjonctive ou une règie ayant un petit nombre de disjonctifs. Qui plus est, 1′apprentissage peut se faire avec des bases de données contenant des donnees bruitees et des cas exceptionnels utilisant des statistiques de bases de données. L'analyse des algorithmes démontre que l'induction orientée sur l'attribut réduit considérablement la complexité informàtique du processus d'apprentissage des bases de données.

A classification rule is a rule which characterizes the properties that distinguish one class from other classes. An attribute-oriented induction algorithm which extracts classification rules from relational databases is developed. The algorithm adopts the artificial intelligence learning from examples paradigm and applies an attribute-oriented concept tree ascending technique in the learning process. The technique integrates database operations with the learning process and provides a simple and efficient way of learning from large databases. The algorithm learns both conjunctive rules and restricted forms of disjunctive rules. Using database statistics, learning can be performed on databases containing noisy data and exceptions. An analysis and comparison with other algorithms show that attribute-oriented induction substantially reduces the complexity of database learning processes.>