The aerial targets with maneuvering trajectories can result in severe defocusing in inverse synthetic aperture radar (ISAR) images, while the low signal-to-noise ratio (SNR) from remote observations can also pose challenges to imaging. However, due to the stable motion state of satellites and their higher speeds compared to aerial targets, the accumulation time required for imaging is relatively short. This makes the impact of aerial target trajectory maneuvers on imaging effects relatively minor, providing a significant advantage for robust imaging of aerial targets with maneuvering trajectory under a spaceborne SAR/ISAR hybrid mode. This letter proposes an algorithm for robust imaging of aerial targets with maneuvering trajectory based on multioptimization strategies (OSs) under a spaceborne SAR/ISAR hybrid mode. In this algorithm, robust imaging is divided into two parts: 1) multistep optimal imaging time interval selection method based on OSs (MS-OITI-OSs). Through multiple optimization steps based on the target’s aerial trajectory and image information, the method achieves robust selection of the OITI by utilizing an “attitude first, quality later” optimization strategy, reducing computational complexity and increasing imaging success rates; and 2) joint translational motion compensation method based on OSs (JTMC-OSs). Characterizing motion parameters using a polynomial model, the method optimizes the motion parameters using image entropy as the objective function through the Grasshopper optimization algorithm (GOA). During the optimization process, a “high-order first, low-order later” optimization strategy is employed based on the impact of motion parameters on imaging quality to achieve robust translational motion compensation. The proposed algorithm enables robust imaging of aerial targets with maneuvering trajectory based on multi-OSs under a spaceborne SAR/ISAR hybrid mode. Extensive experimental validation confirms the effectiveness and robustness of the proposed method.

Room layout estimation predicts layouts from a single panorama. It requires datasets with large-scale and diverse room shapes to well train the models. However, there are significant imbalances in real-world datasets including the dimensions of layout complexity, camera locations, and variation in scene appearance. These issues considerably influence the model training performance. In this work, we propose imBalance-Aware Room Layout Estimation (iBARLE) framework to address these issues. iBARLE consists of: (1) Appearance Variation Generation (AVG) module, which promotes visual appearance domain generalization, (2) Complex Structure Mix-up (CSMix) module, which enhances generalizability w.r.t. room structure, and (3) a gradient-based layout objective function, which allows more effective accounting for occlusions in complex layouts. All modules are jointly trained and help each other to achieve the best performance. Experiments and ablation studies based on ZInD [6] dataset illustrate that iBARLE has state-of-the-art performance compared with other layout estimation baselines.

Memristors are well suited as artificial nerve synapses owing to its unique memory function. This paper establishes a novel flux-controlled memristor model using hyperbolic function series. By taking the memristor as synapses in a Hopfield neural network (HNN), three memristive HNNs are constructed. These memristive HNNs can generate multi-double-scroll chaotic attractors or grid multi-double-scroll chaotic attractors. The number of double scrolls in the attractors is controlled by the memristor. Equilibrium points analysis further reveals the generation mechanism of grid multi-double-scroll chaotic attractors. Moreover, numerical simulations indicate the existence of complex dynamics in the memristive HNNs, including extreme multistability and amplitude control. An approach to physically realize grid multi-double-scroll chaotic attractors is also given. Finally, an encryption scheme based on the proposed memristive HNN is designed to demonstrate application potential of the attractors.

Incomplete multi-view clustering (IMC) aims to integrate the complementary information from incomplete views to improve clustering performance. Most existing IMC methods try to fill the incomplete views or directly learn a common representation based on matrix factorization or subspace learning. The former may introduce useless even noisy information especially for data with a large missing ratio. The latter relies on the initialization and ignores the geometric structure of data. To address these issues, we propose a novel Joint Partition and Graph (JPG) learning method for IMC. Specifically, JPG jointly constructs local incomplete graph matrices, generates incomplete base partition matrices, stretches them to produce a unified partition matrix, and employs it to learn a consensus graph matrix. By this means, we transform incomplete multi-view data into a unified partition space and obtain the consensus graph in a mutual reinforcement manner. Moreover, a partition fusion strategy can allocate a large weight to the stretched base partition that is close to the unified matrix. The objective function is optimized in an alternating optimization fashion. Experimental results on several benchmark datasets demonstrate the effectiveness and superiority of JPG than the state-of-the-art baselines

Memristor is an ideal electronic device used as an artificial nerve synapse due to its unique memory function. This article presents a design of a new Hopfield neural network (HNN) that can generate multiscroll attractors by utilizing a new memristor as a synapse in the HNN. Differing from the others, this memristor is constructed with hyperbolic tangent functions. Taking the memristor as a self-feedback synapse of a neuron in the HNN, the memristive HNN can yield multidouble-scroll attractors, and its parameters can be used to effectively control the number of double scrolls contained in an attractor. Interestingly, the generation of multidouble-scroll attractors is independent of the memductance function but depends only on the internal state equation. Thus, the memductance function can be adjusted to yield various complex dynamical behaviors. Moreover, amplitude control effects and quantitatively controllable multistability are revealed by numerical analysis. The accurate reproduction of some dynamical behaviors by a designed circuit verifies the correctness of the numerical analysis. Finally, based on the proposed memristive HNN, a novel image encryption scheme in the 3-D setting is designed and evaluated, demonstrating its good encryption performances.

In this paper, we propose a new deep learning-based method for estimating room layout given a pair of 360° panoramas. Our system, called Position-aware Stereo Merging Network or PSMNet, is an end-to-end joint layout-pose estimator. PSMNet consists of a Stereo Pano Pose (SP2) transformer and a novel Cross-Perspective Projection (CP2) layer. The stereo-view SP2 transformer is used to implicitly infer correspondences between views, and can handle noisy poses. The pose-aware CP2layer is designed to render features from the adjacent view to the anchor (reference) view, in order to perform view fusion and estimate the visible layout. Our experiments and analysis validate our method, which significantly outperforms the state-of-the-art layout estimators, especially for large and complex room spaces.

Partial domain adaptation (PDA) aims to transfer knowledge from a label-rich source domain to a label-scarce target domain based on an assumption that the source label space subsumes the target label space. The major challenge is to promote positive transfer in the shared label space and circumvent negative transfer caused by the large mismatch across different label spaces. In this article, we propose a dual alignment approach for PDA (DAPDA), including three components: 1) a feature extractor extracts source and target features by the Siamese network; 2) a reweighting network produces "hard" labels, class-level weights for source features and "soft" labels, instance-level weights for target features; 3) a dual alignment network aligns intra domain and interdomain distributions. Specifically, the intra domain alignment aims to minimize the intraclass variances to enhance the intraclass compactness in both domains, and interdomain alignment attempts to reduce the discrepancies across domains by domain-wise and class-wise adaptations. The negative transfer can be alleviated by down-weighting source features with nonshared labels. The positive transfer can be enhanced by upweighting source features with shared labels. The adaptation can be achieved by minimizing the discrepancies based on class-weighted source data with hard labels and instance-weighed target data with soft labels. The effectiveness of our method has been demonstrated by outperforming state-of-the-art PDA methods on several benchmark datasets.

In this article, we investigate the coordinated topology attacks in smart grid, which combine a physical topology attack and a cyber-topology attack. The physical attack first trips a transmission line. In order to deceive the control center, the attacker masks the outage signal of the tripped line in the cyber layer and then creates a fake outage signal for another transmission line. The goal of coordinated topology attacks is to overload a critical line (different from the physical tripped line and the fake outage line) by misleading the control center into making improper dispatch. In order to determine the attack strategy, we propose a deep-reinforcement-learning-based method to identify the physical tripped line and the fake outage line. Besides, in order to block the outage signal of the tripped line and create the fake outage signal with limited attack resources, we propose a deep-reinforcement-learning-based approach to determine the minimal attack resources. Numerical simulations verify the effectiveness of the proposed method.

The existing unsupervised domain adaptation (UDA) methods require not only labeled source samples but also a large number of unlabeled target samples for domain adaptation. Collecting these target samples is generally time-consuming, which hinders the rapid deployment of these UDA methods in new domains. Besides, most of these UDA methods are developed for image classification. In this paper, we address a new problem called one-shot unsupervised domain adaptation for object detection, where only one unlabeled target sample is available. To the best of our knowledge, this is the first time this problem is investigated. To solve this problem, a one-shot feature alignment (OSFA) algorithm is proposed to align the low-level features of the source domain and the target domain. Specifically, the domain shift is reduced by aligning the average activation of the feature maps in the lower layer of CNN. The proposed OSFA is evaluated under two scenarios: adapting from clear weather to foggy weather; adapting from synthetic images to real-world images. Experimental results show that the proposed OSFA can significantly improve the object detection performance in target domain compared to the baseline model without domain adaptation.

Pedestrian regulation can prevent crowd accidents and improve crowd safety in densely populated areas. Recent studies use mobile robots to regulate pedestrian flows for desired collective motion through the effect of passive human-robot interaction (HRI). This paper formulates a robot motion planning problem for the optimization of two merging pedestrian flows moving through a bottleneck exit. To address the challenge of feature representation of complex human motion dynamics under the effect of HRI, we propose using a deep neural network to model the mapping from the image input of pedestrian environments to the output of robot motion decisions. The robot motion planner is trained end-to-end using a deep reinforcement learning algorithm, which avoids hand-crafted feature detection and extraction, thus improving the learning capability for complex dynamic problems. Our proposed approach is validated in simulated experiments, and its performance is evaluated. The results demonstrate that the robot is able to find optimal motion decisions that maximize the pedestrian outflow in different flow conditions, and the pedestrian-accumulated outflow increases significantly compared to cases without robot regulation and with random robot motion.

Multi-modal tasks like visual question answering (VQA) are an important step towards human-level artificial intelligence. In general, the input of the VQA task consists of an image and a related question. In order to correctly answer the question, a model needs to extract and integrate useful information from both the image and the question. In this paper, we propose a model named AnswerNet to tackle this task. In the proposed model, discriminative features are extracted from both the image and the question. Specifically, high-level image features are extracted by the state-of-the-art convolutional neural network, i.e., Deep Residual Net. For question features, the semantic representations of the question and the term frequencies of the distinct words are captured by long short-term memory network and bag-of-words model, respectively. Then, a hierarchical fusion network is proposed to effectively fuse the image features with the question features. Experimental results on three large-scale datasets, VQA, COCO-QA, and VQA2, demonstrate the effectiveness of the proposed AnswerNet.

A smart home with battery energy storage can take part in the demand response program. With proper energy management, consumers can purchase more energy at off-peak hours than at on-peak hours, which can reduce the electricity costs and help to balance the electricity demand and supply. However, it is hard to determine an optimal energy management strategy because of the uncertainty of the electricity consumption and the real-time electricity price. In this paper, a deep reinforcement learning based approach has been proposed to solve this residential energy management problem. The proposed approach does not require any knowledge about the uncertainty and can directly learn the optimal energy management strategy based on reinforcement learning. Simulation results demonstrate the effectiveness of the proposed approach.

Sparse autoencoder is an unsupervised feature extractor and has been widely used in the machine learning and data mining community. However, a sparse hyperparameter has to be determined to balance the trade-off between the reconstruction error and the sparsity of sparse autoencoder. Traditional sparse hyperparameter determination method is time-consuming, especially when the dataset is large. In this paper, we derive a generative model for sparse autoencoder. Based on this model, we derive a formulation to determine the sparse hyperparameter effectively and efficiently. The relationship between the sparse hyperparameter and the average activation of sparse autoencoder hidden units is also presented in this paper. Experimental results and comparative studies over numerous datasets demonstrate the effectiveness of our method to determine the sparse hyperparameter.

Supervised object localization requires detailed image annotation, such as bounding box, to indicate the location of the object. However, labeling image with bounding box is labor-intensive. Besides, the labeling process may involve ambiguous decisions. Weakly supervised object localization only needs category annotation which is available in large amounts. Recently, many weakly supervised object localization methods, based on global pooling, have been proposed. However, these methods only localize part of the object. This paper proposes a deep convolutional neural network with spatial pyramid saliency map to localize the full extent of the object. The experimental result on Cub-200 dataset shows that our method outperforms the traditional ones.

Understanding human behavior is crucial for planning evacuation strategies when an emergency occurs. The social force model, which is a successful quantitative model, has been widely used in investigating human behavior. In this paper, we propose a gradient descent based parameter optimization method to learn the parameters of the social force model from experimental data. Although the original social force model has achieved great success, it does not consider the fact that the response of humans to that happening in front of them is stronger than that happening behind them. In order to model the directional dependency of the interactive force, we propose a modified social force model. Experimental results demonstrate the effectiveness of the modified model.