Attention-based Neural Load Forecasting: A Dynamic Feature Selection Approach

Encoder-decoder-based recurrent neural network (RNN) has made significant progress in sequence-to-sequence learning tasks such as machine translation and conversational models. Recent works have shown the advantage of this type of network in dealing with various time series forecasting tasks. The present paper focuses on the problem of multi-horizon short-term load forecasting, which plays a key role in the power system's planning and operation. Leveraging the encoder-decoder RNN, we develop an attention model to select the relevant features and similar temporal information adaptively. First, input features are assigned with different weights by a feature selection attention layer, while the updated historical features are encoded by a bi-directional long short-term memory (BiLSTM) layer. Then, a decoder with hierarchical temporal attention enables a similar day selection, which re-evaluates the importance of historical information at each time step. Numerical results tested on the dataset of the global energy forecasting competition 2014 show that our proposed model significantly outperforms some existing forecasting schemes.

Online Dictionary Learning Based Fault and Cyber Attack Detection for Power Systems

The emerging wide area monitoring systems (WAMS) have brought significant improvements in electric grids' situational awareness. However, the newly introduced system can potentially increase the risk of cyber-attacks, which may be disguised as normal physical disturbances. This paper deals with the event and intrusion detection problem by leveraging a stream data mining classifier (Hoeffding adaptive tree) with semi-supervised learning techniques to distinguish cyber-attacks from regular system perturbations accurately. First, our proposed approach builds a dictionary by learning higher-level features from unlabeled data. Then, the labeled data are represented as sparse linear combinations of learned dictionary atoms. We capitalize on those sparse codes to train the online classifier along with efficient change detectors. We conduct numerical experiments with industrial control systems cyber-attack datasets. We consider five different scenarios: short-circuit faults, line maintenance, remote tripping command injection, relay setting change, as well as false data injection. The data are generated based on a modified IEEE 9-bus system. Simulation results show that our proposed approach outperforms the state-of-the-art method.

Mitigating Greenhouse Gas Emissions Through Generative Adversarial Networks Based Wildfire Prediction

Over the past decade, the number of wildfire has increased significantly around the world, especially in the State of California. The high-level concentration of greenhouse gas (GHG) emitted by wildfires aggravates global warming that further increases the risk of more fires. Therefore, an accurate prediction of wildfire occurrence greatly helps in preventing large-scale and long-lasting wildfires and reducing the consequent GHG emissions. Various methods have been explored for wildfire risk prediction. However, the complex correlations among a lot of natural and human factors and wildfire ignition make the prediction task very challenging. In this paper, we develop a deep learning based data augmentation approach for wildfire risk prediction. We build a dataset consisting of diverse features responsible for fire ignition and utilize a conditional tabular generative adversarial network to explore the underlying patterns between the target value of risk levels and all involved features. For fair and comprehensive comparisons, we compare our proposed scheme with five other baseline methods where the former outperformed most of them. To corroborate the robustness, we have also tested the performance of our method with another dataset that also resulted in better efficiency. By adopting the proposed method, we can take preventive strategies of wildfire mitigation to reduce global GHG emissions.

Reinforcement Learning for Robot Navigation with Adaptive ExecutionDuration (AED) in a Semi-Markov Model

Deep reinforcement learning (DRL) algorithms have proven effective in robot navigation, especially in unknown environments, through directly mapping perception inputs into robot control commands. Most existing methods adopt uniform execution duration with robots taking commands at fixed intervals. As such, the length of execution duration becomes a crucial parameter to the navigation algorithm. In particular, if the duration is too short, then the navigation policy would be executed at a high frequency, with increased training difficulty and high computational cost. Meanwhile, if the duration is too long, then the policy becomes unable to handle complex situations, like those with crowded obstacles. It is thus tricky to find the "sweet" duration range; some duration values may render a DRL model to fail to find a navigation path. In this paper, we propose to employ adaptive execution duration to overcome this problem. Specifically, we formulate the navigation task as a Semi-Markov Decision Process (SMDP) problem to handle adaptive execution duration. We also improve the distributed proximal policy optimization (DPPO) algorithm and provide its theoretical guarantee for the specified SMDP problem. We evaluate our approach both in the simulator and on an actual robot. The results show that our approach outperforms the other DRL-based method (with fixed execution duration) by 10.3% in terms of the navigation success rate.

DRQN-based 3D Obstacle Avoidance with a Limited Field of View

In this paper, we propose a map-based end-to-end DRL approach for three-dimensional (3D) obstacle avoidance in a partially observed environment, which is applied to achieve autonomous navigation for an indoor mobile robot using a depth camera with a narrow field of view. We first train a neural network with LSTM units in a 3D simulator of mobile robots to approximate the Q-value function in double DRQN. We also use a curriculum learning strategy to accelerate and stabilize the training process. Then we deploy the trained model to a real robot to perform 3D obstacle avoidance in its navigation. We evaluate the proposed approach both in the simulated environment and on a robot in the real world. The experimental results show that the approach is efficient and easy to be deployed, and it performs well for 3D obstacle avoidance with a narrow observation angle, which outperforms other existing DRL-based models by 15.5% on success rate.

Personalized Image Semantic Segmentation

Semantic segmentation models trained on public datasets have achieved great success in recent years. However, these models didn't consider the personalization issue of segmentation though it is important in practice. In this paper, we address the problem of personalized image segmentation. The objective is to generate more accurate segmentation results on unlabeled personalized images by investigating the data's personalized traits. To open up future research in this area, we collect a large dataset containing various users' personalized images called PIS (Personalized Image Semantic Segmentation). We also survey some recent researches related to this problem and report their performance on our dataset. Furthermore, by observing the correlation among a user's personalized images, we propose a baseline method that incorporates the inter-image context when segmenting certain images. Extensive experiments show that our method outperforms the existing methods on the proposed dataset. The code and the PIS dataset will be made publicly available.

W2v-BERT: Combining Contrastive Learning and Masked Language Modeling for Self-Supervised Speech Pre-Training

Motivated by the success of masked language modeling~(MLM) in pre-training natural language processing models, we propose w2v-BERT that explores MLM for self-supervised speech representation learning. w2v-BERT is a framework that combines contrastive learning and MLM, where the former trains the model to discretize input continuous speech signals into a finite set of discriminative speech tokens, and the latter trains the model to learn contextualized speech representations via solving a masked prediction task consuming the discretized tokens. In contrast to existing MLM-based speech pre-training frameworks such as HuBERT, which relies on an iterative re-clustering and re-training process, or vq-wav2vec, which concatenates two separately trained modules, w2v-BERT can be optimized in an end-to-end fashion by solving the two self-supervised tasks~(the contrastive task and MLM) simultaneously. Our experiments show that w2v-BERT achieves competitive results compared to current state-of-the-art pre-trained models on the LibriSpeech benchmarks when using the Libri-Light~60k corpus as the unsupervised data. In particular, when compared to published models such as conformer-based wav2vec~2.0 and HuBERT, our model shows~5\% to~10\% relative WER reduction on the test-clean and test-other subsets. When applied to the Google's Voice Search traffic dataset, w2v-BERT outperforms our internal conformer-based wav2vec~2.0 by more than~30\% relatively.

Neighbor-Vote: Improving Monocular 3D Object Detection through Neighbor Distance Voting

As cameras are increasingly deployed in new application domains such as autonomous driving, performing 3D object detection on monocular images becomes an important task for visual scene understanding. Recent advances on monocular 3D object detection mainly rely on the ``pseudo-LiDAR'' generation, which performs monocular depth estimation and lifts the 2D pixels to pseudo 3D points. However, depth estimation from monocular images, due to its poor accuracy, leads to inevitable position shift of pseudo-LiDAR points within the object. Therefore, the predicted bounding boxes may suffer from inaccurate location and deformed shape. In this paper, we present a novel neighbor-voting method that incorporates neighbor predictions to ameliorate object detection from severely deformed pseudo-LiDAR point clouds. Specifically, each feature point around the object forms their own predictions, and then the ``consensus'' is achieved through voting. In this way, we can effectively combine the neighbors' predictions with local prediction and achieve more accurate 3D detection. To further enlarge the difference between the foreground region of interest (ROI) pseudo-LiDAR points and the background points, we also encode the ROI prediction scores of 2D foreground pixels into the corresponding pseudo-LiDAR points. We conduct extensive experiments on the KITTI benchmark to validate the merits of our proposed method. Our results on the bird's eye view detection outperform the state-of-the-art performance by a large margin, especially for the ``hard'' level detection.

Multi-Modal 3D Object Detection in Autonomous Driving: a Survey

In the past few years, we have witnessed rapid development of autonomous driving. However, achieving full autonomy remains a daunting task due to the complex and dynamic driving environment. As a result, self-driving cars are equipped with a suite of sensors to conduct robust and accurate environment perception. As the number and type of sensors keep increasing, combining them for better perception is becoming a natural trend. So far, there has been no indepth review that focuses on multi-sensor fusion based perception. To bridge this gap and motivate future research, this survey devotes to review recent fusion-based 3D detection deep learning models that leverage multiple sensor data sources, especially cameras and LiDARs. In this survey, we first introduce the background of popular sensors for autonomous cars, including their common data representations as well as object detection networks developed for each type of sensor data. Next, we discuss some popular datasets for multi-modal 3D object detection, with a special focus on the sensor data included in each dataset. Then we present in-depth reviews of recent multi-modal 3D detection networks by considering the following three aspects of the fusion: fusion location, fusion data representation, and fusion granularity. After a detailed review, we discuss open challenges and point out possible solutions. We hope that our detailed review can help researchers to embark investigations in the area of multi-modal 3D object detection.