Device-Free Through-the-Wall Activity Recognition Using Bi-Directional Long Short-Term Memory and WiFi Channel State Information

Activity recognition plays a key role in health management and security.Traditional approaches are based on vision or wearables,which only work under the line of sight(LOS)or require the targets to carry dedicated devices.As human bodies and their movements have influences on WiFi propagation,this paper proposes the recognition of human activities by analyzing the channel state information(CSI)from the WiFi physical layer.The method requires only the commodity:WiFi transmitters and receivers that can operate through a wall,under LOS and non-line of sight(NLOS),while the targets are not required to carry dedicated devices.After collecting CSI,the discrete wavelet transform is applied to reduce the noise,followed by outlier detection based on the local outlier factor to extract the activity segment.Activity recognition is fulfilled by using the bi-directional long short-term memory that takes the sequential features into consideration.Experiments in through-the-wall environments achieve recognition accuracy>95%for six common activities,such as standing up,squatting down,walking,running,jumping,and falling,outperforming existing work in this field.

DPAL-BERT:A Faster and Lighter Question Answering Model

Recent advancements in natural language processing have given rise to numerous pre-training language models in question-answering systems.However,with the constant evolution of algorithms,data,and computing power,the increasing size and complexity of these models have led to increased training costs and reduced efficiency.This study aims to minimize the inference time of such models while maintaining computational performance.It also proposes a novel Distillation model for PAL-BERT(DPAL-BERT),specifically,employs knowledge distillation,using the PAL-BERT model as the teacher model to train two student models:DPAL-BERT-Bi and DPAL-BERTC.This research enhances the dataset through techniques such as masking,replacement,and n-gram sampling to optimize knowledge transfer.The experimental results showed that the distilled models greatly outperform models trained from scratch.In addition,although the distilled models exhibit a slight decrease in performance compared to PAL-BERT,they significantly reduce inference time to just 0.25%of the original.This demonstrates the effectiveness of the proposed approach in balancing model performance and efficiency.

PRI modulation recognition and sequence search under small sample prerequisite

Pulse repetition interval(PRI)modulation recognition and pulse sequence search are significant for effective electronic support measures.In modern electromagnetic environments,different types of inter-pulse slide radars are highly confusing.There are few available training samples in practical situations,which leads to a low recognition accuracy and poor search effect of the pulse sequence.In this paper,an approach based on bi-directional long short-term memory(BiLSTM)networks and the temporal correlation algorithm for PRI modulation recognition and sequence search under the small sample prerequisite is proposed.The simulation results demonstrate that the proposed algorithm can recognize unilinear,bilinear,sawtooth,and sinusoidal PRI modulation types with 91.43% accuracy and complete the pulse sequence search with 30% missing pulses and 50% spurious pulses under the small sample prerequisite.

Practical Options for Adopting Recurrent Neural Network and Its Variants on Remaining Useful Life Prediction

The remaining useful life(RUL)of a system is generally predicted by utilising the data collected from the sensors that continuously monitor different indicators.Recently,different deep learning(DL)techniques have been used for RUL prediction and achieved great success.Because the data is often time-sequential,recurrent neural network(RNN)has attracted significant interests due to its efficiency in dealing with such data.This paper systematically reviews RNN and its variants for RUL prediction,with a specific focus on understanding how different components(e.g.,types of optimisers and activation functions)or parameters(e.g.,sequence length,neuron quantities)affect their performance.After that,a case study using the well-studied NASA’s C-MAPSS dataset is presented to quantitatively evaluate the influence of various state-of-the-art RNN structures on the RUL prediction performance.The result suggests that the variant methods usually perform better than the original RNN,and among which,Bi-directional Long Short-Term Memory generally has the best performance in terms of stability,precision and accuracy.Certain model structures may fail to produce valid RUL prediction result due to the gradient vanishing or gradient exploring problem if the parameters are not chosen appropriately.It is concluded that parameter tuning is a crucial step to achieve optimal prediction performance.

A multi-attention RNN-based relation linking approach for question answering over knowledge base

Aiming at the relation linking task for question answering over knowledge base,especially the multi relation linking task for complex questions,a relation linking approach based on the multi-attention recurrent neural network(RNN)model is proposed,which works for both simple and complex questions.First,the vector representations of questions are learned by the bidirectional long short-term memory(Bi-LSTM)model at the word and character levels,and named entities in questions are labeled by the conditional random field(CRF)model.Candidate entities are generated based on a dictionary,the disambiguation of candidate entities is realized based on predefined rules,and named entities mentioned in questions are linked to entities in knowledge base.Next,questions are classified into simple or complex questions by the machine learning method.Starting from the identified entities,for simple questions,one-hop relations are collected in the knowledge base as candidate relations;for complex questions,two-hop relations are collected as candidates.Finally,the multi-attention Bi-LSTM model is used to encode questions and candidate relations,compare their similarity,and return the candidate relation with the highest similarity as the result of relation linking.It is worth noting that the Bi-LSTM model with one attentions is adopted for simple questions,and the Bi-LSTM model with two attentions is adopted for complex questions.The experimental results show that,based on the effective entity linking method,the Bi-LSTM model with the attention mechanism improves the relation linking effectiveness of both simple and complex questions,which outperforms the existing relation linking methods based on graph algorithm or linguistics understanding.

Ultra-short-term Interval Prediction of Wind Power Based on Graph Neural Network and Improved Bootstrap Technique

Reliable and accurate ultra-short-term prediction of wind power is vital for the operation and optimization of power systems.However,the volatility and intermittence of wind power pose uncertainties to traditional point prediction,resulting in an increased risk of power system operation.To represent the uncertainty of wind power,this paper proposes a new method for ultra-short-term interval prediction of wind power based on a graph neural network(GNN)and an improved Bootstrap technique.Specifically,adjacent wind farms and local meteorological factors are modeled as the new form of a graph from the graph-theoretic perspective.Then,the graph convolutional network(GCN)and bi-directional long short-term memory(Bi-LSTM)are proposed to capture spatiotemporal features between nodes in the graph.To obtain highquality prediction intervals(PIs),an improved Bootstrap technique is designed to increase coverage percentage and narrow PIs effectively.Numerical simulations demonstrate that the proposed method can capture the spatiotemporal correlations from the graph,and the prediction results outperform popular baselines on two real-world datasets,which implies a high potential for practical applications in power systems.

Deep Broad Learning for Emotion Classification in Textual Conversations

Emotion classification in textual conversations focuses on classifying the emotion of each utterance from textual conversations.It is becoming one of the most important tasks for natural language processing in recent years.However,it is a challenging task for machines to conduct emotion classification in textual conversations because emotions rely heavily on textual context.To address the challenge,we propose a method to classify emotion in textual conversations,by integrating the advantages of deep learning and broad learning,namely DBL.It aims to provide a more effective solution to capture local contextual information(i.e.,utterance-level)in an utterance,as well as global contextual information(i.e.,speaker-level)in a conversation,based on Convolutional Neural Network(CNN),Bidirectional Long Short-Term Memory(Bi-LSTM),and broad learning.Extensive experiments have been conducted on three public textual conversation datasets,which show that the context in both utterance-level and speaker-level is consistently beneficial to the performance of emotion classification.In addition,the results show that our proposed method outperforms the baseline methods on most of the testing datasets in weighted-average F1.

Detection and Defense Method Against False Data Injection Attacks for Distributed Load Frequency Control System in Microgrid

In the realm of microgrid(MG),the distributed load frequency control(LFC)system has proven to be highly susceptible to the negative effects of false data injection attacks(FDIAs).Considering the significant responsibility of the distributed LFC system for maintaining frequency stability within the MG,this paper proposes a detection and defense method against unobservable FDIAs in the distributed LFC system.Firstly,the method integrates a bi-directional long short-term memory(Bi LSTM)neural network and an improved whale optimization algorithm(IWOA)into the LFC controller to detect and counteract FDIAs.Secondly,to enable the Bi LSTM neural network to proficiently detect multiple types of FDIAs with utmost precision,the model employs a historical MG dataset comprising the frequency and power variances.Finally,the IWOA is utilized to optimize the proportional-integral-derivative(PID)controller parameters to counteract the negative impacts of FDIAs.The proposed detection and defense method is validated by building the distributed LFC system in Simulink.

Action-Aware Encoder-Decoder Network for Pedestrian Trajectory Prediction

Accurate pedestrian trajectory predictions are critical in self-driving systems,as they are fundamental to the response-and decision-making of ego vehicles.In this study,we focus on the problem of predicting the future trajectory of pedestrians from a first-person perspective.Most existing trajectory prediction methods from the first-person view copy the bird’s-eye view,neglecting the differences between the two.To this end,we clarify the differences between the two views and highlight the importance of action-aware trajectory prediction in the first-person view.We propose a new action-aware network based on an encoder-decoder framework with an action prediction and a goal estimation branch at the end of the encoder.In the decoder part,bidirectional long short-term memory(Bi-LSTM)blocks are adopted to generate the ultimate prediction of pedestrians’future trajectories.Our method was evaluated on a public dataset and achieved a competitive performance,compared with other approaches.An ablation study demonstrates the effectiveness of the action prediction branch.

A Tibetan Sentence Boundary Disambiguation Model Considering the Components on Information on Both Sides of Shad

Sentence Boundary Disambiguation(SBD)is a preprocessing step for natural language processing.Segmenting text into sentences is essential for Deep Learning(DL)and pretraining language models.Tibetan punctuation marks may involve ambiguity about the sentences’beginnings and endings.Hence,the ambiguous punctuation marks must be distinguished,and the sentence structure must be correctly encoded in language models.This study proposed a component-level Tibetan SBD approach based on the DL model.The models can reduce the error amplification caused by word segmentation and part-of-speech tagging.Although most SBD methods have only considered text on the left side of punctuation marks,this study considers the text on both sides.In this study,465669 Tibetan sentences are adopted,and a Bidirectional Long Short-Term Memory(Bi-LSTM)model is used to perform SBD.The experimental results show that the F1-score of the Bi-LSTM model reached 96%,the most efficient among the six models.Experiments are performed on low-resource languages such as Turkish and Romanian,and high-resource languages such as English and German,to verify the models’generalization.

Chinese Word Segmentation via BiLSTM+Semi-CRF with Relay Node

Semi-Markov conditional random fields(Semi-CRFs)have been successfully utilized in many segmentation problems,including Chinese word segmentation(CWS).The advantage of Semi-CRF lies in its inherent ability to exploit properties of segments instead of individual elements of sequences.Despite its theoretical advantage,Semi-CRF is still not the best choice for CWS because its computation complexity is quadratic to the sentenced length.In this paper,we propose a simple yet effective framework to help Semi-CRF achieve comparable performance with CRF-based models under similar computation complexity.Specifically,we first adopt a bi-directional long short-term memory(BiLSTM)on character level to model the context information,and then use simple but effective fusion layer to represent the segment information.Besides,to model arbitrarily long segments within linear time complexity,we also propose a new model named Semi-CRF-Relay.The direct modeling of segments makes the combination with word features easy and the CWS performance can be enhanced merely by adding publicly available pre-trained word embeddings.Experiments on four popular CWS datasets show the effectiveness of our proposed methods.The source codes and pre-trained embeddings of this paper are available on https://github.com/fastnlp/fastNLP/.