D-Fi: Domain adversarial neural network based CSI fingerprint indoor localization

Deep learning based channel state information(CSI)fingerprint indoor localization schemes need to collect massive labeled data samples for training,and the parameters of the deep neural network are used as the fingerprints.However,the indoor environment may change,and the previously constructed fingerprint may not be valid for the changed environment.In order to adapt to the changed environment,it requires to recollect massive amount of labeled data samples and perform the training again,which is labor-intensive and time-consuming.In order to overcome this drawback,in this paper,we propose one novel domain adversarial neural network(DANN)based CSI Fingerprint Indoor Localization(D-Fi)scheme,which only needs the unlabeled data samples from the changed environment to update the fingerprint to adapt to the changed environment.Specifically,the previous environment and changed environment are treated as the source domain and the target domain,respectively.The DANN consists of the classification path and the domain-adversarial path,which share the same feature extractor.In the offline phase,the labeled CSI samples are collected as source domain samples to train the neural network of the classification path,while in the online phase,for the changed environment,only the unlabeled CSI samples are collected as target domain samples to train the neural network of the domainadversarial path to update parameters of the feature extractor.In this case,the feature extractor extracts the common features from both the source domain samples corresponding to the previous environment and the target domain samples corresponding to the changed environment.Experiment results show that for the changed localization environment,the proposed D-Fi scheme significantly outperforms the existing convolutional neural network(CNN)based scheme.

Bearing fault diagnosis based on a multiple-constraint modal-invariant graph convolutional fusion network

Multisensor data fusionmethod can improve the accuracy of bearing fault diagnosis,in order to address the problems of single-sensor data types and the insufficient exploration of redundancy and complementarity between different modal data in most existing multisensor data fusion methods for bearing fault diagnosis,a bearing fault diagnosis method based on a Multiple-Constraint Modal-Invariant Graph Convolutional Fusion Network(MCMI-GCFN)is proposed in this paper.Firstly,a Convolutional Autoencoder(CAE)and Squeeze-and-Excitation Block(SE block)are used to extract features of raw current and vibration signals.Secondly,the model introduces source domain classifiers and domain discriminators to capture modal invariance between different modal data based on domain adversarial training,making use of the redundancy and complementarity between multimodal data.Then,the spatial aggregation property of Graph Convolutional Neural Networks(GCN)is utilized to capture the dependency relationship between current and vibration modes with similar time step features for accurately fusing contextual semantic information.Finally,the validation is conducted on the public bearing damage current and vibration dataset from Paderborn University.The experimental results showed that the delivered fusion method achieved a bearing fault diagnosis accuracy of 99.6%,which was about 9%–11.4%better than that with nonfusion methods.

Short-term Residential Load Forecasting Based on K-shape Clustering and Domain Adversarial Transfer Network

In recent years,the expansion of the power grid has led to a continuous increase in the number of consumers within the distribution network.However,due to the scarcity of historical data for these new consumers,it has become a complex challenge to accurately forecast their electricity demands through traditional forecasting methods.This paper proposes an innovative short-term residential load forecasting method that harnesses advanced clustering,deep learning,and transfer learning technologies to address this issue.To begin,this paper leverages the domain adversarial transfer network.It employs limited data as target domain data and more abundant data as source domain data,thus enabling the utilization of source do-main insights for the forecasting task of the target domain.Moreover,a K-shape clustering method is proposed,which effectively identifies source domain data that align optimally with the target domain,and enhances the forecasting accuracy.Subsequently,a composite architecture is devised,amalgamating attention mechanism,long short-term memory network,and seq2seq network.This composite structure is integrated into the domain adversarial transfer network,bolstering the performance of feature extractor and refining the forecasting capabilities.An illustrative analysis is conducted using the residential load dataset of the Independent System Operator to validate the proposed method empirically.In the case study,the relative mean square error of the proposed method is within 30 MW,and the mean absolute percentage error is within 2%.A signifi-cant improvement in accuracy,compared with other comparative experimental results,underscores the reliability of the proposed method.The findings unequivocally demonstrate that the proposed method advocated in this paper yields superior forecasting results compared with prevailing mainstream forecast-ing methods.

An improved transfer learning strategy for short-term cross-building energy prediction usingdata incremental

The available modelling data shortage issue makes it difficult to guarantee the performance of data-driven building energy prediction(BEP)models for both the newly built buildings and existing information-poor buildings.Both knowledge transfer learning(KTL)and data incremental learning(DIL)can address the data shortage issue of such buildings.For new building scenarios with continuous data accumulation,the performance of BEP models has not been fully investigated considering the data accumulation dynamics.DIL,which can learn dynamic features from accumulated data adapting to the developing trend of new building time-series data and extend BEP model's knowledge,has been rarely studied.Previous studies have shown that the performance of KTL models trained with fixed data can be further improved in scenarios with dynamically changing data.Hence,this study proposes an improved transfer learning cross-BEP strategy continuously updated using the coarse data incremental(CDI)manner.The hybrid KTL-DIL strategy(LSTM-DANN-CDI)uses domain adversarial neural network(DANN)for KLT and long short-term memory(LSTM)as the Baseline BEP model.Performance evaluation is conducted to systematically qualify the effectiveness and applicability of KTL and improved KTL-DIL.Real-world data from six-type 36 buildings of six types are adopted to evaluate the performance of KTL and KTL-DIL in data-driven BEP tasks considering factors like the model increment time interval,the available target and source building data volumes.Compared with LSTM,results indicate that KTL(LSTM-DANN)and the proposed KTL-DIL(LSTM-DANN-CDI)can significantly improve the BEP performance for new buildings with limited data.Compared with the pure KTL strategy LSTM-DANN,the improved KTL-DIL strategy LSTM-DANN-CDI has better prediction performance with an average performance improvement ratio of 60%.

Fault Diagnosis for Rolling Element Bearing in Dataset Bias Scenario

Recently, data-driven methods, especially deep learning, outperform other methods for rolling elementbearing (REB) fault diagnosis. Nevertheless, most research work assumes that REB dataset is unbiased. Inthe real industry applications, the dataset bias exists with REB owing to varying REB working conditions andnoise interference. Recently proposed adversarial discriminative domain adaptation (ADDA) is an increasinglypopular incarnation to solve dataset bias problem. However, it mainly devotes to realizing domain alignments, andignores class-level alignments;it can cause degradation of classification performance. In this study, we proposea new REB fault diagnosis model based on improved ADDA to address dataset bias. The proposed diagnosismodel realizes domain- and class-level alignments in dataset bias scenario;it consists of two feature extractors,a domain discriminator, and two label classifiers. The feature extractors and domain discriminator are trainedin an adversarial manner to minimize the domain difference in feature extractors. The domain discrepancy inlabel classifier is reduced by minimizing correlation alignment (CORAL) loss. We evaluate the proposed model onthe Case Western Reserve University (CWRU) bearing dataset and Paderborn University bearing dataset. Theproposed method yields better results than other methods and has good prospects for industrial applications.