Recently, deep learning based city flow prediction has been extensively used in the establishment of smart cities. These methods are data-hungry, making them unscalable to areas lacking data. Although transfer learning can use data-rich source domains to assist target domain cities in city flow prediction, the performance of existing methods cannot meet the needs of actual use, because the long-distance road network connectivity is ignored. To solve this problem, we propose a transfer learning method based on spatiotemporal graph convolution, in which we construct a co-occurrence space between the source and target domains, and then align the mapping of the source and target domains’ data in this space, to achieve the transfer learning of the source city flow prediction model on the target domain. Specifically, a dynamic spatiotemporal graph convolution module along with a temporal encoder is devised to simultaneously capture the concurrent spatiotemporal features, which implies the inherent relationship among the road network structures, human travel habits, and city bike flow. Then, these concurrent features are leveraged as cross-city invariant representations and nonlinearly spanned to a co-occurrence space. The target domain features are thereby aligned with the source domain features in the co-occurrence space by using a Mahalanobis distance loss, to achieve cross-city bike flow prediction. The proposed method is evaluated on the public bike flow datasets in Chicago, New York, and Washington in 2015, and significantly outperforms state-of-the-art techniques.

Predicting remaining useful life (RUL) of bearings under scarce labeled data is significant for intelligent manufacturing. Current approaches typically encounter the challenge that different degradation stages have similar behaviors in multisensor scenarios. Given that cross-sensor similarity improves the discrimination of degradation features, we propose a multisensor contrast method for RUL prediction under scarce RUL-labeled data, in which we use cross-sensor similarity to mine multisensor similar representations that indicate machine health condition from rich unlabeled sensor data in a co-occurrence space. Specifically, we use ResNet18 to span the features of different sensors into the co-occurrence space. We then obtain multisensor similar representations of abundant unlabeled data through alternate contrast based on cross-sensor similarity in the co-occurrence space. The multisensor similar representations indicate the machine degradation stage. Finally, we focus on finetuning these similar representations to achieve RUL prediction with limited labeled sensor data. The proposed method is evaluated on a publicly available bearing dataset, and the results show that the mean absolute percentage error is reduced by at least 0.058, and the score is improved by at least 0.122 compared with those of state-of-the-art methods.

Abstract During the production of printed circuit board (PCB), there will be defects due to inappropriate operations, which will affect the use of electronic products. Majority defect detection methods cost a large number of annotated samples to train detection models. However, PCB defect samples are difficult to collect. Moreover, existing few-shot object detection methods tend to extracting low-level features from support and query images via the shared backbone such as ResNet-50. However, it is not sufficient to obtain fine-grained prior guidance. To address the above issues, we propose a few-shot PCB defect detection model with double-branch attention. Specifically, the joint attention enhancement (JAE) module is proposed to fully mine effective information of query PCB images in multiple dimensions to enhance the representation of latent defects. Then, the multi-scale guidance (MSG) module is proposed to integrate prior knowledge within support PCB images into vectors to reweight query PCB images. Experiments on the PCB defect dataset demonstrate that AP of FSPDD outperforms state-of-the-art methods under different shot settings (k=1,2,3,5,10,30) and our proposed FSPDD has a good generalization ability, in which AP reachs 0.273 when $$k=30$$ k = 30 and is 5.28% higher than SOTA methods.

The comprehensive regulatory oversight of traffic emissions frequently encounters the missing not-at-random (MNAR) pattern, characterized by the long-term block missing in adjacent road segments, arising from insufficient monitoring points and nonuniform spatiotemporal distribution. The spatiotemporal block missing simultaneously disrupts the spatiotemporal correlation, introducing significant biases in spatiotemporal modeling for incomplete data. The emerging diffusion model recovers the information of the missing regions in a self-supervised manner and focuses on the generation process of the missing regions to address biases. However, the dynamics and spatiotemporal heterogeneity of traffic emissions limit its applicability in unknown spatiotemporal missing. To address this issue, this article proposes a novel progressive Diffusion Model-based framework for SpatioTemporal Imputation of traffic emissions (STI-dm). Specifically, a self-supervised masked training strategy is first devised to construct the nonlocal similarity prior of traffic emission data, explicitly introducing the MNAR missing mechanism for the diffusion process. Furthermore, an enhanced approach of noise injection and supervised denoising is adopted to rectify misconceptions of nonlocal alignment, decreasing modeling biases associated with incomplete data in the generation process. The imputation and prior modeling processes are progressively performed until obtaining stable results, and each of the preceding modeling processes benefits from the gradual improvement results in the other. Experimental evidence indicates that STI-dm surpasses the current state-of-the-art algorithms in scenarios with intricate spatiotemporal patterns and varying rates of missing data.

Spatiotemporal clustering of vehicle emissions, which reveals the evolution pattern of air pollution from road traffic, is a challenging representation learning task due to the lack of supervision. Some recent work building upon graph convolutional network (GCN) models the intrinsic spatiotemporal correlations among the nodes in road networks as graph representations for clustering. However, these existing methods ignore the interactions between spatial and temporal variations in vehicle emissions, resulting in incomplete descriptions and inaccurate detection of the evolution pattern of air pollution. To address this issue, this article proposes a two-way self-supervised spatiotemporal representation learning scheme, in which the temporal and spatial features are progressively learned in a mutually reinforced manner. Our proposed method is based on the observation that though the variation in vehicle emissions in the road network is consistent in the spatial and temporal domains, its expression is more distinct in temporal sequences. To this end, the input emission data are first projected into an initial temporal representation space spanned by the captured features from a pretrained BiLSTM network. Then the generated distribution of temporal features is used to construct an objective constraint for high-purity clustering through a two-way self-supervised mechanism, which is leveraged as a constraint for the feature clustering of a GCN. Furthermore, to eliminate the initial errors, a joint optimization scheme is presented to generate the decoupled clustering results through the progressive refinement of representation and clustering. Our proposed method is evaluated on the traffic emission dataset of Xian city in 2020, and the experimental results have demonstrated the superiority against the state-of-the-art.

Heavy-duty diesel vehicles are important sources of urban nitrogen oxides (NOx) in actual applications for environmental compliance, emitting more than 80% of NOx and more than 90% of particulate matter (PM) in total vehicle emissions. The detection and control of heavy-duty diesel emissions are critical for protecting public health. Currently, vehicles on the road must be regularly tested, every six months or once a year, to filter out high-emission mobile sources at vehicle inspection stations. However, it is difficult to effectively screen high-emission vehicles in time with a long interval between annual inspections, and the fixed threshold cannot adapt to the dynamic changes of vehicle driving conditions. An on-board diagnostic device (OBD) is installed inside the vehicle and can record the vehicle’s emission data in real time. In this paper, we propose a temporal optimization long short-term memory (LSTM) and adaptive dynamic threshold approach to identify heavy-duty high-emitters by using OBD data, which can continuously track and record the emission status in real time. First, a temporal optimization LSTM emission prediction model is established to solve the attention bias discrepancy problem on time steps that is caused by the large number of OBD data streams in practice. Then, the concentration prediction error sequence is detected and distinguished from the anomalous emission contexts using flexible criteria, calculated by an adaptive dynamic threshold with changing driving conditions. Finally, a similarity metric strategy for the time series is introduced to correct some pseudo anomalous results. Experiments on three real OBD time-series emission datasets demonstrate that our method can achieve high accuracy anomalous emission identification.

This article investigates the event-triggered distributed model predictive control (DMPC) for perturbed coupled nonlinear systems subject to state and control input constraints. A novel compound event-triggered DMPC strategy, including a compound triggering condition and a new constraint tightening approach, is developed. In this event-triggered strategy, two stability-related conditions are checked in a parallel manner, which relaxes the requirement of the decrease of the Lyapunov function. An open-loop prediction scheme to avoid periodic transmission is designed for the states in the terminal set. As a result, the number of triggering and transmission instants can be reduced significantly. Furthermore, the proposed constraint tightening approach solves the problem of the state constraint satisfaction, which is quite challenging due to the external disturbances and the mutual influences caused by dynamical coupling. Simulations are conducted at last to validate the effectiveness of the proposed algorithm.

Driving cycles construction, which aims to generate various vehicle driving profiles corresponding to typical traffic conditions, plays an important role in the evaluation of vehicle emissions, economy and mileage. Existing methods usually represent the speed-time distributions of driving data in the space spanned by hand-crafted features, and select typical sequences to combine driving cycle curves. However, since the driving data is treated as static, the inherent dynamic characteristics and temporal dependency tend to be ignored, resulting in low accuracy and insufficient robustness. To address this issue, this paper proposes a dynamic driving cycle construction framework, in which feature extraction and sequence clusters are achieved in an unsupervised joint learning manner. Specifically, the driving data are firstly encoded by a Bi-directional Long Short-Term Memory (BiLSTM) branch to capture the temporal correlation property of driving sequences. Then, a temporal clustering branch is presented to achieve soft distribution clustering of feature sequences by introducing a relative-entropy-based regularization term into the coding unit. The two branches are iteratively updated until stable feature learning and clustering results are obtained. Consequently, each branch benefits from the additional improvement over the previous branch during the iteration process. Finally, typical driving sequences are selected according to the intra-class/extra-class distance and class proportion, and then assembled to generate driving cycles profiles. To verify the performance of our proposed method, evaluations are performed on the on-road driving data of light vehicles in Fuzhou, in which the constructed driving cycle from our methods is substituted into COPERT model to estimate and visualize the road emissions, and the experimental results demonstrate that our proposed methods can greatly improve the accuracy and robustness of the constructed driving cycle.

Urban vehicle emission prediction can help the regulation of vehicle pollution and traffic control. However, it is hard to predict the spatiotemporal variation of vehicle emission because of the spatial interactions and temporal correlations between different road segments as well as the high nonlinearity and complexity of vehicle emission variation. The existing methods solve the problem by splitting the region into standard segments or grids based on conventional deep learning methods, without considering that urban vehicle emission varies by graph-structured traffic road network and depends on many complex external environment factors. To address these issues, a spatiotemporal graph convolution multifusion network (ST-MFGCN) is proposed to leverage the graph structural properties as the inherent connectivity of road network for urban vehicle emission prediction, which can capture the vehicle emission spatiotemporal variation patterns and learn the effects of complex environmental factors. The proposed model consists of three parts: 1) a spatiotemporal graph convolution module to capture spatiotemporal dependencies by merging closeness, period, and trend sequences with temporal convolution as well as graph convolution is introduced to model the spatial dependencies; 2) an external factor component to divide multisource external factors into global and individual external features; and 3) a general fusion component to merge the spatiotemporal patterns and the external features as well as fit the mutation of emission measurement data by multifusion strategy. Finally, the proposed model is evaluated on the practical monitoring data of vehicle emission data in Hefei, and the results demonstrate that our proposed model can predict regional vehicle emissions effectively.

Identifying code has been widely used in man-machine verification to maintain network security. The challenge in engaging man-machine verification involves the correct classification of man and machine tracks. In this study, we propose a random forest (RF) model for man-machine verification based on the mouse movement trajectory dataset. We also compare the RF model with the baseline models (logistic regression and support vector machine) based on performance metrics such as precision, recall, false positive rates, false negative rates, F -measure, and weighted accuracy. The performance metrics of the RF model exceed those of the baseline models.