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This paper studies the convex optimization problem with general constraints, where its global objective function is composed of the sum of local objective functions. The objective is to design a distributed algorithm to cooperatively resolve the optimization problem under the condition that only the information of each node's own local cost function and its neighbors' states can be obtained. To this end, the optimality condition of the researched optimization problem is developed in terms of the saddle point theory. On this basis, the corresponding continuous-time primal-dual algorithm is constructed for the considered constrained convex optimization problem under time-varying undirected and connected graphs. In the case that the parameters involved in the proposed algorithm satisfy certain inequality, the states of all nodes will reach consensus in finite time. Meanwhile, the average state is globally convergent to the optimal solution of the considered optimization problem under some mild and standard assumptions.

Spark-ignited natural gas engines have received increasing attention in the heavy-duty market due to their low cost and reliability advantages. However, there are still some issues with natural gas engines retrofitted from 10 to 15 L diesel engines, which is a valuable medium-term goal for the automotive industry. In this work, the effect of intake port structure on the performance of a spark-ignited heavy-duty natural gas engine was investigated by multidimensional numerical simulations. A newly designed intake port was proposed, with strengthened in-cylinder turbulent kinetic energy and homogeneous air-fuel mixtures. Bench tests show that the proposed intake port has impressive thermal efficiency, cycle variation, and acceptable emissions performance. The effective thermal efficiency improves from 41.0% to 41.4%, and the cycle variation is 36% lower than traditional schemes. However, with the accelerated flame propagation, the in-cylinder temperature and NOx emission of the mixed-flow port increase while the CO emission decreases. In summary, a proper balance of in-cylinder swirl and tumble flow can significantly affect the economy and stability of natural gas engines. The proposed structure solves the inherent problems of slow natural gas flame propagation and harmful cyclic variations.

With the development of electronic technology, intelligent cars can gradually realize more complex artificial intelligence algorithms. The video caption algorithm is one of them. However, current video caption algorithms only consider single-visual information when applied to urban traffic scenes, which leads to the failure to generate accurate captions of complex sets. The multimodal fusion algorithm based on Transformer is one of the solutions to this problem. However, the existing algorithms have the difficulties of a low fusion performance and high computational complexity. We propose a new video caption Transformer-based model, the MFVC (Multimodal Fusion for Video Caption), to solve these issues. We introduce audio modal data and the attention bottleneck module to increase the available information to describe the generative model and improve the model effect with less operation costs through the attention bottleneck module. Finally, the experiment is conducted on the available datasets, MSR-VTT and MSVD. Meanwhile, to verify the effect of the model on the urban traffic scene, the experiment is carried out on the self-built traffic caption dataset BUUISE, and the evaluation index confirms the model. This model can achieve good results on both available datasets and urban traffic datasets and has excellent application prospects in the intelligent driving industry.

Multi-label aerial scene image classification is a long-standing and challenging research problem in the remote sensing field. As land cover objects usually co-exist in an aerial scene image, modeling label dependencies is a compelling approach to improve the performance. Previous methods generally directly model the label dependencies among all the categories in the target dataset. However, most of the semantic features extracted from an image are relevant to the existing objects, making the dependencies among the nonexistant categories unable to be effectively evaluated. These redundant label dependencies may bring noise and further decrease the performance of classification. To solve this problem, we propose S-MAT, a Semantic-driven Masked Attention Transformer for multi-label aerial scene image classification. S-MAT adopts a Masked Attention Transformer (MAT) to capture the correlations among the label embeddings constructed by a Semantic Disentanglement Module (SDM). Moreover, the proposed masked attention in MAT can filter out the redundant dependencies and enhance the robustness of the model. As a result, the proposed method can explicitly and accurately capture the label dependencies. Therefore, our method achieves CF1s of 89.21%, 90.90%, and 88.31% on three multi-label aerial scene image classification benchmark datasets: UC-Merced Multi-label, AID Multi-label, and MLRSNet, respectively. In addition, extensive ablation studies and empirical analysis are provided to demonstrate the effectiveness of the essential components of our method under different factors.

The convergence rate and applicability to directed graphs with interaction topologies are two important features for practical applications of distributed optimization algorithms. In this paper, a new kind of fast distributed discrete-time algorithms is developed for solving convex optimization problems with closed convex set constraints over directed interaction networks. Under the gradient tracking framework, two distributed algorithms are respectively designed over balanced and unbalanced graphs, where momentum terms and two time-scales are involved. Furthermore, it is demonstrated that the designed distributed algorithms attain linear speedup convergence rates provided that the momentum coefficients and the step-size are appropriately selected. Finally, numerical simulations verify the effectiveness and the global accelerated effect of the designed algorithms.

This brief investigates the distributed unconstrained optimization problem, where its global objective function consists of the sum of $N$ local objective functions. The aim for this brief is to design a discrete-time algorithm to solve the considered distributed optimization problem using only local computations and local information exchanges. To this end, a discrete-time algorithm resorting to the conjugate gradient method is proposed, and by it, the optimal solution to a class of distributed optimization problems over a static undirected graph can be obtained with finite-time computations under some mild conditions. Furthermore, simulations are given to verify the validity of the designed algorithm.

The Internet of Vehicles and information security are key components of a smart city. Real-time road perception is one of the most difficult tasks. Traditional detection methods require manual adjustment of parameters, which is difficult, and is susceptible to interference from object occlusion, light changes, and road wear. Designing a robust road perception algorithm is still challenging. On this basis, we combine artificial intelligence algorithms and the 5G-V2X framework to propose a real-time road perception method. First, an improved model based on Mask R-CNN is implemented to improve the accuracy of detecting lane line features. Then, the linear and polynomial fitting methods of feature points in different fields of view are combined. Finally, the optimal parameter equation of the lane line can be obtained. We tested our method in complex road scenes. Experimental results show that, combined with 5G-V2X, this method ultimately has a faster processing speed and can sense road conditions robustly under various complex actual conditions.

Security perception systems based on 5G-V2X have become an indispensable part of smart city construction. However, the detection speed of traditional deep learning models is slow, and the low-latency characteristics of 5G networks cannot be fully utilized. In order to improve the safety perception ability based on 5G-V2X, increase the detection speed in vehicle perception. A vehicle perception model is proposed. First, an adaptive feature extraction method is adopted to enhance the expression of small-scale features and improve the feature extraction ability of small-scale targets. Then, by improving the feature fusion method, the shallow information is fused layer by layer to solve the problem of feature loss. Finally, the attention enhancement method is introduced to increase the center point prediction ability and solve the problem of target occlusion. The experimental results show that the UA-DETRAC data set has a good detection effect. Compared with the vehicle detection capability before the improvement, the detection accuracy and speed have been greatly improved, which effectively improves the security perception capability based on the 5G-V2X system, thereby promoting the construction of smart cities.

In Internet of things, compared with the traditional traceability system, the existing system has difficulties in operation, data are easily lost, data are uncoordinated, standards are not unified, and so on. In this paper, based on the characteristics of blockchain, the traceability model of urban fruits is established to reduce the risk of counterfeit and shoddy urban fruits. The collected data is integrated into the chain for diversified display, providing different permissions and platforms for multiple roles. Blockchain and supporting intelligent hardware are used to realize the real record and tracking of the whole process of fruit. Improve the transparency and efficiency of supply chain, and reduce the supply chain cost. The experimental results show that the proposed algorithm model can be applied to the fruit service field and effectively improve the service level of smart city.

In this article, the constrained optimization problem with its global objective function being the sum of convex local cost functions and the constraint being a closed convex set is researched. The aim of this study is to solve the researched problem in a distributed manner, that is, using only local computations and local information exchanges. Toward this end, two gradient-tracking-based distributed optimization algorithms are designed for the considered problem over weight-balanced and weight-unbalanced graphs, respectively. Since the classical projection method is unsuitable to handle the closed convex set constraint under the gradient-tracking framework, a new indirect projection method is employed in this article to deal with the involved closed convex set constraint. Furthermore, two time scales are introduced to complete the convergence analyses. In addition, under the condition that all local cost functions are strongly convex and L -smooth, it is proved that the algorithms with well-selected fixed step sizes have linear convergence rates.

Semantic segmentation of an image scene provides semantic information of image regions while less information of objects. In this paper, we propose a method of hierarchical semantic segmentation, including scene level and object level, which aims at labeling both scene regions and objects in an image. In the scene level, we use a feature-based MRF model to recognize the scene categories. The raw probability for each category is predicted via a one-vs-all classification mode. The features and raw probability of superpixels are embedded into the MRF model. With the graph-cut inference, we get the raw scene-level labeling result. In the object level, we use a constraint-based geodesic propagation to get object segmentation. The category and appearance features are utilized as the prior constraints to guide the direction of object label propagation. In this hierarchical model, the scene-level labeling and the object-level labeling have a mutual relationship, which regions and objects are optimized interactively. The experimental results on two datasets show the well performance of our method.

In this paper, a class of optimization problems is investigated, where the objective function is the sum of N convex functions viewed as local functions and the constraints are N nonidentical closed convex sets . Additionally, it is aimed to solve the considered optimization problem in a distributed manner and thus a sequence of time-varying unbalanced directed graphs is introduced first to depict the information connection topologies. Then, the novel push-sum based constrained optimization algorithm (PSCOA) is developed, where the new gradient descent-like method is applied to settle the involved closed convex set constraints. Furthermore, the rigorous convergence analysis is shown under some standard and common assumptions and it is proved that the developed distributed discrete-time algorithm owns a convergence rate of O ( ln t t ) in general case. Specially, the convergence rate of O ( 1 t ) can be further obtained under the assumption that at least one objective function is strongly convex. Finally, simulation results are given to demonstrate the validity of the theoretical results.

In recent years, indoor positioning is becoming more and more important. Satellites can position only in the outdoor environment, which is unable to achieve precise positioning in the indoor environment. At present, the indoor positioning is mainly based on wireless signals, such as WiFi, RFID, Zigbee, Bluetooth etc. The cost and power consumption of using WiFi, RFID and Zigbee to realize the indoor positioning is very high and the deployment of WiFi, RFID and Zigbee is inconvenient. In this paper,indoor positioning is based on Bluetooth ibeacon, which is Bluetooth 4.0 standard. The power consumption and the cost of Bluetooth 4.0 is lower than others. In addition, Bluetooth has spread widely in the distance. This paper proposes a new indoor location method, which uses the method of learning to train the Bluetooth signal propagation model in the museum environment and uses the method of weighted least square and four-border positioning to estimate the location of the target object. The experimental result shows that the method is stable and good robustness. The positioning accuracy meets the requirements of the indoor positioning.

In the era of intelligent education, human behavior recognition based on computer vision is an important branch of pattern recognition. Human behavior recognition is a basic technology in the fields of intelligent monitoring and human-computer interaction in education. The dynamic changes of human skeleton provide important information for the recognition of educational behavior. Traditional methods usually use manual information to label or traverse rules only, resulting in limited representation capabilities and poor generalization performance of the model. In this paper, a kind of dynamic skeleton model with residual is adopted—a spatio-temporal graph convolutional network based on residual connections, which not only overcomes the limitations of previous methods, but also can learn the spatio-temporal model from the skeleton data. In the big bone NTU-RGB + D dataset, the network model not only improved the representation ability of human behavior characteristics, but also improved the generalization ability, and achieved better recognition effect than the existing model. In addition, this paper also compares the results of behavior recognition on subsets of different joint points, and finds that spatial structure division have better effects.

Social image tag ranking has emerged as an important research topic due to its application on web image search. This paper presents an adaptive all-season tag ranking algorithm which can handle the images with and without distinct object(s) using different tag ranking strategies. Firstly, based on saliency map derived from the visual attention model, a linear SVM is trained to pre-classify an image as attentive or non-attentive category by using the gray histogram descriptor on the corresponding saliency map. Then, an image with distinct object is processed by the tag saliency ranking algorithm emphasizing distinct object, which combines image saliency map with sparse representation based multi-instance learning algorithm. On the other hand, an image without distinct object can be processed by the tag relevance ranking algorithm via the sparse representation based neighbor-voting strategy. Such adaptive all-season tag ranking strategy can be regarded as taking full advantage of existing tag ranking paradigms. Experiments conducted on well-known image data sets demonstrate the effectiveness of the proposed framework.

Salient object detection is an important process for machines to understand visual contents as humans. Typically, most previous studies on salient object detection infer salient map by only using the visual features. In this paper, we propose a new paradigm on salient object detection, which aims at producing more reliable results by mining the context information from user's annotated tags. To address this problem, we firstly construct a large scale salient object dataset, which includes 5429 images from the NUSWIDE dataset (a real world web image database from National University of Singapore) with tag information and accurate human-labeled masks. Moreover, a specialised conditional random field (CRF) model is also proposed which takes account of both tag contexts and appearance cues. Extensive experiments show the effectiveness and superiority of our model which can accurately locate the salient objects.

The paper proposes a new spatial extension of Bag-of-Features BoF formalism for classification tasks. The scheme is based on multi-annulus partition which contains much spatial information of image space. Experiments are conducted using final super-vector image representation in Support Vector Machine SVM framework for classification on Oxford flowers and 15 scenes data sets. The results of experiment have shown the effectiveness of our scheme in terms of multiple performance metrics. In addition, our scheme is conceptually simple and easily adoptable. It can lead to much more compact representations and more invariance to image transformation compared to several existing works.