Joint Beamforming Design for Double Active RIS-assisted Radar-Communication Coexistence Systems

Integrated sensing and communication (ISAC) technology has been considered as one of the key candidate technologies in the next-generation wireless communication systems. However, when radar and communication equipment coexist in the same system, i.e. radar-communication coexistence (RCC), the interference from communication systems to radar can be large and cannot be ignored. Recently, reconfigurable intelligent surface (RIS) has been introduced into RCC systems to reduce the interference. However, the "multiplicative fading" effect introduced by passive RIS limits its performance. To tackle this issue, we consider a double active RIS-assisted RCC system, which focuses on the design of the radar's beamforming vector and the active RISs' reflecting coefficient matrices, to maximize the achievable data rate of the communication system. The considered system needs to meet the radar detection constraint and the power budgets at the radar and the RISs. Since the problem is non-convex, we propose an algorithm based on the penalty dual decomposition (PDD) framework. Specifically, we initially introduce auxiliary variables to reformulate the coupled variables into equation constraints and incorporate these constraints into the objective function through the PDD framework. Then, we decouple the equivalent problem into several subproblems by invoking the block coordinate descent (BCD) method. Furthermore, we employ the Lagrange dual method to alternately optimize these subproblems. Simulation results verify the effectiveness of the proposed algorithm. Furthermore, the results also show that under the same power budget, deploying double active RISs in RCC systems can achieve higher data rate than those with single active RIS and double passive RISs

Hierarchical Optimization Decision-Making Method to Comply with China’s Fuel Consumption and New Energy Vehicle Credit Regulations

The national targets of reaching carbon peak in 2030 and carbon neutrality in 2060 propose higher requirements for energy conservation and emission reduction of China’s automobile industry. As an important measure for the government, the fuel consumption and new energy vehicle (NEV) credit policy system has a significant impact on the Chinese and even the global vehicle market. Considering the lack of a systematic evaluation model for China’s fuel consumption and NEV credit regulations, this study establishes a hierarchical optimization decision-making model based on technology frontier curves and a multi-dimension database containing extensive data of technologies, products, and enterprises in the Chinese market to simulate and evaluate the technology compliance and policy impact under multiple regulations. The results show that, from the perspective of the technology frontier curve, gasoline technologies still have great cost-effectiveness advantages when the fuel-saving requirement is less than 46%, and the space for plug-in hybrid electric vehicles (PHEVs) and range-extended electric vehicles (REVs) is gradually shrinking due to the cost reduction of battery electric vehicles (BEVs). BEV400 will be better than PHEV70 and REV100 when the fuel-saving requirement is higher than 79%. Diesel vehicles are always not competitive in the passenger car market. In terms of the compliance of corporate average fuel consumption (CAFC) regulation, the start-stop technology will be gradually phased out and mild hybrid electric vehicles will be rapidly introduced due to their high cost-effectiveness in 2025. With the tightening of regulations, the penetration rate of BEVs and PHEVs will be 23.7% and 6.7%, respectively, and mild hybrid electric vehicles will be gradually replaced by strong hybrid electric vehicles in 2030. By 2035, the penetration rate of BEVs and PHEVs will be 43.6% and 6% further. For the CAFC and NEV credit regulation (widely known as the dual credit regulation), the single-vehicle credit poses a greater impact on the penetration of NEVs than corporate credit percentage limitation and is the key factor that should be focused on. The NEV credit limitation in the dual credit regulation could push ‘poor performance’ automakers to produce the required number of NEVs and meet the bottom line. However, in the long term, when compared to the CAFC regulation, the dual credit regulation is more lenient, due to NEVs being able to get double benefits both on NEV credit and CAFC credit, and NEV credit can also unidirectionally compensate CAFC credit under the dual-credit policy context. With the increased penetration and cost reduction of NEVs, the ‘averaging’ effect of dual credit regulation will inhibit the development of energy-saving and new energy vehicles. Therefore, eliminating the connection between NEV credit and CAFC credit or only leaving the CAFC and the fuel consumption limit regulations in the future will be better for the long-term development of the energy-saving and new energy vehicle industry

Impacts of the New Worldwide Light-Duty Test Procedure on Technology Effectiveness and China’s Passenger Vehicle Fuel Consumption Regulations

As a main measure to promote the development of China’s energy–saving and new energy vehicles, the Phase V fuel consumption regulation is dramatically different from the past four phases, especially in the test procedure, moving from the New European Driving Cycle (NEDC) to the worldwide harmonized light duty test cycle (WLTC) and corresponding test procedure (WLTP). The switch of test procedure will not only affect the effectiveness of technologies but also change the fuel consumption target of the industry. However, few studies have systematically investigated the impacts of the new WLTP on the Chinese market. This study establishes a “technology–vehicle–fleet” bottom–up framework to estimate the impacts of test procedure switching on technology effectiveness and regulation stringency. The results show that due to the WLTP being closer to the real driving condition and more stringent, almost all baseline vehicles in the WLTP have higher fuel consumption than that in the NEDC, and diesel vehicles are slightly more impacted than gasoline vehicles. In addition, the impacts are increased with the strengthening of electrification, where the fuel consumption of plug–in hybrid electric vehicles (PHEVs) and range-extended electric vehicles (REEVs) in the WLTP are about 6% higher than that in the NEDC. Engine technologies that gain higher effects in low load conditions, such as turbocharging and downsizing, fuel stratified injection (FSI), lean–burn, and variable valve timing (VVT), are faced with deterioration in the WLTP. Among these, the effect of turbocharging and downsizing shows a maximum decline of 8.5%. The variable compression ratio (VCR) and stoichiometric gasoline direct injection (SGDI) are among the few technologies that benefited from procedure switching, with an average improvement of 1.6% and 0.2% respectively. Except for multi–speed transmissions, which have improvement effects in the WLTP, all automatic transmissions are faced with decreases. From the perspective of the whole fleet and national regulation target, the average fuel consumption in the WLTP will increase by about 7.5% in 2025 compared to 4 L/100 km in the NEDC. According to the current planning of the Chinese government, the fuel consumption target of Phase V is set at 4.6 L/100 km in 2025, which is equivalent to loosening the stringency by 0.3 L/100 km. In Phase VI, the target of 3.2 L/100 km is maintained, which is 30.4% stricter than that of Phase V, and the annual compound tightening rate reaches 7.5%. This means that automakers need to launch their product planning as soon as possible and expand the technology bandwidth to comply with the Phase VI fuel consumption regulation, and the government should evaluate the technical feasibility before determining the evaluation methods and targets of the next phase

An Image Captioning Algorithm Based on Combination Attention Mechanism

With the maturity of computer vision and natural language processing technology, we are becoming more ambitious in image captioning. In particular, we are more ambitious in generating longer, richer, and more accurate sentences as image descriptions. Most existing image caption models use an encoder—decoder structure, and most of the best-performing models incorporate attention mechanisms in the encoder—decoder structure. However, existing image captioning methods focus only on visual attention mechanism and not on keywords attention mechanism, thus leading to model-generated sentences that are not rich and accurate enough, and errors in visual feature extraction can directly lead to generated caption sentences that are incorrect. To fill this gap, we propose a combination attention module. This module comprises a visual attention module and a keyword attention module. The visual attention module helps in performing fast extractions of key local features, and the keyword attention module focuses on keywords that may appear in generated sentences. The results generated by the two modules can be corrected for each other. We embed the combination attention module into the framework of the Transformer, thus constructing a new image caption model CAT (Combination Attention Transformer) to generate more accurate and rich image caption sentences. Extensive experiments on the MSCOCO dataset demonstrate the effectiveness and superiority of our method over many state-of-the-art methods

A Link Prediction Algorithm Based on GAN

Link prediction, as an important research direction in complicated network analysis, has broad application prospects. However, traditional link prediction algorithms are generally designed by the sparse expression of the adjacency matrix, which is computationally expensive and inefficient, being also unable to run on large-scale networks and to preserve their higher order structural features. To fill this gap, we propose a GAN (generative adversarial network)-based link prediction algorithm. The algorithm layers the network graph, preserving the local features and higher-level structural features of the original network graph, and uses a generative adversarial model to recursively and backwardly obtain the low-dimensional vector form of the vertices in each layer of the network graph as the initialization of the network graph in the previous layer. It then obtains the low-dimensional vector form of all the vertices in the original network graph for link prediction, and the problem of local minima that can be generated by random initialization is solved. The experimental results show that our method is superior to many state-of-the-art algorithms

Homomorphic Encryption Based Privacy Preservation Scheme for DBSCAN Clustering

In this paper, we propose a homomorphic encryption-based privacy protection scheme for DBSCAN clustering to reduce the risk of privacy leakage during data outsourcing computation. For the purpose of encrypting data in practical applications, we propose a variety of data preprocessing methods for different data accuracies. We also propose data preprocessing strategies based on different data precision and different computational overheads. In addition, we also design a protocol to implement the cipher text comparison function between users and cloud servers. Analysis of experimental results indicates that our proposed scheme has high clustering accuracy and can guarantee the privacy and security of the data

Underwater Target Tracking Using Forward-Looking Sonar for Autonomous Underwater Vehicles

In the scenario where autonomous underwater vehicles (AUVs) carry out tasks, it is necessary to reliably estimate underwater-moving-target positioning. While cameras often give low-precision visibility in a limited field of view, the forward-looking sonar is still an attractive method for underwater sensing, which is especially effective for long-range tracking. This paper describes an online processing framework based on forward-looking-sonar (FLS) images, and presents a novel tracking approach based on a Gaussian particle filter (GPF) to resolve persistent multiple-target tracking in cluttered environments. First, the character of acoustic-vision images is considered, and methods of median filtering and region-growing segmentation were modified to improve image-processing results. Second, a generalized regression neural network was adopted to evaluate multiple features of target regions, and a representation of feature subsets was created to improve tracking performance. Thus, an adaptive fusion strategy is introduced to integrate feature cues into the observation model, and the complete procedure of underwater target tracking based on GPF is displayed. Results obtained on a real acoustic-vision AUV platform during sea trials are shown and discussed. These showed that the proposed method is feasible and effective in tracking targets in complex underwater environments

A Link Prediction Algorithm Based on GAN

Link prediction, as an important research direction in complicated network analysis, has broad application prospects. However, traditional link prediction algorithms are generally designed by the sparse expression of the adjacency matrix, which is computationally expensive and inefficient, being also unable to run on large-scale networks and to preserve their higher order structural features. To fill this gap, we propose a GAN (generative adversarial network)-based link prediction algorithm. The algorithm layers the network graph, preserving the local features and higher-level structural features of the original network graph, and uses a generative adversarial model to recursively and backwardly obtain the low-dimensional vector form of the vertices in each layer of the network graph as the initialization of the network graph in the previous layer. It then obtains the low-dimensional vector form of all the vertices in the original network graph for link prediction, and the problem of local minima that can be generated by random initialization is solved. The experimental results show that our method is superior to many state-of-the-art algorithms

Homomorphic Encryption Based Privacy Preservation Scheme for DBSCAN Clustering

In this paper, we propose a homomorphic encryption-based privacy protection scheme for DBSCAN clustering to reduce the risk of privacy leakage during data outsourcing computation. For the purpose of encrypting data in practical applications, we propose a variety of data preprocessing methods for different data accuracies. We also propose data preprocessing strategies based on different data precision and different computational overheads. In addition, we also design a protocol to implement the cipher text comparison function between users and cloud servers. Analysis of experimental results indicates that our proposed scheme has high clustering accuracy and can guarantee the privacy and security of the data

Estimating p-y curves for clays by CPTU method : framework and empirical study

Despite its wide use as a tool in foundation design, the piezocone penetration test (CPTU) has been rarely recommended to work for design and analysis of laterally loaded piles. Obtaining lateral response of pile foundations is a complicated engineering problem, especially in nonhomogeneous soils. This paper presents a review of the relationship between the piezocone test and the bearing response of laterally loaded piles and introduces a framework for estimating p-y curves for clays directly using CPTU parameters. To validate this method, full-scale lateral load tests of bored piles with corresponding CPTU tests in Jiangsu soil deposits were conducted and data were compared with the predicted results. In addition, case histories were further considered in detail to study the application of the proposed method for different field conditions. The results predicted by the proposed CPTU-based p-y curve agreed relatively well with the measured results. The proposed method can provide a fast and effective design tool that can be applied to clayey soils with full consideration of soil profiles along the pileembedded depth.Accepted versio