Ling Huang, Xiang Li, Peng-Cheng Gong and Zhiming He He
Frequency diverse array (FDA) radar with uniform frequency offset between antenna elements has been proposed and investigated, which exhibits a range-angle-dependent beampattern…
Abstract
Purpose
Frequency diverse array (FDA) radar with uniform frequency offset between antenna elements has been proposed and investigated, which exhibits a range-angle-dependent beampattern. Nevertheless, because of the coupling in range and angle responses, it cannot estimate directly both the range and angle information of a target.The purpose of this paper is to consider a sub-array scheme of range-angle joint estimation of a target for frequency diverse array (FDA) radar.
Design/methodology/approach
First, The entire array is divided into two sub-arrays, which employs two different frequency offsets. For aperture extension, each sub-array adopts difference co-array structure . Therefore, the targets range and angle can be estimated directly with the subspace-based multiple signal classification algorithms for the decoupling capability of distance and angle dimensional. The estimation performance is examined by analyzing the Cramer-Rao lower bound (CRLB) versus signal-to-noise ratio (SNR).
Findings
Each sub-array adopts difference co-array structure to provide degrees of freedom by only physical sensors when the second-order statistics of the received data is used. And the sub-array is equivalent to two sets of equations to solve two unknown quantities, and the closed solution of the unknown quantity can be directly determined, which cannot be gained by the phase-array radar and basic ULA FDA radar. Finally, numerical simulation results verify the validity of the proposed method.
Originality/value
In this paper, we devise a subarray scheme on FDA radar for range and angle estimation. In order to aperture extension, difference co-array is employed in each subarrays, and more targets can be distinguished than the physical sensors. The range and angle estimation performance is examined by analyzing the CRLB.
Can Zhong Yao, Peng Cheng Kuang and Ji Nan Lin
The purpose of this study is to reveal the lead–lag structure between international crude oil price and stock markets.
Abstract
Purpose
The purpose of this study is to reveal the lead–lag structure between international crude oil price and stock markets.
Design/methodology/approach
The methods used for this study are as follows: empirical mode decomposition; shift-window-based Pearson coefficient and thermal causal path method.
Findings
The fluctuation characteristic of Chinese stock market before 2010 is very similar to international crude oil prices. After 2010, their fluctuation patterns are significantly different from each other. The two stock markets significantly led international crude oil prices, revealing varying lead–lag orders among stock markets. During 2000 and 2004, the stock markets significantly led international crude oil prices but they are less distinct from the lead–lag orders. After 2004, the effects changed so that the leading effect of Shanghai composite index remains no longer significant, and after 2012, S&P index just significantly lagged behind the international crude oil prices.
Originality/value
China and the US stock markets develop different pattens to handle the crude oil prices fluctuation after finance crisis in 1998.
Details
Keywords
Yong Ding, Peixiong Huang, Hai Liang, Fang Yuan and Huiyong Wang
Recently, deep learning (DL) has been widely applied in various aspects of human endeavors. However, studies have shown that DL models may also be a primary cause of data leakage…
Abstract
Purpose
Recently, deep learning (DL) has been widely applied in various aspects of human endeavors. However, studies have shown that DL models may also be a primary cause of data leakage, which raises new data privacy concerns. Membership inference attacks (MIAs) are prominent threats to user privacy from DL model training data, as attackers investigate whether specific data samples exist in the training data of a target model. Therefore, the aim of this study is to develop a method for defending against MIAs and protecting data privacy.
Design/methodology/approach
One possible solution is to propose an MIA defense method that involves adjusting the model’s output by mapping the output to a distribution with equal probability density. This approach effectively preserves the accuracy of classification predictions while simultaneously preventing attackers from identifying the training data.
Findings
Experiments demonstrate that the proposed defense method is effective in reducing the classification accuracy of MIAs to below 50%. Because MIAs are viewed as a binary classification model, the proposed method effectively prevents privacy leakage and improves data privacy protection.
Research limitations/implications
The method is only designed to defend against MIA in black-box classification models.
Originality/value
The proposed MIA defense method is effective and has a low cost. Therefore, the method enables us to protect data privacy without incurring significant additional expenses.
Details
Keywords
Yan Li, Ming K. Lim, Weiqing Xiong, Xingjun Huang, Yuhe Shi and Songyi Wang
Recently, electric vehicles have been widely used in the cold chain logistics sector to reduce the effects of excessive energy consumption and to support environmental…
Abstract
Purpose
Recently, electric vehicles have been widely used in the cold chain logistics sector to reduce the effects of excessive energy consumption and to support environmental friendliness. Considering the limited battery capacity of electric vehicles, it is vital to optimize battery charging during the distribution process.
Design/methodology/approach
This study establishes an electric vehicle routing model for cold chain logistics with charging stations, which will integrate multiple distribution centers to achieve sustainable logistics. The suggested optimization model aimed at minimizing the overall cost of cold chain logistics, which incorporates fixed, damage, refrigeration, penalty, queuing, energy and carbon emission costs. In addition, the proposed model takes into accounts factors such as time-varying speed, time-varying electricity price, energy consumption and queuing at the charging station. In the proposed model, a hybrid crow search algorithm (CSA), which combines opposition-based learning (OBL) and taboo search (TS), is developed for optimization purposes. To evaluate the model, algorithms and model experiments are conducted based on a real case in Chongqing, China.
Findings
The result of algorithm experiments illustrate that hybrid CSA is effective in terms of both solution quality and speed compared to genetic algorithm (GA) and particle swarm optimization (PSO). In addition, the model experiments highlight the benefits of joint distribution over individual distribution in reducing costs and carbon emissions.
Research limitations/implications
The optimization model of cold chain logistics routes based on electric vehicles provides a reference for managers to develop distribution plans, which contributes to the development of sustainable logistics.
Originality/value
In prior studies, many scholars have conducted related research on the subject of cold chain logistics vehicle routing problems and electric vehicle routing problems separately, but few have merged the above two subjects. In response, this study innovatively designs an electric vehicle routing model for cold chain logistics with consideration of time-varying speeds, time-varying electricity prices, energy consumption and queues at charging stations to make it consistent with the real world.