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学者姓名：谢启伟
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Abstract ：
Intelligent sensors serve as crucial elements inthe realm of smart car mobility solutions and urban sens-ing technology. This article presents a novel automotiveenvironment perception system that uses a binocular visionsensor. The binocular camera is used to capture images andobtain cloud points for obstacle perception and environmentpositioning. The proposed system is built on a low-powerembedded platform but maintains a high perception perfor-mance. It can accurately identify and locate obstacles, suchas vehicles and pedestrians. The complete system is compre-hensively described, encompassing the hardware structure,software architecture, and algorithm program. Furthermore,the process of the obstacle detection algorithm, which relieson disparity space and deep learning (DL), is thoroughly presented. The feasibility of the fast stereo-matching algorithmis demonstrated theoretically and validated through experimental verification. Extensive experimental results indicatethat the system is capable of delivering reliable and precise real-time environmental perception for intelligent vehicles.Consequently, the system can be readily implemented in commercial real-time intelligent driving applications. As apertinent research in urban sensing applications, this system holds promise as a viable solution for enhancing smartmobility
Keyword ：
Deep learning (DL) Deep learning (DL) intelligent binocular sensor intelligent binocular sensor environment perception environment perception fast stereo-matching fast stereo-matching
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| GB/T 7714 | Xie, Qiwei , Long, Qian , Li, Jianping et al. Application of Intelligence Binocular VisionSensor: Mobility Solutions for AutomotivePerception System [J]. | IEEE SENSORS JOURNAL , 2024 , 24 (5) : 5578-5592 . |
| MLA | Xie, Qiwei et al. "Application of Intelligence Binocular VisionSensor: Mobility Solutions for AutomotivePerception System" . | IEEE SENSORS JOURNAL 24 . 5 (2024) : 5578-5592 . |
| APA | Xie, Qiwei , Long, Qian , Li, Jianping , Zhang, Liming , Hu, Xiyuan . Application of Intelligence Binocular VisionSensor: Mobility Solutions for AutomotivePerception System . | IEEE SENSORS JOURNAL , 2024 , 24 (5) , 5578-5592 . |
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Abstract ：
This study introduces a novel perspective on financial fraud detection by exploring the utility of managers' abnormal tone. To mitigate bias in indicator selection, we implement a feature selection process involving a comprehensive set of 301 indicators, including financial, nonfinancial, and textual, and various machine learning algorithms. The dataset contains 6077 pairs of fraudulent and non-fraudulent samples in China. Our findings underscore the significance of abnormal tone in fraud detection, establishing it as a prominent factor in the feature selection process. The accuracy outcomes from eight machine learning models further confirm that incorporating abnormal tone can enhance fraud detection performance.
Keyword ：
Financial fraud Financial fraud Managers' abnormal tone Managers' abnormal tone Machine learning Machine learning Feature selection Feature selection
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| GB/T 7714 | Li, Jingyu , Guo, Ce , Lv, Sijia et al. Financial fraud detection for Chinese listed firms: Does managers' abnormal tone matter? [J]. | EMERGING MARKETS REVIEW , 2024 , 62 . |
| MLA | Li, Jingyu et al. "Financial fraud detection for Chinese listed firms: Does managers' abnormal tone matter?" . | EMERGING MARKETS REVIEW 62 (2024) . |
| APA | Li, Jingyu , Guo, Ce , Lv, Sijia , Xie, Qiwei , Zheng, Xiaolong . Financial fraud detection for Chinese listed firms: Does managers' abnormal tone matter? . | EMERGING MARKETS REVIEW , 2024 , 62 . |
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Abstract ：
The transportation industry is considered the foundation and bridge of national economic development, enabling the growth of the social economy. However, it also consumes a considerable amount of energy, resulting in high levels of carbon dioxide (CO2) emissions. As a component of China's vigorous promotion of energy-saving and emissions-reducing policies in recent years, it is crucial to maximize transportation efficiency without increasing transportation energy consumption and CO2 emissions. In this article, the transportation process in China is divided into two stages while maintaining the same total energy consumption and CO2 emissions. Additionally, the generalized equilibrium efficient frontier data envelopment analysis (GEEFDEA) model is enhanced to achieve this. The improved model extends the single-stage GEEFDEA model to a two-stage process, allowing for a more detailed analysis of the internal dynamics within the transportation system. Furthermore, in this article, the fixed inputs and outputs of the original model are further extended to include fixed undesired outputs, expanding the applicability of the model. This also enables the possibility of energy conservation and emissions reduction while promoting development and enhancing efficiency. Based on the improved model, the transport efficiency, energy consumption adjustment, and CO2 emissions adjustment of 30 provinces in China are measured. Finally, the transportation situation and characteristics of three regions, consisting of 30 provinces, are analyzed, and reasonable suggestions for the development of transportation in each region are presented. Furthermore, this article utilizes spatial econometric methods to analyze the impact factors of transportation economic efficiency and their corresponding spatial spillover effects by taking into consideration the intricate interrelationships among regions. The results indicate several findings. First, there is a significant positive spatial correlation in the transportation economic efficiency among Chinese provinces. Second, an increase in per capita gross domestic product, highway transportation, and the proportion of secondary industry have negative effects on transportation economic efficiency. Moreover, the increase in the proportion of secondary industry is negatively correlated with the efficiency of neighboring provinces. Finally, the improvement of energy-saving technology significantly promotes an increase in transportation economic efficiency.
Keyword ：
Carbon dioxide Carbon dioxide Renewable energy sources Renewable energy sources Transportation Transportation Economics Economics Energy consumption Energy consumption Analytical models Analytical models Transportation industry Transportation industry
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| GB/T 7714 | Xie, Qiwei , Shi, Kun , Wu, Xiao et al. Transportation Efficiency Evaluation Under the Policies of Energy Savings and Emissions Reduction [J]. | IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE , 2023 . |
| MLA | Xie, Qiwei et al. "Transportation Efficiency Evaluation Under the Policies of Energy Savings and Emissions Reduction" . | IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE (2023) . |
| APA | Xie, Qiwei , Shi, Kun , Wu, Xiao , Huang, Wuling , Zheng, Xiaolong , Li, Yongjun . Transportation Efficiency Evaluation Under the Policies of Energy Savings and Emissions Reduction . | IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE , 2023 . |
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Abstract ：
Serial section electron microscopy (ssEM) can provide comprehensive 3D ultrastructural information of the brain with exceptional computational cost. Targeted reconstruction of subcellular structures from ssEM da-tasets is less computationally demanding but still highly informative. We thus developed a region-CNN -based deep learning method to identify, segment, and reconstruct synapses and mitochondria to explore the structural plasticity of synapses and mitochondria in the auditory cortex of mice subjected to fear con-ditioning. Upon reconstructing over 135,000 mitochondria and 160,000 synapses, we find that fear condition-ing significantly increases the number of mitochondria but decreases their size and promotes formation of multi-contact synapses, comprising a single axonal bouton and multiple postsynaptic sites from different dendrites. Modeling indicates that such multi-contact configuration increases the information storage ca-pacity of new synapses by over 50%. With high accuracy and speed in reconstruction, our method yields structural and functional insight into cellular plasticity associated with fear learning.
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| GB/T 7714 | Liu, Jing , Qi, Junqian , Chen, Xi et al. Fear memory-associated synaptic and mitochondrial changes revealed by deep learning-based processing of electron microscopy data [J]. | CELL REPORTS , 2022 , 40 (5) . |
| MLA | Liu, Jing et al. "Fear memory-associated synaptic and mitochondrial changes revealed by deep learning-based processing of electron microscopy data" . | CELL REPORTS 40 . 5 (2022) . |
| APA | Liu, Jing , Qi, Junqian , Chen, Xi , Li, Zhenchen , Hong, Bei , Ma, Hongtu et al. Fear memory-associated synaptic and mitochondrial changes revealed by deep learning-based processing of electron microscopy data . | CELL REPORTS , 2022 , 40 (5) . |
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Abstract ：
Background and Objective: The goal of micro-connectomics research is to reconstruct the connectome and elucidate the mechanisms and functions of the nervous system via electron microscopy (EM). Due to the enormous variety of neuronal structures, neuron segmentation is among most difficult tasks in connectome reconstruction, and neuroanatomists desperately need a reliable neuronal structure segmentation method to reduce the burden of manual labeling and validation. Methods: In this article, we proposed an effective deep learning method based on a deep residual contextual and subpixel convolution network to obtain the neuronal structure segmentation in anisotropic EM image stacks. Furthermore, lifted multicut is used for post-processing to optimize the prediction and obtain the reconstruction results. Results: On the ISBI EM segmentation challenge, the proposed method ranks among the top of the leader board and yields a Rand score of 0.98788. On the public data set of mouse piriform cortex, it achieves a Rand score of 0.9562 and 0.9318 in the different testing stacks. The evaluation scores of our method are significantly improved when compared with those of state-of-the-art methods. Conclusions: The proposed automatic method contributes to the development of micro-connectomics, which improves the accuracy of neuronal structure segmentation and provides neuroanatomists with an effective approach to obtain the segmentation and reconstruction of neurons. (C) 2022 The Authors. Published by Elsevier B.V.
Keyword ：
Micro-Connectomics Micro-Connectomics Neuronal structure segmentation Neuronal structure segmentation Subpixel convolution Subpixel convolution Deep learning Deep learning Electron microscopy Electron microscopy
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| GB/T 7714 | Xiao, Chi , Hong, Bei , Liu, Jing et al. Deep residual contextual and subpixel convolution network for automated neuronal structure segmentation in micro-connectomics [J]. | COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE , 2022 , 219 . |
| MLA | Xiao, Chi et al. "Deep residual contextual and subpixel convolution network for automated neuronal structure segmentation in micro-connectomics" . | COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 219 (2022) . |
| APA | Xiao, Chi , Hong, Bei , Liu, Jing , Tang, Yuanyan , Xie, Qiwei , Han, Hua . Deep residual contextual and subpixel convolution network for automated neuronal structure segmentation in micro-connectomics . | COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE , 2022 , 219 . |
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Abstract ：
This study designs a novel image encryption cryptosystem through the two-dimensional partial unwinding decomposition (2D-PUD). It consists of three stages. Firstly, a stream sequence (first part of the security key) is generated by pseudo-random number. Secondly, the plain image is decomposed into three parts by 2D-PUD: one 2D decomposition component, two 1D decomposition components, and the average intensity value of the image. Finally, the 2D decomposition component is shuffled by a generalized Arnold transform where the average intensity value is selected as second part of the security key. The diffusion scheme is subsequently applied to the scrambled image via exclusive OR operations with the randomized 1D decomposition components (third part of the security key) along rows and columns to obtain the cipher image. Due to the adaptive attribute of 2D-PUD, the generated 1D decomposition components are completely distinct for different images. In addition, we can also make them significantly different by tuning the decomposition times for the same image. Thus, the proposed algorithm is an image-content-adaptive encryption scenario that can effectively resist cryptographic attacks. Simulation results demonstrate that our proposed method has excellent encryption performance and can resist against various typical attacks, including brute force, statistical, entropy, and differential attacks. (C) 2020 Elsevier B.V. All rights reserved.
Keyword ：
Partial unwinding decomposition Partial unwinding decomposition Image-content-adaptive encryption Image-content-adaptive encryption Information security Information security Image encryption Image encryption
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| GB/T 7714 | Wu, Yongfei , Zhang, Liming , Qian, Tao et al. Content-adaptive image encryption with partial unwinding decomposition [J]. | SIGNAL PROCESSING , 2021 , 181 . |
| MLA | Wu, Yongfei et al. "Content-adaptive image encryption with partial unwinding decomposition" . | SIGNAL PROCESSING 181 (2021) . |
| APA | Wu, Yongfei , Zhang, Liming , Qian, Tao , Liu, Xilin , Xie, Qiwei . Content-adaptive image encryption with partial unwinding decomposition . | SIGNAL PROCESSING , 2021 , 181 . |
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Abstract ：
In response to the limitation of classical Data Envelopment Analysis (DEA) models, the super efficiency DEA models, including Andersen and Petersen (Manag Sci 39(10): 1261-1264, 1993)'s model (hereafter called AP model) and Li et al. (Eur J Oper Res 255(3): 884-892, 2016)'s cooperative-game-based model (hereafter called L-L model), have been proposed to rank efficient decision-making units (DMUs). Although both models have been widely applied in practice, there is a paucity of research examining the performance of the two models in ranking efficient DMUs. Consequently, it is unclear how close the rankings obtained by the two models are to the "true" ones. Among the very few studies, Banker et al. (Ann Oper Res 250(1): 21-35, 2017) pointed out that the ranking performance of the AP model is unsatisfactory; Li et al. (Eur J Oper Res 255(3): 884-892, 2016) and Hinojosa et al. (Exp Syst Appl 80(9): 273-283, 2017) demonstrated the L-L model's capability of ranking efficient DMUs without addressing the ranking performance. In this study, we, thus, examine the ranking performance of the two super-efficiency models. In evaluating their performance, we carry out Monte Carlo simulations based on the well-known Cobb-Douglas production function and adopt Kendall rank correlation coefficient. Unlike Banker et al. (Ann Oper Res 250(1): 21-35, 2017), we use the rankings obtained based on the two models and the "true" ones as the basis of performance evaluation in our simulations. Moreover, we consider several types of returns to scale (RS) and study the impact of changes of some parameters on the ranking performance. In view of the importance, we also carry out additional simulations to examine the influence of technical inefficiency on the two models' ranking performance. Based on the simulation results, we conclude: (1) Under different RS, the ranking performance of the two models remains the same when changing parameters, e.g., the distribution of input variables; (2) Under different RS, when technical inefficiency (in comparison with random noise) is more important, the two models have satisfactory performance by providing rankings that are close to, or the same as, the "true" ones; (3) The L-L model has better performance than the AP model and is more robust. This is especially true when technical inefficiency is less important; (4) Under different RS, when technical inefficiency is less important, both models have unsatisfactory ranking performance; and (5) The relative importance of technical inefficiency plays an prominent role in ranking efficient DMUs.
Keyword ：
Returns to scale Returns to scale DEA DEA Ranking Ranking Monte Carlo Monte Carlo Super efficiency Super efficiency Technical inefficiency Technical inefficiency
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| GB/T 7714 | Xie, Qiwei , Zhang, Linda L. , Shang, Haichao et al. Evaluating performance of super-efficiency models in ranking efficient decision-making units based on Monte Carlo simulations [J]. | ANNALS OF OPERATIONS RESEARCH , 2021 , 305 (1-2) : 273-323 . |
| MLA | Xie, Qiwei et al. "Evaluating performance of super-efficiency models in ranking efficient decision-making units based on Monte Carlo simulations" . | ANNALS OF OPERATIONS RESEARCH 305 . 1-2 (2021) : 273-323 . |
| APA | Xie, Qiwei , Zhang, Linda L. , Shang, Haichao , Emrouznejad, Ali , Li, Yongjun . Evaluating performance of super-efficiency models in ranking efficient decision-making units based on Monte Carlo simulations . | ANNALS OF OPERATIONS RESEARCH , 2021 , 305 (1-2) , 273-323 . |
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Abstract ：
The extant literature mainly utilized the wavelet tools and EMD-type methods to investigate linkages between different markets based on the frequency-domain information, confronting the difficulties of the wavelet basis selections and scale aliasing phenomenon. To overcome these disadvantages, the present study proposes a BEKK-GARCH-AFD approach based on the adaptive-Fourier-decomposition (AFD) to reveal the linkages between the international crude oil market and the Chinese stock market. According to the spillover effect between markets revealed by BEKK-GARCH, the proposed approach could further disclose the linkages between markets under external shocks with high-resolution information concerning market fluctuations provided by the AFD. Our empirical results demonstrate that the oil supply and demand shocks caused by external events (e.g., the strikes, the geopolitics, and the natural disasters) will put pressure on the Chinese stock market, while the combination of bullish and bearish events (e.g., the reduction of crude oil production and the shale oil boom) contributes to stabilizing the stock market.
Keyword ：
Adaptive-Fourier-decomposition (AFD) Adaptive-Fourier-decomposition (AFD) Oil and stock markets Oil and stock markets Time-frequency domain Time-frequency domain BEKK-GARCH BEKK-GARCH Structural breaks Structural breaks
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| GB/T 7714 | Xie, Qiwei , Liu, Ranran , Qian, Tao et al. Linkages between the international crude oil market and the Chinese stock market: A BEKK-GARCH-AFD approach [J]. | ENERGY ECONOMICS , 2021 , 102 . |
| MLA | Xie, Qiwei et al. "Linkages between the international crude oil market and the Chinese stock market: A BEKK-GARCH-AFD approach" . | ENERGY ECONOMICS 102 (2021) . |
| APA | Xie, Qiwei , Liu, Ranran , Qian, Tao , Li, Jingyu . Linkages between the international crude oil market and the Chinese stock market: A BEKK-GARCH-AFD approach . | ENERGY ECONOMICS , 2021 , 102 . |
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Abstract ：
The majority of data envelopment analysis (DEA) models can be linearized via the classical Charnes-Cooper transformation. Nevertheless, this transformation does not apply to sum-of-fractional DEA efficiencies models, such as the secondary goal I (SG-I) cross efficiency model and the arithmetic mean two-stage network DEA model. To solve a sum-of-fractional DEA efficiencies model, we convert it into bilinear programming. Then, the obtained bilinear programming is relaxed to mixed-integer linear programming (MILP) by using a multiparametric disaggregation technique. We reveal the hidden mathematical structures of sum-of-fractional DEA efficiencies models, and propose corresponding discretization strategies to make the models more easily to be solved. Discretization of the multipliers of inputs or the DEA efficiencies in the objective function depends on the number of multipliers and decision-making units. The obtained MILP provides an upper bound for the solution and can be tightened as desired by adding binary variables. Finally, an algorithm based on MILP is developed to search for the global optimal solution. The effectiveness of the proposed method is verified by using it to solve the SG-I cross efficiency model and the arithmetic mean two-stage network DEA model. Results of the numerical applications show that the proposed approach can solve the SG-I cross efficiency model with 100 decision-making units, 3 inputs, and 3 outputs in 329.6 s. Moreover, the proposed approach obtains more accurate solutions in less time than the heuristic search procedure when solving the arithmetic mean two-stage network DEA model.
Keyword ：
Fractional programming Fractional programming Global optimal solution Global optimal solution Mixed-integer linear programming Mixed-integer linear programming Data envelopment analysis Data envelopment analysis
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| GB/T 7714 | Xie, Jianhui , Xie, Qiwei , Li, Yongjun et al. Solving data envelopment analysis models with sum-of-fractional objectives: a global optimal approach based on the multiparametric disaggregation technique [J]. | ANNALS OF OPERATIONS RESEARCH , 2021 , 304 (1-2) : 453-480 . |
| MLA | Xie, Jianhui et al. "Solving data envelopment analysis models with sum-of-fractional objectives: a global optimal approach based on the multiparametric disaggregation technique" . | ANNALS OF OPERATIONS RESEARCH 304 . 1-2 (2021) : 453-480 . |
| APA | Xie, Jianhui , Xie, Qiwei , Li, Yongjun , Liang, Liang . Solving data envelopment analysis models with sum-of-fractional objectives: a global optimal approach based on the multiparametric disaggregation technique . | ANNALS OF OPERATIONS RESEARCH , 2021 , 304 (1-2) , 453-480 . |
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Abstract ：
Electron microscopy has become the most important technique in the field of connectomics. Several methods have been proposed in the literature to tackle the problem of dense reconstruction. However, sparse reconstruction, which is a promising technique, has not been extensively studied. As a result, we develop an AI integrated system for sparse reconstruction that can automatically trace neurons with only the initial seeded masks. First, as an important part of the system for interlayer information estimation, convolutional LSTMs are employed to estimate the spatial contexts between adjacent sections. Then, the intra-slice information is obtained by a lightweight U-Net. Moreover, we employ a novel recursive training method that can significantly improve the performance. To reduce the tracing errors caused by misalignments in large-scale data, we integrate a shift estimation and correction module that effectively improves the traced neuron length. To the best of our knowledge, this is the first attempt to apply a recurrent neural network to the task of neuron tracing. In addition, our approach performs better than other state-of-the-art methods on two highly anisotropic datasets.
Keyword ：
Deep learning Deep learning Neuron tracing Neuron tracing Convolutional LSTM Convolutional LSTM Electron microscopy Electron microscopy
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| GB/T 7714 | Liu, Jing , Hong, Bei , Chen, Xi et al. An effective AI integrated system for neuron tracing on anisotropic electron microscopy volume [J]. | BIOMEDICAL SIGNAL PROCESSING AND CONTROL , 2021 , 69 . |
| MLA | Liu, Jing et al. "An effective AI integrated system for neuron tracing on anisotropic electron microscopy volume" . | BIOMEDICAL SIGNAL PROCESSING AND CONTROL 69 (2021) . |
| APA | Liu, Jing , Hong, Bei , Chen, Xi , Xie, Qiwei , Tang, Yuanyan , Han, Hua . An effective AI integrated system for neuron tracing on anisotropic electron microscopy volume . | BIOMEDICAL SIGNAL PROCESSING AND CONTROL , 2021 , 69 . |
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