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学者姓名：毕敬
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Abstract ：
Accurately identifying network intrusion cannot only help individuals and enterprises better deal with network security problems, but also maintain the Internet environment. This work proposes a new hybrid classification method named SABD for network intrusion detection. SABD integrates Stacked sparse contractive autoencoders (SSCA), Attention-based Bidirectional long-term and short-term memory (LSTM), and Decision fusion. Specifically, SSCA is used for extracting features, which are sent to the attention-based bidirectional LSTM for the classification. Besides, an improved optimization algorithm named genetic simulated-annealing-based particle swarm optimization is designed to optimize hyperparameters of SSCA. Finally, the decision fusion algorithm is adopted to integrate classification results of multiple classifiers and yield the final results. Based on experimental results from four different types of data sets, the proposed SABD outperforms its most advanced peers in classification accuracy.
Keyword ：
Network intrusion detection Network intrusion detection Decision fusion Decision fusion Autoencoders Autoencoders Long-term and short-term memory Long-term and short-term memory Feature extraction Feature extraction
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| GB/T 7714 | Bi, Jing , Guan, Ziyue , Yuan, Haitao et al. Improved network intrusion classification with attention-assisted bidirectional LSTM and optimized sparse contractive autoencoders [J]. | EXPERT SYSTEMS WITH APPLICATIONS , 2023 , 244 . |
| MLA | Bi, Jing et al. "Improved network intrusion classification with attention-assisted bidirectional LSTM and optimized sparse contractive autoencoders" . | EXPERT SYSTEMS WITH APPLICATIONS 244 (2023) . |
| APA | Bi, Jing , Guan, Ziyue , Yuan, Haitao , Zhang, Jia . Improved network intrusion classification with attention-assisted bidirectional LSTM and optimized sparse contractive autoencoders . | EXPERT SYSTEMS WITH APPLICATIONS , 2023 , 244 . |
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Abstract ：
Traditional path planning algorithms for mobile robots are not effective to solve high-dimensional problems, and suffer from slow convergence and complex modelling. Therefore, it is highly essential to design a more efficient algorithm to realize intelligent path planning of mobile robots. This work proposes an improved path planning algorithm, which is based on the algorithm of Soft Actor-Critic (SAC). It attempts to solve a problem of poor robot performance in complicated environments with static and dynamic obstacles. This work designs an improved reward function to enable mobile robots to quickly avoid obstacles and reach targets by using state dynamic normalization and priority replay buffer techniques. To evaluate its performance, a Pygame-based simulation environment is constructed. The proposed method is compared with a Proximal Policy Optimization (PPO) algorithm in the simulation environment. Experimental results demonstrate that the cumulative reward of the proposed method is much higher than that of PPO, and it is also more robust than PPO. Copyright (C) 2022 The Authors.
Keyword ：
Deep reinforcement learning Deep reinforcement learning path planning path planning Soft Actor-Critic algorithm Soft Actor-Critic algorithm mobile robots mobile robots continuous reward functions continuous reward functions
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| GB/T 7714 | Yang, Laiyi , Bi, Jing , Yuan, Haitao . Dynamic Path Planning for Mobile Robots with Deep Reinforcement Learning [J]. | IFAC PAPERSONLINE , 2022 , 55 (11) : 19-24 . |
| MLA | Yang, Laiyi et al. "Dynamic Path Planning for Mobile Robots with Deep Reinforcement Learning" . | IFAC PAPERSONLINE 55 . 11 (2022) : 19-24 . |
| APA | Yang, Laiyi , Bi, Jing , Yuan, Haitao . Dynamic Path Planning for Mobile Robots with Deep Reinforcement Learning . | IFAC PAPERSONLINE , 2022 , 55 (11) , 19-24 . |
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Abstract ：
Infrastructure in Distributed Green Data Centers (DGDCs) is concurrently shared by multiple different applications to flexibly provide a growing number of services to global users in a cost-effective way. A highly challenging problem is how to maximize the total profit of the DGDC provider in a market where Internet Service Provider (ISP) bandwidth price, availability of green energy, price of power grid, and revenue brought by the execution of tasks all vary with geographical locations. Unlike existing studies, this article proposes a Geography-Aware Task Scheduling (GATS) approach by considering spatial variations in DGDCs to maximize the total profit of the DGDC provider by intelligently scheduling tasks of all applications. In each time slot, the formulated profit maximization problem is solved as a convex optimization one via the interior point method. Trace-driven simulations show that GATS achieves larger total profit and higher throughput than two typical task scheduling approaches.
Keyword ：
Green data centers Green data centers profit maximization profit maximization task scheduling task scheduling convex optimization convex optimization distributed computing distributed computing
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| GB/T 7714 | Yuan, Haitao , Bi, Jing , Zhou, MengChu . Geography-Aware Task Scheduling for Profit Maximization in Distributed Green Data Centers [J]. | IEEE TRANSACTIONS ON CLOUD COMPUTING , 2022 , 10 (3) : 1864-1874 . |
| MLA | Yuan, Haitao et al. "Geography-Aware Task Scheduling for Profit Maximization in Distributed Green Data Centers" . | IEEE TRANSACTIONS ON CLOUD COMPUTING 10 . 3 (2022) : 1864-1874 . |
| APA | Yuan, Haitao , Bi, Jing , Zhou, MengChu . Geography-Aware Task Scheduling for Profit Maximization in Distributed Green Data Centers . | IEEE TRANSACTIONS ON CLOUD COMPUTING , 2022 , 10 (3) , 1864-1874 . |
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Abstract ：
Swarm intelligence in a bat algorithm (BA) provides social learning. Genetic operations for reproducing individuals in a genetic algorithm (GA) offer global search ability in solving complex optimization problems. Their integration provides an opportunity for improved search performance. However, existing studies adopt only one genetic operation of GA, or design hybrid algorithms that divide the overall population into multiple subpopulations that evolve in parallel with limited interactions only. Differing from them, this work proposes an improved self-adaptive bat algorithm with genetic operations (SBAGO) where GA and BA are combined in a highly integrated way. Specifically, SBAGO performs their genetic operations of GA on previous search information of BA solutions to produce new exemplars that are of high-diversity and high-quality. Guided by these exemplars, SBAGO improves both BA's efficiency and global search capability. We evaluate this approach by using 29 widely-adopted problems from four test suites. SBAGO is also evaluated by a real-life optimization problem in mobile edge computing systems. Experimental results show that SBAGO outperforms its widely-used and recently proposed peers in terms of effectiveness, search accuracy, local optima avoidance, and robustness.
Keyword ：
meta-heuristic optimization algorithms meta-heuristic optimization algorithms genetic algorithm (GA) genetic algorithm (GA) learning mechanism learning mechanism Bat algorithm (BA) Bat algorithm (BA) hybrid algorithm hybrid algorithm
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| GB/T 7714 | Bi, Jing , Yuan, Haitao , Zhai, Jiahui et al. Self-adaptive Bat Algorithm With Genetic Operations [J]. | IEEE-CAA JOURNAL OF AUTOMATICA SINICA , 2022 , 9 (7) : 1284-1294 . |
| MLA | Bi, Jing et al. "Self-adaptive Bat Algorithm With Genetic Operations" . | IEEE-CAA JOURNAL OF AUTOMATICA SINICA 9 . 7 (2022) : 1284-1294 . |
| APA | Bi, Jing , Yuan, Haitao , Zhai, Jiahui , Zhou, MengChu , Poor, H. Vincent . Self-adaptive Bat Algorithm With Genetic Operations . | IEEE-CAA JOURNAL OF AUTOMATICA SINICA , 2022 , 9 (7) , 1284-1294 . |
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Abstract ：
The enormous energy consumed by clouds becomes a significant challenge for cloud providers and smart grid operators. Due to performance concerns, applications typically run in different clouds located in multiple sites. In different clouds, many factors, including electricity prices, available servers, and task service rates, exhibit spatial variations. Therefore, it is important to manage and schedule tasks among multiple clouds in a high-quality-of-service and low-energy-cost manner. This work proposes a task scheduling method to jointly minimize energy cost and average task loss possibility (ATLP) of clouds. A problem is formulated and tackled with an adaptive biobjective differential evolution based on simulated annealing to determine a real-time and near-optimal set of solutions. A final knee solution is further chosen to specify suitable servers in clouds and task allocation among web portals. Simulation results based on realistic data prove that less average loss possibility of tasks, and smaller energy cost is obtained with it than its widely used peers. Note to Practitioners-This work considers joint optimization of both ATLP and average energy cost of all clouds. It is of great significance to execute tasks among multiple clouds by jointly allocating all tasks among multiple web portals and specifying suitable servers in different clouds. Yet, it is challenging to achieve joint optimization in a market where factors, including prices of electricity and available servers, show spatial variations. Current studies are coarse-grained and fail to jointly achieve average energy cost minimization and quality-of-service optimization of tasks. In this work, a novel algorithm named adaptive simulated-annealing-based biobjective differential evolution is proposed for an energy cost and quality-of-service-optimized task scheduling strategy in a real-time manner. Experiments prove that it realizes lower energy cost and ATLP compared with its typical widely used peers. It can also be applied to other industrial areas, including smart manufacturing, Internet of Things, and smart city.
Keyword ：
Minimization Minimization smart grid smart grid electricity market electricity market Data centers Data centers task scheduling task scheduling multiobjective optimization multiobjective optimization Quality of service Quality of service Portals Portals Servers Servers Task analysis Task analysis Cloud computing Cloud computing
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| GB/T 7714 | Yuan, Haitao , Bi, Jing , Zhou, MengChu . Energy-Efficient and QoS-Optimized Adaptive Task Scheduling and Management in Clouds [J]. | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING , 2022 , 19 (2) : 1233-1244 . |
| MLA | Yuan, Haitao et al. "Energy-Efficient and QoS-Optimized Adaptive Task Scheduling and Management in Clouds" . | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 19 . 2 (2022) : 1233-1244 . |
| APA | Yuan, Haitao , Bi, Jing , Zhou, MengChu . Energy-Efficient and QoS-Optimized Adaptive Task Scheduling and Management in Clouds . | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING , 2022 , 19 (2) , 1233-1244 . |
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Abstract ：
Current large-scale green cloud data centers (GCDCs) tend to consume a huge amount of energy and generate enormous carbon emissions. Existing studies have tried to solve this problem by either realizing prediction of green energy, or optimizing task scheduling. In contrast, this work seamlessly combines green energy prediction and task scheduling to jointly optimize revenue and energy cost of GCDCs. Specifically, this work designs a prediction method, named Savitzky-Golay and Long Short-Term Memory network (SG-LSTM), to realize noise filtering and forecast green energy. Based on such prediction, a bi-objective optimization method, named Decomposition-based Multi-objective evolutionary algorithm with Gaussian mutation and Crowding distance (DMGC), is developed to optimize the revenue and energy cost of GCDCs. Its performance is demonstrated over real-life datasets including Google cluster traces, wind speeds, solar irradiance and prices of electricity. Experimental results show that SG-LSTM outperforms its two peers, back propagation neural network and gated recurrent unit, in terms of root mean square errors and mean absolute errors. In addition, DMGC surpasses its such peers as NSGA-II, SPEA2, and MOEA/D in terms of revenue, energy cost and average execution time. Particularly, DMGC's revenue is 18%, 20% and 13.1% higher, energy cost is 16%, 19.8% and 15.2% lower, and average execution time is 60.02%, 38.47% and 24.17% lower than those of NSGA-II, SPEA2, and MOEA/D, respectively.
Keyword ：
recurrent neural network recurrent neural network Green products Green products Cloud computing Cloud computing Scheduling Scheduling intelligent optimization intelligent optimization machine learning machine learning task scheduling task scheduling Optimization Optimization Time series analysis Time series analysis multi-objective optimization algorithms multi-objective optimization algorithms Task analysis Task analysis Costs Costs Savitzky-Golay filter Savitzky-Golay filter Green clouds Green clouds
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| GB/T 7714 | Bi, Jing , Yuan, Haitao , Zhang, Jia et al. Green Energy Forecast-Based Bi-Objective Scheduling of Tasks Across Distributed Clouds [J]. | IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING , 2022 , 7 (3) : 619-630 . |
| MLA | Bi, Jing et al. "Green Energy Forecast-Based Bi-Objective Scheduling of Tasks Across Distributed Clouds" . | IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING 7 . 3 (2022) : 619-630 . |
| APA | Bi, Jing , Yuan, Haitao , Zhang, Jia , Zhou, MengChu . Green Energy Forecast-Based Bi-Objective Scheduling of Tasks Across Distributed Clouds . | IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING , 2022 , 7 (3) , 619-630 . |
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Abstract ：
In recent years, cloud computing and big data services are widely adopted by large-scale enterprises. The energy consumption of cloud data centers (CDCs) has also increased dramatically. To effectively reduce the harm on the environment, a growing number of CDCs consider renewable energy instead of fossil energy, and concentrate on reducing idle time of servers by forecasting short-term workload demands for proactively provisioning computational resources and balancing server load in advance. However, due to temporal fluctuation in workload demands and renewable energy, it is a huge challenge to precisely predict their short-term trends. This work adopts basic methods in the field of signal processing and proposes a time series prediction method based on multi-scale wavelet transformation. Extensive experiments based on real-life datasets demonstrate that the proposed method achieves higher accuracy than several typical baseline methods.
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| GB/T 7714 | Bi, Jing , Zhang, Kaiyi , Yuan, Haitao . Workload and Renewable Energy Prediction in Cloud Data Centers with Multi-scale Wavelet Transformation [J]. | 2021 29TH MEDITERRANEAN CONFERENCE ON CONTROL AND AUTOMATION (MED) , 2021 : 506-511 . |
| MLA | Bi, Jing et al. "Workload and Renewable Energy Prediction in Cloud Data Centers with Multi-scale Wavelet Transformation" . | 2021 29TH MEDITERRANEAN CONFERENCE ON CONTROL AND AUTOMATION (MED) (2021) : 506-511 . |
| APA | Bi, Jing , Zhang, Kaiyi , Yuan, Haitao . Workload and Renewable Energy Prediction in Cloud Data Centers with Multi-scale Wavelet Transformation . | 2021 29TH MEDITERRANEAN CONFERENCE ON CONTROL AND AUTOMATION (MED) , 2021 , 506-511 . |
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Abstract ：
Accurate and real-time prediction of network traffic can not only help system operators allocate resources rationally according to their actual business needs but also help them assess the performance of a network and analyze its health status. In recent years, neural networks have been proved suitable to predict time series data, represented by the model of a long short-term memory (LSTM) neural network and a temporal convolutional network (TCN). This article proposes a novel hybrid prediction method named SG and TCN-based LSTM (ST-LSTM) for such network traffic prediction, which synergistically combines the power of the Savitzky-Golay (SG) filter, the TCN, as well as the LSTM. ST-LSTM employs a three-phase end-to-end methodology serving time series prediction. It first eliminates noise in raw data using the SG filter, then extracts short-term features from sequences applying the TCN, and then captures the long-term dependence in the data exploiting the LSTM. Experimental results over real-world datasets demonstrate that the proposed ST-LSTM outperforms state-of-the-art algorithms in terms of prediction accuracy.
Keyword ：
machine learning machine learning Load modeling Load modeling temporal convolutional network (TCN) temporal convolutional network (TCN) Savitzky-Golay (SG) filter Savitzky-Golay (SG) filter Predictive models Predictive models Deep learning Deep learning Long short-term memory (LSTM) Long short-term memory (LSTM) Feature extraction Feature extraction Time series analysis Time series analysis network traffic prediction network traffic prediction Task analysis Task analysis Support vector machines Support vector machines
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| GB/T 7714 | Bi, Jing , Zhang, Xiang , Yuan, Haitao et al. A Hybrid Prediction Method for Realistic Network Traffic With Temporal Convolutional Network and LSTM [J]. | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING , 2021 , 19 (3) : 1869-1879 . |
| MLA | Bi, Jing et al. "A Hybrid Prediction Method for Realistic Network Traffic With Temporal Convolutional Network and LSTM" . | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 19 . 3 (2021) : 1869-1879 . |
| APA | Bi, Jing , Zhang, Xiang , Yuan, Haitao , Zhang, Jia , Zhou, MengChu . A Hybrid Prediction Method for Realistic Network Traffic With Temporal Convolutional Network and LSTM . | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING , 2021 , 19 (3) , 1869-1879 . |
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Abstract ：
The industry of data centers is the fifth largest energy consumer in the world. Distributed green data centers (DGDCs) consume 300 billion kWh per year to provide different types of heterogeneous services to global users. Users around the world bring revenue to DGDC providers according to actual quality of service (QoS) of their tasks. Their tasks are delivered to DGDCs through multiple Internet service providers (ISPs) with different bandwidth capacities and unit bandwidth price. In addition, prices of power grid, wind, and solar energy in different GDCs vary with their geographical locations. Therefore, it is highly challenging to schedule tasks among DGDCs in a high-profit and high-QoS way. This work designs a multiobjective optimization method for DGDCs to maximize the profit of DGDC providers and minimize the average task loss possibility of all applications by jointly determining the split of tasks among multiple ISPs and task service rates of each GDC. A problem is formulated and solved with a simulated-annealing-based biobjective differential evolution (SBDE) algorithm to obtain an approximate Pareto-optimal set. The method of minimum Manhattan distance is adopted to select a knee solution that specifies the Pareto-optimal task service rates and task split among ISPs for DGDCs in each time slot. Real-life data-based experiments demonstrate that the proposed method achieves lower task loss of all applications and larger profit than several existing scheduling algorithms. Note to Practitioners-This work aims to maximize the profit and minimize the task loss for DGDCs powered by renewable energy and smart grid by jointly determining the split of tasks among multiple ISPs. Existing task scheduling algorithms fail to jointly consider and optimize the profit of DGDC providers and QoS of tasks. Therefore, they fail to intelligently schedule tasks of heterogeneous applications and allocate infrastructure resources within their response time bounds. In this work, a new method that tackles drawbacks of existing algorithms is proposed. It is achieved by adopting the proposed SBDE algorithm that solves a multiobjective optimization problem. Simulation experiments demonstrate that compared with three typical task scheduling approaches, it increases profit and decreases task loss. It can be readily and easily integrated and implemented in real-life industrial DGDCs. The future work needs to investigate the real-time green energy prediction with historical data and further combine prediction and task scheduling together to achieve greener and even net-zero-energy data centers.
Keyword ：
quality of service (QoS) quality of service (QoS) task scheduling task scheduling Cloud computing Cloud computing Time factors Time factors Cloud data centers Cloud data centers simulated annealing (SA) simulated annealing (SA) Data centers Data centers Green products Green products Quality of service Quality of service multiobjective differential evolution (DE) multiobjective differential evolution (DE) green computing green computing Task analysis Task analysis Optimization Optimization
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| GB/T 7714 | Yuan, Haitao , Bi, Jing , Zhou, MengChu et al. Biobjective Task Scheduling for Distributed Green Data Centers [J]. | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING , 2021 , 18 (2) : 731-742 . |
| MLA | Yuan, Haitao et al. "Biobjective Task Scheduling for Distributed Green Data Centers" . | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 18 . 2 (2021) : 731-742 . |
| APA | Yuan, Haitao , Bi, Jing , Zhou, MengChu , Liu, Qing , Ammari, Ahmed Chiheb . Biobjective Task Scheduling for Distributed Green Data Centers . | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING , 2021 , 18 (2) , 731-742 . |
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Abstract ：
The significant growth in the number and types of tasks of heterogeneous applications in green cloud data centers (GCDCs) dramatically increases their providers' revenue from users as well as energy consumption. It is a big challenge to maximize such revenue, while minimizing energy cost in a market where prices of electricity, availability of renewable power generation, and behind-The-meter renewable generation contract models differ among the geographical sites of the GCDCs. A multiobjective optimization method that investigates such spatial differences in the GCDCs is for the first time proposed to trade off such two objectives by cost-effectively executing all tasks while meeting their delay constraints. In each time slot, a constrained biobjective optimization problem is formulated and solved by an improved multiobjective evolutionary algorithm based on decomposition. Realistic data-based simulations prove that the proposed method achieves a larger total profit in faster convergence speed than the two state-of-The-Art algorithms. Note to Practitioners-This article considers the tradeoff between profit maximization and energy cost minimization for the green cloud data center (GCDC) providers while meeting the delay constraints of all tasks. Current task-scheduling methods fail to take the advantage of spatial variations in many factors, e.g., prices of electricity and availability of renewable power generation at geographically distributed GCDC locations. As a result, they fail to execute all tasks of heterogeneous applications within their delay bounds in a high-revenue and low-energy-cost manner. In this article, a multiobjective optimization method that addresses the disadvantages of the existing methods is proposed. It is realized by a proposed intelligent optimization algorithm. Simulations demonstrate that in comparison with the two state-of-The-Art scheduling algorithms, the proposed one increases the profit and reduces the convergence time. It can be readily implemented and integrated into actual industrial GCDCs. © 2004-2012 IEEE.
Keyword ：
Multitasking Multitasking Energy utilization Energy utilization Green computing Green computing Evolutionary algorithms Evolutionary algorithms Costs Costs Profitability Profitability Economic and social effects Economic and social effects Multiobjective optimization Multiobjective optimization Constrained optimization Constrained optimization
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| GB/T 7714 | Yuan, Haitao , Liu, Heng , Bi, Jing et al. Revenue and Energy Cost-Optimized Biobjective Task Scheduling for Green Cloud Data Centers [J]. | IEEE Transactions on Automation Science and Engineering , 2021 , 18 (2) : 817-830 . |
| MLA | Yuan, Haitao et al. "Revenue and Energy Cost-Optimized Biobjective Task Scheduling for Green Cloud Data Centers" . | IEEE Transactions on Automation Science and Engineering 18 . 2 (2021) : 817-830 . |
| APA | Yuan, Haitao , Liu, Heng , Bi, Jing , Zhou, Mengchu . Revenue and Energy Cost-Optimized Biobjective Task Scheduling for Green Cloud Data Centers . | IEEE Transactions on Automation Science and Engineering , 2021 , 18 (2) , 817-830 . |
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