Image Encryption Based on Dynamic Filtering and Bit Cuboid Operations

Li, Xinsheng; Xie, Zhen; Wu, Jihuai; Li, Taiyong

doi:10.1155/2019/7485621

Cited by 34 publications

(42 citation statements)

References 45 publications

(66 reference statements)

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“…Our proposed ICEEMDAN-SCA-RVFL uses the framework of "divide and conquer" that was widely used in image processing, fault diagnosis, and so on [47][48][49][50][51]. Firstly, ICEEMDAN is used to divide the nonlinear and nonstationary series of crude oil prices into a group of relatively simple subseries, including several IMFs and one residue.…”

Section: Iceemdan-sca-rvfl: the Proposed Approach For Crude Oil Pricementioning

confidence: 99%

Daily Crude Oil Price Forecasting Based on Improved CEEMDAN, SCA, and RVFL: A Case Study in WTI Oil Market

Miu

2020

Energies

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Crude oil is one of the strategic energies and plays an increasingly critical role effecting on the world economic development. The fluctuations of crude oil prices are caused by various extrinsic and intrinsic factors and usually demonstrate complex characteristics. Therefore, it is a great challenge for accurately forecasting crude oil prices. In this study, a self-optimizing ensemble learning model incorporating the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), sine cosine algorithm (SCA), and random vector functional link (RVFL) neural network, namely ICEEMDAN-SCA-RVFL, is proposed to forecast crude oil prices. Firstly, we employ ICEEMDAN to decompose the raw series of crude oil prices into a group of relatively simple subseries. Secondly, RVFL is used to forecast the target values for each decomposed subseries individually. Due to the complex parameter settings of ICEEMDAN and RVFL, SCA is introduced to optimize the parameters for ICEEMDAN and RVFL in the above decomposition and prediction stages simultaneously. Finally, we assemble the predicted values of all individual subseries as the final predicted values of crude oil prices. Our proposed ICEEMDAN-SCA-RVFL significantly outperforms the single and ensemble benchmark models, as demonstrated by a case study conducted using the time series of West Texas Intermediate (WTI) daily crude oil spot prices.

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Section: Iceemdan-sca-rvfl: the Proposed Approach For Crude Oil Pricementioning

confidence: 99%

Daily Crude Oil Price Forecasting Based on Improved CEEMDAN, SCA, and RVFL: A Case Study in WTI Oil Market

Miu

2020

Energies

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“…First, we set the number of machines to two or four, and the capacity of each machine is represented by a uniform integer [10,40]. Then, random problem instances with number of jobs equals to 10, 20, 50, 100, 200, and 300 are generated, and each job processing time P j is generated by random sampling from a uniform distribution [1,10]. The factor settings of the experiment are summarized in Table 4.…”

Section: Experimental Environmentmentioning

confidence: 99%

“…The job size and machine capacity distribution as shown in Figure 2. Processing time of jobs (P) [1,10] Capacity of machines (K) [10,40] We combine the parameters and randomly generate 50 instances for each combination (a test suite). Each test suite is denoted by a code.…”

Section: Comparison Of 45-approximation Algorithm and Cplexmentioning

confidence: 99%

“…The longest processing time of all jobs in a batch determines the processing time of a batch. The purpose of this problem is to allot each job to a batch and to schedule the batch on the machine to minimize the maximum completion time of the schedule, C max = max j C j , where C j represents the completion time of job j in the schedule [10,11]. Using the notations proposed in [12,13], this problem can be denoted as P s j , p − batch, K i C max .…”

Section: Introductionmentioning

confidence: 99%

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A Novel Fast Parallel Batch Scheduling Algorithm for Solving the Independent Job Problem

Zhang

Song

et al. 2020

Applied Sciences

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With the rapid economic development, manufacturing enterprises are increasingly using an efficient workshop production scheduling system in an attempt to enhance their competitive position. The classical workshop production scheduling problem is far from the actual production situation, so it is difficult to apply it to production practice. In recent years, the research on machine scheduling has become a hot topic in the fields of manufacturing systems. This paper considers the batch processing machine (BPM) scheduling problem for scheduling independent jobs with arbitrary sizes. A novel fast parallel batch scheduling algorithm is put forward to minimize the makespan in this paper. Each of the machines with different capacities can only handle jobs with sizes less than the capacity of the machine. Multiple jobs can be processed as a batch simultaneously on one machine only if their total size does not exceed the machine capacity. The processing time of a batch is determined by the longest of all the jobs processed in the batch. A novel and fast 4.5-approximation algorithm is developed for the above scheduling problem. For the special case of all the jobs having the same processing times, a simple and fast 2-approximation algorithm is achieved. The experimental results show that fast algorithms further improve the competitive ratio. Compared to the optimal solutions generated by CPLEX, fast algorithms are capable of generating a feasible solution within a very short time. Fast algorithms have less computational costs.

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“…e main idea of this framework is to decompose the raw energy data series into several simpler components, then handle each component individually, and finally integrate the result from each component as the final forecasting result. is framework is a typical form of the strategy of "divide and conquer" that is widely used in energy price forecasting [22][23][24], wind speed forecasting [25,26], load forecasting [27,28], biosignal processing [29,30], fault diagnosis [31], image processing [32][33][34][35], and so on. ere are many types of decomposition methods that can be applied to decomposing energy time series.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Load Forecasting with Improved CEEMDAN and GWO-Based Multiple Kernel ELM

Qian

He³

2020

Complexity

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Short-term load forecasting (STLF) is an essential and challenging task for power- or energy-providing companies. Recent research has demonstrated that a framework called “decomposition and ensemble” is very powerful for energy forecasting. To improve the effectiveness of STLF, this paper proposes a novel approach integrating the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), grey wolf optimization (GWO), and multiple kernel extreme learning machine (MKELM), namely, ICEEMDAN-GWO-MKELM, for STLF, following this framework. The proposed ICEEMDAN-GWO-MKELM consists of three stages. First, the complex raw load data are decomposed into a couple of relatively simple components by ICEEMDAN. Second, MKELM is used to forecast each decomposed component individually. Specifically, we use GWO to optimize both the weight and the parameters of every single kernel in extreme learning machine to improve the forecasting ability. Finally, the results of all the components are aggregated as the final forecasting result. The extensive experiments reveal that the ICEEMDAN-GWO-MKELM can outperform several state-of-the-art forecasting approaches in terms of some evaluation criteria, showing that the ICEEMDAN-GWO-MKELM is very effective for STLF.

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Image Encryption Based on Dynamic Filtering and Bit Cuboid Operations

Cited by 34 publications

References 45 publications

Daily Crude Oil Price Forecasting Based on Improved CEEMDAN, SCA, and RVFL: A Case Study in WTI Oil Market

Daily Crude Oil Price Forecasting Based on Improved CEEMDAN, SCA, and RVFL: A Case Study in WTI Oil Market

A Novel Fast Parallel Batch Scheduling Algorithm for Solving the Independent Job Problem

Short-Term Load Forecasting with Improved CEEMDAN and GWO-Based Multiple Kernel ELM

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