The modular multilevel converter has gained popularity in various applications, including photovoltaic (PV) solar energy conversion. Its modular structure allows for the transformation of an MMC into an MMC-based phot...
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The modular multilevel converter has gained popularity in various applications, including photovoltaic (PV) solar energy conversion. Its modular structure allows for the transformation of an MMC into an MMC-based photovoltaic system, sharing key operational characteristics such as modularity, flexibility, redundancy, increased efficiency, and fault tolerance. To ensure the reliability and uninterrupted operation of the modified MMC, even in the event of potential failures in the photovoltaic submodules (PVSMs), a fault-tolerant strategy is developed in this study. It assumes that the Maximum Power Point Tracking (MPPT) of the PVSMs is already guaranteed. Redundant submodules (rSM) are utilized to maintain power balance between the converter arms through voltage control, while reserve submodules (RSMs) are in place to rescue the converter in case of a failure. The detection and localization of faults in the PVSMs/rSMs are achieved through sliding mode observers (SMOs), and the converter reconfiguration is carried out using the proposed permutation algorithms for switching signals and SMs voltages. For precise control of the output current and electrical grid connection, the $dq$ -reference frame control method is employed. To validate these proposed algorithms, time-domain simulations are conducted using the Simulink/Matlab software.
BackgroundDue to the scalability of deep learning technology, researchers have applied it to the non-destructive testing of peach internal quality. In addition, the soluble solids content (SSC) is an important interna...
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BackgroundDue to the scalability of deep learning technology, researchers have applied it to the non-destructive testing of peach internal quality. In addition, the soluble solids content (SSC) is an important internal quality indicator that determines the quality of peaches. Peaches with high SSC have a sweeter taste and better texture, making them popular in the market. Therefore, SSC is an important indicator for measuring peach internal quality and making harvesting *** article presents the High Order Spatial Interaction Network (HOSINet), which combines the Position Attention Module (PAM) and Channel Attention Module (CAM). Additionally, a feature wavelength selection algorithm similar to the Group-based Clustering Subspace Representation (GCSR-C) is used to establish the Position and Channel Attention Module-High Order Spatial Interaction (PC-HOSI) model for peach SSC prediction. The accuracy of this model is compared with traditional machine learning and traditional deep learning models. Finally, the permutation algorithm is combined with deep learning models to visually evaluate the importance of feature wavelengths. Increasing the order of the PC-HOSI model enhances its ability to learn spatial correlations in the dataset, thus improving its predictive *** optimal model, PC-HOSI model, performed well with an order of 3 (PC-HOSI-3), with a root mean square error of 0.421 degrees Brix and a coefficient of determination of 0.864. Compared with traditional machine learning and deep learning algorithms, the coefficient of determination for the prediction set was improved by 0.07 and 0.39, respectively. The permutation algorithm also provided interpretability analysis for the predictions of the deep learning model, offering insights into the importance of spectral bands. These results contribute to the accurate prediction of SSC in peaches and support research on interpretability of neural network models for prediction. (c)
In the manufacturing of highly customized goods and the operation of automatic logistics systems, efficient schedules constitute an everyday challenge. Therefore, the job shop problem is established as a standard mode...
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In the manufacturing of highly customized goods and the operation of automatic logistics systems, efficient schedules constitute an everyday challenge. Therefore, the job shop problem is established as a standard model in scheduling research. While classical variants are well studied, the involvement of practically relevant conditions, such as the absence of intermediate buffers and customer-oriented optimization criteria, shows a lack of theoretical understanding. This work provides a study in this research direction by examining the applicability of a scheduling-tailored heuristic search method to the blocking job shop problem with total tardiness minimization. permutation-based encodings are used to represent a schedule. Appearing redundancy and feasibility issues are discussed. Two well-known neighborhood structures for sequencing problems are applied and an advanced repairing technique to construct feasible blocking job shop schedules is proposed. The computational results obtained by embedding the components in a simulated annealing framework highlight advantages of the heuristic solution approach against existing general-purpose methods. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
In order to solve the problem of short cycle and low precision of one-dimensional (1D) chaotic function, the new compound two-dimensional chaotic function is presented by exploiting two 1D chaotic functions which are ...
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In order to solve the problem of short cycle and low precision of one-dimensional (1D) chaotic function, the new compound two-dimensional chaotic function is presented by exploiting two 1D chaotic functions which are switched randomly. A new chaotic sequence generator is designed by the compound chaos and linear feedback shift register (LFSR). The random properties of compound chaotic functions and LFSR are also proved rigorously. The novel bilateral-diffusion image encryption algorithm and permutation algorithm are proposed based on the compound chaotic function and LFSR, which can produce more avalanche effect and larger key space. The entropy analysis, differential analysis, statistical analysis, cipher random analysis and cipher sensitivity analysis are introduced to test the security of new scheme. The results show that the novel image encryption method passes SP 800-22 standard tests and solves the problem of short cycle and low precision of 1D chaotic function.
In order to solve the problem of short cycle and low precision of one-dimensional (1D) chaotic function, the new compound two-dimensional chaotic function is presented by exploiting two 1D chaotic functions which are ...
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In order to solve the problem of short cycle and low precision of one-dimensional (1D) chaotic function, the new compound two-dimensional chaotic function is presented by exploiting two 1D chaotic functions which are switched randomly. A new chaotic sequence generator is designed by the compound chaos and linear feedback shift register (LFSR). The random properties of compound chaotic functions and LFSR are also proved rigorously. The novel bilateral-diffusion image encryption algorithm and permutation algorithm are proposed based on the compound chaotic function and LFSR, which can produce more avalanche effect and larger key space. The entropy analysis, differential analysis, statistical analysis, cipher random analysis and cipher sensitivity analysis are introduced to test the security of new scheme. The results show that the novel image encryption method passes SP 800-22 standard tests and solves the problem of short cycle and low precision of 1D chaotic function.
This paper presents a scheduling problem on parallel machines with sequence-dependent setup times and setup operations that performed by a single server. The main purpose is to get minimum makespan of the schedule. Th...
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This paper presents a scheduling problem on parallel machines with sequence-dependent setup times and setup operations that performed by a single server. The main purpose is to get minimum makespan of the schedule. The system is formulated as genetic algorithm with problem sizes consisting of two machines and 10, 20 and 30 jobs. A genetic algorithm is developed using random data sets. The optimum results are obtained using a string based permutation algorithm which scans all alternatives. As a result, proposed algorithm is effective to solve P2,S|STsd|Cmax scheduling problem on reasonable runtime and the results of the algorithm which are close to optimum solution values. Effectiveness of the solution is presented considering approximation rates of the genetic algorithm solutions to the optimum results obtained for P2,S|STsd|Cmax problem.
Tradeoffs between time complexities and solution optimalities are important when selecting algorithms for an NP-hard problem in different applications. Also, the distinction between theoretical upper bound and actual ...
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Tradeoffs between time complexities and solution optimalities are important when selecting algorithms for an NP-hard problem in different applications. Also, the distinction between theoretical upper bound and actual solution optimality for realistic instances of an NP-hard problem is a factor in selecting algorithms in practice. We consider the problem of partitioning a sequence of n distinct numbers into minimum number of monotone （increasing or decreasing） subsequences. This problem is NP-hard and the number of monotone subsequences can reach [√2n＋1/1-1/2]in the worst case. We introduce a new algorithm, the modified version of the Yehuda-Fogel algorithm, that computes a solution of no more than [√2n＋1/1-1/2]monotone subsequences in O（n^1.5） time. Then we perform a comparative experimental study on three algorithms, a known approximation algorithm of approximation ratio 1.71 and time complexity O（n^3）, a known greedy algorithm of time complexity O（n^1.5 log n）, and our new modified Yehuda-Fogel algorithm. Our results show that the solutions computed by the greedy algorithm and the modified Yehuda-Fogel algorithm are close to that computed by the approximation algorithm even though the theoretical worst-case error bounds of these two algorithms are not proved to be within a constant time of the optimal solution. Our study indicates that for practical use the greedy algorithm and the modified Yehuda-Fogel algorithm can be good choices if the running time is a major concern.
Examines the internal organization of a magnetic bubble memory. Components of bubble memories; permutation of loop contents; Identification of class of optimum algorithms.
Examines the internal organization of a magnetic bubble memory. Components of bubble memories; permutation of loop contents; Identification of class of optimum algorithms.
This paper presents the background and algorithms for masking the rotational latency of a disk or drum. It discusses the anticipatory input and output of blocks of data to buffer and primary memories for a mono-progra...
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This paper presents the background and algorithms for masking the rotational latency of a disk or drum. It discusses the anticipatory input and output of blocks of data to buffer and primary memories for a mono-programmed computer system. A basic permutation algorithm and several variations are given. Because of the anticipatory nature of the I/O scheduling, these algorithms are restricted to classes of programs with predictable behavior. While the methods are not restricted to numerical computations, matrix and partial differential equation methods are typical examples of their use. It is shown that latency may be masked using a small amount of buffer memory. The methods discussed are independent of the overall size of the data base being considered.
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