The Advanced Encryption Standard (AES) encryption a*algorithm has never been broken since its introduction, and its security has been widely recognized and adopted widely by numerous countries and organizations. However...
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The Advanced Encryption Standard (AES) encryption a*algorithm has never been broken since its introduction, and its security has been widely recognized and adopted widely by numerous countries and organizations. However, the a*algorithm requires dozens of iterations and each iteration involves a large number of computations, so there are problems such as low encryption efficiency and high computational costs. Aiming at these problems, this paper combines the characteristics of ternary optical computer (TOC), such as many data bits, parallel computation, three-valued encoding, to study and propose a parallel AES encryption scheme based on TOC. This scheme leverages the parallel advantages of TOC to improve the block-by-block encryption mode in the AES encryption a*algorithm. By employing a parallel computing strategy, it effectively reduces the execution time of the a*algorithm and significantly enhances encryption efficiency. In addition, this paper introduces threevalued operation logic on the basis of binary Exclusive OR(XOR) operation and constructs a set of three-valued XOR (T-XOR) standards to further improve the operation efficiency of AES encryption a*algorithm on TOC. Through the in-depth analysis of the a*algorithm resource consumption and time efficiency, and combined with the experimental verification, it can be concluded that the parallel AES encryption a*algorithm based on TOC has better computing efficiency and time effectiveness, which demonstrates the advantages of TOC in dealing with large-scale computing tasks.
The phase transformation kinetics of 35CrMo steel during heating have been harnessed to refine thermo-mechanical processing techniques, thereby ensuring the quality of heat-treated components. To accurately forecast t...
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The phase transformation kinetics of 35CrMo steel during heating have been harnessed to refine thermo-mechanical processing techniques, thereby ensuring the quality of heat-treated components. To accurately forecast the austenite phase transformation in 35CrMo steel, a non-isothermal diffusion-type Johnson-Mehl-Avrami-Kolmogorov (J-M-A-K) model was developed under continuous heating conditions. The phase transformation activation energy was determined to be Q = 1.097 x 106 J/mol, with kinetic parameters n = 0.6434 and k0 = 7.8316 x 1052. The adaptive simulated annealing (ASA) a*algorithm was employed to perform inverse estimation, optimizing the J-M-A-K model parameters to n = 0.6306 and k0 = 1.2 x 1053. Taking the cumulative error between the experimental and model values of austenite volume fraction as the objective function, with the minimization of this error as the identification strategy, the optimized model showed an improvement in the prediction correlation coefficient R by 0.285, while the average absolute relative error (AARE) and root-mean-square error (RMSE) decreased by 2.67% and 0.0176, respectively. Through secondary development, the optimized J-M-A-K model was integrated into the SIMHEAT simulation software to simulate the continuous heating of 35CrMo steel. The simulation results correlated highly with experimental data, demonstrating the optimized J-M-A-K model's precision in characterizing the austenitization process of 35CrMo steel during continuous heating.
The objective of the Combined Economic Emissions Dispatch (CEED) problem is to reduce pollutant emissions by lowering the total cost of generating electricity while complying with all other constraints. The multiple o...
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The objective of the Combined Economic Emissions Dispatch (CEED) problem is to reduce pollutant emissions by lowering the total cost of generating electricity while complying with all other constraints. The multiple objective functions of the CEED problem may be converted into a single objective by using a price penalty factor. Then it was solved with the Arithmetic Optimization a*algorithm (AOA) with three-dimensional chaotic mapping in spherical coordinate system. Firstly, five three-dimensional chaotic mappings in Cartesian coordinate system are embedded in the Mathematical Optimization Accelerator (MOA) and the Mathematical Optimization Probability (MOP) of the original a*algorithm. Secondly, 15 three-dimensional chaotic mappings in spherical coordinate system are constructed from the values of 5 three-dimensional chaotic mappings in Cartesian coordinate system through the mathematical expressions of mode length, polar angle and azimuth angle in spherical coordinate system. Then through a large number of simulation experiments, five best three-dimensional chaotic mappings in the spherical coordinate system are selected to be embedded into the two parameters (MOA and MOP) of the original a*algorithm to better balance the a*algorithm's global and local searching ability. The superiority of the improved a*algorithm is verified by employing 12 benchmark test functions in CEC2022. Eventually, the improved optimal a*algorithm (IAOA-r) to solve the power system CEED problem is tested on six generating units with four different loads (150 MW, 175 MW, 200 MW and 225 MW). The results of the improved a*algorithm are compared and analyzed with the results of the Reptile Search a*algorithm (RSA), Prairie Dog Optimization (PDO), Bat a*algorithm (BAT), Whale Optimization a*algorithm (WOA), Harris Hawk Optimization (HHO), Rat Swarm Optimization (RSO). The results demonstrate that the improved a*algorithm is capable of obtaining optimal fuel costs and smaller pollution emissions in every test case.
The rapid development of technologies has attracted significant attention, with the social web and big data becoming key drivers of modern innovation. Although big data in the Social Internet of Things presents variou...
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The rapid development of technologies has attracted significant attention, with the social web and big data becoming key drivers of modern innovation. Although big data in the Social Internet of Things presents various energy-saving merits, problems such as network congestion and data communication reliability occur. In this article, a hybrid channel attention recurrent transformer-based adaptive marine predator a*algorithm is introduced to solve these problems. The main purpose of this approach is to improve the robustness and performance of SIoT systems. The hybrid channel attention recurrent transformer-based adaptive marine predator a*algorithm combines a hybrid recurrent neural network, a channel attention mechanism, and a transformer classifier. In this work, four datasets, including the water treatment plant, GPS trajectories, hepatitis dataset, and Twitter for sentiment analysis in Arabic are employed in validating the performance of a proposed model. The Savitzky-Golay filter is applied to reduce noise and eliminate unnecessary or irrelevant data. After data pre-processing, the hybrid channel attention recurrent transformer-based adaptive marine predator was introduced for classification, and this model is fine-tuned by the adaptive marine predator a*algorithm. In addition, the proposed model demonstrates strong scalability and applicability in real-world applications, making it an ideal solution for future Social Internet of Things systems.
In this paper, we propose an adaptive genetic a*algorithm designed to address the camera calibration problem. This approach facilitates the resolution of a complex optimization challenge. Our objective is to refine the ...
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In this paper, we propose an adaptive genetic a*algorithm designed to address the camera calibration problem. This approach facilitates the resolution of a complex optimization challenge. Our objective is to refine the camera calibration results estimated by the analytical method. For this purpose, a study was conducted on the type and probability of crossover, the probability of mutation and on the adaptation of the initialization intervals. This adaptation consists of adjusting the length of the initialization intervals. The main objective is to find an optimal solution for the camera calibration parameters by minimizing the cost function. This function is reformulated from the relationship between the points of the 3D target and their 2D projection in the image. Experimental tests and evaluations were conducted to validate the proposed approach. The results indicate that our a*algorithm is robust and can achieve very satisfactory calibration results.
Simultaneous regulation of multiple properties in next-generation tokamaks like ITER and fusion pilot plant may require the integration of different plasma control a*algorithms. Such integration requires the conversion ...
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Simultaneous regulation of multiple properties in next-generation tokamaks like ITER and fusion pilot plant may require the integration of different plasma control a*algorithms. Such integration requires the conversion of individual controller commands into physical actuator requests while accounting for the coupling between different plasma properties. This work proposes a tokamak and scenario-agnostic actuator-sharing a*algorithm (ASA) to perform the above-mentioned command-request conversion and, hence, integrate multiple plasma controllers. The proposed a*algorithm implicitly solves a quadratic programming (QP) problem formulated to account for the saturation limits and the relation between the controller commands and physical actuator requests. Since the constraints arising in the QP program are linear, the proposed ASA is highly computationally efficient and can be implemented in the tokamak plasma control system in real time. Furthermore, the proposed a*algorithm is designed to handle real-time changes in the control objectives and actuators' availability. Nonlinear simulations carried out using the Control Oriented Transport SIMulator illustrate the effectiveness of the proposed a*algorithm in achieving multiple control objectives simultaneously.
This paper presents a synchronized Filtered-s Least Mean Squares (SFsLMS) a*algorithm for multichannel Active Noise Control (ANC) systems aimed at mitigating aviation noise. The SFsLMS a*algorithm addresses signal delays ...
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This paper presents a synchronized Filtered-s Least Mean Squares (SFsLMS) a*algorithm for multichannel Active Noise Control (ANC) systems aimed at mitigating aviation noise. The SFsLMS a*algorithm addresses signal delays inherent in aircraft environments, which degrade the performance of traditional ANC a*algorithms. Incorporating delay estimation into the adaptive filtering process ensures accurate alignment of input and reference signals, leading to improved convergence speed and stability. The results demonstrate that the SFsLMS a*algorithm significantly enhances noise cancellation performance in dynamic aviation noise conditions, offering a scalable and robust solution for real-time noise reduction in enclosed areas near airports. This advancement contributes to increased comfort and reduced noise pollution, highlighting the a*algorithm's potential for widespread application in aviation noise control systems. The evaluation is conducted using a (2 x 4 x4) (ANC) system, with performance measured in terms of Averaged Noise Reduction (ANR). The results reveal a marked improvement in convergence speed and stability, as demonstrated by the rapid decrease and sustained low levels of ANR across all microphones.
It is desirable but nontrivial to obtain a portfolio that enjoys both sparsity and optimality. We propose a portfolio model that is rooted in the mean-variance framework, incorporating the 80 constraint as a precise r...
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It is desirable but nontrivial to obtain a portfolio that enjoys both sparsity and optimality. We propose a portfolio model that is rooted in the mean-variance framework, incorporating the 80 constraint as a precise restriction to ensure a sparse portfolio comprising no more than a specified number of assets. Moreover, the simplex constraint is also imposed to ensure the feasibility of portfolio. This model is difficult to solve due to the nonconvexity of the 80 constraint and the geometric complexity of the intersection of the two constraints. To address this issue, we establish the equivalence relation between a local optimum of a general 80-constrained problem and a global optimum on a restricted set of variables. Based on this result, we develop a two-stage accelerated forward-backward a*algorithm that converges to a locally optimal solution to the proposed autonomous sparse Markowitz portfolio model, with an o(1/k2) convergence rate in terms of function value. Extensive experiments on 7 benchmark data sets from real-world financial markets show that the proposed method achieves state-of-the-art performance in various evaluating metrics.
Super-resolution is a promising solution to improve the quality of experience (QoE) for cloud-based video streaming when the network resources between clients and the cloud vendors become scarce. Specifically, the rec...
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Super-resolution is a promising solution to improve the quality of experience (QoE) for cloud-based video streaming when the network resources between clients and the cloud vendors become scarce. Specifically, the received video can be enhanced with a trained super-resolution model running on the client-side. However, all the existing solutions ignore the content-induced performance variability of Super-Resolution Deep Neural Network (SR-DNN) models, which means the same super-resolution models have different enhancement effects on the different parts of videos because of video content variation. That leads to unreasonable bitrate selection, resulting in low video QoE, e.g., low bitrate, rebuffering, or video quality jitters. Thus, in this paper, we propose SR-ABR, a super-resolution integrated adaptive bitrate (ABR) a*algorithm, which considers the content-induced performance variability of SR-DNNs into the bitrate decision process. Due to complex network conditions and video content, SR-ABR adopts deep reinforcement learning (DRL) to select future bitrate for adapting to a wide range of environments. Moreover, to utilize the content-induced performance variability of SR-DNNs efficiently, we first define the performance variability of SR-DNNs over different video content, and then use a 2D convolution kernel to distill the features of the performance variability of the SR-DNNs to a short future video segment (several chunks) as part of the inputs. We compare SR-ABR with the related state-of-the-art works using trace-driven simulation under various real-world traces. The experiments show that SR-ABR outperforms the best state-of-the-art work NAS with the gain in average QoE of 4.3%-46.2% and 18.9%-42.1% under FCC and 3G/HSDPA network traces, respectively.
We reconsider the min-max clustered cycle cover (MM-CCC) problem, which is described as follows. Given an undirected complete graph G = (V, E;w ) with a positive integer k , where the vertex set V is partitioned into ...
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We reconsider the min-max clustered cycle cover (MM-CCC) problem, which is described as follows. Given an undirected complete graph G = (V, E;w ) with a positive integer k , where the vertex set V is partitioned into h clusters V 1 , ... , V h , and w : E -> & Ropf;+ is an edge-weight function satisfying the triangle inequality, it is asked to find k cycles such that they traverse all vertices and the vertices in each cluster are required to be traversed consecutively. The objective is to minimize the weight of the maximum weight cycle. We propose a strongly polynomial time 16- approximation a*algorithm for the MM-CCC problem. The result improves the previous a*algorithm in terms of running time.
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