Methods In Terms Of Time Research Articles

A New Grey Wolf Optimizer Tuned Extended Generalized Predictive Control for Distillation Process.

The distillation process plays an essential role in the petrochemical industry. However, the high-purity distillation column has complicated dynamic characteristics such as strong coupling and large time delay. To control the distillation column accurately, we proposed an extended generalized predictive control (EGPC) method inspired by the principles of extended state observer and proportional-integral-type generalized predictive control method; the proposed EGPC can adaptively compensate the system for the effects of coupling and model mismatch online and performs well in controlling time-delay systems. The strong coupling of the distillation column needs fast control, and the large time delay requires soft control. To balance the requirement for fast and soft control at the same time, a grey wolf optimizer with reverse learning and adaptive leaders number strategies (RAGWO) was proposed to tune the parameters of EGPC, and these strategies enable RAGWO to have a better initial population and improve its exploitation and exploration ability. The benchmark test results indicate that the RAGWO outperforms the existing optimizers for most of the selected benchmark functions. Extensive simulations show that the proposed method in terms of fluctuation and response time is superior to other methods for controlling the distillation process.

Combination prediction method of students' performance based on ant colony algorithm.

Students' performance is an important factor for the evaluation of teaching quality in colleges. The prediction and analysis of students' performance can guide students' learning in time. Aiming at the low accuracy problem of single model in students' performance prediction, a combination prediction method is put forward based on ant colony algorithm. First, considering the characteristics of students' learning behavior and the characteristics of the models, decision tree (DT), support vector regression (SVR) and BP neural network (BP) are selected to establish three prediction models. Then, an ant colony algorithm (ACO) is proposed to calculate the weight of each model of the combination prediction model. The combination prediction method was compared with the single Machine learning (ML) models and other methods in terms of accuracy and running time. The combination prediction model with mean square error (MSE) of 0.0089 has higher performance than DT with MSE of 0.0326, SVR with MSE of 0.0229 and BP with MSE of 0.0148. To investigate the efficacy of the combination prediction model, other prediction models are used for a comparative study. The combination prediction model with MSE of 0.0089 has higher performance than GS-XGBoost with MSE of 0.0131, PSO-SVR with MSE of 0.0117 and IDA-SVR with MSE of 0.0092. Meanwhile, the running speed of the combination prediction model is also faster than the above three methods.

Optimal Sizing of Hybrid Renewable Energy System Using Two-Stage Stochastic Programming

Stochastic programming has become increasingly vital in energy applications, especially in the context of the growing need for renewable energy solutions. This paper presents a significant advancement in this field by introducing an efficient and robust algorithm for optimally sizing hybrid renewable energy systems. Utilizing a two-stage stochastic programming approach, the proposed algorithm addresses the challenges posed by the unpredictability of renewable energy sources. The proposed solution leverages the three-block alternating direction method of multipliers (ADMM), a cutting-edge technique that facilitates parallel computation and enhances computational efficiency. The distinctiveness of this method lies in its ability to solve complex stochastic optimization problems without compromising the mathematical integrity of the model. This is achieved by applying first-order optimality conditions, ensuring both robustness and efficacy. To demonstrate the practical applicability and superiority of the algorithm, a case study was conducted in a rural area of South Africa. The proposed algorithm was applied to design an optimal hybrid renewable energy system, and its performance was compared against traditional methods such as progressive hedging and Monte Carlo techniques. Results affirm the superiority of the approach, saving approximately 8.16% capital cost when compared to progressive hedging. In addition, the proposed algorithm outperforms the Monte Carlo method both in terms of CPU time and the number of cost function evaluations.

GBCA: Graph Convolution Network and BERT combined with Co-Attention for fake news detection

Social media has evolved into a widely influential information source in contemporary society. However, the widespread use of social media also enables the rapid spread of fake news, which can pose a significant threat to national and social stability. Current fake news detection methods primarily rely on graph neural network, which analyze the dissemination patterns of news articles. Nevertheless, these approaches frequently overlook the semantic characteristics of the news content itself. To address this problem, we propose a novel Graph Convolution Network and BERT combined with Co-Attention (GBCA) model. Initially, we conduct training for a graph classification task on the propagation structure of fake news. Subsequently, we employ the BERT model to extract semantic features in fake news. Finally, we utilize co-attention mechanism to integrate the two dimensions of propagation structure and semantic features, which enhances the effectiveness of fake news detection. Our model outperforms baseline methods in terms of accuracy and training time, as demonstrated by experiments on three public benchmark datasets.

Solving the inverse kinematics problem of discretely actuated hyper-redundant manipulators using the multi-module searching method

AbstractHyper-redundant manipulators are produced by cascading several mechanisms on top of each other as modules. The discrete actuation makes their control easier because discrete actuators usually do not need any feedback to control. So far, several methods have been proposed to solve the inverse kinematic problem of discretely actuated, hyper-redundant manipulators. The two-by-two searching method is better than the other methods in terms of CPU time and error. In this article, the mentioned method is generalized by choosing an arbitrary number of modules as pending modules in each step of the solution instead of the necessary two. For validation, the proposed method is compared with nine meta-heuristic searching algorithms: simulated annealing, genetic algorithm, particle swarm optimization, ant colony optimization, gray wolf optimizer, stochastic fractal search, whale optimization algorithm, Giza pyramid construction, and flying fox optimization. Furthermore, the effect of the number of pending modules on CPU time and error is investigated. All the numerical problems have been solved for two case studies, one is planar and the other is spatial.

Towards High-Performance Linear Potential Flow BEM Solver with Low-Rank Compressions

The interaction of water waves with floating bodies can be modeled with linear potential flow theory and numerically solved with the boundary element method (BEM). This method requires the construction of dense matrices and the resolution of the corresponding linear systems. The cost of this method in terms of time and memory grows at least quadratically with the size of the mesh, and the resolution of large problems (such as large farms of wave energy converters) can, thus, be very costly. Approximating some blocks of the matrix with data-sparse matrices can limit this cost. While matrix compression with low-rank blocks has become a standard tool in the larger BEM community, the present paper provides its first application (to our knowledge) to linear potential flows. In this paper, we assess how efficiently low-rank blocks can approximate interaction matrices between distant meshes when using the Green function of linear potential flow. Due to the complexity of this Green function, a theoretical study is difficult, and numerical experiments are used to test the approximation method. Typical results on large arrays of floating bodies show that 99% of the accuracy can be reached with 10% of the coefficients of the matrix.

Informative Trajectory Planning Using Reinforcement Learning for Minimum-Time Exploration of Spatiotemporal Fields.

This article studies the informative trajectory planning problem of an autonomous vehicle for field exploration. In contrast to existing works concerned with maximizing the amount of information about spatial fields, this work considers efficient exploration of spatiotemporal fields with unknown distributions and seeks minimum-time trajectories of the vehicle while respecting a cumulative information constraint. In this work, upon adopting the observability constant as an information measure for expressing the cumulative information constraint, the existence of a minimum-time trajectory is proven under mild conditions. Given the spatiotemporal nature, the problem is modeled as a Markov decision process (MDP), for which a reinforcement learning (RL) algorithm is proposed to learn a continuous planning policy. To accelerate the policy learning, we design a new reward function by leveraging field approximations, which is demonstrated to yield dense rewards. Simulations show that the learned policy can steer the vehicle to achieve an efficient exploration, and it outperforms the commonly-used coverage planning method in terms of exploration time for sufficient cumulative information.

An Edge Computing-Oriented Islanding Detection Using Differential Entropy and Multi-Support Vector Machines

This paper presents an edge computing-oriented islanding detection paradigm using multi-support vector machines (SVM) and differential entropy. The proposed methodology can detect islanding events and locate which microgrid was disconnected in a distribution system with microgrid cluster. The proposed method employs multi-SVMs in the cloud to detect and locate potential islanding events in power distribution systems. The accuracy and reliability of the results of SVMs are enhanced by introducing differential entropy for quantitative evaluation. Additionally, the method decomposes the SVM inference process at the edge, enabling edge devices with low computational power and limited resources to support data-driven algorithms without compromising accuracy and speed. An extensive study is performed on the modified IEEE123 distribution system established in real-time digital simulation. Simulation results elucidate that the proposed method can accurately and promptly detect system islanding operations with zero non-detection zone. Comparison with other methods illustrates the superiority of the proposed method in terms of discrimination accuracy, detection time, and reliability.

Enhancing steganography using modified HHO and RC4

A lot of information is being sent around the globe these days, which increases the risk of knowledge leakage; as a result, it is crucial to preserve information security while exchanging data. Steganography and cryptography are the most essential methods, among many others, to guarantee the data's confidentiality. Encryption is making information unintelligible to anybody but the intended recipients. However, steganography is the study of hiding information in digital media so that nobody can detect it is there. This research introduces two methods for concealing and encrypting messages that improve the Stego image's visual quality and security while offering a sizable embedding capacity. A stream cipher method with a symmetric key, the RC4 algorithm, is utilized to encrypt and decode secret information. At the same time, a population-based metaheuristic, modified HHO, is employed to find the optimum spots in the cover medium to conceal encrypted sensitive data. Regarding security, picture quality, and attack resistance, the testing findings show that the proposed method excels over competing methods in terms of time and cost.

A lightweight GAN-based fault diagnosis method based on knowledge distillation and deep transfer learning

Generative adversarial networks (GANs) have shown promise in the field of small sample fault diagnosis. However, it is worth noting that generating synthetic data using GANs is time-consuming, and synthetic data cannot fully replace real data. To expedite the GAN-based fault diagnostics process, this paper proposes a hybrid lightweight method for compressing GAN parameters. First, three modules are constructed: a teacher generator, a teacher discriminator, and a student generator, based on the knowledge distillation GAN (KD-GAN) approach. The distillation operation is applied to both teacher generator and student generator, while adversarial training is conducted for the teacher generator and the teacher discriminator. Furthermore, a joint loss function is proposed to update the parameters of the student generator by combining distillation loss and adversarial loss. Additionally, the proposed KD-GAN method is combined with deep transfer learning (DTL) and leverages real data to enhance the diagnostic model’s performance. Two numerical experiments are performed to demonstrate that the proposed KD-GAN-DTL method outperforms other GAN-based fault diagnosis methods in terms of computational time and diagnostic accuracy.

A novel regularized weighted estimation method for information diffusion prediction in social networks

In recent years, social networks have become popular among Internet users, and various studies have been performed on the analysis of users’ behavior in social networks. Information diffusion analysis is one of the leading fields in social network analysis. In this context, users are influenced by other users in the social network, such as their friends. User behavior is analyzed using several models designed for information diffusion modeling and prediction. In this paper, first, the problem of estimating the diffusion probabilities for the independent cascade model is studied. We propose a method for estimating diffusion probabilities. This method assigns a weight to each individual diffusion sample within a network. To account for the different effects of diffusion samples, several weighting schemes are proposed. Afterward, the proposed method is applied to real cascade datasets such as Twitter and Digg. We try to estimate diffusion probabilities for the independent cascade model considering the continuous time of nodes’ infections. The results of our evaluation of our methods are presented based on several datasets. The results show the high performance of our methods in terms of training time as well as other metrics such as mean absolute error and F-measure.

Analisis Perbandingan Nilai Kondisi Jalan Menggunakan Metode SDI dan IRI Dari Alat Survei Roadroid

Good road infrastructure plays an important role in the economic development of a city. Therefore, the government must maintain and improve road conditions yearly to help attract investment and ultimately lead to overall economic growth. Presently, road condition surveys are conducted and assessed using the manual method with the Surface Distress Index (SDI), which is time-consuming and expensive due to the large number of access roads. In order to address this issue, a fast and cost-effective alternative survey approach is required, such as the International Roughness Index (IRI) method using the Roadroid application. Roadroid is an application installed on a smartphone and then attached to a car's windshield to record road conditions while the car is in motion. This study aims to compare the use of the Roadroid application with the manual survey method in terms of time and cost efficiency in identifying road conditions in the city of Palembang, which has 1204 roads. In this study, the Roadroid application results is compared to the road condition assessment results from the SDI methods. The results showed that the time required to obtain road conditions with manual survey and Roadroid application was 37.313 minutes and 4.123 minutes, respectively. The cost required to obtain road conditions was IDR 290.937.000 and IDR 68.006.000 for manual surveys and using the Roadroid application, respectively. Furthermore, the comparison of road condition assessment results between the SDI and IRI methods showed that they have average mean differences of 24.86%, so it can be concluded that the road conditions obtained in the IRI method using Roadroid have high accuracy with the conditions in the SDI method. From the results, it can be concluded that using the Roadroid application to obtain road conditions is more effective and efficient than manual surveys.

Efficient Performance Prediction of End-to-End Autonomous Driving Under Continuous Distribution Shifts Based on Anomaly Detection

A Deep Neural Network (DNN)’s prediction may be unreliable outside of its training distribution despite high levels of accuracy obtained during model training. The DNN may experience different degrees of accuracy degradation for different levels of distribution shifts, hence it is important to predict its performance (accuracy) under distribution shifts. In this paper, we consider the end-to-end approach to autonomous driving of using a DNN to map from an input image to the control action such as the steering angle. For each input image with possible perturbations that cause distribution shifts, we design a Performance Prediction Module to compute its anomaly score, and use it to predict the DNN’s expected prediction error, i.e., its expected deviation from the ground truth (optimal) control action, which is not available after deployment. If the expected prediction error is too large, then the DNN’s prediction may no longer be trusted, and remedial actions should be taken to ensure safety. We consider different methods for computing the anomaly score for the input image, including using the reconstruction error of an Autoencoder, or applying an Anomaly Detection algorithm to a hidden layer of the DNN. We present performance evaluation of the different methods in terms of both prediction accuracy and execution time on different hardware platforms, in order to provide a useful reference for the designer to choose among the different methods.

Efficient Analysis of Large-Size Bio-Signals Based on Orthogonal Generalized Laguerre Moments of Fractional Orders and Schwarz–Rutishauser Algorithm

Orthogonal generalized Laguerre moments of fractional orders (FrGLMs) are signal and image descriptors. The utilization of the FrGLMs in the analysis of big-size signals encounters three challenges. First, calculating the high-order moments is a time-consuming process. Second, accumulating numerical errors leads to numerical instability and degrades the reconstructed signals’ quality. Third, the QR decomposition technique is needed to preserve the orthogonality of the higher-order moments. In this paper, the authors derived a new recurrence formula for calculating the FrGLMs, significantly reducing the computational CPU times. We used the Schwarz–Rutishauser algorithm as an alternative to the QR decomposition technique. The proposed method for computing FrGLMs for big-size signals is accurate, simple, and fast. The proposed algorithm has been tested using the MIT-BIH arrhythmia benchmark dataset. The results show the proposed method’s superiority over existing methods in terms of processing time and reconstruction capability. Concerning the reconstructed capability, it has achieved superiority with average values of 25.3233 and 15.6507 with the two metrics PSNR and MSE, respectively. Concerning the elapsed reconstruction time, it also achieved high superiority with an efficiency gain of 0.8. The proposed method is suitable for utilization in the Internet of Healthcare Things.

Performance Evaluation of Priority-based Scheduling in Hybrid VANETs for Different Criteria Weights Using AHP-AHP and AHP-TOPSIS

Multi-Criteria Decision Making (MCDM) approaches have been developed to support a wider range of application areas. For example, channel allocation, among the vehicles in infrastructure-based/hybrid Vehicular Ad hoc NETworks (VANETs), is done by exploiting MCDMs to minimize the waiting time and maximize the request service rate by the Road Side Unit (RSU). The Analytic Hierarchy Process (AHP) developed by Saaty, however, has gained enormous popularity and been used all over the world since its invention in 1977. AHP is capable of prior weight calculation of individual criteria using Criteria Preference Matrix (CPM) and rank computation of alternatives as well. Another MCDM technique, termed Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), was created by Hwang and Yoon in 1981. It is a ranking technique that requires inputs of the weights of the criteria. This paper aims to analyze the impact on ranks of various alternatives for different CPMs using the AHP-AHP method and the AHP-TOPSIS method. For both cases weights of criteria have been calculated using AHP before rank computation. In addition to that, this paper deals with the performance evaluation of infrastructure-based VANETs for priority-based channel allocation using the AHP-AHP and the AHP-TOPSIS method in terms of Request Service Rate and Average Waiting Time for various CPMs. It is observed that AHP-TOPSIS performs better in terms of Request Service Rate with the increasing alternatives. Furthermore, the AHP-AHP method shows frequent rank change while increasing the number of alternatives for various CPMs.

Deep learning based coherence holography reconstruction of 3D objects.

We propose a reconstruction method for coherence holography using deep neural networks. cGAN and U-NET models were developed to reconstruct 3D complex objects from recorded interferograms. Our proposed methods, dubbed deep coherence holography (DCH), predict the non-diffracted fields or the sub-objects included in the 3D object from the captured interferograms, yielding better reconstructed objects than the traditional analytical imaging methods in terms of accuracy, resolution, and time. The DCH needs one image per sub-object as opposed to N images for the traditional sin-fit algorithm, and hence the total reconstruction time is reduced by N×. Furthermore, with noisy interferograms the DCH amplitude mean square reconstruction error (MSE) is 5×104× and 104× and phase MSE is 102× and 3×103× better than Fourier fringe and sin-fit algorithms, respectively. The amplitude peak signal to noise ratio (PSNR) is 3× and 2× and phase PSNR is 5× and 3× better than Fourier fringe and sin-fit algorithms, respectively. The reconstruction resolution is the same as sin-fit but 2× better than the Fourier fringe analysis technique.

Model Compression Algorithm via Reinforcement Learning and Knowledge Distillation

Traditional model compression techniques are dependent on handcrafted features and require domain experts, with a tradeoff between model size, speed, and accuracy. This study proposes a new approach toward resolving model compression problems. Our approach combines reinforcement-learning-based automated pruning and knowledge distillation to improve the pruning of unimportant network layers and the efficiency of the compression process. We introduce a new state quantity that controls the size of the reward and an attention mechanism that reinforces useful features and attenuates useless features to enhance the effects of other features. The experimental results show that the proposed model is superior to other advanced pruning methods in terms of the computation time and accuracy on CIFAR-100 and ImageNet dataset, where the accuracy is approximately 3% higher than that of similar methods with shorter computation times.

Synthesis of Substituted 1,2,4-Triazole Containing Novel Small Molecules by Using Microwave

Green chemistry finds its most promising use in the synthesis of compounds using ecologically friendly, non-hazardous, non-irritant, gentle, and reproducible catalysts and solvents. The adoption of green chemistry approaches has recently been shown to significantly reduce chemical waste and reaction times in a number of organic synthesis processes. The objective of this research work is to develop an eco-friendly method for the Substituted 1,2,4-Triazole containing novel small molecules. The approach makes use of acid as starting material on reaction with hydrazine forms Acetyl-hydrazide. On condensation with nitrile and subsequent ring closure forms substituted 1,2,4-Triazole derivatives. This research work describes the synthesis of thirty-two unique 1,2,4-triazole containing novel small molecules. It has been shown that this technique is superior to other synthetic methods in terms of reaction time, yield, and energy efficiency along with use of green chemistry and microwave.

Better Together: Data-Free Multi-Student Coevolved Distillation

Data-Free Knowledge Distillation (DFKD) aims to craft a customized student model from a pre-trained teacher model by synthesizing surrogate training images. However, a seldom-investigated scenario is to distill the knowledge to multiple heterogeneous students simultaneously. In this paper, we aim to study how to improve the performance by coevolving peer students, termed Data-Free Multi-Student Coevolved Distillation (DF-MSCD). Based on previous DFKD methods, we advance DF-MSCD by improving the data quality from the perspective of synthesizing unbiased, informative and diverse surrogate samples: 1) Unbiased. The disconnection of image synthesis among different timestamps during DFKD will lead to an unnoticed class imbalance problem. To tackle this problem, we reform the prior art into an unbiased variant by bridging the label distribution of the synthesized data among different timestamps. 2) Informative. Different from single-student DFKD, we encourage the interactions not only between teacher–student pairs, but also within peer students, driving a more comprehensive knowledge distillation. To this end, we devise a novel Inter-Student Adversarial Learning method to coevolve peer students with mutual benefits. 3) Diverse. To further promote Inter-Student Adversarial Learning, we develop Mixture-of-Generators, in which multiple generators are optimized to synthesize different yet complementary samples by playing min–max games with multiple students. Experiments are conducted to validate the effectiveness and efficiency of the proposed DF-MSCD, surpassing the existing state-of-the-arts on multiple popular benchmarks. To emphasize, our method can obtain heterogeneous students by training once, which is superior to single-student DFKD methods in terms of both training time and testing accuracy.

XAutn: Blockchain-based cross domain authentication for digital certificates in the education sector

Traditional testimony and electronic endorsements are extremely challenging to uphold and defend, and there is a problem with challenging authentication. The identity of the student is typically not recognized when it comes to requirements for access to a student’s academic credentials that are scattered over numerous sites. This is an issue with cross-domain authentication methods. On the one hand, whenever the volume of cross-domain authentication requests increases dramatically, the response time can become intolerable because of the slow throughput associated with blockchain mechanisms. These systems still do not give enough thought to the cross-domain scenario’s anonymity problem. This research proposes an effective cross-domain authentication mechanism called XAutn that protects anonymity and integrates seamlessly through the present Certificate Transparency (CT) schemes. XAutn protects privacy and develops a fast response correctness evaluation method that is based on the RSA (Rivest, Shamir, and Adleman) cryptographic accumulator, Zero Knowledge Proof Algorithm, and Proof of Continuous work consensus Algorithm (POCW). We also provide a privacy-aware computation authentication approach to strengthen the integrity of the authentication messages more securely and counteract the discriminatory analysis of malevolent requests. This research is primarily used to validate identities in a blockchain network, which makes it possible to guarantee their authenticity and integrity while also increasing security and privacy. The proposed technique greatly outperformed the current methods in terms of authentication time, period required for storage, space for storage, and overall processing cost. The proposed method exhibits a speed gain of authentication of roughly 9% when compared to traditional blockchain systems. The security investigation and results from experiments demonstrate how the proposed approach is more reliable and trustworthy.