Cloning Algorithm Research Articles

Offline reinforcement learning based feeding strategy of ethylene cracking furnace

The feeding process of the ethylene cracking furnace necessitates the synchronized adjustment of multiple controlled factors. The process mainly relies on operators to do it manually, which is burdensome and may lead to significant variations in coil out temperature (COT) due to the differing expertise of operators. This paper proposes a method for learning the feeding strategy of the ethylene cracking furnace using offline reinforcement learning. The agent learns and optimizes the operating strategy directly from datasets, eliminating the need for sophisticated process simulator modeling. In addition, the advantage function is incorporated into the Twin Delayed Deep Deterministic Behavioral Cloning (TD3BC) algorithm, which enables the agent to acquire more effective operational experience. The proposed method is initially evaluated using benchmark datasets. Further, the proposed method is validated through comparative experiments on a feeding process validation model, demonstrating superior rewards and outperforming manual operating experience as well as other offline reinforcement learning methods.

MOQACA: A Novel Multiobjective Quantum Adaptive Cloning Algorithm for Range-Free Localization in Internet of Things

MOQACA: A Novel Multiobjective Quantum Adaptive Cloning Algorithm for Range-Free Localization in Internet of Things

Design of AI-Powered Hybrid Control Algorithm of Robot Vehicle for Enhanced Driving Performance

This paper presents a hybrid control of robot vehicle using behavior cloning algorithm with PID control for enhanced driving performance. The behavior cloning algorithm based on convolutional neural network (CNN) has been implemented to the autonomous driving algorithm of a robot vehicle by implementing supervised learning approach. The algorithm mimics human driving behaviors for autonomous driving by obtaining and learning manual(human) control data. In order to implement the algorithm on the real system, a low-cost camera sensor and controllers with actuators are used so that it achieves robust driving performance. With a successfully developed and validated CNN based behavior cloning model, a classic PID control algorithm has been combined as a lateral controller to propose AI-powered hybrid control architecture which improves the performance of trajectory tracking in various environments using proposed behavior cloning algorithm. The performance of the proposed algorithm has been evaluated via robot vehicle tests. It has been shown from the simulation studies and vehicle tests that the proposed algorithm enhances driving performance.

COLLECT AND PREPARE DATASET FROM HUMAN-DEMONSTRATIONS OF SIMPLE MANIPULATION TASKS USING XARM7 ROBOTIC ARM FOR TRAINING BEHAVIOUR CLONING ALGORITHM

The goal of the study is to understand what data structure is necessary to teach a robot to perform simple manipulation tasks. Training will take place thanks to Imitation learning, using the behavior cloning algorithm. Imitation learning is extremely useful for training any robotic system, especially with multi-jointed robots. This approach allows you to avoid writing complex and cumbersome heuristic algorithms. Heuristic algorithms often contain many hyperparameters that must be chosen empirically. Imitation learning algorithms allow you to develop an End-to-End policy from the collected data, which simplifies the system and increases its reliability. In the realm of training robotic systems through Imitation Learning, the criticality of data collection cannot be overstated. The efficacy of the Behavior Cloning algorithm hinges on the quality and speed at which data is gathered. Properly curated and swiftly acquired datasets serve as the bedrock for training algorithms that emulate human behavior, allowing the robot to seamlessly replicate intricate manipulations. The efficiency of data gathering is paramount in addressing the challenges posed by the dynamic nature of real-world environments. Swift adaptation to varying conditions and unforeseen scenarios necessitates a data collection process that is not only rapid but also comprehensive. A well-curated dataset not only expedites the training process but also enhances the model's adaptability and robustness in real-world applications. In conclusion, the success of Imitation Learning algorithms, particularly those underpinned by the Behavior Cloning approach, is contingent on the meticulous and expeditious gathering of data. This underscores the importance of streamlined data collection processes, ensuring that the resultant models are not only accurate but also capable of navigating the complexities of diverse and dynamic environments. Fastidious attention to data gathering is imperative to ensure a diverse and representative dataset. The xArm7 robot, in collaboration with the Realsense D435 camera, serves as an ideal synergy for capturing a myriad of movements and scenarios. The precision of data collection directly influences the algorithm's ability to generalize, making it adept at handling a spectrum of manipulation tasks.

Optimal Vehicle Scheduling and Charging Infrastructure Planning for Autonomous Modular Transit System

Prioritizing the development of public transport is an effective way to improve the sustainability of the transport system. In recent years, bus passenger flow has been declining in many cities. How to reform the operating mode of the public transportation system is an important issue that needs to be solved. An autonomous modular bus (AMB) is capable of physical coupling and uncoupling to flexibly adjust vehicle capacity as well as provide high-quality service under unbalanced passenger demand conditions. To promote AMB adoption and reduce the operating cost of the bus route, this paper presents a joint optimization method to simultaneously determine the AMB dispatching plan, charging plan, and charging infrastructure configuration scheme. Then, a mixed-integer programming model is formulated to minimize the operating costs of the bus route. A hybrid intelligent algorithm combining enumeration, cloning algorithm, and particle swarm optimization algorithm is designed to resolve the formulated model. Subsequently, an actual bus route is taken as an example to validate the proposed method. Results indicate that the developed method in this paper can reduce the operating costs and operational energy consumption of the route compared with the real route operating plan. Specifically, the reduction ratio of the former is 23.85%, and the reduction ratio of the latter is 5.92%. The results of this study validate the feasibility and advantages of autonomous modular transit service, contributing positively to the sustainable development of the urban public transportation system.

Semi-Markov processes in open quantum systems. II. Counting statistics with resetting.

A semi-Markov process method for obtaining general counting statistics for open quantum systems is extended to the scenario of resetting. The simultaneous presence of random resets and wave function collapses means that the quantum jump trajectories are no longer semi-Markov. However, focusing on trajectories and using simple probability formulas, general counting statistics can still be constructed from reset-free statistics. An exact tilted matrix equationis also obtained. The inputs of these methods are the survival distributions and waiting-time density distributions instead of quantum operators. In addition, a continuous-time cloning algorithm is introduced to simulate the large-deviation properties of open quantum systems. Several quantum optics systems are used to demonstrate these results.

Mixed reality in surgical telepresence: a novel extended mean value cloning with automatic trimap generation and accurate alpha matting for visualization

The aim of this research is to propose an extended mean value cloning algorithm with automatic trimap generation and accurate alpha matting. This implementation improves the visualization accuracy of the merged video by reducing the discolored and smudging artefacts of the remote surgeon’s boundary. It also makes the merge robust for the illumination changes by taking less processing time in real time surgery. The proposed system uses automatic trimap generation from the source video for accurate foreground extraction. Extended mean value cloning with gradient mixing is then applied for the cloning with optimized alpha matting for accurate and realistic video composition. The proposed system improved the visualization accuracy by providing almost 99.7% visibility of the pixels compared to the state-of-the-art solution, which provides 99.1% visibility of pixels. The overlay error was reduced from 0.93 mm to 0.63 mm. The processing time was also reduced. The proposed solution processed 8 frames per second, which is less time than the state-of-the-art solution, which processed 5 frames per second. The extended mean value cloning smooths the differences that presented in the target and source frames for seamless and realistic blending of pixels. The automatic trimap generation reduced the risk of false foreground selection and the generated optimal trimaps improved the alpha matte quality, which is optimized to reduce the smudging artefacts completely and to produce accurate visualization of the final merged image.

Approaches That Use Domain-Specific Expertise: Behavioral-Cloning-Based Advantage Actor-Critic in Basketball Games

Research on the application of artificial intelligence (AI) in games has recently gained momentum. Most commercial games still use AI based on a finite state machine (FSM) due to complexity and cost considerations. However, FSM-based AI decreases user satisfaction given that it performs the same patterns of consecutive actions in the same situations. This necessitates a new AI approach that applies domain-specific expertise to existing reinforcement learning algorithms. We propose a behavioral-cloning-based advantage actor-critic (A2C) that improves learning performance by applying a behavioral cloning algorithm to an A2C algorithm in basketball games. The state normalization, reward function, and episode classification approaches are used with the behavioral-cloning-based A2C. The results of the comparative experiments with the traditional A2C algorithms validated the proposed method. Our proposed method using existing approaches solved the difficulty of learning in basketball games.

Mobile edge computing offloading scheme based on improved multi-objective immune cloning algorithm

Mobile edge computing offloading scheme based on improved multi-objective immune cloning algorithm

Data Cloning Estimation and Identification of a Medium-Scale DSGE Model

We apply the data cloning method to estimate a medium-scale dynamic stochastic general equilibrium model. The data cloning algorithm is a numerical method that employs replicas of the original sample to approximate the maximum likelihood estimator as the limit of Bayesian simulation-based estimators. We also analyze the identification properties of the model. We measure the individual identification strength of each parameter by observing the posterior volatility of data cloning estimates and access the identification problem globally through the maximum eigenvalue of the posterior data cloning covariance matrix. Our results corroborate existing evidence suggesting that the DSGE model of Smeets and Wouters is only poorly identified. The model displays weak global identification properties, and many of its parameters seem locally ill-identified.

Big Data Analysis and Modeling of Higher Education Reform Based on Cloud Computing Technology

In view of the current educational environment, the development of higher education cannot be separated from the support of information technology. The era of big data has had various impacts on higher education and accelerated the development of higher education. Cloud computing technology has good application prospects for the data analysis and mathematical modeling content involved in the reform of higher education. This study analyzes and calculates the concerns of the four main contents involved in the educational reform of a higher education institution as the research object. In order to quantify the application effects of different cloud computing technologies, the paper introduces three typical cloud computing technologies to analyze the research data and establish related mathematical models. The results show that the prediction accuracy based on the immune cloning algorithm is the best. Based on the calculation results, it can be found that the corresponding data of the two main contents of teaching scene setting and teaching quality detection show an obvious polynomial function relationship.

Prediction of Urban Scale Expansion Based on Genetic Algorithm Optimized Neural Network Model

With the continuous development of urbanization, the urban population is becoming more and more dense, and the demand for land is becoming more and more tense. Urban expansion has become an indispensable part of urban development. This paper studies the optimization of neural network structure by genetic algorithm, puts forward the prediction model of urban scale expansion based on a genetic algorithm optimization neural network, and compares the performance of the model with the basic model. A genetic algorithm BP neural network (GA-BP) optimized by the genetic algorithm is used to shorten the running time of the algorithm and improve the prediction accuracy, but it is easy to fall into local solution. The genetic algorithm is improved by immune cloning algorithm, and the CGA-BP neural network model is established to obtain the global optimal solution. Compared with the BP neural network model and GA-BP neural network model, the CGA-BP neural network model converges faster, and the training times reach the error condition after 79 times, while the BP neural network model and GA-BP neural network model need 117 times and 100 times, respectively, and the fitness value corresponding to the number of iterations of the model is larger. Therefore, the CGA-BP neural network algorithm can make prediction more accurately and quickly and predict the expansion of urban scale through urban conditions.

Appearance Fault Diagnosis of a Transformer Based on Data Set Optimization and Heterogeneous Model Ensemble

Prosumers refer to the integration of production and consumption. Due to a large number of access to distributed power sources, electric vehicles, etc., which have a certain impact on power transformers, and increasing potential failures, transformers need to be monitored. In recent years, image recognition technology based on deep learning has been widely used in intelligent inspection image analysis. Aiming at the problem that the accuracy of appearance fault diagnosis in intelligent inspection images is limited by image quantity and quality, an image data set optimization method based on a seamless cloning algorithm and image cleaning is proposed. First, a sample generation method based on the seamless fusion algorithm is proposed, which seamlessly fuses the corroded texture of other power equipment into the transformer image to generate the rust transformer image. On this basis, an image quality evaluation and screening method based on the XGBoost algorithm is proposed to evaluate the image quality of the data set and clean the low-quality images. In addition, aiming at the limitation of a single diagnosis algorithm, an appearance diagnosis method based on heterogeneous model ensemble learning is proposed. By constructing multiple learning models and using a weighted voting strategy to fuse model outputs as final outputs, the accuracy of fault diagnosis is improved.

Research on bin packing problem based on improved immune clonal algorithm

An improved immune cloning algorithm based on clustering competition is proposed, which ensures the diversity of antibody population, effectively improves the convergence speed of the algorithm and avoids immature convergence. The algorithm is applied to packing problem and achieves good results.

Work fluctuations in the active Ornstein–Uhlenbeck particle model

We study the large deviations of the power injected by the active force for an active Ornstein–Uhlenbeck particle (AOUP), free or in a confining potential. For the free-particle case, we compute the rate function analytically in d-dimensions from a saddle-point expansion, and numerically in two dimensions by (a) direct sampling of the active work in numerical solutions of the AOUP equations and (b) Legendre–Fenchel transform of the scaled cumulant generating function obtained via a cloning algorithm. The rate function presents asymptotically linear branches on both sides and it is independent of the system’s dimensionality, apart from a multiplicative factor. For the confining potential case, we focus on two-dimensional systems and obtain the rate function numerically using both methods (a) and (b). We find a different scenario for harmonic and anharmonic potentials: in the former case, the phenomenology of fluctuations is analogous to that of a free particle, but the rate function might be non-analytic; in the latter case the rate functions are analytic, but fluctuations are realised by entirely different means, which rely strongly on the particle-potential interaction. Finally, we check the validity of a fluctuation relation for the active work distribution. In the free-particle case, the relation is satisfied with a slope proportional to the bath temperature. The same slope is found for the harmonic potential, regardless of activity, and for an anharmonic potential with low activity. In the anharmonic case with high activity, instead, we find a different slope which is equal to an effective temperature obtained from the fluctuation–dissipation theorem.

A novel solution of using mixed reality in bowel and oral and maxillofacial surgical telepresence: 3D mean value cloning algorithm.

Most of the mixed reality models used in the surgical telepresence are suffering from the discrepancies in the boundary area and spatial-temporal inconsistency due to the illumination variation in the video frames. The aim behind this work is to propose a new solution that helps produce the composite video by merging the augmented video of the surgery site and virtual hand of the remote expertise surgeon. The purpose of the proposed solution is to decrease the processing time and enhance the accuracy of merged video by decreasing the overlay and visualization error and removing occlusion and artefacts. The proposed system enhanced mean-value cloning algorithm that helps to maintain the spatial-temporal consistency of the final composite video. The enhanced algorithm includes the three-dimensional mean-value coordinates and improvised mean-value interpolant in the image cloning process, which helps to reduce the sawtooth, smudging and discolouration artefacts around the blending region. The accuracy in terms of overlay error of the proposed solution is improved from 1.01 to 0.80mm, whereas the accuracy in terms of visualization error is improved from 98.8% to 99.4%. The processing time is reduced to 0.173s from 0.211s. The processing time and the accuracy of the proposed solution are enhanced as compared to the state-of-art solution. Our solution helps make the object of interest consistent with the light intensity of the target image by adding the space distance that helps maintain the spatial consistency in the final merged video.

State Representation Learning With Adjacent State Consistency Loss for Deep Reinforcement Learning

Through well-designed optimization paradigm and deep neural networks as feature extractor, deep reinforcement learning (DRL) algorithms learn optimal policy on discrete and continuous action space. However, such capability is restricted by the low sampling efficiency. By inspecting the importance of feature extraction in DRL, we find that state feature learning is one of the key obstacles for sampling efficiently. To this end, we propose a new state representation learning scheme with adjacent state consistency loss (ASC loss). The loss is based on the hypothesis that the distance between adjacent states is smaller than that of far apart ones since scenes in videos generally evolve smoothly. We exploit ASC loss as an assistant of RL loss in the training phase to boost the state feature learning, and make evaluation on existing DRL algorithms as well as behavioral cloning algorithm. Experiments on Atari games and MuJoCo continuous control tasks demonstrate the effectiveness of our scheme.

Secure Automatic Speaker Verification (SASV) System Through sm-ALTP Features and Asymmetric Bagging

The growing number of voice-enabled devices and applications consider automatic speaker verification (ASV) a fundamental component. However, maximum outreach for ASV in critical domains e.g., financial services and health care, is not possible unless we overcome security breaches caused by voice cloning algorithms and replayed audios. Therefore, to overcome these vulnerabilities, a secure ASV (SASV) system based on the novel sign modified acoustic local ternary pattern (sm-ALTP) features and asymmetric bagging-based classifier-ensemble with enhanced attack vector is presented. The proposed audio representation approach clusters the high and low frequency components in audio frames by normally distributing frequency components against a convex function. Then, the neighborhood statistics are applied to capture the user specific vocal tract information. The proposed SASV system simultaneously verifies the bonafide speakers and detects the voice cloning attack, cloning algorithm used to synthesize cloned audio (in the defined settings), and voice-replay attacks over the ASVspoof 2019 dataset. In addition, the proposed method detects the voice replay and cloned voice replay attacks over the VSDC dataset. Both the voice cloning algorithm detection and cloned-replay attack detection are novel concepts introduced in this paper. The voice cloning algorithm detection module determines the voice cloning algorithm used to generate the fake audios. Whereas, the cloned voice replay attack detection is performed to determine the SASV behavior when audio samples are simultaneously contemplated with cloning and replay artifacts.

Ground-based visible spectroscopy of asteroids to support the development of an unsupervisedGaiaasteroid taxonomy

Context.TheGaiamission of the European Space Agency is measuring reflectance spectra of a number to the order of 105small Solar System objects. A first sample will be published in theGaiaData Release scheduled for 2021.Aims.The aim of our work was to test the procedure developed to obtain taxonomic classifications for asteroids based only onGaiaspectroscopic data.Methods.We used asteroid spectra obtained using the DOLORES (Device Optimised for the LOw RESolution) instrument, a low-resolution spectrograph and camera installed at the Nasmyth B focus of the Telescopio NazionaleGalileo. Because these spectra have a higher spectral resolution than that typical of theGaiaspectra, we resampled them to more closely match the expectedGaiaspectral resolution. We then developed a cloning algorithm to build a database of asteroid spectra belonging to a variety of taxonomic classes, starting from a set of 33 prototypes chosen from the 50 asteroids in our observing campaign. We used them to generate a simulated population of 10 000 representative asteroid spectra and employed them as the input to the algorithm for taxonomic classification developed to analyzeGaiaasteroid spectra.Results.Using the simulated population of 10 000 representative asteroid spectra in the algorithm to be used to produce theGaiaasteroid taxonomy at the end of the mission, we found 12 distinct taxonomic classes. Two of them, with 53% of the sample, are dominant. At the other extreme are three classes each with <1% of the sample, and these consist of the previously known rare classes A, D/Ld, and V; 99.1% of the simulated population fall into a single class.Conclusions.We demonstrated the robustness of our algorithm for taxonomic classification by using a sample of simulated asteroid spectra fully representative of what is expected to be in theGaiaspectroscopic data catalogue for asteroids. Increasingly larger data sets will become available as soon as they are published in the futureGaiadata releases, with the next one coming in 2021. This will be exploited to develop a correspondingly improved taxonomy, likely with minor tweaks to the algorithm described here, as suggested by the results of this preliminary analysis.

A Novel Solution of Using Mixed Reality in Bowel and Oral and Maxillofacial Surgical Telepresence: 3D Mean Value Cloning algorithm.

Most of the Mixed Reality models used in the surgical telepresence are suffering from the discrepancies in the boundary area and spatial-temporal inconsistency due to the illumination variation in the video frames. The aim behind this work is to propose a new solution that helps produce the composite video by merging the augmented video of the surgery site and virtual hand of the remote expertise surgeon. The purpose of the proposed solution is to decrease the processing time and enhance the accuracy of merged video by decreasing the overlay and visualization error and removing occlusion and artefacts. The proposed system enhanced the mean value cloning algorithm that helps to maintain the spatial-temporal consistency of the final composite video. The enhanced algorithm includes the 3D mean value coordinates and improvised mean value interpolant in the image cloning process, which helps to reduce the sawtooth, smudging and discoloration artefacts around the blending region RESULTS: As compared to the state of art solution, the accuracy in terms of overlay error of the proposed solution is improved from 1.01mm to 0.80mm whereas the accuracy in terms of visualization error is improved from 98.8% to 99.4%. The processing time is reduced to 0.173 seconds from 0.211 seconds CONCLUSION: Our solution helps make the object of interest consistent with the light intensity of the target image by adding the space distance that helps maintain the spatial consistency in the final merged video. This article is protected by copyright. All rights reserved.