Advanced Design of Soft Robots with Artificial Intelligence

In recent years,breakthrough has been made in the field of artificial intelligence(AI),which has also revolutionized the industry of robotics.Soft robots featured with high-level safety,less weight,lower power consumption have always been one of the research hotspots.Recently,multifunctional sensors for perception of soft robotics have been rapidly developed,while more algorithms and models of machine learning with high accuracy have been optimized and proposed.Designs of soft robots with AI have also been advanced ranging from multimodal sensing,human-machine interaction to effective actuation in robotic systems.Nonethe-less,comprehensive reviews concerning the new developments and strategies for the ingenious design of the soft robotic systems equipped with AI are rare.Here,the new development is systematically reviewed in the field of soft robots with AI.First,background and mechanisms of soft robotic systems are briefed,after which development focused on how to endow the soft robots with AI,including the aspects of feeling,thought and reaction,is illustrated.Next,applications of soft robots with AI are systematically summarized and discussed together with advanced strategies proposed for performance enhancement.Design thoughts for future intelligent soft robotics are pointed out.Finally,some perspectives are put forward.

Teaching Design of Course Building Decoration Materials Based on Generative Artificial Intelligence

With the digital transformation of global education and China's emphasis on education digital,generative AI technology has been widely used in the field of higher education.In this paper,the development of generative AI technology and its potential in personalized learning,interactive content creation and adaptive assessment in education were introduced firstly.Then,the application case of generative AI tools in teaching content creation,scenario-based teaching content development,visual teaching content development,complex concept deconstruction and analogy,student-led application practice and other aspects in the teaching of Building Decoration Materials was discussed.Through the teaching experiment and effect evaluation,the positive influence of generative AI technology on the improvement of students'learning effect and teaching efficiency was verified.Finally,some thoughts and inspirations on the combination of educational theory and generative AI technology,the integration of teaching design and generative AI technology,and the practice cases and effect evaluation were put forward,and the importance of teacher role transformation and personalized learning path design was emphasized to provide theoretical and practical support for the innovative development of higher education.

Research and Application of Artificial Intelligence in Electric Guitar Performance

The article explores the application of artificial intelligence technology in electric guitar playing.It delves into the advantages of artificial intelligence and its seamless integration into electric guitar performance.Additionally,it investigates the application of artificial intelligence technology through an intelligent playing robot.The research aims to offer substantial support for the advancement of artificial intelligence in electric guitar performance.

A Generic Intelligent Agent Design Approach Based on Artificial Neural Networks

Artificial intelligence in general and software agents in particular are recognized as computer science disciplines that aim to model or simulate so-called intelligent human behaviors such as perception, decision-making, understanding, learning, etc. This work presents an approach to designing a generic Intelligent Agent that can be used in a multi-agent system to solve a complex problem. The generic agent that is proposed can be instantiated as a concrete agent, which is enabled with learning and autonomy capabilities by using Artificial Neural Networks. To highlight the generic aspect, the proposition is instantiated to be used in agriculture, health and education. The instantiated software agent applied in agriculture can process images in real time and detect defect on plants’ leaf. In the health field, the agent process image to diagnose breast cancer. When applied in Education, the agent can load an image of a student’s script and grade it. The performance of the designed agent system has the same accuracy as that of the respective neural networks used to instantiate them. In the educational field, the software agent has an accuracy of 98.9% and in the health field, it has an accuracy of 99.56% while in the agricultural field, it has an accuracy of 97.2%.

Play by Design:Developing Artificial Intelligence Literacy through Game-based Learning

The paper proposes an innovative approach aimed at fostering AI literacy through interactive gaming experiences.This paper designs a game-based prototype for preparing pre-service teachers to innovate teaching practices across disciplines.The simulation,Color Conquest,serves as a strategic game to encourage educators to reconsider their pedagogical practices.It allows teachers to use and develop various scenarios by customizing maps,giving students agency to engage in the complex decision-making process.Additionally,this engagement process provides teachers with an opportunity to develop students’skills in artificial intelligence literacy as students actively develop strategic thinking,problem-solving,and critical reasoning skills.

Artificial Intelligence in Pharmaceutical Sciences

Drug discovery and development affects various aspects of human health and dramatically impacts the pharmaceutical market.However,investments in a new drug often go unrewarded due to the long and complex process of drug research and development(R&D).With the advancement of experimental technology and computer hardware,artificial intelligence(AI)has recently emerged as a leading tool in analyzing abundant and high-dimensional data.Explosive growth in the size of biomedical data provides advantages in applying AI in all stages of drug R&D.Driven by big data in biomedicine,AI has led to a revolution in drug R&D,due to its ability to discover new drugs more efficiently and at lower cost.This review begins with a brief overview of common AI models in the field of drug discovery;then,it summarizes and discusses in depth their specific applications in various stages of drug R&D,such as target discovery,drug discovery and design,preclinical research,automated drug synthesis,and influences in the pharmaceutical market.Finally,the major limitations of AI in drug R&D are fully discussed and possible solutions are proposed.

Prediction of column failure modes based on artificial neural network

To implement the performance-based seismic design of engineered structures,the failure modes of members must be classified.The classification method of column failure modes is analyzed using data from the Pacific Earthquake Engineering Research Center(PEER).The main factors affecting failure modes of columns include the hoop ratios,longitudinal reinforcement ratios,ratios of transverse reinforcement spacing to section depth,aspect ratios,axial compression ratios,and flexure-shear ratios.This study proposes a data-driven prediction model based on an artificial neural network(ANN)to identify the column failure modes.In this study,111 groups of data are used,out of which 89 are used as training data and 22 are used as test data,and the ANN prediction model of failure modes is developed.The results show that the proposed method based on ANN is superior to traditional methods in identifying the column failure modes.

The Development of Highly Loaded Turbine Rotating Blades by Using 3D Optimization Design Method of Turbomachinery Blades Based on Artificial Neural Network & Genetic Algorithm

In order to improve turbine internal efficiency and lower manufacturing cost, a new highly loaded rotating blade has been developed. The 3D optimization design method based on artificial neural network and genetic algorithm is adopted to construct the blade shape. The blade is stacked by the center of gravity in radial direction with five sections. For each blade section, independent suction and pressure sides are constructed from the camber line using Bezier curves. Three-dimensional flow analysis is carried out to verify the performance of the new blade. It is found that the new blade has improved the blade performance by 0.5%. Consequently, it is verified that the new blade is effective to improve the turbine internal efficiency and to lower the turbine weight and manufacturing cost by reducing the blade number by about 15%.

Codebook design using improved particle swarm optimization based on selection probability of artificial bee colony algorithm

In the paper, a new selection probability inspired by artificial bee colony algorithm is introduced into standard particle swarm optimization by improving the global extremum updating condition to enhance the capability of its overall situation search. The experiment result shows that the new scheme is more valuable and effective than other schemes in the convergence of codebook design and the performance of codebook, and it can avoid the premature phenomenon of the particles.

Artificial neural network based inverse design method for circular sliding slopes

Current design method for circular sliding slopes is not so reasonable that it often results in slope (sliding.) As a result, artificial neural network (ANN) is used to establish an artificial neural network based inverse design method for circular sliding slopes. A sample set containing 21 successful circular sliding slopes excavated in the past is used to train the network. A test sample of 3 successful circular sliding slopes excavated in the past is used to test the trained network. The test results show that the ANN based inverse design method is valid and can be applied to the design of circular sliding slopes.

Hybridizing artificial bee colony with biogeography-based optimization for constrained mechanical design problems

A novel hybrid algorithm named ABC-BBO, which integrates artificial bee colony(ABC) algorithm with biogeography-based optimization(BBO) algorithm, is proposed to solve constrained mechanical design problems. ABC-BBO combined the exploration of ABC algorithm with the exploitation of BBO algorithm effectively, and hence it can generate the promising candidate individuals. The proposed hybrid algorithm speeds up the convergence and improves the algorithm's performance. Several benchmark test functions and mechanical design problems are applied to verifying the effects of these improvements and it is demonstrated that the performance of this proposed ABC-BBO is superior to or at least highly competitive with other population-based optimization approaches.

Artificial Neural Network and Full Factorial Design Assisted AT-MRAM on Fe Oxides, Organic Materials, and Fe/Mn Oxides in Surficial Sediments

Artificial neural network（ANN） and full factorial design assisted atrazine（AT） multiple regression adsorption model（AT-MRAM） were developed to analyze the adsorption capability of the main components in the surficial sediments（SSs）. Artificial neural network was used to build a model（the determination coefficient square r2 is 0.9977） to describe the process of atrazine adsorption onto SSs, and then to predict responses of the full factorial design. Based on the results of the full factorial design, the interactions of the main components in SSs on AT adsorption were investigated through the analysis of variance（ANOVA）, F-test and t-test. The adsorption capability of the main components in SSs for AT was calculated via a multiple regression adsorption model（MRAM）. The results show that the greatest contribution to the adsorption of AT on a molar basis was attributed to Fe/Mn（–1.993 μmol/mol）. Organic materials（OMs） and Fe oxides in SSs are the important adsorption sites for AT, and the adsorption capabilities are 1.944 and 0.418 μmol/mol, respectively. The interaction among the non-residual components（Fe, Mn oxides and OMs） in SSs interferes in the adsorption of AT that shouldn’t be neglected, revealing the significant contribution of the interaction among non-residual components to controlling the behavior of AT in aquatic environments.

Towards data-efficient mechanical design of bicontinuous composites usinggenerative AI

The distribution of material phases is crucial to determine the composite’s mechanical property.While the full structure-mechanics relationship of highly ordered material distributions can be studied with finite number of cases,this relationship is difficult to be revealed for complex irregular distributions,preventing design of such material structures to meet certain mechanical requirements.The noticeable developments of artificial intelligence(AI)algorithms in material design enables to detect the hidden structure-mechanics correlations which is essential for designing composite of complex structures.It is intriguing how these tools can assist composite design.Here,we focus on the rapid generation of bicontinuous composite structures together with the stress distribution in loading.We find that generative AI,enabled through fine-tuned Low Rank Adaptation models,can be trained with a few inputs to generate both synthetic composite structures and the corresponding von Mises stress distribution.The results show that this technique is convenient in generating massive composites designs with useful mechanical information that dictate stiffness,fracture and robustness of the material with one model,and such has to be done by several different experimental or simulation tests.This research offers valuable insights for the improvement of composite design with the goal of expanding the design space and automatic screening of composite designs for improved mechanical functions.

Effect of Injection Angle on Artificial Cavitation Using the Design of Experiment Method

Using the supercavitation phenomenon is necessary to reach high velocities underwater. Supercavitation can be achieved in two ways: natural and artificial. In this article, the simulation of flows around a torpedo was studied naturally and artificially. The validity of simulation using theoretical and practical data in the natural and artificial phases was evaluated. Results showed that the simulations were consistent with the laboratory results. The results in different injection coefficient rates, injection angles, and cavitation numbers were studied. The obtained results showed the importance of cavitation number, injection rate coefficient, and injection angle in cavity shape. At the final level, determining the performance conditions using the Design of Experiment (DOE) method was emphasized, and the performance of cavitation number, injection rate coefficient, and injection angle in drag and lift coefficient was studied. The increase in injection angle in the low injection rate coefficient resulted in a diminished drag coefficient and that in the high injection rate coefficient resulted in an enhanced drag coefficient.

Introducing 3D-potting:a novel production process for artificial membrane lungs with superior blood flow design

Currently,artificial-membrane lungs consist of thousands of hollow fiber membranes where blood flows around the fibers and gas flows inside the fibers,achieving diffusive gas exchange.At both ends of the fibers,the interspaces between the hollow fiber membranes and the plastic housing are filled with glue to separate the gas from the blood phase.During a uniaxial centrifugation process,the glue forms the“potting.”The shape of the cured potting is then determined by the centrifugation process,limiting design possibilities and leading to unfavorable stagnation zones associated with blood clotting.In this study,a new multiaxial centrifugation process was developed,expanding the possible shapes of the potting and allowing for completely new module designs with potentially superior blood flow guidance within the potting margins.Two-phase simulations of the process in conceptual artificial lungs were performed to explore the possibilities of a biaxial centrifugation process and determine suitable parameter sets.A corresponding biaxial centrifugation setup was built to prove feasibility and experimentally validate four conceptual designs,resulting in good agreement with the simulations.In summary,this study shows the feasibility of a multiaxial centrifugation process allowing greater variety in potting shapes,eliminating inefficient stagnation zones and more favorable blood flow conditions in artificial lungs.

Research on Multi-Dimensional Aesthetic Perception of Art Design in the Era of Artificial Intelligence

With the rapid development of artificial intelligence technology,the way we interact with products and the environment has undergone great changes compared with the past.While the technology of the artificial intelligence era affects our lives and redefines our relationship with machines,it is also changing our aesthetic and perception of art design.The article mainly starts from the perspective of art design interaction mode and experience innovation,and studies the multi-dimensional aesthetic perception and interaction mode of art design in the era of artificial intelligence in human vision,touch and hearing.

Application of Artificial Neural Networks Based Monte Carlo Simulation in the Expert System Design and Control of Crude Oil Distillation Column of a Nigerian Refinery

This research work investigated comparative studies of expert system design and control of crude oil distillation column (CODC) using artificial neural networks based Monte Carlo (ANNBMC) simulation of random processes and artificial neural networks (ANN) model which were validated using experimental data obtained from functioning crude oil distillation column of Port-Harcourt Refinery, Nigeria by MATLAB computer program. Ninety percent (90%) of the experimental data sets were used for training while ten percent (10%) were used for testing the networks. The maximum relative errors between the experimental and calculated data obtained from the output variables of the neural network for CODC design were 1.98 error % and 0.57 error % when ANN only and ANNBMC were used respectively while their respective values for the maximum relative error were 0.346 error % and 0.124 error % when they were used for the controller prediction. Larger number of iteration steps of below 2500 and 5000 were required to achieve convergence of less than 10-7?for the training error using ANNBMC for both the design of the CODC and controller respectively while less than 400 and 700 iteration steps were needed to achieve convergence of 10-4?using ANN only. The linear regression analysis performed revealed the minimum and maximum prediction accuracies to be 80.65% and 98.79%;and 98.38% and 99.98% when ANN and ANNBMC were used for the CODC design respectively. Also, the minimum and maximum prediction accuracies were 92.83% and 99.34%;and 98.89% and 99.71% when ANN and ANNBMC were used for the CODC controller respectively as both methodologies have excellent predictions. Hence, artificial neural networks based Monte Carlo simulation is an effective and better tool for the design and control of crude oil distillation column.

The Design and Implementation of the Data Buffer Unit in an Artificial intelligence Computer ITM-1

This paper describes the function,structure and working status of the data buffer unitDBU,one of the most important functional units on ITM-1.It also discusses DBU’s supportto the multiprocessor system and Prolog language.

Optimization method of reef structure design based on hydrodynamic model

Artificial fish reef is a kind of artificial structure in water,which provides a necessary and safe place for aquatic life such as fish to inhabit,grow,and breed,and creates an environment suitable for fish growth,so as to protect and multiply fishery resources.In a large time scale,the physical process of sea area can deeply affect the chemical process and biological process,so the structure characteristics of artificial reef are the key factors affecting the flow field effect around the reef.In this study,through the hydrodynamic experiments of four kinds of reef models,including big windows box reef,big and small windows box reef,"(卐)"shaped reef and double-layer shellfish breeding reef,the influence of single reef structure on the flow field effect is analyzed,and the force conditions of different reefs under the same incoming current velocity are obtained.According to the simulation results,the safety research and calculation of five kinds of reef models are carried out,and the volumes of vortex area and upwelling area behind four kinds of reef are obtained.Using hydrodynamic model to simulate the flow field effect of reef area,optimizing the reef structure design,improving the maximum biological trapping and proliferation effect of reef,can provide theoretical guidance and scientific and technological support for the construction of reef area.

Transit Network Design:a Hybrid Enhanced Artificial Bee Colony Approach and a Case Study

A bus network design problem in a suburban area of Hong Kong is studied.The objective is to minimize the weighted sum of the number of transfers and the total travel time of passengers by restructuring bus routes and determining new frequencies.A mixed integer optimization model is developed and was solved by a Hybrid Enhanced Artificial Bee Colony algorithm(HEABC).A case study was conducted to investigate the effects of different design parameters,including the total number of bus routes available,the maximum route duration within the study area and the maximum allowable number of bus routes that originated from each terminal.The model and results are useful for improving bus service policies.