Teaching Design on Digital Environment of Intelligence Learning

The practice and research on the establishment of digital course environment and the reform of learning way breaks through the integration of information technology and teaching courses. Digital course environment promotes the teaching interest and becomes the learning under the support of learning terminal, which must make students happy. The digitalization of teaching materials, platform of learning and learning according to requirements promotes the establishment of happy class. Establishment of learning environment is the basis of realizing the reform of learning and teaching ways. With the rapid development of information technology, intelligence learning is the necessary outcome of deep integration with education learning.

Environmental Analysis Using Integrated GIS and Spatial Configurations in Israel

The scope for environmental analysis constitutes a critical factor in recent times, yet demanding importance due to the concerns of environmental sustainability. The study aims at analysing the prospects of implementing an integrated GIS and spatial configuration for environment analysis in Israel. The study adopts an empirical study design to consider the multi-dimensional utilisation of an integrated GIS and spatial configuration for environment analysis. The study considers the materials and methods of the GIS system modelling as well, consisting of satellite imagery, GPS-based location identification, Esri ArcGIS, CyberGIS, and BIM integration to present a comprehensive system for the environmental analysis of Israel. The results of the study indicate that the threats of natural disasters and climate change can be identified based on the synergy of spatial data within an integrated GIS modelling. In many cases, it is also used in collaboration with a BIM to ensure that planning and decision-making processes are sustainable, economically beneficial and environmentally considered. Thus, it is concluded that environmental analysis through the projection of visually represented satellite imagery within an integrated GIS with spatial configurations in Israel can minimise the conflicts between the infrastructural designs, human activities, and environmental sustainability.

Framework and Key Technologies of Human-machine Hybrid-augmented Intelligence System for Large-scale Power Grid Dispatching and Control

With integration of large-scale renewable energy,new controllable devices,and required reinforcement of power grids,modern power systems have typical characteristics such as uncertainty,vulnerability and openness,which makes operation and control of power grids face severe security challenges.Application of artificial intelligence(AI)technologies represented by machine learning in power grid regulation is limited by reliability,interpretability and generalization ability of complex modeling.Mode of hybrid-augmented intelligence(HAI)based on human-machine collaboration(HMC)is a pivotal direction for future development of AI technology in this field.Based on characteristics of applications in power grid regulation,this paper discusses system architecture and key technologies of human-machine hybrid-augmented intelligence(HHI)system for large-scale power grid dispatching and control(PGDC).First,theory and application scenarios of HHI are introduced and analyzed;then physical and functional architectures of HHI system and human-machine collaborative regulation process are proposed.Key technologies are discussed to achieve a thorough integration of human/machine intelligence.Finally,state-of-theart and future development of HHI in power grid regulation are summarized,aiming to efficiently improve the intelligent level of power grid regulation in a human-machine interactive and collaborative way.

Vision‐audio fusion SLAM in dynamic environments

Moving humans,agents,and subjects bring many challenges to robot self‐localisation and environment perception.To adapt to dynamic environments,SLAM researchers typically apply several deep learning image segmentation models to eliminate these moving obstacles.However,these moving obstacle segmentation methods cost too much computation resource for the onboard processing of mobile robots.In the current industrial environment,mobile robot collaboration scenario,the noise of mobile robots could be easily found by on‐board audio‐sensing processors and the direction of sound sources can be effectively acquired by sound source estimation algorithms,but the distance estimation of sound sources is difficult.However,in the field of visual perception,the 3D structure information of the scene is relatively easy to obtain,but the recognition and segmentation of moving objects is more difficult.To address these problems,a novel vision‐audio fusion method that combines sound source localisation methods with a visual SLAM scheme is proposed,thereby eliminating the effect of dynamic obstacles on multi‐agent systems.Several heterogeneous robots experiments in different dynamic scenes indicate very stable self‐localisation and environment reconstruction performance of our method.

An Intelligent Agent Based on Virtual Geographic Environment System

On the basis of previous work, this paper designs an intelligent agent based on virtual geographic environment (VGE) system that is characterized by huge data, rapid computation, multi\|user, multi\|thread and intelligence and issues challenges to traditional GIS models and algorithms. The new advances in software and hardware technology lay a reliable basis for system design, development and application.

Deep Learning Based Energy Consumption Prediction on Internet of Things Environment

The creation of national energy strategy cannot proceed without accurate projections of future electricity consumption;this is because EC is intimately tied to other forms of energy,such as oil and natural gas.For the purpose of determining and bettering overall energy consumption,there is an urgent requirement for accurate monitoring and calculation of EC at the building level using cutting-edge technology such as data analytics and the internet of things(IoT).Soft computing is a subset of AI that tries to design procedures that are more accurate and reliable,and it has proven to be an effective tool for solving a number of issues that are associated with the use of energy.The use of soft computing for energy prediction is an essential part of the solution to these kinds of challenges.This study presents an improved version of the Harris Hawks Optimization model by combining it with the IHHODL-ECP algorithm for use in Internet of Things settings.The IHHODL-ECP model that has been supplied acts as a useful instrument for the prediction of integrated energy consumption.In order for the raw electrical data to be compatible with the subsequent processing in the IHHODL-ECP model,it is necessary to perform a preprocessing step.The technique of prediction uses a combination of three different kinds of deep learning models,namely DNN,GRU,and DBN.In addition to this,the IHHO algorithm is used as a technique for making adjustments to the hyperparameters.The experimental result analysis of the IHHODL-ECP model is carried out under a variety of different aspects,and the comparison inquiry highlighted the advantages of the IHHODL-ECP model over other present approaches.According to the findings of the experiments conducted with an hourly time resolution,the IHHODL-ECP model obtained a MAPE value of 33.85,which was lower than those produced by the LR,LSTM,and CNN-LSTM models,which had MAPE values of 83.22,44.57,and 34.62 respectively.These findings provided evidence of the IHHODL-ECP model’s improved ability to provide accurate forecasts.

Applicability of denoising-based artificial intelligence to forecast the environmental externalities

The current study attempts to compare the hybrid artificial intelligence models to forecast the environmental externalities in Saudi Arabia.We have used the denoising based artificial intelligence models to construct hybrid models.While comparing the denoising techniques,the CSD-based denoising has outperformed.However,we have used the CSD-based hybrid models.CSD-ANN and CSD-RNN are used for denoising-based artificial intelligence models,whereas CSD-ARIMA is used for denoising-based traditional models.All these models are used to check and compare their performance in terms of level and direction of prediction for PM_(10).The results show that the CSD-based ANN model has a higher predictability for PM_(10) levels in Saudi Arabia due to low error values and higher Dstat values.In comparing original and forecasted data,the superiority of CSD-ANN is evident in predicting the PM_(10) in Saudi Arabia.Hence,this hybrid model can predict the environmental externalities for non-linear and highly noised data.Moreover,the findings can be useful in achieving the sustainable development goal.

INTELLIGENT MECHANISM TO SUPPORT DFX ABILITIES IN AUTOMATED DESIGNER'S ENVIRONMENT

The paper aims at providing a computerized design environment to support product design practically. The authors solved this problem by taking the product life-cycle issues into consideration as more as possible during the design process from the designer-oriented perspective of view. Design for X-abilities (DFX) is an effective approach to this philosophy. So the paper mainly presents the infrastructure of an intelligent DFX mechanism which is the essential part of the developed product design environment. At first the designer-oriented computer environment DesignerSpace is introduced for understanding the designer how to implement design activities and DFX method better. In order to integrate design knowledge from downstream aspects for the optimization and the design decision-making, an intelligent DFX mechanism is developed to incorporate knowledge base, algorithm-base and monitoring/debugging tools. DesignerSpace is implemented with DFX abilities and applied to the blisk design of aircraft engine and the further development is strongly intended.

Review on the Sensor Technology Applied in the Intelligent Learning Environment

The development of sensor technology promotes the transformation of the intelligent learning environment. Through the research of the sensor technology application, this paper described the intelligent learning environment application system of the sensor technology and the management functions, conference organization, a library building, information collection, monitoring and equipment sharing function the role of sensor technology played in the intelligent learning environment.

Design and Implementation of an Intelligent Monitoring and Early Warning System for Kitchen Garbage Treatment

With a population of 1.4 billion in China and a huge daily output of kitchen waste,intelligent treatment of kitchen waste is imperative.This article elaborates on the design and implementation of an intelligent monitoring and early warning system from five aspects:system architecture design,hardware equipment selection and configuration,data collection and processing flow,early warning algorithm and model development,and system integration and testing verification.It also points out the advantages of the intelligent monitoring and early warning system in kitchen waste treatment.

Research on the Multi-Interactive Intelligent Teaching Model of College English in the Information Technology Environment

The multi-interactive teaching mode based on the information technology is a new teaching mode, which promotes students' autonomous learning and research-oriented learning through the online and offline effective interaction in and out of class, fully embodying the principle of the learning-orientation. Based on this, this paper focuses on the connotation and the theoretical basis o f the multi-interactive teaching model, as well as its implementation practices and characteristics, with a view o f providing help to improve the quality of the college English teaching.

Heading toward Artificial Intelligence 2.0

With the popularization of the Intemet, permeation of sensor networks, emergence of big data, increase in size of the information community, and interlinking and fusion of data and information throughout human society, physical space, and cyberspace, the information environment related to the current development of artificial intelligence （AI） has profoundly changed. AI faces important adjustments, and scientific foundations are confronted with new breakthroughs, as AI enters a new stage： AI 2.0. This paper briefly reviews the 60-year developmental history of AI, analyzes the external environment promoting the formation of AI 2.0 along with changes in goals, and describes both the beginning of the technology and the core idea behind AI 2.0 development. Furthermore, based on combined social demands and the information environment that exists in relation to Chinese development, suggestions on the develoDment of Al 2.0 are given.

Human-Machine Shared Lateral Control Strategy for Intelligent Vehicles Based on Human Driver Risk Perception Reliability

Intelligent vehicle(Ⅳ)technology has developed rapidly in recent years.However,achieving fully unmanned driving still presents numerous challenges,which means that human drivers will continue to play a vital role in vehicle operation for the foreseeable future.Human-machine shared driving,involving cooperation between a human driver and an automated driving system(AVS),has been widely regarded as a necessary stage for the development of IVs.Focusing onⅣdriving safety,this study proposed a human-machine shared lateral control strategy(HSLCS)based on the reliability of driver risk perception.The HSLCS starts by identifying the effective areas of driver risk perception based on eye movements.It establishes an anisotropic driving risk field,which serves as the foundation for the AVS to assess risk levels.Building upon the cumulative and diminishing effects of risk perception,the proposed approach leverages the driver's risk perception effective area and converts the risk field into a representation aligned with the driver's perspective.Subsequently,it quantifies the reliability of the driver's risk perception by using area-matching rules.Finally,based on the driver’s risk perception reliability and dif-ferences in lateral driving operation between the human driver and the AVS,the dynamic distribution of driving authority is achieved through a fuzzy rule-based system,and the human-machine shared lateral control is completed by using model predictive control.The HSLCS was tested across various scenarios on a driver-in-the-loop test platform.The results show that the HSLCS can realize the synergy and complementarity of human and machine intelligence,effectively ensuring the safety ofⅣoperation.

Design of an Information Security Service for Medical Artificial Intelligence

The medical convergence industry has gradually adopted ICT devices,which has led to legacy security problems related to ICT devices.However,it has been difficult to solve these problems due to data resource issues.Such problems can cause a lack of reliability in medical artificial intelligence services that utilize medical information.Therefore,to provide reliable services focused on security internalization,it is necessary to establish a medical convergence environment-oriented security management system.This study proposes the use of system identification and countermeasures to secure systemreliabilitywhen using medical convergence environment information in medical artificial intelligence.We checked the life cycle of medical information and the flow and location of information,analyzed the security threats that may arise during the life cycle,and proposed technical countermeasures to overcome such threats.We verified the proposed countermeasures through a survey of experts.Security requirements were defined based on the information life cycle in the medical convergence environment.We also designed technical countermeasures for use in the security management systems of hospitals of diverse sizes.

Paving the Way for Economically Accepted and Technically Pronounced Smart Radio Environment

Recently,Reconfigurable Intelligent Surfaces(RISs)have been introduced to provide the necessary flexibility for the design of the Smart Radio Environment(SRE),which can be optimally shaped to facilitate efficient signal transmissions.In line with the concept of smart cities,SRE needs to be carefully designed with respect to the city infrastructure and utilization of resources.In this paper,we provide our vision on RIS integration into future Smart Cities by highlighting the potential technical,environmental,and economic motivations of RIS deployment in harmony with various ecosystems at a city level such as buildings facades.To this end,we are pointing out some scenarios for mitigating the conflict between RIS realization and the existing building facade’s ecosystems such as advertising display and solar cells on building walls.Also,in this fashion,the proposed vision supports a win-win relationship between all stakeholders of different ecosystems.This study presents guidelines for not only enabling seamless economically accepted RIS widespread utilization but,also more technically sounding SRE by supporting enhanced RIS features and more advanced applications that cannot be attained by traditional passive RIS.Moreover,based on the current research directions,we offer promising insights for cost-effective mass pro-duction through motivating two scenarios of“all on silicon”and“all as metasurface”fabrication technology.With this study,we aim to encourage the metasurface researchers,that for a broad deployment of the technical solution,economic,environmental,and other commercial requirements should be planned together,early on in the design phase.

Swarm Intelligence Research:From Bio-inspired Single-population Swarm Intelligence to Human-machine Hybrid Swarm Intelligence

Swarm intelligence has become a hot research field of artificial intelligence.Considering the importance of swarm intelli-gence for the future development of artificial intelligence,we discuss and analyze swarm intelligence from a broader and deeper perspect-ive.In a broader sense,we are talking about not only bio-inspired swarm intelligence,but also human-machine hybrid swarm intelli-gence.In a deeper sense,we discuss the research using a three-layer hierarchy:in the first layer,we divide the research of swarm intelli-gence into bio-inspired swarm intelligence and human-machine hybrid swarm intelligence;in the second layer,the bio-inspired swarm intelligence is divided into single-population swarm intelligence and multi-population swarm intelligence;and in the third layer,we re-view single-population,multi-population and human-machine hybrid models from different perspectives.Single-population swarm intel-ligence is inspired by biological intelligence.To further solve complex optimization problems,researchers have made preliminary explor-ations in multi-population swarm intelligence.However,it is difficult for bio-inspired swarm intelligence to realize dynamic cognitive in-telligent behavior that meets the needs of human cognition.Researchers have introduced human intelligence into computing systems and proposed human-machine hybrid swarm intelligence.In addition to single-population swarm intelligence,we thoroughly review multi-population and human-machine hybrid swarm intelligence in this paper.We also discuss the applications of swarm intelligence in optimization,big data analysis,unmanned systems and other fields.Finally,we discuss future research directions and key issues to be studied in swarm intelligence.

Hybrid Deep Learning Enabled Air Pollution Monitoring in ITS Environment

Intelligent Transportation Systems(ITS)have become a vital part in improving human lives and modern economy.It aims at enhancing road safety and environmental quality.There is a tremendous increase observed in the number of vehicles in recent years,owing to increasing population.Each vehicle has its own individual emission rate;however,the issue arises when the emission rate crosses a standard value.Owing to the technological advances made in Artificial Intelligence(AI)techniques,it is easy to leverage it to develop prediction approaches so as to monitor and control air pollution.The current research paper presents Oppositional Shark Shell Optimization with Hybrid Deep Learning Model for Air Pollution Monitoring(OSSOHDLAPM)in ITS environment.The proposed OSSO-HDLAPM technique includes a set of sensors embedded in vehicles to measure the level of pollutants.In addition,hybridized Convolution Neural Network with Long Short-Term Memory(HCNN-LSTM)model is used to predict pollutant level based on the data attained earlier by the sensors.In HCNN-LSTM model,the hyperparameters are selected and optimized using OSSO algorithm.In order to validate the performance of the proposed OSSO-HDLAPM technique,a series of experiments was conducted and the obtained results showcase the superior performance of OSSO-HDLAPM technique under different evaluation parameters.

Design Path of Ecological Learning Environment Based on Spatial Resilience

Nowadays,the city’s focus on education makes the improvement of learning environment become a new demand for development.From the the perspective of ecology,the theory of"resilience"is introduced to break the stereotype of the existing learning environment and create a space for dialogue with nature from inside to outside.At the same time,the learning environment is divided into observation learning space,exploration learning space and communication learning space by theory and method of multiple intelligence integrating teaching and learning.Through the analysis on resilience characteristics of three types of space,eight basic unit forms of learning environment design are obtained.Arrangement and combination of its form can improve the campus environment,increase children’s learning efficiency and the risk response ability of campus environment,and give children the opportunity to interact with and understand nature in the learning process,so as to provide reference for the diversified development of education and learning space.

Research on Novel and Innovative Music Education Model based on Multiple Intelligences Theory and New Media

With the rapid development of new media and education theories, the combination of the two aspects is urgently needed. In this paper, we conduct theoretical analysis on novel and innovative music education model based on multiple intelligences theory and new media. The theory of multiple intelligence education concept is to change too pay attention to the tendency of knowledge. The novelty of new media, not only in the form of new information carrier and communication channel, also include technological progress brings new propagation mode. Our proposed combined model will enhance the performance of the current teaching activity which is meaningful.

Interaction and the Genesis of Experience: A Phenomenological Contribution for Meaningful Embodied Artificial Intelligence

In this article I will address the issue of the meaning of Embodied Artificial Intelligence(EAI)as it is configured today.My starting point is the refined interactive perspective on the semantics of EAI,as was recently suggested by Froese and colleagues.This perspective rests on the assumption that the concept of human bodily subjectivity must be extended to include meaning-making processes,which are enabled by advanced AI systems that may be incorporated in the human biological body.After having clarified the technical background,I will introduce the genetic component of the phenomenological method as a suitable tool to face the aforementioned issue.Towards this end,I will place the genetic method in the context of the so-called New Human-Machine Interaction(New HMI).I will further outline a genetic phenomenology of visual embodiment,suggesting a futuristic application based on the thesis of the“technological supplementation of phenomenological methodology”through the synthetic method.The case at stake is that of patients with a severe clinical picture characterised by the loss of corneal function,who in the near future could be treated with synthetic corneal prosthetic implants produced by a 3D bio-printing process by using an advanced EAI technique.I will conclude this article with a brief review of the main problems that still remain open.