Early warning of core network capacity in space-terrestrial integrated networks

With the rapid development of low-orbit satellite com-munication networks both domestically and internationally,space-terrestrial integrated networks will become the future development trend.For space and terrestrial networks with limi-ted resources,the utilization efficiency of the entire space-terres-trial integrated networks resources can be affected by the core network indirectly.In order to improve the response efficiency of core networks expansion construction,early warning of the core network elements capacity is necessary.Based on the inte-grated architecture of space and terrestrial network,multidimen-sional factors are considered in this paper,including the number of terminals,login users,and the rules of users’migration during holidays.Using artifical intelligence(AI)technologies,the regis-tered users of the access and mobility management function(AMF),authorization users of the unified data management(UDM),protocol data unit(PDU)sessions of session manage-ment function(SMF)are predicted in combination with the num-ber of login users,the number of terminals.Therefore,the core network elements capacity can be predicted in advance.The proposed method is proven to be effective based on the data from real network.

Artificial intelligence based real-time microcirculation analysis system for laparoscopic colorectal surgery

BACKGROUND Colonic perfusion status can be assessed easily by indocyanine green(ICG)angiography to predict ischemia related anastomotic complications during laparoscopic colorectal surgery.Recently,various parameter-based perfusion analysis have been studied for quantitative evaluation,but the analysis results differ depending on the use of quantitative parameters due to differences in vascular anatomical structure.Therefore,it can help improve the accuracy and consistency by artificial intelligence(AI)based real-time analysis microperfusion(AIRAM).AIM To evaluate the feasibility of AIRAM to predict the risk of anastomotic complication in the patient with laparoscopic colorectal cancer surgery.METHODS The ICG curve was extracted from the region of interest(ROI)set in the ICG fluorescence video of the laparoscopic colorectal surgery.Pre-processing was performed to reduce AI performance degradation caused by external environment such as background,light source reflection,and camera shaking using MATLAB 2019 on an I7-8700k Intel central processing unit(CPU)PC.AI learning and evaluation were performed by dividing into a training patient group(n=50)and a test patient group(n=15).Training ICG curve data sets were classified and machine learned into 25 ICG curve patterns using a self-organizing map(SOM)network.The predictive reliability of anastomotic complications in a trained SOM network is verified using test set.RESULTS AI-based risk and the conventional quantitative parameters including T1/2max,time ratio(TR),and rising slope(RS)were consistent when colonic perfusion was favorable as steep increasing ICG curve pattern.When the ICG graph pattern showed stepped rise,the accuracy of conventional quantitative parameters decreased,but the AI-based classification maintained accuracy consistently.The receiver operating characteristic curves for conventional parameters and AI-based classification were comparable for predicting the anastomotic complication risks.Statistical performance verifications were improved in the AI-based analysis.AI analysis was evaluated as the most accurate parameter to predict the risk of anastomotic complications.The F1 score of the AI-based method increased by 31% for T1/2max,8% for TR,and 8% for RS.The processing time of AIRAM was measured as 48.03 s,which was suitable for real-time processing.CONCLUSION In conclusion,AI-based real-time microcirculation analysis had more accurate and consistent performance than the conventional parameter-based method.

Application of Artificial Neural Network in Predicting Crop Yield： A Review

Agricultural system is very complex since it deals with large data situation which comes from a number of factors. A lot of techniques and approaches have been used to identify any interactions between factors that affecting yields with the crop performances. The application of neural network to the task of solving non-linear and complex systems is promising. This paper presents a review on the use of artificial neural network （ANN） in predicting crop yield using various crop performance factors. General overview on the application of ANN and the basic concept of neural network architecture are also presented. From the literature, it has been shown that ANN provides better interpretation of crop variability compared to the other methods.

Intelligent PID controller based on ant system algorithm and fuzzy inference and its application to bionic artificial leg

A designing method of intelligent proportional-integral-derivative(PID) controllers was proposed based on the ant system algorithm and fuzzy inference. This kind of controller is called Fuzzy-ant system PID controller. It consists of an off-line part and an on-line part. In the off-line part, for a given control system with a PID controller,by taking the overshoot, setting time and steady-state error of the system unit step response as the performance indexes and by using the ant system algorithm, a group of optimal PID parameters K*p , Ti* and T*d can be obtained, which are used as the initial values for the on-line tuning of PID parameters. In the on-line part, based on Kp* , Ti*and Td* and according to the current system error e and its time derivative, a specific program is written, which is used to optimize and adjust the PID parameters on-line through a fuzzy inference mechanism to ensure that the system response has optimal transient and steady-state performance. This kind of intelligent PID controller can be used to control the motor of the intelligent bionic artificial leg designed by the authors. The result of computer simulation experiment shows that the controller has less overshoot and shorter setting time.

Forecasting Monsoon Precipitation Using Artificial Neural Networks

This paper explores the application of Artificial Intelligent (AI) techniques for climate forecast. It presents a study on modelling the monsoon precipitation forecast by means of Artificial Neural Networks (ANNs). Using the historical data of the total amount of summer rainfall over the Delta Area of Yangtze River in China, three ANNs models have been developed to forecast the monsoon precipitation in the corresponding area one year, five-year, and ten-year forward respectively. Performances of the models have been validated using a 'new' data set that has not been exposed to the models during the processes of model development and test. The experiment results are promising, indicating that the proposed ANNs models have good quality in terms of the accuracy, stability and generalisation ability.

Artificial Intelligence Technique and Wireless Sensor Networks in En­ergy Management System for Secure Power Optimization

The modern basic building blocks of a control system consist of data acquisition,dispensation of data by the system operators and the remote control of system devices.However,the physical controls,technical ex­aminations and deductions were originally implemented to aid the process and control of power system design.The complexity of the power system keeps increasing due the technical improvements,diversity and dynamic requirements.Artificial intelligence is the science of automating intelligent activities presently attainable by individuals.Intelligent system techniques may be of excessive benefit in the application of area power system con­trols.Whereas smart grid can be measured as a modern electric power grid structure for better productivity and dependability via automatic control,excessive power converters,modern communications setup,sensing and metering equipment,and modern energy management techniques estab­lished on the optimization of demand,energy and network accessibility,and so on.The enormous depiction of the entire transmission grid,in the perspective of smart grids,is quite unclear;and in Nigeria no studies have been put on ground in order for the existing network to be turn into a smart grid.In this research work emphasis is placed on generation and transmis­sion stations;power optimization using artificial intelligent techniques and wireless sensor networks for power control management system.

Prediction of landslide displacement with dynamic features using intelligent approaches

Landslide displacement prediction can enhance the efficacy of landslide monitoring system,and the prediction of the periodic displacement is particularly challenging.In the previous studies,static regression models(e.g.,support vector machine(SVM))were mostly used for predicting the periodic displacement.These models may have bad performances,when the dynamic features of landslide triggers are incorporated.This paper proposes a method for predicting the landslide displacement in a dynamic manner,based on the gated recurrent unit(GRU)neural network and complete ensemble empirical decomposition with adaptive noise(CEEMDAN).The CEEMDAN is used to decompose the training data,and the GRU is subsequently used for predicting the periodic displacement.Implementation procedures of the proposed method were illustrated by a case study in the Caojiatuo landslide area,and SVM was also adopted for the periodic displacement prediction.This case study shows that the predictors obtained by SVM are inaccurate,as the landslide displacement is in a pronouncedly step-wise manner.By contrast,the accuracy can be significantly improved using the dynamic predictive method.This paper reveals the significance of capturing the dynamic features of the inputs in the training process,when the machine learning models are adopted to predict the landslide displacement.

Optimal fuzzy PID controller with adjustable factors based on flexible polyhedron search algorithm

A new kind of optimal fuzzy PID controller is proposed, which contains two parts. One is an on line fuzzy inference system, and the other is a conventional PID controller. In the fuzzy inference system, three adjustable factors x p, x i , and x d are introduced. Their functions are to further modify and optimize the result of the fuzzy inference so as to make the controller have the optimal control effect on a given object. The optimal values of these adjustable factors are determined based on the ITAE criterion and the Nelder and Mead′s flexible polyhedron search algorithm. This optimal fuzzy PID controller has been used to control the executive motor of the intelligent artificial leg designed by the authors. The result of computer simulation indicates that this controller is very effective and can be widely used to control different kinds of objects and processes.

Temperature Intelligent Control System of Large-Scale Standing Quench Furnace

Considering some characteristics of large-scale standing quench furnace, such as great heat inertia, evident time lag, strong coupling influence, hard to establish exact mathematical models of plant and etc, an artificial intelligent fuzzy control algorithm is put forward in this paper. Through adjusting the on-off ratio of electric heating elements, the temperature in furnace is controlled accurately. This paper describes structure and qualities of the large-scale standing quench furnace briefly, introduces constitution of control system, and expounds principle and implementation of intelligent control algorithm. The applied results prove that the intelligent control system can completely satisfy the technological requirements. Namely, it can realize fast increasing temperature with a little overshoot, exact holding temperature, and well-distributed temperature in quench furnace. It has raised the output and quality of aluminum material, and brought the outstanding economic and social benefits.

Machinery fault diagnosis expert system based on case-based reasoning

A mechinery fault diagnosis expert system based on case-based reasoning (CBR) technology was established. The process of the CBR fault diagnosis is analyzed from three main aspects: expression and memory, retrieving and matching, and modification and maintenance of a case. The results indicate that the CBR method is flexible and simple to implement, and it has strong self-studying ability. Using a large enough number of case reasoning sets, it can accumulate the experience of problem solving, avoid the difficulty of knowledge acquisition, shorten the course of solving problems, improve efficiency of reasoning, and save the time of developing.

Nano-frictional mechano-reinforcing porous nanowires scaffolds

Artificial biomaterials with dynamic mechano-responsive behaviors similar to those of biological tissues have been drawing great attention.In this study,we report a TiO_(2)-based nanowire(TiO_(2)NWs)scaffolds,which exhibit dynamic mechano-responsive behaviors varying with the number and amplitude of nano-deformation cycles.It is found that the elastic and adhesive forces in the TiO_(2)NWs scaffolds can increase significantly after multiple cycles of nano-deformation.Further nanofriction experiments show the triboelectric effect of increasing elastic and adhesive forces during the nano-deformation cycles of TiO_(2)NWs scaffolds.These properties allow the TiO_(2)NW scaffolds to be designed and applied as intelligent artificial biomaterials to simulate biological tissues in the future.

The national multi-center artificial intelligent myopia prevention and control project

In recent years,the incidence of myopia has increased at an alarming rate among children and adolescents in China.The exploration of an effective prevention and control method for myopia is in urgent need.With the development of information technology in the past decade,artificial intelligence with the Internet of Things technology(AIoT)is characterized by strong computing power,advanced algorithm,continuous monitoring,and accurate prediction of long-term progression.Therefore,big data and artificial intelligence technology have the potential to be applied to data mining of myopia etiology and prediction of myopia occurrence and development.More recently,there has been a growing recognition that myopia study involving AIoT needs to undergo a rigorous evaluation to demonstrate robust results.

Artificial intelligence powered large-scale renewable integrations in multi-energy systems for carbon neutrality transition: Challenges and future perspectives

The vigorous expansion of renewable energy as a substitute for fossil energy is the predominant route of action to achieve worldwide carbon neutrality. However, clean energy supplies in multi-energy building districts are still at the preliminary stages for energy paradigm transitions. In particular, technologies and methodologies for large-scale renewable energy integrations are still not sufficiently sophisticated, in terms of intelligent control management. Artificial intelligent (AI) techniques powered renewable energy systems can learn from bioinspired lessons and provide power systems with intelligence. However, there are few in-depth dissections and deliberations on the roles of AI techniques for large-scale integrations of renewable energy and decarbonisation in multi-energy systems. This study summarizes the commonly used AI-related approaches and discusses their functional advantages when being applied in various renewable energy sectors, as well as their functional contribution to optimizing the operational control modalities of renewable energy and improving the overall operational effectiveness. This study also presents practical applications of various AI techniques in large-scale renewable energy integration systems, and analyzes their effectiveness through theoretical explanations and diverse case studies. In addition, this study introduces limitations and challenges associated with the large-scale renewable energy integrations for carbon neutrality transition using relevant AI techniques, and proposes further promising research perspectives and recommendations. This comprehensive review ignites advanced AI techniques for large-scale renewable integrations and provides valuable informational instructions and guidelines to different stakeholders (e.g., engineers, designers and scientists) for carbon neutrality transition.

Development of new correlations for the oil formation volume factor in oil reservoirs using artificial intelligent white box technique

Oil formation volume factor(OFVF)is considered one of the main parameters required to characterize the crude oil.OFVF is needed in reservoir simulation and prediction of the oil reservoir performance.Existing correlations apply for specific oils and cannot be extended to other oil types.In addition,big errors were obtained when we applied existing correlations to predict the OFVF.There is a massive need to have a global OFVF correlation that can be used for different oils with less error.The objective of this paper is to develop a new empirical correlation for oil formation volume factor(OFVF)prediction using artificial intelligent techniques(AI)such as;artificial neural network(ANN),adaptive neuro-fuzzy inference system(ANFIS),and support vector machine(SVM).For the first time we changed the ANN model to a white box by extracting the weights and the biases from AI models and form a new empirical equation for OFVF prediction.In this paper we present a new empirical correlation extracted from ANN based on 760 experimental data points for different oils with different compositions.The results obtained showed that the ANN model yielded the highest correlation coefficient(0.997)and lowest average absolute error(less than 1%)for OFVF prediction as a function of the specific gravity of gas,the dissolved gas to oil ratio,the oil specific gravity,and the temperature of the reservoir compared with ANFIS and SVM.The developed empirical equation from the ANN model outperformed the previous empirical correlations and AI models for OFVF prediction.It can be used to predict the OFVF with a high accuracy.

Enabling technology and core theory of synthetic biology

Synthetic biology provides a new paradigm for life science research(“build to learn”)and opens the future journey of biotechnology(“build to use”).Here,we discuss advances of various principles and technologies in the mainstream of the enabling technology of synthetic biology,including synthesis and assembly of a genome,DNA storage,gene editing,molecular evolution and de novo design of function proteins,cell and gene circuit engineering,cell-free synthetic biology,artificial intelligence(AI)-aided synthetic biology,as well as biofoundries.We also introduce the concept of quantitative synthetic biology,which is guiding synthetic biology towards increased accuracy and predictability or the real rational design.We conclude that synthetic biology will establish its disciplinary system with the iterative development of enabling technologies and the maturity of the core theory.

A data-driven approach to predict compressional and shear wave velocities in reservoir rocks

Compressional and shear wave velocities(V_(p)and V_(s)respectively)are essential reservoir parameters that can be used to delineate lithology,calculate porosity,identify reservoir fluids,evaluate fracture and calculate mechanical properties of rocks.In this study,the potential application of intelligent systems in predicting V_(p)and V_(s)of reservoir rocks is presented.To date,considerable efforts are being carried out to obtain the best set of parameters capable of predicting V_(p)and V_(s)with a high degree of accuracy.Three intelligent models namely artificial neural network(ANN),adaptive neuro-fuzzy inference system(ANFIS)and least square support vector machine(LSSVM)were used in this study.The different models were based on the available information sourced from wireline log data.Parametric studies showed that measured depth,neutron porosity,gamma-ray,and density log data are vital in predicting both V_(p)and V_(s).In developing the models,a comprehensive dataset available from one of the oil fields in the Norwegian North Basin was used.In evaluating the different models,two different statistical parameters namely Pearson’s correlation coefficient(R^(2))and root mean square error(RMSE)were considered.It was found that the LSSVM model is the most accurate technique for predicting both V_(p)and V_(s).LSSVM model predicted the V_(p)with R^(2)and RSME of 0.9706 and 0.0893 respectively.In addition,the model showed an excellent accuracy level in the prediction of V_(s)with R^(2)and RMSE of 0.9991 and 0.0457 respectively.The proposed approach,if implemented,is crucial for geoscientists,reservoir and drilling engineers working on reservoir characterization and drilling operations.

From Eliza to XiaoIce：challenges and opportunities with social chatbots

Conversational systems have come a long way since their inception in the 1960 s.After decades of research and development,we have seen progress from Eliza and Parry in the 1960 s and 1970 s,to task-completion systems as in the Defense Advanced Research Projects Agency(DARPA) communicator program in the 2000 s,to intelligent personal assistants such as Siri,in the 2010 s,to today's social chatbots like Xiao Ice.Social chatbots' appeal lies not only in their ability to respond to users' diverse requests,but also in being able to establish an emotional connection with users.The latter is done by satisfying users' need for communication,affection,as well as social belonging.To further the advancement and adoption of social chatbots,their design must focus on user engagement and take both intellectual quotient(IQ) and emotional quotient(EQ) into account.Users should want to engage with a social chatbot;as such,we define the success metric for social chatbots as conversation-turns per session(CPS).Using Xiao Ice as an illustrative example,we discuss key technologies in building social chatbots from core chat to visual awareness to skills.We also show how Xiao Ice can dynamically recognize emotion and engage the user throughout long conversations with appropriate interpersonal responses.As we become the first generation of humans ever living with artificial intelligenc(AI),we have a responsibility to design social chatbots to be both useful and empathetic,so they will become ubiquitous and help society as a whole.

ATC simulation for flight training:The missing link

This paper presents the state of piloted flight simulation fidelity with a focus on the missing link needed to complete the flight simulation experience,namely the simulated ATC environment(SATCE).To date,there has been a great deal of effort invested in providing the highest level of flight realism possible.However,little investment has gone into systems which are used to improve communication skills with ATC while in a populated active airspace.It is important to note that the relatively few SATCEs is not due to the lack of technology,since such products have been available for about a decade.The primary reason for its absence is the inability and unwillingness for operators to justify the investment in such a training tool.In the meantime,the aviation industry has recognized that pilots need to have better communication skills while operating in various conditions.Consequently ICAO,with help from ARINC Industry Activities/FSEMC,has already taken steps to recommend the inclusion of SATCE characteristics in flight simulation devices.The aviation and research communities need to assist efforts by producing the necessary studies and metrics which can be used to evaluate and validate SATCEs used in the flight training.

Reducing deep learning network structure through variable reduction methods in crop modeling

Crop models are widely used to predict plant growth,water input requirements,and yield.However,existing models are very complex and require hundreds of variables to perform accurately.Due to these shortcomings,large-scale applications of crop models are limited.In order to address these limitations,reliable crop models were developed using a deep neural network(DNN)–a new approach for predicting crop yields.In addition,the number of required input variables was reduced using three common variable selection techniques:namely Bayesian variable selection,Spearman's rank correlation,and Principal Component Analysis Feature Extraction.The reduced-variableDNN modelswere capable of estimating future crop yields for 10,000,000 differentweather and irrigation scenarios while maintaining comparable accuracy levels to the original model that used all input variables.To establish clear superiority of the methodology,the results were also compared with a very recent feature selection algorithm called min-redundancy max-relevance(mRMR).The results of this study showed that the Bayesian variable selection was the best method for achieving the aforementioned goals.Specifically,the final Bayesian-based DNN model with a structure of 10 neurons in 5 layers performed very similarly(78.6%accuracy)to the original DNN cropmodel with 400 neurons in 10 layers,even though the size of the neural network was reduced by 80-fold.This effort can help promote sustainable agricultural intensifications through the large-scale application of crop models.