Archimedean copula estimation of distribution algorithm based on artificial bee colony algorithm

The artificial bee colony （ABC） algorithm is a com- petitive stochastic population-based optimization algorithm. How- ever, the ABC algorithm does not use the social information and lacks the knowledge of the problem structure, which leads to in- sufficiency in both convergent speed and searching precision. Archimedean copula estimation of distribution algorithm （ACEDA） is a relatively simple, time-economic and multivariate correlated EDA. This paper proposes a novel hybrid algorithm based on the ABC algorithm and ACEDA called Archimedean copula estima- tion of distribution based on the artificial bee colony （ACABC） algorithm. The hybrid algorithm utilizes ACEDA to estimate the distribution model and then uses the information to help artificial bees to search more efficiently in the search space. Six bench- mark functions are introduced to assess the performance of the ACABC algorithm on numerical function optimization. Experimen- tal results show that the ACABC algorithm converges much faster with greater precision compared with the ABC algorithm, ACEDA and the global best （gbest）-guided ABC （GABC） algorithm in most of the experiments.

“Deep-time Digital Basin” Based on Big Data and Artificial Intelligence

1 Introduction Information technology has been playing an ever-increasing role in geoscience.Sphisicated database platforms are essential for geological data storage,analysis and exchange of Big Data(Feblowitz,2013;Zhang et al.,2016;Teng et al.,2016;Tian and Li,2018).The United States has built an information-sharing platform for state-owned scientific data as a national strategy.

Prediction of Superconductivity for Oxides Based on Structural Parameters and Artificial Neural Network Method

Superconductive properties for oxides were predicted by artificial neural network (ANN) method with structural and chemical parameters as inputs. The predicted properties include superconductivity for oxides, distributed ranges of the superconductive transition temperature (Tc) for complex oxides, and Tc values for cuprate superconductors. The calculated results indicated that the adjusted ANN can be used to predict superconductive properties for unknown oxides.

Artificial Neural Networks for Event Based Rainfall-Runoff Modeling

The Artificial Neural Network (ANN) approach has been successfully used in many hydrological studies especially the rainfall-runoff modeling using continuous data. The present study examines its applicability to model the event-based rainfall-runoff process. A case study has been done for Ajay river basin to develop event-based rainfall-runoff model for the basin to simulate the hourly runoff at Sarath gauging site. The results demonstrate that ANN models are able to provide a good representation of an event-based rainfall-runoff process. The two important parameters, when predicting a flood hydrograph, are the magnitude of the peak discharge and the time to peak discharge. The developed ANN models have been able to predict this information with great accuracy. This shows that ANNs can be very efficient in modeling an event-based rainfall-runoff process for determining the peak discharge and time to the peak discharge very accurately. This is important in water resources design and management applications, where peak discharge and time to peak discharge are important input

Artificial Intelligence-Driven Fog-Computing-Based Radio Access Networks

The edge cache is an effective way to reduce the heavy traffic load and the end-to-end latency in radio access networks(RANs)for supporting a number of critical Internet of Things(IoT)services and applications.It has been verified to provide high spectral efficiency,high energy efficiency,and low latency.To exploit the advantages of edge cache,a paradigm of fog computing-based radio access networks(F-RANs)has emerged to provide great flexibility to satisfy quality-of-service requirements of various IoT applications in the fifth generation(5G)wireless systems.

Prediction Model of Soil Nutrients Loss Based on Artificial Neural Network

On the basis of Artificial Neural Network theory, a back propagation neural network with one middle layer is building in this paper, and its algorithms is also given, Using this BP network model, study the case of Malian-River basin. The results by calculating show that the solution based on BP algorithms are consis- tent with those based multiple - variables linear regression model. They also indicate that BP model in this paper is reasonable and BP algorithms are feasible.

Yarn Quality Prediction and Diagnosis Based on Rough Set and Knowledge-Based Artificial Neural Network

In the spinning process, some key process parameters( i. e.,raw material index inputs) have very strong relationship with the quality of finished products. The abnormal changes of these process parameters could result in various categories of faulty products. In this paper, a hybrid learning-based model was developed for on-line intelligent monitoring and diagnosis of the spinning process. In the proposed model, a knowledge-based artificial neural network( KBANN) was developed for monitoring the spinning process and recognizing faulty quality categories of yarn. In addition,a rough set( RS)-based rule extraction approach named RSRule was developed to discover the causal relationship between textile parameters and yarn quality. These extracted rules were applied in diagnosis of the spinning process, provided guidelines on improving yarn quality,and were used to construct KBANN. Experiments show that the proposed model significantly improve the learning efficiency, and its prediction precision is improved by about 5. 4% compared with the BP neural network model.

Artificial Intelligence Embedded Object-Oriented Methodology For Model Based Decision Support

The paper presents the coupling of artificial intelligence-AI and Object-oriented methodology applied for the construction of the model-based decision support system MBDSS.The MBDSS is designed for support the strategic decision making lead to the achievemellt of optimal path towardsmarket economy from the central planning situation in China. To meet user's various requirements,a series of innovations in software development have been carried out, such as system formalization with OBFRAMEs in an object-oriented paradigm for problem solving automation and techniques of modules intelligent cooperation, hybrid system of reasoning, connectionist framework utilization,etc. Integration technology has been highly emphasized and discussed in this article and an outlook to future software engineering is given in the conclusion section.

Artificial Neural Network Method Based on Expert Knowledge and Its Application to Quantitative Identification of Potential Seismic Sources

In this paper,an approach is developed to optimize the quality of the training samples in the conventional Artificial Neural Network(ANN)by incorporating expert knowledge in the means of constructing expert-rule samples from rules in an expert system,and through training by using these samples,an ANN based on expert-knowledge is further developed.The method is introduced into the field of quantitative identification of potential seismic sources on the basis of the rules in an expert system.Then it is applied to the quantitative identification of the potential seismic sources in Beijing and its adjacent area.The result indicates that the expert rule based on ANN method can well incorporate and represent the expert knowledge in the rules in an expert system,and the quality of the samples and the efficiency of training and the accuracy of the result are optimized.

Physics-Based Active Learning for Design Space Exploration and Surrogate Construction for Multiparametric Optimization

The sampling of the training data is a bottleneck in the development of artificial intelligence(AI)models due to the processing of huge amounts of data or to the difficulty of access to the data in industrial practices.Active learning(AL)approaches are useful in such a context since they maximize the performance of the trained model while minimizing the number of training samples.Such smart sampling methodologies iteratively sample the points that should be labeled and added to the training set based on their informativeness and pertinence.To judge the relevance of a data instance,query rules are defined.In this paper,we propose an AL methodology based on a physics-based query rule.Given some industrial objectives from the physical process where the AI model is implied in,the physics-based AL approach iteratively converges to the data instances fulfilling those objectives while sampling training points.Therefore,the trained surrogate model is accurate where the potentially interesting data instances from the industrial point of view are,while coarse everywhere else where the data instances are of no interest in the industrial context studied.

An elasto-plastic constitutive model of moderate sandy clay based on BC-RBFNN

Application research of neural networks to geotechnical engineering has become a hotspot nowadays.General model may not reach the predicting precision in practical application due to different characteristics in different fields.In allusion to this,an elasto-plastic constitutive model based on clustering radial basis function neural network(BC-RBFNN) was proposed for moderate sandy clay according to its properties.Firstly,knowledge base was established on triaxial compression testing data;then the model was trained,learned and emulated using knowledge base;finally,predicting results of the BC-RBFNN model were compared and analyzed with those of other intelligent model.The results show that the BC-RBFNN model can alter the training and learning velocity and improve the predicting precision,which provides possibility for engineering practice on demanding high precision.

基于tree part-based模型的目标识别和定位

提出了一种基于tree part-based模型的目标识别和定位的方法。该方法将tree part-based模型应用于静态图像中单一目标的识别定位,使用可变形模板(Deformable template)处理测试图像,提取与训练模型中的每一部分关联度在一定阈值范围内的特征点,结合外观模型和空间模型实现目标识别和定位。实验结果表明,该方法提高了目标识别和定位的准确率和可靠性。

A brief overview of traditional Chinese medicine prescription powered by artificial intelligence

Traditional Chinese medicine prescription is one of the treasures of traditional Chinese medicine(TCM).There are tens of thousands TCM prescriptions accumulated in the past thousands of years,corresponding to different diseases,symptoms and therapeutic goals.The correspondences are so complicated that there is an urgent need to leverage new technologies such as artificial intelligence(AI)to analyze,understand and utilize them effectively.In this paper,we present a brief overview of this direction,where current research progress on TCM prescription powered by AI is summarized.Our summarization focuses on three aspects,TCM prescription mining that aims at understanding the TCM prescription,TCM prescription or herb knowledge base construction that aims at extracting knowledge to support the TCM prescription-related study,and TCM prescription discovery that aims at utilizing AI technologies to further energize TCM.It is encouraging to see that steady progress in this direction has been made recently.Besides,a toy experiment on image-based TCM herb recognition by using convolutional neural networks is also conducted.It basically verifies that it is promising to use AI technologies to address challenging tasks in TCM.We also point out several research topics that could be cooperatively performed by researchers from the two disciplines.

Integrated Digital Design for Radar Typical Structure Using Knowledge Based Engineering

With the deep research of knowledge engineering and the widespread applications of CAD technology, the joining of knowledge engineering with CAD is the focus of advanced manufacturing. An intelligent approach is presented for configurating the typical structural components of radar. Case based reasoning, rule based reasoning, geometric, constraint solving and domain ontology are merged into a compound knowledge model. The main frame and workflow of radar typical structural component design system are illustrated. Experiments show this approach is efficient and effective.

HYBRID STRATIFIED ATMS AND ANN FOR CASE-BASED REASONING

Case Based Reasoning (CBR) is a powerful problem solving technique in AI, but the traditional CBR techniques have its limitations. We hybridized stratified ATMS and ANN for CBR which can deal with case representation, case retrieving, case adapting, learning from failure more effectively. The structure of our CBR system and algorithms of case base reasoning in our CBR system were presented.

CASE STORAGE BASED ON RELATIONAL DATABASE

This paper focused on the integration of case base and relational database management system (RDBMS). The organizational and commercial impact will be far greater if the case based reasoning (CBR) system is integrated with main stream information system, which is exemplified by RDBMS. The scalability, security and robustness provided by a commercial RDBMS facilitate the CBR system to manage the case base. The virtual table in relational database (RDB) is important for CBR systems to implement the flexibility of case template. It was discussed how to implement a flexible and succinct case template, and a mapping model between case template and RDB was proposed. The key idea is to build the case as the virtual view of underlying data.

Lateral interaction by Laplacian‐based graph smoothing for deep neural networks

Lateral interaction in the biological brain is a key mechanism that underlies higher cognitive functions.Linear self‐organising map(SOM)introduces lateral interaction in a general form in which signals of any modality can be used.Some approaches directly incorporate SOM learning rules into neural networks,but incur complex operations and poor extendibility.The efficient way to implement lateral interaction in deep neural networks is not well established.The use of Laplacian Matrix‐based Smoothing(LS)regularisation is proposed for implementing lateral interaction in a concise form.The authors’derivation and experiments show that lateral interaction implemented by SOM model is a special case of LS‐regulated k‐means,and they both show the topology‐preserving capability.The authors also verify that LS‐regularisation can be used in conjunction with the end‐to‐end training paradigm in deep auto‐encoders.Additionally,the benefits of LS‐regularisation in relaxing the requirement of parameter initialisation in various models and improving the classification performance of prototype classifiers are evaluated.Furthermore,the topologically ordered structure introduced by LS‐regularisation in feature extractor can improve the generalisation performance on classification tasks.Overall,LS‐regularisation is an effective and efficient way to implement lateral interaction and can be easily extended to different models.

Intelligent Agent Based Mapping of Software Requirement Specification to Design Model

Automatically mapping a requirement specification to design model in Software Engineering is an open complex problem. Existing methods use a complex manual process that use the knowledge from the requirement specification/modeling and the design, and try to find a good match between them. The key task done by designers is to convert a natural language based requirement specification (or corresponding UML based representation) into a predominantly computer language based design model—thus the process is very complex as there is a very large gap between our natural language and computer language. Moreover, this is not just a simple language conversion, but rather a complex knowledge conversion that can lead to meaningful design implementation. In this paper, we describe an automated method to map Requirement Model to Design Model and thus automate/partially automate the Structured Design (SD) process. We believe, this is the first logical step in mapping a more complex requirement specification to design model. We call it IRTDM (Intelligent Agent based requirement model to design model mapping). The main theme of IRTDM is to use some AI (Artificial Intelligence) based algorithms, semantic representation using Ontology or Predicate Logic, design structures using some well known design framework and Machine Learning algorithms for learning over time. Semantics help convert natural language based requirement specification (and associated UML representation) into high level design model followed by mapping to design structures. AI method can also be used to convert high level design structures into lower level design which then can be refined further by some manual and/or semi automated process. We emphasize that automation is one of the key ways to minimize the software cost, and is very important for all, especially, for the “Design for the Bottom 90% People” or BOP (Base of the Pyramid People).

CucumberAI:Cucumber Fruit Morphology Identification System Based on Artificial Intelligence

Cucumber is an important vegetable crop that has high nutritional and economic value and is thus favored by consumers worldwide.Exploring an accurate and fast technique for measuring the morphological traits of cucumber fruit could be helpful for improving its breeding efficiency and further refining the development models for pepo fruits.At present,several sets of measurement schemes and standards have been proposed and applied for the characterization of cucumber fruits;however,these manual methods are time-consuming and inefficient.Therefore,in this paper,we propose a cucumber fruit morphological trait identification framework and software called CucumberAI,which combines image processing techniques with deep learning models to efficiently identify up to 51 cucumber features,including 32 newly defined parameters.The proposed tool introduces an algorithm for performing cucumber contour extraction and fruit segmentation based on image processing techniques.The identification framework comprises 6 deep learning models that combine fruit feature recognition rules with MobileNetV2 to construct a decision tree for fruit shape recognition.Additionally,the framework employs U-Net segmentation models for fruit stripe and endocarp segmentation,a MobileNetV2 model for carpel classification,a ResNet50 model for stripe classification and a YOLOv5 model for tumor identification.The relationships between the image-based manual and algorithmic traits are highly correlated,and validation tests were conducted to perform correlation analyses of fruit surface smoothness and roughness,and a fruit appearance cluster analysis was also performed.In brief,CucumberAI offers an efficient approach for extracting and analyzing cucumber phenotypes and provides valuable information for future cucumber genetic improvements.

Development of tomographic reconstruction for three-dimensional optical imaging:From the inversion of light propagation to artificial intelligence

Optical molecular tomography(OMT)is an imaging modality which uses an optical signal,especially near-infrared light,to reconstruct the three-dimensional information of the light source in biological tissue.With the advantages of being low-cost,noninvasive and having high sensitivity,OMT has been applied in preclinical and clinical research.However,due to its serious ill-posedness and illcondition,the solution of OMT requires heavy data analysis and the reconstruction quality is limited.Recently,the artificial intelligence(commonly known as AI)-based methods have been proposed to provide a different tool to solve the OMT problem.In this paper,we review the progress on OMT algorithms,from conventional methods to AI-based methods,and we also give a prospective towards future developments in this domain.