Innovative Scientific Discoveries:The Role of Intelligent Computing in the Fifth Paradigm Shift

This article explores the key role of intelligent computing in driving the paradigm shift of scientific discovery.The article first outlines the five paradigms of scientific discovery,from empirical observation to theoretical models,then to computational simulation and data intensive science,and finally introduces intelligent computing as the core of the fifth paradigm.Intelligent computing enhances the ability to understand,predict,and automate scientific discoveries of complex systems through technologies such as deep learning and machine learning.The article further analyzes the applications of intelligent computing in fields such as bioinformatics,astronomy,climate science,materials science,and medical image analysis,demonstrating its practical utility in solving scientific problems and promoting knowledge development.Finally,the article predicts that intelligent computing will play a more critical role in future scientific research,promoting interdisciplinary integration,open science,and collaboration,providing new solutions for solving complex problems.

From Digitalized to Intelligentized Surveying and Mapping: Fundamental Issues and Research Agenda

Nowadays Surveying and Mapping(S&M)production and services are facing some serious challenges such as real-timization of data acquisition,automation of information processing,and intellectualization of service applications.The main reason is that current digitalized S&M technologies,which involve complex algorithms and models as the core,are incapable of completely describing and representing the diverse,multi-dimensional and dynamic real world,as well as addressing high-dimensional and nonlinear spatial problems using simple algorithms and models.In order to address these challenges,it is necessary to explore the use of natural intelligence in S&M,and to develop intelligentized S&M technologies,which are knowledge-guided and algorithm-based.This paper first discusses the basic concepts and ideas of intelligentized S&M,and then analyzes and defines its fundamental issues in the analysis and modeling of natural intelligence in S&M,the construction and realization of hybrid intelligent computing paradigm,and the mechanism and path of empowering production.Further research directions are then proposed in the four areas,including knowledge systems,technologies and methodologies,application systems,and instruments and equipments of intelligentized S&M.Finally,some institutional issues related to promoting scientific research and engineering applications in this area are discussed.

Design and Implementation of Cloud Platform for Intelligent Logistics in the Trend of Intellectualization

Intellectualization has become a new trend for telecom industry, driven by intelligent technology including cloud computing, big data, and Internet of things. In order to satisfy the service demand of intelligent logistics, this paper designed an intelligent logistics platform containing the main applications such as e-commerce, self-service transceiver, big data analysis, path location and distribution optimization. The intelligent logistics service platform has been built based on cloud computing to collect, store and handling multi-source heterogeneous mass data from sensors, RFID electronic tag, vehicle terminals and APP, so that the open-access cloud services including distribution, positioning, navigation, scheduling and other data services can be provided for the logistics distribution applications. And then the architecture of intelligent logistics cloud platform containing software layer(SaaS), platform layer(PaaS) and infrastructure(IaaS) has been constructed accordance with the core technology relative high concurrent processing technique, heterogeneous terminal data access, encapsulation and data mining. Therefore, intelligent logistics cloud platform can be carried out by the service mode for implementation to accelerate the construction of the symbiotic win-winlogistics ecological system and the benign development of the ICT industry in the trend of intellectualization in China.

Employing Computational Intelligence to Generate More Intelligent and Energy Efficient Living Spaces

Our living environments are being gradually occupied with an abundant number of digital objects that have networking and computing capabilities. After these devices are plugged into a network, they initially advertise their presence and capabilities in the form of services so that they can be discovered and, if desired, exploited by the user or other networked devices. With the increasing number of these devices attached to the network, the complexity to configure and control them increases, which may lead to major processing and communication overhead. Hence, the devices are no longer expected to just act as primitive stand-alone appliances that only provide the facilities and services to the user they are designed for, but also offer complex services that emerge from unique combinations of devices. This creates the necessity for these devices to be equipped with some sort of intelligence and self-awareness to enable them to be self-configuring and self-programming. However, with this ＂smart evolution＂, the cognitive load to configure and control such spaces becomes immense. One way to relieve this load is by employing artificial intelligence （AI） techniques to create an intelligent ＂presence＂ where the system will be able to recognize the users and autonomously program the environment to be energy efficient and responsive to the user＇s needs and behaviours. These AI mechanisms should be embedded in the user＇s environments and should operate in a non-intrusive manner. This paper will show how computational intelligence （CI）, which is an emerging domain of AI, could be employed and embedded in our living spaces to help such environments to be more energy efficient, intelligent, adaptive and convenient to the users.

Human Being Emotion in Cognitive Intelligent Robotic Control Pt I: Quantum/Soft Computing Approach

The article consists of two parts.Part I shows the possibility of quantum/soft computing optimizers of knowledge bases(QSCOptKB™)as the toolkit of quantum deep machine learning technology implementation in the solution’s search of intelligent cognitive control tasks applied the cognitive helmet as neurointerface.In particular case,the aim of this part is to demonstrate the possibility of classifying the mental states of a human being operator in on line with knowledge extraction from electroencephalograms based on SCOptKB™and QCOptKB™sophisticated toolkit.Application of soft computing technologies to identify objective indicators of the psychophysiological state of an examined person described.The role and necessity of applying intelligent information technologies development based on computational intelligence toolkits in the task of objective estimation of a general psychophysical state of a human being operator shown.Developed information technology examined with special(difficult in diagnostic practice)examples emotion state estimation of autism children(ASD)and dementia and background of the knowledge bases design for intelligent robot of service use is it.Application of cognitive intelligent control in navigation of autonomous robot for avoidance of obstacles demonstrated.

Intelligent Server Management Framework over Extensible Messaging and Presence Protocol

With the increasing importance of cloud services worldwide, the cloud infrastructure and platform management has become critical for cloud service providers. In this paper, a novel architecture of intelligent server management framework is proposed. In this framework, the communication layer is based on the Extensible Messaging and Presence Protocol (XMPP), which was developed for instant messaging and has been proven to be highly mature and suitable for mobile and large scalable deployment due to its extensibility and efficiency. The proposed architecture can simplify server management and increase flexibility and scalability when managing hundreds of thousands of servers in the cloud era.

Advances of embedded resistive random access memory in industrial manufacturing and its potential applications

Embedded memory,which heavily relies on the manufacturing process,has been widely adopted in various industrial applications.As the field of embedded memory continues to evolve,innovative strategies are emerging to enhance performance.Among them,resistive random access memory(RRAM)has gained significant attention due to its numerousadvantages over traditional memory devices,including high speed(<1 ns),high density(4 F^(2)·n^(-1)),high scalability(~nm),and low power consumption(~pJ).This review focuses on the recent progress of embedded RRAM in industrial manufacturing and its potentialapplications.It provides a brief introduction to the concepts and advantages of RRAM,discusses the key factors that impact its industrial manufacturing,and presents the commercial progress driven by cutting-edge nanotechnology,which has been pursued by manysemiconductor giants.Additionally,it highlights the adoption of embedded RRAM in emerging applications within the realm of the Internet of Things and future intelligent computing,with a particular emphasis on its role in neuromorphic computing.Finally,the review discusses thecurrent challenges and provides insights into the prospects of embedded RRAM in the era of big data and artificial intelligence.

Computational intelligence interception guidance law using online off-policy integral reinforcement learning

Missile interception problem can be regarded as a two-person zero-sum differential games problem,which depends on the solution of Hamilton-Jacobi-Isaacs(HJI)equa-tion.It has been proved impossible to obtain a closed-form solu-tion due to the nonlinearity of HJI equation,and many iterative algorithms are proposed to solve the HJI equation.Simultane-ous policy updating algorithm(SPUA)is an effective algorithm for solving HJI equation,but it is an on-policy integral reinforce-ment learning(IRL).For online implementation of SPUA,the dis-turbance signals need to be adjustable,which is unrealistic.In this paper,an off-policy IRL algorithm based on SPUA is pro-posed without making use of any knowledge of the systems dynamics.Then,a neural-network based online adaptive critic implementation scheme of the off-policy IRL algorithm is pre-sented.Based on the online off-policy IRL method,a computa-tional intelligence interception guidance(CIIG)law is developed for intercepting high-maneuvering target.As a model-free method,intercepting targets can be achieved through measur-ing system data online.The effectiveness of the CIIG is verified through two missile and target engagement scenarios.

Particle Swarm Optimization-Based Hyperparameters Tuning of Machine Learning Models for Big COVID-19 Data Analysis

Analyzing big data, especially medical data, helps to provide good health care to patients and face the risks of death. The COVID-19 pandemic has had a significant impact on public health worldwide, emphasizing the need for effective risk prediction models. Machine learning (ML) techniques have shown promise in analyzing complex data patterns and predicting disease outcomes. The accuracy of these techniques is greatly affected by changing their parameters. Hyperparameter optimization plays a crucial role in improving model performance. In this work, the Particle Swarm Optimization (PSO) algorithm was used to effectively search the hyperparameter space and improve the predictive power of the machine learning models by identifying the optimal hyperparameters that can provide the highest accuracy. A dataset with a variety of clinical and epidemiological characteristics linked to COVID-19 cases was used in this study. Various machine learning models, including Random Forests, Decision Trees, Support Vector Machines, and Neural Networks, were utilized to capture the complex relationships present in the data. To evaluate the predictive performance of the models, the accuracy metric was employed. The experimental findings showed that the suggested method of estimating COVID-19 risk is effective. When compared to baseline models, the optimized machine learning models performed better and produced better results.

INNOVATIVE PREDATORY SEARCH ALGORITHM FOR AIRCRAFT ARRIVAL SEQUENCING AND SCHEDULING PROBLEMS

To solve aircraft arrival sequencing and scheduling problems,and improve the typical predatory search algorithm（PSA）,an innovative PSA is developed.The new PSA uses variable constraints of local search and global search to avoid falling into local optimal solutions and the degeneration of solutions.To test the performance of new PSA,a case study with ten arriving flights and two runways is performed.Test results show that the new PSA performs much better than typical PSA and genetic algorithm（GA）in the aspects of the rate of gaining optimal solutions and the computational time.

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Computational intelligence is one of the most powerful data processing tools to solve complex nonlinear problems, and thus plays a significant role in intelligent fault diagnosis and prediction. However, only few com- prehensive reviews have summarized the ongoing efforts of computational intelligence in machinery condition moni- toring and fault diagnosis. The recent research and devel- opment of computational intelligence techniques in fault diagnosis, prediction and optimal sensor placement are reviewed. The advantages and limitations of computational intelligence techniques in practical applications are dis- cussed. The characteristics of different algorithms are compared, and application situations of these methods are summarized. Computational intelligence methods need to be further studied in deep understanding algorithm mech- anism, improving algorithm efficiency and enhancing engineering application. This review may be considered as a useful guidance for researchers in selecting a suit- able method for a specific situation and pointing out potential research directions.

Artificial intelligence-assisted colonoscopy:A review of current state of practice and research

Colonoscopy is an effective screening procedure in colorectal cancer prevention programs;however,colonoscopy practice can vary in terms of lesion detection,classification,and removal.Artificial intelligence(AI)-assisted decision support systems for endoscopy is an area of rapid research and development.The systems promise improved detection,classification,screening,and surveillance for colorectal polyps and cancer.Several recently developed applications for AIassisted colonoscopy have shown promising results for the detection and classification of colorectal polyps and adenomas.However,their value for real-time application in clinical practice has yet to be determined owing to limitations in the design,validation,and testing of AI models under real-life clinical conditions.Despite these current limitations,ambitious attempts to expand the technology further by developing more complex systems capable of assisting and supporting the endoscopist throughout the entire colonoscopy examination,including polypectomy procedures,are at the concept stage.However,further work is required to address the barriers and challenges of AI integration into broader colonoscopy practice,to navigate the approval process from regulatory organizations and societies,and to support physicians and patients on their journey to accepting the technology by providing strong evidence of its accuracy and safety.This article takes a closer look at the current state of AI integration into the field of colonoscopy and offers suggestions for future research.

Model of Land Suitability Evaluation Based on Computational Intelligence

A novel model of land suitability evaluation is built based on computational intelligence (CI). A fuzzy neural network (FNN) is constructed by the integration of fuzzy logic and artificial neural network (ANN). The structure and process of this network is clear. Fuzzy rules (knowledge) are expressed in the model explicitly, and can be self-adjusted by learning from samples. Genetic algorithm (GA) is employed as the learning algorithm to train the network, and makes the training of the model efficient. This model is a self-learning and self-adaptive system with a rule set revised by training.

Towards a Theoretical Framework of Autonomous Systems Underpinned by Intelligence and Systems Sciences

Autonomous systems are an emerging AI technology functioning without human intervention underpinned by the latest advances in intelligence,cognition,computer,and systems sciences.This paper explores the intelligent and mathematical foundations of autonomous systems.It focuses on structural and behavioral properties that constitute the intelligent power of autonomous systems.It explains how system intelligence aggregates from reflexive,imperative,adaptive intelligence to autonomous and cognitive intelligence.A hierarchical intelligence model(HIM)is introduced to elaborate the evolution of human and system intelligence as an inductive process.The properties of system autonomy are formally analyzed towards a wide range of applications in computational intelligence and systems engineering.Emerging paradigms of autonomous systems including brain-inspired systems,cognitive robots,and autonomous knowledge learning systems are described.Advances in autonomous systems will pave a way towards highly intelligent machines for augmenting human capabilities.

Bio-inspired computational techniques based on advanced condition monitoring

The application of bio-inspired computational techniques to the field of condition monitoring is addressed. First, the bio-inspired computational techniques are briefly addressed; the advantages and disadvantages of these computational methods are made clear. Then, the roles of condition monitoring in the predictive maintenance and failures prediction and the development trends of condition monitoring are discussed. Finally, a case study on the condition monitoring of grinding machine is described, which shows the application of bio-inspired computational technique to a practical condition monitoring system.

High-throughput phenotyping of two plant-size traits of Eucalyptus species using neural networks

In forest modeling to estimate the volume of wood,artificial intelligence has been shown to be quite effi-cient,especially using artificial neural networks(ANNs).Here we tested whether diameter at breast height(DBH)and the total plant height(Ht)of eucalyptus can be pre-dicted at the stand level using spectral bands measured by an unmanned aerial vehicle(UAV)multispectral sensor and vegetation indices.To do so,using the data obtained by the UAV as input variables,we tested different configurations(number of hidden layers and number of neurons in each layer)of ANNs for predicting DBH and Ht at stand level for different Eucalyptus species.The experimental design was randomized blocks with four replicates,with 20 trees in each experimental plot.The treatments comprised five Eucalyptus species(E.camaldulensis,E.uroplylla,E.saligna,E.gran-dis,and E.urograndis)and Corymbria citriodora.DBH and Ht for each plot at the stand level were measured seven times in separate overflights by the UAV,so that the multispectral sensor could obtain spectral bands to calculate vegetation indices(VIs).ANNs were then constructed using spectral bands and VIs as input layers,in addition to the categorical variable (species), to predict DBH and Ht at the stand level simultaneously. This report represents one of the first appli-cations of high-throughput phenotyping for plant size traits in Eucalyptus species. In general, ANNs containing three hidden layers gave better statistical performance (higher esti-mated r, lower estimated root mean squared error-RMSE) due to their greater capacity for self-learning. Among these ANNs, the best contained eight neurons in the first layer, seven in the second, and five in the third (8 − 7 − 5). The results reported here reveal the potential of using the gener-ated models to perform accurate forest inventories based on spectral bands and VIs obtained with a UAV multispectral sensor and ANNs, reducing labor and time.

Multi-objective forest harvesting under sustainable and economic principles

Selective logging is well-recognized as an effective practice in sustainable forest management.However,the ecological efficiency or resilience of the residual stand is often in doubt.Recovery time depends on operational variables,diversity,and forest structure.Selective logging is excellent but is open to changes.This may be resolved by mathematical programming and this study integrates the economic-ecological aspects in multi-objective function by applying two evolutionary algorithms.The function maximizes remaining stand diversity,merchantable logs,and the inverse of distance between trees for harvesting and log landings points.The Brazilian rainforest database(566 trees)was used to simulate our 216-ha model.The log landing design has a maximum volume limit of 500 m3.The nondominated sorting genetic algorithm was applied to solve the main optimization problem.In parallel,a sub-problem(p-facility allocation)was solved for landing allocation by a genetic algorithm.Pareto frontier analysis was applied to distinguish the gradientsα-economic,β-ecological,andγ-equilibrium.As expected,the solutions have high diameter changes in the residual stand(average removal of approximately 16 m^(3) ha^(-1)).All solutions showed a grouping of trees selected for harvesting,although there was no formation of large clearings(percentage of canopy removal<7%,with an average of 2.5 ind ha^(-1)).There were no differences in floristic composition by preferentially selecting species with greater frequency in the initial stand for harvesting.This implies a lower impact on the demographic rates of the remaining stand.The methodology should support projects of reduced impact logging by using spatial-diversity information to guide better practices in tropical forests.

Broad Learning System for Tackling Emerging Challenges in Face Recognition

Face recognition has been rapidly developed and widely used.However,there is still considerable uncertainty in the computational intelligence based on human-centric visual understanding.Emerging challenges for face recognition are resulted from information loss.This study aims to tackle these challenges with a broad learning system(BLS).We integrated two models,IR3C with BLS and IR3C with a triplet loss,to control the learning process.In our experiments,we used different strategies to generate more challenging datasets and analyzed the competitiveness,sensitivity,and practicability of the proposed two models.In the model of IR3C with BLS,the recognition rates for the four challenging strategies are all 100%.In the model of IR3C with a triplet loss,the recognition rates are 94.61%,94.61%,96.95%,96.23%,respectively.The experiment results indicate that the proposed two models can achieve a good performance in tackling the considered information loss challenges from face recognition.

Data-driven based four examinations in TCM:a survey

Traditional Chinese medicine(TCM)diagnosis is a unique disease diagnosis method with thousands of years of TCM theory and effective experience.Its thinking mode in the process is different from that of modern medicine,which includes the essence of TCM theory.From the perspective of clinical application,the four diagnostic methods of TCM,including inspection,auscultation and olfaction,inquiry,and palpation,have been widely accepted by TCM practitioners worldwide.With the rise of artificial intelligence(AI)over the past decades,AI based TCM diagnosis has also grown rapidly,marked by the emerging of a large number of data-driven deep learning models.In this paper,our aim is to simply but systematically review the development of the data-driven technologies applied to the four diagnostic approaches,i.e.the four examinations,in TCM,including data sets,digital signal acquisition devices,and learning based computational algorithms,to better analyze the development of AI-based TCM diagnosis,and provide references for new research and its applications in TCM settings in the future.

A Non-Singleton Type-3 Fuzzy Modeling: Optimized by Square-Root Cubature Kalman Filter

In many problems,to analyze the process/metabolism behavior,a mod-el of the system is identified.The main gap is the weakness of current methods vs.noisy environments.The primary objective of this study is to present a more robust method against uncertainties.This paper proposes a new deep learning scheme for modeling and identification applications.The suggested approach is based on non-singleton type-3 fuzzy logic systems(NT3-FLSs)that can support measurement errors and high-level uncertainties.Besides the rule optimization,the antecedent parameters and the level of secondary memberships are also adjusted by the suggested square root cubature Kalmanfilter(SCKF).In the learn-ing algorithm,the presented NT3-FLSs are deeply learned,and their nonlinear structure is preserved.The designed scheme is applied for modeling carbon cap-ture and sequestration problem using real-world data sets.Through various ana-lyses and comparisons,the better efficiency of the proposed fuzzy modeling scheme is verified.The main advantages of the suggested approach include better resistance against uncertainties,deep learning,and good convergence.