ARTIFICIAL NEURAL NETWORKS BASED GEARS MATERIAL SELECTION HYBRID INTELLIGENT SYSTEM

An artificial neural networks(ANNs) based gear material selection hybrid intelligent system is established by analyzing the individual advantages and weakness of expert system (ES) and ANNs and the applications in material select of them. The system mainly consists of tow parts: ES and ANNs. By being trained with much data samples, the back propagation (BP) ANN gets the knowledge of gear materials selection, and is able to inference according to user input. The system realizes the complementing of ANNs and ES. Using this system, engineers without materials selection experience can conveniently deal with gear materials selection.

Joint Active and Passive Beamforming Design for Hybrid RIS-Aided Integrated Sensing and Communication

Integrated sensing and communication(ISAC) is considered an effective technique to solve spectrum congestion in the future. In this paper, we consider a hybrid reconfigurable intelligent surface(RIS)-assisted downlink ISAC system that simultaneously serves multiple single-antenna communication users and senses multiple targets. Hybrid RIS differs from fully passive RIS in that it is composed of both active and passive elements, with the active elements having the effect of amplifying the signal in addition to phase-shifting. We maximize the achievable sum rate of communication users by collaboratively improving the beamforming matrix at the dual function base station(DFBS) and the phase-shifting matrix of the hybrid RIS, subject to the transmit power constraint at the DFBS, the signal-to-interference-plus-noise-ratio(SINR) constraint of the radar echo signal and the RIS constraint are satisfied at the same time. The builtin RIS-assisted ISAC design problem model is significantly non-convex due to the fractional objective function of this optimization problem and the coupling of the optimization variables in the objective function and constraints. As a result, we provide an effective alternating optimization approach based on fractional programming(FP) with block coordinate descent(BCD)to solve the optimization variables. Results from simulations show that the hybrid RIS-assisted ISAC system outperforms the other benchmark solutions.

Hybrid Intelligent Approach for the Selection of Third-Party Reverse Logistics Provider under Uncertainty

A hybrid intelligent approach is proposed to help the decision maker to select the appropriate third-party reverse logistics provider. The following process is included: firstly,the evaluation team is established to determine the selection criteria and evaluate them by triangular fuzzy numbers; secondly,calculate the weight of criteria by the proposed hybrid algorithm integrating particle swarm optimization( PSO) and simulated annealing( SA); then, the performance evaluation for each supplier is predicted by the proposed self-feedback neural network( SFBNN) based on the historical data. A numerical example is also presented to interpret the methodology above.

MAHIS-based analysis and design of financial investment planning system

In financial investment planning,a large number of components that interact in varying and complexways are involved.This leads to complex behavior that is difficult to understand,predict and manage.Asingle intelligent technique can not solve the complicated and elaborate investment planning problems.Itis necessary to study those problems synthetically by combining the multiple intelligent techniques.Weemployed fussy algorithms,genetic algorithms,etc.to solve complicated financial portfolio managementin this paper.We analyse and design an agent-based hybrid intelligent system by following the methodolo-gy for constructing agent-based hybrid intelligent system(MAHIS).The system starts with the financialrisk tolerance evaluation based on fussy algorithms.Asset allocation,portfolio selections,interest predic-tions,and ordered weighted averaging can be conducted by using hybrid intelligent techniques.The plan-ning agent in the system can easily access all the intelligent processing agents,including the agents of fi-nancial risk tolerance assessment,asset allocation,portfolio selection,interest prediction,and decisionaggregation.Overall system robustness is facilitated.

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.

A Knowledge-reuse Based Intelligent Reasoning Model for Worsted Process Optimization

The textile process planning is a knowledge reuse process in nature, which depends on the expert’s knowledge and experience. It seems to be very difficult to build up an integral mathematical model to optimize hundreds of the processing parameters. In fact, the existing process cases which were recorded to ensure the ability to trace production steps can also be used to optimize the process itself. This paper presents a novel knowledge-reuse based hybrid intelligent reasoning model (HIRM) for worsted process optimization. The model architecture and reasoning mechanism are respectively described. An applied case with HIRM is given to demonstrate that the best process decision can be made, and important processing parameters such as for raw material optimized.

DESIGNING A HYBRID INTELLIGENT MINING SYSTEM FOR CREDIT RISK EVALUATION

In this study, a novel hybrid intelligent mining system integrating rough sets theory and support vector machines is developed to extract efficiently association rules from original information table for credit risk evaluation and analysis. In the proposed hybrid intelligent system, support vector machines are used as a tool to extract typical features and filter its noise, which are different from the previous studies where rough sets were only used as a preprocessor for support vector machines. Such an approach could reduce the information table and generate the final knowledge from the reduced information table by rough sets. Therefore, the proposed hybrid intelligent system overcomes the difficulty of extracting rules from a trained support vector machine classifier and possesses the robustness which is lacking for rough-set-based approaches. In addition, the effectiveness of the proposed hybrid intelligent system is illustrated with two real-world credit datasets.

System Strength Assessment Based on Multi-task Learning

Increase in permeability of renewable energy sources(RESs)leads to the prominent problem of voltage stability in power system,so it is urgent to have a system strength evaluation method with both accuracy and practicability to control its access scale within a reasonable range.Therefore,a hybrid intelligence enhancement method is proposed by combining the advantages of mechanism method and data driven method.First,calculation of critical short circuit ratio(CSCR)is set as the direction of intelligent enhancement by taking the multiple renewable energy station short circuit ratio as the quantitative indicator.Then,the construction process of CSCR dataset is proposed,and a batch simulation program of samples is developed accordingly,which provides a data basis for subsequent research.Finally,a multi-task learning model based on progressive layered extraction is used to simultaneously predict CSCR of each RESs connection point,which significantly reduces evaluation error caused by weak links.Predictive performance and anti-noise performance of the proposed method are verified on the CEPRI-FS-102 bus system,which provides strong technical support for real-time monitoring of system strength.

精锻工艺数字化智能化方法探讨

精锻是零部件制造的一种重要方法,研究其数字化和智能化成形方法对产业转型升级具有重要意义。首先介绍了我国精锻成形工艺同国外先进水平的差距,分析了目前精锻工艺方案设计方法的优点及存在的问题。随后论述了精锻成形工艺数字化智能化方法的内涵,以及精锻成形工艺方案的数字化智能化设计与智能自动生产线的建立。最后,以气门电热镦粗成形工艺为例,阐述了数字化智能化方法与软件系统的开发及应用情况。应用结果表明,实现精锻成形工艺方案及工艺参数的数字化智能化设计不仅能够显著提高设计效率,而且可以达到自动优化的目标,实现精锻成形工艺与信息技术的深度融合,从而达到提质增效的效果。

HISP： a hybrid intelligent approach for identifying Jirected signaling pathways

Signal transduction plays important roles in biological systems. Unfortunately, our knowledge about signaling pathways is far from complete. Specifically, the direction of signaling flows is less known even though the signaling molecules of some signaling pathways have been determined. In this paper, we propose a novel hybrid intelligent method, namely HISP （Hybrid Intelligent approach for identifying directed Signaling Pathways）, to determine both the topologies of signaling pathways and the direction of signaling flows within a pathway based on integer linear programming and genetic algorithm. By integrating the protein-protein interaction, gene expression, and gene knockout data, our HISP approach is able to determine the optimal topologies of signaling pathways in an accurate way. Benchmark results on yeast MAPK signaling pathways demonstrate the efficiency of our proposed approach. When applied to the EGFR/ErbB signaling pathway in human hepatocytes, HISP unveils a high-resolution signaling path- way, where many signaling interactions were missing by existing computational approaches.

Robot stereo vision calibration method with genetic algorithm and particle swarm optimization

Accurate stereo vision calibration is a preliminary step towards high-precision visual posi- tioning of robot. Combining with the characteristics of genetic algorithm （GA） and particle swarm optimization （PSO）, a three-stage calibration method based on hybrid intelligent optimization is pro- posed for nonlinear camera models in this paper. The motivation is to improve the accuracy of the calibration process. In this approach, the stereo vision calibration is considered as an optimization problem that can be solved by the GA and PSO. The initial linear values can be obtained in the frost stage. Then in the second stage, two cameras＇ parameters are optimized separately. Finally, the in- tegrated optimized calibration of two models is obtained in the third stage. Direct linear transforma- tion （DLT）, GA and PSO are individually used in three stages. It is shown that the results of every stage can correctly find near-optimal solution and it can be used to initialize the next stage. Simula- tion analysis and actual experimental results indicate that this calibration method works more accu- rate and robust in noisy environment compared with traditional calibration methods. The proposed method can fulfill the requirements of robot sophisticated visual operation.

Robot and its living space:A roadmap for robot development based on the view of living space

Robot’s living space Human-robot relationship Robot humanization Hybrid intelligence Human-robot integrated society Robots,as the creation of humans,became an irreplaceable component in human society.With the advancement of technologies,robots have become more and more intelligent and have been widely used in many fields,such as disease diagnosis,customer services,healthcare for the older people,and so on.As robots made our lives much more convenient than ever before,they also brought many potential risks and challenges in technology,security,and ethic.To better understand the development of robots,we proposed a concept of a robot’s living space and analyzed the role of robots in our society.In this paper,we focus on setting a theoretical framework of the robot’s living space to further understand the human-robot relationship.The research in this paper contains three central aspects.First,we interpret the concept of the robot’s living space and the functions of each space.Second,we analyze and summarize the relative technologies which support robots living well in each space.Finally,we provide advice and improvement measures based on a discussion of potential problems caused by the developments of robots.With the trend of robots humanization and human-robot society integration,we should seriously consider how to collaborate with intelligent robots to achieve hybrid intelligence.To build a harmonious human-robot integrated society,studying the robot’s living space and its relationship with humans is the prerequisite and roadmap.

Hybrid intelligent modeling approach for online predicting and simulating surface temperature of HVs

Online prediction as well as online simulation of surface temperature will play a sig-nificant role in flight safety of future near space hypersonic vehicles(HVs).But it still remains a classical scientific problem both in thermodynamics and aerospace sci-ence.In view of the complex HV structure and complex heat conduction procedure,three-dimensional numerical simulation is too inefficient for online prediction,while cur-rent rapid computation methods cannot meet the requirement of accuracy.Therefore,a hybrid intelligent dynamic modeling approach is proposed to estimate the surface temperature of HV with the combination of mechanism equations,test data and intel-ligent modeling technology.A simplified model based on a mechanism equation and experimental formulas is presented for predicting or simulating transient heat conduc-tion procedure efficiently,while a case-based reasoning(CBR)algorithm is developed to estimate two uncertain coefficients in the simplified model.Furthermore,a support vector regression(SVR)-based model is developed to compensate the modeling error.With the data both from high-precision finite element computation and from real-world HV thermal protection experiments,a number of comparative simulations demonstrate the effectiveness of the proposed hybrid intelligent modeling approach.

Evaluating performance of cutting machines during sawing dimension stones

The performance of cutting machines in terms of energy consumption and vibration directly affects the production costs. In this work, our aim was to evaluate the performance of cutting machines using hybrid intelligent models. For this purpose, a systematic experimental work was performed. A database of the carbonate and granite rocks was established, in which the physical and mechanical properties of these rocks (i.e., UCS, elastic modulus, Mohs hardness, and Schmiazek abrasivity factor) and the operational parameters (i.e., depth of cut and feed rate) were considered as the input parameters. The predictive models were developed incorporating a combination of the multi-layered perceptron artificial neural networks and genetic algorithm (GANN-BP) and the support vector regression method and Cuckoo optimization algorithm (COA-SVR). The results obtained indicated that the performance of the developed GANN-BP and COA-SVR models was close to each other and that these models had good agreements with the measured values. These results also showed that these proposed models were suitable tools in evaluating the performance of cutting machines.

High resolution 3D nonlinear integrated inversion

The high resolution 3D nonlinear integrated inversion method is based on nonlinear theory. Under layer control, the log data from several wells （or all wells） in the study area and seismic trace data adjacent to the wells are input to a network with multiple inputs and outputs and are integratedly trained to obtain an adaptive weight function of the entire study area. Integrated nonlinear mapping relationships are built and updated by the lateral and vertical geologic variations of the reservoirs. Therefore, the inversion process and its inversion results can be constrained and controlled and a stable seismic inversion section with high resolution with velocity inversion, impedance inversion, and density inversion sections, can be gained. Good geologic effects have been obtained in model computation tests and real data processing, which verified that this method has high precision, good practicality, and can be used for quantitative reservoir analysis.

The Role of Computational Intelligence in Sensory Evaluation

Sensory evaluation is the evaluation of signals that a human receives via its senses of sight, smell, taste, touch and hearing. In today’s industrial companies, sensory evaluation is widely used in quality inspection of products, in marketing study and in many other fields such as risk evaluation, investment evaluation and safety evaluation. In practice, setting up a suitable mathematical formulation, an efficient working procedure and a pertinent computing method for sensory evaluation is quite difficult because of uncertainty and imprecision in sensory panels and their results involving linguistic expressions, non normalized data, data reliability, etc. At the present a prime problem of the practitioner is not the lack of useful methods but the lack of transparency in this area. In this tutorial lecture, we briefly describe some of the technology in the computational intelligence (CI) areas that has been developed for application to sensory evaluation and related fields. Moreover, we will illustrate the role of CI in sensory evaluation related applications from some recent publications.

Survey and Tutorial on Hybrid Human-Artificial Intelligence

The growing computing power,easy acquisition of large-scale data,and constantly improved algorithms have led to a new wave of artificial intelligence(AI)applications,which change the ways we live,manufacture,and do business.Along with this development,a rising concern is the relationship between AI and human intelligence,namely,whether AI systems may one day overtake,manipulate,or replace humans.In this paper,we introduce a novel concept named hybrid human-artificial intelligence(H-AI),which fuses human abilities and AI capabilities into a unified entity.It presents a challenging yet promising research direction that prompts secure and trusted AI innovations while keeping humans in the loop for effective control.We scientifically define the concept of H-AI and propose an evolution road map for the development of AI toward H-AI.We then examine the key underpinning techniques of H-AI,such as user profile modeling,cognitive computing,and human-in-the-loop machine learning.Afterward,we discuss H-AI’s potential applications in the area of smart homes,intelligent medicine,smart transportation,and smart manufacturing.Finally,we conduct a critical analysis of current challenges and open gaps in H-AI,upon which we elaborate on future research issues and directions.

Real-Time Control Strategy of Exoskeleton Locomotion Trajectory Based on Multi-modal Fusion

The exoskeleton robot is a typical man–machine integration system in the human loop.The ideal man–machine state is to achieve motion coordination,stable output,strong personalization,and reduce man–machine confrontation during motion.In order to achieve an ideal man–machine state,a Time-varying Adaptive Gait Trajectory Generator(TAGT)is designed to estimate the motion intention of the wearer and generate a personalized gait trajectory.TAGT can enhance the hybrid intelligent decision-making ability under human–machine collaboration,promote good motion coordination between the exoskeleton and the wearer,and reduce metabolic consumption.An important feature of this controller is that it utilizes a multi-layer control strategy to provide locomotion assistance to the wearer,while allowing the user to control the gait trajectory based on human–robot Interaction(HRI)force and locomotion information.In this article,a Temporal Convolutional Gait Prediction(TCGP)model is designed to learn the personalized gait trajectory of the wearer,and the control performance of the model is further improved by fusing the predefined gait trajectory method with an adaptive interactive force control model.A human-in-the-loop control strategy is formed with the feedback information to stabilize the motion trajectory of the output joints and update the system state in real time based on the feedback from the inertial and interactive force signal.The experimental study employs able-bodied subjects wearing the exoskeleton for motion trajectory control to evaluate the performance of the proposed TAGT model in online adjustments.Data from these evaluations demonstrate that the controller TAGT has good motor coordination and can satisfy the subject to control the motor within a certain range according to the walking habit,guaranteeing the stability of the closed-loop system.

Relevance of hybrid artificial intelligence for improving the forecasting accuracy of natural resource prices

The prediction performance of traditional forecasting methods is low due to the high level of complexity in a series of energy prices.The present study attempts to compare the traditional regression,machine learning tools and hybrid models to conclude the outperforming model.The first step is to propose the effective denoising technique for Tadawul energy index,which has confirmed the superiority of CSD based denoising.However,we use the CSD-ARIMA,CSD-ANN,and CSD-RNN as hybrid models.As a result,CSD-RNN outperforms both other models in terms of MSE,MAPE,RMSE and Dstat.The findings are useful for policy makers,investors and portfolio managers to forecast the energy trends,and hedge the portfolio risk accordingly.

Flow-Shop Scheduling Models with Parameters Represented by Rough Variables

In reality, processing times are often imprecise and this imprecision is critical for the scheduling procedure. This research deals with flow-shop scheduling in rough environment. In this type of scheduling problem, we employ the rough sets to represent the job parameters. The job processing times are assumed to be rough variables, and the problem is to minimize the makespan. Three novel types of rough scheduling models are presented. A rough simulation-based genetic algorithm is designed to solve these models and its effectiveness is well illustrated by numerical experiments.