scholarly journals Many-Objective Optimization and Decision-Making Method for Selective Assembly of Complex Mechanical Products Based on Improved NSGA-III and VIKOR

Processes ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 34
Author(s):  
Rongshun Pan ◽  
Jiahao Yu ◽  
Yongman Zhao
Keyword(s):  
Success Rate ◽  
Intelligent Systems ◽  
Intelligent System ◽  
Optimal Solution ◽  
Solution Set ◽  
Selection Strategy ◽  
Selective Assembly ◽  
Assembly Quality ◽  
Mutation Operators ◽  
Mechanical Products

In Industry 4.0, data are sensed and merged to drive intelligent systems. This research focuses on the optimization of selective assembly of complex mechanical products (CMPs) under intelligent system environment conditions. For the batch assembly of CMPs, it is difficult to obtain the best combinations of components from combinations for simultaneous optimization of success rate and multiple assembly quality. Hence, the Taguchi quality loss function was used to quantitatively evaluate each assembly quality and the assembly success rate is combined to establish a many-objective optimization model. The crossover and mutation operators were improved to enhance the ability of NSGA-III to obtain high-quality solution set and jump out of a local optimal solution, and the Pareto optimal solution set was obtained accordingly. Finally, considering the production mode of Human–Machine Intelligent System interaction, the optimal compromise solution is obtained by using fuzzy theory, entropy theory and the VIKOR method. The results show that this work has obvious advantages in improving the quality of batch selective assembly of CMPs and assembly success rate and gives a sorting selection strategy for non-dominated selective assembly schemes while taking into account the group benefit and individual regret.

scholarly journals Results of Evolution Supervised by Genetic Algorithms

2010 ◽  
Vol 2 (3) ◽  
pp. 12-15 ◽  
Author(s):  
Lorentz JÄNTSCHI ◽  
Sorana D. BOLBOACĂ ◽  
Mugur C. BĂLAN ◽  
Radu E. SESTRAS ◽  
Mircea V. DIUDEA
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimal Solution ◽  
Structure Characterization ◽  
Survival Strategies ◽  
Selection Strategy ◽  
Structure Activity Relationships ◽  
Mutation Operators ◽  
Structure Activity ◽  
Survival Selection

The efficiency of a genetic algorithm is frequently assessed using a series of operators of evolution like crossover operators, mutation operators or other dynamic parameters. The present paper aimed to review the main results of evolution supervised by genetic algorithms used to identify solutions to agricultural and horticultural hard problems and to discuss the results of using a genetic algorithms on structure-activity relationships in terms of behavior of evolution supervised by genetic algorithms. A genetic algorithm had been developed and implemented in order to identify the optimal solution in term of estimation power of a multiple linear regression approach for structure-activity relationships. Three survival and three selection strategies (proportional, deterministic and tournament) were investigated in order to identify the best survival-selection strategy able to lead to the model with higher estimation power. The Molecular Descriptors Family for structure characterization of a sample of 206 polychlorinated biphenyls with measured octanol-water partition coefficients was used as case study. Evolution using different selection and survival strategies proved to create populations of genotypes living in the evolution space with different diversity and variability. Under a series of criteria of comparisons these populations proved to be grouped and the groups were showed to be statistically different one to each other. The conclusions about genetic algorithm evolution according to a number of criteria were also highlighted.

Artificial intelligence as a moving force of improvement and innovative development in education and pedagogic

2019 ◽  
pp. 21-30
Author(s):  
M. G. Koliada ◽  
T. I. Bugayova
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Systems ◽  
Intelligent System ◽  
Interaction Analysis ◽  
Educational Data Mining ◽  
Main Task ◽  
Intellectual System ◽  
Will Force ◽  
To Come ◽  
Artificial Intelligence Systems

The article discusses the history of the development of the problem of using artificial intelligence systems in education and pedagogic. Two directions of its development are shown: “Computational Pedagogic” and “Educational Data Mining”, in which poorly studied aspects of the internal mechanisms of functioning of artificial intelligence systems in this field of activity are revealed. The main task is a problem of interface of a kernel of the system with blocks of pedagogical and thematic databases, as well as with the blocks of pedagogical diagnostics of a student and a teacher. The role of the pedagogical diagnosis as evident reflection of the complex influence of factors and reasons is shown. It provides the intelligent system with operative and reliable information on how various reasons intertwine in the interaction, which of them are dangerous at present, where recession of characteristics of efficiency is planned. All components of the teaching and educational system are subject to diagnosis; without it, it is impossible to own any pedagogical situation optimum. The means in obtaining information about students, as well as the “mechanisms” of work of intelligent systems based on innovative ideas of advanced pedagogical experience in diagnostics of the professionalism of a teacher, are considered. Ways of realization of skill of the teacher on the basis of the ideas developed by the American scientists are shown. Among them, the approaches of researchers D. Rajonz and U. Bronfenbrenner who put at the forefront the teacher’s attitude towards students, their views, intellectual and emotional characteristics are allocated. An assessment of the teacher’s work according to N. Flanders’s system, in the form of the so-called “The Interaction Analysis”, through the mechanism of fixing such elements as: the verbal behavior of the teacher, events at the lesson and their sequence is also proposed. A system for assessing the professionalism of a teacher according to B. O. Smith and M. O. Meux is examined — through the study of the logic of teaching, using logical operations at the lesson. Samples of forms of external communication of the intellectual system with the learning environment are given. It is indicated that the conclusion of the found productive solutions can have the most acceptable and comfortable form both for students and for the teacher in the form of three approaches. The first shows that artificial intelligence in this area can be represented in the form of robotized being in the shape of a person; the second indicates that it is enough to confine oneself only to specially organized input-output systems for targeted transmission of effective methodological recommendations and instructions to both students and teachers; the third demonstrates that life will force one to come up with completely new hybrid forms of interaction between both sides in the form of interactive educational environments, to some extent resembling the educational spaces of virtual reality.

The Central Role of Cognitive Computations in Human-Information Interaction

2018 ◽  
Author(s):  
Wai-Tat Fu ◽  
Jessie Chin ◽  
Q. Vera Liao
Keyword(s):  
Information Systems ◽  
Cognitive Science ◽  
Intelligent Systems ◽  
Intelligent System ◽  
Levels Of Abstraction ◽  
Intelligent Information Systems ◽  
Emergent Behaviour ◽  
Information Interaction ◽  
Intelligent Information

Cognitive science is a science of intelligent systems. This chapter proposes that cognitive science can provide useful perspectives for research on technology-mediated human-information interaction (HII) when HII is cast as emergent behaviour of a coupled intelligent system. It starts with a review of a few foundational concepts related to cognitive computations and how they can be applied to understand the nature of HII. It discusses several important properties of a coupled cognitive system and their implication to designs of information systems. Finally, it covers how levels of abstraction have been useful for cognitive science, and how these levels can inform design of intelligent information systems that are more compatible with human cognitive computations.

scholarly journals Theoretical, Measured, and Subjective Responsibility in Aided Decision Making

2021 ◽  
Vol 11 (1) ◽  
pp. 1-37
Author(s):  
Nir Douer ◽  
Joachim Meyer
Keyword(s):  
Intelligent Systems ◽  
Intelligent System ◽  
Classification Systems ◽  
Strongly Correlated ◽  
Human Capabilities ◽  
Legal Decisions ◽  
Subjective Perceptions ◽  
Causal Responsibility ◽  
Quantification Model ◽  
Human Responsibility

When humans interact with intelligent systems, their causal responsibility for outcomes becomes equivocal. We analyze the descriptive abilities of a newly developed responsibility quantification model (ResQu) to predict actual human responsibility and perceptions of responsibility in the interaction with intelligent systems. In two laboratory experiments, participants performed a classification task. They were aided by classification systems with different capabilities. We compared the predicted theoretical responsibility values to the actual measured responsibility participants took on and to their subjective rankings of responsibility. The model predictions were strongly correlated with both measured and subjective responsibility. Participants’ behavior with each system was influenced by the system and human capabilities, but also by the subjective perceptions of these capabilities and the perception of the participant's own contribution. A bias existed only when participants with poor classification capabilities relied less than optimally on a system that had superior classification capabilities and assumed higher-than-optimal responsibility. The study implies that when humans interact with advanced intelligent systems, with capabilities that greatly exceed their own, their comparative causal responsibility will be small, even if formally the human is assigned major roles. Simply putting a human into the loop does not ensure that the human will meaningfully contribute to the outcomes. The results demonstrate the descriptive value of the ResQu model to predict behavior and perceptions of responsibility by considering the characteristics of the human, the intelligent system, the environment, and some systematic behavioral biases. The ResQu model is a new quantitative method that can be used in system design and can guide policy and legal decisions regarding human responsibility in events involving intelligent systems.

The hybrid intelligent systems design using grey systems theory

2015 ◽  
Vol 5 (2) ◽  
pp. 194-205 ◽  
Author(s):  
Scarlat Emil ◽  
Virginia Mărăcine
Keyword(s):  
Systems Theory ◽  
Intelligent Systems ◽  
Explicit Knowledge ◽  
Intelligent System ◽  
Grey System Theory ◽  
Knowledge Flows ◽  
Content Type ◽  
Hybrid Intelligent Systems ◽  
Advantages And Disadvantages ◽  
Grey Systems Theory

Purpose – The purpose of this paper is to discuss how tacit and explicit knowledge determine grey knowledge and how these are stimulated through interactions within networks, forming the grey hybrid intelligent systems (HISs). The feedback processes and mechanisms between internal and external knowledge determine the apparition of grey knowledge into an intelligent system (IS). The extension of ISs is determined by the ubiquity of the internet but, in our framework, the grey knowledge flows assure the viability and effectiveness of these systems. Design/methodology/approach – Some characteristics of the Hybrid Intelligent Knowledge Systems are put forward along with a series of models of hybrid computational intelligence architectures. More, relevant examples from the literature related to the hybrid systems architectures are presented, underlying their main advantages and disadvantages. Findings – Due to the lack of a common framework it remains often difficult to compare the various HISs conceptually and evaluate their performance comparatively. Different applications in different areas are needed for establishing the best combinations between models that are designed using grey, fuzzy, neural network, genetic, evolutionist and other methods. But all these systems are knowledge dependent, the main flow that is used in all parts of every kind of system being the knowledge. Grey knowledge is an important part of the real systems and the study of its proprieties using the methods and techniques of grey system theory remains an important direction of the researches. Originality/value – The paper discusses the differences among the three types of knowledge and how they and the grey systems theory can be used in different hybrid architectures.

scholarly journals Diagnostics of psychological readiness of high school students for research activities in mathematics in the design context of a hybrid intellectual learning environment

2021 ◽  
Vol 54 (6) ◽  
pp. 192-210
Author(s):  
Svetlana N. Dvoryatkina ◽  
◽  
Vera S. Merenkova ◽  
Eugeny I. Smirnov ◽  
◽  
...  
Keyword(s):  
High School Students ◽  
Learning Environment ◽  
Boundary Conditions ◽  
Intelligent Systems ◽  
Intelligent System ◽  
Educational Institution ◽  
Value Orientations ◽  
School Students ◽  
Research Activities ◽  
Scientific Potential

Introduction. The problem of improving the process of organizing and supporting the project and research activities of schoolchildren through intelligent management for the purpose of self-organization of the individual, understanding and comprehending complex mathematical knowledge as a principle of personal development is relevant and far from solved. Intelligent systems provide the process of individualization of learning, the establishment of personalized and computerized feedback of cognitive and creative processes. The purpose of the article is to assess the student's readiness for research activities in the context of designing a hybrid intelligent learning environment. Materials and methods. The assessment of the student's psychological readiness for research activities in the conditions of using a hybrid intellectual environment was carried out on an experimental representative sample of students of 1-2 courses of secondary vocational education (n1=42) and students of the senior classes of secondary schools (n2=30). The diagnosis was carried out using the intelligence structure test of R. Amthauer, the creativity questionnaire of D. Johnson, the test "Individual styles of thinking" by A. Alekseev, L. Gromova, the methods of value orientations by M. Rokich, etc. The significance of the differences was established by means of Student's t-test, Fisher's angular transformation, χ2-test. The results of the study. The assessment of psychological readiness for research activities in mathematics was carried out on the basis of the developed nine parameters of scientific potential. The presented results allow us to pre-set the framework of boundary conditions in order to minimize the imprinting time of a hybrid intelligent system (including the selection of the neural network topology). For all three groups of criteria, differences by gender were established, for example, by the parameter "value orientations" (temp  = 2.26 > tcr = 2.02); by the parameter "creativity" (χemp2 = 6,02 ≥ χcr2 (0,05;2) = 5,99). And also by the type of educational institution, for example, by the parameter “motivation to achieve the result” (φemp = 0,186 > φcr = 1,64). Conclusion. The results of the research are of practical value, as they serve as a technological basis for establishing the boundaries and boundary conditions of the most significant parameters for the effective realization of scientific potential, expressed in the work of a specialized web interface created with the student's personal account.

scholarly journals Become A Star: Teaching the Process of Design and Implementation of an Intelligent System

10.28945/2894 ◽  
2005 ◽  
Author(s):  
Anne Venables ◽  
Grace Tan
Keyword(s):  
Intelligent Systems ◽  
Knowledge Engineering ◽  
Intelligent System ◽  
Development Project ◽  
Project Manager ◽  
Domain Experts ◽  
Software Development Project ◽  
Intelligent Software ◽  
Practical Experiences ◽  
Complete Process

Teaching future knowledge engineers, the necessary skills for designing and implementing intelligent software solutions required by business, industry and research today, is a very tall order. These skills are not easily taught in traditional undergraduate computer science lectures; nor are the practical experiences easily reinforced in laboratory sessions. In an attempt to address this issue, a software development project, designed to take students through a complete process of knowledge engineering, was introduced in an undergraduate Intelligent Systems subject. In this project, students were required to act as domain experts, knowledge engineers, programmers, end users and project manager in the production of a game-playing expert system. The paper describes the project, its objectives and development, as well as some of the benefits.

scholarly journals Analysis and Implementation  of Reinforcement Learning  on a GNU Radio Cognitive  Radio Platform

2021 ◽  
Author(s):  
◽  
Yu Ren
Keyword(s):  
Cognitive Radio ◽  
Success Rate ◽  
Large Scale ◽  
Channel Selection ◽  
Packet Transmission ◽  
Selection Strategy ◽  
Gnu Radio ◽  
Selection Scheme ◽  
Q Learning ◽  
Transmission Success

<p>Spectrum today is regulated based on fixed licensees. In the past radio operators have been allocated a frequency band for exclusive use. This has become problem for new users and the modern explosion in wireless services that, having arrived late find there is a scarcity in the remaining available spectrum. Cognitive radio (CR) presents a solution. CRs combine intelligence, spectrum sensing and software reconfigurable radio capabilities. This allows them to opportunistically transmit among several licensed bands for seamless communications, switching to another channel when a licensee is sensed in the original band without causing interference. Enabling this is an intelligent dynamic channel selection strategy capable of finding the best quality channel to transmit on that suffers from the least licensee interruption. This thesis evaluates a Q-learning channel selection scheme using an experimental approach. A cognitive radio deploying the scheme is implemented on GNU Radio and its performance is measured among channels with different utilizations in terms of its packet transmission success rate, goodput and interference caused. We derive similar analytical expressions in the general case of large-scale networks. Our results show that using the Q-learning scheme for channel selection significantly improves the goodput and packet transmission success rate of the system.</p>

Prediction of International Stock Markets Based on Hybrid Intelligent Systems

2016 ◽  
pp. 110-124
Author(s):  
Salim Lahmiri
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Stock Market ◽  
Intelligent Systems ◽  
Fuzzy Inference ◽  
Intelligent System ◽  
Market Price ◽  
Hybrid Intelligent Systems ◽  
Out Of Sample ◽  
Artificial Neural Network Ann

This paper compares the accuracy of three hybrid intelligent systems in forecasting ten international stock market indices; namely the CAC40, DAX, FTSE, Hang Seng, KOSPI, NASDAQ, NIKKEI, S&P500, Taiwan stock market price index, and the Canadian TSE. In particular, genetic algorithms (GA) are used to optimize the topology and parameters of the adaptive time delay neural networks (ATNN) and the time delay neural networks (TDNN). The third intelligent system is the adaptive neuro-fuzzy inference system (ANFIS) that basically integrates fuzzy logic into the artificial neural network (ANN) to better model information and explain decision making process. Based on out-of-sample simulation results, it was found that contrary to the literature GA-TDNN significantly outperforms GA-ATDNN. In addition, ANFIS was found to be more effective in forecasting CAC40, FTSE, Hang Seng, NIKKEI, Taiwan, and TSE price level. In contrary, GA-TDNN and GA-ATDNN were found to be superior to ANFIS in predicting DAX, KOSPI, and NASDAQ future prices.

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