Relevant bibliographies by topics / Intelligent model

Academic literature on the topic 'Intelligent model'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Intelligent model"

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LIAO, Yunyan, Qing HUANG, Changjing WANG, Zhengkang ZUO, and Jiaxing LU. "Course Intelligent Brain Model Based on Crowd Intelligence." Wuhan University Journal of Natural Sciences 27, no. 4 (August 2022): 331–40. http://dx.doi.org/10.1051/wujns/2022274331.

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The development of artificial intelligence in education promotes the reform of teaching methods in the direction of intelligence and individuation. In this paper, the programming course is taken as an example to propose a curriculum intelligent brain model for open source swarm intelligence based on knowledge graph, and the bootstrapping framework is introduced to try to make the intelligent brain track the frontier like human beings and study several courses vertically. It studies the knowledge of subgraphs fusion of open-source software resources and domain semantics as well as the mining method of potential relationship, so that the intelligent brain can digest knowledge like human, and get through the course horizontally. Finally, knowledge discovery and natural representation based on knowledge graph enable intelligent brain to discover knowledge and solve problems just like human. This study provides new ideas, strategies, and application paths for the construction of knowledge graph based on big data and the integration of heterogeneous knowledge graph.
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Korovin, I. S., and M. G. Tkachenko. "Intelligent Oilfield Model." Procedia Computer Science 101 (2016): 300–303. http://dx.doi.org/10.1016/j.procs.2016.11.035.

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Zhao, Min, Zhenbo Ning, Baicun Wang, Chen Peng, Xingyu Li, and Sihan Huang. "Understanding the Evolution and Applications of Intelligent Systems via a Tri-X Intelligence (TI) Model." Processes 9, no. 6 (June 21, 2021): 1080. http://dx.doi.org/10.3390/pr9061080.

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The evolution and application of intelligence have been discussed from perspectives of life, control theory and artificial intelligence. However, there has been no consensus on understanding the evolution of intelligence. In this study, we propose a Tri-X Intelligence (TI) model, aimed at providing a comprehensive perspective to understand complex intelligence and the implementation of intelligent systems. In this work, the essence and evolution of intelligent systems (or system intelligentization) are analyzed and discussed from multiple perspectives and at different stages (Type I, Type II and Type III), based on a Tri-X Intelligence model. Elemental intelligence based on scientific effects (e.g., conscious humans, cyber entities and physical objects) is at the primitive level of intelligence (Type I). Integrated intelligence formed by two-element integration (e.g., human-cyber systems and cyber-physical systems) is at the normal level of intelligence (Type II). Complex intelligence formed by ternary-interaction (e.g., a human-cyber-physical system) is at the dynamic level of intelligence (Type III). Representative cases are analyzed to deepen the understanding of intelligent systems and their future implementation, such as in intelligent manufacturing. This work provides a systematic scheme, and technical supports, to understand and develop intelligent systems.
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He, Lin, Dongliang Yuan, Lianwei Ren, Ming Huang, Wenyu Zhang, and Jie Tan. "Evaluation Model Research of Coal Mine Intelligent Construction Based on FDEMATEL-ANP." Sustainability 15, no. 3 (January 25, 2023): 2238. http://dx.doi.org/10.3390/su15032238.

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To improve intelligent construction standard systems in coal mines, we must promote the high-quality development of the coal mining industry. The current intelligent construction of coal mines is inefficient. Considering the complexity and diversity of coal mine intelligent construction index factors, this paper proposes an intelligent coal mine construction evaluation model that integrates the fuzzy decision-making trial and evaluation laboratory (FDEMATEL) and the analytical network process (ANP). Firstly, the evaluation index system is established based on the intelligent construction of coal mines. Secondly, the FDEMATEL is applied to deal with the fuzziness in the evaluation process and determine the influence relationship between the evaluation indexes of coal mine intelligent construction to draw the ANP network structure diagram. Finally, super decision software is used to calculate the weight of coal mine intelligent construction evaluation indexes, and then obtain the combination weight and correlation degree of each evaluation index. By applying the evaluation model to conduct a comprehensive evaluation of coal mine intelligent construction, the results show that there is a significant correlation between the indexes affecting the intelligent construction of coal mines. Basic platform intelligence and safety monitoring intelligence are the two most important aspects of intelligent coal mine construction. Database construction, mobile internet construction, big data support, and model algorithm support are the key indexes affecting the intelligent construction of coal mines.
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Lv, Chengshuang, Jiaojiao Xu, and Caihui Wang. "Intelligent Strategies to Improve Food Safety Supervision Model." International Journal of Education and Humanities 1, no. 1 (December 15, 2021): 21–27. http://dx.doi.org/10.54097/ijeh.v1i1.197.

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Intelligent supervision effectively deals with food safety problems from four aspects: concept, subject, activity and object. This paper makes a qualitative analysis on the current situation of intelligent supervision of food safety in Beijing, Tianjin and Hebei, compares and studies the intelligent supervision modes of food safety in three coastal areas in eastern China, constructs the analysis framework of intelligent supervision of food safety, and improves the intelligent supervision mode of food safety in Beijing, Tianjin and Hebei. By studying the policy path of intelligent supervision of food safety, extract the three-stage three source stream model of supervision mode from standard cultivation, informatization to standard unification and intelligence, to better promote the intelligent supervision mode in the country. For the challenges still faced by food safety supervision, it is proposed to improve the top-level design and strengthen the intelligent supervision mechanism of cross regional coordination; Promote the cooperation and sharing of data resources and optimize the cross regional risk early warning mechanism; Consolidate the rural digital foundation and realize the integration mechanism of urban and rural food safety supervision.
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Liu, Runnan, Guangze Liu, Pengfei He, and Xingzhi Lin. "Research on artificial intelligence safety prediction and intervention model based on ship driving habits." MATEC Web of Conferences 355 (2022): 03032. http://dx.doi.org/10.1051/matecconf/202235503032.

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Based on the analysis of the causes of ship accidents, the development prospect and development direction of ship intelligent safe driving, the artificial intelligence safety prediction and intervention model is put forward. This model solves the problem of ship intelligent safety prediction by using intelligent analysis technology and network technology, and promotes the development of ship intelligence and ship safety navigation technology. Additionally, it expands the channels of obtaining information, connects the ship's mechanical and electrical equipment, collects, stores and analyzes the data reasonably, and constructs the intelligent analysis and processing platform of ship small-world data processing to implement intelligent intervention. What is impressive is that it makes ship navigation safer, more economical, more reasonable and optimized, and accelerates the development of ship artificial intelligence safe navigation.
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Li, Jing. "Construction and Model Realization of Financial Intelligence System Based on Multisource Information Feature Mining." Computational Intelligence and Neuroscience 2022 (July 4, 2022): 1–13. http://dx.doi.org/10.1155/2022/9363023.

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Multisource information mining systems and related business intelligence technology are currently a hot topic of research. However, the current commercial applications and applications are not ideal in terms of application. Because there is still much work to be done before decision support, it is best to transition to them only financially. This paper examines the multisource part of the information used in mining and introduces research hotspots in the fields of accounting informatization, the development status of intelligent financial analysis software, the research and application status of data warehouse, data mining, and decision support systems. This paper examines the specific composition and content of a financial information system using information mining to lay a solid foundation. Financial intelligent analysis, financial intelligent monitoring, financial intelligent decision-making, and financial intelligent early warning are the four parts of the financial intelligent system. It then examined the structure and processing of the financial intelligence system and proposed a financial intelligence system operation strategy. Financial intelligence low-risk integrated implementation strategies and ideal financial intelligence models, according to the current state of research and practical applications. According to the findings, the overall discrimination accuracy of the financial information system based on mining multisource information features is up to 95%, which is 42% higher than the traditional model. The development and use of financial information benefit from the realization and exploration of the financial intelligence system model.
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Tian, Yu, and Yonghong Zhang. "Evaluation of Accounting Data of Water Company Based on Combination Model." Scientific Programming 2022 (April 11, 2022): 1–9. http://dx.doi.org/10.1155/2022/3373420.

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Driven by the dual effects of artificial intelligence and accounting transformation, it has become one of the research hotspots of accounting intelligence in China to replace accountants with computers to make professional judgments and automatically evaluate accounting. On the basis of summarizing and analyzing the research status in the field of intelligent accounting, the realization path of intelligent accounting in water companies is put forward in this paper. By introducing the improved Apriori learning algorithm based on Boolean mapping matrix, intelligent data mining is carried out for evaluation of water accounting. With the help of the improved attribute inductive learning algorithm, the corresponding relationship between the original water consumption attribute and the water right transaction is mined and output. In addition, with the help of forward reasoning in inference engine technology, the computer intelligent data evaluation function is realized. Finally, the functional architecture of accounting system of the water company is designed, which provides reference for the development and application of intelligent accounting system, and explores a new path for the integration of accounting and intelligent algorithms.
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Majstorović, Vidosav. "Application of Industry 4.0 model in Oil and Gas companies." Journal of Engineering Management and Competitiveness 12, no. 1 (2022): 77–84. http://dx.doi.org/10.5937/jemc2201077m.

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Although the concept of Industry 4.0 was defined a decade ago, experts have only been talking about the "Oil and Gas 4.0" model in the last few years. Experts in this industry agree that the "Oil and Gas 4.0" model in use, based on the digitalization and intelligence of the oil and gas industry, can bring huge benefits to the company. However, the "Oil and Gas 4.0" model is still in its infancy, but applications in areas such as big data (BDA), industrial Internet of Things (IIoT), or analysis of typical oil and gas industry chain application scenarios through examples, such as an intelligent oil field, an intelligent pipeline, and an intelligent refinery, make today's challenges real. So the essence of the "Oil and Gas 4.0" model is a data-driven intelligence system based on high digitization. This paper provides an overview of the state and development of the Industry 4.0 concept in the Oil and Gas Industry.
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Liu, Zhenghao, and Xi Zeng. "Hybrid Intelligence in Big Data Environment: Concepts, Architectures, and Applications of Intelligent Service." Data and Information Management 5, no. 2 (January 5, 2021): 262–76. http://dx.doi.org/10.2478/dim-2020-0051.

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Abstract Based on the emerging concept of “Hybrid Intelligence,” this paper aims to explore a new model of human–computer interaction, and deeply research on its development and application of Intelligent Service in the big data environment. It systematically explores the related academic concepts of hybrid intelligence, and establishes its architecture model. The development of hybrid intelligence is faced with cognitive differences, system fragmentation, human–machine digital divide, and other issues. Strengthening the interaction between cognition and perception can be the key to break through the bottleneck. The intelligent service system based on the hybrid intelligent architecture takes knowledge fusion as the core, and “cloud intelligent brain” is making it possible for the human–computer symbiosis driven by hybrid intelligence. The proposed advanced human–computer interaction mode constructs a hybrid intelligent architecture model, enriches the concept system of human–machine hybrid intelligence, and provides a new landing scheme for intelligent services based on complex scenes in the big data environment.
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Dissertations / Theses on the topic "Intelligent model"

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Sime, Julie-Ann. "Model switching in intelligent training systems." Thesis, Heriot-Watt University, 1994. http://hdl.handle.net/10399/1396.

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Cisneros, Marco Antonio Perez. "Intelligent model structures in visual servoing." Thesis, University of Manchester, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.682243.

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This thesis focuses on visual servoing (VS) control systems, particularly on image-based visual servoing (IBVS) control structures. In IBVS, the error signal is computed in the image plane and the regulation commands are generated with respect to such error by means of a visual Jacobian. The main design challenge is the high latency of the visual sensor which affects the overall performance and limits the design. The primary objective is to develop a complete framework for simulation and real-time experimentation of VS schemes. One commercial CCD camera is attached to the TQ MA2000 robotic manipulator. The framework has been employed to investigate the use of RL algorithms to increase the performance of the IBVS control structure. The classic RL actor-critic structure has been used to perform on-line adjustment of the gains driving the linear trajectory regulator inside the IBVS control structure. The neural system learns directly from data in the image space and the state of the robot. Two feedforward networks are used, the actor directly modifies the regulator gains whereas the adaptive critic stores and assigns action values. By using the adaptive heuristic critic approach (AHC), the training aims to achieve real-time improvement and adaptation without losing an acceptable regulation of the visual servoing task. A compact model and a flexible framework host the reinforcement learning algorithm in order to enable its inclusion within the IBVS control structure. The approach in this thesis has solved critical neuro-dynamic problems which are derived from the interaction between the imaging model and the robot’s dynamics. The VS toolkit also provides a real-time library to implement and test the IBVS control structure. The libraries have proven effective to construct both the linear IBVS and the RL-supported IBVS system thanks to its layered architecture which facilitates the inclusion of control en› tities of different nature such as the neural networks and the learning framework. Two case studies demonstrate the applicability of the CSC VS toolkit to integrate all the required components and to implement each of the VS experiments in real-time. Performance comparison between the linear IBVS and the RL-supported system are also documented to show the effectiveness of the actor-critic structure.
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SANTOS, Luiz José Rodrigues dos. "Generalização do modelo computacional de tráfego veicular IDM (Intelligent Driver Model)." Universidade Federal Rural de Pernambuco, 2008. http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/5172.

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Submitted by (ana.araujo@ufrpe.br) on 2016-08-03T14:07:18Z No. of bitstreams: 1 Luiz Jose Rodrigues dos Santos.pdf: 1081987 bytes, checksum: 435fc2cb438881b9c3905e16b4b41ed0 (MD5)
Made available in DSpace on 2016-08-03T14:07:42Z (GMT). No. of bitstreams: 1 Luiz Jose Rodrigues dos Santos.pdf: 1081987 bytes, checksum: 435fc2cb438881b9c3905e16b4b41ed0 (MD5) Previous issue date: 2008-02-28
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES
Urban traffic represents a phenomenon of great socioeconomic importance,whose modeling from the point of view of prevision on the basis of initial conditions, still represents a challenge for modern science. Computational methods (computer simulations) represent a powerful tool for modeling and prediction of a number of effects, where systems of coupled differential equations may be used to simulate different phenomena observed in traffic systems. In particular, a quantity of high importance for maintenance and planning of road systems is the vehicular capacity which can be supported without traffic jams, whose description and prevision is still not well understood. In this work, a generalization of an existing microscopic traffic model, the Intelligent Driver Model (IDM), is proposed by implementing a distribution of desired velocities, where it is shown that vehicle capacity of multiple lane roads can be measured in a rather realistic manner, as a function of model parameters,which may be adjusted to real observations.
O tráfego urbano representa um fenômeno de grande importância sócio econômica, cuja modelagem de ponto de vista de previsão a partir de condições iniciais, ainda representa um desafio para a ciência moderna. Métodos computacionais (simulação computacional) representam uma ferramenta poderosa para modelagem e previsão de diversos efeitos, nos quais sistemas de equações diferenciais acopladas podem simular diversos fenômenos observados no sistema de tráfego. Em particular, uma grandeza de alto impacto para o gerenciamento e planejamento de rodovias é a capacidade veicular que elas podem suportar sem que aconteça o efeito de congestionamento, cuja descrição e previsão ainda não estão bem entendida. Neste trabalho, propõe-se uma generalização de um modelo microscópico computacional existente, o Intelligent Driver Model (IDM), aplicando uma distribuição de velocidades desejadas, onde torna-se possível medir de forma bastante realista a capacidade veicular de rodovias com múltiplas faixas, em função de parâmetros de modelo, que podem ser ajustados às observações reais.
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Jing, Yanguo. "A model based intelligent interface agent architecture." Thesis, Heriot-Watt University, 2004. http://hdl.handle.net/10399/328.

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Colombini, Esther Luna. "An attentional model for intelligent robotics agents." Instituto Tecnológico de Aeronáutica, 2014. http://www.bd.bibl.ita.br/tde_busca/arquivo.php?codArquivo=3201.

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As the field of autonomous robotics grows and its applications broaden up, an enormous amount of sensors and actuators, sometimes redundant, have been added to mobile robots. These now fully equipped entities are expected to perceive and act in their surrounding world in a human-like fashion, through perception, reasoning, planning and decision making processes. The higher complexity level of the resulting system and the nature of the environments where autonomous robots are usually expected to operate - continuous, partially unknown and usually unpredictable - demand the application of techniques to deal with this overload of data. In humans, that face the same problem when sounds, images and smells are presented to their sensors in a daily scene, a natural filter is applied: Attention. Although there are many computational models that apply attentive systems to Robotics, they usually are restricted to two classes of systems: a) those that have complex biologically-based attentional visual systems and b) those that have simpler attentional mechanisms with a larger variety of sensors. In order to evaluate an attentional system that operates with other robotics sensors than visual ones, this work presents a biologically inspired computational attentional model that can handle both top-down and bottom-up attention and that is able to learn how to re-distribute its limited resources over time and space. Experiments performed on a high fidelity simulator demonstrates the feasibility of the proposed attentional model and its capability on performing decision making and learning processes over attentional modulated data. The proposed system promotes a significant reduction on the original state space (96%) that was created over multiple sensory systems.
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Yan, Jingsheng. "Platoon modal operations under vehicle autonomous adaptive cruise control model." Thesis, This resource online, 1994. http://scholar.lib.vt.edu/theses/available/etd-07102009-040612/.

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Chandrasekar, Subramanian. "The intelligent manufacturing workstation." Ohio : Ohio University, 1999. http://www.ohiolink.edu/etd/view.cgi?ohiou1175887566.

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Bostan, Burcin. "A Fuzzy Petri Net Model For Intelligent Databases." Phd thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/3/12605962/index.pdf.

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Knowledge intensive applications require an intelligent environment, which can perform deductions in response to user queries or events that occur inside or outside of the applications. For that, we propose a Fuzzy Petri Net (FPN) model to represent the knowledge and the behavior in an intelligent object-oriented database environment, which integrates fuzzy, active and deductive rules with database objects. By gaining intelligent behaviour, the system maintains objects to perceive dynamic occurences and user queries. Thus, objects can produce new knowledge or keep themselves in a consistent, stable, and upto-date state. The behavior of a system can be unpredictable due to the rules triggering or untriggering each other (non-termination). Intermediate and final database states may also differ according to the order of rule executions (non-confluence). In order to foresee and solve problematic behavior patterns, we employ static rule analysis on the FPN structure that provides easy checking of the termination property without requiring any extra construct. In addition, with our proposed inference algorithm, we guarantee confluent rule executions. The techniques and solutions provided in this study can be utilized in various complex systems, such as weather forecasting applications, environmental information systems, defense applications, video database applications, etc. We implement a prototype of the model for the weather forecasting of the Central Anatolia Region
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Zhou, Xiaowei. "Evolving Intelligent Systems with a Neuro-Fuzzy Model." Thesis, Lancaster University, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.524755.

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Zantout, Hind. "Intelligent document management through enrichment : a conceptual model." Thesis, Kingston University, 2000. http://eprints.kingston.ac.uk/20669/.

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With ever more enterprises holding most of their text documents electronically, this research considers the implications that follow and the opportunities that are created by this situation. It introduces the notion of intelligent document management through enrichment as a novel interpretation of the Push Technology paradigm and develops a conceptual model for a software assistant that can carry out this enrichment task. Such an Intelligent Document Enriching Assistant, IDEA, will perform simple reasoning to retrieve, automatically, previously stored text documents which are relevant to the task at hand. However, before such an assistant can actually be implemented, the underlying concepts, which are taken from a range of topics on which such an IDEA is founded, have to be researched and assembled. This is what is of concern here, namely the analysis of the background on which the IDEA is based, together with the development of its conceptual model. The implementation of this knowledge-based component does not form part of this work. The semantic content of the text document needs to be captured using a representation that is richer than simple key words, and is based on Speech Act Theory. For the development of the conceptual model for the IDEA, the CommonKADS method is used. The main deliverable of this method is the model of expertise, which describes the knowledge, and the reasoning capabilities needed to carry out the required enrichment task. The generic enrichment problem solving method is also developed. The underlying domain knowledge is taken from a Sales and Marketing scenario. This research answers the call for good practice in Knowledge Management that requires the development of new knowledge, making accessible new and existing knowledge, and distributing and combining knowledge. The IDEA is an example of how intelligence, through the provision of relevant information, can be realised within this context.
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Books on the topic "Intelligent model"

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Levitt, Raymond E. Merging artificial intelligence with CAD: Intelligent, model-based engineering. Raleigh, N.C: Dept. of Civil Engineering, College of Engineering, North Carolina State University, 1991.

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Autonomous, model-based diagnosis agents. Boston: Kluwer, 1998.

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Magedanz, Thomas. An integrated management model for intelligent networks. Munich: R. Oldenbourg, 1994.

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Su, Xiaojie, Yao Wen, Yue Yang, and Peng Shi. Intelligent Control, Filtering and Model Reduction Analysis for Fuzzy-Model-Based Systems. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-81214-0.

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Shaw, Michael. Learning model management knowledge in intelligent decision support systems. [Urbana, Ill.]: College of Commerce and Business Administration, University of Illinois at Urbana-Champaign, 1988.

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Akinsola, Abiodun Olanrewaju. An intelligent model of variations' contingency on constructions projects. Wolverhampton: University of Wolverhampton, 1997.

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Odincov, Boris. Models and intelligent systems. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1060845.

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The monograph consists of three chapters, the first of which outlines the theoretical foundations of intelligent information systems. Special attention is paid to the disclosure of the term "model" as the intended meaning depends on the understanding of the material. Introduces and examines the new concepts such as the associative and intuitive knowledge while in the creation of intellectual information systems are not used. The second Chapter contains the analysis of problems of development of artificial intelligence (AI), developed in two directions: classical and statistical. Discusses difficulties in the development of the classical approach, associated with identifying the meaning of words, phrases, text, and formulating thoughts. The analysis of problems arising in the play of imagination and insight, machine understanding of natural language texts, play, verbalization and reflection. The third Chapter contains examples of the development of intelligent information systems and technologies in practice of management of economic objects. Theoretical bases of construction of information robots designed to support the task hierarchy of the knowledge base and generating control regulations. The technology of their creation and application in the management of the business efficiency of enterprise business processes and its investment activities. Focused on researchers and developers, AI and intelligent information systems, as well as graduate students and faculty in related academic disciplines.
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Balakirsky, Stephen. Vehicle level world model manager interface description. Gaithersburg, MD: U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 1999.

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Balakirsky, Stephen. Vehicle level world model manager interface description. Gaithersburg, MD: U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 1999.

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Balakirsky, Stephen. Vehicle level world model manager interface description. Gaithersburg, MD: U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 1999.

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Book chapters on the topic "Intelligent model"

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Precup, Radu-Emil, Raul-Cristian Roman, and Ali Safaei. "Intelligent PID Controllers." In Data-Driven Model-Free Controllers, 81–136. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003143444-3.

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Mattos, Juliana Barcellos, Eraylson G. Silva, Paulo S. G. de Mattos Neto, and Renato Vimieiro. "Exceptional Survival Model Mining." In Intelligent Systems, 307–21. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61380-8_21.

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Zheng, Peng, Zhong-Qiu Zhao, and Jun Gao. "A Multi-modal SPM Model for Image Classification." In Intelligent Computing Methodologies, 525–35. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63315-2_46.

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Getseva, Vanesa, and Amruth N. Kumar. "Comparing Bayesian Knowledge Tracing Model Against Naïve Mastery Model." In Intelligent Tutoring Systems, 73–77. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80421-3_9.

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Hollatz, J. "Fuzzy Identification Using Methods of Intelligent Data Analysis." In Fuzzy Model Identification, 165–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-60767-7_5.

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Wang, Yutao, and Neil T. Heffernan. "The Student Skill Model." In Intelligent Tutoring Systems, 399–404. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30950-2_51.

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Merzouki, Rochdi, Arun Kumar Samantaray, Pushparaj Mani Pathak, and Belkacem Ould Bouamama. "Physical Model-Based Control." In Intelligent Mechatronic Systems, 231–80. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4628-5_4.

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Knobbe, Arno, Ad Feelders, and Dennis Leman. "Exceptional Model Mining." In Intelligent Systems Reference Library, 183–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-23241-1_9.

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Fermé, Eduardo, and Sven Ove Hansson. "The AGM Model." In SpringerBriefs in Intelligent Systems, 9–24. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-60535-7_3.

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Bück, Andreas. "Model-Based Control." In Intelligent Control in Drying, 53–61. Boca Raton : Taylor & Francis, a CRC title, part of the Taylor & Francis imprint, a member of the Taylor & Francis Group, the academic division of T&F Informa, plc, 2018. | Series: Advances in drying science & technology: CRC Press, 2018. http://dx.doi.org/10.1201/9780429443183-4.

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Conference papers on the topic "Intelligent model"

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Jun-Min Luo. "Intelligence and an intelligent model." In Proceedings of 2005 International Conference on Machine Learning and Cybernetics. IEEE, 2005. http://dx.doi.org/10.1109/icmlc.2005.1527938.

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Xue, Zhongjie, and Danhua Zhao. "Design Innovation Based on Classification Model of Intelligent Products." In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1001934.

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Rencently, with the development and application of IoT, AI, and Internet technology, traditional products have been transformed into intelligent products with different levels of intelligence and improved human life’s quality. However, it does not provide a comprehensive and holistic design method for new intelligent products. The overall aims of service design are to system and experience and design research based on users, technologies, user-behaviors, environment, etc. it can provide guidance and direction for the designer and upgrading of the complex intelligent system. In this paper, we used service design thinking to give intelligent product suggestions for the design process from dependent factors, like the user behavior, technologies, material. We selected the target users, used the user journey map analyzed the stakeholder's activities, and think to extract the user value. Finally, this paper proposes a design process based on service design, which takes into account the value of the different levels of intelligent products.
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Mussbacher, Gunter, Benoit Combemale, Silvia Abrahão, Nelly Bencomo, Loli Burgueño, Gregor Engels, Jörg Kienzle, et al. "Towards an assessment grid for intelligent modeling assistance." In MODELS '20: ACM/IEEE 23rd International Conference on Model Driven Engineering Languages and Systems. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3417990.3421396.

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Jahanbin, Sorour, Dimitris Kolovos, and Simos Gerasimou. "Intelligent run-time partitioning of low-code system models." In MODELS '20: ACM/IEEE 23rd International Conference on Model Driven Engineering Languages and Systems. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3417990.3420198.

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Huang, W. C., C. M. Lai, B. Luo, C. K. Tsai, M. H. Chih, C. W. Lai, C. C. Kuo, R. G. Liu, and H. T. Lin. "Intelligent model-based OPC." In SPIE 31st International Symposium on Advanced Lithography, edited by Donis G. Flagello. SPIE, 2006. http://dx.doi.org/10.1117/12.657792.

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Geffner, Hector. "Model-free, Model-based, and General Intelligence." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/2.

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During the 60s and 70s, AI researchers explored intuitions about intelligence by writing programs that displayed intelligent behavior. Many good ideas came out from this work but programs written by hand were not robust or general. After the 80s, research increasingly shifted to the development of learners capable of inferring behavior and functions from experience and data, and solvers capable of tackling well-defined but intractable models like SAT, classical planning, Bayesian networks, and POMDPs. The learning approach has achieved considerable success but results in black boxes that do not have the flexibility, transparency, and generality of their model-based counterparts. Model-based approaches, on the other hand, require models and scalable algorithms. Model-free learners and model-based solvers have indeed close parallels with Systems 1 and 2 in current theories of the human mind: the first, a fast, opaque, and inflexible intuitive mind; the second, a slow, transparent, and flexible analytical mind. In this paper, I review developments in AI and draw on these theories to discuss the gap between model-free learners and model-based solvers, a gap that needs to be bridged in order to have intelligent systems that are robust and general.
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Oleshchuk, Vladimir. "Trust-enhanced intelligent security model." In 2012 6th IEEE International Conference Intelligent Systems (IS). IEEE, 2012. http://dx.doi.org/10.1109/is.2012.6335179.

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Zhou, Chun, Hao Zhang, Ying Xu, Zhanwei Xing, and Yin Liu. "Multidimensional Channel Intelligent Supervision Model." In 2018 International Symposium on Communication Engineering & Computer Science (CECS 2018). Paris, France: Atlantis Press, 2018. http://dx.doi.org/10.2991/cecs-18.2018.42.

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Valchev, Evgeni, Jordan Todorov, Ivan Stoyanov, Vladimir Monov, Borislav Dimitrov, and Lyubka Doukovska. "Infrastructure Model of Intelligent Pasture." In 2021 International Conference Automatics and Informatics (ICAI). IEEE, 2021. http://dx.doi.org/10.1109/icai52893.2021.9639655.

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Qizhi Wang, Xiaoxia Wang, and Zhiren Cai. "Model reference based on sliding mode control." In 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS 2009). IEEE, 2009. http://dx.doi.org/10.1109/icicisys.2009.5358283.

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Reports on the topic "Intelligent model"

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Albus, James Sacra. Concept for a reference model architecture for real-time intelligent control systems :. Gaithersburg, MD: National Bureau of Standards, 1990. http://dx.doi.org/10.6028/nist.tn.1277.

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Peglow, S. NA22 Model Cities Project - LL244T An Intelligent Transportation System-Based Radiation Alert and Detection System. Office of Scientific and Technical Information (OSTI), February 2004. http://dx.doi.org/10.2172/892074.

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Ryder, Joan M., Jennifer l. DePaul, Wayne W. Zachary, and Vassil Iordanov. Automated Tutoring Environment for Command (ATEC): Using an Intelligent Tutor to Model Expert Mentor Interactions. Fort Belvoir, VA: Defense Technical Information Center, March 2002. http://dx.doi.org/10.21236/ada400648.

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Huang, Hui-Min. Outline of a multiple dimensional reference model architecture and a knowledge engineering methodology for intelligent systems control. Gaithersburg, MD: National Institute of Standards and Technology, 1995. http://dx.doi.org/10.6028/nist.ir.5643.

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Reno, Matthew, Logan Blakely, Rodrigo Trevizan, Bethany Pena, Matthew Lave, Joseph Azzolini, Jubair Yusuf, et al. IMoFi - Intelligent Model Fidelity: Physics-Based Data-Driven Grid Modeling to Accelerate Accurate PV Integration Final Report. Office of Scientific and Technical Information (OSTI), January 2022. http://dx.doi.org/10.2172/1855058.

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Reno, Matthew, Logan Blakely, Rodrigo Trevizan, Bethany Pena, Matthew Lave, Joseph Azzolini, jubair yusuf, et al. IMoFi (Intelligent Model Fidelity): Physics-Based Data-Driven Grid Modeling to Accelerate Accurate PV Integration Updated Accomplishments. Office of Scientific and Technical Information (OSTI), September 2022. http://dx.doi.org/10.2172/1888157.

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Kumar, Ratnesh, and Lawrence E. Holloway. DEPSCOR: Research on ARL's Intelligent Control Architecture: Hierarchical Hybrid-Model Based Design, Verification, Simulation, and Synthesis of Mission Control for Autonomous Underwater Vehicles. Fort Belvoir, VA: Defense Technical Information Center, February 2007. http://dx.doi.org/10.21236/ada464977.

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Chen, H. H., and Y. C. Lee. Connectionist Models for Intelligent Computation. Fort Belvoir, VA: Defense Technical Information Center, July 1994. http://dx.doi.org/10.21236/ada286436.

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Chen, H. H., and Y. C. Lee. Connectionist Models for Intelligent Computation. Fort Belvoir, VA: Defense Technical Information Center, July 1994. http://dx.doi.org/10.21236/ada296789.

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Chen, H. H., and Y. C. Lee. Connectionist Models for Intelligent Computation. Fort Belvoir, VA: Defense Technical Information Center, July 1989. http://dx.doi.org/10.21236/ada228949.

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