Artificial intelligence in engineering

Pham, Duc Truong; Pham, P. T. N.

doi:10.1016/s0890-6955(98)00076-5

Cited by 133 publications

(74 citation statements)

References 26 publications

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“…Researchers studied the application of AI from two main aspects. (1) designing and realizing the prototype system and experimental system, such as ES, DSS, KBS, MAS, by the method and model to solve the application problems; (2) employing the method, model, or algorithm of AI to solve practical applications in some fields. Based on the analysis of academic output, the number of articles increased substantially in most application fields in which optimization, prediction, classification, and data mining play an important role.…”

Section: Abstract Hot Issues and Research Trend Analysismentioning

confidence: 99%

“…Application domains of AI include robotics, voice recognition, image recognition, natural language processing, and essence of intelligence and design intelligent machines that can act as human behavior. AI has attracted researchers with respect to its theories and principles since the 1956 Dartmouth conference [2,3]. Since the 1960s, AI-related researches have been heavily funded, and many laboratories have been established around the world.…”

Section: Introductionmentioning

confidence: 99%

“…Until the 1990s, AI achieved its greatest success, which can be confirmed by the scientific articles indexed by the Science Citation Index Expanded (SCI-Expanded) ( Figure 1). It is during this period that AI is widely used for logistics, data mining, medical diagnosis, and many other areas throughout industry [2,5]. From the early 21st century, AI research enters a period with numerous research outputs.…”

Section: Introductionmentioning

confidence: 99%

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Global Research on Artificial Intelligence from 1990–2014: Spatially-Explicit Bibliometric Analysis

Niu

Tang

et al. 2016

IJGI

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Abstract:In this article, we conducted the evaluation of artificial intelligence research from 1990-2014 by using bibliometric analysis. We introduced spatial analysis and social network analysis as geographic information retrieval methods for spatially-explicit bibliometric analysis. This study is based on the analysis of data obtained from database of the Science Citation Index Expanded (SCI-Expanded) and Conference Proceedings Citation Index-Science (CPCI-S). Our results revealed scientific outputs, subject categories and main journals, author productivity and geographic distribution, international productivity and collaboration, and hot issues and research trends. The growth of article outputs in artificial intelligence research has exploded since the 1990s, along with increasing collaboration, reference, and citations. Computer science and engineering were the most frequently-used subject categories in artificial intelligence studies. The top twenty productive authors are distributed in countries with a high investment of research and development. The United States has the highest number of top research institutions in artificial intelligence, producing most single-country and collaborative articles. Although there is more and more collaboration among institutions, cooperation, especially international ones, are not highly prevalent in artificial intelligence research as expected. The keyword analysis revealed interesting research preferences, confirmed that methods, models, and application are in the central position of artificial intelligence. Further, we found interesting related keywords with high co-occurrence frequencies, which have helped identify new models and application areas in recent years. Bibliometric analysis results from our study will greatly facilitate the understanding of the progress and trends in artificial intelligence, in particular, for those researchers interested in domain-specific AI-driven problem-solving. This will be of great assistance for the applications of AI in alternative fields in general and geographic information science, in particular.

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Section: Abstract Hot Issues and Research Trend Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Global Research on Artificial Intelligence from 1990–2014: Spatially-Explicit Bibliometric Analysis

Niu

Tang

et al. 2016

IJGI

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“…According to PHAM & PHAM (1999), SA can also be defined as a probabilistic version of the hill-climbing algorithm. The pseudocode is shown in Figure 1.…”

Section: Proceeding To Apply Simulated Annealingmentioning

confidence: 99%

Application of simulated annealing in simulation and optimization of drying process of Zea mays malt

et al. 2011

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Kinetic simulation and drying process optimization of corn malt by Simulated Annealing (SA) for estimation of temperature and time parameters in order to preserve maximum amylase activity in the obtained product are presented here. Germinated corn seeds were dried at 54-76 °C in a convective dryer, with occasional measurement of moisture content and enzymatic activity. The experimental data obtained were submitted to modeling. Simulation and optimization of the drying process were made by using the SA method, a randomized improvement algorithm, analogous to the simulated annealing process. Results showed that seeds were best dried between 3h and 5h. Among the models used in this work, the kinetic model of water diffusion into corn seeds showed the best fitting. Drying temperature and time showed a square influence on the enzymatic activity. Optimization through SA showed the best condition at 54 ºC and between 5.6h and 6.4h of drying. Values of specific activity in the corn malt were found between 5.26±0.06 SKB/mg and 15.69±0,10% of remaining moisture.KEYWORDS: simulated annealing, Zea mays, drying curves, optimization, simulation. APLICAÇÃO DO SIMULATED ANNEALING NA SIMULAÇÃO E OTIMIZAÇÃO DO PROCESSO DE SECAGEM DO MALTE DE Zea mays RESUMO:Este trabalho objetivou a simulação da cinética e a otimização do processo de secagem do malte de milho por meio da técnica Simulated Annealing (SA), para estimação dos parâmetros de temperatura e tempo, tais que mantenham a atividade máxima das enzimas amilases no produto obtido. Para tanto, as sementes de milho germinadas foram secas entre 54-76°C, em um secador convectivo de ar. De tempo em tempo, a umidade e a atividade enzimática foram medidas. Esses dados experimentais foram usados para testar os modelos. A simulação e a otimização do processo foram feitas por meio do método SA, um algoritmo de melhoria randômica, análogo ao processo de têmpera simulada. Os resultados mostram que as sementes estavam secas após 3 h ou 5 h de secagem. Entre os modelos usados, o modelo cinético de difusão da água através das sementes apresentou o melhor ajuste. O tempo e a temperatura de secagem mostraram influência quadrática sobre a atividade enzimática. A otimização usando SA mostrou que a melhor condição de processo foi quando se trabalhou a 54 ºC e entre 5,6 h e 6,4 h. No malte produzido, foram encontrados valores da atividade específica de 5,26±0,06 SKB/mg e umidade residencial de 15,69±0,10%. PALAVRAS-CHAVE:Simulated annealing, Zea mays, curvas de secagem, otimização, simulação.

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“…These methods, with appropriately directed research, may offer solutions for the critical technology needs in manufacturing and machine monitoring. The growing interest in the use of artificial intelligence for the solution of engineering problems is visible from the considerable number of articles published in the last decade [1].…”

Section: Introductionmentioning

confidence: 99%