Academic literature on the topic 'GA. Information industry'
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Journal articles on the topic "GA. Information industry"
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Rao, Xiaoyang, and Xuesong Yan. "Particle Swarm Optimization Algorithm Based on Information Sharing in Industry 4.0." Wireless Communications and Mobile Computing 2022 (March 10, 2022): 1–11. http://dx.doi.org/10.1155/2022/4328185.
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Intelligent manufacturing is an important part of Industry 4.0; artificial intelligence technology is a necessary means to realize intelligent manufacturing. This requires the exploration of pattern recognition, computer vision, intelligent optimization, and other related technologies. Particle swarm optimization (PSO) algorithm is an optimization algorithm inspired by the foraging behavior of birds. PSO was an intelligent technology and an efficient optimization algorithm verified by a lot of research and experiments. In this paper, the traditional PSO algorithm is compared with genetic algorithms (GA) to illustrate the performance of the traditional PSO algorithm. By analyzing the advantages and disadvantages of the traditional PSO algorithm, the traditional PSO algorithm is improved through introducing into the sharing information mechanism and the competition strategy, called information sharing based PSO (IPSO). The novel algorithm IPSO was the rapid convergence speed similar to the traditional PSO and enhanced the global search capability. Our experimental results show that IPSO has better performance than the traditional PSO and the GA algorithm on benchmark functions, especially for difficult functions.
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Kim, Seonhyeon, Ephraim Kwashie Thompson, and Changki Kim. "Insurance Transactions through Blockchain Applications: Current Status and Implications for the Domestic Insurance Industry." Crisis and Emergency Management: Theory and Praxis 17, no. 11 (November 30, 2021): 137–56. http://dx.doi.org/10.14251/crisisonomy.2021.17.11.137.
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This paper discusses the necessity for introducing blockchain technology into insurance sales channels. We focus on two factors that have hindered the sustainable development of the domestic insurance industry. The first problem is that insurance companies’ profitability is on the decline and the second issue relates to information asymmetry problems besetting insurance sales channels. The channels are heavily concentrated on general agencies (GA) but the GA channel has been characterized by problems such as incomplete sales and high termination rates. Due to the advantages of blockchain technology such as the transparency of information and reduction in transaction and security costs, insurance sales channels such as GA can help reduce insurance companies’ business costs and alleviate information asymmetry by adopting blockchain technology. Thus, in this paper, we discuss ways to utilize applications using blockchain platforms in insurance sales channels. This paper also introduces the web-based blockchain application of International Business Machines (IBM) and suggests that various stakeholders of insurance can interact through blockchain applications.
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Jendrzejczyk-Handzlik, D. "Phase equilibria in the ternary Ag-Au-Ga system: Isothermal sections at 250°C and 450°C." Journal of Mining and Metallurgy, Section B: Metallurgy 53, no. 3 (2017): 215–22. http://dx.doi.org/10.2298/jmmb170531043j.
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The ternary Ag-Au-Ga system seems to be interesting in jeweller?s craft as a joint. Moreover, the ternary systems based on gold and silver have found applications in the dental industry. A literature overview of the Ag-Au-Ga system shows that the information about phase equilibria of this system does not exist. In the present work, phase equilibria in the Ag-Au-Ga ternary system have been studied by using scanning electron microscopy (SEM) with energy dispersive spectroscopy (EDS) analysis and X-ray diffraction analysis (XRD). Twenty two annealed alloys in the 10-70 at.% Ga region have been investigated. Obtained experimental results were compared with the predicted isothermal sections at two temperatures (250?C and 450?C). No ternary compounds are found.
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Liu, Chao, Yixin Fan, and Xiangyu Zhu. "Fintech Index Prediction Based on RF-GA-DNN Algorithm." Wireless Communications and Mobile Computing 2021 (June 7, 2021): 1–9. http://dx.doi.org/10.1155/2021/3950981.
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The Fintech index has been more active in the stock market with the Fintech industry expanding. The prediction of the Fintech index is significant as it is capable of instructing investors to avoid risks and provide guidance for financial regulators. Traditional prediction methods adopt the deep neural network (DNN) or the combination of genetic algorithm (GA) and DNN mostly. However, heavy computational load is required by these algorithms. In this paper, we propose an integrated artificial intelligence-based algorithm, consisting of the random frog algorithm (RF), GA, and DNN, to predict the Fintech index. The proposed RF-GA-DNN prediction algorithm filters the key input variables and optimizes the hyperparameters of DNN. We compare the proposed RF-GA-DNN with the traditional GA-DNN in terms of convergence time and prediction accuracy. Results show that the convergence time of GA-DNN is up to 20 hours and its prediction accuracy is 97.4%. In comparison, the convergence time of our RF-GA-DNN is only about 1.5 hours and the prediction accuracy reaches 97.0%. These results demonstrate that the proposed RF-GA-DNN prediction algorithm significantly reduces the convergence time with the promise of competitive prediction accuracy. Thus, the proposed algorithm deserves to be widely recommended for predicting the Fintech index.
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Kandiri, Amirreza, Farid Sartipi, and Mahdi Kioumarsi. "Predicting Compressive Strength of Concrete Containing Recycled Aggregate Using Modified ANN with Different Optimization Algorithms." Applied Sciences 11, no. 2 (January 6, 2021): 485. http://dx.doi.org/10.3390/app11020485.
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Using recycled aggregate in concrete is one of the best ways to reduce construction pollution and prevent the exploitation of natural resources to provide the needed aggregate. However, recycled aggregates affect the mechanical properties of concrete, but the existing information on the subject is less than what the industry needs. Compressive strength, on the other hand, is the most important mechanical property of concrete. Therefore, having predictive models to provide the required information can be helpful to convince the industry to increase the use of recycled aggregate in concrete. In this research, three different optimization algorithms including genetic algorithm (GA), salp swarm algorithm (SSA), and grasshopper optimization algorithm (GOA) are employed to be hybridized with artificial neural network (ANN) separately to predict the compressive strength of concrete containing recycled aggregate, and a M5P tree model is used to test the efficiency of the ANNs. The results of this study show the superior efficiency of the modified ANN with SSA when compared to other models. However, the statistical indicators of the hybrid ANNs with SSA, GA, and GOA are so close to each other.
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Wang, Di, Lin Xie, Simon Yang, and Fengchun Tian. "Support Vector Machine Optimized by Genetic Algorithm for Data Analysis of Near-Infrared Spectroscopy Sensors." Sensors 18, no. 10 (September 25, 2018): 3222. http://dx.doi.org/10.3390/s18103222.
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Near-infrared (NIR) spectral sensors deliver the spectral response of the light absorbed by materials for quantification, qualification or identification. Spectral analysis technology based on the NIR sensor has been a useful tool for complex information processing and high precision identification in the tobacco industry. In this paper, a novel method based on the support vector machine (SVM) is proposed to discriminate the tobacco cultivation region using the near-infrared (NIR) sensors, where the genetic algorithm (GA) is employed for input subset selection to identify the effective principal components (PCs) for the SVM model. With the same number of PCs as the inputs to the SVM model, a number of comparative experiments were conducted between the effective PCs selected by GA and the PCs orderly starting from the first one. The model performance was evaluated in terms of prediction accuracy and four parameters of assessment criteria (true positive rate, true negative rate, positive predictive value and F1 score). From the results, it is interesting to find that some PCs with less information may contribute more to the cultivation regions and are considered as more effective PCs, and the SVM model with the effective PCs selected by GA has a superior discrimination capacity. The proposed GA-SVM model can effectively learn the relationship between tobacco cultivation regions and tobacco NIR sensor data.
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Praveen, S. Phani, Hesam Ghasempoor, Negar Shahabi, and Fatemeh Izanloo. "A Hybrid Gravitational Emulation Local Search-Based Algorithm for Task Scheduling in Cloud Computing." Mathematical Problems in Engineering 2023 (February 4, 2023): 1–9. http://dx.doi.org/10.1155/2023/6516482.
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The flexibility of cloud computing to provide a dynamic and adaptable infrastructure in the context of information technology and service quality has made it one of the most challenging issues in the computer industry. Task scheduling is a major challenge in cloud computing. Scheduling tasks so that they may be processed by the most effective cloud network resources has been identified as a critical challenge for maximizing cloud computing’s performance. Due to the complexity of the issue and the size of the search space, random search techniques are often used to find a solution. Several algorithms have been offered as possible solutions to this issue. In this study, we employ a combination of the genetic algorithm (GA) and the gravitational emulation local search (GELS) algorithm to overcome the task scheduling issue in cloud computing. GA and the particle swarm optimization (PSO) algorithms are compared to the suggested algorithm to demonstrate its efficacy. The suggested algorithm outperforms the GA and PSO, as shown by the experiments.
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Chen, Chen, Thomas Phang, and Lee Kong Tiong. "Planning semi-automated precast production using GA." International Journal of Industrialized Construction 1, no. 1 (July 27, 2020): 48–63. http://dx.doi.org/10.29173/ijic215.
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Although fully automated production systems have been developed and used in some industry leaders, most of the precast factories have yet to be developed to that stage. Semi-automated production lines are still popularly used. As production productivity can be maximally improved within the physical constraints by applying a sound production plan, this paper tends to propose a production planning method for the semi-automated precast production line using genetic algorithm (GA). The production planning problem is formulated into a flexible job shop scheduling problem (FJSSP) model and solved using an integrated approach. Thanks to the development of new technologies such as building information modeling (BIM) platform and radio frequency identification (RFID), implementation of a just-in-time (JIT) schedule in the semi-automated precast production line becomes practicable on the grounds of risk mitigation and enhanced demand forecast capability. In this regard, the optimization objectives are minimum makespan, station idle time, and earliness and tardiness penalty. An example was applied to validate the integrated GA approach. The experimental results show that the developed GA approach is a useful and effective method for solving the problem that it can return high-quality solutions. This paper thus contributes to the body of knowledge new precast production planning method for practical usage.
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Kourousis, Kyriakos I. "A HOLISTIC APPROACH TO GENERAL AVIATION AIRCRAFT STRUCTURAL FAILURE PREVENTION IN AUSTRALIA." Aviation 17, no. 3 (October 7, 2013): 98–103. http://dx.doi.org/10.3846/16487788.2013.840055.
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Ageing aircraft are becoming a major issue in the general aviation (GA) industry, both in terms of safety and maintenance and support cost. Ensuring a sound structure is considered one of the primary challenges in this area, it is, therefore, attracting the attention of the regulating authorities. The Civil Aviation Safety Agency (CASA) has taken a mixture of actions to tackle the various issues related to the diverse Australian GA ageing aircraft fleet. Further efforts focus on increasing the awareness of the different parties engaged in aircraft operations, maintenance and design, as well as quantification of the associated risk. In this frame a holistic approach is proposed to cover the various aspects, emphasizing the use of cost-effective structural health monitoring (SHM) systems and web-based education and information dissemination on ageing aircraft issues.
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Yang, Zeyin. "Application and Development of Digital Enhancement of Traditional Sculpture Art." Scientific Programming 2022 (February 3, 2022): 1–8. http://dx.doi.org/10.1155/2022/9095577.
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Sculpture art, as an important carrier of spiritual civilization, also portrays a prosperous scene as an industry with urban and cultural development. Three-dimensional technology offers a new platform for sculpture creation, allowing for the digitization of sculpture works via electronic information technology, and the display of sculpture works in front of people via displays, facilitating the exchange and dissemination of information and promoting the growth and progress of the entire sculpture creation industry. We plan to use digital enhancement technology to conduct small-scale creation experiments on traditional sculpture works, discuss the method of GA (Genetic Algorithm) in image restoration processing, investigate the method of image segmentation processing based on the genetic algorithm, and propose the method of image segmentation processing based on the fuzzy membership surface genetic algorithm, in order to verify and solve the creation difficulties of traditional sculpture works.
Dissertations / Theses on the topic "GA. Information industry"
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Amorim, Antonio Marcos. "A globalização do mercado de periódicos científicos eletrônicos e os consórcios de bibliotecas universitárias brasileiras: desafios à democratização do conhecimento científico." Thesis, Universidade de São Paulo, 2002. http://eprints.rclis.org/17713/1/dissertacao_final2006.pdf.
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The research aimed at evaluating the consortia activities in terms of its performance as social instruments of democratization and broadening of scientific information access, by means of its policies of collection development. A case of study of brazilian University libraries consortia for the acquisition of scientific journals was carried out. By means of a dialectal point of view as a way to develop new knowledge and methodology for this research s subject matter, an analyses of the electronic globalization s social and economic context was performed, as for the growing of social exclusion, and the change in the flow of scientific knowledge towards a digital culture. The analysis above struggles to check implicit and explicit relations affecting collection development and widening a trend, marked by the gravity of current digital exclusion in Brazil. Research findings revealed that : the presence of scientific publishers monopoly power and the lower budgets of the brazilian libraries in the nineties, have restricted consortia s purposes as well as blocked likely long-term social gains; challenges for collection development through cooperative activities still persist among them, the strenghtening of marketing actions performed by those participating libraries, allowing greater electronic resources dissemination, as well as greater negotiation posssibilities between consortium and monopolies in the scientific periodicals market ; lack of new libraries consortia embracing other types of documents non-scientific journals associated with public and regional libraries, allowing knowledge democratization to greater extents of society, minimizing digital exclusion
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Rapetti, Elsa. "Formación de bibliotecarios en las escuelas universitarias de bibliotecología del Mercosur : impacto de las tecnologías de información en los planes de estudio." Thesis, 2005. http://eprints.rclis.org/7708/1/rapeti.pdf.
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The contemponary world, dominated by the perspectives, both of information and knowledge, suggests the overbearing necessity of educating and enabling library personnel in sustainable professional competeces. Todays personnel will have to subject to the challenges and the impact of the Information Technologies and shall have to make important contributions to the Society of Information. The present work presents the educational scenery of the university schools of Library Science and Information Science, at Mercosur wtih respect to the inclusion of the Technologies of Information in their programs of study, underlying the necessity of an standardized model, flexible and up to date which allows to give the knowledges and skills requiered to work successfully and a good professional performance. It shows some reflexions about the new technological paradigms, its impact over the librarian profession, the structures of the programs of study and the differences between the schools evaluated.
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Accart, Jean-Philippe. "Dispositifs de capitalisation des connaissances : Repérage de l’offre et construction d’une typologie." Thesis, 2001. http://eprints.rclis.org/8162/1/7F72B910.pdf.
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The dissertation explains the knowldege capitalisation process based on a survey in 12 French companies (Rhone-Alpes Region, South-East of France)conducted in 2001: information management, document management and knowledge management are very closed concepts and are sometimes not very well undestand or implement by companies. The following aspects are discussed and analysed: individual or collective practices, technological supports and the role of experts.
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Sánchez-Sánchez, Pedro. "Los clubes del libro en el mundo editorial: el caso del Círculo de Lectores : trabajo de grado." Thesis, 2005. http://eprints.rclis.org/16330/1/Clubes-Circulo%20Lectores.pdf.
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Book clubs or reading clubs are a special type of editorial service that sends directly to the homes of their members a selection of books, usually published earlier by other publishers. In this thesis, a general description and characteristics of one of these clubs are presented.
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Akkaya, Mehmet Ali. "Endüstriyel örgütlerde bilgi hizmetleri." Thesis, 2010. http://eprints.rclis.org/24765/1/tez%20onay%20sayfal%C4%B1%20son%20hal%208%20mart.pdf.
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The “knowledge” which keeps its place and importance in every period of history, together with “knowledge age”, became the most important source and power on providing a long term sustainable economical superiority and competition adventage to the communities and organizations. At this point, the Formula of being successful is the use the knowledge as effective and productive. Specializing in knowledge’s meaning and becoming widespread in its impression increased it’s emphasis of being renewable and continual. Depending on this concepts of “producing the knowledge” and “creating the knowledge” started to be used in a widespread manner. These concepts are the forms of handling knowledge in the way of managing it. Here in this study; with the “knowledge development” concept mentioned for the first time, based on the knowledge itself and by introducing renewability, developability, ability of getting up to date and convertible talents of knowledge, it is tried to exhibit the impression of all these added to organizations in terms of competition perceptive and economical priority. For this aim; also depending on the technology the industrial world which is leading the fields using the knowledge intensively, is chosen. But, with the experiences along the study it is ascertained that; especially in communities which are developing and in which the importance and meaning of knowledge has not been understood yet; like our country, it is being quite early to be adopted for the “knowledge development” process. Also it is ascertained that it will take time for this process to get worth that it deserves.
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Ortizá, Palomares Javier. "Blockchain como tecnología para la agricultura." Thesis, 2022. http://eprints.rclis.org/43466/1/TFG%20Blockchain%20como%20tecnologi%CC%81a%20para%20la%20agricultura.pdf.
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Blockchain technology is a unique technology that combines a number of features: decentralised structure, storage mechanism, consensus algorithm or asymmetric encryption that ensure transparency, efficiency and security of information. This unique combination has the potential to transform a key sector such as agriculture, as well as a challenge for information science professionals.
This thesis aims to provide an approach to the technology and its application in the agri-food sector at a theoretical and real level through the opinion of professionals working with blockchain in the agricultural sector.
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Ortizá, Palomares Javier. "Blockchain como tecnología para la agricultura." Thesis, 2022. http://eprints.rclis.org/43470/1/TFG%20Blockchain%20como%20tecnologi%CC%81a%20para%20la%20agricultura.pdf.
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Blockchain technology is a unique technology that combines a number of features: decentralised structure, storage mechanism, consensus algorithm or asymmetric encryption that ensure transparency, efficiency and security of information. This unique combination has the potential to transform a key sector such as agriculture, as well as a challenge for information science professionals.
This thesis aims to provide an approach to the technology and its application in the agri-food sector at a theoretical and real level through the opinion of professionals working with blockchain in the agricultural sector.
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Beneyto, Raul G. "El Community Manager y las Ciencias de la Documentación." Thesis, 2013. http://eprints.rclis.org/20196/1/El_Community_Manager_y_las_Ciencias_de_la_Documentacion.pdf.
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The current Information Society and information technology (IT) evolve at the same time. That is why professions are changing and adapting to the new reality. This fact results in the new professional community manager (CM), closely related to the new communication models of the XXI century. These are social networks. How is this professional profile? What skills this professional should have?
This paper aims to find the link there, or could have, among this profession in association with social networks and Web 2.0, and Information Science.
Learn the skills needed to perform the job of community manager, define their duties, and see if the studies in Information Science in Spanish universities are supporting and forming effectively in tasks and abilities about community managemen
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Соляник, Алла. "Система документопостачання фондів бібліотек України: теоретико-методологічний аспект." Thesis, 2005. http://eprints.rclis.org/18794/1/solanyk_aref.pdf.
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The dissertation is devoted to the elaboration of the theoretic-methodical foundation of forming the suitable libraries’ collections supplying system in the times of informatization society. The evolution of the scientific views concerning the problem of the supplying the libraries’ collections is investigated, and the theoretical elaboration level is revealed. The term system of the researching sphere is improved, the definitions of its basic terms are offered. Main periods of the beginning and the development of the components of the supplying libraries’ collections system are described, conformities to natural laws of development and factors that define the modern condition of functioning of the libraries’ collections supplying system are found out. Foreign experience of the libraries’ collections supplying is generalized. Ways of improving the Ukrainian libraries’ supplying system in the times of forming the national electronic library are scientifically grounded. The theoretical model of Ukrainian libraries’ supplying system is elaborated.
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Rodríguez-Yunta, Luis. "Ciencia y Documentación en español en el espacio virtual iberoamericano : Rol de las revistas científicas y desarrollo de nuevos productos electrónicos de información bibliográfica." Thesis, 2010. http://eprints.rclis.org/15402/1/Tesis_LuisRodriguezYunta.pdf.
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The thesis analyzes the role taken by both scientific journals and bibliographic information products, in the new relations between Spain and Latin American. The combination of technological development with the promotion of cultural and scientific relations in the Iberoamerican Community of Nations, form a virtual space, which defines the framework of the study. The research methodology is based on both quantitative and qualitative techniques. Despite the growing importance of scientific and academic relations, there is considerable room for improvement to seek greater inter-Iberoamerican scientific community. Internet facilitates scientific cooperation and online higher education in their own language. The publication of scientific journals is a communication channel which can be enhanced scientific exchange further by the international contribution of Hispanic, Iberian and Latin American Studies. Documentary resources focused on the national and regional scientific publications, make a wide range of products. Iberoamerican coverage, encompassing the sources of Spain, Portugal and Latin America, is emerging in the design of new sources of scientific documentation, enhanced by the facilities for dissemination via Internet. In the production of different resources from literature in Spanish language, the thesis highlights the work of the UNAM in Mexico (especially CLASE and PERIODICA databases) and the CSIC in Spain (ISOC, ICYT and IME databases). It also analyzes the system Latindex, Virtual Health Library, SciELO, Redalyc and Dialnet. These products are compared, concluding that the major databases are complementary and therefore there are possibilities for cooperation. Scientific information systems are essential to build the target of a Iberoamerican Area of Knowledge.
Books on the topic "GA. Information industry"
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Jōhō rikkoku: Nani ga Ajia no dainamizumu o tsukuridashite iru no ka. Tōkyō: NTT Shuppan, 1990.
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Jōhōka ga shakai seikatsu ni oyobosu inpakuto ni kansuru kisotiki kenkyū. [Tokyo]: Denki Tsūshin Sōgō Kenkyūjo, 1986.
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Joho gijutsu ga mane o kaeru. Fujitsu Keiei Kenshujo, 1992.
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Maruchimedia kakumei: "yume no okoku" ga kyodai shijo o umu /Nikkei sangyo shinbun hen. Nihon Keizai Shinbunsha, 1993.
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Takemochi, Ishii. Nihon kara shinseiki ga hajimaru: Sekai o miryosuru Nihon-gata raifusutairu no shinario = Japanese lifestyles, a template for tomorrow. PHP Kenkyujo, 1990.
Find full textBook chapters on the topic "GA. Information industry"
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Zaichenko, Dmitry S., and Irina S. Sineva. "The Study of Genetic Type Steganographic Models to Increase Noise Immunity of IoT Systems." In Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms, 1414–29. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-8048-6.ch066.
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Research and development in the field of the Internet of Things, or more generally M2M systems security, is the subject of daily discussion in the ICT market. With the rapid development of intelligent devices, the necessity of valuable information protection has generated many new methods and technologies. Stegoimages, along with genetic algorithms (GA), are a relatively new object in the field of information hiding. The assumption that their application can significantly improve the noise-resistant properties of stegofiles is justified by the properties of the GA, but it is a subject for detailed study, since in such an application the GA has not yet been considered. The proposed method is based on genetic coding that hides messages between Internet of Things devices and is capable of detecting both internal and external attacks in the intellectual infrastructure. A sufficiently high efficiency of preliminary GA coding is shown for objects such as hiding graphic information in a graphic stegocontainer.
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Chawla, Suruchi. "Web Page Recommender System using hybrid of Genetic Algorithm and Trust for Personalized Web Search." In Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms, 656–75. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-8048-6.ch034.
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The main challenge to effective information retrieval is to optimize the page ranking in order to retrieve relevant documents for user queries. In this article, a method is proposed which uses hybrid of genetic algorithms (GA) and trust for generating the optimal ranking of trusted clicked URLs for web page recommendations. The trusted web pages are selected based on clustered query sessions for GA based optimal ranking in order to retrieve more relevant documents up in ranking and improves the precision of search results. Thus, the optimal ranking of trusted clicked URLs recommends relevant documents to web users for their search goal and satisfy the information need of the user effectively. The experiment was conducted on a data set captured in three domains, academics, entertainment and sports, to evaluate the performance of GA based optimal ranking (with/without trust) and search results confirms the improvement of precision of search results.
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PriyaDarshani, Manu, Mohan Prasad Sinha, and Keshav Sinha. "A Study on Evolutionary Technique to Predict the Sales During COVID-19." In Advances in Library and Information Science, 376–402. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-6449-3.ch020.
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COVID-19 has affected the growth of every industry; in the meantime, an enormous amount of demand is present in the field of telecom and automobiles. In this chapter, the authors present case studies based on sales prediction for the Indian market. The analysis of the study is based on the various traditional methods like growth rate (GR), percentage growth rate (PGR), and the evolutionary techniques like genetic algorithms (GA). The data are collected for the report of telecommunication and heavy industry ministry (Republic of India). The results are used to analyze the sale of automobiles and telecommunication devices and to predict the growth at the time of the COVID-19 pandemic. The prediction is used to identify the upcoming sale and counterparts with demand.
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Celik, Gaffari. "Determining Headache Diseases With Genetic Algorithm." In Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms, 642–55. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-8048-6.ch033.
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Currently, medical diagnosis has a strong relation with the artificial-intelligence-oriented approaches. Because it is practical to employ intelligent mechanisms over some input data-expert knowledge and design effective solution ways, even the biomedical engineering field is interested in taking support from artificial intelligence. If applications in this manner are taken into consideration, we can see that medical diagnoses have a big percentage. In the sense of the explanations, the objective of this chapter is to use genetic algorithm (GA) for diagnosing headache diseases. As a popular and essential technique benefiting from evolutionary mechanisms, GA can deal with many different types of real-world problems. So, it has been chosen as the solution way/algorithm over the headache disease detection problem, which shapes the research framework of the study. The chapter content gives information about the performed diagnosis application and the results.
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Chakraborty, Shouvik, Sankhadeep Chatterjee, Amira S. Ashour, Kalyani Mali, and Nilanjan Dey. "Intelligent Computing in Medical Imaging." In Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms, 592–608. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-8048-6.ch030.
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Biomedical imaging is considered main procedure to acquire valuable physical information about the human body and some other biological species. It produces specialized images of different parts of the biological species for clinical analysis. It assimilates various specialized domains including nuclear medicine, radiological imaging, Positron emission tomography (PET), and microscopy. From the early discovery of X-rays, progress in biomedical imaging continued resulting in highly sophisticated medical imaging modalities, such as magnetic resonance imaging (MRI), ultrasound, Computed Tomography (CT), and lungs monitoring. These biomedical imaging techniques assist physicians for faster and accurate analysis and treatment. The present chapter discussed the impact of intelligent computing methods for biomedical image analysis and healthcare. Different Artificial Intelligence (AI) based automated biomedical image analysis are considered. Different approaches are discussed including the AI ability to resolve various medical imaging problems. It also introduced the popular AI procedures that employed to solve some special problems in medicine. Artificial Neural Network (ANN) and support vector machine (SVM) are active to classify different types of images from various imaging modalities. Different diagnostic analysis, such as mammogram analysis, MRI brain image analysis, CT images, PET images, and bone/retinal analysis using ANN, feed-forward back propagation ANN, probabilistic ANN, and extreme learning machine continuously. Various optimization techniques of ant colony optimization (ACO), genetic algorithm (GA), particle swarm optimization (PSO) and other bio-inspired procedures are also frequently conducted for feature extraction/selection and classification. The advantages and disadvantages of some AI approaches are discussed in the present chapter along with some suggested future research perspectives.
Conference papers on the topic "GA. Information industry"
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Yu, Wennian, Chris K. Mechefske, and Il Yong Kim. "Cutting Tool Wear Estimation Using a Genetic Algorithm Based Long Short-Term Memory Neural Network." In ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/detc2018-85253.
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On-line cutting tool wear monitoring plays a critical role in industry automation and has the potential to significantly increase productivity and improve product quality. In this study, we employed the long short-term memory neural network as the decision model of the tool condition monitoring system to predict the amount of cutting tool wear. Compared with the traditional recurrent neural networks, the long short-term memory (LSTM) network can capture the long-term dependencies within a time series. To further decrease the training error and enhance the prediction performance of the network, a genetic algorithm (GA) is applied to find the initial values of the networks that minimize the objective (training error). The proposed methodology is applied on a publicly available milling data set. Comparisons of the prediction performance between the Elman network and the LSTM with and without using GA optimization proves that the GA based LSTM shows an enhanced prediction performance on this data set.
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Liszkai, T. R. "Application of an Implicit Redundant Genetic Algorithm for Structural Damage Identification of Flexible Structures." In ASME/JSME 2004 Pressure Vessels and Piping Conference. ASMEDC, 2004. http://dx.doi.org/10.1115/pvp2004-2582.
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Detecting structural damage is critical in assessing current condition, calculating remaining life, and developing rehabilitation strategies for existing structures. Many structural damage identification methods (SDIM) use vibration data to localize and identify deterioration of structural members. Due to practical constraints, such as cost, number of input channels of the measuring device, or lack of access of parts of the structure, the actual number of sensors used to collect measurement data is much smaller then the number of possible sensor locations. Therefore, the inverse problem associated with structural damage identification is ill formulated and often difficult to solve explicitly. This research addresses the problem of structural damage detection using the linear vibration information contained in frequency response functions (FRF). A structural damage identification method (SDIM) is proposed, which minimizes the error between the analytically computed and measured vibration signatures of structures. The SDIM is formulated as an unconstrained optimization problem, which is solved using genetic algorithms (GA). The implicit redundant representation (IRR) of genes allows the formulation of unstructured optimization problems in which the number of unknown variables is indefinite. The IRR GA efficiently exploits the unstructured nature of structural damage detection by allowing the number of assumed damaged elements to change throughout the optimization. The accuracy and efficiency of SDIM is increased when the IRR GA is used instead of the simple fixed representation GA. The procedure is applied to flexible structures to show that the proposed SDIM is capable of identifying damages in structures often used in the nuclear industry. Noisy measurements are also considered in the simulations to investigate their effect on the proposed SDIM accuracy. Test case results using different measurement noise levels show that the IRR GA has superior performance over the standard fixed representation GA in correctly identifying both the location and extent of damages.
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Chen, Yuxuan, Wei Yan, Hua Zhang, Ying Liu, Zhigang Jiang, and Xumei Zhang. "A Data-Driven Design Approach for Carbon Emission Prediction of Machining." In ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/detc2022-90465.
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Abstract The issue of carbon emission reduction for manufacturing industry attracts increasing attention. As a major contributor in the manufacturing industry, machining has generated large amounts of carbon emissions through the resource consumption, energy consumption, and waste disposal. The carbon emission prediction of machining is a priori technology for its reduction, and has been established as one of the most crucial research targets. The purpose of this study is to design a carbon emission prediction model of machining through a data-driven approach. First of all, the multiple sources and impact factors of carbon emissions in machining are studied, and the relationship between these factors is also studied to describe the carbon emissions. Then, a data-driven approach is designed to predict the carbon emission of machining, which consists of data collection and preprocessing, feature extraction, prediction model establishment and model validation. The ridge regression, BP neural network based on Genetic Algorithm (GA-BP), root means square error (RMSE) and mean relative percentage error (MPAE) are respectively employed to fulfill the above tasks in the design approach. Finally, an experimental study of a real turning machining is proposed to verify the feasibility and merits of the designed approach.
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Sharp, Chris, and Bryony DuPont. "Wave Energy Converter Array Optimization: A Review of Current Work and Preliminary Results of a Genetic Algorithm Approach Introducing Cost Factors." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-46553.
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Currently, ocean wave energy is a novel means of electricity generation that is projected to potentially serve as a primary energy source in coastal areas. However, for wave energy converters (WECs) to be applicable on a scale that allows for grid implementation, these devices will need to be placed in close relative proximity to each other. From what’s been learned in the wind industry of the U.S., the placement of these devices will require optimization considering both cost and power. However, current research regarding optimized WEC layouts only considers the power produced. This work explores the development of a genetic algorithm (GA) that will create optimized WEC layouts where the objective function considers both the economics involved in the array’s development as well as the power generated. The WEC optimization algorithm enables the user to either constrain the number of WECs to be included in the array, or allow the algorithm to define this number. To calculate the objective function, potential arrays are evaluated using cost information from Sandia National Labs Reference Model Project, and power development is calculated such that WEC interaction affects are considered. Results are presented for multiple test scenarios and are compared to previous literature, and the implications of a priori system optimization for offshore renewables are discussed.
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Li, Meifang, and Mian Li. "Decision Support for Performance Arts Using Support Vector Regression With Genetic and Particle Swarm Algorithms." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-47059.
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Different from typical mechanical products, tickets for movies and performing arts can be considered as a special type of consumer products. Compared to widely known box-office receipts prediction with single-output in movie industry, estimating the market share and price for performing arts is still a challenging problem due to high dimensional datasets yet limited number of samples. This paper describes a data-driven decision support system to help arts managers make strategic decisions, especially on session-determination and price-setting, considering price discrimination and prediction on the corresponding sales volume. Eight different attributes from the database, with multiple labels in each attribute, are used to accurately and comprehensively represent and classify the characteristics of performing arts in each genre. A web-based influence factor is also defined to quantify the popularity and publicity of performing arts. For this multi-input and multi-output problem, support vector regression (SVR) is employed and its optimal parameters are determined using genetic algorithm (GA) and particle swarm optimization (PSO) respectively. Price utility axiom with the law of demand is applied to maximize the receipts. Compared to artificial neural networks (ANN), those two optimization based SVR methods perform much better, in terms of effectiveness and reliability.
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Hegde, Shailesh, Angelo Bonfitto, Hadi Rahmeh, Nicola Amati, and Andrea Tonoli. "Optimal Selection of Equivalence Factors for ECMS in Mild Hybrid Electric Vehicles." In ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/detc2021-71621.
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Abstract The increasing stringent emissions regulation over the years have shifted the focus of automotive industry towards more efficient fuel economy solutions. One such solution is Hybrid electric architecture, which is able to improve the fuel economy and consequently cutting down emissions. A well known control strategy to solve optimization problem for energy management of Hybrid electric vehicles is ECMS (Equivalent Consumption Minimization Strategy). Finding the best control parameters (equivalence factors) of this strategy may become quite involved. This paper proposes a method for the selection of the optimal equivalence factors, for charging and discharging, by applying genetic algorithm in the case of a P0 mild hybrid electric vehicle. This method is a systematic and deterministic way to guarantee an optimal solution with respect to the trial and error method. The proposed ECMS is compared to a technique available in literature, known as the shooting method, which relies only on one equivalence factor for discharging. It is demonstrated that the performance in terms of pollutant emissions are comparable. However, ECMS with GA always guarantees an optimal solution even in the case of heavy accessory load, when shooting method is not valid anymore, as it does not guarantee a charge sustaining condition.
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Chen, Shiang-Fong, and Xiao-Yun Liao. "Stable Assembly Sequence Planning Using a Genetic Algorithm." In ASME 1999 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/detc99/dfm-8912.
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Abstract Stability problems in assembly sequence planning have drawn great research interest in recent years. Most proposed methodologies are based on graph theory and involve complex geometric and physical analyses. As a result, even for a simple structure, it is difficult to take all the criteria into account and to implement real world solutions. This paper uses a genetic algorithm (GA) to synthesize different criteria fo generating a stable assembl plan. Three matrices (Connection Matrix, Supporting Matrix, and Interference-Free Matrix) are generated from an input B-rep file to represent the CAD information of a given product. The stability of a given assembly plan and reorientation numbers are incorporated into the fitness function of the genetic assembly planner. The proposed planning algorithm has been successfull implemented. This paper also presents implemented planne performance as measured for two industry-standard structures.
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Wishart, Jeffrey D., Zuomin Dong, and Marc M. Secanell. "Optimization of a PEM Fuel Cell System for Low-Speed Hybrid Electric Vehicles." In ASME 2006 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2006. http://dx.doi.org/10.1115/detc2006-99606.
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Design optimization is performed by presenting a systematic method to obtain the optimal operating conditions of a Proton Exchange Membrane (PEM) fuel cell system targeted towards a vehicular application. The fuel cell stack model is a modified version of the semi-empirical model introduced by researchers at the Royal Military College of Canada and one that is widely used by industry. Empirical data obtained from tests of PEM fuel cell stacks are used to determine the empirical parameters of the fuel cell performance model. Based on this stack model, a fuel cell system model is built in MATLAB. Included in the system model are heat transfer and gas flow considerations and the associated Balance of Plant (BOP) components. The modified ADVISOR vehicle simulation tool is used to integrate the New York City Cycle (NYCC) drive cycle and vehicle model to determine the power requirements and hence the load cycle of the fuel cell system for a low-speed fuel cell hybrid electric vehicle (LSFCHEV). The optimization of the powerplant of this vehicle type is unique. The vehicle model has been developed in the work to describe the characteristics and performance of an electric scooter, a simple low-speed vehicle (LSV). The net output power and system exergetic efficiency of the system are maximized for various system operating conditions using the weighted objective function based on the load cycle requirement. The method is based on the coupling of the fuel cell system model with three optimization algorithms (a) sequential quadratic programming (SQP); (b) simulated annealing (SA); and (c) genetic algorithm (GA). The results of the optimization provide useful information that will be used in future study on control algorithms for LSFCHEVs. This study facilitates research on more complex fuel cell system modeling and optimization, and provides a basis for experimentation to verify the fuel cell system model.
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Dominique, S., and J. Y. Tre´panier. "Optimization of a Gas Turbine Engine Rotor Disc Using Case-Based Reasoning and the GATE Genetic Algorithm." In ASME Turbo Expo 2010: Power for Land, Sea, and Air. ASMEDC, 2010. http://dx.doi.org/10.1115/gt2010-23011.
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The implementation of an automated decision support system in the field of structural design and optimization can give a significant advantage to any industry working on mechanical design. Such a system can reduce the project cycle time or allow more time to produce a better design by providing solution ideas to a designer or by upgrading existing design solutions while the designer is not at work. This paper presents an approach to automating the process of designing a gas turbine engine rotor disc using case-based reasoning (CBR), combined with a new genetic algorithm, the Genetic Algorithm with Territorial core Evolution (GATE). GATE was specifically created to solve problems in the mechanical structural design field, and is essentially a real number genetic algorithm that prevents new individuals from being born too close to previously evaluated solutions. The restricted area becomes smaller or larger during optimization to allow global or local searches when necessary. The CBR process uses a databank filled with every known solution to similar design problems. The closest solutions to the current problem in terms of specifications are selected, along with an estimated solution from an artificial neural network. Each solution selected by the CBR is then used to initialize the population of a GATE island. Our results show that CBR may significantly upgrade the performance of an optimization algorithm when sufficient preliminary information is known about the design problem. It provides an average solution 5.0% lighter than the average solution found using random initialization. The results are compared to other results obtained for the same problems by four optimization algorithms from the I-SIGHT 3.5 software: the sequential quadratic programming algorithm (SQP), the insular genetic algorithm (GA), the Hookes & Jeeves generalized pattern search (HJ) and POINTER. Results show that GATE can be a very good candidate for automating and accelerating the structural design of a gas turbine engine rotor disc, providing an average disc 18.9% lighter than SQP, 11.2% lighter than HJ, 23.9% lighter than GA and 4.3% lighter than POINTER, even when starting with the same solution set.