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Zhang, Xuexia, Zixuan Yu, and Weirong Chen. "Life Prediction Based on D-S ELM for PEMFC." Energies 12, no. 19 (September 30, 2019): 3752. http://dx.doi.org/10.3390/en12193752.
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The proton exchange membrane fuel cell (PEMFC) is an extremely clean and efficient power generation device. However, its limited lifespan has restricted the large-scale commercial development of PEMFCs. Life prediction is a promising solution for the further life extension of PEMFCs. In this paper, D-S ELM(DWT-SaDE ELM), define as, an enhanced extreme learning machine (ELM) optimized by discrete wavelet transform (DWT) and self-adaptive differential evolutionary algorithm (SaDE), is proposed to predict the remaining useful life (RUL) of PEMFCs. In D-S ELM, DWT is employed to extract available features from multi-input data with stochastic noise. Then, SaDE explores the optimal parameter configuration for the ELM neural network. Moreover, the influence of training data sizes on the prediction results is discussed. Simulations show that D-S ELM has obvious advantages in prediction accuracy. Furthermore, the superiority of D-S ELM in small sample applicability, prediction speed and robustness make it more suitable for the online prediction of PEMFCs.
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Tang, Chaofei, Nurbol Luktarhan, and Yuxin Zhao. "SAAE-DNN: Deep Learning Method on Intrusion Detection." Symmetry 12, no. 10 (October 15, 2020): 1695. http://dx.doi.org/10.3390/sym12101695.
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Intrusion detection system (IDS) plays a significant role in preventing network attacks and plays a vital role in the field of national security. At present, the existing intrusion detection methods are generally based on traditional machine learning models, such as random forest and decision tree, but they rely heavily on artificial feature extraction and have relatively low accuracy. To solve the problems of feature extraction and low detection accuracy in intrusion detection, an intrusion detection model SAAE-DNN, based on stacked autoencoder (SAE), attention mechanism and deep neural network (DNN), is proposed. The SAE represents data with a latent layer, and the attention mechanism enables the network to obtain the key features of intrusion detection. The trained SAAE encoder can not only automatically extract features, but also initialize the weights of DNN potential layers to improve the detection accuracy of DNN. We evaluate the performance of SAAE-DNN in binary-classification and multi-classification on an NSL-KDD dataset. The SAAE-DNN model can detect normally and attack symmetrically, with an accuracy of 87.74% and 82.14% (binary-classification and multi-classification), which is higher than that of machine learning methods such as random forest and decision tree. The experimental results show that the model has a better performance than other comparison methods.
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Bocca, Francisco Verardi. "La Mettrie: nem santo, nem pecador." Sofia 6, no. 2 (December 20, 2017): 128–45. http://dx.doi.org/10.47456/sofia.v6i2.17490.
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Neste artigo procuro desfazer a opinião compartilhada entre leitores e historiadores da filosofia que consideram imoralista tanto a pessoa como a obra do médico-filósofo Julien Offray de La Mettrie, portanto perniciosa e merecedora das censuras sofridas. De suas causas, destaco as biografias dedicadas a ele. Também o recurso que Marquês de Sade, ao adotá-lo como seu filósofo, fez de suas teses, atribuindo-lhe sua inspiração hedonista e libertina. Por último, o histrionismo de seus escritos. Todas podem ser questionadas, mas somente a leitura atenta de sua obra pode lhe fazer justiça. Quanto aos biógrafos nada tenho a dizer. Quanto a Sade, me apoio em Jean Dupran (1976), para quem realizou uma apropriação tendenciosa de suas teses. Assim, apresentarei um confronto entre as obras L`homme machine (1747) e Anti-Sénèque (1748), a fim de melhor compreender sua noção de moralidade, prazer, remorso, convívio social e o papel da filosofia na construção da felicidade virtuosa, longe da imoderação e devassidão que o público lhe atribui.
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Chaulwar, Amit. "Sampling Algorithms Combination with Machine Learning for Efficient Safe Trajectory Planning." International Journal of Machine Learning and Computing 11, no. 1 (January 2021): 1–11. http://dx.doi.org/10.18178/ijmlc.2021.11.1.1007.
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The planning of safe trajectories in critical traffic scenarios using model-based algorithms is a very computationally intensive task. Recently proposed algorithms, namely Hybrid Augmented CL-RRT, Hybrid Augmented CL-RRT+ and GATE-ARRT+, reduce the computation time for safe trajectory planning drastically using a combination of a deep learning algorithm 3D-ConvNet with a vehicle dynamic model. An efficient embedded implementation of these algorithms is required as the vehicle on-board micro-controller resources are limited. This work proposes methodologies for replacing the computationally intensive modules of these trajectory planning algorithms using different efficient machine learning and analytical methods. The required computational resources are measured by downloading and running the algorithms on various hardware platforms. The results show significant reduction in computational resources and the potential of proposed algorithms to run in real time. Also, alternative architectures for 3D-ConvNet are presented for further reduction of required computational resources.
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A, Hajare Prashantbhai. "Prediction of safe working frequencies of screening machine by Harmonic Analysis." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (June 30, 2018): 74–77. http://dx.doi.org/10.31142/ijtsrd12838.
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Gopal, Madhwaraj Kango. "A Novel Machine Learning Technique towards Predicting the Sale of Washing Machines in a Small Organization." International Journal of Psychosocial Rehabilitation 24, no. 5 (May 25, 2020): 6969–76. http://dx.doi.org/10.37200/ijpr/v24i5/pr2020697.
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Chiwamba, Simon Hawatichke, Jackson Phiri, Philip O. Y. Nkunika, Mayumbo Nyirenda, Monica M. Kabemba, and Philemon H. Sohati. "Machine Learning Algorithms for automated Image Capture and Identification of Fall Armyworm (FAW) Moths." Zambia ICT Journal 3, no. 1 (March 7, 2019): 1–4. http://dx.doi.org/10.33260/zictjournal.v3i1.69.
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Automated entomology is one of the field that has received a fair attention from the computer scientists and its support disciplines. This can further be confirmed by the recent attention that the Fall Armyworm (FAW) (Spodoptera frugiperda) has received in Africa particularly the Southern African Development Community (SADC). As the FAW is known for its devastating effects, stakeholders such as the Food and Agriculture Organization (FAO), SADC and University of Zambia (UNZA) have agreed to develop robust early monitoring and warning system. To supplement the stakeholders’ efforts, we choose a branch of artificial intelligence that employs deep neural network architectures known as Google TensorFlow. It is an advanced state-of-the-art machine learning technique that can be used to identify the FAW moths. In this paper, we use Google TensorFlow, an open source deep learning software library for defining, training and deploying machine learning models. We use the transfer learning technique to retrain the Inception v3 model in TensorFlow on the insect dataset, which reduces the training time and improve the accuracy of FAW moth identification. Our retrained model achieves a train accuracy of 57 – 60 %, cross entropy of 65 – 70% and validation accuracy of
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Shoko, Claris, Caston Sigauke, and Peter Njuho. "Short-term forecasting of confirmed daily COVID-19 cases in the Southern African Development Community region." African Health Sciences 22, no. 4 (December 23, 2022): 534–50. http://dx.doi.org/10.4314/ahs.v22i4.60.
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Background: The coronavirus pandemic has resulted in complex challenges worldwide, and the Southern African Development Community (SADC) region has not been spared. The region has become the epicentre for coronavirus in the African continent. Combining forecasting techniques can help capture other attributes of the series, thus providing crucial information to address the problem.
Objective: To formulate an effective model that timely predicts the spread of COVID-19 in the SADC region.
Methods: Using the Quantile regression approaches; linear quantile regression averaging (LQRA), monotone composite quantile regression neural network (MCQRNN), partial additive quantile regression averaging (PAQRA), among others, we combine point forecasts from four candidate models namely, the ARIMA (p, d, q) model, TBATS, Generalized additive model (GAM) and a Gradient Boosting machine (GBM).
Results: Among the single forecast models, the GAM provides the best model for predicting the spread of COVID-19 in the SADC region. However, it did not perform well in some periods. Combined forecasts models performed significantly better with the MCQRNN being the best (Theil’s U statistic=0.000000278).
Conclusion: The findings present an insightful approach in monitoring the spread of COVID-19 in the SADC region. The spread of COVID-19 can best be predicted using combined forecasts models, particularly the MCQRNN approach.
Keywords: Combined Forecasts; LQRA; PLAQR; OPERA; Quantile Regression Neural Networks; COVID-19.
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da Silva, Paulo, and Ikuo Tanabe. "Analytical models for machine tool motion behavior assessment bench mark subjected to great earthquake." International Journal of Safety and Security Engineering 9, no. 2 (June 30, 2019): 121–36. http://dx.doi.org/10.2495/safe-v9-n2-121-136.
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Gaffoor, Zaheed, Kevin Pietersen, Nebo Jovanovic, Antoine Bagula, and Thokozani Kanyerere. "Big Data Analytics and Its Role to Support Groundwater Management in the Southern African Development Community." Water 12, no. 10 (October 9, 2020): 2796. http://dx.doi.org/10.3390/w12102796.
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Big data analytics (BDA) is a novel concept focusing on leveraging large volumes of heterogeneous data through advanced analytics to drive information discovery. This paper aims to highlight the potential role BDA can play to improve groundwater management in the Southern African Development Community (SADC) region in Africa. Through a review of the literature, this paper defines the concepts of big data, big data sources in groundwater, big data analytics, big data platforms and framework and how they can be used to support groundwater management in the SADC region. BDA may support groundwater management in SADC region by filling in data gaps and transforming these data into useful information. In recent times, machine learning and artificial intelligence have stood out as a novel tool for data-driven modeling. Managing big data from collection to information delivery requires critical application of selected tools, techniques and methods. Hence, in this paper we present a conceptual framework that can be used to manage the implementation of BDA in a groundwater management context. Then, we highlight challenges limiting the application of BDA which included technological constraints and institutional barriers. In conclusion, the paper shows that sufficient big data exist in groundwater domain and that BDA exists to be used in groundwater sciences thereby providing the basis to further explore data-driven sciences in groundwater management.
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Geving, Stig, Øyvind Norvik, and Lars Gullbrekken. "The Effect of Repeated Moisture Cycles on the Air Tightness of Traditional Clamped Vapour Barrier Joints." Journal of Sustainable Architecture and Civil Engineering 24, no. 1 (April 17, 2019): 44–51. http://dx.doi.org/10.5755/j01.sace.24.1.23232.
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In this study it has been investigated whether and to what extent, repeated moisture cycles affect the airleakage through clamped overlap joints in the vapour barrier layer. Use of clamped joints is a traditionalway to make airtight joints in the wind- and vapour barrier used in wood frame walls in Norway and othercountries. A laboratory test has been carried out, with a total of 63 pressure tests, being carried out on 9 testsamples, consisting of overlap joints of 0.15 mm polyethylene film clamped between a wooden batten andstud. Each sample was tested seven times after repeated drying and humidification, where the moisturevalues of the sub-cycles were chosen to represent the annual variations of indoor relative humidity.The laboratory test materials were mounted with machine nails with various center spacing (150 mm,300 mm and 450 mm). The overlap joints of the vapour barrier were in the end of the test sealed withadhesive tape, revealing to what extent this over a longer period of time will be beneficial.The results showed that the first moisture cycle (drying) resulted in significant increase of air leakage forall the sample variants. Throughout the moisture cycles, a further strong leakage development for centerspacing 450 mm was observed, which was less for 300 mm, and non-existent for 150 mm. The gain ofusing structural adhesive tape was found to largely depend on the level of perforation resulting from thenails and their center distance. Adhesive tape on the joints resulted in the greatest reduction in leakagenumbers where the center distance between the nails was high, i.e. the reduction was 58% for centerdistance 450 mm. However, with shorter center distance the use of tape only decreased the air leakagebetween 22-39%, revealing the fact that a large part of the joint leakage is through the nail perforations.
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Gireesh, Midhula, Jhalendra P. Rijal, and Shimat V. Joseph. "Spatial Distribution of Hunting Billbugs (Coleoptera: Curculionidae) in Sod Farms." Insects 12, no. 5 (April 30, 2021): 402. http://dx.doi.org/10.3390/insects12050402.
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The hunting billbug, Sphenophorus venatus vestitus Chittenden (Coleoptera: Curculionidae), is an important turfgrass pest, especially in sod farms. S. venatus vestitus larvae feed on the stems and roots of turfgrass. Damaged turfgrass is loosely held together and poses a challenge for machine harvesting. Additionally, the normal growth of turfgrass is affected, especially after winter dormancy. Because S. venatus vestitus larvae are hidden inside the stems or under the soil, larval management is challenging. To improve sampling and management, the spatial distribution patterns of S. venatus vestitus larvae and adults were assessed at four sod farm sites with a history of S. venatus vestitus infestation in central Georgia (USA). The larvae were sampled by soil cores using a hole cutter, whereas adults were collected using pitfall traps for 7 d. The spatial distributions of larvae and adults was analyzed using SADIE and variograms. The SADIE and variogram analyses revealed a significant aggregation pattern for adults, whereas aggregated distributions were detected for larvae with variogram analyses. The average ranges of spatial dependence for larval and adult samples were 3.9 m and 5.4 m, respectively. Interpolated distribution maps were created to visually depict S. venatus vestitus infestation hotspots within the sod farms.
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WDOWIAK, AGNIESZKA, and JANUSZ BROL. "METHODS OF STRENGTH GRADING OF STRUCTURAL TIMBER – COMPARATIVE ANALYSIS OF VISUAL AND MACHINE GRADING ON THE EXAMPLE OF SCOTS PINE TIMBER FROM FOUR NATURAL FOREST REGIONS OF POLAND." Structure and Environment 11, no. 3 (September 30, 2019): 210–24. http://dx.doi.org/10.30540/sae-2019-016.
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Rana, Prashant Singh, Harish Sharma, Mahua Bhattacharya, and Anupam Shukla. "Quality assessment of modeled protein structure using physicochemical properties." Journal of Bioinformatics and Computational Biology 13, no. 02 (April 2015): 1550005. http://dx.doi.org/10.1142/s0219720015500055.
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Physicochemical properties of proteins always guide to determine the quality of the protein structure, therefore it has been rigorously used to distinguish native or native-like structure from other predicted structures. In this work, we explore nine machine learning methods with six physicochemical properties to predict the Root Mean Square Deviation (RMSD), Template Modeling (TM-score), and Global Distance Test (GDT_TS-score) of modeled protein structure in the absence of its true native state. Physicochemical properties namely total surface area, euclidean distance (ED), total empirical energy, secondary structure penalty (SS), sequence length (SL), and pair number (PN) are used. There are a total of 95,091 modeled structures of 4896 native targets. A real coded Self-adaptive Differential Evolution algorithm (SaDE) is used to determine the feature importance. The K-fold cross validation is used to measure the robustness of the best predictive method. Through the intensive experiments, it is found that Random Forest method outperforms over other machine learning methods. This work makes the prediction faster and inexpensive. The performance result shows the prediction of RMSD, TM-score, and GDT_TS-score on Root Mean Square Error (RMSE) as 1.20, 0.06, and 0.06 respectively; correlation scores are 0.96, 0.92, and 0.91 respectively; R2 are 0.92, 0.85, and 0.84 respectively; and accuracy are 78.82% (with ± 0.1 err), 86.56% (with ± 0.1 err), and 87.37% (with ± 0.1 err) respectively on the testing data set. The data set used in the study is available as supplement at http://bit.ly/RF-PCP-DataSets .
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Zhao, Zhenxing, Kaijie Chen, Ying Chen, Yuxing Dai, Zeng Liu, Kuiyin Zhao, Huan Wang, and Zishun Peng. "An Ultra-Fast Power Prediction Method Based on Simplified LSSVM Hyperparameters Optimization for PV Power Smoothing." Energies 14, no. 18 (September 13, 2021): 5752. http://dx.doi.org/10.3390/en14185752.
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With existing power prediction algorithms, it is difficult to satisfy the requirements for prediction accuracy and time when PV output power fluctuates sharply within seconds, so this paper proposes a high-precision and ultra-fast PV power prediction algorithm. Firstly, in order to shorten the optimization time and improve the optimization accuracy, the single-iteration Gray Wolf Optimization (SiGWO) method is used to simplify the iteration process of the hyperparameters of Least Squares Support Vector Machine (LSSVM), and then the hybrid local search algorithm composed of Iterative Local Search (ILS) and Self-adaptive Differential Evolution (SaDE) is used to improve the accuracy of hyperparameters, so as to achieve high-precision and ultra-fast PV power prediction. The power prediction model is established, and the proposed algorithm is applied in a test experiment which can complete the power prediction within 3 s, and the RMSE is only 0.44%. Finally, combined with the PV-storage advanced smoothing control strategy, it is verified that the performance of the proposed algorithm can satisfy the system’s requirements for prediction accuracy and time under the condition of power mutation in a PV power generation system.
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Riquieri, Manuella Ribeiro Lira, André Luís Bonifácio de Carvalho, Assis Luiz Mafort Ouverney, and Thiago Dias Sarti. "Profile of municipal managers of healthcare in Brazil: an overview of three decades." Revista de Administração Pública 56, no. 5 (September 2022): 683–93. http://dx.doi.org/10.1590/0034-761220220132x.
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Abstract Municipal health secretaries are strategic actors in the construction of the Brazilian national health system - SUS, actively participating in the system’s governance. The comparative analysis of the profile of municipal secretaries over three decades (1996, 2006, and 2017) was carried out using the database of three cross-sectional surveys carried out at the national level with structured questionnaires aimed at all municipal health secretaries in the country. The increase in female participation, greater diversity in ethnic and racial terms, greater professional qualification of managers, and the renewal of the generational profile, reiterates the advance of the democratization of these management spaces. Despite the renewal of the profile during SUS’s construction, the challenge remains to make the public machine more representative of Brazilian society as a whole.
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Andrić-Petrović, Sanja, and Nađa Marić. "Improvement of the psychiatric care through outsourcing artificial intelligence technologies: Where are we now?" Medicinska istrazivanja 55, no. 2 (2022): 19–29. http://dx.doi.org/10.5937/medi55-37718.
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Currently, the world is entering the fourth industrial revolution - marked by artificial intelligence (AI) powered technologies. The growing ubiquity of AI technologies is already present in many sectors of modern society, but caution still prevails in medicine where their application is far from routine, although it is on the constant rise. Psychiatry has been recognized as one of the disciplines where significant contribution of AI technologies is expected for prediction, diagnosis, treatment and monitoring of persons with psychiatric disorders. Nearly half of the world's population live in countries that have fewer than one psychiatrist per 100 000 inhabitants, which is far below the health needs as the prevalence of psychiatric disorders is within the range of 10-20%. Thus, the question arises - whether AI technologies can help to fill the gap in unmet needs in psychiatry? The main types of autonomous technologies currently applied in psychiatry are machine learning and its subsets deep learning and computer vision, alongside natural language processing and chatbots. The present review will focus on the brief history of the concept, the utility of AI technologies in psychiatry, clinicians' attitudes, ethical dilemmas, clinical and scientific challenges. This review emphasizes that the psychiatric community should not be ignorant but could try to leave the comfort zone and do more to raise the awareness of AI technologies development achievements.
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do Nascimento, Carla Ferreira, Hellen Geremias dos Santos, André Filipe de Moraes Batista, Alejandra Andrea Roman Lay, Yeda Aparecida Oliveira Duarte, and Alexandre Dias Porto Chiavegatto Filho. "Cause-specific mortality prediction in older residents of São Paulo, Brazil: a machine learning approach." Age and Ageing 50, no. 5 (May 3, 2021): 1692–98. http://dx.doi.org/10.1093/ageing/afab067.
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Abstract Background Populational ageing has been increasing in a remarkable rate in developing countries. In this scenario, preventive strategies could help to decrease the burden of higher demands for healthcare services. Machine learning algorithms have been increasingly applied for identifying priority candidates for preventive actions, presenting a better predictive performance than traditional parsimonious models. Methods Data were collected from the Health, Well Being and Aging (SABE) Study, a representative sample of older residents of São Paulo, Brazil. Machine learning algorithms were applied to predict death by diseases of respiratory system (DRS), diseases of circulatory system (DCS), neoplasms and other specific causes within 5 years, using socioeconomic, demographic and health features. The algorithms were trained in a random sample of 70% of subjects, and then tested in the other 30% unseen data. Results The outcome with highest predictive performance was death by DRS (AUC−ROC = 0.89), followed by the other specific causes (AUC−ROC = 0.87), DCS (AUC−ROC = 0.67) and neoplasms (AUC−ROC = 0.52). Among only the 25% of individuals with the highest predicted risk of mortality from DRS were included 100% of the actual cases. The machine learning algorithms with the highest predictive performance were light gradient boosted machine and extreme gradient boosting. Conclusion The algorithms had a high predictive performance for DRS, but lower for DCS and neoplasms. Mortality prediction with machine learning can improve clinical decisions especially regarding targeted preventive measures for older individuals.
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Li, Aihua, Lei An, and Zihui Che. "A Facial Expression Recognition Model Based on Texture and Shape Features." Traitement du Signal 37, no. 4 (October 10, 2020): 627–32. http://dx.doi.org/10.18280/ts.370411.
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With the development of computer vision, facial expression recognition has become a research hotspot. To further improve the accuracy of facial expression recognition, this paper probes deep into image segmentation, feature extraction, and facial expression classification. Firstly, the convolution neural network (CNN) was adopted to accurately separate the salient regions from the face image. Next, the Gaussian Markov random field (GMRF) model was improved to enhance the ability of texture features to represent image information, and a novel feature extraction algorithm called specific angle abundance entropy (SAAE) was designed to improve the representation ability of shape features. After that, the texture features were combined with shape features, and trained and classified by the support vector machine (SVM) classifier. Finally, the proposed method was compared with common methods of facial expression recognition on a standard facial expression database. The results show that our method can greatly improve the accuracy of facial expression recognition.
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Revishvili, A. Sh, N. N. Lomidze, A. S. Abdrakhmanov, A. A. Nechepurenko, E. A. Ivanitsky, O. V. Belyaev, S. V. Popov, et al. "REMOTE MONITORING FOR EARLY DIAGNOSTICS OF PATIENT’S STATE CHANGES WITH HOME MONITORING TECHNOLOGY." Jounal of arrhythmology 26, no. 2(96) (October 10, 2019): 5–13. http://dx.doi.org/10.35336/va-2019-2-5-13.
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Aims. Analysis of the prevalence of clinical events and of the trends of the physiologically significant parameters in patients with cardiac implantable electronic devices (CIEDs) with the remote monitoring options.Methods. In 9 clinical centers of the Russian Federation and 2 clinical centers of the Republic of Kazakhstan, 126 patients with an ICD or a pacemaker provided with the Home Monitoring technology (BIOTRONIK, Berlin, Germany) have been enrolled into the ReHoming (Registry Home Monitoring) clinical study. Based on the daily data transmission, all alarm alerts and all the Home Monitoring options changes have been registered with dated alert content and undertaken measures.Results. The study patients, followed up at least for one year, demonstrated 42 adverse events (AE), 26 of which were serious AE (SAE) and 3 SAE were defined as device related (SADE). ICD patients (n=90) had statistically significantly higher SAE prevalence with attendant coronary artery disease (CAD) (p=0.0249). Patients with CRT/D compared to patients with dual-chamber or single-chamber ICD had less SAE rate (р=0.046). Downloads of Home Monitoring parameters for retrospective mathematical analysis were available for 60 ICD patients, 47 of which had episodes of ventricular tachycardia (VT), ventricular fibrillation (VF) and/or atrial tachyarrhythmia (AT). Machine learning analysis of the trends of the physiologically meaningful parameters revealed correlation of the changes with arrhythmia episodes, the random forest method and the gradient boosting method giving the results strongly exceeding a random guess.Conclusion. Home Monitoring of CIED patients enables evaluation of clinical advantages of different device types application, also in regard to prevention of adverse events and possible iatrogenic effects of electrotherapy of the heart. The study results demonstrate a possibility to develop a predictor of arrhythmia episodes, based on daily transmission of trends of physiologically meaningful Home Monitoring parameters.
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Lyaletski, O. V. "V.M. Glushkov and automated theorem proving in Ukraine: evidence algorithm evidence algorithm and SAD systems." Mathematical machines and systems 4 (2020): 3–10. http://dx.doi.org/10.34121/1028-9763-2020-4-3-10.
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Fifty years ago, in 1970, Academician V.M. Glushkov published a paper, in which he, along with a discussion of some problems of artificial intelligence, formulated a research program called Evidence Algorithm (EA) describing his vision of the problem of a computer support of human activity in looking for a proof of a particular theorem. V.M. Glushkov proposed to focus attention on the construction of an automated theorem-proving system performing simultaneous investigations in: creating formal natural languages for writing mathematical texts in a form accustomed to a human, constructing a procedure for a proof search based on the evolutionary developing of the machine notion of an evidence of a computer-made proof step, using the knowledge gained by the system during its operation and providing a user with the opportunity to assist to the system in its proof search process. Since the inception of EA, two serious attempts have been made to implement this program. The first led to the emergence in 1978 of a Russian-language automated theorem proving and the second led to the appearance in 2002 of its English-language modification named System for Automated Deduction (SAD). And if the development and trial operation of the first system were discontinued in 1992 after the output from service of the ES-line computers, on which it was realized, the SAD system, being placed on the website “nevidal.org”, is now still available in online mode. That is, at the current time, it is possible to carry out different experiments with the SAD system and to solve various problems that require rigorous mathematical reasoning. This work is devoted to a chronological description of studies on the implementation of the EA program for the entire period of its existence and to the highlighting of peculiarities of both the systems, as well as of their common features and distinguishes. Some possible ways of the further development of the SAD system are given.
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Carvalho, Alexandre Filordi de, and Silvio Gallo. "É preciso defender a amizade." Educação e Filosofia 36, no. 78 (January 4, 2023): 1763–97. http://dx.doi.org/10.14393/revedfil.v36n78a2022-66281.
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Este artigo homenageia a atualidade do curso A hermenêutica do sujeito, proferido por Foucault no Collège de France em 1982. Sabe-se que o tema central do curso é o cuidado de si como tekhné tou biou, examinado pelo filósofo nos momentos socrático-platônico, helenístico e cristão. Porém, tomaremos a questão da amizade, que atravessa as técnicas de si antigas analisadas por Foucault, para problematizar nosso presente. Após discutir a recepção que fazemos do curso quarenta anos depois, discutimos o atual momento do neoliberalismo e as políticas de inimizade por ele engendradas, de modo a pensar a prática da amizade como contraconduta, como prática de resistência. A amizade anarquista como forma de associação é tomada como máquina de guerra possível contra as atuais políticas de inimizade neoliberais. Palavras-chave: amizade; técnicas de si; ética; política; anarquismo. In defense of friendship: life’s proofs in the subjecvity wars of the contemporary enmity policies Abstract: This article pays homage to the actuality of the course The hermeneutics of the subject, given by Foucault at the Collège de France in 1982. It is known that the central theme of the course is the care of the self as tekhné tou biou, examined by the philosopher in three moments: Socratic-Platonic, Hellenistic, and Christian. However, we will take the question of friendship, which crosses the ancient techniques of the self analyzed by Foucault, to problematize our present. After discussing our reception of the course forty years later, we discuss the current moment of neoliberalism and the enmity policies engendered by it, in order to think about the practice of friendship as a counter-conduct, as a practice of resistance. Anarchist friendship as a form of association is taken as a possible war machine against current neoliberal policies of enmity. Key words: friendship; techniques of the self; ethics; politics; anarchism. En defensa de la amistad: pruebas de vida en las guerras de subjetividad de las políticas de enemistad contemporáneas Resumen: En este artículo prestamos homenaje a la actualidad del curso La hermenéutica del sujeto, presentado por Foucault en 1982 en el Collège de France. Se sabe que el tema que es el centro del curso ha sido la inquietud de sí como tekhné tou biou, examinada por el filósofo en tres momentos de la antigüedad: el socrático-platónico, el helenístico y el cristiano. Aún así, tomamos la cuestión de la amistad, que es transversal a las técnicas de sí antiguas analizadas por Foucault, para problematizar nuestro tiempo presente. Después de discutir la recepción que nosotros hacemos del curso cuarenta años más tarde, ponemos en debate el momento actual del neoliberalismo y sus políticas de enemistad, de manera a pensar la práctica de la amistad como contra conducta, como práctica de resistencia. Tomamos la amistad anarquista como como forma de asociación como posible maquina de guerra contra las políticas de enemistad neoliberales actuales. Palabras clave: amistad; técnicas de sí; ética; política; anarquismo Data de registro: 10/07/2022 Data de aceite: 26/10/2022
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Meng, Yanmei, Jinlai Zhang, Johnny Qin, Qiliang Lan, Yanpeng Xie, and Feihong Hu. "Research on the Adaptive Control in Sugar Evaporative Crystallization Using LSSVM and SaDE-ELM." International Journal of Food Engineering 15, no. 5-6 (March 12, 2019). http://dx.doi.org/10.1515/ijfe-2018-0203.
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AbstractThe process of sugar evaporative crystallization is a nonlinear process with large time lag and strong coupling. It is difficult to establish a reasonable mechanism model. In this paper, we use the data driving modeling method to establish an Adaptive Control model for batch boiling sugar crystallization process. First, by analyzing the main influencing factors of the evaporative crystallization process of intermittent boiling sugar, the most important two parameters, brix and liquid level, are selected as the control object. The self-adaptive differential evolution Extreme Learning Machine (SaDE-ELM) is used to construct the control model. A least squares support vector machine (LSSVM) is established and connected in the control loop to control the opening of the feed valve so that to control the feed flowrate according to the objective values of syrup Brix and liquid level. Experiments are conducted and the obtained data are used to train and verify the learning machines. Experiments indicate that the learning machines are able to realize adaptive control to key parameters of the crystallization process. Comparison of different neural networks indicates that the LSSVM performs better than BP, RBF and ELM and SaDE-ELM with prediction error of below 0.01, and training time of below 0.05 s.
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Chen, Peng, Jiahao Chen, and Zhiqiang Hu. "Software-in-the-Loop Combined Reinforcement Learning Method for Dynamic Response Analysis of FOWTs." Frontiers in Marine Science 7 (January 28, 2021). http://dx.doi.org/10.3389/fmars.2020.628225.
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Floating offshore wind turbines (FOWTs) still face many challenges on how to better predict the dynamic responses. Artificial intelligence (AI) brings a new solution to overcome these challenges with intelligent strategies. A new AI technology-based method, named SADA, is proposed in this paper for the prediction of dynamic responses of FOWTs. Firstly, the methodology of SADA is introduced with the selection of Key Disciplinary Parameters (KDPs). The AI module in SADA was built in a coupled aero-hydro-servo-elastic in-house program DARwind and the policy decision is provided by the machine learning algorithms deep deterministic policy gradient (DDPG). Secondly, a set of basin experimental results of a Hywind Spar-type FOWT were employed to train the AI module. SADA weights KDPs by DDPG algorithms' actor network and changes their values according to the training feedback of 6DOF motions of Hywind platform through comparing the DARwind simulation results and that of experimental data. Many other dynamic responses that cannot be measured in basin experiment could be predicted in higher accuracy with this intelligent DARwind. Finally, the case study of SADA method was conducted and the results demonstrated that the mean values of the platform's motions can be predicted by AI-based DARwind with higher accuracy, for example the maximum error of surge motion is reduced by 21%. This proposed SADA method takes advantage of numerical-experimental method and the machine learning method, which brings a new and promising solution for overcoming the handicap impeding direct use of traditional basin experimental technology in FOWTs design.
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Arthur, Clement Kweku, Victor Amoako Temeng, and Yao Yevenyo Ziggah. "A Self-adaptive differential evolutionary extreme learning machine (SaDE-ELM): a novel approach to blast-induced ground vibration prediction." SN Applied Sciences 2, no. 11 (October 20, 2020). http://dx.doi.org/10.1007/s42452-020-03611-3.
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Santos, Hellen Geremias dos, Carla Ferreira do Nascimento, Rafael Izbicki, Yeda Aparecida de Oliveira Duarte, and Alexandre Dias Porto Chiavegatto Filho. "Machine learning para análises preditivas em saúde: exemplo de aplicação para predizer óbito em idosos de São Paulo, Brasil." Cadernos de Saúde Pública 35, no. 7 (2019). http://dx.doi.org/10.1590/0102-311x00050818.
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Este estudo objetiva apresentar as etapas relacionadas à utilização de algoritmos de machine learning para análises preditivas em saúde. Para isso, foi realizada uma aplicação com base em dados de idosos residentes no Município de São Paulo, Brasil, participantes do estudo Saúde Bem-estar e Envelhecimento (SABE) (n = 2.808). A variável resposta foi representada pela ocorrência de óbito em até cinco anos após o ingresso do idoso no estudo (n = 423), e os preditores, por 37 variáveis relacionadas ao perfil demográfico, socioeconômico e de saúde do idoso. A aplicação foi organizada de acordo com as seguintes etapas: divisão dos dados em treinamento (70%) e teste (30%), pré-processamento dos preditores, aprendizado e avaliação de modelos. Na etapa de aprendizado, foram utilizados cinco algoritmos para o ajuste de modelos: regressão logística com e sem penalização, redes neurais, gradient boosted trees e random forest. Os hiperparâmetros dos algoritmos foram otimizados por validação cruzada 10-fold, para selecionar aqueles correspondentes aos melhores modelos. Para cada algoritmo, o melhor modelo foi avaliado em dados de teste por meio da área abaixo da curva (AUC) ROC e medidas relacionadas. Todos os modelos apresentaram AUC ROC superior a 0,70. Para os três modelos com maior AUC ROC (redes neurais e regressão logística com penalização de lasso e sem penalização, respectivamente), foram também avaliadas medidas de qualidade da probabilidade predita. Espera-se que, com o aumento da disponibilidade de dados e de capital humano capacitado, seja possível desenvolver modelos preditivos de machine learning com potencial para auxiliar profissionais de saúde na tomada de melhores decisões.