Journal articles on the topic 'Multi-index Monte Carlo'
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Crisan, Dan, Pierre Del Moral, Jeremie Houssineau, and Ajay Jasra. "Unbiased multi-index Monte Carlo." Stochastic Analysis and Applications 36, no. 2 (December 7, 2017): 257–73. http://dx.doi.org/10.1080/07362994.2017.1394880.
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Jasra, Ajay, Kengo Kamatani, Kody J. H. Law, and Yan Zhou. "A MULTI-INDEX MARKOV CHAIN MONTE CARLO METHOD." International Journal for Uncertainty Quantification 8, no. 1 (2018): 61–73. http://dx.doi.org/10.1615/int.j.uncertaintyquantification.2018021551.
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Haji-Ali, Abdul-Lateef, Fabio Nobile, and Raúl Tempone. "Multi-index Monte Carlo: when sparsity meets sampling." Numerische Mathematik 132, no. 4 (June 27, 2015): 767–806. http://dx.doi.org/10.1007/s00211-015-0734-5.
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Wang, Christoph Reisinger and Zhenru. "Analysis of Multi-Index Monte Carlo Estimators for a Zakai SPDE." Journal of Computational Mathematics 36, no. 2 (June 2018): 202–36. http://dx.doi.org/10.4208/jcm.1612-m2016-0681.
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Robbe, Pieterjan, Dirk Nuyens, and Stefan Vandewalle. "A Multi-Index Quasi--Monte Carlo Algorithm for Lognormal Diffusion Problems." SIAM Journal on Scientific Computing 39, no. 5 (January 2017): S851—S872. http://dx.doi.org/10.1137/16m1082561.
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LI, TING, HUI GONG, and QINGMING LUO. "MCVM: MONTE CARLO MODELING OF PHOTON MIGRATION IN VOXELIZED MEDIA." Journal of Innovative Optical Health Sciences 03, no. 02 (April 2010): 91–102. http://dx.doi.org/10.1142/s1793545810000927.
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The Monte Carlo code MCML (Monte Carlo modeling of light transport in multi-layered tissue) has been the gold standard for simulations of light transport in multi-layer tissue, but it is ineffective in the presence of three-dimensional (3D) heterogeneity. New techniques have been attempted to resolve this problem, such as MCLS, which is derived from MCML, and tMCimg, which draws upon image datasets. Nevertheless, these approaches are insufficient because of their low precision or simplistic modeling. We report on the development of a novel model for photon migration in voxelized media (MCVM) with 3D heterogeneity. Voxel crossing detection and refractive-index-unmatched boundaries were considered to improve the precision and eliminate dependence on refractive-index-matched tissue. Using a semi-infinite homogeneous medium, steady-state and time-resolved simulations of MCVM agreed well with MCML, with high precision (~100%) for the total diffuse reflectance and total fractional absorption compared to those of tMCimg (< 70%). Based on a refractive-index-matched heterogeneous skin model, the results of MCVM were found to coincide with those of MCLS. Finally, MCVM was applied to a two-layered sphere with multi-inclusions, which is an example of a 3D heterogeneous media with refractive-index-unmatched boundaries. MCVM provided a reliable model for simulation of photon migration in voxelized 3D heterogeneous media, and it was developed to be a flexible and simple software tool that delivers high-precision results.
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Haji-Ali, Abdul-Lateef, and Raúl Tempone. "Multilevel and Multi-index Monte Carlo methods for the McKean–Vlasov equation." Statistics and Computing 28, no. 4 (September 12, 2017): 923–35. http://dx.doi.org/10.1007/s11222-017-9771-5.
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Ramdhani, Faisal, and Hartrisari Hardjomidjojo. "ANALISIS INDEKS KINERJA USAHA KECIL MENENGAH DI KOTA BOGOR." JURNAL REKAYASA DAN MANAJEMEN AGROINDUSTRI 7, no. 1 (April 2, 2019): 133. http://dx.doi.org/10.24843/jrma.2019.v07.i01.p14.
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Small and Medium Enterprises (SMEs) is the locomotive of economic development of Indonesia. Within the framework of the national economy, SMEs have contributed to the recruitment of labor, the increase of Gross Domestic Product (GDP) as well as the increase of export value and national investment. In the vision and mission of Bogor city government, SMEs are expected to become the economic buffer of the city of Bogor in the face of free market competition. Currently there are 13.953 SMEs in the city of Bogor. Sustainability Index based on multi dimensional scaling can be used to measure the performance status of SMEs and formulate specific strategies that need to be done to improve their competitiveness. This research was conducted from dimension setting, attribute determination, attribute review, multidimentional scaling analysis, Monte Carlo and Leverage. The value of Stress and R2 resulted is less than 0.25 and more than 0.80 so that the data has good of fit. The Monte Carlo analysis results for all SMEs have been valid because it has sustainability index ratio with Monte Carlo index less than 5%. The results show that the average sustainability index for SMEs in Bogor city is 49.8, which means that the city of Bogor is in sufficient condition (index value is below 50). The result also shows that ecological and economical factors are the important factors for increasing the performance of SMEs. The simulation results show that changes in related dimensions can increase the average value of the sustainability index to above 60 (good category).
Key Words : Small and Medium Enterprises (SMEs), Sustainability Index, competitiveness, multi dimensional scaling.
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Shao, Jiang, Keyong Zhu, Xiaochuan Liu, Guodong Shi, and Yong Huang. "A general Monte Carlo method for polarized radiative transfer in multi-dimensional graded-index media." Journal of Quantitative Spectroscopy and Radiative Transfer 278 (February 2022): 108041. http://dx.doi.org/10.1016/j.jqsrt.2021.108041.
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Nazmul Huda, A. S., and Rastko Živanovic. "Estimation of Distribution Systems Expected Energy Not Supplied Index by Multi-level Monte Carlo Method." Electric Power Components and Systems 47, no. 9-10 (June 15, 2019): 810–22. http://dx.doi.org/10.1080/15325008.2019.1628120.
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Hosseini Sarvari, S. M. "Multi-grid Monte Carlo method for radiative transfer in multi-dimensional graded index media with diffuse-specular-gray boundaries." Journal of Quantitative Spectroscopy and Radiative Transfer 219 (November 2018): 61–73. http://dx.doi.org/10.1016/j.jqsrt.2018.08.004.
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Yang, Chiu Jung, Chien Sheng Huang, Ji Nan Jeng, and Po Wen Chen. "The Optimal Design of Multi-Chip White-Light LEDs for Color Uniformity." Applied Mechanics and Materials 415 (September 2013): 465–72. http://dx.doi.org/10.4028/www.scientific.net/amm.415.465.
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A multi-chip white-light modeling algorithm for high color rendering index and high color uniformity of surface mounted LED devices is demonstrated. The simulation of white-light LED model, according to the principle of additive color mixture by four color of red, amber, green, blue LEDs and using the commercial software program to perform Monte Carlo ray tracing has been developed. The simulation and experimental results show that the performance can be achieved with a number of LED chips.
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Sato, Hiroshi. "Influence of index table accuracy on roundness calibration in the multi-step method using monte carlo simulation." MAPAN 26, no. 1 (March 2011): 37–46. http://dx.doi.org/10.1007/s12647-011-0004-7.
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Lu, Chengchao, and Zhongjie Wang. "A cluster-autonomous partitioning algorithm in electrical power grid using Monte Carlo simulation." International Journal of Modeling, Simulation, and Scientific Computing 09, no. 01 (January 23, 2018): 1750053. http://dx.doi.org/10.1142/s1793962317500532.
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Power grid partitioning decomposes a large power grid into several clusters. Most of the existing partitioning methods suffer from a limitation that the buses within a cluster are severely topologically disconnected after partitioning in some cases. As a result, a cluster will inevitably be assigned to two or more power grid corporations. This assignment obstructs inner-cluster monitoring and control applications of the transmission system. To overcome the limitation, this paper proposes a multi-index power grid partitioning approach using Monte Carlo simulation guaranteeing cluster connectivity to ensure the cluster autonomy. A line-based binary coding technique is developed to ensure the cluster connectivity. Three partitioning indices are considered: the coherency, the cluster connectivity, and the number of clusters. Finally, the proposed partitioning method is applied to IEEE 9-bus system, IEEE 39-bus system and IEEE 145-bus system and compared with Fuzzy C-medoid (FCMdd) algorithm.
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Hassan, Mustafa Hamid, Salama A. Mostafa, Aida Mustapha, Mohd Zainuri Saringat, Bander Ali Saleh Al-rimy, Faisal Saeed, A. E. M. Eljialy, and Mohammed Ahmed Jubair. "A New Collaborative Multi-Agent Monte Carlo Simulation Model for Spatial Correlation of Air Pollution Global Risk Assessment." Sustainability 14, no. 1 (January 4, 2022): 510. http://dx.doi.org/10.3390/su14010510.
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Air pollution risk assessment is complex due to dynamic data change and pollution source distribution. Air quality index concentration level prediction is an effective method of protecting public health by providing the means for an early warning against harmful air pollution. However, air quality index-based prediction is challenging as it depends on several complicated factors resulting from dynamic nonlinear air quality time-series data, such as dynamic weather patterns and the verity and distribution of air pollution sources. Subsequently, some minimal models have incorporated a time series-based predicting air quality index at a global level (for a particular city or various cities). These models require interaction between the multiple air pollution sensing sources and additional parameters like wind direction and wind speed. The existing methods in predicting air quality index cannot handle short-term dependencies. These methods also mostly neglect the spatial correlations between the different parameters. Moreover, the assumption of selecting the most recent part of the air quality time series is not valid considering that pollution is cyclic behavior according to various events and conditions due to the high possibility of falling into the trap of local minimum and poor generalization. Therefore, this paper proposes a new air pollution global risk assessment (APGRA) prediction model for an air quality index of spatial correlations to address these issues. The APGRA model incorporates an autoregressive integrated moving average (ARIMA), a Monte Carlo simulation, a collaborative multi-agent system, and a prediction algorithm for reducing air quality index prediction error and processing time. The proposed APGRA model is evaluated based on Malaysia and China real-world air quality datasets. The proposed APGRA model improves the average root mean squared error by 41%, mean and absolute error by 47.10% compared with the conventional ARIMA and ANFIS models.
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GRECA, F. M., R. L. BARDDAL, S. C. RAVACHE, D. G. SILVA, A. CATAPAN, and P. F. MARTINS. "ANALYSIS OF AN INVESTMENT PROJECT TO MINIMIZE THE BREAKS OF STOCK WITH THE USE OF MULTI-INDEX METHODOLOGY AND MONTE CARLO SIMULATION." Revista Gestão, Inovação e Tecnologias 4, no. 3 (October 2, 2014): 1092–107. http://dx.doi.org/10.7198/s2237-0722201400030008.
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Tatipikalawan, Jomima Martha, Fransiskus Trisakti Haryadi, Endang Sulastri, and Tri Satya Mastuti Widi. "A Multi-Dimensional Approach to the Sustainable Development of Moa Buffaloes in Maluku Province, Indonesia." Buletin Peternakan 45, no. 4 (November 30, 2021): 254. http://dx.doi.org/10.21059/buletinpeternak.v45i4.68838.
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The purpose of the study was to determine the level of sensitivity of the indicator for the sustainability of the Moa Buffalo development based on the results of the identification and measurement of the index with a multidimensional approach on ecological, socio-cultural, and economic dimensions. The unit of analysis in this study, which was conducted on Moa Island, Maluku Province, was 261 farmer households and 16 experts who were selected using purposive sampling technique. Data collection method: literature study, Focus Group Discussion, in-depth interview, survey, measurement. The research variable is the development potential of the Moa Buffalo from 3 dimensions of sustainable Moa Buffalo development (ecology, socio-culture, economy), including 40 indicators. Data analysis using ordination technique Rap-BANGKER through the Multi-Dimensional Scaling (MDS) method to measure the index and status of sustainability and Leverage Analysis, to determine the key factors, Monte Carlo Analysis to assess the effect of errors on the estimated value of ordination for the development of the Moa Buffalo. The results showed that the average value of the Moa buffalo development index was 52.72% (sustainable). The ecological dimension is 41.15% (less sustainable) with 7 sensitive indicators, socio-cultural dimension is 60.28% (sustainable) with 5 sensitive indicators, economic dimension is 56.73% (sustainable) with 3 sensitive indicators. The Monte Carlo analysis of the three dimensions shows the value of the sustainability index at the confidence level of 95%, the difference in value is <1, the coefficient of determination (R2) for the third dimension is 95.00%. The MDS Rap-BANGKER analysis model is adequate to estimate the sustainability of the development of Moa Buffalo, has a high level of confidence, can be used as an evaluation tool to rapid appraisal sustainability analysis of buffalo development in an area.
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Li, Ning, Junfeng Duan, Jun Ma, Wei Qiu, Wei Zhang, and Zhaosheng Teng. "Gaussian Process based Remaining useful life Prediction for Electric Energy Metering Equipment." Journal of Physics: Conference Series 2125, no. 1 (November 1, 2021): 012032. http://dx.doi.org/10.1088/1742-6596/2125/1/012032.
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Abstract Electric energy metering equipment (EEME) will fail in advance not as designed running in extreme environments. A multi-kernel Gaussian process regression model using measurement error data to perceive remaining useful life (RUL) for EEME is proposed. Firstly, the gauss kernel and periodic kernel are used to match the health index trend of EEME under a variety of typical environmental stresses. Furthermore, the Bayesian method and Monte Carlo Markov chain method are used to solve the model, and the Weibull distribution is used to fit the posterior trajectory to get the probability density estimation of the RUL.
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Zhu, Yuxin, Dazuo Tian, and Feng Yan. "Effectiveness of Entropy Weight Method in Decision-Making." Mathematical Problems in Engineering 2020 (March 26, 2020): 1–5. http://dx.doi.org/10.1155/2020/3564835.
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Entropy weight method (EWM) is a commonly used weighting method that measures value dispersion in decision-making. The greater the degree of dispersion, the greater the degree of differentiation, and more information can be derived. Meanwhile, higher weight should be given to the index, and vice versa. This study shows that the rationality of the EWM in decision-making is questionable. One example is water source site selection, which is generated by Monte Carlo Simulation. First, too many zero values result in the standardization result of the EWM being prone to distortion. Subsequently, this outcome will lead to immense index weight with low actual differentiation degree. Second, in multi-index decision-making involving classification, the classification degree can accurately reflect the information amount of the index. However, the EWM only considers the numerical discrimination degree of the index and ignores rank discrimination. These two shortcomings indicate that the EWM cannot correctly reflect the importance of the index weight, thus resulting in distorted decision-making results.
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Caricimi, Rudimar, and Jose Donizetti Lima de. "Economic Analysis for Small Hydroelectric Power Plant using Extended Multi-Index Methodology ‑ An Approach Stochastic by the Monte Carlo Simulation." IEEE Latin America Transactions 16, no. 8 (August 2018): 2184–91. http://dx.doi.org/10.1109/tla.2018.8528233.
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Zhu, Yongjia, Yuyao He, Ye Fan, and Rugui Yao. "Protection scheme of subcarrier index in OFDM with index modulation aided by LDPC coding." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 39, no. 4 (August 2021): 818–23. http://dx.doi.org/10.1051/jnwpu/20213940818.
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The receiver of OFDM with Index Modulation (OFDM-IM) usually adopts a Log Likelihood Ratio (LLR) detection algorithm based on the activation state of subcarriers. However, the LLR detection algorithm will cause detection errors in subcarrier activation pattern (SAP) or get illegal SAP. Consequently, further errors occur in demodulation, increasing the bit error rate (BER). To solve this problem, we propose the protection scheme of subcarrier index aided by LDPC coding, which reduces the SAP detection errors by encoding the index information bits. On the receiver, the LDPC Coding Aided (LA) detection algorithm is designed, and the formula of LLR of index information bits is derived in detail. Monte Carlo simulation is carried out over multi-path fading channel by MATLAB software. The results show that under the condition that the spectrum efficiency is not lower than the classical OFDM-IM scheme, the proposed protection scheme can obtain a gain of about 5~9 dB when the BER is 10-4, effectively improving the BER performance of OFDM-IM scheme.
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Sudiono, NFN, Surjono Hadi Sutjahyo, Nurheni Wijayanto, Purnama Hidayat, and Rachman Kurniawan. "Analisis Berkelanjutan Usahatani Tanaman Sayuran Berbasis Pengendalian Hama Terpadu di Kabupaten Tanggamus Provinsi Lampung." Jurnal Hortikultura 27, no. 2 (February 19, 2018): 297. http://dx.doi.org/10.21082/jhort.v27n2.2017.p297-310.
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<p>Produktivitas usahatani sayuran menghadapi kendala produksi akibat gangguan organisme pengganggu tanaman, hal tersebut dapat diselesaikan melalui praktek pertanian yang baik dan pengendalian hama terpadu. Penelitian ini bertujuan untuk menentukan indikator pengelolaan usahatani tanaman sayuran berkelanjutan dan menganalisis nilai indeks keberlanjutan pengelolaan usahatani berbasis pengendalian hama terpadu. Penelitian dilaksanakan dari bulan Maret sampai Oktober 2015 di Kabupaten Tanggamus, Provinsi Lampung. Metode penelitian menggunakan analisis multi dimensional scaling (MDS), leverage analysis, analisis Monte Carlo dengan teknik rapid appraisal for integrated pest management (Rap IPM) yang hasilnya dinyatakan dalam bentuk nilai indeks dan status keberlanjutan. Hasil analisis menunjukkan bahwa terdapat 60 atribut yang di antaranya terdapat 20 faktor pengungkit atau atribut yang sensitif terhadap nilai indeks dan status keberlanjutan. Indeks keberlanjutan usahatani tanaman sayuran berbasis PHT di Kabupaten Tanggamus termasuk kriteria kurang berkelanjutan, dengan indeks gabungan sebesar 48,13. Indeks keberlanjutan yang paling tinggi adalah dimensi sosial dan ekonomi masing-masing sebesar 60,90 dan 51,39 termasuk kriteria cukup berkelanjutan, sedangkan dimensi ekologi, teknologi, dan kelembagaan masing-masing sebesar 48,54; 38,36; dan 40,61 termasuk kriteria kurang berkelanjutan.</p><p>The yield of vegetable is at risk due to the incidence of pests and pathogens. It was related to good agricultural practices and integrated pest management. The purposes of this research were to identify indicators of sustainable vegetables farm and to analyze index sustainability of vegetable farm based on integrated pest management. The research was conducted from March to October 2015 in Tanggamus District, Lampung Province. This research applied multi dimensional scaling (MDS), leverage analysis, and Monte Carlo analysis by rapid appraisal for integrated pest management (Rap IPM). Research showed that among 60 indicators analyzed there were 20 sensitive indicators that affected sustainability index and status. Sustainability index in Tanggamus District were dimension of social and economy obtained value 60.90 and 51.39, it was categorized as sufficiently sustainable, while sustainability index of ecology, technology, and institution dimensions were 48.54, 38.36, and 40.61 respectively, which were considered as less sustainable.</p>
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García-Soto, Adrián-David, Felícitas Calderón-Vega, César Mösso, Jesús-Gerardo Valdés-Vázquez, and Alejandro Hernández-Martínez. "Revisiting Two Simulation-Based Reliability Approaches for Coastal and Structural Engineering Applications." Applied Sciences 10, no. 22 (November 18, 2020): 8176. http://dx.doi.org/10.3390/app10228176.
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The normality polynomial and multi-linear regression approaches are revisited for estimating the reliability index, its precision, and other reliability-related values for coastal and structural engineering applications. In previous studies, neither the error in the reliability estimation is mathematically defined nor the adequacy of varying the tolerance is investigated. This is addressed in the present study. First, sets of given numbers of Monte Carlo simulations are obtained for three limit state functions and probabilities of failure are computed. Then, the normality polynomial approach is applied to each set and mean errors in estimating the reliability index are obtained, together with its associated uncertainty; this is defined mathematically. The data is also used to derive design points and sensitivity factors by multi-linear regression analysis for given tolerances. Results indicate that power laws define the mean error of the reliability index and its standard deviation as a function of the number of simulations for the normality polynomial approach. Results also indicate that the multi-linear regression approach accurately predicts reliability-related values if enough simulations are performed for a given tolerance. It is concluded that the revisited approaches are a valuable option to compute reliability-associated values with reduced simulations, by accepting a quantitative precision level.
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Souza, Alceu, Ariane Maria Machado de Oliveira, Dayla Karolina Fossile, Emmanuel Óguchi Ogu, Luciano Luiz Dalazen, and Claudimar Pereira da Veiga. "Business Plan Analysis Using Multi-Index Methodology: Expectations of Return and Perceived Risks." SAGE Open 10, no. 1 (January 2020): 215824401990017. http://dx.doi.org/10.1177/2158244019900171.
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This article aims to show that business plan risks are clearer and more consistent, compared with classical methodology, when evaluated through the multi-index methodology. To do so, it is necessary to identify the expectations of return and perceived risks in evaluating a business plan. To explain the above statement, we used a case example of a manufacturing unit of purses made of tilapia leather in the city of Campo Mourão, Paraná State, Brazil. Relevant information was collected through documenting research and semi-structured interviews, which were conducted in 2016 and 2017. The adoption of the cost leadership strategy, practicable due to the presence of local production arrangements, proved crucial to this project’s viability. Following this strategy, the demand, initial investments, production costs, and selling price were estimated. The multi-index methodology was used for the generation and analysis of the return indicators vis-à-vis the perceived risks. The multi-index methodology perceptual map signals a medium/high return (return on investment assets = 23.71% per year) and that the perceived risks are compatible with profit expectations. The sensitivity analysis of the results, using the Monte Carlo method, shows that P(net present value ≤ 0) ≈ 0.0002, thus corroborating the decision to invest in this business. This article contributes to the literature on the use of existing productive arrangements in various stages of the production and marketing process and the use of an analysis methodology that leads decision-makers to specify the risks associated with their decision.
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Zhan, Yuan, Yang Liu, and Shiqing Zou. "Research on Logistic Operation of Public 3A Grade hospitals." E3S Web of Conferences 218 (2020): 03001. http://dx.doi.org/10.1051/e3sconf/202021803001.
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Research on hospital logistics management is conducive to improving the efficiency of logistics operations and promoting the economic development of the hospital. In order to solve the problem of logistics management affected by a large number of uncertainties and multi-state factors, a set of 12-factor research index system was constructed from three aspects: basic skills, element skills, and task skills, and at the same time, the level of evaluation indicators was determined. In the interval, a research method of hospital logistics management based on cloud model is proposed. And according to Monte Carlo simulation, find out specific sensitive factors. Finally, the effectiveness and feasibility of the method is verified through the logistic operation and maintenance examples of 4 Grade-A public hospitals in Wuhan.
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Liao, Ziyan, and Alessandra Parisio. "Reliability Optimization of Multi-Energy System Considering Energy Storage Devices Effects under Weather Uncertainties." Energies 15, no. 3 (January 18, 2022): 696. http://dx.doi.org/10.3390/en15030696.
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Multi-energy systems (MES) allow various energy forms, such as electricity, gas, and heat, to interact and achieve energy transfer and mutually benefit, reducing the probability of load cutting in the event of a failure, increasing the energy utilization efficiency, and improving the reliability and robustness of the overall energy supply system. Since energy storage systems can help to restore power in the case of failure and store the surplus energy to enhance the flexibility of MES, this work provides a methodology for reliability optimization, considering different energy storage configuration schemes under weather uncertainties. First of all, a reliability evaluation model of a multi-energy system under weather uncertainties based on a sequential Monte Carlo simulation is established. Then, the reliability optimization problem is formulated as a multi-objective optimization problem to minimize the reliability index, SAIDI (system average interruption duration index), and the reliability cost. Finally, a case study implemented on a typical MES layout is used to demonstrate the proposed methodology. A comparative analysis of three widely adopted multi-objective metaheuristic algorithms, including NSGA-II (non-dominated sorting genetic algorithm II), MOPSO (multiple objective particle swarm optimization), and SPEA2 (strength Pareto evolution algorithm 2), is performed to validate the effectiveness of the proposed method. The simulation results show that the NSGA-II algorithm leads to better optimal values and converges the fastest compared to the other two methods.
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Yang, Qiang, Zhi Li Sun, Yan Yan Shi, Dan Dan Jia, and Yu Tao Yan. "Kinematic Reliability Analysis of Five-Axis Machine Tool." Advanced Materials Research 308-310 (August 2011): 1292–96. http://dx.doi.org/10.4028/www.scientific.net/amr.308-310.1292.
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Five-axis CNC machine tool is the top product of numerically controlled machine tool. Kinmatic accuracy is an important index to evaluate the quality of mechanism. Kinematic accuracy analysis of the Five-axis machine tool in previous study ignores the randomness of each input error component, so the conclusions are imprecise. Using the homogeneous transformation, inverse kinematics of VMC650 Five-axis machine tool is calculated. Based on Multi-body System Theory, error model of the machine tool is presented. Considering the randomness of every input error components, applying the Monte Carlo method, math model of kinematic reliability of the mechanism is deduced. The calculation method of kinematics reliability are put forward applying point evaluation and track evaluation. Kinematic reliability analysis provides some theoretical reference for improving the machining accuracy and operation life of machine tool.
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Ngaile, J. E., P. K. Msaki, S. A. Suleiman, F. M. Chuma, W. E. Muhogora, and A. M. Jusabani. "ESTIMATION OF PATIENT RADIATION DOSES FROM MULTI-DETECTOR COMPUTED TOMOGRAPHY ANGIOGRAPHY PROCEDURES IN TANZANIA." Radiation Protection Dosimetry 197, no. 2 (November 3, 2021): 63–77. http://dx.doi.org/10.1093/rpd/ncab159.
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Abstract The aim of the present study was to estimate the volume CT dose index (CTDIvol), dose length product (DLP) and effective dose (ED) to patients from five multi-detector computed tomography angiography (MDCTA) procedures: brain, carotid, coronary, entire aorta and lower limb from four medical institutions in Tanzania; to compare these doses to those reported in the literature, and to compare the data obtained with ICRP 103 and Monte Carlo software. The radiation doses for 217 patients were estimated using patient demographics, patient-related exposure parameters, the geometry of examination and CT-Expo V 2.4 Monte Carlo-based software. The median values of the CTDIvol, DLP and ED for MDCTA procedures of the brain and carotids were 36.8 mGy, 1481.0 mGy∙cm and 5.2 mSv, and 15.9 mGy, 1224.0 mGy∙cm and 7.8 mSv, respectively; while for the coronary, entire aortic, and lower limbs were 49.4 mGy, 1493.0 mGy∙cm and 30.6 mSv; 16.2 mGy, 2287.0 mGy∙cm and 41.1 mSv; and 6.4 mGy, 1406.0 mGy∙cm and 10.5 mSv, respectively. The ratio of the maximum to minimum ED values to individual patients across the four medical centers were 41.4, 11.1, 4.6, 9.5 and 37.4, respectively, for the brain, carotid, coronary, entire aortic and lower limb CT angiography procedures. The mean values of CTDIvol, DLP and ED in the present study were typically higher than the values reported from Kenya, Korea and Saudi Arabia. The 75th percentile values of the DLP were above the preliminary diagnostic references levels proposed by Kenya, Switzerland and Korea. The observed wide range of examination scanning protocols and patient doses for similar MDCTA procedures within and across hospitals; and the observed relatively high patient doses compared to those reported in the literature, call for the need to standardize scanning protocols and optimise patient dose from MDCTA procedures.
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Cheng, Xiao Yue, and Jin Ling Lu. "Optimal Siting and Sizing of Distributed Generation Planning in a Standalone Microgrid." Advanced Materials Research 960-961 (June 2014): 1048–53. http://dx.doi.org/10.4028/www.scientific.net/amr.960-961.1048.
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This paper introduces renewable energy to ensure offshore platforms as a stand-alone microgrid operate reliably economically in low-carbon environment, considering volatility of CO2 emission price. A probability distribution model and economic model containing investment cost and low-carbon effect is established respectively, using latin hypercube sampling (LHS), meanwhile, the stochastic power is adopted to judge whether the built planning model can meet power flow constraint, Monte Carlo method is used to simulate characteristics of distributed generation ( DG), a probabilistic power flow is made to illustrate reliability index. The model takes the types, sites and sizes of DG as decision variables, economy and environment as the objective function, particle swarm algorithm is proposed to solve the multi-objective optimization problem .A case study based on data from offshore oilfield is presented to verify the advantages of the improved optimal sizing method.
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Zhao, Wei. "Target Tracking Algorithm in Football Match Video Based on Deep Learning." Discrete Dynamics in Nature and Society 2022 (May 31, 2022): 1–9. http://dx.doi.org/10.1155/2022/2769606.
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After the introduction of online learning mechanism, the traditional target tracking algorithm in football game video based on TLD has good tracking ability, but it will lose the target when the target is seriously obscured. Therefore, a soccer game video target tracking algorithm based on deep learning is proposed. The target detection algorithm of GoogLeNet-LSTM is used for faltung to obtain the feature mapping array. After processing, a high reliability candidate box for training and matching is obtained, and the feature maps of the detection results are collected to obtain the depth features required for tracking. Scale space discriminant tracking algorithm and Markov Monte Carlo algorithm are used to track single target or multi-target, respectively. Experimental results show that the average frame rate of the algorithm is maintained above 35 Hz, and the tracking time is about 12.5 s. The average center position deviation index is 39, the average coverage index is 40, and the resource utilization is low. The algorithm can track the target in the football game video well.
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Shi, Zejing, Ninghui Zhu, and Jinsong Yu. "The Electric Vehicle Time-of-Use Price Optimization Model Considering the Demand Response." MATEC Web of Conferences 160 (2018): 02009. http://dx.doi.org/10.1051/matecconf/201816002009.
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A large number of electric vehicles connecting to the distribution grids usually introduce significant fluctuations to the grid and the loads. To solve the problem, guiding the users coordinated charging is proposed. Firstly, the uncontrolled charging power prediction models of electric vehicles are established, and the Monte Carlo method is adopted to simulate the power demands of different electric vehicles, and the influences on the load peak-valley ratios and the voltages and losses of the grid are all analyzed. Then the vehicle responses model considering the time-of-use price is analyzed, and the vehicle response ratios are obtained under different time-of-use prices. Finally the multi-objective optimization model is constructed including the minimum peak-valley ratio, maximum consumption satisfaction index and cost satisfaction index. In the procedure, vehicles and the grid are both taken into account. The results indicate the proposed method could guide the users coordinated charging, and the peak shaving and valley filling is also achieved, and the operation of the distribution grid is improved.
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Sari, K. I., Budimawan, and M. B. Selamat. "Sustainability Study of Mangrove Area Management in the North Coast of Makassar City (Case Study: Lantebung and Untia)." IOP Conference Series: Earth and Environmental Science 1134, no. 1 (January 1, 2023): 012050. http://dx.doi.org/10.1088/1755-1315/1134/1/012050.
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Abstract Among the mangroves area on the north coast of Makassar are in Lantebung and Untia. The mangrove area is denominated as an ecotourism protected area based on community empowerment and sustainable tourism. As a coastal area that has potential resource, it needs to be managed so that the resource remains sustainable even though they are used optimally. The purpose of this study as to analyse the status of the sustainability of mangrove management in Lantebung and Untia. Analysis of mangrove management sustainability in Lantebung and Untia using Multi Criteria Analysis (MCA) method with Rap-Mforest approach modified from Rapfish. The result of the analysis show that the mangrove area management sustainability index in Lantebung and Untia is in the moderate sustainable category for the ecological dimension. Meanwhile, in the socio-economic and institutional dimensions, Lantebung was included in the moderate sustainable category and Untia was included in the less sustainable category. The Monte Carlo analysis method shows that the sustainability index is classified as good and acceptable because the stress value is less than 25%.
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Safari, E., N. M. Fogarty, and A. R. Gilmour. "Sensitivity of response of multi-trait index selection to changes in genetic correlations between production traits in sheep." Australian Journal of Experimental Agriculture 46, no. 3 (2006): 283. http://dx.doi.org/10.1071/ea04232.
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Complex sheep breeding objectives and the inclusion of more traits in genetic evaluation requires more accurate genetic parameters. There are few estimates of genetic correlations, especially between the major sheep production trait groups and they are generally variable with large standard errors. This study examines the sensitivity of response to multi-trait index selection for a range of genetic correlations between major traits (growth, wool, carcass, reproduction and worm resistance) under 3 breeding objectives (fine wool, dual-purpose wool/meat and meat) relevant to Australian sheep enterprises. Factor analysis identified the major factors explaining the underlying variability in the matrix of base genetic correlations for each breeding objective. Monte Carlo simulation studied the effect of changes in the genetic correlations under 3 index scenarios with the genetic and environmental variance–covariance matrices sampled independently from a Wishart distribution. The sensitivity of responses to selection over the range of correlations varied with the breeding objective. Selection responses were sensitive under all objectives for the range of correlations for reproduction with wool traits and in at least some objectives for reproduction with growth, worm resistance and carcass muscling. Selection responses in the fine wool objective were highly sensitive to the correlation between growth and fibre diameter. The results provide a logical basis for setting priorities for studies to obtain more accurate estimates of genetic correlations among sheep production traits.
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Imani, Mahdi, Seyede Fatemeh Ghoreishi, Douglas Allaire, and Ulisses M. Braga-Neto. "MFBO-SSM: Multi-Fidelity Bayesian Optimization for Fast Inference in State-Space Models." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 7858–65. http://dx.doi.org/10.1609/aaai.v33i01.33017858.
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Nonlinear state-space models are ubiquitous in modeling real-world dynamical systems. Sequential Monte Carlo (SMC) techniques, also known as particle methods, are a well-known class of parameter estimation methods for this general class of state-space models. Existing SMC-based techniques rely on excessive sampling of the parameter space, which makes their computation intractable for large systems or tall data sets. Bayesian optimization techniques have been used for fast inference in state-space models with intractable likelihoods. These techniques aim to find the maximum of the likelihood function by sequential sampling of the parameter space through a single SMC approximator. Various SMC approximators with different fidelities and computational costs are often available for sample-based likelihood approximation. In this paper, we propose a multi-fidelity Bayesian optimization algorithm for the inference of general nonlinear state-space models (MFBO-SSM), which enables simultaneous sequential selection of parameters and approximators. The accuracy and speed of the algorithm are demonstrated by numerical experiments using synthetic gene expression data from a gene regulatory network model and real data from the VIX stock price index.
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Godo, Masazumi, Takeshi Takatsuka, Mutsuya Tomonari, Kunio Miura, Seiichi Takami, Momoji Kubo, and Akira Miyamoto. "Adsorption Behavior of Chemical Contaminants by Molecular Simulation." Journal of the IEST 46, no. 1 (September 14, 2003): 98–102. http://dx.doi.org/10.17764/jiet.46.1.7450w638377v5872.
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The adsorption process of chemical contaminants on silicon wafers was investigated by using molecular mechanics, molecular dynamics, Monte Carlo method, and computer graphics. The adsorption energy of organic gaseous contaminants, such as DBP, DOP, and D5, on the hydrogen terminated silicon surface and its oxide surface, were estimated and compared. The adsorption energy of chemical contaminants was larger than that of low molecular weight species, such as benzene, H2O and NH3. The adsorption energy on the hydrogen terminated silicon surface was smaller than that on its oxide surface. The equilibrium adsorption amount on the silicon wafer was also calculated. It was indicated that the configuration of the adsorbed DBP molecules over its gas concentration of 1.9x103 ng/m3 showed multi-layer adsorption. It was indicated that adsorption energy and the equilibrium adsorption amount is a good index to estimate the adsorption properties of organic molecules.
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Morala, L., A. Serrano, and J. A. Garcia. "Detecting quasi-oscillations in the monthly precipitation regimes of the Iberian Peninsula." Annales Geophysicae 21, no. 3 (March 31, 2003): 819–32. http://dx.doi.org/10.5194/angeo-21-819-2003.
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Abstract. A spectral analysis of the time series corresponding to the main monthly precipitation regimes of the Iberian Peninsula was performed using two methods, the Multi-Taper Method and Monte Carlo Singular Spectrum Analysis. The Multi-Taper Method gave a preliminary view of the presence of signals in some of the time series. Monte Carlo Singular Spectrum Analysis discriminated between potential oscillations and noise. From the results of the two methods it is concluded that there exist three significant quasi-oscillations at the 95% level of confidence: a 5.0 year quasi-oscillation and a long-term trend in the Atlantic pattern of March, a 3.2 year quasi-oscillation in the Cantabrian pattern of January, and a 4.0 year quasi-oscillation in the Catalonian pattern of February. These quasi-oscillations might be related to climatic variations with similar periodicities over the North Atlantic Ocean. The possible simultaneity of high values of precipitation generated by the significant quasi-oscillations and high sea–level pressures was studied by means of composite maps. It was found that high values of precipitation generated by the oscillations of the Atlantic patterns of January and March exist simultaneously with a specific high pressure structure over the North Atlantic Ocean, that allow cyclonic perturbations to cross the Iberian Peninsula. During the non-wet years, this high pressure structure moves northwards, keeping the track of the low pressure centers to the north, far from the Iberian Peninsula. On the other hand, high values of precipitation generated by the oscillation of the Cantabrian pattern of January exist simultaneously with a high pressure structure over the Galicia region and the Cantabrian Sea, that allow a northerly flow over the region. Also, a positive trend in the NAO index for March has been found, starting in the sixties, which is not evident for other winter months. This trend agrees with the decreasing trend found in the March Atlantic pattern.Key words. Meteorology and atmospheric dynamics (climatology; precipitation) Oceanography: general (climate and interannual variability)
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Dong, Xinyu, and Yu Sun. "Precision Design of Transmission Mechanism of Toggle Press Based on Error Modeling." Machines 10, no. 8 (August 21, 2022): 717. http://dx.doi.org/10.3390/machines10080717.
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The position accuracy of the bottom dead centre (BDC) of a slider is a crucial performance index to measure the quality of a multi-link mechanical press. At present, the research on improving the position accuracy of the BDC mainly focuses on error compensation, and there is a lack of research on the tolerance design of the transmission mechanism according to the accuracy requirements of the BDC. In this paper, the motion output accuracy of the press is characterized by the position error of the BDC of the slider and the inclined angle error of the slider. Based on the loop increment method, the error transmission model of the transmission mechanism of a multi-link mechanical press was established. The key error factors that affect the output accuracy of the press were determined by analysis of error sensitivity. On this basis, a precision synthesis method considering manufacturing cost and BDC precision reliability was proposed. Finally, a Monte Carlo simulation and experiments on a toggle mechanical press were conducted to verify the model. The results show that the proposed error model and optimization method are feasible and effective.
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Yang, Wenyuan, Hong Liang, Feng Peng, Zili Liu, Jie Liu, and Zhiwei Qiao. "Computational Screening of Metal–Organic Framework Membranes for the Separation of 15 Gas Mixtures." Nanomaterials 9, no. 3 (March 20, 2019): 467. http://dx.doi.org/10.3390/nano9030467.
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The Monte Carlo and molecular dynamics simulations are employed to screen the separation performance of 6013 computation-ready, experimental metal–organic framework membranes (CoRE-MOFMs) for 15 binary gas mixtures. After the univariate analysis, principal component analysis is used to reduce 44 performance metrics of 15 mixtures to a 10-dimension set. Then, four machine learning algorithms (decision tree, random forest, support vector machine, and back propagation neural network) are combined with k times repeated k-fold cross-validation to predict and analyze the relationships between six structural feature descriptors and 10 principal components. Based on the linear correlation value R and the root mean square error predicted by the machine learning algorithm, the random forest algorithm is the most suitable for the prediction of the separation performance of CoRE-MOFMs. One descriptor, pore limiting diameter, possesses the highest weight importance for each principal component index. Finally, the 30 best CoRE-MOFMs for each binary gas mixture are screened out. The high-throughput computational screening and the microanalysis of high-dimensional performance metrics can provide guidance for experimental research through the relationships between the multi-structure variables and multi-performance variables.
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Zhang, Ziling, Shuo Feng, Yan Ding, Xiao Mei, and Zhiqiang Tao. "Thermal error modeling of spindle and dynamic machining accuracy reliability analysis of CNC machine tools based on IA and LHSMC." Eksploatacja i Niezawodnosc - Maintenance and Reliability 24, no. 1 (January 17, 2022): 100–113. http://dx.doi.org/10.17531/ein.2022.1.12.
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Machining accuracy reliability as a key index of CNC machine tools is seriously influenced by the geometric and thermal errors. In the paper, a spindle unit thermal error modeling and machining accuracy reliability analysis method is proposed. By analyzing the heat generation mechanism, a thermal error model was developed to describe the thermal deformation of the electric spindle. Based on the immune algorithm (IA), the heat generation power and the heat transfer coefficient were optimized, and the thermal error was obtained by finite element thermal-mechanical coupling. By adopting the multi-body system theory (MBS), a dynamic machining accuracy model was put forward including the geometric and thermal errors. Based on the Latin hypercube sampling Monte Carlo method (LHSMC), a machining accuracy reliability analysis method was proposed to characterize the machining accuracy reliability considering the geometric and thermal errors. The method was employed to a machine tool, and the experimental results indicate the verification and superiority of the method.
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Herbst, M., H. V. Gupta, and M. C. Casper. "Mapping model behaviour using Self-Organizing Maps." Hydrology and Earth System Sciences Discussions 5, no. 6 (December 4, 2008): 3517–55. http://dx.doi.org/10.5194/hessd-5-3517-2008.
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Abstract. Hydrological model evaluation and identification essentially depends on the extraction of information from model time series and its processing. However, the type of information extracted by statistical measures has only very limited meaning because it does not relate to the hydrological context of the data. To overcome this inadequacy we exploit the diagnostic evaluation concept of Signature Indices, in which model performance is measured using theoretically relevant characteristics of system behaviour. In our study, a Self-Organizing Map (SOM) is used to process the Signatures extracted from Monte-Carlo simulations generated by a distributed conceptual watershed model. The SOM creates a hydrologically interpretable mapping of overall model behaviour, which immediately reveals deficits and trade-offs in the ability of the model to represent the different functional behaviours of the watershed. Further, it facilitates interpretation of the hydrological functions of the model parameters and provides preliminary information regarding their sensitivities. Most notably, we use this mapping to identify the set of model realizations (among the Monte-Carlo data) that most closely approximate the observed discharge time series in terms of the hydrologically relevant characteristics, and to confine the parameter space accordingly. Our results suggest that Signature Index based SOMs could potentially serve as tools for decision makers inasmuch as model realizations with specific Signature properties can be selected according to the purpose of the model application. Moreover, given that the approach helps to represent and analyze multi-dimensional distributions, it could be used to form the basis of an optimization framework that uses SOMs to characterize the model performance response surface. As such it provides a powerful and useful way to conduct model identification and model uncertainty analyses.
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Herbst, M., H. V. Gupta, and M. C. Casper. "Mapping model behaviour using Self-Organizing Maps." Hydrology and Earth System Sciences 13, no. 3 (March 18, 2009): 395–409. http://dx.doi.org/10.5194/hess-13-395-2009.
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Abstract. Hydrological model evaluation and identification essentially involves extracting and processing information from model time series. However, the type of information extracted by statistical measures has only very limited meaning because it does not relate to the hydrological context of the data. To overcome this inadequacy we exploit the diagnostic evaluation concept of Signature Indices, in which model performance is measured using theoretically relevant characteristics of system behaviour. In our study, a Self-Organizing Map (SOM) is used to process the Signatures extracted from Monte-Carlo simulations generated by the distributed conceptual watershed model NASIM. The SOM creates a hydrologically interpretable mapping of overall model behaviour, which immediately reveals deficits and trade-offs in the ability of the model to represent the different functional behaviours of the watershed. Further, it facilitates interpretation of the hydrological functions of the model parameters and provides preliminary information regarding their sensitivities. Most notably, we use this mapping to identify the set of model realizations (among the Monte-Carlo data) that most closely approximate the observed discharge time series in terms of the hydrologically relevant characteristics, and to confine the parameter space accordingly. Our results suggest that Signature Index based SOMs could potentially serve as tools for decision makers inasmuch as model realizations with specific Signature properties can be selected according to the purpose of the model application. Moreover, given that the approach helps to represent and analyze multi-dimensional distributions, it could be used to form the basis of an optimization framework that uses SOMs to characterize the model performance response surface. As such it provides a powerful and useful way to conduct model identification and model uncertainty analyses.
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Suryawati, Siti Hajar, and Tajerin Tajerin. "PENILAIAN KESIAPAN MALUKU SEBAGAI LUMBUNG IKAN NASIONAL." Jurnal Sosial Ekonomi Kelautan dan Perikanan 10, no. 1 (June 17, 2016): 1. http://dx.doi.org/10.15578/jsekp.v10i1.1244.
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Maluku merupakan propinsi kepulauan dengan potensi sumberdaya perikanan tangkap yang besar. Potensi tersebut meliputi kelompok jenis ikan pelagis besar seperti tuna dan cakalang, pelagis kecil, demersal, udang, cumi-cumi dan ikan karang. Hal tersebut mendorong pemerintah menjadikan wilayah Maluku menjadi lumbung ikan nasional (M-LIN). Penelitian ini bertujuan untuk menganalisis status kesiapan Maluku sebagai lumbung ikan nasional. Metode analisis yang digunakan adalah Multi Dimensional Scaling (MDS) dala bentuk RAP-MLIN (Rapid Appraisal for Maluku as ‘Lumbung Ikan Nasional’) yang merupakan modifikasi dari software RAPFISH (Rapid Appraisal for Fisheries). Hasil analisisnya dinyatakan dalam bentuk indeks dan kesiapan program tersebut. Analisis leverage dan Monte-Carlo digunakan untuk mengetahui faktor pengungkit yang merupakan atribut-atribut yang sensitif terhadap indeks dan status kesiapan. Hasil penelitian menunjukkan bahwa pada dimensi ekologi statusnya cukup siap (50,33%), dimensi ekonomi cukup siap (67,62%), dimensi sosial siap (92,37%), dimensi teknologi siap (99,90%), dimensi infrastruktur cukup siap (70,56%), dan dimensi kelembagaan dan kebijakan siap (86,26%). Dari 47 atribut yang dianalisis, terdapat 18 atribut yang merupakan faktor pengungkit terhadap indeks dan status kesiapan, sehingga perlu dilakukan upaya perbaikan atau intervensi terhadap atribut-atribut tersebut. Dengan melakukan intervensi terhadap 18 faktor tersebut diharapkan dapat meningkatkan status kesiapan Maluku sebagai Lumbung Ikan Nasional ke tingkat yang lebih siap.(Evaluation of Readiness for Maluku as “Lumbung Ikan Nasional”)Maluku is an archipilagic province with large potential for fisheries resources including pelagic groups such as tuna and skipjack tuna, small pelagic, demersal, shrimp, squid and reef fish. This situation encourages the government to establish Maluku as “Lumbung Ikan Nasional (M-LIN)”. This study aimed to analyze the status of readiness of Maluku as “Lumbung Ikan Nasional”. Analytical method was used Multi Dimensional Scaling (MDS) which is so called RAP-MLIN (Rapid Appraisal for Maluku as Lumbung Ikan Nasional) which is a modification of the software RAPFISH (Rapid Appraisal for Fisheries). Analysis results expressed in terms of index and status of program readiness. Leverage and Monte Carlo analysis was used to determine attributes that are sensitive to the index and readiness status. Results showed that the ecological dimension was quite ready status (50.33%), the economic dimension was quite ready (67.62%), the social dimension ready (92.37%), the dimensions of the technology is ready (99.90%), the dimensions of the infrastructure was quite ready (70.56%), and the institutional and policy dimensions were ready (86.26%). Of the 47 attributes to be analyzed, there were 18 attributes enter during to factor of the index and the readiness status of the project, so that improvement and precise intervention can be made. With those intervention the implementation of Maluku as ‘Lumbung Ikan Nasional’ can be ensured.
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Ding, Qing, Runqiu Huang, Fengyan Wang, Jianping Chen, Mingchang Wang, and Xuqing Zhang. "Multi-Parameter Dominant Grouping of Discontinuities in Rock Mass Using Improved ISODATA Algorithm." Mathematical Problems in Engineering 2018 (December 12, 2018): 1–10. http://dx.doi.org/10.1155/2018/5619404.
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The dominant grouping of the discontinuities in a rock mass is crucial to investigating the rock structure and stability. The traditional grouping methods are mostly based on the discontinuity orientation. However, other discontinuity parameters nonnegligibly impact the properties of a rock mass. If two discontinuities have the same orientation but differ otherwise, their mechanical and hydraulic properties would differ. In the present study, orientation, trace length, opening degree, and undulation of discontinuities were used to develop a multi-parameter dominant discontinuity grouping method that utilizes an improved ISODATA algorithm. The developed method uses several indicators, such as the weighted Euclidean distance and standard deviation, to iteratively update the clustering centers of the discontinuities. A clustering validity index was introduced for assessment and optimization of the discontinuity grouping results, and an adaptive grouping model that considers the weight of each grouping parameter was developed. By grouping discontinuities generated by Monte Carlo stochastic simulation, defects existing in the grouping based on the orientation only were illustrated, and the rationality of the established adaptive grouping model was verified. The engineering practicability of the method was further verified by using it to group discontinuities measured in the Dongsheng Quarry in Jingyuetan, Changchun, China.
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Huang, Su Fen, Zhi Gang Song, and Bin Li. "Reliability Research of Building Fire Based on Numerical Simulation and Uniform Design." Advanced Materials Research 368-373 (October 2011): 665–72. http://dx.doi.org/10.4028/www.scientific.net/amr.368-373.665.
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Existing safety study of building fire is mainly based on the ISO834 temperature-time curve, which is a theoretical curve and not fully reflect the influencing factors of fire such as the distribution of fuel and ventilation of the building. Secondly, the reliability analysis of building fire lacks explicit limit state function, especially when the reliability calculation considers the internal force redistribution of the structure. Direct Monte Carlo simulation has no requirement of explicit limit state function, but it needs huge calculation efforts. To solve these two problems, the response surface method is proposed from the view point of numerical simulation and experiment design. Using the fire modeling software CFAST the actual result of temperature and thickness of smoke layer can be obtained. On this basis, the reliability index can be calculated with the response surface method，which can solve the problem of lacking explicit limit state function by regressing multi-variable function based on the inputs and outputs. Uniform design (UD) method can allocate more parameters without greatly increasing the calculation efforts. Using a case the calculation process is explained with. The results show that this method can quickly obtain the reliability index in the premise of less calculation.
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Li, Xiangqi, Yunfeng Li, Li Liu, Weiyu Wang, Yong Li, and Yijia Cao. "Latin Hypercube Sampling Method for Location Selection of Multi-Infeed HVDC System Terminal." Energies 13, no. 7 (April 2, 2020): 1646. http://dx.doi.org/10.3390/en13071646.
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Owing to the stochastic states of power systems with large-scale renewable generation, the impact of high-voltage direct current (HVDC) systems on the stability of the power system should be examined in a probabilistic manner. A probabilistic small signal stability assessment methodology to select the best locations for multi-infeed high-voltage direct current systems in alternating current (AC) grids is proposed in this paper. The Latin hypercube sampling-based Monte Carlo simulation approach is taken to generate the stochastic operation scenarios of power systems with the consideration of several stochastic factors, i.e., load demand and power generation. The damping ratio of the critical oscillation modes and the controllability of power injection to oscillation modes are analyzed by the probabilistic small signal stability. A probabilistic index is proposed to select the best locations of high-voltage direct current systems for improving the damping of the oscillation modes. The proposed methodology is applied to an IEEE 39 bus system considering the stochastic load demand and power generation. The results of probabilistic small signal stability assessment and a time-domain simulation show that the installation of a high-voltage direct current system on the selected locations can effectively improve the system damping.
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da Silva Júnior, Antônio Carlos, Isabela de Castro Sant’Anna, Michele Jorge Silva Siqueira, Cosme Damião Cruz, Camila Ferreira Azevedo, Moyses Nascimento, and Plínio César Soares. "Multi-trait and multi-environment Bayesian analysis to predict the G x E interaction in flood-irrigated rice." PLOS ONE 17, no. 5 (May 3, 2022): e0259607. http://dx.doi.org/10.1371/journal.pone.0259607.
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The biggest challenge for the reproduction of flood-irrigated rice is to identify superior genotypes that present development of high-yielding varieties with specific grain qualities, resistance to abiotic and biotic stresses in addition to superior adaptation to the target environment. Thus, the objectives of this study were to propose a multi-trait and multi-environment Bayesian model to estimate genetic parameters for the flood-irrigated rice crop. To this end, twenty-five rice genotypes belonging to the flood-irrigated rice breeding program were evaluated. Grain yield and flowering were evaluated in the agricultural year 2017/2018. The experimental design used in all experiments was a randomized block design with three replications. The Markov Chain Monte Carlo algorithm was used to estimate genetic parameters and genetic values. The flowering is highly heritable by the Bayesian credibility interval: h2 = 0.039–0.80, and 0.02–0.91, environment 1 and 2, respectively. The genetic correlation between traits was significantly different from zero in the two environments (environment 1: -0.80 to 0.74; environment 2: -0.82 to 0.86. The relationship of CVe and CVg higher for flowering in the reduced model (CVg/CVe = 5.83 and 13.98, environments 1 and 2, respectively). For the complete model, this trait presented an estimate of the relative variation index of: CVe = 4.28 and 4.21, environments 1 and 2, respectively. In summary, the multi-trait and multi-environment Bayesian model allowed a reliable estimate of the genetic parameter of flood-irrigated rice. Bayesian analyzes provide robust inference of genetic parameters. Therefore, we recommend this model for genetic evaluation of flood-irrigated rice genotypes, and their generalization, in other crops. Precise estimates of genetic parameters bring new perspectives on the application of Bayesian methods to solve modeling problems in the genetic improvement of flood-irrigated rice.
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Lin, Wu-Ting, Ru-Yin Tsai, Hsiu-Ling Chen, Yaw-Shyan Tsay, and Ching-Chang Lee. "Probabilistic Prediction Models and Influence Factors of Indoor Formaldehyde and VOC Levels in Newly Renovated Houses." Atmosphere 13, no. 5 (April 23, 2022): 675. http://dx.doi.org/10.3390/atmos13050675.
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Rapid urbanization has promoted house renovations and refurbishment in urban and rural cities. Indoor pollutants emitted through renovations and refurbishment processes have raised public concerns owing to their adverse effects on human health. In the present study, the sources of formaldehyde and specific volatile organic compounds (VOCs) are used to model the health effects associated with exposure to formaldehyde and specific VOCs and the loading factors of building materials for newly renovated homes. The present study is carried out to identify the sources of formaldehyde and specific VOCs in newly renovated houses and develop probabilistic prediction models of the health effects to explore the health risks of residents and the potential contributions of multilayer wood materials responsible for indoor pollutants. In living rooms and bedrooms, the average concentrations of formaldehyde and TVOCs in closed window conditions were higher than those in opened window conditions. Multi-layer wooden structures were a significant predictor of indoor VOC concentrations in houses. The 95 percentile values of Monte Carlo simulations (MCS P95) of the hazard index and cancer risk were lower and slightly higher than the acceptable level, respectively. Prediction models for the concentrations of formaldehyde and selected VOCs in newly renovated houses were first established using probabilistic and sensitive approaches. The multi-layer wood materials, including the wooden floor, cold paint multi-layer wooden materials, and multi-layer materials for system furniture, were responsible for the contribution of these levels of formaldehyde and selected VOCs in the newly renovated houses. Our results provide a strategy for eliminating indoor pollutants emitted from construction and building/furnishing materials.
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NARENDRA, BUDI HADI, WIDIATMAKA WIDIATMAKA, CECEP KUSMANA, LINA KARLINASARI, and MACHFUD MACHFUD. "Multi-dimensional rapid appraisal technique for evaluating the sustainability of energy plantation forests in East Lombok District, Indonesia." Biodiversitas Journal of Biological Diversity 20, no. 4 (March 19, 2019): 1027–33. http://dx.doi.org/10.13057/biodiv/d200413.
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Abstract. Narendra BH, Widiatmaka, Kusmana C, Karlinasari L, Machfud. 2019. Multi-dimensional rapid appraisal technique for evaluating the sustainability of energy plantation forests in East Lombok District, Indonesia. Biodiversitas 20: 1027-1033. The increased use of woody biomass as a renewable energy source should be supported by development of sustainable energy plantation forest. This study aims to evaluate the sustainability status of energy plantation forest in East Lombok District and to determine the leverage attributes affecting sustainability. The evaluation was carried out using rapid appraisal approach, consisting of multi-dimensional scaling (MDS) analysis to provide sustainability indices for ecological, economic and social dimensions, followed by Monte Carlo simulation to define the validity. Dominant attributes affecting the sustainability were assessed by sensitivity analysis. All analyses were executed using the R software. The evaluation of eight locations generally results in a quite sustainable status for ecological, social, and economic dimensions with the validity index values of 72.73%, 61.07%, and 58.49%, respectively. To maintain or enhance sustainability, some leverage attributes need to be prioritized for policy intervention. In the ecological dimension, increasing species diversity using some potential energy woody species should be prioritized. In the social dimension, optimizing the role and capability of forest farmer groups and their members is needed, while for the economic dimension, government should support the expanding area of energy plantation forest managed by each farmer.
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Diao, Wei, Peiyi Peng, Chunze Zhang, Shuqing Yang, and Xujin Zhang. "Multi-objective optimal operation of reservoir group in Jialing River based on DREAM algorithm." Water Supply 21, no. 5 (March 11, 2021): 2518–31. http://dx.doi.org/10.2166/ws.2021.064.
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Abstract With the implementation of the most stringent water resources management system and the advancement of the construction process of reservoir terrace basins, the research and application of the theory and method of joint operation of reservoir groups are becoming more and more important. The Differential Evolution Adaptive Metropolis (DREAM) algorithm is a sampling algorithm based on the Markov Chain Monte Carlo method proposed in recent years. The algorithm satisfies ergodicity and is good at handling problems with multivariate nonlinearity, high dimensionality, and multi-peaks, and as such the algorithm is a new global optimization solution. This paper elaborated the solution mechanism of the standard DREAM algorithm, and the algorithm was applied to the optimal operation model of the reservoir group in Jialing River. First, we optimized and analyzed the multi-objective supply operation model of the reservoir group water in Jialing River. Then the multi-attribute decision-making and evaluation index system of water supply operation rules for the reservoir group to assess the optimization of the operation model was adopted. Finally based on the results of the evaluation, the best water supply operation scheme for the reservoir group of Jialing River was selected. The results show that the Baozhusi Reservoir can fully meet the planned water supply requirements in dry years, while the two reservoirs, Shengzhong and Tingzikou, need to be weighed against the evaluation indexes of water supply operation. The research provides a theoretical basis for the DREAM algorithm in the optimal operation of the reservoir group and the actual operation scheme for the reservoir group of Jialing River.
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Cendrakasih, Yuwana Utami, Indra Gumay Yudha, Darma Yuliana, and Henni W. Maharani. "THE STATUS ANALYSIS OF SUSTAINABILITY OF TOURISM DEVELOPMENT GUCI BATU KAPAL BEACH IN MAJA VILLAGE, KALIANDA, SOUTH LAMPUNG." Journal of Aquatropica Asia 6, no. 2 (October 3, 2021): 60–71. http://dx.doi.org/10.33019/aquatropica.v6i2.2615.
Full textAbstract:
Tourism is an activity that supported with all the facilities at once the tourist activities which gained benefits parties included tourism or visitors, society and local government. Tourism as one of a development sector and driving force of the wheels of economy could not be released the connection with the sustainable development that had been planned by the government. The sustainable tourism development namely a development that could be supported as ecologically, economy worthy, also fair in ethics and social to society. The purpose of this research was to: (1) analyzing the status of tourist sustainability of Guci Batu Kapal beach from five sustainable dimensions (ecology, economy, technology infrastructure, also law and institutional); (2) Identifying factors that influencing sustainable index of Guci Batu Kapal beach tourism. This research was held on February 2021, in Guci Batu Kapal beach, Maja village, Kalianda, South Lampung. The methods that were used for this research was qualitative methods by descriptive research. Analyzing data that used was analyzing multi-dimensional scaling (MDS) with rapfish approach and leverage analysis. The result of this research showed that tourism of Guci Batu Kapal beach had an index score as 21,58 on a sustainable scale 0-100, which mean include in the bad impact (unsustainable) because of the index score was between in index score of 0-25,00. This analysis of Monte-Carlo showed that the index score of tourism Guci Batu Kapal beach sustainability was not that much different with the Rap-beachtour analysis, the difference obtained only for 0,0001-0,00003. The result was quite stable also the mistake in the input could not be avoided.