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Enhancing computational efficiency in topology-optimized mode converters via dynamic update rate strategies

SCIENTIFIC REPORTS 2024年第1期14卷 1-14页

作者： Cao, Pengfei Duan, Ning Zhao, Zhikai Yu, Mengqiang Li, Congcong Yuan, Mingrui Cheng, Lin Yan, Ge Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Peoples R China Latitude Design Automat Inc Wuxi 214000 Peoples R China Lanzhou Univ Sch Phys Sci & Technol Lanzhou 730000 Peoples R China Lanzhou Inst Phys Lanzhou 730000 Peoples R China

In the big data era, mode division multiplexing, as a technology for extended channel capacity, demonstrates potential in enhancing parallel data processing capability. Consequently, developing a compact, high-performance mode converter through efficient design methods is an urgent requirement. However, traditional design methodologies for these converters face significant computational complexities and inefficiencies. Addressing this challenge, this paper introduces a novel topology optimization design method for mode converters employing a Dynamic Adjustment of Update Rate (DAUR). This approach markedly reduces computational overhead, accelerating the design process while ensuring high performance and compactness. As a proof-of-concept, an ultra-compact dual-mode converter was designed. The DAUR method demonstrated an 80% reduction in computational time compared to traditional methods, while maintaining a compact design (only 1.4 mu m x 1.4 mu m) and an insertion loss under 0.68 dB across a wavelength range of 1525 nm to 1575 nm. Meanwhile, simulated inter-mode crosstalk remained below - 24 dB across a 40 nm bandwidth. A comprehensive comparison with traditional inverse design algorithms is presented, demonstrating our method's superior efficiency and effectiveness. Our findings suggest that DAUR not only streamlines the design process but also facilitates exploration into more complex micro-nano photonic structures with reduced resource investment.

关键词： Mode division multiplexing Integrated optics Topology optimization Mode converter optimization algorithm Ultra compact design

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algorithm of Finding Hypo-Critical Path in Network Planning

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Physics Procedia 2012年 24卷 1520-1529页

作者： Jianxun Qi Xiuhua Zhao School of Business Administration North China Electric Power University NCEPU Beijing China

Network planning technology could be used to represent project plan management, such Critical Path Method (CPM for short) and Performance Evaluation Review Technique (PERT for short) etc. Aiming at problem that how to find hypo-critical path in network planning, firstly, properties of total float. free float and safety float are analyzed, and total float theorem is deduced on the basis of above analysis; and secondly, simple algorithm of finding the hypo-critical path is designed by using these properties of float and total theorem, and correctness of the algorithm is analyzed. Proof shows that the algorithm could realize effect of whole optimization could be realized by part optimization. Finally, one illustration is given to expatiate the algorithm.

关键词： Project management operational research network planning total float theorem optimization algorithm float

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History matching and uncertainty quantification for velocity dependent relative permeability parameters in a gas condensate reservoir

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ARABIAN JOURNAL OF GEOSCIENCES 2019年第15期12卷 454-454页

作者： Farahani, Zohreh Dermanaki Ahmadi, Mohammad Sharifi, Mohammad Amirkabir Univ Technol Hafez Ave 424 Tehran Iran

In gas condensate reservoirs, gas flow at large velocities enhances the gas permeability due to gas-liquid positive coupling which results in near-miscible flow condition. On the other hand, augmented pressure drop due to non-Darcy flow, reduces the gas permeability. Models for the Positive Coupling or non-Darcy flow include several parameters, which are rarely known from reliable lab special core analysis. We offer a good alternative for tuning of these parameters in which the observed production history data are reproduced from the readjusted simulation model. In this study, history matching on observed production data was carried out using evolutionary optimization algorithms including genetic algorithms, neighborhood algorithm, differential evolution algorithm and particle swarm optimization algorithm, where a faster convergence and lower misfit value were obtained from a genetic algorithm. Then, the Neighborhood algorithm-Bayes was used to perform Bayesian posterior inference on the history matched models and create the posterior cumulative probability distributions for all uncertain parameters. Finally, Bayesian credible intervals for production rate and wellhead pressure were computed in the long-range forecast. Our new approach enables to not only calibrate the gas effective permeability parameters to dynamic reservoir data, but allows to capture the uncertainty with parameter estimation and production forecast.

关键词： History matching Uncertainty quantification Velocity dependent relative permeability Gas condensate reservoirs optimization algorithm

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Increasing the essential oil yield from Shorea roxburghii inflorescences using an eco-friendly solvent-free microwave extraction method for fragrance applications

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JOURNAL OF APPLIED RESEARCH ON MEDICINAL AND AROMATIC PLANTS 2021年 24卷

作者： Yingngam, Bancha Navabhatra, Abhiruj Brantner, Adelheid Ubon Ratchathani Univ Fac Pharmaceut Sci Dept Pharmaceut Chem & Technol Ubon Ratchathani 34190 Thailand Rangsit Univ Coll Pharm Dept Pharmacol Pathum Thani 12000 Thailand Karl Franzens Univ Graz Inst Pharmaceut Sci Dept Pharmacognosy Univ Pl 4-1 A-8010 Graz Austria

The short bloom period of Shorea roxburghii *** is a technical challenge in the development of a fast and efficient process for recovering essential oils. This study aimed to optimize the conditions for the isolation of essential oil and the determination of the resulting oil's possible bioactivities for fragrance applications. Solvent-free microwave extraction (SFME) coupled with response surface methodology (RSM) was applied to study essential oil from fresh inflorescences. The microwave power and irradiation time were chosen as crucial independent variables, while essential oil yield was the response variable. The physicochemical, antioxidant, olfactory, cytotoxic, and irritant properties of the resulting oil were evaluated. SFME yielded essential oil with a unique and pleasant scent, similar to that of fresh flowers. Increased microwave power and irradiation time increased the oil yield. The proportions of oxygenated compounds with high antioxidant properties were notably higher after SFME than after hydrodistillation. The optimal conditions for maximizing the oil yield were 800 W and 35 min. Among 51 volatile substances, germacrene B, kaur-16-ene, beta-caryophyllene, and (E)-linalool oxide (furanoid) were the oil's main components. Low toxicity to primary human dermal fibroblast cells was defined at an oil concentration < 50 mu g/mL. Moreover, the application of the extracted volatile oil to hen's egg-chorioallantoic membrane resulted in a low degree of irritation. This research establishes optimal extraction conditions for the isolation of essential oil with potential for fragrance applications.

关键词： Aromatic plants Essential oil Extraction conditions Fragrance ingredients optimization algorithm Response surface methodology

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Integrating socio-psychological factors in the SEIR model optimized by a genetic algorithm for COVID-19 trend analysis

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SCIENTIFIC REPORTS 2024年第1期14卷 1-12页

作者： Wang, Haonan Wu, Danhong Luo, Jie Zhang, Junhui Southwest Med Univ Sch Publ Hlth 1Sect 1Xianglin Rd Luzhou 646000 Sichuan Peoples R China Chengdu Univ Informat Technol Dept Appl Math Chengdu 610225 Sichuan Peoples R China Univ Durham Dept Psychol Durham DH1 3LE England

The global spread of COVID-19 has profoundly affected health and economies, highlighting the need for precise epidemic trend predictions for effective interventions. In this study, we used infectious disease models to simulate and predict the trajectory of COVID-19. An SEIR (susceptible, exposed, infected, removed) model was established using Wuhan data to reflect the pandemic. We then trained a genetic algorithm-based SEIR (GA-SEIR) model using data from a specific U.S. region and focused on individual susceptibility and infection dynamics. By integrating socio-psychological factors, we achieved a significant enhancement to the GA-SEIR model, leading to the development of an optimized version. This refined GA-SEIR model significantly improved our ability to simulate the spread and control of the epidemic and to effectively track trends. Remarkably, it successfully predicted the resurgence of COVID-19 in mainland China in April 2023, demonstrating its robustness and reliability. The refined GA-SEIR model provides crucial insights for public health authorities, enabling them to design and implement proactive strategies for outbreak containment and mitigation. Its substantial contributions to epidemic modelling and public health planning are invaluable, particularly in managing and controlling respiratory infectious diseases such as COVID-19.

关键词： COVID-19 Dynamic model GA-SEIR model Epidemic forecasting Socio-psychological factors optimization algorithm

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Optimized machine learning model for air quality index prediction in major cities in India

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SCIENTIFIC REPORTS 2024年第1期14卷 1-18页

作者： Natarajan, Suresh Kumar Shanmurthy, Prakash Arockiam, Daniel Balusamy, Balamurugan Selvarajan, Shitharth Jain Sch Comp Sci & Engn Bengaluru Karnataka India Presidency Univ Sch Comp Sci & Engn & Informat Sci Bengaluru Karnataka India Amity Univ ASET CSE Gwalior Madhya Pradesh India Shiv Nadar Inst Eminence Delhi India Leeds Beckett Univ Sch Built Environm Engn & Comp Leeds LS1 3HE W Yorkshire England

Industrial advancements and utilization of large amount of fossil fuels, vehicle pollution, and other calamities increases the Air Quality Index (AQI) of major cities in a drastic manner. Major cities AQI analysis is essential so that the government can take proper preventive, proactive measures to reduce air pollution. This research incorporates artificial intelligence in AQI prediction based on air pollution data. An optimized machine learning model which combines Grey Wolf optimization (GWO) with the Decision Tree (DT) algorithm for accurate prediction of AQI in major cities of India. Air quality data available in the Kaggle repository is used for experimentation, and major cities like Delhi, Hyderabad, Kolkata, Bangalore, Visakhapatnam, and Chennai are considered for analysis. The proposed model performance is experimentally verified through metrics like R-Square, RMSE, MSE, MAE, and accuracy. Existing machine learning models, like k-nearest Neighbor, Random Forest regressor, and Support vector regressor, are compared with the proposed model. The proposed model attains better prediction performance compared to traditional machine learning algorithms with maximum accuracy of 88.98% for New Delhi city, 91.49% for Bangalore city, 94.48% for Kolkata, 97.66% for Hyderabad, 95.22% for Chennai and 97.68% for Visakhapatnam city.

关键词： Air pollution Air quality index Machine learning optimization algorithm Grey-wolf optimization Decision tree regression

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Efficient feature selection for histopathological image classification with improved multi-objective WOA

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SCIENTIFIC REPORTS 2024年第1期14卷 1-17页

作者： Sharma, Ravi Sharma, Kapil Bala, Manju Delhi Technol Univ New Delhi 110042 India Univ Delhi Indraprastha Coll Women Civil Lines New Delhi 110054 India

The difficulty of selecting features efficiently in histopathology image analysis remains unresolved. Furthermore, the majority of current approaches have approached feature selection as a single objective issue. This research presents an enhanced multi-objective whale optimisation algorithm-based feature selection technique as a solution. To mine optimal feature sets, the suggested technique makes use of a unique variation known as the enhanced multi-objective whale optimisation algorithm. To verify the optimisation capability, the suggested variation has been evaluated on 10 common multi-objective CEC2009 benchmark functions. Furthermore, by comparing five classifiers in terms of accuracy, mean number of selected features, and calculation time, the effectiveness of the suggested strategy is verified against three other feature-selection techniques already in use. The experimental findings show that, when compared to the other approaches under consideration, the suggested method performed better on the assessed parameters.

关键词： Image classification Multi-objective grey wolf optimizer optimization algorithm Pre processing

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A new approach of deep neural computing for spatial prediction of wildfire danger at tropical climate areas

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ECOLOGICAL INFORMATICS 2021年 63卷 101300-101300页

作者： Hung Van Le Duc Anh Hoang Chuyen Trung Tran Phi Quoc Nguyen Van Hai Thi Tran Nhat Duc Hoang Amiri, Mahdis Thao Phuong Thi Ngo Ha Viet Nhu Thong Van Hoang Dieu Tien Bui Hanoi Univ Min & Geol Fac Informat Technol Hanoi Vietnam Hanoi Univ Min & Geol Dept Environm Sci Hanoi Vietnam Duy Tan Univ Inst Res & Dev P809-03 Quang Trung Da Nang 550000 Vietnam Duy Tan Univ Fac Civil Engn P809-03 Quang Trung Da Nang 550000 Vietnam Gorgan Univ Agr Sci & Nat Resources Dept Watershed & Arid Zone Management Gorgan Golestan Iran Hanoi Univ Min & Geol Dept Geol Geotech Engn Hanoi Vietnam Univ Transport & Commun Informat Technol Fac Hanoi Vietnam Univ South Eastern Norway Dept Business & IT GIS Grp Gullbringvegen 36 N-3800 Bo I Telemark Norway

Wildfire is an environmental hazard that has both local and global effects, causing economic losses and various severe environmental problems. Due to the adverse effects of climate changes and anthropogenic activities, wildfire is anticipated more frequent and extreme;therefore, new and more efficient tools for forest fire prevention and control are essential. This study proposes a new deep neural computing approach for spatial prediction of wildfire in a tropical climate area. For this purpose, deep neural computing (Deep-NC) with a structure of 3 hidden layers was proposed. The Rectified Linear Unit (ReLU) activation function was adopted to infer wildfire dangers from the input factors. To search and optimize the weights of the model, Stochastic Gradient Descent (SGD), Root Mean Square Propagation (RMSProp), Adaptive Moment Estimation (Adam), and Adadelta optimizers were employed. Also, this study has established a Geographic Information System (GIS) database for Gia Lai province (Vietnam) to train and verify the newly developed deep computing approach. The twelve ignition factors, namely, slope, aspect, elevation, curvature, land use, NVDI, NDWI, NDMI, temperature, wind speed, relative humidity, and rainfall, have been used to characterize the study area with respect to forest fire susceptibility. According to experimental results, the Adam optimized Deep-NC model delivered the highest predictive accuracy (AUC = 0.894, Kappa = 0.63). Accordingly, this model has been employed to establish a forest fire susceptibility map for Gia Lai province. The proposed Deep-NC model and the newly constructed forest fire susceptibility map can help local authorities in land use planning and hazard mitigation/prevention.

关键词： Forest fire Deep neural computing optimization algorithm Geographical information system Gia Lai Vietnam

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A heuristic approach for the positioning of elevator hoistways based on the utilization intensity index

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ARCHITECTURAL ENGINEERING AND DESIGN MANAGEMENT 2013年第4期9卷 209-228页

作者： Markos, P. A. Dentsoras, A. J. Univ Patras Mach Design Lab Dept Mech Engn & Aeronaut Patras 26500 Greece

In modern buildings, the position(s) of elevator system(s) tends to be a crucial factor for the unimpeded vertical and on-floor circulation of their residents. The present paper introduces a method that correlates circulation data for the population of a typical office building (size, density and possibility for elevator use) with structural and architectural data of each one of its floors (net usable space, circulation space, structural intrusions, etc.). The method provides an optimum position for the elevator system hoistway(s) so that all walking distances on all floors between the elevator system and usable spaces are as small as possible. Every single floor of the building is partitioned into cells that form a grid covering its surface. An index of the intensity of elevator utilization (EUII) is introduced, defined as a function of the data mentioned above. Then, Euclidean norms, based on (EUII) values, provide weighted mean distances for locating the point on the vertical projection of the building that corresponds to the minimal mean distance from every cell of its usable spaces. A case study of a typical office building served by a single elevator system exemplifies the proposed approach, initially for a single floor and then for all the floors of the building.

关键词： architectural design elevator design optimization algorithm building circulation

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Predicting the small strain shear modulus of sands and sand-fines binary mixtures using machine learning algorithms

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TRANSPORTATION GEOTECHNICS 2024年 44卷

作者： Khodkari, Naser Hamidian, Pouria Khodkari, Homayoun Payan, Meghdad Behnood, Ali Univ Tehran Coll Engn Sch Civil Engn Tehran Iran Amirkabir Univ Technol Tehran Polytech Turbulence Lab Tehran Iran Univ Guilan Fac Engn Dept Civil Engn Rasht Iran Univ Mississippi Dept Civil Engn Oxford MS USA

This study aims to develop several novel machine learning (ML) evolutionary algorithms for the prediction of small strain shear modulus (Gmax) of clean sands and sand-fines binary mixtures. To this end, five key features of isotropic confining pressure (p), void ratio (e), uniformity coefficient (Cu), particle shape descriptor (rho), and non plastic fines content (FC) are adopted as the inputs to artificial neural network (ANN) models as well as genetic programming (GP) algorithm so as to render the maximum shear modulus of granular soils as the output. Accordingly, a comprehensive dataset containing 1055 Gmax data points is exploited to develop ML simulations. The validity of ML-based models in estimating the Gmax of clean sands and sand-silt mixtures is rigorously examined through various statistical indices and measurement criteria. The results show that a novel ML model utilizing ANN-Levenberg Marquardt (LM) in conjunction with an evolutionary optimization method named Success History-based Adaptive Differential Evolution with Linear population size reduction (LSHADE) is capable of predicting Gmax data with a very high precision rendering R2 values of 0.9833, 0.9841, 0.9802, and 0.9835 for the whole, training, validation, and test datasets, respectively. Meanwhile, using the well-established GP algorithm, a new practical model is proposed to predict the Gmax of clean sands and sand-fines mixtures containing non-cohesive silt inclusion with R2 values of 0.9323, 0.9351, and 0.9312 for the whole, training, and test datasets, respectively. Finally, the proposed models of ANN-LSHADE-LM and GP are shown to be appreciably superior, in terms of accuracy, to all commonly used empirical correlations in the literature for Gmax estimation.

关键词： Small strain shear modulus Artificial neural network (ANN) optimization algorithm Differential evolution (DE) Genetic programming (GP) Sand-silt mixtures

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