a novel parameter decision approach in hobbing process

HDDM: Hierarchical Bayesian estimation of the Drift

METHODS ARTICLE published: 02 August 2013 doi: 10.3389/fninf.2013.00014 HDDM: Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python Thomas V. Wiecki* †,ImriSoferand Michael J. Frank Department of Cognitive, Linguistic and Psychological

A (Long) Peek into Reinforcement Learning

In this post, we are gonna briefly go over the field of Reinforcement Learning (RL), from fundamental concepts to classic algorithms. Hopefully, this review is helpful enough so that newbies would not get lost in specialized terms and jargons while starting. [WARNING] This is a long read.

Estimating Multiparameter Partial Expected Value of

2013/11/18We describe a novel nonparametric regression-based method for estimating partial EVPI that requires only the probabilistic sensitivity analysis sample (i.e., the set of samples drawn from the joint distribution of the parameters and the corresponding net benefits).

Learning Linear Programs from Optimal Decisions

optimization process, which in our case is assumed linear. We view IO as the problem of inferring a constrained optimization model that gives identical (or equivalent) decisions, and which generalizes to novel conditions u. The family of candidate models is

Subtype

While novel omics data are integrated into the approach, Subtype-GAN only needs to update the corresponding independent layer and the original model's shared layer. From subtyping results, on most TCGA datasets, hierarchical clustering believes that Subtype-GAN is consistent with VAE but is quite different from AE.

A novel parameter decision approach in hobbing process

2020/11/11A novel parameter decision approach in hobbing process for minimizing carbon footprint and processing time Hengxin Ni 1, Chunping Yan 1, Weidong Cao 2 Liu 1 The International Journal of Advanced Manufacturing Technology volume 111, pages 2020)

GitHub

The Canadian Hydrological Model (CHM) is a novel modular unstructured mesh based approach for hydrological modelling. It can move between spatial scale, temporal scale, and spatial extents. It is designed for developing and testing process representations for hydrological models.

Multi

Dry hobbing has received extensive attention for its environmentally friendly processing pattern. Due to the absence of lubricants, hobbing process is highly dependent on process parameters combination since using unreasonable parameters tends to affect the machining performance. Besides, the consideration of tool life is frequently ignored in gear hobbing. Thus, to settle the above issues, a

Modeling wine preferences by data mining from physicochemical properties

parameter, in order to get good predictive accuracy (see Section 2.3). The use of decision support systems by the wine industry is mainly focused on the wine production phase [12]. Despite the potential of DM techniques to predict wine quality based on 3

Practical and Intuitive Surgical Approach Renal Ranking to

Conclusion: Surgical Approach Renal Ranking is a simple, practical and intuitive classification for renal tumors that can be used in the decision-making process and to predict outcomes in the surgical treatment of patients with renal tumors.

A novel machine learning strategy for model selections

2020/8/27An essential aspect of medical research is the prediction for a health outcome and the scientific identification of important factors. As a result, numerous methods were developed for model selections in recent years. In the era of big data, machine learning has been broadly adopted for data analysis. In particular, the Support Vector Machine (SVM) has an excellent performance in

Singapore Institute of Manufacturing Technology

Inventory planning is a process involving inventory performance analysis planning, and purchase decision-making; by reaching right-sized inventory through the process, the organisation will be able to mitigate inventory risk in short term and maximise inventory

BIOSTAT STR Bioreactors for Scale

The scale-conversion tool described above is a novel multiple-parameter scale-up approach to optimizing the agitation and gassing strategy for five different-sized single-use bioreactors. Bioreactors ranged from those for cell culture process development (ambr bioreactors), through pilot to manufacturing scale (BIOSTAT STR bioreactors) for volumes from 15 mL to 2,000 L.

International Journal of Information Technology

The Analytic Hierarchy Process (AHP) is one of the most widely used quantitative tools in multi-criteria decision-making problems. Despite its popularity and use due to its simple but systematic procedure, AHP has limitations especially in terms of the numerical comparison scale used in one of its core steps: pairwise comparisons.

Optimizing time–cost in generalized construction

Design/methodology/approach In this paper, a novel MOGSO to mimic the time–cost tradeoff problem in generalized construction projects is proposed. The MOSGO has slightly modified the mechanism operation from the original algorithm to be a free-parameter

A novel approach to an old problem Analysis of systematic errors

A novel approach to an old problem: Analysis of systematic errors in two models of recognition memory Adam J.O. Dedea,b,n, Larry R. Squirea,b,c,d, John T. Wixtedb a Veterans Affairs San Diego Healthcare System, San Diego, CA 92161, USA b Department of Psychology, University of California, San Diego, CA 92093, USA

Novel Methods For Text Generation Using Adversarial

Just two years ago, text generation models were so unreliable that you needed to generate hundreds of samples in hopes of finding even one plausible sentence. Nowadays, OpenAI's pre-trained language model can generate relatively coherent news articles given only two sentence of context. Other approaches like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) have

Multi

Process parameter optimization is an effective method of decreasing energy from process control perspective. However, hobbing parameter optimization is rarely involved in previous studies. To this end, a multi-component energy model is first developed on a basis of energy characteristics analysis of dry gear hobbing machines.

A (Long) Peek into Reinforcement Learning

In this post, we are gonna briefly go over the field of Reinforcement Learning (RL), from fundamental concepts to classic algorithms. Hopefully, this review is helpful enough so that newbies would not get lost in specialized terms and jargons while starting. [WARNING] This is a long read.

Biopharmaceutical Manufacturing Process Validation and

This article was published in the May/June 2016 edition of Pharmaceutical Engineering magazine. It is one of five articles nominated for the Roger F. Sherwood Article of the Year Award, all which will be posted to iSpeak throughout the week of 5 December.

About Hob Idle Distance in Gear Hobbing Operation

This task is of importance in two aspects: to cut hobbing time and to reduce axial size of a hobbed cluster gear, gear with shoulder etc. The necessity of cutting hobbing time is evident. Reduction of axial size of a hobbed gear cluster leads to reduction of size and weight of the gear cluster itself and of the gear train housing, and therefore its necessity is also evident.

HDDM: Hierarchical Bayesian estimation of the Drift

METHODS ARTICLE published: 02 August 2013 doi: 10.3389/fninf.2013.00014 HDDM: Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python Thomas V. Wiecki* †,ImriSoferand Michael J. Frank Department of Cognitive, Linguistic and Psychological

A novel hybrid deep learning scheme for four

2019/10/16Approach. An end-to-end novel hybrid deep learning scheme is developed to decode the MI task from EEG data. The proposed algorithm consists of two parts: a. A one-versus-rest filter bank common spatial pattern is adopted to preprocess and pre-extract theb.

A new approach to solving the feature

One approach that has been proposed for solving the feature-binding problem is known as feature integration theory (FIT) [].This theory makes the assumption that there is only a single spatial locus of attention within the visual field where features are bound together.

Thoracic fluid content: a novel parameter for predicting

2020/3/5Thoracic fluid content: a novel parameter for predicting failed weaning from mechanical ventilation Shymaa Fathy 1, Ahmed M. Hasanin 1, Mohamed Raafat 1, Maha M. A. Mostafa 1, Ahmed M. Fetouh 1, Mohamed Elsayed 1, Esraa M. Badr 1, Hanan M. Kamal 1

A novel approach for solid particle erosion prediction

2021/2/15Based on Gaussian Process Regression (GPR) method, a novel non-linear approach is adopted to predict solid particle erosion in standard elbows. Several data sets extracted from Computational Fluid Dynamics (CFD) results and experimental tests are used to evaluate the effectiveness of the approach for gas-solid as well as gas-liquid-solid flows.

From DFT to machine learning: recent approaches to

2019/5/16Resulting data, irrespective of its origin, is then used as a substrate to the learning process, within the ML approach, resulting in extraction of knowledge from the patterns discovered. Considering the historical development of research in computational materials science, we can classify the different problems and methods used to tackle them into three generations related to the topics

BIOSTAT STR Bioreactors for Scale

The scale-conversion tool described above is a novel multiple-parameter scale-up approach to optimizing the agitation and gassing strategy for five different-sized single-use bioreactors. Bioreactors ranged from those for cell culture process development (ambr bioreactors), through pilot to manufacturing scale (BIOSTAT STR bioreactors) for volumes from 15 mL to 2,000 L.

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