scholarly journals Optimal composition of tasks in cloud manufacturing platform: a novel hybrid GWO-GA approach

2019 ◽  
Author(s):  
Hamed Bouzary ◽  
F. Frank Chen ◽  
Mohammad Shahin
Keyword(s):  
Large Scale ◽  
Combinatorial Problem ◽  
Cloud Manufacturing ◽  
Superior Performance ◽  
Grey Wolf Optimizer ◽  
Mutation Operators ◽  
Discrete Variations ◽  
Np Hard Problem ◽  
Manufacturing Platform ◽  
Evolutionary Operators

Emerging cloud manufacturing paradigm aims towards providing highly integrated solutions via enabling cooperation between distributed manufacturing resources and capabilities. Implementation of this groundbreaking idea, however, is facing serious challenges springing from the currently centralized industrial structures. In order to help it make its way out, researchers have to address a number of pivotal issues in this regard. Service composition and optimal selection (SCOS), which tackles the problem of optimally selecting and combining available resources into a composite service is one of them. To deal with this NP-hard problem, we developed a novel hybrid algorithm based on the recentlyintroduced grey wolf optimizer (GWO) in which evolutionary operators are also embedded into the hunting mechanism of the basic algorithm.This approach not only makes it possible to adapt an algorithm with continuous structure such as GWO to solve a combinatorial problem such as SCOS, but also empowers it with providing higher exploration through crossover and mutation operators. Experiments conducted clearly proves the superior performance of the proposed algorithm over existing discrete variations of GWO and genetic algorithm, especially in large-scale SCOS problems.

A Scalable Redefined Stochastic Blockmodel

2021 ◽  
Vol 15 (3) ◽  
pp. 1-28
Author(s):  
Xueyan Liu ◽  
Bo Yang ◽  
Hechang Chen ◽  
Katarzyna Musial ◽  
Hongxu Chen ◽  
...  
Keyword(s):  
Large Scale ◽  
Network Science ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Real World Data ◽  
Computational Overhead ◽  
Stochastic Blockmodel ◽  
Np Hard Problem ◽  
Large Scale Networks ◽  
The Cost

Stochastic blockmodel (SBM) is a widely used statistical network representation model, with good interpretability, expressiveness, generalization, and flexibility, which has become prevalent and important in the field of network science over the last years. However, learning an optimal SBM for a given network is an NP-hard problem. This results in significant limitations when it comes to applications of SBMs in large-scale networks, because of the significant computational overhead of existing SBM models, as well as their learning methods. Reducing the cost of SBM learning and making it scalable for handling large-scale networks, while maintaining the good theoretical properties of SBM, remains an unresolved problem. In this work, we address this challenging task from a novel perspective of model redefinition. We propose a novel redefined SBM with Poisson distribution and its block-wise learning algorithm that can efficiently analyse large-scale networks. Extensive validation conducted on both artificial and real-world data shows that our proposed method significantly outperforms the state-of-the-art methods in terms of a reasonable trade-off between accuracy and scalability. 1

On a Vector Space Representation in Genetic Algorithms for Sensor Scheduling in Wireless Sensor Networks

2014 ◽  
Vol 22 (3) ◽  
pp. 361-403 ◽  
Author(s):  
F. V. C. Martins ◽  
E. G. Carrano ◽  
E. F. Wanner ◽  
R. H. C. Takahashi ◽  
G. R. Mateus ◽  
...  
Keyword(s):  
Fitness Landscape ◽  
Combinatorial Problem ◽  
Wireless Sensor ◽  
Space Representation ◽  
Network Dynamic ◽  
Decision Variable ◽  
Exact Methods ◽  
Variable Space ◽  
Combinatorial Space ◽  
Evolutionary Operators

Recent works raised the hypothesis that the assignment of a geometry to the decision variable space of a combinatorial problem could be useful both for providing meaningful descriptions of the fitness landscape and for supporting the systematic construction of evolutionary operators (the geometric operators) that make a consistent usage of the space geometric properties in the search for problem optima. This paper introduces some new geometric operators that constitute the realization of searches along the combinatorial space versions of the geometric entities descent directions and subspaces. The new geometric operators are stated in the specific context of the wireless sensor network dynamic coverage and connectivity problem (WSN-DCCP). A genetic algorithm (GA) is developed for the WSN-DCCP using the proposed operators, being compared with a formulation based on integer linear programming (ILP) which is solved with exact methods. That ILP formulation adopts a proxy objective function based on the minimization of energy consumption in the network, in order to approximate the objective of network lifetime maximization, and a greedy approach for dealing with the system's dynamics. To the authors’ knowledge, the proposed GA is the first algorithm to outperform the lifetime of networks as synthesized by the ILP formulation, also running in much smaller computational times for large instances.

scholarly journals 3D Photon-To-Digital Converter for Radiation Instrumentation: Motivation and Future Works

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 598
Author(s):  
Jean-François Pratte ◽  
Frédéric Nolet ◽  
Samuel Parent ◽  
Frédéric Vachon ◽  
Nicolas Roy ◽  
...  
Keyword(s):  
Large Scale ◽  
Single Photon ◽  
Time Of Flight ◽  
Digital Signal ◽  
Liquid Argon ◽  
Cmos Technology ◽  
System Level ◽  
Superior Performance ◽  
Digital Converter ◽  
Positron Emission

Analog and digital SiPMs have revolutionized the field of radiation instrumentation by replacing both avalanche photodiodes and photomultiplier tubes in many applications. However, multiple applications require greater performance than the current SiPMs are capable of, for example timing resolution for time-of-flight positron emission tomography and time-of-flight computed tomography, and mitigation of the large output capacitance of SiPM array for large-scale time projection chambers for liquid argon and liquid xenon experiments. In this contribution, the case will be made that 3D photon-to-digital converters, also known as 3D digital SiPMs, have a potentially superior performance over analog and 2D digital SiPMs. A review of 3D photon-to-digital converters is presented along with various applications where they can make a difference, such as time-of-flight medical imaging systems and low-background experiments in noble liquids. Finally, a review of the key design choices that must be made to obtain an optimized 3D photon-to-digital converter for radiation instrumentation, more specifically the single-photon avalanche diode array, the CMOS technology, the quenching circuit, the time-to-digital converter, the digital signal processing and the system level integration, are discussed in detail.

scholarly journals Multiscale Semantic Feature Optimization and Fusion Network for Building Extraction Using High-Resolution Aerial Images and LiDAR Data

2021 ◽  
Vol 13 (13) ◽  
pp. 2473
Author(s):  
Qinglie Yuan ◽  
Helmi Zulhaidi Mohd Shafri ◽  
Aidi Hizami Alias ◽  
Shaiful Jahari Hashim
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Spatial Information ◽  
Feature Fusion ◽  
Aerial Images ◽  
Semantic Gap ◽  
Superior Performance ◽  
Lidar Data ◽  
Building Extraction ◽  
Hierarchical Features

Automatic building extraction has been applied in many domains. It is also a challenging problem because of the complex scenes and multiscale. Deep learning algorithms, especially fully convolutional neural networks (FCNs), have shown robust feature extraction ability than traditional remote sensing data processing methods. However, hierarchical features from encoders with a fixed receptive field perform weak ability to obtain global semantic information. Local features in multiscale subregions cannot construct contextual interdependence and correlation, especially for large-scale building areas, which probably causes fragmentary extraction results due to intra-class feature variability. In addition, low-level features have accurate and fine-grained spatial information for tiny building structures but lack refinement and selection, and the semantic gap of across-level features is not conducive to feature fusion. To address the above problems, this paper proposes an FCN framework based on the residual network and provides the training pattern for multi-modal data combining the advantage of high-resolution aerial images and LiDAR data for building extraction. Two novel modules have been proposed for the optimization and integration of multiscale and across-level features. In particular, a multiscale context optimization module is designed to adaptively generate the feature representations for different subregions and effectively aggregate global context. A semantic guided spatial attention mechanism is introduced to refine shallow features and alleviate the semantic gap. Finally, hierarchical features are fused via the feature pyramid network. Compared with other state-of-the-art methods, experimental results demonstrate superior performance with 93.19 IoU, 97.56 OA on WHU datasets and 94.72 IoU, 97.84 OA on the Boston dataset, which shows that the proposed network can improve accuracy and achieve better performance for building extraction.

Cloud Manufacturing Resource Management Based on Metadata

2015 ◽  
Author(s):  
Chun Zhao ◽  
Lin Zhang ◽  
Xuesong Zhang ◽  
Liang Zhang
Keyword(s):  
Resource Management ◽  
Cloud Manufacturing ◽  
State Monitoring ◽  
Knowledge Resources ◽  
Centralized Management ◽  
Service Oriented ◽  
Manufacturing Resources ◽  
Resource Data ◽  
Manufacturing Platform ◽  
Management Approaches

Centralized management and sharing of manufacturing resources is one of the important functions of cloud manufacturing platform. There are many kinds of manufacturing resources, centralized management, optimized scheduling, quick searching for various manufacturing resources become important issues in a cloud manufacturing platform. This paper presents a resource management model based on metadata to realize the access and unified management of the hardware resources, software resources and knowledge resources. Two management approaches respectively for static and dynamic resource data are introduced to realize resource state monitoring and real-time information collecting. On this basis, the relationship between static and dynamic data is determined and service-oriented of resources is realized.

Multilevel Regression and Poststratification Versus Survey Sample Weighting for Estimating Population Quantities in Large Population Health Studies

2020 ◽  
Vol 189 (7) ◽  
pp. 717-725 ◽  
Author(s):  
Marnie Downes ◽  
John B Carlin
Keyword(s):  
Population Health ◽  
Large Scale ◽  
Census Data ◽  
Large Population ◽  
Population Data ◽  
Superior Performance ◽  
Population Parameter ◽  
Multilevel Regression ◽  
Sample Weighting ◽  
Survey Weighting

Abstract Multilevel regression and poststratification (MRP) is a model-based approach for estimating a population parameter of interest, generally from large-scale surveys. It has been shown to be effective in highly selected samples, which is particularly relevant to investigators of large-scale population health and epidemiologic surveys facing increasing difficulties in recruiting representative samples of participants. We aimed to further examine the accuracy and precision of MRP in a context where census data provided reasonable proxies for true population quantities of interest. We considered 2 outcomes from the baseline wave of the Ten to Men study (Australia, 2013–2014) and obtained relevant population data from the 2011 Australian Census. MRP was found to achieve generally superior performance relative to conventional survey weighting methods for the population as a whole and for population subsets of varying sizes. MRP resulted in less variability among estimates across population subsets relative to sample weighting, and there was some evidence of small gains in precision when using MRP, particularly for smaller population subsets. These findings offer further support for MRP as a promising analytical approach for addressing participation bias in the estimation of population descriptive quantities from large-scale health surveys and cohort studies.

scholarly journals Shift Scheduling of Short Time Workers in Large-Scale Home Improvement Center by using Cooperative Evolution

MENDEL ◽  
2017 ◽  
Vol 23 (1) ◽  
pp. 21-28
Author(s):  
Makoto Ohki
Keyword(s):  
Large Scale ◽  
Shift Scheduling ◽  
Time Zone ◽  
Cooperative Evolution ◽  
Shift Schedule ◽  
Home Improvement ◽  
Mutation Operators ◽  
Break Time ◽  
Working Day ◽  
Short Time

There are a lot of large-scale Home Improvement Center (HIC) in Japan. In the large-scale HIC,about hundred short time workers are registered. And about forty workers are working every day. A managercreates a monthly shift schedule. The manager selects the workers required for each working day, assigns theworking time and break time for each worker and also work placement. Because there are many requirementsfor the scheduling, the scheduling consumes time costs and efforts. Therefore, we propose the technique to createand optimize the schedule of the short time workers in order to reduce the burden of the manager. A cooperativeevolution is applied for generating and optimizing the shift schedule of short time worker. Several problems hasbeen found in carrying out this research. This paper proposes techniques to automatically create and optimize theshift schedule of workers in large-scale HIC by using a Cooperative Evolution (CE), to solve the situation thatmany workers concentrate in a speci c time zone, and to solve the situation where many breaks are concentratedin a speci c break time zone, and an effective mutation operators.

scholarly journals The Speed of Large-Scale Transformation of Political and Economic Institutions

2018 ◽  
Vol 18 (4) ◽  
pp. 555-583
Author(s):  
Ružica Šimić Banović ◽  
Martina Basarac Sertić ◽  
Valentina Vučković
Keyword(s):  
Market Economy ◽  
Large Scale ◽  
Shock Therapy ◽  
The Body ◽  
Economic Indicators ◽  
Superior Performance ◽  
Scale Transformation ◽  
Member States ◽  
Therapy Approach ◽  
Eu Member States

This article compares the applicability of both the gradual and the shock therapy approach to reform implementation in large-scale change. Using quantitative data, it aims to provide more evidence for the lessons learned from post-socialist transformation. Hence it adds a theoretical and an empirical contribution to the body of literature on great transformations, focusing on their speed and the acceptability of related policy solutions. Despite the predominant inclination towards the gradualist approach to reforms in the initial transition years, economic indicators suggest that the big bang reformers have demonstrated a superior performance over the last (few) decade(s). Still, the approach to (post-)transition processes should be multidimensional and include more than the speed of transformation and key economic indicators. Therefore, a quantitative analysis covers several aspects of socioeconomic change. The analysis of the quality of democracy, market economy, and management performance in post-socialist EU member states indicates that over the last decade the countries that applied the shock therapy approach have performed significantly better in all these areas. This suggests that slow reformers are lagging behind in the development of democratic institutions and a modern market economy, and presumably have insufficient capacities to rapidly catch up with fast reformers. Further research on this topic should tackle the deep roots of socioeconomic development and path-dependent choices (reform speed included), proximity to Western countries, the possible effects of other specific circumstances (such as war), the importance of selected institutions on the performance of post-socialist non-EU member states, and other limitations.

scholarly journals Expression and manufacturing of protein therapeutics in spirulina

2021 ◽  
Author(s):  
Benjamin Jester ◽  
Hui Zhao ◽  
Mesfin Gewe ◽  
Thomas Adame ◽  
Lisa Perruzza ◽  
...  
Keyword(s):  
Large Scale ◽  
Genetic Manipulation ◽  
Low Cost ◽  
Downstream Processing ◽  
Arthrospira Platensis ◽  
Protein Therapeutics ◽  
High Productivity ◽  
Medical Needs ◽  
Manufacturing Platform ◽  
Safe Food

ABSTRACTArthrospira platensis (commonly known as spirulina) is a photosynthetic cyanobacterium1. It is a highly nutritious food that has been consumed for decades in the US, and even longer by indigenous cultures2. Its widespread use as a safe food source and proven scalability have driven frequent attempts to convert it into a biomanufacturing platform. But these were repeatedly frustrated by spirulina’s genetic intractability. We report here efficient and versatile genetic engineering methodology for spirulina that allows stable expression of bioactive protein therapeutics at high levels. We further describe large-scale, indoor cultivation and downstream processing methods appropriate for the manufacturing of biopharmaceuticals in spirulina. The potential of the platform is illustrated by pre-clinical development and human testing of an orally delivered antibody therapeutic against campylobacter, a major cause of infant mortality in the developing world and a growing antibiotic resistance threat3,4. This integrated development and manufacturing platform blends the safety of food-based biotechnology with the ease of genetic manipulation, rapid growth rates and high productivity characteristic of microbial platforms. These features combine for exceptionally low-cost production of biopharmaceuticals to address medical needs that are unfeasible with current biotechnology platforms.

Sign in / Sign up

Export Citation Format

Share Document