Module Partition Research Articles

Modular design for optimum granularity with auto-generated DSM and improved elbow assessment method

To cope with an increasingly competitive market environment, manufacturers are utilizing modular technology to guide the production process, and a vital activity in the module partition is to determine the optimal granularity levels. A modular design methodology is developed for obtaining the optimal granularity of a modularized architecture in this paper. A relationship extraction solution is executed to automatically construct the design structure matrix (DSM) from the 3D CAD assembly model. Hierarchical clustering algorithm is implemented to form a hierarchical dendrogram with different granularity levels. An improved Elbow method is proposed to determine the optimum granularity level and corresponding modularity spectrum during the dendrogram process. The computational framework for hierarchical clustering and modularization with improved Elbow assessment operators is explained. Based on a existing literature example and a jaw crusher modular design case, comparative studies are carried out to verify the effectiveness and practicality of the proposed method. The methodology is characterized by running independently on the computer in data visualization format without human involvement, and the obtained result with optimized granularity favor further modular design work.

A modular design method based on TRIZ and AD and its application to cutter changing robot

Modular design, Axiomatic design (AD) and Theory of inventive problem solving (TRIZ) have been increasingly popularized in concept design of modern mechanical product. Each method has their own advantages and drawbacks. The benefit of modular design is reducing the product design period, and AD has the capability of problem analysis, while TRIZ’s expertise is innovative idea generation. According to the complementarity of these three approaches, an innovative and systematic methodology is proposed to design big complex mechanical system. Firstly, the module partition is executed based on scenario decomposition. Then, the behavior attributes of modules are listed to find the design contradiction, including motion form, spatial constraints, and performance requirements. TRIZ tools are employed to deal with the contradictions between behavior attributes. The decomposition and mapping of functional requirements and design parameters are carried out to construct the structural hierarchy of each module. Then, modules are integrated considering the connections between each other. Finally, the operation steps in application scenario are designed in temporal and spatial dimensions. Design of cutter changing robot for shield tunneling machine is taken as an example to validate the feasibility and effectiveness of the proposed method.

Application of Service Modular Design Based on a Fuzzy Design Structure Matrix: A Case Study from the Mining Industry

The development of customized service is an important way to transform and upgrade China’s mining industry. However, in practice, there remain problems, such as the slow market response speed of service providers and the contradiction between the large-scale development of service providers and the personalized service needs of service demanders. This paper uses the theory and method of service modular design to solve these problems and explores the process-based service modular design method. Service modular design depends largely on the determination of the relationship between service activities and the reasonable division of modules. However, previous research has rarely made use of modular design methods and modeling tools in the mining service context. At the same time, evaluations of the relationship between service activities relying on knowledge and those relying on experience have been inconclusive. Therefore, this paper proposes a service modularization design method based on the fuzzy relation analysis of a design structure matrix (DSM) that solves the optimal module partition scheme. Triangular fuzzy number and fuzzy evidence theory are used to evaluate and fuse the multidimensional and heterogeneous relationship between service activities, and the quantitative processing of the comprehensive relationship between service activities is carried out. On this basis, the service module structure is divided, followed by the construction of the mathematical programming model with the maximum sum of the average cohesion degree in the module and the average coupling degree between modules as the driving goal. The genetic algorithm is used to solve the problem, and the optimal module division result is obtained. Finally, taking the service modular design of SHD coal production enterprises in China as an example, the feasibility of the proposed method is verified.

Module Partition Method for Complex Products Considering the Change Risk

Module Partition Method for Complex Products Considering the Change Risk

Module partitioning study of specialized engineering services based on modular theory ——case on coal production firms

Outsourcing of specialized engineering services has become one of the routes to the innovation and high-quality development of the commercial model of coal production enterprises in China. Currently, although there is practice, no scale-up development has been achieved.In this paper, based on modular theory, under the condition of unknown expert weights, triangular fuzzy number method is used to construct a service unit association relationship evaluation model, and the correlation matrix is calculated. Then, a genetic algorithm is used to cluster the association matrix and obtain the results of the division of the outsourced module of engineering services. Finally, the proposed method is validated using the coal mine development process of coal producers as an example.

Modeling the impacts of diffuse light fraction on photosynthesis in ORCHIDEE (v5453) land surface model

Abstract. Aerosol- and cloud-induced changes in diffuse light have important impacts on the global land carbon cycle, as they alter light distribution and photosynthesis in vegetation canopies. However, this effect remains poorly represented or evaluated in current land surface models. Here, we add a light partitioning module and a new canopy light transmission module to the ORCHIDEE (Organising Carbon and Hydrology In Dynamic Ecosystems) land surface model (trunk version, v5453) and use the revised model, ORCHIDEE_DF, to estimate the fraction of diffuse light and its effect on gross primary production (GPP) in a multilayer canopy. We evaluate the new parameterizations using flux observations from 159 eddy covariance sites over the globe. Our results show that, compared with the original model, ORCHIDEE_DF improves the GPP simulation under sunny conditions and captures the observed higher photosynthesis under cloudier conditions in most plant functional types (PFTs). Our results also indicate that the larger GPP under cloudy conditions compared with sunny conditions is mainly driven by increased diffuse light in the morning and in the afternoon as well as by a decreased vapor pressure deficit (VPD) and decreased air temperature at midday. The observations show that the strongest positive effects of diffuse light on photosynthesis are found in the range from 5 to 20 ∘C and at a VPD < 1 kPa. This effect is found to decrease when the VPD becomes too large or the temperature falls outside of the abovementioned range, which is likely due to the increasing stomatal resistance to leaf CO2 uptake. ORCHIDEE_DF underestimates the diffuse light effect at low temperature in all PFTs and overestimates this effect at high temperature and at a high VPD in grasslands and croplands. The new model has the potential to better investigate the impact of large-scale aerosol changes and long-term changes in cloudiness on the terrestrial carbon budget, both in the historical period and in the context of future air quality policies and/or climate engineering.

HFADE-FMD: a hybrid approach of fireworks algorithm and differential evolution strategies for functional module detection in protein-protein interaction networks

Functional module detection in protein-protein interaction (PPI) network is one important content of the proteomics research in the post-genomic era. Nowadays the swarm intelligence and evolutionary based approaches have become effective ways for detecting functional modules. This paper proposes a novel hybrid approach of fireworks algorithm and differential evolution strategies for functional module detection in PPI networks (called HFADE-FMD). HFADE-FMD first initializes each firework individual into a candidate functional module partition based on label propagation according to the topological and functional information between protein nodes. Then HFADE-FMD uses the explosion operator of firework algorithm, and mutation, crossover and selection strategies of differential evolution algorithm to iteratively search for better functional module partitions. To verify the performance of HFADE-FMD, this paper compared it with ten competitive methods on four public PPI datasets. The experimental results show that HFADE-FMD achieves prominent performance with respective to Recall, Sn, PPV, and ACC metrics while performing well in terms of Precision and F-measure metrics. Thus, it is able to more accurately detect functional modules and help biologists to find some novel biological insights.

Throughput-Aware Partitioning and Placement of Applications in Fog Computing

Fog computing promises to extend cloud computing to match emerging demands for low latency, location-awareness and dynamic computation. It thus brings data processing close to the edge of the network by leveraging on devices with different computational characteristics. However, the heterogeneity, the geographical distribution, and the data-intensive profiles of IoT deployments render the placement of fog applications a fundamental problem to guarantee target performance figures. This is a core challenge for fog computing providers to offer fog infrastructure as a service, while satisfying the requirements of this new class of microservices-based applications. In this article we root our analysis on the throughput requirements of the applications while exploiting offloading towards different regions. The resulting resource allocation problem is developed for a fog-native application architecture based on containerised microservice modules. An algorithmic solution is designed to optimise the placement of applications modules either in cloud or in fog. Finally, the overall solution consists of two cascaded algorithms. The first one performs a throughput-oriented partitioning of fog application modules. The second one rules the orchestration of applications over a region-based infrastructure. Extensive numerical experiments validate the performance of the overall scheme and confirm that it outperforms state-of-the-art solutions adapted to our context.

Modularization of smart product service: A framework integrating smart product service blueprint and weighted complex network

Modularization of smart product service (SPS) emerges as a priority strategy to agilely satisfy the dynamic customization requirement and to flexibly response to the rapid market change. Compared with the traditional product service, the SPS have more complicated interactions between the service components due to the novel characteristics caused by the application of smart technologies. The SPS modularization presents great differences from the identification of service component, correlation evaluation and module partition with the traditional product service modularization. However, most the existing research mainly focuses on the context of traditional product service, while containing scant study of smart product service. Therefore, this study proposes a hybrid framework for SPS modularization. In the framework, a cyber-physical product service blueprint is firstly proposed to represent the SPS operation process and identify the SPS components. Then, a rough-fuzzy correlation matrix is presented to determine the comprehensive interdependence between all pairs of SPS components with fully considering the hybrid decision uncertainties involved in the evaluation process, i.e., intrapersonal linguistic vagueness and interpersonal preference diversity. After that, the complex network theory is used to construct the SPS network and a modified Girvan-Newman algorithm is adopted for the SPS module partition. Finally, an illustrative modularization case of smart gearbox maintenance service and some caparisons with other methods demonstrate the feasibility and validity of the proposed approach.

Fast intra-coding unit partition decision in H.266/FVC based on deep learning

In the recent Future Video Coding (FVC) standard developed by the Joint Video Exploration Team (JVET), the quad-tree binary-tree (QTBT) block partition module makes use of rectangular block forms and additional square block sizes compared to quad-tree (QT) block partitioning module proposed in the predecessor High-Efficiency Video Coding (HEVC) standard. This block flexibility, induced with the QTBT module, significantly improves compression performance while it dramatically increases coding complexity due to the brute force search for Rate Distortion Optimization (RDO). To cope with this issue, it is necessary to consider the unique characteristics of QTBT in FVC. In this paper, we propose a fast QT partitioning algorithm based on a deep convolutional neural network (CNN) model to predict coding unit (CU) partition instead of RDO which enhances considerably QTBT performance for intra-mode coding. Based on a suitable diversified CU partition patterns database, the optimization process is set up with three levels CNN structure developed to learn the split or non-split decision from the established database. Experimental results reveal that the proposed algorithm can accelerate the QTBT block partition structure by reducing the intra-mode encoding time by an average of 35% with a bit rate increase of 1.7%, allowing its application in practical scenarios.

Finding modular structure in multiplex networks by sequential intra-layer edge elimination

Identifying modular properties of multiplex networks presents additional subtleties to the already corresponding difficult problem in single layer networks. One most evident issue is the presence of conflicting module partitions, when two or more layers have very clear community structure that differ from one another. Based on the well known Newman-Girvan method, a framework (MultiNG) to reach this goal is developed and tested. Taking into account that the community structure of any multiplex layer can be evaluated in advance, and that multiplex nodes have an intrinsic identity, MultiNG is targeted at finding one sole global structure, meaning that to any node a single module is assigned in all layers. As a consequence, inter-layer connections are preserved throughout the process, and only intra-layer edges are eligible to be eliminated. The reliability of the procedure is tested by investigating different cases, as synthetic multiplex networks and multiplex networks obtained from real data. Results are compared with those obtained by other methods.

SSAP: Single-Shot Instance Segmentation With Affinity Pyramid

Proposal-free instance segmentation methods mainly generate instance-agnostic semantic segmentation labels and instance-aware features to group pixels into different object instances. However, previous methods mostly employ separate modules for these two sub-tasks and require multiple passes for inference. In addition to the lack of efficiency, previous methods also failed to perform as well as proposal-based approaches. To this end, this work proposes a single-shot proposal-free instance segmentation method that requires only one single pass for prediction. Our method is based on learning an affinity pyramid, which computes the probability that two pixels belong to the same instance in a hierarchical manner. Moreover, incorporating with the learned affinity pyramid, a novel cascaded graph partition (CGP) module is presented to fuse the two predictions and segment instances efficiently. As an additional contribution, we conduct an experiment to demonstrate the benefits of proposal-free methods in capturing detailed structures from finely annotated training examples. Our approach is evaluated on the Cityscapes and COCO datasets and achieves state-of-the-art performance.

EPC global network design using agent-based simulation

ABSTRACT The goal of this study is to introduce a new design approach of the EPC global network considering activities of actors in a supply chain via agent-based simulation (ABS). The EPC global network provides track and trace services of shipments for internal and external subscribers, based on electronic product code (EPC) data generated from radio frequency identification (RFID) tags. To maintain dependable mean response time and service failure of the network, the proposed approach consists of two modules: (1) ABS module with AnyLogic® simulation software involving transactions between actors (manufacturers, distributors, and retailers) and (2) Network partitioning module to find appropriate structure of the network based on responses given by the ABS module. Demonstration of the proposed approach will be conducted with data of an automotive supply chain in Republic of Korea. This preventive maintenance approach will help engineers avoid over-investment on implementation of the network for their efficient supply chain management.

Investigation of Potential Genetic Biomarkers and Molecular Mechanism of Ulcerative Colitis Utilizing Bioinformatics Analysis.

Objectives To reveal the molecular mechanisms of ulcerative colitis (UC) and provide potential biomarkers for UC gene therapy. Methods We downloaded the GSE87473 microarray dataset from the Gene Expression Omnibus (GEO) and identified the differentially expressed genes (DEGs) between UC samples and normal samples. Then, a module partition analysis was performed based on a weighted gene coexpression network analysis (WGCNA), followed by pathway and functional enrichment analyses. Furthermore, we investigated the hub genes. At last, data validation was performed to ensure the reliability of the hub genes. Results Between the UC group and normal group, 988 DEGs were investigated. The DEGs were clustered into 5 modules using WGCNA. These DEGs were mainly enriched in functions such as the immune response, the inflammatory response, and chemotaxis, and they were mainly enriched in KEGG pathways such as the cytokine-cytokine receptor interaction, chemokine signaling pathway, and complement and coagulation cascades. The hub genes, including dual oxidase maturation factor 2 (DUOXA2), serum amyloid A (SAA) 1 and SAA2, TNFAIP3-interacting protein 3 (TNIP3), C-X-C motif chemokine (CXCL1), solute carrier family 6 member 14 (SLC6A14), and complement decay-accelerating factor (CD antigen CD55), were revealed as potential tissue biomarkers for UC diagnosis or treatment. Conclusions This study provides supportive evidence that DUOXA2, A-SAA, TNIP3, CXCL1, SLC6A14, and CD55 might be used as potential biomarkers for tissue biopsy of UC, especially SLC6A14 and DUOXA2, which may be new targets for UC gene therapy. Moreover, the DUOX2/DUOXA2 and CXCL1/CXCR2 pathways might play an important role in the progression of UC through the chemokine signaling pathway and inflammatory response.

Hypergraph Clustering Based on Game-Theory for Mining Microbial High-Order Interaction Module.

Microbial community is ubiquitous in nature, which has a great impact on the living environment and human health. All these effects of microbial communities on the environment and their hosts are often referred to as the functions of these communities, which depend largely on the composition of the communities. The study of microbial higher-order module can help us understand the dynamic development and evolution process of microbial community and explore community function. Considering that traditional clustering methods depend on the number of clusters or the influence of data that does not belong to any cluster, this paper proposes a hypergraph clustering algorithm based on game theory to mine the microbial high-order interaction module (HCGI), and the hypergraph clustering problem naturally turns into a clustering game problem, the partition of network modules is transformed into finding the critical point of evolutionary stability strategy (ESS). The experimental results show HCGI does not depend on the number of classes, and can get more conservative and better quality microbial clustering module, which provides reference for researchers and saves time and cost. The source code of HCGI in this paper can be downloaded from https://github.com/ylm0505/HCGI.

Modulation of Protein Partition in an Aqueous Two Phase System by Inclusion Complexation of Cyclodextrins

We focused on introducing inclusion capability of β-cyclodextrin (β-CD) in an aqueous two-phase system (ATPS). A phase separation between two aqueous solutions of poly(ethylene glycol) (PEG) and de...

Automatic detection of small bowel tumors in wireless capsule endoscopy images using ensemble learning.

Wireless Capsule Endoscopy (WCE) is a minimally invasive diagnosis tool for lesion detection in the gastrointestinal tract, reaching places where conventional endoscopy is unable to. However, the significant amount of acquired data leads to difficulties in the diagnosis by the physicians; which can be eased with computer assistance. This paper addresses a method for the automatic detection of tumors in WCE by using a two-step based procedure: region of interest selection and classification. The first step aims to separate abnormal from normal tissue by using automatic segmentation based on a Gaussian Mixture Model (GMM). A modified version of the Anderson method for convergence acceleration of the expectation-maximization (EM) algorithm is proposed. The proposed features for both segmentation and classification are based on the CIELab color space, as a way of bypassing lightness variations, where the L component is discarded. Tissue variability among subjects, light inhomogeneities and even intensity differences among different devices can be overcome by using simultaneously features from both regions. In the second step, an ensemble system with partition of the training data with a new training scheme is proposed. At this stage, the gating network is trained after the experts have been trained decoupling the joint maximization of both modules. The partition module is also used at the test step, leading the incoming data to the most likely expert allowing incremental adaptation by preserving data diversity. This algorithm outperforms others based on texture features selected from Wavelets and Curvelets transforms, classified by a regular support vector machine (SVM) in more than 5%. This work shows that simpler features can outperform more elaborate ones if appropriately designed. In the current case, luminance was discarded to cope with saturated tissue, facilitating the color perception. Ensemble systems remain an open research field. In the current case, changes in both topology and training strategy have led to significant performance improvements. A system with this level of performance can be used in current clinical practice.

Multi-dimensional mining and analysis of core prescriptions in launched Chinese patent medicine for treatment of influenza

Through the multi-dimensional mining and analysis of launched anti-influenza proprietary Chinese medicines,this paper explores the study of the prescriptions and pharmacodynamics of traditional Chinese medicines for influenza. We established a standardized database by collecting and excavating the launched Chinese patent medicines that clearly describe the treatment of influenza. Frequency analysis and association rules were used to analyze the frequency of Chinese patent medicines for the treatment of influenza in the aspects of dosage form,category and prescription drugs. The network module partitioning method was used to excavate the core drug combination for influenza. The relationship between functional nouns was used to construct a network of functional terminology and analyze the relationship between its main functions. The pharmacological characteristics quantitative method was used to analyze the pharmacological characteristics of three heat-clearing and detoxifying type Chinese patent medicines for influenza. This article shows the traditional Chinese medicine syndrome differentiation ideas and medication rules for influenza treatment in many aspects and from multiple perspectives,so as to provide a certain reference for the clinical application of proprietary Chinese medicines for influenza and the development of new influenza drugs.

Module partitioning for multilayer brain functional network using weighted clustering ensemble

Module can not only affect the integration of network functions, but also contribute to understand the characteristics of local connections in the network. However, the connection between nodes in the network changes with the passage of time, and the module structure changes accordingly. To overcome this drawback, we propose a method of applying weighted clustering ensemble to partition multilayer brain functional networks into modules. Firstly, k-means clustering is adopted to carry out base clustering for a certain layer of functional network for several times, and each clustering is corresponding to a subordinate matrix and a similarity matrix. Then clustering validity index is used to assess each partitioning and the assessed values are taken as the weights of similarity matrix. Finally, the weighted similarity matrix is partitioned by means of fuzzy C-means clustering, and the results are evaluated by modularity function to obtain the optimal partitioned modules. Experimental results show that the effect of module partitioning resulting from weighted clustering ensemble is better than that of other comparable methods. The proposed framework could be promising to analyze the differences between corresponding modules of patients with Alzheimer’s disease and normal people, so as to better understanding some dynamical pathological changes in brain connectome of patients.

Temporal stability of functional brain modules associated with human intelligence.

Individual differences in general cognitive ability (i.e., intelligence) have been linked to individual variations in the modular organization of functional brain networks. However, these analyses have been limited to static (time‐averaged) connectivity, and have not yet addressed whether dynamic changes in the configuration of brain networks relate to general intelligence. Here, we used multiband functional MRI resting‐state data (N = 281) and estimated subject‐specific time‐varying functional connectivity networks. Modularity optimization was applied to determine individual time‐variant module partitions and to assess fluctuations in modularity across time. We show that higher intelligence, indexed by an established composite measure, the Wechsler Abbreviated Scale of Intelligence (WASI), is associated with higher temporal stability (lower temporal variability) of brain network modularity. Post‐hoc analyses reveal that subjects with higher intelligence scores engage in fewer periods of extremely high modularity — which are characterized by greater disconnection of task‐positive from task‐negative networks. Further, we show that brain regions of the dorsal attention network contribute most to the observed effect. In sum, our study suggests that investigating the temporal dynamics of functional brain network topology contributes to our understanding of the neural bases of general cognitive abilities.