【计算机类】9月截稿SCI期刊专刊约稿信息5条

计算机体系结构,并行与分布式计算

Future Generation Computer Systems

Special Issue on Emerging Services Science for Cloud Computing

全文截稿: 2017-09-14

影响因子: 3.997

期刊难度: ★★★

CCF分类: C类

网址: http://www.journals.elsevier.com/future-generation-computer-systems/

Cloud Computing has reached maturity in software architecture, methods and technologies. The research and development work has moved from context of exploration and formalization to the application. Nowadays, Cloud Computing offers unprecedented possibilities in a wide range of new computation areas, becoming a key topic in the academia and industry, not only contributing to the critical questions of the How, but also opening new scientific questions needing foundation for the What and Why matters.

In this sense, in the past years of the International Conference on Cloud Computing and Service Science (CLOSER), we have realized the about specific conformational work in emerging service science for cloud computing. In one hand, pioneers are building the theoretical pillars of the future generation of service science. In the other hand, the service science frontiers for industrial applications are moving ahead in parallel to the foundation, providing new problems definition and also experimental feedback. This virtuous circle between theory and practice has caught our attention because the exciting possibilities offered in the scientific context as well as the social impact of the works.

The present Special Issue is offered to those interested in that powerful vision of Service Science for Cloud Computing, with strong synergies between topics building a common knowledge. Few CLOSER 2017 top papers in the areas are invited to extend their work and to participate in opening competition with other submissions.

Topics of Interest

- Service Privacy, Security and Trust

- Resource Management for Cloud Services

- Fog/Edge Computing Service Oriented Automation

- Foundation of the Internet of Services

- Application Service Discovery and Allocation

- Application Service Adaption Automation

- Model-driven Web Service Science

- Service Composition and Mashups

- Service Modeling and Specification

- Service Security, Reliability and Access Control for Web Services

- Business Process Management and Web Services

- Business Services Realized by IT Services

- Enterprise Architectures and Services

- Industrial Applications of Services Science

- Information and Service Economy

- Service Marketing and Management

- Services for Federated Cloud, Bridging and Bursting

- Big Data Cloud Service Science

- Service Science for Cloud-based Mobile Media Systems and Social Networks

计算机体系结构,并行与分布式计算

Journal of Parallel and Distributed Computing

Special Issue on Parallel/Distributed Computing and Optimization

全文截稿: 2017-09-15

影响因子: 1.93

期刊难度: ★★★★

CCF分类: B类

网址: http://www.journals.elsevier.com/journal-of-parallel-and-distributed-computing/

In the last years, we have experienced a change in computing towards parallelism. The physical limits of integrated circuits being reached, computing performance now keep Moore’s law thanks to the replication of components. The default computer is nowadays a parallel machine. In this scenario, parallelism is a must in any modern software in order to make an effective use of the available resources.

The purpose of this special issue is to collect the main recent trends and designs in parallel and distributed computing for solving hard optimization problems.

Topics of interests include:

•Integer programming, linear programming, nonlinear programming;

•Global optimization, combinatorial optimization, multi-objective optimization, dynamic optimization;

•Exact methods, heuristics, metaheuristics, nature inspired algorithms, machine learning;

•Cooperative methods;

•Hybrid methods;

•Multi-core, many-core, GPGPU, cluster, Grid, and Cloud computing platforms;

•Parallel sparse matrix computations, graph algorithms, load balancing;

•Peer to peer computing and optimization problems;

•Theoretical studies on parallel and distributed computing

•Real world applications

数据库管理与信息检索

Advanced Engineering Informatics

Special Issue on Transdisciplinary Design|Analysis|and Implementation of Advanced Systems

全文截稿: 2017-09-30

影响因子: 2.68

期刊难度: ★★★★

CCF分类: B类

网址: http://www.journals.elsevier.com/advanced-engineering-informatics

From the perspectives of computer supported cooperative work (CSCW), concurrent engineering (CE), and product lifecycle management (PLM), to collaborative product creation, manufacturing, and service delivery, transdisciplinary design, analysis, and implementation of advanced systems have drawn strong attention in both theoretical and practical studies in recent years. The complexity of problems and challenges scientists are facing in engineering areas has highly increased, while the necessary knowledge and understanding required to tackle these problems has been evolving rapidly. Furthermore, the involvement of multiple, very different, domains with enhanced ranges of variety require multiple methods to be used.

To deal with such challenges, new approaches such as a transdisciplinary approach are necessary. It describes a critical and self-reflective scientific methodology that crosses many boundaries of singular scientific disciplines to compose a holistic approach. A transdisciplinary approach raises the need for concurrent handling of architectural and operational aspects, relations, and parameters in a socio-technical system. A transdisciplinary approach requires not only technical disciplines to interact, but also interaction with disciplines from social sciences, since information needs to be acquired from user communities and consumers, while also approaches are needed to validate results in these communities and implement them in practice.

This special issue is aimed at receiving the state-of-the-art research and applications, addressing the major challenges and issues of designing, analysing, validating and implementing applications and systems in transdisciplinary engineering. The scope of the issue covers, but is not limited to:

- Mining the Customer’s Voice and Patent Data for Strategic Product Quality Function Deployment.

- Multi-criteria data analysis and model building using domain knowledge for collaborative product and service creation and evaluation.

- Utilizing Text Mining and Kansei Engineering to Support Data-driven Design Automation.

- Ontology-based product affective properties identification approaches.

- Interoperability and interconnectivity of smart devices and Internet of Things for collaborative systems in design, manufacturing, and services.

- Analysing Engineering Knowledge in CAD-models and Spread Sheets using Graph Theory and Filtering.

- Collaborative Data Management System and supply/demand/value chain management.

- Selection of smart services for Industry 4.0 based on intelligent decision models.

- Assessment of interoperation strategies for pervasive and smart collaborative systems.

计算机体系结构,并行与分布式计算

Computers & Electrical Engineering

Special Issue on Computational Intelligence and Smart Power Systems

全文截稿: 2017-09-30

影响因子: 1.57

期刊难度: ★★

CCF分类: 无

网址: http://www.journals.elsevier.com/computers-and-electrical-engineering/

Computational Intelligence (CI) emerges as a significant computing field to facilitate the operation, maintenance and control of power systems. CI can transform the traditional power grid to a smart power grid by effective conditioning and control of the production and distribution of electric power. For the power industry to evolve due to deregulation, engineers require CI tools for appropriate planning, operation and control of the power system. The CI tools can be broadly categorized as optimization methods and decision making methods. They can offer power utilities with novel solutions for efficient analysis, optimal operation and control, and intelligent decision making.

The objective of this special issue is to address and disseminate the latest CI applied to generation and distribution of alternate energy, and to manage smart power systems. It plans to cover various aspects of power quality in smart grids and the perspectives of addressing them by researchers of both academia and industry.

Authors are invited to submit original and unpublished submissions that exploit CI approaches such as artificial intelligence, soft computing, bio-inspired computing, fuzzy set, etc, for power quality analysis, distributed generation of alternative energy, unbalanced distribution of electrical energy, controlling and maintenance of nonlinear loads and enhancement in smart grids.

Topics of interest are limited to:

- Power quality issues in smart power grid

- Power quality conditioners in smart grids

- Coordinated control in inter-area inter-connected power systems and smart distribution network

- Power quality issues in multi-micro-grid systems, mini-grids and remote areas

- Techno-economic effects of power quality problems in smart grids

- ICT and communication requirements for smart power quality assessment and improvement

- Micro-grids and distributed generators (DGs) with power quality ancillary services such as voltage support, reactive power flow control, low voltage ride-through, unbalance and harmonics compensation

- Active damping control methods for DGs, micro-grids, and smart grids

- Islanding detection and synchronization regarding power quality issues of DGs and micro-grids

- Protections during faults and severe power quality issues in smart grids and micro-grids

计算机体系结构,并行与分布式计算

Computers & Electrical Engineering

SPECIAL ISSUE ON HYBRID ARTIFICAL INTELLIGENCE APPLICATIONS

全文截稿: 2017-09-30

影响因子: 1.57

期刊难度: ★★

CCF分类: 无

网址: http://www.journals.elsevier.com/computers-and-electrical-engineering/

Artificial intelligence has grown widely for various applications. The current trends of Artificial Intelligence focus on hybridization to improve the performance of system. Despite the immense growth of various AI techniques, there are many challenges and threats which limits the performance these techniques. This special issue invites researchers to provide new directions in addressing the current challenges in identifying various hybrid AI methodologies to combat the current research problems. It will focus very specifically on bringing the current edge opinions on the hybrid artificial intelligence techniques.

Authors are invited to submit original unpublished research manuscripts focused on the latest developments in hybrid artificial intelligence or machine learning methodologies. The topic of interest are:

- Hybrid machine learning methods or Artificial neural networks

- Cognitive frame work using ANN

- Clustering process using the artificial neural networks

- Fast stable learning in the hybrid neural network

- Neural Network based power management systems

- Information storage and quality prediction using ANN

- FPGA implementation process in ANN

- Distance recognition in wireless sensor network using ANN

- Analyzing different hybrid activation function using the supervised learning algorithm

- Self learning, adaptability, deployment process in the neural networks and machine learning methods

- Hybridization of machine learning tools

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