计算机科学与技术 | SCI期刊专刊信息4条

全文截稿: 2021-01-24

影响因子: 1.045

CCF分类: C类

中科院JCR分区:

• 大类 : 工程技术 - 4区

• 小类 : 计算机：硬件 - 4区

• 小类 : 计算机：理论方法 - 4区

• 小类 : 工程：电子与电气 - 4区

网址: https://www.journals.elsevier.com/microprocessors-and-microsystems

Description

Intelligent collaborating systems are networked systems that have the capacity to gather and analyse complex data, take decisions based on the data analysis, actuate the decisions, as well as, communicate with other systems to form intelligent infrastructures (systems-of-systems) that can perform complex tasks to achieve common higher-level goals. Such systems include autonomous vehicles, intelligent transportation systems (ITS), and industry 4.0. Applications such as Autonomous cars, Vehicle-to-X (V2X) communications, in-vehicle infotainment, and assistance for elderly and disabled drivers require powerful processing and communication capabilities. Moreover, modern intelligent systems play a leading role in strategically worldwide projects such as green and sustainable mobility in future smart cities and smart roads.

In this context, system safety and security are becoming increasingly relevant as humans are progressively ruled out from the decision/control loop of intelligent and learning-enabled machines. In particular, the technical foundations and assumptions on which traditional system engineering principles are based are inadequate for systems in which Artificial Intelligence (AI) algorithms, in particular Machine Learning (ML) algorithms, are interacting with the physical world at increasingly higher levels of autonomy.

Topic

The special issue “Architectures and Systems for Automotive, Aeronautic and Intelligent Transportation (ASAASIT’2020)” is addressing all technological and methodological aspects related to intelligent systems. Papers on any of the following and related topics can be submitted to the special issue:

System architecture and software design for efficient, safe, green and autonomous vehicles.

Emerging technologies enabling intelligent connected vehicles and collaborative systems.

Hardware and software design for intelligent systems based on Multicore, FPGA, GPU and heterogeneous architectures.

Hardware and software solutions for run-time system management, power management, diagnostics and self-adaptation.

Machine and Deep Learning for autonomous vehicles and industry 4.0: Hardware accelerators, GPU, Multi-cores, etc.

Safe human-machine interaction in automated decision-making systems.

Safety in AI-based system architectures.

Safety and security assessment and detection of security threats and vulnerabilities.

Design of security and privacy for intelligent systems

Sensors, actuators and wireless technologies for intelligent systems

Design of efficient computing and reliable communication systems for harsh environments

Energy modeling and optimization for low powered embedded devices, such as for UAS (Unmanned Aerial System)

全文截稿: 2021-03-15

影响因子: 1.045

CCF分类: C类

中科院JCR分区:

• 大类 : 工程技术 - 4区

• 小类 : 计算机：硬件 - 4区

• 小类 : 计算机：理论方法 - 4区

• 小类 : 工程：电子与电气 - 4区

网址: https://www.journals.elsevier.com/microprocessors-and-microsystems

Cognitive Internet of Things (CIoT) is an emerging field where in IoT systems are made more intelligent and smarter using cognitive computing. The intuitive ability of IoT combined with cognitive power of Machine learning and security of blockchain is capable of building transformative techniques. CIoT with machine learning and blockchain presents enormous opportunities for building intelligent and smart applications. Machine learning enables cognitive computing to develop a “thinking” system. It allows the system to learn and analyze the new data as it comes. The blockchain technology provides more security to the data and disables any kind of breaches. The enormous data generated by connected devices essentially needs cognitive power to build intelligent systems. On the other hand, the security of this data is of utmost importance for various applications.

In the coming decade it is estimated that more than 30 billion IoT devices will be generating data. Thus, there is an emerging requirement of development of technologies that can process, store and secure this data. The use of cognitive computing, Machine learning and blockchain will enable us to handle this data effectively. This special issue aims to publish high quality research papers that focus on the power of machine learning and blockchain for cognitive IoT. These technologies can be used together for automation, resource optimizations, sustainable systems and better security of data.

Topics of interest include, but are not limited to:

Innovative architecture, infrastructure, techniques for CIoT

Intelligent models and applications for CIoT

Intelligent systems for information fusion in CIoT

Architectures and platforms for blockchain and IoT

IoT and blockchain convergence

IoT malicious transactions detection

Blockchain schemes for decentralization in IoT

Machine learning algorithms for IoT

Machine Learning for decision support systems in IoT

Cognitive aspects of Machine learning

Knowledge-based techniques for IoT

Optimization methods for IoT

Automated reasoning in IoT

Big data analytics to identify malicious behaviours on blockchain for IoT

Intelligent blockchain driven IoT applications

AI-enabled scalable Blockchain for IoT

IoT data encryption and security

全文截稿: 2021-03-31

影响因子: 4.769

CCF分类: 无

中科院JCR分区:

• 大类 : 工程技术 - 2区

• 小类 : 计算机：跨学科应用 - 2区

网址: https://www.journals.elsevier.com/computers-in-industry

Objectives

While digitalisation is at the heart of the strong research stream on Factory of the Future and Industry 4.0, the convergence between digitalization and business innovations through servitization leads to a new and already very active area of research on Digital Servitization. Part of this transition are Product-Service-Systems (PSS), which have generated a large amount of research work over the past decade and which have recently evolved towards the ‘smart PSS’ concept, by integrating artificial intelligence and digital supports. These new generation Product-Service-Systems are not only smart in terms of functionalities for the final users, but also smarter and smarter along most of the phases of their proper life cycle, thus providing added-value to most of the actors of the ecosystem in addition to the final user: smart at design time, smart for installation and delivery phase, smart for usage and maintenance, smart for end-of life. Digital Servitization opens opportunities for multi-dimensional and multi-actor value exchanges, increasing its strategical innovative impact for industrial companies.

This Special Issue is launched as an extension of the preliminary state of the art ‘Digital technologies in product-service systems: a literature review and a research agenda’ already published by Computers in Industry and currently available [1]. This literature review presents the key concepts, scope and issues of this Special Issue more extensively. It emphasizes a large research agenda on Digital Servitization at 3 levels : (i) Engineering challenges to develop, experiment and validate new engineering capabilities for smart PSS, (ii) Managerial issues to support firm’s innovation strategies and (iii) Conceptual researches to build a theoretical background of research on Digital Servitization. This special issue is positioned on the first of these three levels: the deployment of the full potential of digitalization to support an integrated design and life cycle management of smart PSS. The potential of digital solutions to support value-creation is addressed in a broad sense: digitalisation is today mature to address distinct issues of PSS deployment, not only the PSS solution life cycle traceability, but also the collaborative PSS delivery network configuration and management, as well as the PSS Ecosystem perception and change management. The interoperability among various key technologies able to support smart PSS (Artificial Intelligence, IoT, Cyber-Physical-Systems, Digital Twin, Cloud Manufacturing, etc…) opens a large avenue to improve lifecycle management of integrated product-service solutions.

The call for papers is open to both theoretical and applied research papers, intending to gather scientific papers illustrating distinct complementary aspects of the added-value of digitalisation, when supporting life cycle management of smart Product-Service-Systems.

Topics

Data-Driven PSS engineering and management

IT-based decision making for smart PSS value network design and engineering

Modelling frameworks and model-based integration of Product & Service Life Cycle management

Digital solution for uncertainty and risk management along product-service value network’s life cycles

Digital solution, artificial intelligence and digital twins for PSS delivery configuration and management

Data management & decision-making to support the delivery of Cyber-Physical System-based PSS

Data management for Product-Service Ecosystems

Digital technologies for knowledge management along the PSS lifecycle phases

Data-based decision making and decision support system for Smart PSS operations

Influence of a dynamic digitalized ecosystem and lifecycle on Smart PSS requirements and engineering

New competence and new stakeholder roles along the life cycles of Smart PSS ecosystems

Digital management of PSS solutions for XaaS (e.g. Manufacturing as a Service) models