Journal Information
IEEE Transactions on Industrial Informatics (TII)
Impact Factor:

Call For Papers
Scope: IEEE Transactions on Industrial Informatics focuses on knowledge-based factory automation as a means to enhance industrial fabrication and manufacturing processes. This embraces a collection of techniques that use information analysis, manipulation, and distribution to achieve higher efficiency, effectiveness, reliability, and/or security within the industrial environment. The scope of the Transaction includes reporting, defining, providing a forum for discourse, and informing its readers about the latest developments in intelligent and computer control systems, robotics, factory communications and automation, flexible manufacturing, vision systems, and data acquisition and signal processing
Last updated by Xin Yao in 2017-08-21
Special Issues
Special Issue on Deep Learning Models for Industry Informatics
Submission Date: 2017-09-30

Deep learning is a novel research direction in machine learning field. In recent years, it has made breakthrough progress in many applications such as speech recognition, computer vision, industrial control and automation etc. The motivation of deep learning is to establish a model to simulate the neural connection structure of human brain. While dealing with outside complex signals, it adopts a number of transformation stages to give the in-depth interpretation of the data. Shallow learning is to rely on the artificial experience to extract the characteristics of the sample datasets, and the network model is obtained after the study which has no hierarchical structure; while the deep learning treats the original signal with layer by layer feature transformation, and transforms the feature representation of the sample in the original space into the new feature space, and automatically learns the hierarchical representation of the feature, which is more conducive to the classification or feature visualization. Deep learning achieves exceptional power and flexibility by learning torepresent the task as a nested hierarchy of layers, with more abstract representations computed in terms of less abstract ones. The current resurgence is a result of the breakthroughs in efficient layer-wise training, availability of big datasets, and faster computers. It is expected that the development of deep learning theories and applications would further influence the field of industry informatics. This special issue mainly focuses on deep learning models for industry informatics, addressing both original algorithmic development and new applications of deep learning. We are soliciting original contributions, of leading researchers and practitioners from academia as well as industry, which address a wide range of theoretical and application issues in deep learning for industry informatics. Topics for this special issue include, but are not limited to: Topics include, but are not limited to, the following research topics and technologies: - Deep learning for Internet-based monitoring and control systems - Deep learning for collaborative factory automation - Deep learning for distributed industrial control and computing paradigms - Deep learning for real-time control software for industrial processes - Deep learning for control of wireless sensors and actuators - Deep learning for systems interoperability and human machine interface - Deep learning for industrial focused software development - Deep learning for reusable analytics tools and frameworks - Deep learning for urban informatics - Deep learning for statistical tools for electric machine and drives condition monitoring - Deep learning for DSP and FPGA-based system implementation - Deep learning for industrial system security and intrusion
Last updated by Dou Sun in 2017-07-01
Special Issue on Embedded and Networked Systems for Intelligent Vehicles and Robots
Submission Date: 2017-09-30

Embedded and networked systems for intelligent e-vehicles and robots are emerging, with a high economic, societal and industrial impact. They will improve safety (reducing accidents caused by human errors), sustainability (increasing transport system efficiency), comfort/inclusivity (ensuring user's freedom for other activities and mobility for all), logistic & factory automation (with a key role in industry 4.0, allowing industrial robots moving and operating autonomously and cooperating). There are many key enabling technologies for the revolution, such as networked sensors, actuators and embedded computing/control platforms distributed onboard the vehicle/robot...etc. Moreover, Artificial Intelligence (AI) and deep learning computing platform are emerging to achieve full intelligent autonomous mobility of vehicles and robots. Solving these issues for intelligent e-vehicles and robots will have benefit for other applications such as unmanned vehicles and Industry 4.0 scenarios. Last but not least, worldwide standardization and homogenization efforts are needed to ensure interoperability of the solutions. We solicit papers covering the following topics of interest, but not limited to: - Embedded systems, embedded software, hardware/software partition & real-time systems in vehicles - Real-time communications in vehicles, e.g., AVB, TSN, CAN, Flexray - Scheduling and schedulability analysis techniques - Models, languages and techniques to deal with the complexity of vehicle software - Advanced powerful execution platforms, e.g., multi-core Electronic Control Units - Functional safety and certification (e.g., according to ISO 26262) aspects in vehicles. - Authentication, privacy and security issues in automated and connected vehicles/robots - Networking for E-transportation and smart grid - Peer-to-peer and cooperating vehicles and robots - Over-the-air diagnostic and firmware/software update - AI and deep learning computing for self-driving vehicles/robots - V2X/M2X wireless transceivers, data-link, MAC and networking layers - Synergies among vehicular, robotics and Industry4.0 technologies, tools and industrial case studies - Homogenization and standardization to ensure interoperability, security and functional safety
Last updated by Dou Sun in 2017-07-01
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