Internet of Things (IoT) has provided a promising opportunity to build powerful industrial systems and applications by leveraging the growing ubiquity of radio-frequency identification (RFID), and wireless, mobile, and sensor devices. A wide range of industrial IoT applications have been developed and deployed in recent years. In an effort to understand the development of IoT in industries, this paper reviews the current research of IoT, key enabling technologies, major IoT applications in industries, and identifies research trends and challenges. A main contribution of this review paper is that it summarizes the current state-of-the-art IoT in industries systematically.

Internet of Things in Industries: A Survey

The organization of behavior: A neuropsychological theory

Rapid advances in industrialisation and informatisation methods have spurred tremendous progress in developing the next generation of manufacturing technology. Today, we are on the cusp of the Fourth Industrial Revolution. In 2013, amongst one of 10 ‘Future Projects’ identified by the German government as part of its High-Tech Strategy 2020 Action Plan, the Industry 4.0 project is considered to be a major endeavour for Germany to establish itself as a leader of integrated industry. In 2014, China’s State Council unveiled their ten-year national plan, Made-in-China 2025, which was designed to transform China from the world’s workshop into a world manufacturing power. Made-in-China 2025 is an initiative to comprehensively upgrade China’s industry including the manufacturing sector. In Industry 4.0 and Made-in-China 2025, many applications require a combination of recently emerging new technologies, which is giving rise to the emergence of Industry 4.0. Such technologies originate from different disciplines ...

Industry 4.0: state of the art and future trends

In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to:  • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information.  Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

IEEE transactions on pattern analysis and machine intelligence

Stochastic equations in infinite dimensions

Evolutionary algorithms have been shown to be very successful in solving multi-objective optimization problems (MOPs). However, their performance often deteriorates when solving MOPs with irregular Pareto fronts. To remedy this issue, a large body of research has been performed in recent years and many new algorithms have been proposed. This paper provides a comprehensive survey of the research on MOPs with irregular Pareto fronts. We start with a brief introduction to the basic concepts, followed by a summary of the benchmark test problems with irregular problems, an analysis of the causes of the irregularity, and real-world optimization problems with irregular Pareto fronts. Then, a taxonomy of the existing methodologies for handling irregular problems is given and representative algorithms are reviewed with a discussion of their strengths and weaknesses. Finally, open challenges are pointed out and a few promising future directions are suggested.

A Survey of Evolutionary Algorithms for Multi-Objective Optimization Problems With Irregular Pareto Fronts

Existing multiobjective evolutionary algorithms (MOEAs) perform well on multiobjective optimization problems (MOPs) with regular Pareto fronts in which the Pareto optimal solutions distribute continuously over the objective space. When the Pareto front is discontinuous or degenerated, most existing algorithms cannot achieve good results. To remedy this issue, a clustering-based adaptive MOEA (CA-MOEA) is proposed in this paper for solving MOPs with irregular Pareto fronts. The main idea is to adaptively generate a set of cluster centers for guiding selection at each generation to maintain diversity and accelerate convergence. We investigate the performance of CA-MOEA on 18 widely used benchmark problems. Our results demonstrate the competitiveness of CA-MOEA for multiobjective optimization, especially for problems with irregular Pareto fronts. In addition, CA-MOEA is shown to perform well on the optimization of the stretching parameters in the carbon fiber formation process.

A Clustering-Based Adaptive Evolutionary Algorithm for Multiobjective Optimization With Irregular Pareto Fronts

The Internet of Things (IoT) is emerging as the major trend in shaping the development of the next generation of information networks. The challenges of the enormous, dynamic, incredibly diverse and high complexity of the IoT urgently require novel self-organization scheme because most of the existing distributed self-organization schemes cannot be directly applied to it. In this paper, we propose an intelligent self-organizing scheme (ISOS) for the IoT inspired by the endocrine regulating mechanism. For each node in the network, an autonomous area is established, where the node can effectively interact with its peers and perform self-control according to its own status and dynamic circumstance in a decentralized infrastructure. By introducing the hormone mechanism as the medium for information transmission and data sharing, the nodes can collaborate with each other and work in a cooperative way. Through adjusting the release procedure of the hormones, the ability to effectively detect service randomly generated can also be guaranteed in the probabilistic partially-working IoT. Simulation results verify the performance of the proposed mechanism that entitles the IoT to the ability of maintaining its status in a globally stable status, while effectively discovering the random service requests in a resource-critical configuration. The ISOS would be of great significance for the practical implementation of the IoT.

An Intelligent Self-Organization Scheme for the Internet of Things

Particle filter algorithm is widely used for target tracking using video sequences, which is of great importance for intelligent surveillance applications. However, there is still much room for improvement, e.g. the so-called ''sample impoverishment''. It is brought by re-sampling which aims to avoid particle degradation, and thus becomes the inherent shortcoming of the particle filter. In order to solve the problem of sample impoverishment, increase the number of meaningful particles and ensure the diversity of the particle set, an evolutionary particle filter with the immune genetic algorithm (IGA) for target tracking is proposed by adding IGA in front of the re-sampling process to increase particle diversity. Particles are regarded as the antibodies of the immune system, and the state of target being tracked is regarded as the external invading antigen. With the crossover and mutation process, the immune system produces a large number of new antibodies (particles), and thus the new particles can better approximate the true state by exploiting new areas. Regulatory mechanisms of antibodies, such as promotion and suppression, ensure the diversity of the particle set. In the proposed algorithm, the particle set optimized by IGA can better express the true state of the target, and the number of meaningful particles can be increased significantly. The effectiveness and robustness of the proposed particle filter are verified by target tracking experiments. Simulation results show that the proposed particle filter is better than the standard one in particle diversity and efficiency. The proposed algorithm can easily be extended to multiple objects tracking problems with occlusions.

An evolutionary particle filter with the immune genetic algorithm for intelligent video target tracking

Multi-robot systems can be applied to patrol a concerned environment for security purposes. According to different goals, this work reviews the existing researches in a multi-robot patrolling field from the perspectives of regular and adversarial patrolling. Regular patrolling requires robots to visit important locations as frequently as possible and a series of deterministic strategies are proposed, while adversarial one focuses on unpredictable robots&#x02BC moving patterns to maximize adversary detection probability. Under each category, a systematic survey is done including problem statements and modeling, patrolling objectives and evaluation criteria, and representative patrolling strategies and approaches. Existing problems and open questions are presented accordingly.

Kuangrong Hao

Papers

A Survey of Evolutionary Algorithms for Multi-Objective Optimization Problems With Irregular Pareto Fronts

A Clustering-Based Adaptive Evolutionary Algorithm for Multiobjective Optimization With Irregular Pareto Fronts

An Intelligent Self-Organization Scheme for the Internet of Things

An evolutionary particle filter with the immune genetic algorithm for intelligent video target tracking

A survey of multi-robot regular and adversarial patrolling