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

This note investigates the output feedback stabilization of networked control systems (NCSs). The sensor-to-controller (S-C) and controller-to-actuator (C-A) random network-induced delays are modeled as Markov chains. The focus is on the design of a two-mode-dependent controller that depends on not only the current S-C delay but also the most recent available C-A delay at the controller node. The resulting closed-loop system is transformed to a special discrete-time jump linear system. Then, the sufficient and necessary conditions for the stochastic stability are established. Further, the output feedback controller is designed via the iterative linear matrix inequality (LMI) approach. Simulation examples illustrate the effectiveness of the proposed method.

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Output Feedback Stabilization of Networked Control Systems With Random Delays Modeled by Markov Chains

Distributed networked control systems

Brief paper: New delay-dependent stability criteria for systems with interval delay

This note is concerned with stability and controller design of networked control systems (NCSs) with packet dropouts. New NCS models are provided considering both single- and multiple-packet transmissions. Both sensor-to-controller (S/C) and controller-to-actuator (C/A) packet dropouts are modeled and their history behavior is described by different independent Markov chains. In term of the given models, sufficient conditions for stochastic stability are derived in the form of linear matrix inequalities (LMIs) and corresponding control laws are given. Numerical examples illustrate the effectiveness of the results.

Design of Networked Control Systems With Packet Dropouts

Delay-Dependent Robust Stability and H∞ Control for Jump Linear Systems with Delays

False data injection (FDI) attacks have recently been introduced as an important class of cyberattacks in modern power systems. By coordinating the injection of false data in selected meters readings, an FDI attacks can bypass bad data detection methods in power system state estimation. In this paper, we propose a strategy to enhance detection and identification of an FDI that leverages reactance perturbation. We begin by deriving conditions to mitigate attacks in noiseless systems that relates the likelihood of attack detection and identification to the rank of the composite matrix, limited by power system topology and the deployment of meters. Based on such conditions, we design a secure reactance perturbation algorithm that maximizes the likelihood of an FDI attack detection and identification while minimizing the effect on the operational cost of power systems, e.g., power losses on transmission lines. Simulations on a 6-bus and the IEEE 57-bus system verify the performance of the secure reactance perturbation and the effect on power losses in both noiseless and noisy systems.

Reactance Perturbation for Detecting and Identifying FDI Attacks in Power System State Estimation

A novel thermal storage strategy for CCHP system based on energy demands and state of storage tank

Drug traceability system is essentially important for public drug security and business of pharmaceutical companies, which aims to track or trace where the drug has been and where it has gone along the drug supply chain. Traditional centralized server-client technical solutions have been far from satisfying for their bad performances in data authenticity, privacy, system resilience and flexibility. In this paper, we propose a scenario-oriented blockchain system for drug traceability and regulation called Drugledger, which reconstructs the whole service architecture by separating service provider into three independent service components and ensures the authenticity and privacy of traceability data. Drugledger is more resilient than traditional solutions with its p2p architecture. Furthermore, Drugledger could efficiently prune its storage, achieving a finally stable and acceptable blockchain storage. Besides, algorithms reflecting the real drug supply chain logic (e.g, package, repackage, unpackage, etc.) are designed based on the expanded UTXO workflow in Drugledger. To our knowledge, it is the first systematic work from both a technical and practical perspective on how blockchain system could be designed for drug traceability and regulation.

Jing Wu

Papers

Design of Networked Control Systems With Packet Dropouts

Delay-Dependent Robust Stability and H∞ Control for Jump Linear Systems with Delays

Reactance Perturbation for Detecting and Identifying FDI Attacks in Power System State Estimation

A novel thermal storage strategy for CCHP system based on energy demands and state of storage tank

Drugledger: A Practical Blockchain System for Drug Traceability and Regulation