Hierarchical Control for Large-Scale Systems

A class of large-scale systems, where the overall objective function is a nonlinear function of performance index of each subsystem, is investigated in this paper. This type of large-scale control problem is non-separable in the sense of conventional hierarchical control. Hierarchical control is extended in the paper to large-scale non-separable control problems, where multiobjective optimization is used as separation strategy. The large-scale non-separable control problem is embedded, under certain conditions, into a family of the weighted Lagrangian formulation. The weighted Lagrangian formulation is separable with respect to subsystems and can be effectively solved using the interaction balance approach at the two lower levels in the proposed three-level solution structure. At the third level, the weighting vector for the weighted Lagrangian formulation is adjusted iteratively to search the optimal weighting vector with which the optimal of the original large-scale non-separable control problem is obtained. Theoretical base of the algorithm is established. Simulation shows that the algorithm is effective.

Development of a distribution management system in refinery industry

Developing an information system for managing the product distribution in a refinery demands sophisticated design solutions. First, organization models are created to describe the structural relationships among employees and the ways in which they will use the information system. Second, function models are created to hierarchically describe how sales, transports, deliveries and vehicles are managed. Thirdly, an information model is built to represent the data entities and their dynamic interactions. Based on the system design, a distribution management system is developed and implemented in a refinery, which has considerably increased the distribution management efficiency and effectiveness.

Reasoning about knowledge, belief and certainty in hierarchical multi-agent systems

Multi-agent systems （MAS） have received exten- sive studies in the last decade. However, little attention is paid to investigation on reasoning about logics in MAS with hier- archical structures. This paper proposes a complete quantified temporal KBC （knowledge, belief and certainty） logic and corresponding reasoning in hierarchical multi-agent systems （HMAS）. The key point is that internal beliefs and certainty, and external belief and certainty are considered in our logic. The internal beliefs and certainty show every agent is au- tonomous, while the external belief and certainty indicate the mutual influence of mental attitudes between two different agents on different layers in HMAS. To interpret this logic, we propose four classes of corresponding quantified interpreted systems, and define first-order KBC axiomatisations over HMAS, which are sound and complete with respect to the corresponding semantical classes. Finally, we give a case study to show the advantages in terms of expressiveness of our logic.

Automatic and Hierarchical Verification for Concurrent Systems

Proving correctness of concurrent systems is quite difficult because of the high level of nondeterminism,especially in large and complex ones.AMC is a model checking system for verifying asynchronous concurrent systems by using branching time temporal logic.This paper introduces the tech- niques of the modelling approach,especially how to construct models for large concurrent systems with the concept of hierarchy,which has been proved to be effective and practical in verifying large systems without a large growth of cost.

A review of security issues and solutions for precision health in Internet-of-Medical-Things systems

Precision medicine provides a holistic perspective of an individual's health,including genetic,environmental,and lifestyle aspects to realize individualized therapy.The development of the internet of things(IoT)devices,the widespread emergence of electronic medical records(EMR),and the rapid progress of cloud computing and artificial intelli-gence provide an opportunity to collect healthcare big data throughout the lifespan and analyze the disease risk at all stages of life.Thus,the focus of precision medicine is shift-ing from treatment toward prediction and prevention,i.e.,precision health.To this end,various types of data such as omics,imaging,EMR,continuous physiological monitoring,lifestyle,and environmental information,need to be collected,tracked,managed and shared.Thus,internet-of-medical things(IoMT)is crucial for assimilating the health systems,appli-cations,services,and devices that can improve the speed and accuracy of diagnosis and treatments along with real-time monitoring and modification of patient behavior as well as health status.However,security has emerged as a growing concern owing to the prolifera-tion of IoMT devices.The increasing interconnectivity of IoMT-enabled devices with health data reception,transmission,and processing significantly increases the number of potential vulnerabilities within a system.To address the security issues of precision health in IoMT systems,this study reviews the state-of-the-art techniques and schemes from the perspective of a hierarchical system architecture.We present an IoMT system model comprising three layers:the sensing layer,network layer,and cloud infrastructure layer.In particular,we dis-cuss the vulnerabilities and threats to security in each layer and review the existing security techniques and schemes corresponding to the system components along with their function-alities.Owing to the unique nature of biometric features in medical and health services,we highlight the biometrics-based technologies applied in IoMT systems,which contribute toward a considerable difference between the security solutions of existing IoT systems.Fur-thermore,we summarize the challenges and future research directions of IoMT systems to ensure an improved and more secure future of precision health.