Hai Wan

A novel fault diagnosis mechanism for wireless sensor networks

This paper considers a novel fault diagnosis mechanism for wireless sensor networks (WSNs). Without additional agents, the built-in and self-organized diagnosis mechanism can monitor each node in real time and identify faulty nodes. As the diagnosis is operated within a cluster of nodes, it can reduce power consumption and communication traffic. We present a modeling of the diagnosis algorithm for WSNs, with a probabilistic analysis of the local and global performances of our approach. Extensive experiments demonstrate the effectiveness of the proposed method.

Multitask Oriented Virtual Resource Integration and Optimal Scheduling in Cloud Manufacturing

To deal with the problem of resource integration and optimal scheduling in cloud manufacturing, based on the analyzation of the existing literatures, multitask oriented virtual resource integration and optimal scheduling problem is presented from the perspective of global optimization based on the consideration of sharing and correlation among virtual resources. The correlation models of virtual resources in a task and among tasks are established. According to the correlation model and characteristics of resource sharing, the formulation in which resource time-sharing scheduling strategy is employed is put forward, and then the formulation is simplified to solve the problem easily. The genetic algorithm based on the real number matrix encoding is proposed. And crossover and mutation operation rules are designed for the real number matrix. Meanwhile, the evaluation function with the punishment mechanism and the selection strategy with pressure factor are adopted so as to approach the optimal solution more quickly. The experimental results show that the proposed model and method are feasible and effective both in situation of enough resources and limited resources in case of a large number of tasks.

Formalization and Verification of PLC Timers in Coq

Programmable logic controllers (PLCs) are widely used in embedded systems. A timer plays a pivotal role in PLC real-time applications.The paper presents a formalization of TON-timers of PLC programs in the theorem proving system Coq. The behavior of a timer is characterized by a set of axioms at an abstract level. PLC programs with timers are modeled in Coq. As a case study, the quiz machine problem with timer is investigated. Relevant timing properties of practical interests are proposed and proven in Coq. This work unveils the hardness of timer modeling in embedded systems. It is an attempt of formally proving the correctness of PLC programs with timer control.

Modeling and Verification of Component-Based Systems with Data Passing Using BIP

Large-scale systems are often modeled and verified in a component-based way. BIP (Behavior, Interaction, Priority) is a flexible component-based framework which supports hierarchical design of heterogeneous systems. BIP components interact via connectors in which data can be passed among multiple components. It also support the modeling of time. Due to its expressiveness and flexibility, many real-time systems can be modeled easily in BIP. Verification, however, is not well supported in the current BIP framework. That is a major disadvantage when it is used in a model-driven design flow. To fill this gap, we propose a translation from slightly restricted BIP models to timed automata. Then model checking can be applied to the latter using Uppaal (which is a sophisticated model checker for timed automata). The correctness of translation is proven formally and the translation is implemented as a tool Bip2Uppaal. Three industrial case studies show that our approach is practical and effective.

A Novel Two-Terminal Reliability Analysis for MANET

Mobile ad hoc network (MANET) is a dynamic wireless communication network. Because of the dynamic and infrastructureless characteristics, MANET is vulnerable in reliability. This paper presents a novel reliability analysis for MANET. The node mobility effect and the node reliability based on a real MANET platform are modeled and analyzed. An effective Monte Carlo method for reliability analysis is proposed. A detailed evaluation is performed in terms of the experiment results.

Formal component-based modeling and synthesis for PLC systems

Programmable Logic Controllers (PLCs) are widely used in industry. PLC systems are reactive systems which run cyclically. In each cycle, the system state is checked and the program is executed once to determine the system behavior for a single cycle. Development of PLC systems conventionally follows the V-model, but increasing demand for efficiency and reliability requires a new rigorous and rapid design flow. In this paper, we propose a component-based formal modeling and synthesis method for cyclic execution platforms and apply it to PLC. Our method consists of three main phases: modeling, verification and code synthesis. In the modeling phase, the BIP (Behavior-Interaction-Priority) framework which is flexible and expressive is used as the modeling language. Real-time behavior, which is intensely concerned in PLC systems, can be modeled as well. In the verification phase, the system model is translated to timed automata and checked by Uppaal. Verification helps to ensure correctness of the model and further increases reliability of the implementation. In the code synthesis phase, the software part of the system model is extracted and synthesized to cyclic code. Although the PLC software runs cyclically, the software model is not necessarily given in a cyclic manner. We propose an algorithm which can generate high-performance cyclic code from a model which describes the business work-flow. This feature significantly simplifies program development. A set of tools is implemented to support our design flow and they are applied to an industrial case study for a PLC system that controls dozens of physical devices in a huge palace.

Vertex-Weighted Hypergraph Learning for Multi-View Object Classification.

3D object classification with multi-view representation has become very popular, thanks to the progress on computer techniques and graphic hardware, and attracted much research attention in recent years. Regarding this task, there are mainly two challenging issues, i.e., the complex correlation among multiple views and the possible imbalance data issue. In this work, we propose to employ the hypergraph structure to formulate the relationship among 3D objects, taking the advantage of hypergraph on high-order correlation modelling. However, traditional hypergraph learning method may suffer from the imbalance data issue. To this end, we propose a vertex-weighted hypergraph learning algorithm for multi-view 3D object classification, introducing an updated hypergraph structure. In our method, the correlation among different objects is formulated in a hypergraph structure and each object (vertex) is associated with a corresponding weight, weighting the importance of each sample in the learning process. The learning process is conducted on the vertex-weighted hypergraph and the estimated object relevance is employed for object classification. The proposed method has been evaluated on two public benchmarks, i.e., the NTU and the PSB datasets. Experimental results and comparison with the state-of-the-art methods and recent deep learning method demonstrate the effectiveness of our proposed method.

Representative band selection for hyperspectral image classification

Abstract High dimensional curse for hyperspectral images is one major challenge in image classification. In this work, we introduce a novel spectral band selection method by representative band mining. In the proposed method, the distance between two spectral bands is measured by using disjoint information. For band selection, all spectral bands are first grouped into clusters, and representative bands are selected from these clusters. Different from existing clustering-based band selection methods which select bands from each cluster individually, the proposed method aims to select representative bands simultaneously by exploring the relationship among all band clusters. The optimal representative band selection is based on the criteria of minimizing the distance inside each cluster and maximizing the distance among different representative bands. These selected bands can be further applied in hyperspectral image classification. Experiments are conducted on the 92AV3C Indian Pine data set. Experimental results show that the disjoint information-based spectral band distance measure is effective and the proposed representative band selection approach outperforms state-of-the-art methods for high dimensional image classification.

Parameterized Specification and Verification of PLC Systems in Coq

Programmable logic controllers (PLCs) represent a typical class of embedded software systems. They are widely used in safety-critical industrial applications, such as railways, automotive applications, etc. The paper presents a novel method to specify and verify PLC software systems with the theorem proving system Coq. Dependent inductive data types are harnessed to represent the component specifications. Modular and parameterized specification and verification are proposed. An illustrative example demonstrates the effectiveness of the method.

Formal Proof of a Machine Closed Theorem in Coq

The paper presents a formal proof of a machine closed theorem of TLA+ in the theorem proving system Coq. A shallow embedding scheme is employed for the proof which is independent of concrete syntax. Fundamental concepts need to state that the machine closed theorems are addressed in the proof platform. A useful proof pattern of constructing a trace with desired properties is devised. A number of Coq reusable libraries are established.