Hongnian Yu

Elderly activities recognition and classification for applications in assisted living

Assisted living systems can help support elderly persons with their daily activities in order to help them maintain healthy and safety while living independently. However, most current systems are ineffective in actual situation, difficult to use and have a low acceptance rate. There is a need for an assisted living solution to become intelligent and also practical issues such as user acceptance and usability need to be resolved in order to truly assist elderly people. Small, inexpensive and low-powered consumption sensors are now available which can be used in assisted living applications to provide sensitive and responsive services based on users current environments and situations. This paper aims to address the issue of how to develop an activity recognition method for a practical assisted living system in term of user acceptance, privacy (non-visual) and cost. The paper proposes an activity recognition and classification method for detection of Activities of Daily Livings (ADLs) of an elderly person using small, low-cost, non-intrusive non-stigmatize wrist worn sensors. Experimental results demonstrate that the proposed method can achieve a high classification rate (>90%). Statistical tests are employed to support this high classification rate of the proposed method. Also, we prove that by combining data from temperature sensor and/or altimeter with accelerometer, classification accuracy can be improved.

Decentralised power system load frequency control beyond the limit of diagonal dominance

The design of decentralised robust load frequency control for interconnected multi-area power systems is studied in this paper. It is shown that although the design can be naturally formulated as a large-scale system decentralised control problem, it can be translated into an equivalent problem of decentralised controller design for a multi-input multi-output (MIMO) control system. It is known that simple controllers can be designed to achieve satisfactory performances if diagonal dominance can be achieved in a multivariable system. This is further extended in this paper. Using the design method proposed in this paper, even when the diagonal dominance cannot be achieved, subject to a condition based on the structured singular values (SSVs), each local area load-frequency controller can be designed independently. The robust stability condition for the overall system can be easily stated as to achieve a sufficient interaction margin, and a sufficient gain and phase margin during each independent design.

Combined Petri net modelling and AI-based heuristic hybrid search for flexible manufacturing systems-part I: Petri net modelling and heuristic search

This two-part paper presents modelling and scheduling approaches for flexible manufacturing systems (FMS) using Petri nets (PNs) and artificial intelligence (AI) based heuristic search methods. In part I, the description of FMS formulation that will be considered throughout the paper is presented. A new class of PNs, Buffer-nets, for defining FMS is proposed, which enhances the modelling techniques for manufacturing systems with features that are considered difficult to model. An input language for automatic synthesis of these nets is developed. A scheduling architecture, which integrates PN models and AI techniques, is proposed. Finally, the complexity issues of manufacturing systems are addressed.

Activity classification using a single wrist-worn accelerometer

Automatic identification of human activity has led to a possibility of providing personalised services in different domains i.e. healthcare, security and sport etc. With advancement in sensor technology, automatic activity recognition can be done in an unobtrusive and non-intrusive way. The placement of the sensor and wearability are ones of vital keys in the successful activity recognition of free space livings. Experiments were carried out to investigate the use of a single wrist-worn accelerometer for automatic activity classification. The performances of two classification algorithms namely Decision Tree C4.5 and Artificial Neural Network were compared using four different sets of features to classify five daily living activities. The result revealed that Decision Tree C4.5 has outperformed Neural Network regardless of the different sets of features used. The best classification result was achieved using the set containing the most popular and accurate features i.e. mean, minimum, energy and sample differences etc. The best accuracy of 94.13% was achieved using only wrist-worn accelerometer showing a possibility of automatic activity classification with no movement constrain, discomfort and stigmatisation caused by the sensor.

Integrating Petri nets and hybrid heuristic search for the scheduling of FMS

This paper studies modelling and scheduling of Flexible Manufacturing Systems (FMS) using Petri Nets (PNs) and Artificial Intelligence (AI) based on heuristic search methods. A subclass of PNs, Buffer nets or B-nets is obtained by the systematic synthesis of PN models from FMS formulations. Scheduling is performed as heuristic search in the reachability tree, which is guided by a new heuristic function that exploits PN information. This heuristic is derived from a new concept, the Resource Cost Reachability (RCR) matrix which builds on the properties of B-nets. To mitigate the complexity problem, a hybrid search algorithm is proposed. The algorithm combines dispatching rules based on analysis information provided by the PN simulation with a modified stage-search algorithm. Experimental results are provided and indicate the effectiveness of the approach and the potential of PN-based heuristic search for FMS scheduling.

Hybrid heuristic search for the scheduling of flexible manufacturing systems using Petri nets

The combination of Petri nets (PNs) as an analysis tool for discrete-event dynamic systems and artificial intelligence heuristic search has been shown to be a promising way to solve flexible manufacturing systems (FMS) scheduling problems. However, the NP hard nature of the problem obscures the PN capability of reasoning about the behavior of the system. In this paper, two techniques to alleviate this drawback are presented: a systematic method to avoid the generation of futile paths within the search graph and a novel hybrid stage-search algorithm. The new algorithm is based on the application of A* guided by a PN-based heuristic within a limited local search frame. An optimization policy is applied to maintain, under evaluation, only the most promising paths. For each system state, the algorithm is able to decide whether an enabled operation should be applied and to maintain this decision until new information forces reconsideration. This eliminates permutation paths and useless scheduling sequences. Experimental results show that the algorithms cost does not grow exponentially with the size of the problem. Comparison with previous work is given to show the superiority of our approach and the potential of PN-based heuristic search.

Co-simulation of wireless networked control systems over mobile ad hoc network using SIMULINK and OPNET

Wireless networked control systems (WNCS) over mobile ad hoc network (MANET) is a new area of research and has many potential applications, for instance, military or rescue missions, exploring hazardous environments, and so on. For performance evaluation, researchers mostly rely on computer simulations as WNCS experiments are expensive to execute. It will generate a significant benefit to conduct performance analysis of WNCS over MANET using co-simulation that utilises SIMULINK and OPtimised Network Engineering Tool (OPNET) to simulate plant/controller behaviour and the MANET, respectively. Previous conference papers by the authors reported the initial SIMULINK-OPNET co-simulation for only one network size. Here the authors present an extention of their previous work, and presents the SIMULINK-OPNET co-simulation, methodology and comprehensive simulation results that have not been reported previously. It also considers the impact of five network sizes with stationary and mobile nodes. The proposed SIMULINK-OPNET co-simulation is applied to WNCS over MANET using a realistic wireless communication model. It investigates the impact of network data rates, node mobility, the packet delay, packet drop on the system stability and performance.

Intelligent Learning Algorithms for Active Vibration Control

This correspondence presents an investigation into the comparative performance of an active vibration control (AVC) system using a number of intelligent learning algorithms. Recursive least square (RLS), evolutionary genetic algorithms (GAs), general regression neural network (GRNN), and adaptive neuro-fuzzy inference system (ANFIS) algorithms are proposed to develop the mechanisms of an AVC system. The controller is designed on the basis of optimal vibration suppression using a plant model. A simulation platform of a flexible beam system in transverse vibration using a finite difference method is considered to demonstrate the capabilities of the AVC system using RLS, GAs, GRNN, and ANFIS. The simulation model of the AVC system is implemented, tested, and its performance is assessed for the system identification models using the proposed algorithms. Finally, a comparative performance of the algorithms in implementing the model of the AVC system is presented and discussed through a set of experiments.

Combined Petri net modelling and AI-based heuristic hybrid search for flexible manufacturing systems-part II: heuristic hybrid search

This two-part paper presents modelling and scheduling approaches of flexible manufacturing systems using Petri nets (PNs) and artificial intelligence (AI)-based heuristic search methods. In Part I, PN-based modelling approaches and basic AI-based heuristic search algorithms were presented. In Part II, a new heuristic function that exploits PN information is proposed. Heuristic information obtained from the PN model is used to dramatically reduce the search space. This heuristic is derived from a new concept, the resource cost reachability matrix, which builds on the properties of B-nets proposed in Part I. Two hybrid search algorithms, (1) an approach to model dispatching rules using analysis information provided by the PN simulation and (2) an approach of the modified stage-search algorithm, are proposed to reduce the complexity of large systems. A random problem generator is developed to test the proposed methods. The experimental results show promising results.

Closed-loop tracking control of a pendulum-driven cart-pole underactuated system

This paper investigates a new modelling and control issue: the tracking control problem of underactuated dynamic systems by using a special example – a pendulum-driven cart-pole system. A dynamic model of a pendulum-driven cart-pole system is firstly presented. Then, a six-step motion strategy of the pendulum-driven cart-pole system is proposed. After that, a desired profile of the pendulum joint velocity is developed based on the proposed six-step motion strategy. The desired joint position and acceleration can be computed using the desired joint velocity profile. A closed-loop control approach is proposed using the partial feedback linearization technique. Finally, extensive simulation studies are conducted to demonstrate the proposed approaches.