Dynamic control of skilled and unskilled labour task assignments

Efficient allocation of skilled and non-skilled workers allow a company to improve productivity and usually requires an understanding of personnel capability, operating conditions and resource availability. This paper examines a labour control strategy that optimises labour skill level, utilisation, task execution time and processing error. The proposed controller manages different labour groups in a multiple work cell environment, providing real-time job assignment, as well as guiding and navigation features. These features can be used to enhance the performance of existing MRP-based or Just-In-Time production systems. A discrete event simulation-based manufacturing model has been developed to assess the performance of the labour controller. Experiments conducted for the selected production scenarios have demonstrated a productivity improvement when using the proposed control. A second experiment has shown that when a skilled labour uses the labour controller to guide them through the job, their utilisation also increases. The proposed controller also has potential application in other domains, such as minimising the shopping time at a supermarket

An efficient scheduling method for grid systems based on a hierarchical stochastic petri net

This paper addresses the problem of resource scheduling in a grid computing environment. One of the main goals of grid computing is to share system resources among geographically dispersed users, and schedule resource requests in an efficient manner. Grid computing resources are distributed, heterogeneous, dynamic, and autonomous, which makes resource scheduling a complex problem. This paper proposes a new approach to resource scheduling in grid computing environments, the hierarchical stochastic Petri net (HSPN). The HSPN optimizes grid resource sharing, by categorizing resource requests in three layers, where each layer has special functions for receiving subtasks from, and delivering data to, the layer above or below. We compare the HSPN performance with the Min-min and Max-min resource scheduling algorithms. Our results show that the HSPN performs better than Max-min, but slightly underperforms Min-min.

Dynamic path determination policy for distributed multimedia content adaptation

Multimedia content adaptation allows the ever increasing variety of handheld devices such as Smartphones to access distributed rich media resources available on the Internet today. Path planning and determination is a fundamental problem in enhancing performance of distributed multimedia content adaptation systems. Most of the existing path determination mechanisms use static path determination criteria based solely on associating a path with a single behavior aggregate score. However, some criteria such as availability are best represented using different functionality rather than being accumulated into the aggregate score. Moreover, since selection criteria have different behavior towards the score, this principle need to be considered. In this paper, we propose a dynamic multi-criteria path determination policy that selects an optimal path to the content adaptation services that best meet the user preferences and QoS requirements. The performance of the proposed approach is studied in terms of score’s fairness and reliability under different variations. The results indicate that the proposed policy performs substantially better than the baseline policy.

Intelligent techniques for recommender systems

This thesis focuses on the data sparsity issue and the temporal dynamic issue in the context of collaborative filtering, and addresses them with imputation techniques, low-rank subspace techniques and optimizations techniques from the machine learning perspective. A comprehensive survey on the development of collaborative filtering techniques is also included.

Factories of the future : a system of systems engineering perspective

Factories of the Future will be distinguished by intelligent machines, automation, human factors integration and knowledge management. Modelling and simulation is recognised as a key enabling technology essential to economic, social and environmental sustainability of future manufacturing systems. This talk will explore the history, recent achievements and directions in modelling and simulation for 21st century factories and supply chains. A systems science approach is employed, from stakeholder engagement through participative modelling to self-tuning and self-assembling simulations. Our contributions lower the cost of the application of modelling and simulation to manufacturing processes, enabling real time planning, dynamic risk analysis, dashboards and 3D visualisation. This realisation of the virtual factory integrates human factors and decisions into the core technology platform. The implications to future manufacturing enterprises are explored through a series of case studies from aerospace, mining and small and medium manufacturing enterprises.

Small-signal stability assessment of active distribution networks with dynamic loads

This paper investigates small-signal stability of a distribution system with distributed generator and induction motor load, as a dynamic element. The analysis is carried out over a distribution test system with different types of induction motor loads. The system is linearised by the perturbation method. Eigenvalues and participation factors are calculated to see the modal interaction of the system. The study indicates that load voltage dynamics significantly influence the damping of a newly identified voltage mode. This mode has frequency of oscillation between the electromechanical and subsynchronous oscillation of power systems. To justify the validity of the modal analysis time domain simulation is also carried out. Finally, significant parameters of the system that affect the damping and frequency of the oscillation are identified.

Impact of SCIG and DFIG type wind turbine on the stability of distribution networks: Static and dynamic aspects

This paper presents potential barriers to integrate the squirrel cage induction generator (SCIG) and doubly fed induction generator (DFIG) type wind turbine in distribution networks. The analysis is carried out over a 16 bus distribution test system. Both static and dynamic analyses are performed to see the impact of two different generators on the distribution system. The simulation results show that both SCIG and DFIG type wind turbines have significant impact on the static voltage stability, power loss, and dynamic behavior of the system, which should be taken into account to improve systems performance before integrating wind generation in existing distribution networks.

Enhancement of transient stability limit and voltage regulation with dynamic loads using robust excitation control

In stressed power systems with large induction machine component, there exist undamped electromechanical modes and unstable montonic voltage modes. This article proposes a sequential design of an excitation controller and a power system stabiliser (PSS) to stabilise the system. The operating region, with induction machines in stressed power systems, is often not captured using a linearisation around an operating point, and to alleviate this situation a robust controller is designed which guaruntees stable operation in a large region of operation. A minimax linear quadratic Gaussian design is used for the design of the supplementary control to automatic voltage regulators, and a classical PSS structure is used to damp electromechanical oscillations. The novelty of this work is in proposing a method to capture the unmodelled nonlinear dynamics as uncertainty in the design of the robust controller. Tight bounds on the uncertainty are obtained using this method which enables high-performance controllers. An IEEE benchmark test system has been used to demonstrate the performance of the designed controller

The totalitarian dynamic behind HRM's democratic façade

The terms 'authoritarian' and 'democratic' are political concepts often applied as a means of distinguishing human resource management (HRM) from older forms of labour management, the common assertion being that former authoritarian practices have become more democratic under HRM. This article challenges this view by arguing that the foundational principles and practices of HRM, when orientated by the expectations of a 'desired' organisational culture, cast it into a role that involves mobilising the collective psychology of organisational members to accept willingly the legitimacy of managerial authority and the virtues of firm loyalty. It is suggested that such a role has no parallel in orthodox democratic arrangements; that if political concepts are to be applied to the way labour is managed under HRM, a clearer affinity exists with the aims and practices of totalitarian regimes and their use of propaganda and other means to control civilian populations. Key points: This article challenges the assumption that HRM is cast in the spirit of democracy. HRM's foundational principles and functional practices are instead more closely aligned with totalitarian conceptions of social control. This is evidenced by HRM's role in mobilising the collective psychology of employees in accordance with a 'desired' workplace culture proscribed by organisational leaderships.

A comparative study of different power system stabilizers for dynamic stability analysis

Power system stabilizers (PSSs) are extensively used to ensure the dynamic stability of power systems through the modulation of excitation signals supplied to synchronous generators. This paper presents a comparative study of two different PSSs: STAB1 and IEEEST. The stabilizers are designed for the linearized model of a single machine infinite bus (SMIB) system with different loads. Both time-and frequency-domain simulations are carried out to investigate the performance of these stabilizers. For all PSSs, the time-domain simulations are performed by applying a three-phase short-circuit fault at the terminal of the synchronous generator. These simulation results are compared against the open-loop characteristics of the SMIB system where no PSS is implemented. Simulation results demonstrate that the speed-fed PSS provides more damping as compared to frequency- and power-fed stabilizers.

Detection of dynamic background due to swaying movements from motion features

Dynamically changing background (dynamic background) still presents a great challenge to many motion-based video surveillance systems. In the context of event detection, it is a major source of false alarms. There is a strong need from the security industry either to detect and suppress these false alarms, or dampen the effects of background changes, so as to increase the sensitivity to meaningful events of interest. In this paper, we restrict our focus to one of the most common causes of dynamic background changes: 1) that of swaying tree branches and 2) their shadows under windy conditions. Considering the ultimate goal in a video analytics pipeline, we formulate a new dynamic background detection problem as a signal processing alternative to the previously described but unreliable computer vision-based approaches. Within this new framework, we directly reduce the number of false alarms by testing if the detected events are due to characteristic background motions. In addition, we introduce a new data set suitable for the evaluation of dynamic background detection. It consists of real-world events detected by a commercial surveillance system from two static surveillance cameras. The research question we address is whether dynamic background can be detected reliably and efficiently using simple motion features and in the presence of similar but meaningful events, such as loitering. Inspired by the tree aerodynamics theory, we propose a novel method named local variation persistence (LVP), that captures the key characteristics of swaying motions. The method is posed as a convex optimization problem, whose variable is the local variation. We derive a computationally efficient algorithm for solving the optimization problem, the solution of which is then used to form a powerful detection statistic. On our newly collected data set, we demonstrate that the proposed LVP achieves excellent detection results and outperforms the best alternative adapted from existing art in the dynamic background literature.

AMC: an adaptive multi-level cache algorithm in hybrid storage systems

Hybrid storage systems that consist of flash-based solid state drives (SSDs) and traditional disks are now widely used. In hybrid storage systems, there exists a two-level cache hierarchy that regard dynamic random access memory (DRAM) as the first level cache and SSD as the second level cache for disk storage. However, this two-level cache hierarchy typically uses independent cache replacement policies for each level, which makes cache resource management inefficient and reduces system performance. In this paper, we propose a novel adaptive multi-level cache (AMC) replacement algorithm in hybrid storage systems. The AMC algorithm adaptively adjusts cache blocks between DRAM and SSD cache levels using an integrated solution. AMC uses combined selective promote and demote operations to dynamically determine the level in which the blocks are to be cached. In this manner, the AMC algorithm achieves multi-level cache exclusiveness and makes cache resource management more efficient. By using real-life storage traces, our evaluation shows the proposed algorithm improves hybrid multi-level cache performance and also increases the SSD lifetime compared with traditional multi-level cache replacement algorithms.

Future integrated factories: a system of systems engineering perspective

The manufacturing sector has gone through tremendous change in the last decade. We have witnessed the transformation from stand alone, manual processes to smart and integrated systems, from hand written reports to interactive computer-based dashboards. Future integrated factories will operate as a system of systems through intelligent machines, human factors integration, and integrated supply chains. To effectively operate and manage these emerging enterprises, a systems science approach is required. Modelling and simulation is recognised as a key enabling technology, with application from stakeholder engagement and knowledge elicitation to operational decision support through self-tuning and self-assembling simulations. Our research has led to the introduction of effective modelling and simulation methods and tools to enable real time planning, dynamic risk analysis and effective visualisation for production processes, resources and systems. This paper discusses industrial applicable concepts for real-time simulation and decision support, and the implications to future integrated factories, or factories of the future, are explored through relevant case studies from aerospace manufacturing to mining and materials processing enterprises.

Distributed multi-agent scheme to improve dynamic voltage stability of distribution networks

In this paper, a distributed multi-agent scheme is presented for reactive power management with renewable energy sources (RESs). The multi-agent system (MAS) framework is developed for distribution systems to improve the stability which is mostly dominated by voltage and the agents in this framework coordinate among themselves using online information and energy flow. In this paper, the agents basically perform two tasks- reactive power estimation and necessary control actions. The topology of distribution network is used to estimate the required reactive power for maintaining voltage stability where distributed static synchronous compensators (DSTATCOMs) are used to supply this reactive power. The DSTATCOM is controlled by using a linear quadratic regulator (LQR) controller within the agent framework. The proposed scheme is further compared with the conventional approach to validate the simulation results.

Nonlinear adaptive excitation controller design for multimachine power systems

This paper presents a nonlinear adaptive excitation control scheme to enhance the dynamic stability of multimachine power systems. The proposed controller is designed based on the adaptive backstepping technique where the mechanical power input to the generators and the damping coefficient of each generator are considered as unknown. These unknown quantities are estimated through the adaption laws. The adaption laws are obtained from the formulation of Lyapunov functions which guarantee the convergence of different physical quantities of generators such as the relative speed, terminal voltage, and electrical power output. The proposed scheme is evaluated by applying a three-phase short-circuit fault at one of the key transmission lines in an 11-bus test power system and compared with an existing backstepping controller and conventional power system stabilizer (CPSS). Simulation results show that the proposed scheme is much more effective than existing controllers.