Intelligent kanban : evaluation of a supply chain MAS application using benchmarking

The real world challenges of incomplete information access and bounded computational resources in supply chain management motivate us to propose a bottom up approach to supply chain intelligence, built over a widely used reactive card-based replenishments system (kanban). The rationale is to use agent technology to improve the performance of the traditional kanban system while maintaining its recognized usability. Instead of optimizing a system utility function, we encode the system goal in desired behaviours of individual agents that reason about their own behaviours in the local context. This paper discusses a rigorous framework for evaluation of the proposal based on the concept of benchmarking. Preliminary results from these simulations show remarkable improvements over the traditional system. Furthermore, use of the benchmarking framework gives confidence that these results translate into real performance gains in practical implementations.

Experiments toward a practical implementation of an intelligent kanban system

This paper presents laboratory experiments to test a bottom up approach to production control and supply chain management. Built upon the successful traditional kanban (Card) system, the new intelligent system associates a kanban agent to each physical kanban. Instead of relying on demand forecast and planning, kanban agents reason about their own movements to adapt to changing demands. After previous simulations results of the intelligent system showed significant performance improvements over the traditional system, we further use the Auto-ID Laboratory at Cambridge University to test the feasibility of the idea in a realistic manufacturing environment. The results from the experiments demonstrated the superiority on several performance measures of the intelligent system compared to the traditional system used as a benchmark. Moreover, the implementation of the experiments exposed several real world constraints not shown in the simulation study and practical solutions were adopted to address these.

Intelligent animal fibre identification and classification

To develop an objective and repeatable method of identification and classification of animal fibres, two different integrated systems were developed to mimic the human brain's ability to undertake feature extraction and discrimination of animal fibres. Both integrated systems are basically composed of an image processing system and an artificial neural network system.

Intelligent energy management in hybrid electric vehicles

The modelling and simulation approach is employed to develop an intelligent energy management system for hybrid electric vehicles. The aim is to optimize fuel consumption and reduce emissions. An analysis of the role of drivetrain, energy management control strategy and the associated impacts on the fuel consumption with combined wind/drag, slope, rolling, and accessories loads are included.

Intelligent based mobility models for data management in wireless sensor networks

Wireless sensor networks (WSNs) are proposed as powerful means for fine grained monitoring in different classes of applications at very low cost and for extended periods of time. Among various solutions, supporting WSNs with intelligent mobile platforms for handling the data management, proved its benefits towards extending the network lifetime and enhancing its performance. The mobility model applied highly affects the data latency in the network as well as the sensors’ energy consumption levels. Intelligent-based models taking into consideration the network runtime conditions are adopted to overcome such problems. In this chapter, existing proposals that use intelligent mobility for managing the data in WSNs are surveyed. Different classifications are presented through the chapter to give a complete view on the solutions lying in this domain. Furthermore, these models are compared considering various metrics and design goals.

Applying fair intelligent congestion control in a hybrid QoS architecture for wireless environment

This simple and scalable Differentiated Services (DiffServ) QoS control model is acceptable for the core of the network. However, more explicit and stringent admission and reservation based QoS mechanisms are required in the wireless access segment of the network, where available resources are severely limited and the degree of traffic aggregation is not significant, thus rendering the DiffServ principles less effective. In this paper we present a suitable hybrid QoS architecture framework to address the problem. At the wireless access end, the local QoS mechanism is designed in the context of IEEE 802.11 WLAN with 802.11e QoS extensions. At the edge and over the DiffServ domain, the Fair Intelligent Congestion Control (FICC) algorithm is applied to provide fairness among traffic aggregates and control congestion at the bottleneck interface between the wireless link and the network core.

Beyond Simon’s means-ends analysis : natural creativity and the unanswered ‘why’ in the design of intelligent systems for problem-solving

Goal-directed problem solving as originally advocated by Herbert Simon’s means-ends analysis model has primarily shaped the course of design research on artificially intelligent systems for problem-solving. We contend that there is a definite disregard of a key phase within the overall design process that in fact logically precedes the actual problem solving phase. While systems designers have traditionally been obsessed with goal-directed problem solving, the basic determinants of the ultimate desired goal state still remain to be fully understood or categorically defined. We propose a rational framework built on a set of logically interconnected conjectures to specifically recognize this neglected phase in the overall design process of intelligent systems for practical problem-solving applications.

Adaptive intelligent systems for energy management of vehicles

The thesis demonstrated the architecture of adaptive intelligent systems for energy management that is capable of interacting with complex systems including the vehicle, environment, and driver components, as well as the interrelationships between these variables, to deliver fuel consumption improvements.

Intelligent clothing for automated recognition of human physical activities in free-living environment

This paper presents an intelligent clothing framework for human daily activity recognition using a single waist-worn tri-axial accelerometer sensor coupled with a robust pattern recognition system. The activity recognition algorithm is realized to distinguish six different physical activities through three major steps: acceleration signal collection/pre-processing, wavelet-based principle component analysis, and a support vector machine classifier. The proposed activity recognition method has been experimentally validated through two batches of trials with an overall mean classification accuracy of 95.25 and 94.87%, respectively. These results suggest that the intelligent clothing is not only able to learn the activity patterns but also capable of generalizing new data from both known and unknown subjects. This enables the proposed intelligent clothing to be applied in a comfortable and in situ assessment of human physical activities, which would open up new market segments to the textile industry.