21082976
Real-time machine learning in embedded software and hardware platforms
This paper describes on-going research work into real-time machine learning using embedded software and reconfigurable hardware. The main focus of the work is to develop real-time incremental learning methods particularly targeted at demonstration in mobile robot environments. Three main areas are described. The first represents reactive robot navigation knowledge using a novel frequency table technique whose memory requirement is known a priori. The second area investigates a Genetic Algorithm (GA) method that combines planning and reactive approaches to allow navigation to proceed even in the face of time constraints. In the third area we are developing novel hardware-based machine learning systems suitable for implementation in reconfigurable platforms.
16750916
Mobile Robot Path Planning in Dynamic Environments
This paper introduces a genetic algorithm (GA) planner that is able to rapidly determine optimal or near-optimal solutions for mobile robot path planning problems in environments containing moving obstacles. The method restricts the search space to the vertices of the obstacles, obviating the need to search the entire environment as in earlier GA-based approaches. The new approach is able to produce an off-line plan through an environment containing dynamic obstacles, and can also re-calculate the plan on-line to deal with any motion changes encountered. A particularly novel aspect of the work is the incorporation of the selection of robot speed into the GA genes. The results from a number of realistic environments demonstrate that planning changes in robot speed significantly improves the efficiency of movement through the static and moving obstacles.
3104032
Robot Navigation by Waypoints
In this paper we propose a novel waypoint-based robot navigation method that combines reactive and deliberative actions. The approach uses reactive exploration to generate waypoints that can then be used by a deliberative system to plan future movements through the same environment. The waypoints are used largely to provide the interface between reactive and deliberative navigation and a range of methods could be used for either type of navigation. In the current work, an incremental decision tree method is used to navigate the robot reactively from the specified initial position to its destination avoiding obstacles in its path and a genetic algorithm method is used to perform the deliberative navigation. The new method is shown to have a number of practical advantages. Firstly, in contrast with many deliberative approaches, complete knowledge of the environment is not required, nor is it necessary to make assumptions regarding the geometry of obstacles. Secondly, the presence of a reactive navigator means it is always possible to continue directed movements in unknown or changing environments or when time constraints become particularly demanding. Thirdly, the use of waypoints allows escape from certain obstacle configurations that would normally trap robots navigated under the control of purely reactive methods. In addition, the results presented in this paper from a number of realistic simulated environments show that the adoption of waypoints significantly reduces the time to calculate a deliberative path.
1772294
Scalable real-time system design using preemption thresholds
The maturity of schedulability analysis techniques for fixed-priority preemptive scheduling has enabled the consideration of timing issues at design time using a specification of the tasking architecture and estimates of execution times for tasks. While successful, this approach has limitations since the preemptive multi-tasking model does not scale well for a large number of tasks, and the fixed-priority scheduling theory does not work well with many object-oriented design methods. In this paper, we present an approach that scales well even when the design consists of a large number of concurrent jobs. The approach avoids any unnecessary preemptability in the system, thereby resulting in reduced run-time overheads from preemptions and associated context switches. It also allows significant memory savings by grouping jobs into non-preemptive groups and then sharing the stack space between them. Our approach is based on our earlier work on scheduling using preemption thresholds that allows parametric control over preemptability in a priority-based system. We show that our approach provides significant advantages over one using a fixed-priority preemptive scheduling architecture. The benefits include higher schedulability for small numbers of tasks and lower run-time overheads, and hence better scalability. We develop algorithms that allow design-time consideration of schedulability and automatic synthesis of an implementation model to minimize run-time overheads.
6870100
A hybrid cultural ecology: world of warcraft in China
We analyze online gaming as a site of collaboration in a digital-physical hybrid. We ground our analysis in findings from an ethnographic study of the online game World of Warcraft in China. We examine the interplay of collaborative practices across the physical environment of China's Internet cafes and the virtual game space of World of Warcraft. Our findings suggest that it may be fruitful to broaden existing notions of physical-digital hybridity by considering the nuanced interplay between the digital and physical as a multi-dimensional environment or "ecology". We illustrate how socio-economics, government regulations and cultural value systems shaped a hybrid cultural ecology of online gaming in China.
31735857
Research on Vehicle Image Classifier Based on Concentration Regulating of Immune Clonal Selection
For time-variation and nonlinearity of vehicle image classifier design in complicated environment, a novel classifier based on Immune Clone Concentration Clustering Algorithm (ICCCA) was proposed, inspired by the clone selection theory in the natural immune system. It overcame the defects of traditional classification algorithms as multi-restraint and trap of local optimum. It also avoided the high complexity and regulating dissimilation caused by the cross and variation rules of exiting immune clone clustering algorithms. Directing towards two-class image pattern recognition problem, we defined the antibody and antigen of vehicle image, established class divisibility standard, and designed the affine mathematical model based on the antibody and antigen's Euclidean distance and their concentration. We proposed a method to create immune clone memory cell by the antibodies concentration as long as concentration regulating. The algorithm can guarantee the antibody diversity, and obtain the global optimal solution quickly and precisely. The experimental results verified that the classification accuracy of the algorithm is superior to other classification algorithm.
6225988
A global optimal algorithm for class-dependent discretization of continuous data
This paper presents a new method to convert continuous variables into discrete variables for inductive machine learning. The method can be applied to pattern classification problems in machine learning and data mining. The discretization process is formulated as an optimization problem. We first use the normalized mutual information that measures the interdependence between the class labels and the variable to be discretized as the objective function, and then use fractional programming (iterative dynamic programming) to find its optimum. Unlike the majority of class-dependent discretization methods in the literature which only find the local optimum of the objective functions, the proposed method, OCDD, or Optimal Class-Dependent Discretization, finds the global optimum. The experimental results demonstrate that this algorithm is very effective in classification when coupled with popular learning systems such as C4.5 decision trees and Naive-Bayes classifier. It can be used to discretize continuous variables for many existing inductive learning systems.
14783345
Designing of LED Illuminating System and Testing Notice
With the development of LED, LED lighting has been gradually developed to commercial stage for its advantage of non-pollution, long life, easy to transport, and other advantages. Because of uneven development of LED industry in China and some engineering staff is short of professional knowledge, which result in the LED lighting was used inappropriately and unreasonable and unqualified phenomenon of the LED lighting system. This paper was based on the principles and characteristics of LED lighting, discussed the LED lighting system design process and points for attention, combined with experience in engineering practice, and provided suggestions to resolve some related problems. We hope to help the engineering staff of LED lighting system design.