Web-based collaborative inquiry learning

Abstract This study proposes a web-based collaborative inquiry learning system. This system uses the World-wide web (WWW) as a source of knowledge exploration, and provides exploratory problems to guide students to think and explore. A concept map is used as a tool of anchoring and representing knowledge during inquiry process. In the process of learning, learners are allowed to exchange the evidence they have collected, their personal opinions, and the concept maps that they have built. In order to effectively integrate the inquiry learning, collaborative learning, and concept map in the system, this study proposes a collaborative inquiry learning model and related learning activities. Two studies were constructed based on the collaborative inquiry learning model to investigate students' learning processes in the collaborative inquiry learning on the web.     

Voltage-Clock Scaling for Low Energy Consumption in Fixed-Priority Real-Time Systems

Power and energy constraints are becoming increasingly prevalent in real-time embedded systems. Voltage-scaling is a promising technique to reduce energy and power consumption: clock speed tends to decrease linearly with supply voltage while power consumption goes down quadratically. We therefore have a tradeoff between the energy consumption of a task and the speed with which it can be completed. The timing constraints associated with real-time tasks can be used to resolve this tradeoff. In this paper, we present two algorithms for voltage-scaling. Assuming that a processor can operate in one of two modes: high voltage and low voltage, we show how to schedule the voltage settings so that deadlines are met while reducing the total energy consumed. We show that significant reductions can be made in energy consumption.     

Realization of a self-recognition algorithm based on the biological immune system

As the use of the computer is popularized, the damage from computer viruses and hacking by malicious users is increasing rapidly. To block the hacking that is an intrusion into a person's computer, and the viruses that destroy data, a study into an intrusion detection and virus detection system based on the biological immune system is in progress. In this article, we describe a model of positive and negative selection for self-recognition, which has a similar function to the cytotoxic T cells that play an important role in the biological immune system. We propose a self/nonself discrimination algorithm for a computer system, which will the important when we detect data infected by a computer virus, of data modified by an intrusion from outside. We also show the validity and effectiveness of the proposed self-recognition algorithm by a computer simulation of some infected data obtained from cell changes and string changes in the self-file. This work was presented, in part, at the Seventh International Symposium on Artificial Life and Robotics, Oita, Japan, January 16–18, 2002     

On Advances in Statistical Modeling of Natural Images

Statistical analysis of images reveals two interesting properties: (i) invariance of image statistics to scaling of images, and (ii) non-Gaussian behavior of image statistics, i.e. high kurtosis, heavy tails, and sharp central cusps. In this paper we review some recent results in statistical modeling of natural images that attempt to explain these patterns. Two categories of results are considered: (i) studies of probability models of images or image decompositions (such as Fourier or wavelet decompositions), and (ii) discoveries of underlying image manifolds while restricting to natural images. Applications of these models in areas such as texture analysis, image classification, compression, and denoising are also considered.     

A Noninformative Prior for Neural Networks

While many implementations of Bayesian neural networks use large, complex hierarchical priors, in much of modern Bayesian statistics, noninformative (flat) priors are very common. This paper introduces a noninformative prior for feed-forward neural networks, describing several theoretical and practical advantages of this approach. In particular, a simpler prior allows for a simpler Markov chain Monte Carlo algorithm. Details of MCMC implementation are included.     

Edge-preserving texture filtering for real-time rendering

Texture filtering is essential in enhancing the visual quality of real-time rendering. Conventional schemes do not consider the characteristics of texture content, thus the sharpness of edges in texture images cannot be retained. This paper proposes a novel texture-filtering algorithm, which consists of edge-preserving interpolation and edge-preserving MIP-map prefiltering. The memory bandwidth requirement is kept the same as in conventional schemes by dynamically adjusting the interpolation kernel. Hardware implementation is also provided to show the real-time processing capability. Published online: 28 January 2003     

双机器人协调作业下碰撞算法及仿真研究

本文立足于关节水平，从双机器人碰撞研究入手，提出了机器人协同作业下碰撞检测 、干涉空间的算法，解决了机器人与周边环境任意障碍物碰撞问题．建立了连杆与连杆，连 杆与平面，连杆与任意三维实体避障理论模型，给出了相应的计算方法及计算机仿真结果．     

Robust stability analysis and design method for the fuzzy feedbacklinearization regulator

A robust stability analysis and design method for a fuzzy feedback linearization regulator is presented. The well-known Takagi-Sugeno fuzzy model is used as the nonlinear plant model. Uncertainties and disturbance are assumed to be included in the model structure with known bounds. For these structured uncertainties, stability robustness of the closed system is analyzed in both input-output sense and Lyapunov sense. The robust stability conditions are proposed using multivariable circle criterion and the relationship between input-output stability and Lyapunov stability. Also, based on the stability analysis, a systematic design procedure for the fuzzy feedback linearization regulator is provided. The effectiveness of the proposed analysis and design method is illustrated by a simple example     

Sensor Fusion and Planning with Perception–Action Network

Robot intelligence requires a real-time connection between sensing and action. A new computation principle of robotics that efficiently implements such a connection is utmost important for the new generation of robotics. In this paper, a perception–action network is presented as a means of efficiently integrating sensing, knowledge, and action for sensor fusion and planning. The network consists of a number of heterogeneous computational units, representing feature transformation and decision-making for action, which are interconnected as a dynamic system. New input stimuli to the network invoke the evolution of network states to a new equilibrium, through which a real-time integration of sensing, knowledge, and action can be accomplished. The network provides a formal, yet general and efficient, method of achieving sensor fusion and planning. This is because the uncertainties of signals, propagated in the network, can be controlled by modifying sensing parameters and robot actions. Algorithms for sensor planning based on the proposed network are established and applied to robot self-localization. Simulation and experimental results are shown.     

Disk placement for arbitrary-rate playback in an interactive video server

Multimedia data, especially continuous media including video and audio objects, represent a rich and natural stimulus for humans, but require large amount of storage capacity and real-time processing. In this paper, we describe how to organize video data efficiently on multiple disks in order to support arbitrary-rate playback requested by different users independently. Our approach is to segment and decluster video objects and to place the segments in multiple disks using a restricted round-robin scheme, called prime round-robin (PRR). Its placement scheme provides uniform load balance of disks for arbitrary retrieval rate as well as normal playback, since it eliminates hot spots. Moreover, it does not require any additional disk bandwidth to support VCR-like operations such as fast-forward and rewind. We have studied the various effects of placement and retrieval schemes in a storage server by simulation. The results show that PRR offers even disk accesses, and the failure in reading segment by deadline occurs only at the beginning of new operations. In addition, the number of users admitted is not decreased, regardless of arbitrary-rate playback requests.