Short-term load forecasting using bayesian neural networks learned by Hybrid Monte Carlo algorithm

This paper presents a short term load forecasting model based on Bayesian neural network (shorted as BNN) learned by the Hybrid Monte Carlo (shorted as HMC) algorithm. The weight vector parameter of the Bayesian neural network is a multi-dimensional random variable. In learning process, the Bayesian neural network is considered as a special Hamiltonian dynamical system, and the weights vector as the system position variable. The HMC algorithm is used to learn the weight vector parameter with respect to Normal prior distribution and Cauchy prior distribution, respectively. The Bayesian neural networks learned by Laplace algorithm and HMC algorithm and the artificial neural network (ANN) learned by the BP algorithm were used to forecast the hourly load of 25 days of April (Spring), August (Summer), October (Autumn) and January (Winter), respectively. The roots mean squared error (RMSE) and the mean absolute percent errors (MAPE) were used to measured the forecasting performance. The experimental result shows that the BNNs learned by HMC algorithm have far better performance than the BNN learned by Laplace algorithm and the neural network learned BP algorithm and the BNN learned by HMC has powerful generalizing capability, it can welly solve the overfitting problem.     

An efficient initialization approach of Q-learning for mobile robots

This article demonstrates that Q-learning can be accelerated by appropriately specifying initial Q-values using dynamic wave expansion neural network. In our method, the neural network has the same topography as robot work space. Each neuron corresponds to a certain discrete state. Every neuron of the network will reach an equilibrium state according to the initial environment information. The activity of the special neuron denotes the maximum cumulative reward by following the optimal policy from the corresponding state when the network is stable. Then the initial Q-values are defined as the immediate reward plus the maximum cumulative reward by following the optimal policy beginning at the succeeding state. In this way, we create a mapping between the known environment information and the initial values of Q-table based on neural network. The prior knowledge can be incorporated into the learning system, and give robots a better learning foundation. Results of experiments in a grid world problem show that neural network-based Q-learning enables a robot to acquire an optimal policy with better learning performance compared to conventional Q-learning and potential field-based Qlearning.     

中医正骨复位手法信息采集系统的设计

研究中医正骨复位手法,建立虚拟信息系统,但是由于手法的使用形式繁多,实施操作方法经验性强,对操作的技巧性和力度大小要求非常高等原因,使得正骨技术在临床中的应用不易推广,影响着中医正骨复位手法的的继承和发扬。针对上述原因,提出构建虚拟正骨手法复位信息采集系统,通过LabVIEW软件、角度传感器,信息采集卡等硬件设施有机的结合,把抽象的中医骨伤手法复位操作技术用角度、位移参数值具体、直观的表示出来,并以波形图表的形式显示。为初学者认识和学习正骨手法提供了生动形象的教材,改进了传统的教学模式、教学手段和教学方法,进一步促进虚拟技术在中医正骨中的应用。     

Adsorption characteristics of hexadecyl ammonium with different numbers of carbon chains in montmorillonite and the structure of the prepared composites

Journal of Porous Materials - The dynamics and thermodynamics of adsorption of hexadecyl ammonium with different numbers of carbon chains in montmorillonite (Mt) with different layer charge density...     

A one-shot bargaining strategy for dealing with multifarious opponents

Bargaining is an effective paradigm to solve the problem of resource allocation. The consideration of factors such as bounded rationality of negotiators, time constraints, incomplete information, and complexity of dynamic environment make the design of optimal strategy for one-shot bargaining much tougher than the situation that all bargainers are assumed to be absolutely rational. Lots of prediction-based strategies have been explored either based on assuming a finite number of models for opponents, or focusing on the prediction of opponent’s reserve price, deadline, or the probabilities of different behaviors. Following the methods of estimating opponent’s private information, this paper gives a strategy which improves the BLGAN strategy to adapt to various possible bargaining situations and deal with multifarious opponents. In addition, this paper compares the improved BLGAN strategy with related work. Experimental results show that the improved BLGAN strategy can outperform related ones when faced with various opponents, especially the agents who frequently change their strategies for anti-learning.     

案例教学方法在《土木工程CAD》教学中的应用

基于对《土木工程CAD》教学中案例教学方法存在的问题的分析,结合《土木工程CAD》课程教学内容,通过精心选择案例,在课堂教学、实训教学和课程考核中应用教学案例,改善课程教学效果。教学实践表明,案例教学方法的应用明确课程的学习内容,激发学生的学习兴趣,保证课程教学效果,提高学生分析和解决问题的能力。     

Mapping streaming applications on multiprocessors with time-division-multiplexed network-on-chip

This paper addresses mapping of streaming applications (such as MPEG) on multiprocessor platforms with time-division-multiplexed network-on-chip. In particular, we solve processor selection, path selection and router configuration problems. Given the complexity of these problems, state of the art approaches in this area largely rely on greedy heuristics, which do not guarantee optimality. Our approach is based on a constraint programming formulation that merges a number of steps, usually tackled in sequence in classic approaches. Thus, our method has the potential of finding optimal solutions with respect to resource usage under throughput constraints. The experimental evaluation presented in here shows that our approach is capable of exploring a range of solutions while giving the designer the opportunity to emphasize the importance of various design metrics.     

Hierarchical representation of video sequences for annotation

Video annotation is an important issue in video content management systems. Rapid growth of the digital video data has created a need for efficient and reasonable mechanisms that can ease the annotation process. In this paper, we propose a novel hierarchical clustering based system for video annotation. The proposed system generates a top-down hierarchy of the video streams using hierarchical k-means clustering. A tree-based structure is produced by dividing the video recursively into sub-groups, each of which consists of similar content. Based on the visual features, each node of the tree is partitioned into its children using k-means clustering. Each sub-group is then represented by its key frame, which is selected as the closest frame to the centroids of the corresponding cluster, and then can be displayed at the higher level of the hierarchy. The experiments show that very good hierarchical view of the video sequences can be created for annotation in terms of efficiency.