Optimal cyclic single crane scheduling for two parallel train oilcan repairing lines

Crane is widely used to move a heavy object from one place to another not only in manufacturing industry but also service industry. As an important resource in the train oilcan repairing, crane scheduling affects directly the productivity of the systems. In this paper, we study cyclic single crane scheduling problem with two parallel train oilcan repairing lines, where jobs are loaded into the line at one end and unloaded at the other end. The processing time at each workstation must be within a given range. There is no buffer between these stations. A crane is used to move jobs between the workstations in two parallel lines. The objective is to schedule the moves to minimize the production cycle. We proposed a time way diagram for two parallel lines and developed a mixed integer linear programming model. Then we extended the model to the scheduling problem with multi-station to eliminate the bottleneck in lines. Examples are given to demonstrate the effectiveness of the model.     

Tree-turn routing: an efficient deadlock-free routing algorithm for irregular networks

In this paper, we propose a general turn model, called a Tree-turn model, for tree-based routing algorithms on irregular topologies. In the Tree-turn model, links are classified as either a tree link or a cross link and six directions are associated with the channels of links. Then we can prohibit some of the turns formed by these six directions such that an efficient deadlock-free routing algorithm, Tree-turn routing, can be derived. There are three phases to develop the Tree-turn routing. First, a coordinated tree for a given topology is created. Second, a communication graph is constructed based on the topology and the corresponding coordinated tree. Third, the forwarding table is set up by using all-pairs shortest path algorithm according to the prohibited turns in the Tree-turn model and the directions of the channels in the communication graph. To evaluate the performance of the proposed Tree-turn routing, we develop a simulator and implement Tree-turn routing along with up*/down* routing, L-turn routing, and up*/down* routing with DFS methodology. The simulation results show that Tree-turn routing outperforms other routing algorithms for all the test cases.     

Contextual correlation based thread detection in short text message streams

Short text message streams are produced by Instant Messaging and Short Message Service which are wildly used nowadays. Each stream contains more than one thread usually. Detecting threads in the streams is helpful to various applications, such as business intelligence, investigation of crime and public opinion analysis. Existing works which are mainly based on text similarity encounter many challenges including the sparse eigenvector and anomaly of short text message. This paper introduces a novel concept of contextual correlation instead of the traditional text similarity into single-pass clustering algorithm to cover the challenges of thread detection. We firstly analyze the contextually correlative nature of conversations in short text message streams, and then propose an unsupervised method to compute the correlative degree. As a reference, a single-pass algorithm employing the contextual correlation is developed to detect threads in massive short text stream. Experiments on large real-life online chat logs show that our approach improves the performance by 11% when compared with the best similarity-based algorithm in terms of F1 measure.     

Relevance feature mapping for content-based multimedia information retrieval

This paper presents a novel ranking framework for content-based multimedia information retrieval (CBMIR). The framework introduces relevance features and a new ranking scheme. Each relevance feature measures the relevance of an instance with respect to a profile of the targeted multimedia database. We show that the task of CBMIR can be done more effectively using the relevance features than the original features. Furthermore, additional performance gain is achieved by incorporating our new ranking scheme which modifies instance rankings based on the weighted average of relevance feature values. Experiments on image and music databases validate the efficacy and efficiency of the proposed framework.     

Robust and efficient ridge-based palmprint matching

During the past decade, many efforts have been made to use palmprints as a biometric modality. However, most of the existing palmprint recognition systems are based on encoding and matching creases, which are not as reliable as ridges. This affects the use of palmprints in large-scale person identification applications where the biometric modality needs to be distinctive as well as insensitive to changes in age and skin conditions. Recently, several ridge-based palmprint matching algorithms have been proposed to fill the gap. Major contributions of these systems include reliable orientation field estimation in the presence of creases and the use of multiple features in matching, while the matching algorithms adopted in these systems simply follow the matching algorithms for fingerprints. However, palmprints differ from fingerprints in several aspects: 1) Palmprints are much larger and thus contain a large number of minutiae, 2) palms are more deformable than fingertips, and 3) the quality and discrimination power of different regions in palmprints vary significantly. As a result, these matchers are unable to appropriately handle the distortion and noise, despite heavy computational cost. Motivated by the matching strategies of human palmprint experts, we developed a novel palmprint recognition system. The main contributions are as follows: 1) Statistics of major features in palmprints are quantitatively studied, 2) a segment-based matching and fusion algorithm is proposed to deal with the skin distortion and the varying discrimination power of different palmprint regions, and 3) to reduce the computational complexity, an orientation field-based registration algorithm is designed for registering the palmprints into the same coordinate system before matching and a cascade filter is built to reject the nonmated gallery palmprints in early stage. The proposed matcher is tested by matching 840 query palmprints against a gallery set of 13,736 palmprints. Experimental results show that the proposed matcher outperforms the existing matchers a lot both in matching accuracy and speed.     

Reference price effect and its implications for decision making in online auctions: An empirical study

Simultaneous supplies of an item by multiple online auction vendors greatly reduce the burden of arduous search that a consumer has to conduct in a brick-and-mortar market. The enriched information set endows all auction participants with added decision flexibility and complexity. One of such flexibilities or complications is the potential of information cross-referencing. In this study, we empirically tested if and how bidders interacted with each other through cross-referencing. Our results strongly supported the existence of cross-referencing. We also identified new strategies and tactical moves as best responses for sellers and bidders under the online simultaneous condition. This research provided another support for the path-dependent nature of online auctions as bidders were found to actively adjust their behavior in avoidance of winner's curse.     

Modeling NOx emissions from coal-fired utility boilers using support vector regression with ant colony optimization

Modeling NO_x emissions from coal fired utility boiler is critical to develop a predictive emissions monitoring system (PEMS) and to implement combustion optimization software package for low NO_x combustion. This paper presents an efficient NO_x emissions model based on support vector regression (SVR), and compares its performance with traditional modeling techniques, i.e., back propagation (BPNN) and generalized regression (GRNN) neural networks. A large number of NO_x emissions data from an actual power plant, was employed to train and validate the SVR model as well as two neural networks models. Moreover, an ant colony optimization (ACO) based technique was proposed to select the generalization parameter C and Gaussian kernel parameter γ. The focus is on the predictive accuracy and time response characteristics of the SVR model. Results show that ACO optimization algorithm can automatically obtain the optimal parameters, C and γ, of the SVR model with very high predictive accuracy. The predicted NO_x emissions from the SVR model, by comparing with the BPNN model, were in good agreement with those measured, and were comparable to those estimated from the GRNN model. Time response of establishing the optimum SVR model was in scale of minutes, which is suitable for on-line and real-time modeling NO_x emissions from coal-fired utility boilers.     

小型无人直升机地面控制站设计与实现

通过分析小型无人直升机对地面控制站的需求,介绍地面控制站的结构和功能,分析开发地面控制站涉及的关键技术和解决方案,控制站采用基于面向对象的VC++开发环境和嵌入式Matlab混合编程技术。通过在某小型无人直升机飞行实验中的应用,证明该系统具有实时性强、稳定性好、人机界面友好、可扩展能力强等优点。     

装甲车车身自动调平系统原理及算法设计

为了提高6点装甲车辆调平的快速性和稳定性,根据装甲车结构,考虑响应时间、速度和可靠性等综合因素,将车身系统由6点调平系统等效为4点调平系统。利用相应的角度和压力传感器检测车架的水平状态,以确定油气簧的动作量。采用双速调整的方法计算比例阀门的开启大小及时间,运用西门子控制器实现对调平系统的控制。试验结果表明,该系统具有较高的快速性和稳定性。