攀钢钢包自动开浇技术研究

通过信息调研、实验室研究及现场工业试验 ,研制出了适应攀钢生产条件的钢包引流剂 ,使模铸自开率从2 0 %～ 30 %提高到 95%以上 ,连铸钢包自开率从 50 %提高到 80 %以上。     

Stabilizing static output feedback receding horizon controls for linear discrete time-invariant systems

In this paper, a stabilizing static output feedback receding horizon control (RHC) is proposed for linear discrete time-invariant systems. An inequality condition on the terminal weighting matrices is presented under which the closed-loop stability of static output feedback receding horizon controls is guaranteed. It is shown that the stabilizing static output feedback receding horizon control problem can be represented as a non-linear minimization problem based on linear matrix inequalities (LMIs). An algorithm for solving the presented non-linear minimization problem is proposed. In addition, this result is extended to an arbitrary reference tracking problem that is one of the main features of receding horizon control. Finally, the efficiency of the proposed algorithm is illustrated by numerical examples.     

A study on the student pilot's mental workload due to personality types of both instructor and student

The main purpose of this paper is to provide the ideal flight crew combination for instructor and student pilots in order to enhance the flight training effects based on Myers Briggs Type Indicator (MBTI) theory. In addition to personality, various levels of flight difficulty are considered in order to investigate their potential interaction effect in terms of student pilots' mental workload measured in heart rate, altitude deviation, NASA-TLX (Task Load Index) and subjective degree of personality harmony. Based on an experiment performed in a real flight situation, we found significant effects of personality combinations in terms of all four outcome measures. Both group C types of instructors and students who are concrete, realistic and have mechanical skills turn out to be the ideal flight crew combination. A structural equation model, fitted to analyse causality among the four response variables, implied that as the heart rate increased, the altitude deviation increased. In addition, as the altitude deviation and personality harmony increased, NASA-TLX increased. The results of this study are expected to provide a theoretical basis for manning the flight crew combinations and thereby enhancing the efficiency of flight training.     

Interleaved Traffic Splitting: A promising technique to solve False Timeout

In this paper, we analyze the False Timeout (FTO) problem that TCP flows suffered in OBS networks and propose Interleaved Traffic Splitting (ITS) to solve the problem. We show that the collision loss of ACK bursts may also cause FTO, which has been neglected by previous researchers. As TCP Sack is designed to recover from multiple packet losses, we find that a Sack flow can recover its data transmission without triggering retransmission timeout if packet loss is not too severe. This suggests limiting the number of packets carried by a single burst. In ITS, this is achieved by carrying out a parallel, interleaved burst assembly process at each ingress OBS edge router. We show that ITS tackles the FTO problem caused by the loss of both data and ACK bursts. Unlike other existing approaches, the ITS scheme requires only modification to the assembly/de-assembly mechanisms implemented at the edge routers. It does not add complexities to the network management or the router design. In this paper, both analytical and simulation results are presented to demonstrate the performance improvement of ITS. Our analysis further shows that the use of two parallel bursts is optimum for ITS.     

Filtering Data Streams for Entity-Based Continuous Queries

The idea of allowing query users to relax their correctness requirements in order to improve performance of a data stream management system (e.g., location-based services and sensor networks) has been recently studied. By exploiting the maximum error (or tolerance) allowed in query answers, algorithms for reducing the use of system resources have been developed. In most of these works, however, query tolerance is expressed as a numerical value, which may be difficult to specify. We observe that in many situations, users may not be concerned with the actual value of an answer, but rather which object satisfies a query (e.g., "who is my nearest neighbor?”). In particular, an entity-based query returns only the names of objects that satisfy the query. For these queries, it is possible to specify a tolerance that is "nonvalue-based.” In this paper, we study fraction-based tolerance, a type of nonvalue-based tolerance, where a user specifies the maximum fractions of a query answer that can be false positives and false negatives. We develop fraction-based tolerance for two major classes of entity-based queries: 1) nonrank-based query (e.g., range queries) and 2) rank-based query (e.g., k-nearest-neighbor queries). These definitions provide users with an alternative to specify the maximum tolerance allowed in their answers. We further investigate how these definitions can be exploited in a distributed stream environment. We design adaptive filter algorithms that allow updates be dropped conditionally at the data stream sources without affecting the overall query correctness. Extensive experimental results show that our protocols reduce the use of network and energy resources significantly.     

3D human modeling from a single depth image dealing with self-occlusion

This paper presents a 2D to 3D conversion scheme to generate a 3D human model using a single depth image with several color images. In building a complete 3D model, no prior knowledge such as a pre-computed scene structure and photometric and geometric calibrations is required since the depth camera can directly acquire the calibrated geometric and color information in real time. The proposed method deals with a self-occlusion problem which often occurs in images captured by a monocular camera. When an image is obtained from a fixed view, it may not have data for a certain part of an object due to occlusion. The proposed method consists of following steps to resolve this problem. First, the noise in a depth image is reduced by using a series of image processing techniques. Second, a 3D mesh surface is constructed using the proposed depth image-based modeling method. Third, the occlusion problem is resolved by removing the unwanted triangles in the occlusion region and filling the corresponding hole. Finally, textures are extracted and mapped to the 3D surface of the model to provide photo-realistic appearance. Comparison results with the related work demonstrate the efficiency of our method in terms of visual quality and computation time. It can be utilized in creating 3D human models in many 3D applications.     

A nonlinear minimization approach to multiobjective and structured controls for discrete‐time systems

In this paper, a nonlinear minimization approach is proposed for multiobjective and structured controls for discrete‐time systems. The problem of finding multiobjective and structured controls for discrete‐time systems is represented as a quadratic matrix inequality problem. It is shown that the problem is reduced to a nonlinear minimization problem that has a concave objective function and linear matrix inequality constraints. An algorithm for the nonlinear minimization problem is proposed, which is easily implemented with existing semidefinite programming algorithms. The validity of the proposed algorithm is illustrated by comparisons with existing methods. In addition, applications of this work are demonstrated via numerical examples. Copyright © 2004 John Wiley & Sons, Ltd.     

Personalized trip recommendation for tourists based on user interests,points of interest visit durations and visit recency

Tour recommendation and itinerary planning are challenging tasks for tourists, due to their need to select points of interest (POI) to visit in unfamiliar cities and to select POIs that align with their interest preferences and trip constraints. We propose an algorithm called PersTour for recommending personalized tours using POI popularity and user interest preferences, which are automatically derived from real-life travel sequences based on geo-tagged photographs. Our tour recommendation problem is modeled using a formulation of the Orienteering problem and considers user trip constraints such as time limits and the need to start and end at specific POIs. In our work, we also reflect levels of user interest based on visit durations and demonstrate how POI visit duration can be personalized using this time-based user interest. Furthermore, we demonstrate how PersTour can be further enhanced by: (i) a weighted updating of user interests based on the recency of their POI visits and (ii) an automatic weighting between POI popularity and user interests based on the tourist’s activity level. Using a Flickr dataset of ten cities, our experiments show the effectiveness of PersTour against various collaborative filtering and greedy-based baselines, in terms of tour popularity, interest, recall, precision and F\(_1\)-score. In particular, our results show the merits of using time-based user interest and personalized POI visit durations, compared to the current practice of using frequency-based user interest and average visit durations.     

Chart Constellations: Effective Chart Summarization for Collaborative and Multi‐User Analyses

Many data problems in the real world are complex and require multiple analysts working together to uncover embedded insights by creating chart‐driven data stories. How, as a subsequent analysis step, do we interpret and learn from these collections of charts? We present Chart Constellations, a system to interactively support a single analyst in the review and analysis of data stories created by other collaborative analysts. Instead of iterating through the individual charts for each data story, the analyst can project, cluster, filter, and connect results from all users in a meta‐visualization approach. Constellations supports deriving summary insights about prior investigations and supports the exploration of new, unexplored regions in the dataset. To evaluate our system, we conduct a user study comparing it against data science notebooks. Results suggest that Constellations promotes the discovery of both broad and high‐level insights, including theme and trend analysis, subjective evaluation, and hypothesis generation.