预分解窑空气炮运用的节能效果

随着能源成本的增加,水泥行业不断寻求利用石油焦炭等替代燃料,但这些替代燃料的使用增加了物料在预热器、窑、冷却机中的阻塞和堆积。对物料的堵塞和堆积,常用的清除方法会带来许多弊端。利用空气炮防止物料的阻塞和堆积比较有效,可以从熟料质量、熟料产量、耐火材料寿命、能源、安全性等方面达到降低成本的目的。马丁公司在预热器和冷却器的改造方面与水泥行业有非常紧密的合作,墨西哥水泥厂是这种合作关系成功的典范。从1996年至今,墨西哥水泥厂已经安装了大约2900个马丁公司的BIG BLASTER空气炮,每年因为安装空气炮带来的收益达27万美元。基于此,UCC万吨熟料生产线也不例外地选用于马丁公司的空气炮。     

Range Unit‐Root (RUR) Tests: Robust against Nonlinearities,Error Distributions,Structural Breaks and Outliers

Abstract. Since the seminal paper by Dickey and Fuller in 1979, unit‐root tests have conditioned the standard approaches to analysing time series with strong serial dependence in mean behaviour, the focus being placed on the detection of eventual unit roots in an autoregressive model fitted to the series. In this paper, we propose a completely different method to test for the type of long‐wave patterns observed not only in unit‐root time series but also in series following more complex data‐generating mechanisms. To this end, our testing device analyses the unit‐root persistence exhibited by the data while imposing very few constraints on the generating mechanism. We call our device the range unit‐root (RUR) test since it is constructed from the running ranges of the series from which we derive its limit distribution. These nonparametric statistics endow the test with a number of desirable properties, the invariance to monotonic transformations of the series and the robustness to the presence of important parameter shifts. Moreover, the RUR test outperforms the power of standard unit‐root tests on near‐unit‐root stationary time series; it is invariant with respect to the innovations distribution and asymptotically immune to noise. An extension of the RUR test, called the forward–backward range unit‐root (FB‐RUR) improves the check in the presence of additive outliers. Finally, we illustrate the performances of both range tests and their discrepancies with the Dickey–Fuller unit‐root test on exchange rate series.     

An autonomous educational mobile robot mediator

So far, most of the applications of robotic technology to education have mainly focused on supporting the teaching of subjects that are closely related to the Robotics field, such as robot programming, robot construction, or mechatronics. Moreover, most of the applications have used the robot as an end or a passive tool of the learning activity, where the robot has been constructed or programmed. In this paper, we present a novel application of robotic technologies to education, where we use the real world situatedness of a robot to teach non-robotic related subjects, such as math and physics. Furthermore, we also provide the robot with a suitable degree of autonomy to actively guide and mediate in the development of the educational activity. We present our approach as an educational framework based on a collaborative and constructivist learning environment, where the robot is able to act as an interaction mediator capable of managing the interactions occurring among the working students. We illustrate the use of this framework by a 4-step methodology that is used to implement two educational activities. These activities were tested at local schools with encouraging results. Accordingly, the main contributions of this work are: i) A novel use of a mobile robot to illustrate and teach relevant concepts and properties of the real world; ii) A novel use of robots as mediators that autonomously guide an educational activity using a collaborative and constructivist learning approach; iii) The implementation and testing of these ideas in a real scenario, working with students at local schools.

Key elements on team achievement: a retrospective analysis

This study is a historical reconstruction and in-depth scrutiny of one very successful team initiative within a Quality Improvement (QI) program in a municipal government organization. The identification of the essential concepts that contributed to the team's success and their integration into a local explanatory theory of team achievement is the final purpose of the study. The focus of this retrospective study is on the team process, with primary attention given to team members’ perspectives and reflections on the project development. Data collection and analysis were conducted with methods drawn from the qualitative research tradition and from Quality Management.

Main findings point to the importance of consistent management support, correct team composition with an emphasis on the team leader choice and demeanor, and to the central role of training in the group conduct. The negotiated approach to decision-making employed by the team, which reflected its organizational context, and the effort and mechanisms that allowed the group to reach equilibrium between internal and external interests proved critical for its ultimate achievement.     

DeepClassRooms: a deep learning based digital twin framework for on-campus class rooms

Neural Computing and Applications - A lot of different methods are being opted for improving the educational standards through monitoring of the classrooms. The developed world uses Smart...     

TWC LOGD: A portal for linked open government data ecosystems

International open government initiatives are releasing an increasing volume of raw government datasets directly to citizens via the Web. The transparency resulting from these releases not only creates new application opportunities but also imposes new burdens inherent to large-scale distributed data integration, collaborative data manipulation and transparent data consumption. The Tetherless World Constellation (TWC) at Rensselaer Polytechnic Institute (RPI) has developed the Semantic Web-based TWC LOGD portal to support the deployment of linked open government data (LOGD). The portal is both an open source infrastructure supporting linked open government data production and consumption and a vibrant community portal that educates and serves the growing international open government community of developers, data curators and end users. This paper motivates and introduces the TWC LOGD Portal and highlights innovative aspects and lessons learned.     

Light-weight salient foreground detection for embedded smart cameras

An embedded smart camera is a stand-alone unit that not only captures images, but also includes a processor, memory and communication interface. Battery-powered, embedded smart cameras introduce many additional challenges since they have very limited resources, such as energy, processing power and memory. Computer vision algorithms running on these camera boards should be light-weight and efficient. Considering the memory requirements of an algorithm and its portability to an embedded processor should be an integral part of the algorithm design in addition to the accuracy requirements. This paper presents a light-weight and efficient background modeling and foreground detection algorithm that is highly robust against lighting variations and non-static backgrounds including scenes with swaying trees, water fountains and rain. Compared to many traditional methods, the memory requirement for the data saved for each pixel is very small in the proposed algorithm. Moreover, the number of memory accesses and instructions are adaptive, and are decreased depending on the amount of activity in the scene. Each pixel is treated differently based on its history, and instead of requiring the same number of memory accesses and instructions for every pixel, we require less instructions for stable background pixels. The plot of the number of unstable pixels at each frame also serves as a tool to find the video portions with high activity. The proposed method selectively updates the background model with an automatically adaptive rate, thus can adapt to rapid changes. As opposed to traditional methods, pixels are not always treated individually, and information about neighbors is incorporated into decision making. The results obtained with nine challenging outdoor and indoor sequences are presented, and compared with the results of different state-of-the-art background subtraction methods. The ROC curves and memory comparison of different background subtraction methods are also provided. The experimental results demonstrate the success of the proposed light-weight salient foreground detection method.     

Shape-sensitive MLS deformation

This work presents methods for deforming meshes in a shape-sensitive way using Moving Least Squares (MLS) optimization. It extends an approach for deforming space (Cuno et al. in Proceedings of the 27th Computer Graphics International Conference, pp. 115–122, 2007) by showing how custom distance metrics may be used to achieve deformations which preserve the overall mesh shape. Several variant formulations are discussed and demonstrated, including the use of geodesic distances, distances constrained to paths contained in the mesh, the use of skeletons, and a reformulation of the MLS scheme which makes it possible to affect the bending behavior of the deformation. Finally, aspects of the implementation of these techniques in parallel architectures such as GPUs (graphics processing units) are described and compared with CPU-only implementations.     

A urine patch framework to simulate nitrogen leaching on New Zealand dairy farms

On New Zealand dairy farms, it is the nitrogen excreted directly onto pasture, particularly urine, that drives nitrogen (N) leaching from the farm. A new framework (UPF: Urine Patch Framework) is presented that post-processes the results of a whole farm model and runs a mechanistic soil model to simulate the urine patches. Two alternative methods to simulate the spatial distribution of urine patches were implemented and compared (Grid: spatially explicit, and Probabilistic: based on the probability of different temporal urination patterns). This paper describes the implementation of these two methods in connection with a Whole Farm Model; and compares the N leaching predictions with observed data. Two examples are provided, one analyzing the impact of urine patch overlap and another, the relative risk of N leaching at different times of urinary N deposition. The model showed good correlation and predictive ability between simulated annual N leaching results and observed data [R² = 94 %, mean relative prediction error (MRPE) = 10 % for Grid and R² = 72 %, MRPE = 20 % for Probabilistic]. The two methods produced similar results across an 8-year period for monthly and annual N leaching (R² = 96 %, MRPE = 10 % and R² = 86 %, MRPE = 8 %; respectively). Only 8 % of the paddock area was covered with multiple urinations during 1 year, but as much as 39 % of the total urine volume was deposited on overlapped patches. Systematically removing all urinary N for 1 month in either May or June reduced N leaching by approximately 20 %. Avoiding urinary N deposition during autumn or early winter could be highly effective in mitigating N leached during the following winter.