Apparatus for steady-state kinetic measurements of the catalytic reduction of nitric oxide by ammonia on iron oxide

The reduction of nitric oxide with ammonia on an unsupported iron oxide catalyst has been studied in a continuous-flow recycle reactor using simulated flue gas. The responses of the employed reactor system to step and pulse inputs of tracer indicate that the system could be regarded as a continuous stirred tank reactor (CSTR). Preliminary tests were carried out to determine the effect of temperature and particle size on the measured reaction rates. Additional experiments were performed in order to study the influence of oxygen and water concentration on these rates. A gas chromatographic system has been developed to analyze the gas components NO, N₂O, NO₂, NH₃, H₂O, O₂, CO₂ and N₂. In addition, the concentrations of NO and NO₂ were measured with a nondisperse infrared (NDUV/NDIR) analyzer.     

Modal logic as metalogic

The goal of this paper is to show how modal logic may be conceived as recording the derived rules of a logical system in the system itself. This conception of modal logic was propounded by Dana Scott in the early seventies. Here, similar ideas are pursued in a context less classical than Scott's.First a family of propositional logical systems is considered, which is obtained by gradually adding structural rules to a variant of the nonassociative Lambek calculus. In this family one finds systems that correspond to the associative Lambek calculus, linear logic, relevant logics, BCK logic and intuitionistic logic. Above these basic systems, sequent systems parallel to the basic systems are constructed, which formalize various notions of derived rules for the basic systems. The deduction theorem is provable for the basic systems if, and only if, they are at least as strong as systems corresponding to linear logic, or BCK logic, depending on the language, and their deductive metalogic is not stronger than they are.However, though we do not always have the deduction theorem, we may always obtain a modal analogue of the deduction theorem for conservative modal extensions of the basic systems. Modal postulates which are necessary and sufficient for that are postulates of S4 plus modal postulates which mimic structural rules. For example, the modal postulates which Girard has recently considered in linear logic are necessary and sufficient for the modal analogue of the deduction theorem.All this may lead towards results about functional completeness in categories. When functional completeness, which is analogous to the deduction theorem, fails, we may perhaps envisage a modal analogue of functional completeness in a modal category, of which our original category is a full subcategory.     

分层网格划分实现海量地图标记物聚散可视化

在传统的地图可视化中,面对海量地图标记物展示会采用点聚合的方式,但是各类点聚合算法都是运行时计算,没有分层机制,在海量点的散开展示时,对于地图标记物堆叠没有过滤机制。针对这一问题,提出了分层的网格划分实现海量地图标记物聚散一体化解决方案。该方法对分层网格中心点构建K-D树索引,对海量点构建四叉树索引,通过索引和存储技术,实现了聚合的高效查询。对海量点散开时增加网格过滤,消除堆叠问题。在实验案例数据集上进行对比,结果表明,与传统的点聚合方案相比,在数据量大的情况下,计算性能显著提高,对海量标记物散开展示增加过滤算法,有效提升了用户体验。     

An adaptive method based on the improved LPA-ICI algorithm for MRI enhancement

Various diseases are diagnosed using medical imaging used for analysing internal anatomical structures. However, medical images are susceptible to noise introduced in both acquisition and transmission processes. We propose an adaptive data-driven image denoising algorithm based on an improvement of the intersection of confidence intervals (ICI), called relative ICI (RICI) algorithm. The 2D mask of the adaptive size and shape is calculated for each image pixel independently, and utilized in the design of the 2D local polynomial approximation (LPA) filters. Denoising performances, in terms of the PSNR, are compared to the original ICI-based method, as well as to the fixed sized filtering. The proposed adaptive RICI-based denoising outperformed the original ICI-based method by up to 1.32 dB, and the fixed size filtering by up to 6.48 dB. Furthermore, since the denoising of each image pixel is done locally and independently, the method is easy to parallelize.     

Liquidus temperatures for primary crystallization of cryolite in molten salt systems of interest for aluminum electrolysis

Temperatures for primary crystallization of Na₃AlF₆ in multicomponent electrolyte systems of interest for the aluminum electrolysis process were determined by thermal analysis. The results are presented as binary and quasibinary diagrams and discussed in view of the literature data. An empirical equation describing liquidus temperatures for primary crystallization of Na₃AlF₆ was derived: $$\begin{gathered} t/(^\circ C) = 1011 + 0.50[AlF_3 ] - 0.13[AIF_3 ] - \frac{{3.45[CaF_2 ]}}{{1 + 0.0173[CaF_2 ]}} \hfill \\ + 0.124[CaF_2 ] \cdot [AlF_3 ] - 0.00542([CaF_2 ] \cdot [AlF_3 ])^{1.5} \hfill \\ - \frac{{7.93[Al_2 O_3 ]}}{{1 + 0.0936[Al_2 O_3 ] - 0.0017[Al_2 O_3 ]^2 - 0.0023[AlF_3 ] \cdot [Al_2 O_3 ]}} \hfill \\ - \frac{{8.90[LiF]}}{{1 + 0.0047[LiF] + 0.0010[AlF3]^2 }} - 3.95[MgF_2 ] - 3.95 \hfill \\ \end{gathered} $$ wheret is the temperature in degree Celsius and the square brackets denote the weight percent of components in the system Na₃AlF₆-AlF₃-CaF₂-Al₂O₃-LiF-MgF₂-KF. The composition limitations are [AlF₃] ≈ [CaF₂] ≈ [LiF] < 20 wt pct, [MgF₂] ≈ [KF] < 5 wt pct, and [A1₂O₃] up to saturation.     

Medical application of information gain based artificial immune recognition system (AIRS): Diagnosis of thyroid disease

In this paper, we have made medical application of a new artificial immune system named the information gain based artificial immune recognition system (IG-AIRS) which minimizes the negative effects of taking into account all attributes in calculating Euclidean distance in shape–space representation which is used in many artificial immune systems. For medical data, thyroid disease data set was applied in the performance analysis of our proposed system. Our proposed system reached 95.90% classification accuracy with 10-fold CV method. This result ensured that IG-AIRS would be helpful in diagnosing thyroid function based on laboratory tests, and would open the way to various ill diagnoses support by using the recent clinical examination data, and we are actually in progress.     

面向存储资源优化的遥感卫星任务规划算法

针对遥感卫星的成像任务规划问题,按照任务重要性的不同,提出了两阶段调度策略,并设计了两个时间窗口选择规则。以星上存储资源优化为目标,利用地面站的下传活动将整个规划过程分为较小的调度区间,提出了可用存储空间上界估计算法计算每个调度区间的可调度存储资源容量,以此为基础对问题进行了简化,并给出了基于规则的启发式求解算法。实验结果表明,该算法是有效的。     

Fuzzy functions with LSE

“Fuzzy Functions” are proposed to be determined by the least squares estimation (LSE) technique for the development of fuzzy system models. These functions, “Fuzzy Functions with LSE” are proposed as alternate representation and reasoning schemas to the fuzzy rule base approaches. These “Fuzzy Functions” can be more easily obtained and implemented by those who are not familiar with an in-depth knowledge of fuzzy theory. Working knowledge of a fuzzy clustering algorithm such as FCM or its variations would be sufficient to obtain membership values of input vectors. The membership values together with scalar input variables are then used by the LSE technique to determine “Fuzzy Functions” for each cluster identified by FCM. These functions are different from “Fuzzy Rule Base” approaches as well as “Fuzzy Regression” approaches. Various transformations of the membership values are included as new variables in addition to original selected scalar input variables; and at times, a logistic transformation of non-scalar original selected input variables may also be included as a new variable. A comparison of “Fuzzy Functions-LSE” with Ordinary Least Squares Estimation (OLSE)” approach show that “Fuzzy Function-LSE” provide better results in the order of 10% or better with respect to RMSE measure for both training and test cases of data sets.     

A new supervised classification algorithm in artificial immune systems with its application to carotid artery Doppler signals to diagnose atherosclerosis

Because of its self-regulating nature, immune system has been an inspiration source for usually unsupervised learning methods in classification applications of Artificial Immune Systems (AIS). But classification with supervision can bring some advantages to AIS like other classification systems. Indeed, there have been some studies, which have obtained reasonable results and include supervision in this branch of AIS. In this study, we have proposed a new supervised AIS named as Supervised Affinity Maturation Algorithm (SAMA) and have presented its performance results through applying it to diagnose atherosclerosis using carotid artery Doppler signals as a real-world medical classification problem. We have employed the maximum envelope of the carotid artery Doppler sonograms derived from Autoregressive (AR) method as an input of proposed classification system and reached a maximum average classification accuracy of 98.93% with 10-fold cross-validation method used in training-test portioning. To evaluate this result, comparison was done with Artificial Neural Networks and Decision Trees. Our system was found to be comparable with those systems, which are used effectively in literature with respect to classification accuracy and classification time. Effects of system's parameters were also analyzed in performance evaluation applications. With this study and other possible contributions to AIS, classification algorithms with effective performances can be developed and potential of AIS in classification can be further revealed.