The Design of Experimental Performance Analysis and Visualization System

This paper deals with an experimental analysis of performance and emissions of a single cylinder, air cooled, direct injection, compression ignition engine using synthetic fuel extracted from Tilia B233 refrigerator lubricating oil and waste cooking oil biodiesel. Biodiesel and synthetic fuel are produced by transesterification process using methanol and potassium hydroxide. Physical properties as well as liquid density, liquid viscosity, total acidity number and flash point are measured. Physical properties and their blends are compared with conventional diesel fuel properties. A first insight to the results indicates that, unlike biodiesel, synthetic fuel which is distinguished by their its low acidity, which enables its storage. In addition, their viscosity is quite high and require the use of synthetic fuel blends with diesel fuel. A careful investigation according to current limitations and standards leads us to a direct engine test with a maximum blending rate of 50%. Thus, blends are achieved by mixing 15%, 30% and 45% of biodiesels with a complementary amount of commercial diesel fuel. The experimental results are obtained on single cylinder diesel engine operating at 1600 rpm for a wide range of loads. Brake specific fuel consumption, specific NOx and CO emissions are compared for each blend with those obtained by conventional diesel fuel.

A single-stage axial-flow compressor which incorporates rotor inlet counterswirl to improve stage performance is discussed. Results for two rotor configurations are presented, including design and experimental test data. In this compressor design, inlet guide vanes were used to add counterswirl to the inlet of the rotor. The magnitude of the counterswirl was radially distributed to maximize the overall stage efficiency by minimizing the rotor combined losses (diffusion losses and shock losses). The shock losses were minimized by simultaneously optimizing the rotor blade section geometry, through-blade static pressure distribution, and leading edge aerodynamic/geometric shock sweep angles. Results from both the design and experimental performance analyses are presented and comparisons are made between the experimental data and the analyses and between the performances of both rotor designs. The computation of the flow field for both rotor designs and for the analysis of both tests was performed in an identical fashion using an axisymmetric, streamline-curvature-type code. Results presented include tip section blade-to-blade static pressure distributions and rotor through-blade and exit distributions of various aerodynamic parameters. The performance of this compressor stage represents a significant improvement in axial compressor performance compared to previous attempts to use rotor inlet counterswirl and to current, more conventional, state-of-the-art axial compressors operating under similar conditions.

A single-stage axial-flow compressor which incorporates rotor inlet counterswirl to improve stage performance is discussed. Results for two rotor configurations are presented, including design and experimental test data. In this compressor design, inlet guide vanes were used to add counterswirl to the inlet of the rotor. The magnitude of the counterswirl was radially distributed to maximize the overall stage efficiency by minimizing the rotor combined losses (diffusion losses and shock losses). The shock losses were minimized by simultaneously optimizing the rotor blade section geometry, through-blade static pressure distribution, and leading edge aerodynamic/geometric shock sweep angles. Results from both the design and experimental performance analyses are presented and comparisons are made between the experimental data and the analyses and between the performance of both rotor designs. The computation of the flow field for both rotor designs and for the analysis of both tests was performed in an identical fashion using an axisymmetric, streamline-curvature-type code. Results presented include tip section blade-to-blade static pressure distributions and rotor through-blade and exit distributions of various aerodynamic parameters. The performance of this compressor stage represents a significant improvement in axial compressor performance compared to previous attempts to use rotor inlet counterswirl and to current, more conventional, state-of-the-art axial compressors operating under similar conditions.

Experimental Performance Analysis and Prediction of Output Performance of Solar Powered Boost Converter at Different Weather Conditions

Social network visualization focuses on nodes lacking inherent layout semantics. Many previous mapping algorithms locating graph data into physical space are based on assumptions that space is in two-dimensions and that the visualization of huge graphs can be realized by dividing them into subgraphs independently for easier exploration. However, when physical space extends to three dimensions, the automatic layout becomes a problem. Different from sparse graphs, nodes in social networks may be closely connected to each other, which raises another problem that how to explore them in undivided ways. For these reasons, the primary motivation for this work is to provide a unified framework optimized for social network a 3D exploration by combining techniques widely used in data mining and computational graphics. In the framework, we borrow octree structure to solve nodes self-adapting layout problem in 3D space and propose an algorithm to map a social network into an octree without losing connection information after space division, and define two practical operations (shifting and shading) to satisfy the details-on-demand requirement of data visualization. By building a prototype system on Java 3D, the experiment visualizes the social networks extracted from real organizations and demonstrates the flexibility of the framework. Our methods produce a satisfying performance.

Pottery is a unique art that involves creating items out of clay. It creates a variety of clay and ceramic products, including as flowerpots, water and grain storage jars, by distilling rice spirits. Almost all pottery manufacture is done in the conventional manner, which uses more energy and emits hazardous emissions. This project primarily focuses on experimental performance analysis, calculating temperature distribution, fuel required per batch, pottery production per batch, maximum temperature reading, flame temperature, and energy audit for an existing furnace and thermal analysis of pottery furnace giving optimum design for future construction and modification of existing furnace. Energy calculation shows that specific fuel consumption of different furnaces in Thimi varies from 0.98MJ/kg to 2.4MJ/kg. Experimental data are collected and validated by transient thermal analysis using ANSYS.

This study focuses on predicting which firms will increase capital by issuing new stocks in the near future. Many stakeholders, including banks, credit rating agencies and investors, performs a variety of analyses for firms' growth, profitability, stability, activity, productivity, etc., and regularly report the firms' financial analysis indices. In the paper, we develop predictive models for rights issues using these financial analysis indices and data mining techniques. This study approaches to building the predictive models from the perspective of two different analyses. The first is the analysis period. We divide the analysis period into before and after the IMF financial crisis, and examine whether there is the difference between the two periods. The second is the prediction time. In order to predict when firms increase capital by issuing new stocks, the prediction time is categorized as one year, two years and three years later. Therefore Total six prediction models are developed and analyzed. In this paper, we employ the decision tree technique to build the prediction models for rights issues. The decision tree is the most widely used prediction method which builds decision trees to label or categorize cases into a set of known classes. In contrast to neural networks, logistic regression and SVM, decision tree techniques are well suited for high-dimensional applications and have strong explanation capabilities. There are well-known decision tree induction algorithms such as CHAID, CART, QUEST, C5.0, etc. Among them, we use C5.0 algorithm which is the most recently developed algorithm and yields performance better than other algorithms. We obtained data for the rights issue and financial analysis from TS2000 of Korea Listed Companies Association. A record of financial analysis data is consisted of 89 variables which include 9 growth indices, 30 profitability indices, 23 stability indices, 6 activity indices and 8 productivity indices. For the model building and test, we used 10,925 financial analysis data of total 658 listed firms. PASW Modeler 13 was used to build C5.0 decision trees for the six prediction models. Total 84 variables among financial analysis data are selected as the input variables of each model, and the rights issue status (issued or not issued) is defined as the output variable. To develop prediction models using C5.0 node (Node Options: Output type = Rule set, Use boosting = false, Cross-validate = false, Mode = Simple, Favor = Generality), we used 60% of data for model building and 40% of data for model test. The results of experimental analysis show that the prediction accuracies of data after the IMF financial crisis (59.04% to 60.43%) are about 10 percent higher than ones before IMF financial crisis (68.78% to 71.41%). These results indicate that since the IMF financial crisis, the reliability of financial analysis indices has increased and the firm intention of rights issue has been more obvious. The experiment results also show that the stability-related indices have a major impact on conducting rights issue in the case of short-term prediction. On the other hand, the long-term prediction of conducting rights issue is affected by financial analysis indices on profitability, stability, activity and productivity. All the prediction models include the industry code as one of significant variables. This means that companies in different types of industries show their different types of patterns for rights issue. We conclude that it is desirable for stakeholders to take into account stability-related indices and more various financial analysis indices for short-term prediction and long-term prediction, respectively. The current study has several limitations. First, we need to compare the differences in accuracy by using different data mining techniques such as neural networks, logistic regression and SVM. Second, we are required to develop and to evaluate new prediction models including variables which research in the theory of capital structure has mentioned about the relevance to rights issue.

In this paper, we focus on the robustness in noise tolerance of spatial domain optical flow and we present a performance study of bidirectional confidential with median filter on spatial domain optical flow (spatial correlation, local based, and global based) under non Gaussian noise where several noise tolerance models on spatial domain optical flow are used in comparison. The experiment results are investigated on robustness under noisy condition by using non Gaussian noise (Poisson Noise, Salt & Pepper noise, and Speckle Noise) in contamination with several standard sequences. The experiment concentrates on Error Vector Magnitude (EVM) as a performance indicator where the result in motion vector (MV) of each method is used to compare with the ground truth vector in the experimental performance analysis.

In order to foster the healthy inspection professional's innovative ability,Jining Medical University constructs the innovation personnel training system of healthy inspection major.It includes four aspects:innovation course content and the curriculum system which highlights the characteristic and the personalized raise hygienic examination;Innovation experiment teaching system which is for the purpose of fostering abilities of experimental performance,operation and problem analysis and solving;Innovation latter practice teaching system which improves abilities to combine theory and practice;Extracurricular innovative activities and the social practice system which has the rich innovation color.This article has made the summary and the elaboration separately. Key words: Healthy inspection ; Innovative talent ; Training system ; Construction ; Practice ;

Experimental study and simulation analysis of seismic performance of weakened filled energy-dissipating steel beam-column joints

In the humidification and dehumidification solar desalination system, the recovery of vapor condensation latent heat is the key problem. Using a cascaded phase change heat storage method to recover vapor condensation latent heat can improve the phase change heat storage rate and the water production performance of dehumidifier. The exergy analysis and experimental methods are used to study the cascaded phase change storage dehumidifier. The results show that the more stages of phase change materials in the cascaded phase change heat storage device, the greater the exergy efficiency will be. The heat transfer performance of phase change materials increases with the increase of hot and wet air temperature and flow at the inlet of the dehumidifier. The exergy efficiency and gain output ratio of three-stage phase change heat storage are higher than that of the single-stage. The three-stage one is recommended. If the heat recovered by the cascaded phase change heat storage device is supplied to the passive humidification dehumidification desalinator for secondary water output, the water output and gain output ratio will increase by 25% and the water production cost will be reduced by 20%. The results can provide a basis for the design and application of a cascaded phase change heat storage dehumidifier.

The identification of useful patterns from a medical dataset benefits early diagnosis. Classification techniques in data mining are famous for medical data prediction. Decision Tree (J48) and Navie Bayes are the most popular and frequently used algorithms in the field of prediction analysis. The efficiency of such prediction algorithms can be improved with better pre-processing approaches. This paper aims at finding optimum features using permutation combination of input data attributes for improving the classifier accuracy. The Pima Indian Diabetes Dataset from UCI repository is used for experimentation. The performance of J48 and Navie Bayes has been tested for different combination of features. Keywords: Diabetes; J48; Navie Bayes; Permutation

Preparing a data set for analysis is generally the most time consuming task in a data mining project, requiring many complex SQL queries, joining tables and aggregating columns. Existing SQL aggregations have limitations to prepare data sets because they return one column per aggregated group. In general, a significant manual effort is required to build data sets, where a horizontal layout is required. We propose simple, yet powerful, methods to generate SQL code to return aggregated columns in a horizontal tabular layout, returning a set of numbers instead of one number per row. This new class of functions is called horizontal aggregations. Horizontal aggregations build data sets with a horizontal denormalized layout (e.g. point-dimension, observation-variable, instance-feature), which is the standard layout required by most data mining algorithms. We propose three fundamental methods to evaluate horizontal aggregations: CASE: Exploiting the programming CASE construct; SPJ: Based on standard relational algebra operators (SPJ queries); PIVOT: Using the PIVOT operator, which is offered by some DBMSs. Experiments with large tables compare the proposed query evaluation methods. Our CASE method has similar speed to the PIVOT operator and it is much faster than the SPJ method. In general, the CASE and PIVOT methods exhibit linear scalability, where as the SPJ method does not. In a relational database, especially with normalized tables, a significant effort is required to prepare a summary data set that can be used as input for a data mining or statistical algorithm. Most algorithms require as input a data set with a horizontal layout, with several Records and one variable or dimension per column. That is the case with models like clustering, classification, regression and PCA; consult. Each research discipline uses different terminology to describe the data set. In data mining the common terms are point-dimension. Statistics literature generally uses observation-variable. Machine learning research uses instance-feature. This article introduces a new class of aggregate functions that can be used to build data sets in a horizontal layout (denormalized with aggregations), automating SQL query writing and extending SQL capabilities. We show evaluating horizontal aggregations is a challenging and interesting problem and we introduced alternative methods and optimizations for their efficient evaluation. II. MOTIVATION As mentioned above, building a suitable data set for data mining purposes is a time- consuming task. This task generally requires writing long SQL statements or customizing SQL Code if it is automatically generated by some tool. There are two main ingredients in such SQL code: joins and aggregations; we focus on the second one. The most widely- known aggregation is the sum of a column over groups of rows. Some other aggregations return the average, maximum, minimum or row count over groups of rows. There exist many aggregations functions and operators in SQL. Unfortunately, all these aggregations have limitations to build data sets for data mining purposes. The main reason is that, in general, data sets that are stored in a relational database (or a data warehouse) come

This paper initiates perceptions and algorithms of feature selection, survey of existing feature selection algorithms and assesses diverse algorithms with a classifying frame based on search approaches, valuation criteria, and provides strategy in selecting feature selection algorithms. A unifying platform is projected to continue our efforts headed for building an incorporated system for intelligent feature selection. Feature selection intends to reduce the dimensionality of patterns for classification by choosing the most informative instead of irrelevant and/or redundant features. In this proposed work Intuitionistic fuzzy based feature clustering is proposed for grouping features based on the degree of membership and degree of indeterminacy among the attributes and clusters. In this proposed work a novel approach which uses an Intuitionistic fuzzy version of k-means has been introduced for grouping interdependent features. Genetic algorithm reinstating which is the variation of traditional genetic algorithm is then applied to appraise whether the measured feature is independent of class labels; hence, it leads to eliminate unrelated clusters to classification process and progress the selection of features. The proposed method achieves improvement on classification accuracy and perhaps to select less number of features which show the way to simplification of learning task to a big extent. The Experiment results have been demonstrated by the good performance and also find good enough subset features of this method on using UCI benchmark datasets that are for data mining methods such as Breast Cancer, Sensor and Iris Records.

Dengue fever is widespread disease in the tropical areas caused by bite of female Eddie mosquito. Pakistan has been victim of this rapidly growing disease since last few years. The world health organization identified the four types of dengue fever. The experts are facing the problem of misdiagnosis of dengue fever. The Tests needed for empirical Classification of Dengue Fever takes a lot of time and money especiallyin epidemic situation in a country with limited resources. Therefore, we have used data mining techniques for the efficient classification of the dengue fever Type. The decision tree learning algorithm has been used as a classification Model. We performed two experiments using decision tree. The first general experiment shows the accuracy of 99.44%. It prunes the attributes which classify the dengue fever on the basis of the values in the dataset. The Second experiment classifies the dengue fever on the basis of expert weighted attributes, which are used in the classification on the basis of Minimum Cost and resource availability. The accuracy of this Model is still high 98.62%. We compared the performance in term of Type II error. We found that the Type II error is very low in second experiment