Mining of High-Utility Patterns in Big IoT-based Databases

When focusing on the general area of data mining, high-utility itemset mining (HUIM) can be defined as an offset of frequent itemset mining (FIM). It is known to emphasize more factors critically, which gives HUIM its intrinsic edge. Due to the flourishing development of the IoT technique, the uncertainty patterns mining is also attractive. Potential high-utility itemset mining (PHUIM) is introduced to reveal valuable patterns in an uncertainty database. Unfortunately, even though the previous methods are all very effective and powerful to mine, the potential high-utility itemsets quickly. These algorithms are not specifically designed for a database with an enormous number of records. In the previous methods, uncertainty transaction datasets would be load in the memory ultimately. Usually, several pre-defined operators would be applied to modify the original dataset to reduce the seeking time for scanning the data. However, it is impracticable to apply the same way in a big-data dataset. In this work, a dataset is assumed to be too big to be loaded directly into memory and be duplicated or modified; then, a MapReduce framework is proposed that can be used to handle these types of situations. One of our main objectives is to attempt to reduce the frequency of dataset scans while still maximizing the parallelization of all processes. Through in-depth experimental results, the proposed Hadoop algorithm is shown to perform strongly to mine all of the potential high-utility itemsets in a big-data dataset and shows excellent performance in a Hadoop computing cluster.

     

Fuzzy-PSO controller design for maximum power point tracking in photovoltaic system

Photovoltaic power generation system becomes increasingly important, highly attractive as a clean and renewable energy sources, widely used today in many applications. Recently, researchers have strongly promoted the use of solar energy as a viable source of energy due to its advantages and which it can be integrated into local and regional power supplies. The P–V curve of photovoltaic system exhibits multiple peaks under various conditions of functioning and changes in meteorological conditions which reduces the effectiveness of conventional maximum power point tracking (MPPT) methods and the Particle swarm optimization (PSO) algorithm is considered to be highly efficient for the solution of complicated problems.In this paper, the application of this approach based MPPT algorithm for Photovoltaic power generation system operating under variable conditions is proposed to optimize and to design an intelligent controller comparing to conventional one. PSO Approaches is considered to select and generate an optimal duty cycle which varies with photovoltaic parameters in order to extract the maximum Power. Simulation results show that the proposed approach can track the maximum power point faster and can improve the performance of the system compared to the conventional method.     

Robust diagnosis to measurement uncertainties using bond graph approach: Application to intelligent autonomous vehicle

This paper deals with robust bond graph model-based fault detection and isolation to improve the robustness of the diagnosis system in presence of measurements and parameters uncertainties. We develop a procedure of measurement uncertainties modeling directly on the graph. By using the structural and causal properties of the bond graph, the robust diagnosis is performed. The interest of the developed methodology consists in using the graphical tool not only for measurement uncertainties modeling, but also for designing robust fault detection and isolation algorithms. Moreover, this method can be easily automated. The developed approach is validated by an application to an electromechanical traction system of intelligent autonomous vehicle.     

Redundant versus orthogonal wavelet decomposition for multisensor image fusion

The wavelet decomposition has become an attractive tool for fusing multisensor images. Usually, the input images are decomposed with an orthogonal wavelet in order to extract features, which are combined through an appropriate fusion rule. The fused image is then reconstructed by applying the inverse wavelet transform. In this paper, we investigate the use of the nonorthogonal (or redundant) wavelet decomposition as an alternative approach for feature extraction. By using test and remote sensing images, various fusion rules are considered and the detailed comparison indicates the superiority of this approach compared to the standard orthogonal wavelet decomposition for image fusion.     

激光增材制造FeNi坡莫合金及其磁性能研究

以Fe-50%Ni预合金粉末为原材料,并采用激光选区熔化（Selective laser melting, SLM）技术制备坡莫合金,通过光学显微镜、X射线衍射、扫描电子显微镜等对采用不同工艺参数制备的SLM Fe-50%Ni合金的微观组织进行观察表征,分别在直流及交流条件下采用磁性能测试仪对其稳态磁性能及动态磁性能进行测试分析,其中直流测试中最大外磁场强度为5000 A/m．同时,研究了在交流条件下外磁场强为10 mT时不同频率下的铁损变化．研究结果表明：随着扫描速度的降低,即激光体积能量密度的增加,合金样品的孔隙率降低,显微硬度升高,但是过高的能量密度也使得热应力增加而产生微裂纹,晶粒尺寸也较大;合金样品的主相均为γ-(Fe-Ni)相,微观组织主要为沿打印方向的柱状晶组织,但在其熔池边界附近也存在少量的等轴晶,晶粒大小呈现微观不均匀性;选择适合的能量密度对合金的内部质量及性能至关重要,在使用较适合的体积能量密度制备的SLM合金样品的相对孔隙率低于0.5%、矫顽力为270 A/m、饱和磁感应强度为1.2 T、剩余饱和磁感应强度为0.4 T;当磁场强度为10 mT时,随着频率从10 kHz增加至100 kHz,铁损随着频率的升高而升高．      

Distribution of the solvent-extractable organic compounds in fine (PM1) and coarse (PM1-10) particles in urban, industrial and forest atmospheres of Northern Algeria

The distribution of the solvent-extractable organic components in the fine (PM₁) and coarse (PM_1-10) fractions of airborne particulate was studied for the first time in Algeria. That was done during October 2006 concurrently in a big industrial district, a busy urban area, and a forest national park located in Algiers, Boumerdes, Blida, respectively, which are the three biggest provinces of Northern Algeria. Most of the organic matter identified in both particle size ranges consisted of n-alkanes and n-alkanoic acids, with minor contributions coming from polycyclic aromatic hydrocarbons (PAHs), nitrated polycyclic aromatic hydrocarbons (NPAHs), oxygenated PAHs, and other polar compounds (e.g., caffeine and nicotine). The potential emission sources of airborne contaminants were reconciled by combining the values of n-alkane carbon preference index (CPI) and selected diagnostic ratios of PAHs, calculated in both size ranges. The mean cumulative concentrations of PAHs reached 3.032 ng m^− 3 at the Boumerdes site, urban, 80% of which (i.e. 2.246 ng m^− 3) in the PM₁ fraction, 6.462 ng m^− 3 at Rouiba-Réghaia, industrial district, (5.135 ng m^− 3 or 80% in PM₁), and 0.512 ng m^− 3 at Chréa, forested mountains (0.370 ng m^− 3 or 72% in PM₁). Similar patterns were shown by all organic groups, which resulted overall enriched in the fine particles at the three sites. Carcinogenic and mutagenic potencies associated to PAHs were evaluated by multiplying the concentrations of “toxic” compounds times the corresponding potency factors normalized vs. benzo(a)pyrene (BaP), and were found to be both acceptable.     

Removal of Copper(II) from a Concentrated Sulphate Medium by Cloud Point Extraction Using an N,N′-Bis(salicylaldehyde)Ethylenediimine Di-Schiff Base Chelating Ligand

Various chelating ligands have been investigated for the cloud point extraction of several metal ions. However, limited studies on the use of the Schiff base ligands have been reported. In this work, cloud point extraction behavior of copper(II) with N,N′‐bis(salicylaldehyde)Ethylenediimine Schiff base chelating ligand, (H₂SALEN), was investigated in aqueous concentrated sulphate medium. The extraction process used is based on the formation of hydrophobic H₂SALEN–copper(II) complexes that are solubilized in the micellar phase of a non‐ionic surfactant, i.e. ethoxylated (9.5EO) tert‐butylphenol. The copper(II) complexes are then extracted into the surfactant‐rich phase above cloud point temperature. Different parameters affecting the extraction process of Cu(II), such as equilibrium pH, extractant concentration, and non‐ionic surfactant concentration were explored. The extraction of Cu(II) was studied in the pH range of 2–11. The results obtained showed that it was profoundly influenced by the pH of the aqueous medium. The concentration factor, C_f, of about 17 with extraction efficiency of E % ≈100 was achieved. The stoichiometry of the extracted complex of copper(II) was ascertained by the Yoe–Jones method to give a composition of 1:1 (Cu:H₂L). The optimum conditions of the extraction‐removal have been established as the following: (1) 1.86 × 10^?3 mol/L ligand; (2) 3 wt% surfactant; (3) pH of 8 (4) 0.5 mol/L Na₂SO₄ and (5) temperature of 60 °C.     

Yttria-stabilized zirconia in-flight particle characteristics under vacuum plasma spray conditions

This paper deals with the diagnostic yttria-stabilized zirconia (YSZ) in-flight particles in Vacuum Plasma Spray (VPS) process using an optical measurement device. Particle velocity, temperature and diameter were correlated to spray distance under a fixed chamber pressure of about 14 kPa. Experiments were carried out with a two-color pyrometer. Results show that correlations can be satisfactory described with linear relationships. Particle velocity and temperature decrease when increasing spray distance whereas particle diameter exhibits a linear increase with the spray distance.