On an ant colony-based approach for business fraud detection

Nowadays we witness an increasing number of business frauds. To protect investors’ interest, a financial firm should possess an effective means to detect such frauds. In this regard, artificial neural networks (ANNs) are widely used for fraud detection. Traditional back-propagation-based algorithms used for training an ANN, however, exhibit the local optima problem, thus reducing the effectiveness of an ANN in detecting frauds. To alleviate the problem, this paper proposes an approach to training an ANN using an ant colony optimization technique, through which the local optima problem can be solved and the effectiveness of an ANN in fraud detection can be improved. Based on our approach, an associated prototype system is designed and implemented, and an exploratory study is performed. The results of the study are encouraging, showing the viability of our proposed approach

Distributionally robust distributed generation hosting capacity assessment in distribution systems

© 2018 by the authors. Uncertainties associated with the loads and the output power of distributed generations create challenges in quantifying the integration limits of distributed generations in distribution networks, i.e., hosting capacity. To address this, we propose a distributionally robust optimization-based method to determine the hosting capacity considering the voltage rise, thermal capacity of the feeders and short circuit level constraints. In the proposed method, the uncertain variables are modeled as stochastic variables following ambiguous distributions defined based on the historical data. The distributionally robust optimization model guarantees that the probability of the constraint violation does not exceed a given risk level, which can control robustness of the solution. To solve the distributionally robust optimization model of the hosting capacity, we reformulated it as a joint chance constrained problem, which is solved using the sample average approximation technique. To demonstrate the efficacy of the proposed method, a modified IEEE 33-bus distribution system is used as the test-bed. Simulation results demonstrate how the sample size of historical data affects the hosting capacity. Furthermore, using the proposed method, the impact of electric vehicles aggregated demand and charging stations are investigated on the hosting capacity of different distributed generation technologies

Multi-attention graph neural networks for city-wide bus travel time estimation using limited data

Multi-attention graph neural networks for city-wide bus travel time estimation using limited dat

Probabilistic assessment of hosting capacity in radial distribution systems

High penetration of distributed generation (DG) is mainly constrained by voltage-related issues. Due to the uncertainties associated with type, size, and location of DGs, it is difficult to quantify their integration limits in distribution networks, i.e., hosting capacity (HC). To address this issue, this paper proposes a probabilistic-based framework to determine the maximum integration limits of DGs considering the voltage rise and voltage deviation constraints. Such framework requires the use of the HC model, which can be formulated as a nonlinear optimization problem. Adding the voltage deviation constraint in the HC problem makes the model unsolvable. We address this issue by proposing a two-step algorithm to linearize the HC model. Then, using the linearized model, a probabilistic framework is proposed for considering the load variability and DGs uncertainties. To validate the efficacy and accuracy of the proposed framework, we identify the HC of a balanced and an unbalanced distribution networks and compare our results with those obtained from comprehensive power flow method and the traditional conservative planning. Finally, using the proposed framework, the impact of voltage deviation constraint, load growth, DG type and network structure on the HC are comprehensively studied using different DG technologies (i.e., Photovoltaics and wind). © 2010-2012 IEEE

Anisotropy in the mechanical properties of AZ31 magnesium alloy after being compressed at high temperatures (up to 823K)

The mechanical properties of AZ31 magnesium alloy compressed at high temperature (623-823K) are investigated. The alloy was compressed along the rolling direction (RD) at a strain rate of 1s-1. Optical microstructure observation and electron backscatter diffraction analysis reveal that the grain size and texture have a great influence on the mechanical properties. The fracture elongations (FE) of the compressed AZ31 Mg alloy samples are in the vicinity of 17% when the tensile direction is in the transverse direction (TD). When the tensile direction is in the normal direction (ND), the FE values grew abruptly at above 723K. The yield stress (YS) value decreased linearly with the increasing deformation temperature along the TD. However, along the ND, the YS values decreased linearly at temperatures below 723K but decreased more rapidly at temperatures higher than 723K. The results show that mechanical anisotropy becomes stronger with the increasing temperature. This is because the orientations of the basal planes are rotated 90° from the ND to the RD at lower temperatures but about 45° from the ND to the RD after deformation at temperatures up to 823K. The effects of grain size and texture on the mechanical properties are analyzed in detail. © 2013 Elsevier B.V

RecKGC: Integrating recommendation with knowledge graph completion

Both recommender systems and knowledge graphs can provide overall and detailed views on datasets, and each of them has been a hot research domain by itself. However, recommending items with a pre-constructed knowledge graph or without one often limits the recommendation performance. Similarly, constructing and completing a knowledge graph without a target is insufficient for applications, such as recommendation. In this paper, we address the problems of recommendation together with knowledge graph completion by a novel model named RecKGC that generates a completed knowledge graph and recommends items for users simultaneously. Comprehensive representations of users, items and interactions/relations are learned in each respective domain, such as our attentive embeddings that integrate tuples in a knowledge graph for recommendation and our high-level interaction representations of entities and relations for knowledge graph completion. We join the tasks of recommendation and knowledge graph completion by sharing the comprehensive representations. As a result, the performance of recommendation and knowledge graph completion are mutually enhanced, which means that the recommendation is getting more effective while the knowledge graph is getting more informative. Experiments validate the effectiveness of the proposed model on both tasks. © 2019, Springer Nature Switzerland AG

Mechanical properties and grain growth kinetics in magnesium alloy after accumulative compression bonding

An ultrafine-grained AZ31 magnesium alloy with a mean grain size less than 1μm was fabricated using three-passes of accumulative compression bonding. Three passes were made at temperatures of 693K, 643K and 593K, respectively, with a strain rate of 1.5s-1 and a reduction of 75% for each pass. After the first pass, the mean grain size was abruptly refined from 23μm to 5.1μm and most of the basal planes had been rotated 90° perpendicular to the compression direction. After three passes, the microstructure was homogenous and excellent mechanical properties were obtained: 245MPa yield stress, 372MPa ultimate tensile strength and 14.8% fracture elongation. And a study on the kinetics of grain growth was carried out at temperatures range of 623-723K. The effect of annealing temperature, T, and time, t, on the grain growth kinetics can be well interpreted by the kinetics equation Dn-D0n=kt, where k=k0e(-Eg/RT). According to the experimental data, the activation energy for grain growth Eg was measured to be 105kJ/mol. © 2012

Enhanced stretch formability and mechanical properties of a magnesium alloy processed by cold forging and subsequent annealing

A magnesium alloy AZ21 was multi-directionally forged at room temperature to cumulative strains of ΣΔ. e{open}= 1.5 and then subjected to various annealing treatments. The results showed that the initial grains were gradually subdivided into ultrafine ones by mechanical twins. The annealing temperature had a pronounced effect on the microstructural evolution. At 523. K, a homogeneous structure with a mean grain size of 3.8 μm was obtained, which exhibited remarkable texture weakening compared with the as-annealed specimen, resulting in significantly increase of ductility and stretch formability. Increased and decreased annealing temperature can both lead to a coarsening of grain size. Moreover, the analysis on the recrystallization kinetics during annealing indicated that it could be well described by Johnson-Mehl-Avrami-Kolmogorov model and the activation energy for recrystallization was calculated to be about 63.4. kJ/mol. © 2012

Texture weakening of AZ31 magnesium alloy sheet obtained by a combination of bidirectional cyclic bending at low temperature and static recrystallization

In this work, the grain refinement and texture weakening in the sheets of AZ31 magnesium alloy were studied by means of bidirectional cyclic bending for 6 passes at 423 K and subsequent static recrystallization (SRX) on two annealing conditions. The deformed and annealed samples were examined by optical microscopy and electron backscatter diffraction analysis. The results showed that a gradient structure with fine grains in the regions near the surfaces and, in contrast, coarse grains in the middle of the sheet were induced. The texture of the annealed samples was dramatically weakened, and the intensity decreased gradually from the center of the sheet to two surfaces. The different SRX mechanisms significantly affected the different weakening for the basal texture. The cumulative strain energy achieved by twinning played a more important role in the formation of an asymmetric gradient texture intensity distribution after annealing at 523 K for 1000 s. On the contrary, thermal energy dominated a symmetric gradient under annealing at 573 K for 100 s because of the preferential growth of new grains produced by SRX. The ductility is enhanced outstandingly with no remarkable improvement for the strength. © 2012 Springer Science+Business Media, LLC

Enhancement of tensile ductility and stretch formability of AZ31 magnesium alloy sheet processed by cross-wavy bending

The microstructure and texture evolution in the sheets of AZ31 magnesium alloy was studied by means of cross-wavy bending for 4 passes at 673 K. The bended samples were examined by optical microscopy and electron backscatter diffraction analysis. Finite element analysis suggested an inhomogeneous deformation at each pass. Following cross-wavy bending, a fine-grained microstructure with an average grain size of ∼8 μm and a weak and random basal texture were achieved. Accumulative effective strain was almost equal in the whole sheet at the end. Different work softening mechanisms significantly affected the evolution of the microstructure. Dynamic recovery played an important role during the first three bending passes whereas, in contrast, dynamic recrystallization dominated the evident grain refinement during the last pass. The tensile ductility and stretch formability of the 4-pass sheet at room temperature were distinctly enhanced compared to the initial sheet (1.55 and 2 times larger, respectively). These prominent increases were mainly attributed to texture randomizing rather than texture weakening alone. © 2013 Elsevier B.V. All rights reserved