Hyperparameter Selection with Good Region Recognition for SVM Based Fault Diagnosis

This paper proposes a novel method of good region recognition for hyperparameter selection of SVM. The method can provide a much smaller good region for optimization search-based methods, and thus it can greatly save computation time. Experimental results show that the proposed method improves effi ciency of fault diagnosis of rolling bearing with no accuracy loss

Online Influence Maximization (Extended Version)

Social networks are commonly used for marketing purposes. For example, free samples of a product can be given to a few influential social network users (or "seed nodes"), with the hope that they will convince their friends to buy it. One way to formalize marketers' objective is through influence maximization (or IM), whose goal is to find the best seed nodes to activate under a fixed budget, so that the number of people who get influenced in the end is maximized. Recent solutions to IM rely on the influence probability that a user influences another one. However, this probability information may be unavailable or incomplete. In this paper, we study IM in the absence of complete information on influence probability. We call this problem Online Influence Maximization (OIM) since we learn influence probabilities at the same time we run influence campaigns. To solve OIM, we propose a multiple-trial approach, where (1) some seed nodes are selected based on existing influence information; (2) an influence campaign is started with these seed nodes; and (3) users' feedback is used to update influence information. We adopt the Explore-Exploit strategy, which can select seed nodes using either the current influence probability estimation (exploit), or the confidence bound on the estimation (explore). Any existing IM algorithm can be used in this framework. We also develop an incremental algorithm that can significantly reduce the overhead of handling users' feedback information. Our experiments show that our solution is more effective than traditional IM methods on the partial information.Comment: 13 pages. To appear in KDD 2015. Extended versio

Learning and Prediction Theory of Distributed Least Squares

With the fast development of the sensor and network technology, distributed estimation has attracted more and more attention, due to its capability in securing communication, in sustaining scalability, and in enhancing safety and privacy. In this paper, we consider a least-squares (LS)-based distributed algorithm build on a sensor network to estimate an unknown parameter vector of a dynamical system, where each sensor in the network has partial information only but is allowed to communicate with its neighbors. Our main task is to generalize the well-known theoretical results on the traditional LS to the current distributed case by establishing both the upper bound of the accumulated regrets of the adaptive predictor and the convergence of the distributed LS estimator, with the following key features compared with the existing literature on distributed estimation: Firstly, our theory does not need the previously imposed independence, stationarity or Gaussian property on the system signals, and hence is applicable to stochastic systems with feedback control. Secondly, the cooperative excitation condition introduced and used in this paper for the convergence of the distributed LS estimate is the weakest possible one, which shows that even if any individual sensor cannot estimate the unknown parameter by the traditional LS, the whole network can still fulfill the estimation task by the distributed LS. Moreover, our theoretical analysis is also different from the existing ones for distributed LS, because it is an integration of several powerful techniques including stochastic Lyapunov functions, martingale convergence theorems, and some inequalities on convex combination of nonnegative definite matrices.Comment: 14 pages, submitted to IEEE Transactions on Automatic Contro

Mezoporozni katalizator PtSnO2/C s povećanom katalitičkom aktivnošću za elektrooksidaciju etanola

In this paper, we report the synthesis, characterization, and electrochemical evaluation of a mesoporous PtSnO2/C catalyst, called PtSnO2(M)/C, with a nominal Pt : Sn ratio of 3 : 1. Brunauer–Emmett–Teller and transmission electron microscopy characterizations showed the obvious mesoporous structure of SnO2 in PtSnO2(M)/C catalyst. X-ray photoelectron spectroscopy analysis exhibited the interaction between Pt and mesoporous SnO2. Compared with Pt/C and commercial PtSnO2/C catalysts, PtSnO2(M)/C catalyst has a lower active site, but higher catalytic activity for ethanol electro-oxidation reaction (EOR). The enhanced activity could be attributed to Pt nanoparticles deposited on mesoporous SnO2 that could decrease the amount of poisonous intermediates produced during EOR by the interaction between Pt and mesoporous SnO2. This work is licensed under a Creative Commons Attribution 4.0 International License.U ovom radu prikazana je sinteza, karakterizacija i elektrokemijska ocjena mezoporoznog katalizatora PtSnO2/C (PtSnO2(M)/C), s nominalnim omjerom Pt : Sn od 3 : 1. Karakterizacije Brunauer-Emmett-Tellerovom metodom i transmisijskom elektronskom mikroskopijom pokazale su očitu mezoporoznu strukturu SnO2 u katalizatoru PtSnO2(M)/C. Analiza rendgenskom fotoelektronskom spektroskopijom pokazala je interakciju između Pt i mezoporoznog SnO2. U usporedbi s Pt/C i komercijalnim katalizatorima PtSnO2/C, katalizator PtSnO2(M)/C ima slabije aktivno mjesto, ali veću katalitičku aktivnost za reakciju elektrooksidacije etanola (EOR). Poboljšana aktivnost mogla bi se pripisati nanočesticama Pt pohranjenim na mezoporoznom SnO2, što bi moglo smanjiti količinu otrovnih međuprodukata proizvedenih tijekom elektrooksidacije etanola interakcijom između Pt i mezoporoznog SnO2. Ovo djelo je dano na korištenje pod licencom Creative Commons Imenovanje 4.0 međunarodna

Dynamic characteristic of spur gear with flexible support of gearbox

In this study, a nonlinear translation-torsion model of spur gear pair with flexible support of gearbox is proposed. The time-varying meshing stiffness, transmission error and backlash are considered in this model. Lagrange’s equations are used for establishing the mathematic model. The numerical method is presented for solutions of nonlinear differential equations. The effect of rotating speed and support stiffness of gearbox is analyzed. The numerical results show that the flexibility of the support of gearbox has a significant effect on the amplitude-frequency characteristic of the spur gear pair at low rotating speeds. The response shows flexibility while the support stiffness is smaller than the bearings and rigidity while the support stiffness is larger than the bearings. The maximum deformation of the driving gear bearings under the flexible support is generally greater than the one under rigid support

AFPN: Asymptotic Feature Pyramid Network for Object Detection

Multi-scale features are of great importance in encoding objects with scale variance in object detection tasks. A common strategy for multi-scale feature extraction is adopting the classic top-down and bottom-up feature pyramid networks. However, these approaches suffer from the loss or degradation of feature information, impairing the fusion effect of non-adjacent levels. This paper proposes an asymptotic feature pyramid network (AFPN) to support direct interaction at non-adjacent levels. AFPN is initiated by fusing two adjacent low-level features and asymptotically incorporates higher-level features into the fusion process. In this way, the larger semantic gap between non-adjacent levels can be avoided. Given the potential for multi-object information conflicts to arise during feature fusion at each spatial location, adaptive spatial fusion operation is further utilized to mitigate these inconsistencies. We incorporate the proposed AFPN into both two-stage and one-stage object detection frameworks and evaluate with the MS-COCO 2017 validation and test datasets. Experimental evaluation shows that our method achieves more competitive results than other state-of-the-art feature pyramid networks. The code is available at \href{https://github.com/gyyang23/AFPN}{https://github.com/gyyang23/AFPN}