Pattern-based event detection in sensor networks

Many applications of wireless sensor networks monitor the physical world and report events of interest. To facilitate event detection in these applications, in this paper we propose a pattern-based event detection approach and integrate the approach into an in-network sensor query processing framework. Different from existing threshold-based event detection, we abstract events into patterns in sensory data and convert the problem of event detection into a pattern matching problem. We focus on applying single-node temporal patterns, and define the general patterns as well as five types of basic patterns for event specification. Considering the limited storage on sensor nodes, we design an on-node cache manager to maintain the historical data required for pattern matching and develop event-driven processing techniques for queries in our framework. We have conducted experiments using patterns for events that are extracted from real-world datasets. The results demonstrate the effectiveness and efficiency of our approach.     

Improving neighborhood based Collaborative Filtering via integrated folksonomy information

Personalized recommender systems which can provide people with suggestions according to individual interests usually rely on Collaborative Filtering (CF). The neighborhood based model (NBM) is a common choice when implementing such recommenders due to the intuitive nature; however, the recommendation accuracy is a major concern. Current NBM based recommenders mostly address the accuracy issue based on the rating data alone, whereas research on hybrid recommender systems suggests that users enjoy specifying feedback about items across multiple dimensions. In this work we aim to improve the accuracy of NBM via integrating the folksonomy information. To achieve this objective, we first propose the folksonomy network (FN) to analyze the item relevance described by the folksonomy data. We subsequently integrate the obtained folksonomy information into the global-optimization based NBM for making multi-source based recommendations. Experiments on the MovieLens dataset suggest positive results, which prove the efficiency of our strategy.     

A parallel matrix factorization based recommender by alternating stochastic gradient decent

Collaborative Filtering (CF) can be achieved by Matrix Factorization (MF) with high prediction accuracy and scalability. Most of the current MF based recommenders, however, are serial, which prevent them sharing the efficiency brought by the rapid progress in parallel programming techniques. Aiming at parallelizing the CF recommender based on Regularized Matrix Factorization (RMF), we first carry out the theoretical analysis on the parameter updating process of RMF, whereby we can figure out that the main obstacle preventing the model from parallelism is the inter-dependence between item and user features. To remove the inter-dependence among parameters, we apply the Alternating Stochastic Gradient Solver (ASGD) solver to deal with the parameter training process. On this basis, we subsequently propose the parallel RMF (P-RMF) model, of which the training process can be parallelized through simultaneously training different user/item features. Experiments on two large, real datasets illustrate that our P-RMF model can provide a faster solution to CF problem when compared to the original RMF and another parallel MF based recommender.     

Wafer-level multilayer integration of RF passives with thick BCB/metal interlayer connection in silicon-based SiP

This paper presents an integration technology for RF passives using benzocyclobutene (BCB)/metal multilayer interconnection for system-in-package applications. This technology has been specially developed for RF subsystem packages in which a thick polymer, BCB (more than 15 μm thick), is adopted as dielectric with lossy silicon as substrate for its excellent characteristics. Both dry-etch BCB and photosensitive BCB are applied in this work, and their processes are briefly introduced and compared. An RF power divider, an MIM capacitor, different types of RF inductors as well as a coupled microstrip based band-pass filter are fabricated and measured at wafer level. The results show good electrical performances, and accordingly the passives are well applicable in RF band. Moreover, the subsystem models including monolithic chips connected with passives are presented.     

Application of fuzzy analytic hierarchy process and neural network in power transformer risk assessment

In operation, risk arising from power transformer faults is of much uncertainty and complicacy. To timely and objectively control the risks, a transformer risk assessment method based on fuzzy analytic hierarchy process (FAHP) and artificial neural network (ANN) from the perspective of accuracy and quickness is proposed. An analytic hierarchy process model for the transformer risk assessment is built by analysis of the risk factors affecting the transformer risk level and the weight relation of each risk factor in transformer risk calculation is analyzed by application of fuzzy consistency judgment matrix; with utilization of adaptive ability and nonlinear mapping ability of the ANN, the risk factors with large weights are used as input of neutral network, and thus intelligent quantitative assessment of transformer risk is realized. The simulation result shows that the proposed method increases the speed and accuracy of the risk assessment and can provide feasible decision basis for the transformer risk management and maintenance decisions.     

Stretch rate and deformation for pre-stretching aluminum alloy sheet

Numerical simulation combined with experimental test was carried out to analyze the pre-stretching process of the 7075 aluminum alloy sheet, from which the stress variation curves and residual stress of aluminum alloy sheet in different stretch rates were obtained. The results show that the residual stress in length direction is released after unloading the stretch force, while the residual stress in width direction is released during the stretching process. The study of residual stress elimination is beneficial for optimizing stretch rate on the basis of residual stress distribution law. By comparing the variation principle of residual stress in length direction, the size range of three deformation areas and elimination percentage of residual stress were obtained. The residual stresses of clamping area and transition area are not eliminated effectively, so sawing quantity should be the sum of both the areas. The elimination rate of residual stress in even deformation area could reach 90% after choosing a proper stretch rate, which is verified by both simulation and experiment.     

激光角度欺骗仿真系统的软件可靠性指标分配

在激光角度欺骗仿真系统的需求分析和概要设计阶段,使用基于模糊故障模式、影响及危害性分析(FMECA)框架的分配法对该系统软件可靠性分配。首先建立影响因素集、评价集合;然后基于FMECA框架进行模糊综合评价;再利用层次分析法(AHP)得到权重集;最后通过量化手段得到各子系统的不可靠系数并将它们转化为平均无故障时间(MTBF)。最终得到的可靠性指标符合专家评估和类似软件系统的可靠性分配结果。     

高精度磁测在沉积型磁铁矿区的找矿效果

沉积型磁铁矿脉特点是矿脉宽度小,品位低,综合磁性不均匀。通过对已知矿体进行剖面测量,证明高精度磁测的有效性,对野外实测数据进行日变校正、正常场校正、高度校正、水平梯度校正,化极、向上延拓、求取垂向二阶导数,以及对实测磁异常剖面进行人机对话反演等处理获得良好的效果。本次高精度磁测工作查明了矿区内磁铁矿（化）体的空间分布范围及大致埋深情况,圈定了几个有价值的磁铁矿（化）体靶区,取得了良好的找矿效果。     

综合物探方法在新凉亭铜铅锌矿区的试验效果

通过在新凉亭铜铅锌矿区已知矿上开展可控源音频大地电磁法（CSAMT）、复电阻率法（CR）、时域激电法（TDIP）的方法试验,为该矿区下一步找矿选择合适的物探方法。经试验认为,CSAMT能有效探测容矿断裂构造,TDIP能确定矿体顶板位置及矿体倾向,CR能有效圈定矿体空间范围,下一步物探找矿应以CSAMT、TDIP为主,CR法因成本过高,仅在重要成矿有利地段辅以少量工作进行异常评价。