盐城市农村水利现代化建设思考

盐城市是农业大市、水利大市,实现农村水利现代化是全面推进水利现代化的基础和保障.在全面分析农村水利现状与水利现代化目标差距的基础上,提出了建立农村水利规划、防洪排涝、农业灌溉、饮水安全、服务体系、工程管理、建设管理、资金投入、科技创新、督察考核等十大保障体系.     

基于光伏微网的PI-dP&O高性能MPPT控制器的设计

通过采用PI-dP&O模块、Buck-Boost电路与具有电流闭环控制的抑制积分饱和的PI控制器相结合的策略,设计出一种具有高性能的MPPT控制器。此控制器具有软件实现简单、适应性好、成本低廉等优点。在Matlab实验平台上将此高性能的MPPT控制器与传统的MPPT控制器进行对比,并应用到光伏微网中,充分显示其优越性和可行性。     

基于MSP430的酒驾报警减速器的研制

以MSP430为核心控制器,利用测速器、FPGA、减速器、声光报警模块和LCD1602液晶显示器,设计了一款防止酒后驾车的汽车报警减速器,实现对车速的控制和平稳停车。对数据采集过程中对数据的处理方法以及控制汽车停车控制算法作了详细阐述,整个系统经调试,能运行正常。     

基于无线的物联智能家居控制系统设计

提出一种基于无线的物联智能家居控制系统设计,该系统通过PXA270和nRF24L01模块实现无线采集、发送各项家居信息;通过GSM通信将烟雾、煤气、红外等传感器的异常报警信息以短信方式通知用户,并连接PXA270的PC实现实时远程监控。经过测试,该系统工作稳定,便于实际应用,市场前景广阔。     

Multi-operator image retargeting with automatic integration of direct and indirect seam carving

Multi-operator image resizing can preserve important objects and structure in an image by combining multiple image resizing operators. However, traditional multi-operator methods do not take both horizontal and vertical content-aware resizing potential into consideration, which essentially leads to squeeze/stretch effect in the resultant images. In this paper, we propose a new multi-operator scheme that addresses aforementioned issue by integrating direct and indirect seam carving. Compared with previous methods, the proposed scheme remarkably reduces the cost of deciding when to change operators, by employing a newly defined image artifact measure. Furthermore, we propose a novel seam carving enhancement, named ACcumulated Energy Seam Carving (ACESC), as a basic operator to improve global structure preservation. By combining horizontal and vertical seam carving, our scheme preserves the shapes of important objects well. We present typical results to demonstrate the effectiveness of our method. User study shows that our method has high user preference.     

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.     

Gaussian process assisted coevolutionary estimation of distribution algorithm for computationally expensive problems

In order to reduce the computation of complex problems, a new surrogate-assisted estimation of distribution algorithm with Gaussian process was proposed. Coevolution was used in dual populations which evolved in parallel. The search space was projected into multiple subspaces and searched by sub-populations. Also, the whole space was exploited by the other population which exchanges information with the sub-populations. In order to make the evolutionary course efficient, multivariate Gaussian model and Gaussian mixture model were used in both populations separately to estimate the distribution of individuals and reproduce new generations. For the surrogate model, Gaussian process was combined with the algorithm which predicted variance of the predictions. The results on six benchmark functions show that the new algorithm performs better than other surrogate-model based algorithms and the computation complexity is only 10% of the original estimation of distribution algorithm.