RFID复杂事件处理关键技术的研究与改进

RFID复杂事件处理是一个新兴的技术领域,它用来处理大量的简单事件,并从中整理出有价值的事件。RFID事件和传统的事件相比较具有海量性、空间性和时间性、数据不准确性等特征。文中在分析RFID数据特点的基础上,对RFID复杂事件处理的关键技术进行研究和改进,主要介绍RFID数据的清洗和事件检测技术。对于RFID数据清洗部分,提出了多层次过滤的方法使得到的数据更接近真实情况,而事件检测方面则提出了局部检测和全局检测相结合的方法对相关数据进行检测以得到更有意义的数据供上层应用使用。最后,对RFID复杂事件处理的发展趋势做出展望。     

MicroPort: A general simulation platform for seaport container terminals

Seaport container terminals are essential nodes in sea cargo transportation networks. As such, the operational efficiency of container terminals in handling containers passing through them plays a critical role in a globalized world economy. Many models and algorithms have been developed to address various decision problems in container terminals to help improve operational efficiency. These decision support tools are usually used separately for specific purposes. However, the problems they are trying to tackle are often interrelated. Therefore, in this regard, an evaluation tool which can capture as many operational conditions as possible for different decision problems is necessary. This paper introduces a general simulation platform, named MicroPort, which aims to provide an integrated and flexible modeling system for evaluating the operational capability and efficiency of different designs of seaport container terminals. The software structure of MicroPort comprises three programming layers: (1) the Functions layer; (2) the Applications layer; and (3) the Extensions layer. Different layers are bound by Application Programming Interfaces (APIs). Basic functions built in the Functions layer support the Applications layer in which major operation processes can be modeled by an agent-based method. External modules and decision support tools in the Extensions layer then use APIs to adjust the system to produce suitable simulation models for specific purposes.     

On mapping processes to processors in distributed systems

This paper is concerned with the implementation of parallel programs on networks of processors. In particular, we study the use of the network augmenting approach as an implementation tool. According to this approach, the capabilities of a given network of processors can be increased by adding some auxiliary links among the processors. We prove that the minimum set of edges needed to augment a line-like network so that it can accommodate a parallel program is determined by an optimal path cover of the graph representation of the program. Anoptimal path cover of a simple graphG is a set of vertex-disjoint paths that cover all the vertices ofG and has the maximum possible number of edges. We present a linear time optimal path covering algorithm for a class of sparse graphs. This algorithm is of special interest since the optimal path covering problem is NP-complete for general graphs. Our results suggest that a cover and augment scheme can be used for optimal implementation of parallel programs in line-like networks.A preliminary version of this paper was presented at the 6th IEEE Conference on Computer Communications (INFOCOM '87).This reseach is supported in part by National Semiconductor (Israel), Ltd.     

D-2-DenseNet噪音鲁棒的城市音频分类模型

为了提高噪音环境下城市音频分类系统的鲁棒性,提出了一种双特征2阶密集卷积神经网络（D-2-DenseNet）噪音鲁棒的城市音频分类模型.首先介绍了噪音添加和噪音鲁棒处理,阐述了一种双特征互补偿的算法;然后结合2阶密集卷积神经网络与自适应机制提出了一种噪音鲁棒音频分类模型：双特征2阶密集卷积神经网络.模型采用双特征互补偿自适应算法,可在特征提取与模型训练中更有针对性地提取有效音频信息,降低噪音干扰,以提高噪音鲁棒性.最后,基于Dcase2016数据集开展噪音环境下城市音频分类测试.实验结果表明,模型分类准确率分别可达77.12%、75.52%,与基线模型相比,平均分类准确率分别提高了8.51%和10.38%,验证了模型良好的噪音鲁棒性.     

基于三维特征向量的非侵入式电热负荷细分算法

非侵入式负荷监测分解（NILMD）技术是当前居民用能服务深化提升和电力供需互动的重要数据获取手段,然而当前工程上应用广泛的事件驱动型NILMD技术一直无法准确细化分解电热负荷。针对这一问题,文中提出了一种基于三维特征向量的典型电热负荷细化分解算法。首先,基于有功、无功功率和电流谐波等电气负荷特征采用事件检测方法提取电热事件,在有功功率的基础上,引入运行时长、频繁启停次数等非电气负荷特征共同构建三维特征向量电器模型。然后,采用序贯覆盖法设计典型电热负荷细化分解命题学习规则和细化分解算法。最后,基于实证实验数据进行分解验证,发现4种典型电热负荷的细化分解准确率超过85%。实验结果表明,文中所提典型电热负荷细化分解算法有效地提高了4种典型电热负荷分解的准确率。     

硅烷偶联剂-树脂作软岩防崩解涂料的研究

软岩的崩解、泥化往往给工程带来困难和损失。崩解、泥化实际上是岩体失水-吸水-膨胀的过程，水在这个过程中起着关键性的作用。硅烷偶联剂一树脂涂料中由于硅烷偶联剂在岩体表面和树脂问的架桥作用，使得涂层具有吸附牢固、疏水性好的特点，起到了阻止岩体与外界水分交换、加固岩体的作用。硅烷偶联剂一树脂涂料在软岩工程方面有着广阔的应用前景。     

Automatic annotation of tennis games: An integration of audio,vision, and learning

Fully automatic annotation of tennis game using broadcast video is a task with a great potential but with enormous challenges. In this paper we describe our approach to this task, which integrates computer vision, machine listening, and machine learning. At the low level processing, we improve upon our previously proposed state-of-the-art tennis ball tracking algorithm and employ audio signal processing techniques to detect key events and construct features for classifying the events. At high level analysis, we model event classification as a sequence labelling problem, and investigate four machine learning techniques using simulated event sequences. Finally, we evaluate our proposed approach on three real world tennis games, and discuss the interplay between audio, vision and learning. To the best of our knowledge, our system is the only one that can annotate tennis game at such a detailed level.     

A novel algorithm for layout optimization of injection process with random demands and sequence dependent setup times

Injection molding is an ideal manufacturing process for producing high volumes of products from both thermoplastic and thermo setting materials. Nevertheless, in some cases, this type of manufacturing process decelerates the production rate as a bottleneck. Thus, layout optimization plays a crucial role in this type of problem in terms of increasing the efficiency of the production line. In this regard, a novel computer simulation–stochastic data envelopment analysis (CS-SDEA) algorithm is proposed in this paper to deal with a single row job-shop layout problem in an injection molding process. First, the system is modeled with discrete-event-simulation as a powerful tool for analyzing complex stochastic systems. Then, due to lack of information about some operational parameters, theory of uncertainty is imported to the simulation model. Finally, an output-oriented stochastic DEA model is used for ranking the outputs of simulation model. The proposed CS-SDEA algorithm is capable of modeling and optimizing non-linear, stochastic, and uncertain injection process problems. The solution quality is illustrated by an actual case study in a refrigerator manufacturing company.