TCBR-HMM: An HMM-based text classifier with a CBR system

This paper presents an innovative solution to model distributed adaptive systems in biomedical environments. We present an original TCBR-HMM (Text Case Based Reasoning-Hidden Markov Model) for biomedical text classification based on document content. The main goal is to propose a more effective classifier than current methods in this environment where the model needs to be adapted to new documents in an iterative learning frame. To demonstrate its achievement, we include a set of experiments, which have been performed on OSHUMED corpus. Our classifier is compared with Naive Bayes and SVM techniques, commonly used in text classification tasks. The results suggest that the TCBR-HMM Model is indeed more suitable for document classification. The model is empirically and statistically comparable to the SVM classifier and outperforms it in terms of time efficiency.     

SINA: Semantic interpretation of user queries for question answering on interlinked data

The architectural choices underlying Linked Data have led to a compendium of data sources which contain both duplicated and fragmented information on a large number of domains. One way to enable non-experts users to access this data compendium is to provide keyword search frameworks that can capitalize on the inherent characteristics of Linked Data. Developing such systems is challenging for three main reasons. First, resources across different datasets or even within the same dataset can be homonyms. Second, different datasets employ heterogeneous schemas and each one may only contain a part of the answer for a certain user query. Finally, constructing a federated formal query from keywords across different datasets requires exploiting links between the different datasets on both the schema and instance levels. We present Sina, a scalable keyword search system that can answer user queries by transforming user-supplied keywords or natural-languages queries into conjunctive SPARQL queries over a set of interlinked data sources. Sina uses a hidden Markov model to determine the most suitable resources for a user-supplied query from different datasets. Moreover, our framework is able to construct federated queries by using the disambiguated resources and leveraging the link structure underlying the datasets to query. We evaluate Sina over three different datasets. We can answer 25 queries from the QALD-1 correctly. Moreover, we perform as well as the best question answering system from the QALD-3 competition by answering 32 questions correctly while also being able to answer queries on distributed sources. We study the runtime of SINA in its mono-core and parallel implementations and draw preliminary conclusions on the scalability of keyword search on Linked Data.     

马尔可夫跳变线性系统最优控制的研究现状与进展

马尔可夫跳变线性系统（MJLS）是一种具有多个模态的随机系统，系统在各个模态之间的跳变转移由一组马尔可夫链来决定。MJLS模型因其在表示过程中可以产生突变而更能精确的描述实际工程应用中的系统。近年来，MJLS的最优控制问题成为了研究的热点，动态规划、极大值原理以及线性矩阵不等式等成为了解决此类问题的主流方法。本文对MJLS最优控制领域的研究现状进行了综述。分别对一般情况下、带有噪声的情况下、带有时滞的情况下以及某些特定情况下的MLJS最优控制问题的国内外研究现状进行论述。最后进行了总结并提出MJLS最优控制领域未来值得关注的研究方向。     

Robust nonlinear feedback–feedforward control of a coupled kinetic Monte Carlo–finite difference simulation

Robust nonlinear feedforward–feedback controllers are designed for a multiscale system that dynamically couples kinetic Monte Carlo (KMC) and finite difference (FD) simulation codes. The coupled codes simulate the copper electrodeposition process for manufacturing on-chip copper interconnects in electronic devices. The control objective is to regulate the current density subject to the condition that the steady-state fluctuation of the overpotential remains bounded within ±0.01 V. The controller designs incorporate a low-order stochastic model that captures the input–output behavior of the coupled KMC–FD code. The controllers achieve the objectives and the closed-loop responses implemented on the low-order model and the coupled KMC–FD code match well within stochastic variations. The nonlinear feedforward control reduces the rise time of the controller response while the feedback control ensures robustness in the presence of model uncertainty.     

Fast handoff with reducing waste rate approach for wireless resources management in UMTS mobile networks

3G Wideband CDMA systems adopt the Orthogonal Variable Spreading Factor code tree as the channelization codes management for achieving high data rate transmission in personal multimedia communications. It assigns a single channelization code for each accepted connection. Nevertheless, it wastes the system capacity when the required rate is not powers of two of the basic rate. One good solution is to assign multiple codes for each accepted connection but it causes two inevitable drawbacks: long handoff delay and new call setup delay due to high complexity of processing with multiple channelization codes, and high cost of using more number of rake combiners. Especially, long handoff delay may result in more call dropping probability and higher Grade of Service, which will degrade significantly the utilization and revenue of the 3G cellular systems. Therefore, we propose herein an adaptive efficient codes determination algorithm based on the Markov Decision Process analysis approach to reduce the waste rate and reassignments significantly while providing fast handoff. Numerical results demonstrate that the proposed approach yields several advantages, including the lowest GOS, the least waste rate, and the least number of reassignments. Meanwhile, the optimal number of rake combiners is also analyzed in this paper. This research was supported in part by the National Science Council of Taiwan, ROC, under contract NSC-93-2213-E-324-018.     

多带抗噪声语音识别算法研究

根据Flether等人的研究,基于感知独立性假设的子带识别方法被用于抗噪声鲁棒语音识别。本文拓展子带方法,采用基于噪声污染假定的多带框架来减少噪声影响。论文不仅从理论上分析了噪声污染假定多带框架在识别性能上的潜在优势,而且提出了多带环境下的鲁棒语音识别算法。研究表明：多带框架不仅回避了独立感知假设要求,而且与子带方法相比,多带方法能更好的减少噪声影响,提高系统识别性能。     

On the effects of the packet size distribution on the packet loss process

Real-time multimedia applications have to use forward error correction (FEC) anderror concealment techniques to cope with losses in today’s best-effort Internet. The efficiency of these solutions is known however to depend on the correlation between losses in the media stream. In this paper we investigate how the packet size distribution affects the packet loss process, that is, the distribution of the number of lost packets in a block, the related FEC performance and the average loss run length. We present mathematical models for the loss process of the MMPP+M/D/1/K and the MMPP+M/M/1/K queues; we validate the models via simulations, and compare the results to simulation results with an MPEG-4 coded video trace. We conclude that the deterministic packet size distribution (PSD) not only results in lower stationary loss probability than the exponential one, but also gives a less correlated loss process, both at a particular average link load and at a particular stationary loss probability as seen by the media stream.Our results show that for applications that can only measure the packet loss probability, the effects of the PSD on FEC performance are higher in access networks, where a single multimedia stream might affect the multiplexing behavior. Our results show that the effects of the PSD on FEC performance are higher in access networks, where a single multimedia stream might affect the multiplexing behavior and thus can improve the queuing performance by decreasing the variance of its PSD.     

多业务移动通信系统中的呼叫接入控制

在多业务无线移动通信系统的呼叫接入控制中,不仅要考虑业务之间的优先级,同时也必须考虑切换呼叫的优先级。本文结合可移动边界算法和保护信道策略,提出了一种有效的多业务呼叫接入控制算法。为了方便地分析方案的性能,设计了一个二维的Markov链对方案的性能进行理论上的分析。通过数值分析结果表明,提出的算法同时保证了两种优先级,并提高了系统的性能。1     

Quality Control for Scalable Media Processing Applications

Many media processing applications create a load that varies significantly over time. Hence, if such an application is assigned a lower processing-time budget than needed in its worst-case load situation, deadline misses are likely to occur. This problem can be dealt with by designing media processing applications in a scalable fashion. A scalable media processing application can run in multiple qualities, leading to correspondingly different resource demands. The problem we consider is to find an accompanying quality control strategy, which minimizes both the number of deadline misses and the number of quality changes, while maximizing the quality of processing. We present an initial approach to the above problem by modeling it as a Markov decision process (MDP). Our model is based on measuring relative progress at milestones. Solving the MDP results in a quality control strategy that can be applied during runtime with only little overhead. We evaluate our approach by means of a practical example, which concerns a scalable MPEG-2 decoder.