基于超图正则化的域适应偏最小二乘多工况软测量模型

针对流程工业中,因多工况导致数据分布变化引起传统软测量模型预测性能恶化问题,本文提出一种基于超图正则化的域适应多工况软测量回归模型框架.首先,采用非线性迭代偏最小二乘回归算法为基模型,在潜变量空间利用历史工况数据重构当前工况数据,以增强工况间的相关性,有效减小数据分布差异;同时,对重构系数施加低秩稀疏约束,保留了数据的局部和全局子空间结构;其次,通过超图拉普拉斯正则项对域适应潜变量求解过程进行约束,避免在寻找潜变量过程中破坏数据结构.最后,利用交替方向乘子法优化求解模型参数.在多个数据集上的实验表明,本文方法在多工况环境下可有效提高软测量模型的预测精度和泛化性能.     

男同性恋群体的城市空间分布及文化生产:以沈阳为例

From the pespective of space-culture, this paper explores how gay people's body, behavior and social relations are organized, represented and understood in Shenyang city by adopting participating observation, in-depth individual interview and historical literature review. Through the interaction between the urban space and the gay groups' inner characteristics, this paper finds out that the urban space constructs a dynamic platform for the mobilization of homosexual behavior, emotion and identity.     

The dynamics of disruption from altered auditory feedback: Further evidence for a dissociation of sequencing and timing.

Three experiments were designed to test whether perception and action are coordinated in a way that distinguishes sequencing from timing (Pfordresher, 2003). Each experiment incorporated a trial design in which altered auditory feedback (AAF) was presented for varying lengths of time and then withdrawn. Experiments 1 and 2 included AAF that resulted in action-effect asynchronies (delayed auditory feedback) during simple tapping (Experiment 1) and melody production (Experiment 2). Asynchronous AAF immediately slowed production; this effect then diminished rapidly after removal of AAF. By contrast, sequential alterations of feedback pitch during melody production (Experiment 3) had an effect that varied over successive presentations of AAF (by increasing error rates) that lasted after its withdrawal. The presence of auditory feedback after withdrawal of asynchronous AAF (Experiments 1 and 2) led to overcompensation of timing, whereas the presence of auditory feedback did not influence performance after withdrawal of AAF in Experiment 3. Based on these results, we suggest that asynchronous AAF perturbs the phase of an internal timekeeper, whereas alterations to feedback pitch over time degrade the internal representation of sequence structure. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Unsupervised adaptation of electroencephalogram signal processing based on fuzzy C‐means algorithm

This paper studies an unsupervised approach for online adaptation of electroencephalogram (EEG) based brain–computer interface (BCI). The approach is based on the fuzzy C‐means (FCM) algorithm. It can be used to improve the adaptability of BCIs to the change in brain states by online updating the linear discriminant analysis classifier. In order to evaluate the performance of the proposed approach, we applied it to a set of simulation data and compared with other unsupervised adaptation algorithms. The results show that the FCM‐based algorithm can achieve a desirable capability in adapting to changes and discovering class information from unlabeled data. The algorithm has also been tested by the real EEG data recorded in experiments in our laboratory and the data from other sources (set IIb of the BCI Competition IV). The results of real data are consistent with that of simulation data. Copyright © 2011 John Wiley & Sons, Ltd.     

Toward Robust Speech Recognition and Understanding

The principal cause of speech recognition errors is a mismatch between trained acoustic/language models and input speech due to the limited amount of training data in comparison with the vast variation of speech. It is crucial to establish methods that are robust against voice variation due to individuality, the physical and psychological condition of the speaker, telephone sets, microphones, network characteristics, additive background noise, speaking styles, and other aspects. This paper overviews robust architecture and modeling techniques for speech recognition and understanding. The topics include acoustic and language modeling for spontaneous speech recognition, unsupervised adaptation of acoustic and language models, robust architecture for spoken dialogue systems, multi-modal speech recognition, and speech summarization. This paper also discusses the most important research problems to be solved in order to achieve ultimate robust speech recognition and understanding systems. Dr. Sadaoki Furui is currently a Professor at Tokyo Institute of Technology, Department of Computer Science. He is engaged in a wide range of research on speech analysis, speech recognition, speaker recognition, speech synthesis, and multimodal human-computer interaction and has authored or coauthored over 450 published articles. From 1978 to 1979, he served on the staff of the Acoustics Research Department of Bell Laboratories, Murray Hill, New Jersey, as a visiting researcher working on speaker verification. He is a Fellow of the IEEE, the Acoustical Society of America and the Institute of Electronics, Information and Communication Engineers of Japan (IEICE). He was President of the Acoustical Society of Japan (ASJ) from 2001 to 2003 and the Permanent Council for International Conferences on Spoken Language Processing (PC-ICSLP) from 2000 to 2004. He is currently President of the International Speech Communication Association (ISCA). He was a Board of Governor of the IEEE Signal Processing Society from 2001 to 2003. He has served on the IEEE Technical Committees on Speech and MMSP and on numerous IEEE conference organizing committees. He has served as Editor-in-Chief of both Journal of Speech Communication and the Transaction of the IEICE. He is an Editorial Board member of Speech Communication, the Journal of Computer Speech and Language, and the Journal of Digital Signal Processing. He has received the Yonezawa Prize and the Paper Awards from the IEICE (1975, 88, 93, 2003), and the Sato Paper Award from the ASJ (1985, 87). He has received the Senior Award from the IEEE ASSP Society (1989) and the Achievement Award from the Minister of Science and Technology, Japan (1989). He has received the Technical Achievement Award and the Book Award from the IEICE (2003, 1990). He has also received the Mira Paul Memorial Award from the AFECT, India (2001). In 1993 he served as an IEEE SPS Distinguished Lecturer. He is the author of “Digital Speech Processing, Synthesis, and Recognition” (Marcel Dekker, 1989, revised, 2000) in English, “Digital Speech Processing” (Tokai University Press, 1985) in Japanese, “Acoustics and Speech Processing” (Kindai-Kagaku-Sha, 1992) in Japanese, and “Speech Information Processing” (Morikita, 1998) in Japanese. He edited “Advances in Speech Signal Processing” (Marcel Dekker, 1992) jointly with Dr. M.M. Sondhi. He has translated into Japanese “Fundamentals of Speech Recognition,” authored by Drs. L.R. Rabiner and B.-H. Juang (NTT Advanced Technology, 1995) and “Vector Quantization and Signal Compression,” authored by Drs. A. Gersho and R. M. Gray (Corona-sha, 1998).     

无线Mesh网络速率自适应算法研究

自适应速率选择是影响无线Mesh网性能的关键问题。提出了一种基于动态解调门限的速率自适应选择算法,该算法通过统计传输速率进行速率选择,能够针对不同信道环境对每种调制速率所需最小信噪比门限进行自动调整。同时,采用跨层设计思想,采用路由协议广播报文作为MESH设备间传输信噪比信息的载体,从而能够根据信噪比和传输成功率信息得到信道环境下的最佳发送速率。实际测试结果表明,所提算法能够在较短时间内选择出最佳速率,而且具有较低的抖动和稳定的吞吐量。     

VHE平台在移动业务中的应用

文章提出了一个用于移动业务的虚拟原藉环境(VHE)平台,该平台具备支撑移动业务的高级特征,可以支持不同网络和终端间的业务环境的可移植性;给出了VHE平台的基本结构和相关功能模块;分析了VHE平台可以提供的高级业务特征(如业务自适应、业务个性化、业务持续性和业务提供);简述了基于VHE的移动智能业务平台的发展趋势和尚需继续解决的问题.     

Spectral Efficiency Evaluation for MRC Diversity Schemes Over Generalized Rician Fading Channels

In this paper, closed-form expressions for the capacities per unit bandwidth for generalized Rician fading channels are derived for power and rate adaptation, constant transmit power, channel inversion with fixed rate, and truncated channel inversion adaptation policies. The closed-form solutions are derived for the single antenna reception (without diversity combining) and maximal-ratio combining (MRC) diversity cases. Truncated channel inversion adaptation policy is the best policy for the single antenna reception case, while the channel inversion with fixed rate policy is the best policy for the MRC diversity case. Constant transmit power policy provides the lowest spectral efficiency as compared to the other policies with and without diversity.     

Defect Engineering in Ambipolar Layered Materials for Mode-Regulable Nociceptor

Defect engineering represents a significant approach for atomically thick 2D semiconductor material development to explore the unique material properties and functions. Doping-induced conversion of conductive polarity is particularly beneficial for optimizing the integration of layered electronics. Here, controllable doping behavior in palladium diselenide (PdSe₂) transistor is demonstrated by manipulating its adatom-vacancy groups. The underlying mechanisms, which originate from reversible adsorption/desorption of oxygen clusters near selenide vacancy defects, are investigated systematically via their dynamic charge transfer characteristics and scanning tunneling microscope analysis. The modulated doping effect allows the PdSe₂ transistor to emulate the essential characteristics of photo nociceptor on a device level, including firing signal threshold and sensitization. Interestingly, electrostatic gating, acting as a neuromodulator, can regulate the adaptive modes in nociceptor to improve its adaptability and perceptibility to handle different danger levels. An integrated artificial nociceptor array is also designed to execute unique image processing functions, which suggests a new perspective for extension of the promise of defect engineered 2D electronics in simplified sensory systems toward use in advanced humanoid robots and artificial visual sensors.     

Sample selection for visual domain adaptation via sparse coding

Visual domain adaptation has attracted much attention and has made great achievement in recent years. It deals with the problem of distribution divergence between source and target domains. Current methods mostly focus on transforming images from different domains into a common space to minimize the distribution divergence. However, there are many irrelevant source samples for target domain even after the transformation. In order to eliminate the irrelevant samples, we develop a sample selection algorithm using sparse coding theory. We do the sample selection in a common subspace of source and target data to find as many as relevant source samples. In the common subspace, data characteristics are preserved by using graph regularization. Therefore, we can select the most relevant samples for our target image classification task. Moreover, in order to build a discriminative classifier for the target domain, we use not only the common part of source and target domains learned in the common subspace but also the specific part of target domain. The algorithm can be extended to handle samples from multiple source domains. Experimental results show that our visual domain adaptation method on the image classification tasks can be very effective for the state-of-the-art datasets.