Emotional speech feature normalization and recognition based on speaker-sensitive feature clustering

In this paper we propose a feature normalization method for speaker-independent speech emotion recognition. The performance of a speech emotion classifier largely depends on the training data, and a large number of unknown speakers may cause a great challenge. To address this problem, first, we extract and analyse 481 basic acoustic features. Second, we use principal component analysis and linear discriminant analysis jointly to construct the speaker-sensitive feature space. Third, we classify the emotional utterances into pseudo-speaker groups in the speaker-sensitive feature space by using fuzzy k-means clustering. Finally, we normalize the original basic acoustic features of each utterance based on its group information. To verify our normalization algorithm, we adopt a Gaussian mixture model based classifier for recognition test. The experimental results show that our normalization algorithm is effective on our locally collected database, as well as on the eNTERFACE’05 Audio-Visual Emotion Database. The emotional features achieved using our method are robust to the speaker change, and an improved recognition rate is observed.     

Extended minimum-squared error algorithm for robust face recognition via auxiliary mirror samples

The changes of face images with poses and polarized illuminations increase data uncertainty in face recognition. In fact, synthesized mirror samples can be recognized as representations of the left–right deflection of poses or illuminations of the face. Symmetrical face images generated from the original face images also provide more observations of the face which is useful for improving the accuracy of face recognition. In this paper, to the best of our knowledge, it is the first time that the well-known minimum squared error classification (MSEC) algorithm is used to perform face recognition on an extended face database using synthesized mirror training samples, which is titled as extended minimum squared error classification (EMSEC). By modifying the MSE classification rule, we append the mirror samples to the training set for gaining better classification performance. First, we merge original training samples and mirror samples synthesized from original training samples per subject as mixed training samples. Second, EMSEC algorithm exploits mixed training samples to obtain the projection matrix that can best transform the mixed training samples into predefined class labels. Third, the projection matrix is exploited to simultaneously obtain transform results of the test sample and its nearest neighbor from the mixed training sample set. Finally, we ultimately classify the test sample by combining the transform results of the test sample and the nearest neighbor. As an extension of MSEC, EMSEC reduces the uncertainty of the face observation by auxiliary mirror samples, so that it has better robustness classification performance than traditional MSEC. Experimental results on the ORL, GT, and FERET databases show that EMSEC has better generalization ability than traditional MSEC.     

Adiabatic passage for one-step generation of <Emphasis Type="Italic">n</Emphasis>-qubit Greenberger–Horne–Zeilinger states of superconducting qubits via quantum Zeno dynamics

An efficient scheme is proposed for generating n-qubit Greenberger–Horne–Zeilinger states of n superconducting qubits separated by (\(n-1\)) coplanar waveguide resonators capacitively via adiabatic passage with the help of quantum Zeno dynamics in one step. In the scheme, it is not necessary to precisely control the time of the whole operation and the Rabi frequencies of classical fields because of the introduction of adiabatic passage. The numerical simulations for three-qubit Greenberger–Horne–Zeilinger state show that the scheme is insensitive to the dissipation of the resonators and the energy relaxation of the superconducting qubits. The three-qubit Greenberger–Horne–Zeilinger state can be deterministically generated with comparatively high fidelity in the current experimental conditions, though the scheme is somewhat sensitive to the dephasing of superconducting qubits.     

Polygonal multiresolution topology optimization (PolyMTOP) for structural dynamics

We use versatile polygonal elements along with a multiresolution scheme for topology optimization to achieve computationally efficient and high resolution designs for structural dynamics problems. The multiresolution scheme uses a coarse finite element mesh to perform the analysis, a fine design variable mesh for the optimization and a fine density variable mesh to represent the material distribution. The finite element discretization employs a conforming finite element mesh. The design variable and density discretizations employ either matching or non-matching grids to provide a finer discretization for the density and design variables. Examples are shown for the optimization of structural eigenfrequencies and forced vibration problems.     

A two-time-scale load balancing framework for minimizing electricity bills of Internet Data Centers

Internet Data Center (IDC) is one of important emerging cyber-physical systems. To guarantee the quality of service for their worldwide users, large Internet service providers usually operate multiple geographically distributed IDCs. The enormous power consumption of these data centers may lead to both huge electricity bills and considerable carbon emissions. To mitigate these problems, on-site renewable energy plants are emerging in recent years. Since the renewable energy is intermittent, greening geographical load balancing (GGLB for short) has been proposed to reduce both the electricity bills and carbon emissions by following the renewables. However, GGLB is not able to well deal with the wildly fluctuating wind power when applied into IDCs with on-site wind energy plants. It may either fail to minimize the total electricity bills or incur the costly frequent on–off switching of servers. In order to minimize the total electricity bills of geographically distributed IDCs with on-site wind energy plants, we formulate the total electricity bills minimization problem and propose a novel two-time-scale load balancing framework TLB to solve it. First, TLB models the runtime cooling efficiency for each IDC. Then it predicts the future fine-grained (e.g., 10-min) on-site wind power output at the beginning of each scheduling period (e.g., an hour). After that, TLB transforms the primal optimization problem into a typical mixed-integer linear programming problem and solves it to finally obtain the optimal scheduling policy including the open server number as well as the request routing policy. It is worth noting that the open server number of each IDC will remain the same during each scheduling period. As an application instance of TLB, we also design a two-time-scale load balancing algorithm TLB-ARMA for our experimental scenario. Evaluation results based on real-life traces show that TLB-ARMA is able to reduce the total electricity bills by as much as 12.58 % compared with the hourly executed GGLB without incurring the costly repeated on–off switching of servers.     

山西省近50年风速变化及突变特征分析

利用线性拟合、M-K方法对山西省22个气象观测站1960—2010年的逐日风速资料进行了倾向、突变、逐年代变化等特征分析。研究表明,近50年来山西省平均风速存在明显的下降趋势,平均风速由20世纪60年代的2.16 m/s下降到21世纪初的1.86 m/s;不同时间段呈现不同的变化特征,1979—1989年风速出现显著下降,年均递减率达到0.046 m/s,变率远超出其他时段;对近50年山西省平均风速进行M-K检验后发现,在1982年出现明显的突变点。山西省四季平均风速均呈下降趋势,四季风速下降速率最快的为春季,冬季次之,夏季风速下降速率最慢。同样,四季风速均发生突变,突变时间点分布在1980—1985年间。除8月外,山西省各个月份风速也呈下降趋势,且均出现突变现象。     

供电企业绩效管理对策研究

绩效管理是人力资源管理中的重要举措之一,对现代企业增强对员工的掌控力和核心竞争力具有重要作用。随着我国供电体制改革的不断深入,供需平衡已成为电力能源供应的发展趋势。基于当前成功企业的绩效管理方法和供电企业在实施绩效管理工作中存在的突出问题,从绩效管理的计划、指标、流程和应用等环节,深入研究了供电企业建立符合自身特点的全员绩效管理体系的方法。     

混合休眠策略的ZigBee多点监控自组网设计

分析了ZigBee多点监控网络中休眠节点工作不同步、网络性能不稳定等问题,提出基于混合休眠策略的 ZigBee多点监控自组网设计方案,完成基于Z Stack的软件设计,并组建ZigBee星型网络进行测试。实验结果表明,节点在维持低功耗的同时,系统的同步性、稳定性得到了提高。     

应用型本科院校“数据结构”课程教学探讨

针对应用型本科院校对学生动手能力要求高,但学生学习"数据结构"课程觉得枯燥无味、难以理解的情况,对课堂教学和课后实训两方面改革进行了探讨。