乐音音形的感知与谐和性分析

针对乐音的谐和性问题,以听觉与视觉之间的通感现象为基础,从听音联想的角度将"音形"定义为听到乐音时主观联想到的图形。在保证乐音信号等声压级的前提下,通过乐音音形的感知实验和谐和性评价实验,类比视觉审美法则,探索感知音形与听觉谐和性之间的关系。实验及分析结果显示,乐音的感知音形呈现一定的规律性,且感知音形与乐音谐和性评价之间相互关联。乐器单音音形多被感知为单一图形,音程的感知音形多由两个图形组成,且这两个图形分别对应于构成音程的两个单音的感知音形。乐器单音及音程的感知音形与听感谐和性之间存在四种关系,分别是一致、比例、对称和对比融合。     

Stabilization and synchronization of 5-D memristor oscillator using sliding mode control

ABSTRACT

This article presents a control design method for asymptotic stabilization, synchronization, and antisynchronization of a five dimensional chaotic memristor oscillator system. Two cases are considered: (i) with known parameters, (ii) and with unknown parameters. When a system’s parameters are known, then controllers are designed using sliding mode approach and when system’s parameters are unknown then controllers are devised using adaptive integral sliding mode control method. Hurwitz surfaces are constructed in both approaches. To employ the integral sliding mode, firstly, the system is transformed into a special structure containing a nominal part and some unknown terms computed adaptively. Then, the system is stabilized using integral sliding mode control. The stabilizing controller, consisting of the nominal control plus some compensator control, is then designed. The compensator controller and the adapted laws are derived in such a way that the time derivative of a Lyapunov function becomes strictly negative. The effectiveness of the proposed scheme is tested through computer simulation.     

Invariant cone and synchronization state stability of the mean field models

In this article we prove the stability of some mean field systems similar to the Winfree model in the synchronized state. The model is governed by the coupling strength parameter κ and the natural frequency of each oscillator. The stability is proved independently of the number of oscillators and the distribution of the natural frequencies. The main result is proved using the positive invariant cone method for the linearized system. This method can be applied to other mean field models as in the Kuramoto model.     

Supply chain relationship quality and performance in technological turbulence: an artificial neural network approach

A well-functioning supply chain management relationship cannot only develop seamless coordination with valuable members, but also improve operational efficiency to secure greater market share, increased profits and reduced costs. An accurate decision-making system considering multifactor relationship quality is highly desired. This study offers an alternative perspective and characterisation of the supply chain relationship quality and performance. A decision-making model is proposed with an artificial neural network approach for supply chain continuous performance improvement. Supply chain performance is analysed via a supervised learning back-propagation neural network. An ‘inverse’ neural network model is proposed to predict the supply chain relationship quality conditions. Optimal performance parameters can be obtained using the proposed neural network scheme, providing significant advantages in terms of improved relationship quality. This study demonstrates a new solution with the combination of qualitative and quantitative methods for performance improvement. The overall accuracy rate of the decision-making model is 88.703%. The results indicated that trust has the greatest influence on the supply chain performance. Relationship quality among supply chain partners impacts performance positively as the pace of technological turbulence increases.     

Fabrication of an aluminum, Caribbean-style, musical pan: Metallurgical and acoustical characterization

We report herein the first development and fabrication of a 6061 aluminum alloy pan and compare its tuning and acoustic spectra for selected notes with a standard low-carbon steel Caribbean pan fabricated from a 210-L barrel. The experimental aluminum alloy pan was completely manufactured by welding a 1.68-mm-thick head sheet to a 9-mm² aluminum alloy hoop, sinking the head by pneumatic hammering and welding a 1.15-mm-thick aluminum alloy side or skirt to the hoop. This experimental pan was 0.66 m in diameter, in contrast to the 210-L steel barrel standard, which had a diameter of 0.57 m. Chromatic tones were observed for most rim notes on the aluminum alloy pan, but the highest octave range notes at the pan bottom were not tuned. Microstructural characterization by light optical metallography and transmission electron microscopy illustrated the necessity for high dislocation densities and associated hardness in order to stabilize the notes and to assure their chromatic tuning.     

Optimization and evaluation of time-dependent tablets comprising an immediate and sustained release profile using artificial neural network

The aim of this work was to optimize time-dependent tablets using artificial neural network (ANN). The time-dependent tablet consisted of a tablet core, which contained sustained release pellets (70% isosorbide-5-mononitrate [5-ISMN]), immediate release granules (30% 5-ISMN), superdisintegrating agent (sodium carboxymethylstarch, CMS-Na), and other excipients, surrounded by a coating layer composed of a water-insoluble ethylcellulose and a water-soluble channeling agent. The chosen independent variables, i.e., X₁ coating level of tablets, X₂ coating level of pellets, and X₃ CMS-Na level, were optimized with a three-factor, three-level Box-Behnken design. Data were analyzed for modeling and optimizing the release profile using ANN. Response surface plots were used to relate the dependent and the independent variables. The optimized values for the factors X₁-X₃ were 4.1, 14.1, and 29.8%, respectively. Optimized formulations were prepared according to the factor combinations dictated by ANN. In each case, the observed drug release data of the optimized formulations were close to the predicted release pattern. An in vitro model for predicting the effect of food on release behavior of optimized products was used in this study. It was concluded that neural network technique could be particularly suitable in the pharmaceutical technology of time-dependent dosage forms where systems were complex and nonlinear relationships often existed between the independent and the dependent variables.     

An intelligent system for gastrointestinal polyp detection in endoscopic video using fusion of bidimensional empirical mode decomposition and convolutional neural network features

This paper presents an intelligent system for gastrointestinal polyp detection in endoscopic video. Video endoscopy is a popular diagnostic modality in assessing the gastrointestinal polyps. But the accuracy of diagnosis mostly depends on doctors' experience that is crucial to detect polyps in many cases. Computer-aided polyp detection is promising to reduce the miss detection rate of polyp and thus improve the accuracy of diagnosis results. The proposed method illustrates an automatic system based on a new color feature extraction scheme as a support for gastrointestinal polyp detection. The scheme is the combination of color empirical mode decomposition features and convolutional neural network features extracted from video frames. The features are fed into a linear support vector machine to train the classifier. Experiments on standard public databases show that the proposed scheme outperforms the previous conventional methods, gaining accuracy of 99.53%, sensitivity of 99.91%, and specificity of 99.15%.     

M-type potassium conductance controls the emergence of neural phase codes: a combined experimental and neuron modelling study

Rate and phase codes are believed to be important in neural information processing. Hippocampal place cells provide a good example where both coding schemes coexist during spatial information processing. Spike rate increases in the place field, whereas spike phase precesses relative to the ongoing theta oscillation. However, what intrinsic mechanism allows for a single neuron to generate spike output patterns that contain both neural codes is unknown. Using dynamic clamp, we simulate an in vivo-like subthreshold dynamics of place cells to in vitro CA1 pyramidal neurons to establish an in vitro model of spike phase precession. Using this in vitro model, we show that membrane potential oscillation (MPO) dynamics is important in the emergence of spike phase codes: blocking the slowly activating, non-inactivating K⁺ current (I_M), which is known to control subthreshold MPO, disrupts MPO and abolishes spike phase precession. We verify the importance of adaptive I_M in the generation of phase codes using both an adaptive integrate-and-fire and a Hodgkin–Huxley (HH) neuron model. Especially, using the HH model, we further show that it is the perisomatically located I_M with slow activation kinetics that is crucial for the generation of phase codes. These results suggest an important functional role of I_M in single neuron computation, where I_M serves as an intrinsic mechanism allowing for dual rate and phase coding in single neurons.     

A Novel Approach for FMEA: Combination of Interval 2‐Tuple Linguistic Variables and Gray Relational Analysis

Failure mode and effect analysis (FMEA) is a powerful tool for defining, identifying, and eliminating potential failures from the system, design, process, or service before they reach the customer. Since its appearance, FMEA has been extensively used in a wide range of industries. However, the conventional risk priority number (RPN) method has been criticized for having a number of drawbacks. In addition, FMEA is a group decision behavior and generally performed by a cross‐functional team. Multiple experts tend to express their judgments on the failure modes by using multigranularity linguistic term sets, and there usually exists uncertain and incomplete assessment information. In this paper, we present a novel FMEA approach combining interval 2‐tuple linguistic variables with gray relational analysis to capture FMEA team members’ diversity opinions and improve the effectiveness of the traditional FMEA. An empirical example of a C‐arm X‐ray machine is given to illustrate the potential applications and benefits of the proposed approach. Copyright © 2014 John Wiley & Sons, Ltd.     

Crushing behavior of laterally compressed composite elliptical tubes: Experiments and predictions using artificial neural networks

Composite materials have been increasingly used in the automobile industry for weight saving and part integration purposes. In this regard, composite elliptical tubes have been effectively employed as energy absorber devices. This increases the need for accurate and simple prediction techniques to optimize these structures.

The present work deals with the implementation of artificial neural networks (ANN) technique in the prediction of the crushing behavior and energy absorption characteristics of laterally loaded glass fiber/epoxy composite elliptical tubes. Predicted results are compared with actual experimental data in terms of load carrying capacity and energy absorption capability showing good agreement. This shows that ANN techniques could effectively be used to predict the response of collapsible composite energy absorber devices subjected to different loading conditions. As is the case for experimental findings, the predictions obtained using ANN also show the significant effect of the ellipticity ratio on the crushing behavior of laterally loaded tubes.     

An analysis of the influence of hand hole and ventilation hole design on compressive strength of corrugated fiberboard boxes by an artificial neural network model

This research aimed at developing a high-performing corrugated fiberboard box compression strength prediction model and to analyze the influences of ventilation and hand hole designs for these containers on the box compression test (BCT) by applying artificial neural network (ANN) modeling. The input variables considered in this study are composed of nine parameters including box dimension as well as shapes, sizes, positions, and locations of ventilations and hand holes of a regular slotted container (RSC, FEFCO 0201). Back propagation algorithms (BPNs) of ANN models were developed from 970 BCT testing data points (single wall boards, C flute, 205/112/205 g/m²). Tested data was randomly broken into three groups for the model development as 80:10:10 for the training set, testing set, and validating set. According to the analysis performed, a BPN 9-13-1 model reflected the highest prediction performance with R² = 0.97. According to the analysis, BCT was significantly affected by the hand hole location followed by the geometrical dimensions of the box (height, length, and width) and the ventilation factors (shape, number, and location) in that order. Hand holes at the top flaps caused a lower BCT reduction compared with those at the vertical locations of the box. Slight changes to the eliminated board area for both hand holes and ventilation (±5%) contributed to less BCT reduction compared with its locations and shapes. Interestingly, increasing the box height significantly increased the BCT, and this was found to be limited only to shorter boxes fabricated from a high stiffness corrugated board.     

Biotreatment of zinc-containing wastewater in a sulfidogenic CSTR: Performance and artificial neural network (ANN) modelling studies

Sulfidogenic treatment of sulfate (2-10g/L) and zinc (65-677mg/L) containing simulated wastewater was studied in a mesophilic (35 degrees C) CSTR. Ethanol was supplemented (COD/sulfate=0.67) as carbon and energy source for sulfate-reducing bacteria (SRB). The robustness of the system was studied by increasing Zn, COD and sulfate loadings. Sulfate removal efficiency, which was 70% at 2g/L feed sulfate concentration, steadily decreased with increasing feed sulfate concentration and reached 40% at 10g/L. Over 99% Zn removal was attained due to the formation of zinc-sulfide precipitate. COD removal efficiency at 2g/L feed sulfate concentration was over 94%, whereas, it steadily decreased due to the accumulation of acetate at higher loadings. Alkalinity produced from acetate oxidation increased wastewater pH remarkably when feed sulfate concentration was 5g/L or lower. Electron flow from carbon oxidation to sulfate reduction averaged 83+/-13%. The rest of the electrons were most likely coupled with fermentative reactions as the amount of methane production was insignificant. The developed ANN model was very successful as an excellent to reasonable match was obtained between the measured and the predicted concentrations of sulfate (R=0.998), COD (R=0.993), acetate (R=0.976) and zinc (R=0.827) in the CSTR effluent.     

Optimization of a passive structure for active vibration isolation: an interval-computation- and constraint-propagation-based approach

This article is concerned with the optimization of the mechanical structure of a one-degree-of-freedom vibration isolator. An efficient optimization algorithm based on an interval-computation method is used. The authors’ general objective is to include and develop an optimization step in the pre-design process of a smart structure. In this article, the idea of this pre-design is to obtain, very quickly and very simply, the global structural geometry of the passive isolation device. For this purpose, a simplified mathematical model is built, which describes the main natural mode shapes of the suspension device. The method is applicable to large-scale dynamic systems for a first optimization process step because it's clearly an effective time-saving optimization approach. The results obtained are quite sufficient for a first pre-design step. For a real case scenario, the optimized structure is applied to a numerical active vibration control process.     

A neural feature extraction model for classification of firms and prediction of outsourcing success: advantage of using relational sources of information for new suppliers

Clustering is a family of classification techniques, often preceding further analysis or application in a number of fields like data analysis, strategy selection, supplier selection, etc. Data based neural techniques are gaining popularity in clustering applications due to flexibility and adaptability. Kohonen’s Self Organizing Map (SOM) is often used when the objects to be clustered have many attributes. In both supervised and un-supervised modes, Kohonen’s map exhibit good capability to extract a classification which assigns highest weight to the most important attribute. In this paper, we have applied SOM for classification of firms based on their sources of information for new suppliers/customers. Additional data regarding the outsourcing success of the firms’ is added to see if there is an association between a particular set of information sources and the probability of firms’ success to outsource to partner firms. Using data from World Bank BEEPS survey of German industries, we could produce three distinct clusters of industries. When successful outsourcing data were included, it still showed three clusters. The hits were obtained using specific support vector for identification of clusters. We found evidence of associations between relational sources and firms’ ability to outsource successfully.     

文创产品的信息传播与感知差异性研究——以青年群体的视角为例

为探析普通消费者对文创产品的感性认知差异,从消费末端探究文创产品开发的基本依据。在以往研究和专家深度访谈基础上建构了青年群体对文创产品感性认知的基本框架,具体表现在基本信息、产品特征与使用方法三个方面,在每个层面采用两个变量对其进行解释并形成最终量表。以一个经典文创产品“书灯”(Lumio)为研究对象,通过问卷调查与同步的深度访谈系统收集用户反馈数据,以统计学软件SPSS19.00的独立样本T检定模块进行统计学分析。结果发现,对青年群体而言,性别因素导致对文创产品的感性认知差异主要集中在产品特征和使用方法上;专业背景因素导致的感性认知差异主要集中在基本信息和使用方法上;而产品的IP辨识度因素并没有在青年群体中带来对文创产品的感性认知差异。     

A linguistic risk prioritization approach for failure mode and effects analysis: A case study of medical product development

Failure mode and effects analysis (FMEA) is a widely used risk management technique for identifying the potential failures from a system, design, or process and determining the most serious ones for risk reduction. Nonetheless, the traditional FMEA method has been criticized for having many deficiencies. Further, in the real world, FMEA team members are usually bounded rationality, and thus, their psychological behaviors should be considered. In response, this study presents a novel risk priority model for FMEA by using interval two‐tuple linguistic variables and an integrated multicriteria decision‐making (MCDM) method. The interval two‐tuple linguistic variables are used to capture FMEA team members' diverse assessments on the risk of failure modes and the weights of risk factors. An integrated MCDM method based on regret theory and TODIM (an acronym in Portuguese for interactive MCDM) is developed to prioritize failure modes taking experts' psychological behaviors into account. Finally, an illustrative example regarding medical product development is included to verify the feasibility and effectiveness of the proposed FMEA. By comparing with other existing methods, the proposed linguistic FMEA approach is shown to be more advantageous in ranking failure modes under the uncertain and complex environment.     

声发射技术在纤维增强复合材料损伤检测和破坏过程分析中的应用研究进展

声发射作为一种无损检测技术,具有主动性、几何形状不敏感性、即时性和特征性等优点。声发射技术通过建立复合材料损伤和破坏特征与声发射信号间的关联,分辨复合材料随加载过程的各种失效模式,结合加载过程中的应力应变曲线,从而获得失效机制。本文对声发射检测技术在纤维增强复合材料研究中的应用和分析方法进行了综述,并对其在复合材料领域的应用趋势进行了展望。     

Coupled stress and energy criterion for composite failure: Pointwise versus averaged evaluation of the stress criterion

Abstract

In this work, failure loads and failure modes of single lap adhesive joints between composite laminates are investigated. To this aim, a coupled stress and energy criterion is applied and results are compared to numerical reference solutions using cohesive zone modeling and to experimental values from literature. Possible failure modes are adhesive failure along the adherend/adhesive interface, adherend failure as intralaminar failure in the first ply closest to the adhesive layer and interlaminar failure between the first and second ply. Suitable failure criteria adressing the different failure modes are implemented within the framework of the coupled criterion. The stress criterion is carried out in a pointwise or in an averaged manner, called point method or line method respectively. It is shown that two physically sound failure modes can only be predicted using the stress criterion in an averaged manner since the pointwise evaluation does not allow the formation of certain types of cracks.     

Determination of the optimal burn-in time and cost using an environmental stress approach: a case study in switch mode rectifier

Stress burn-in is an effective burn-in means of screening out the infant mortality components of a system which are conducted under an extremely stressful environment. While investigating stresses, screening burn-in by thermal stress, voltage stress, or mechanical shock stress, most related studies failed to develop an effective method to determine the optimal burn-in time and burn-in cost for a practical operation. Therefore, this study presents an effective procedure that adopts robust design techniques and the accelerated stress test to determine the optimal burn-in time and burn-in cost. A case study of the production of switch mode rectifier demonstrates the proposed procedure's effectiveness. Moreover, the results show that the proposed procedure generalizes well, and can screen out the early failure from material and manufacturing process.