Dynamic and contact analysis of a bimodal ultrasonic motor

A bimodal ultrasonic motor, which operates with only one power amplifier, uses two simultaneously excited modes to drive the rotor; a longitudinal mode and a flexural mode. The equations of motion describing the vibrations and contact behavior are derived by Hamilton's principle and the geometry constraint. The Lagrange multiplier method is used to treat the frictional contact problem. The finite element method and numerical integration scheme are used to simulate the dynamic responses of this system with and without contact. Some important factors are studied for the bimodal ultrasonic motor design. The factors include structure design, amplitude of input voltage, phase displacement, exciting frequency, and contact behavior.     

A coordinated approach to hedge the risks in stochastic inventory-routing problem

The inventory routing problem (IRP) studied in this research involves repeated delivery of products from a depot to a set of retailers that face stochastic demands over a long period. The main objective in the IRP is to design the set of routes and delivery quantities that minimize transportation cost while controlling inventory costs. Traditional IRP focuses on risk-neutral decision makers, i.e., characterizing replenishment policies that maximize expected total net present value, or equivalently, minimize expected total cost over a planning horizon. In this research, for incorporating risk aversion, a hedge-based stochastic inventory-routing system (HSIRS) integrated with Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model and Forward Option Pricing (FOP)model based on Black-Scholes model, from hedge point of view, is proposed to solve the multi-product multi-period inventory routing problem with stochastic demand. Computational results demonstrate the importance of this approach not only to risk-averse decision makers, but also to maximize the net present value at an acceptable service level. As a result, an optimal portfolio (R, s, S) system of product group can be generated to maximize the net present value under an acceptable service level in a given planning horizon. Meanwhile, the target group needed to be served and the relative transportation policy also can be determined accordingly based on the time required to be served as a priori partition to minimize the average transportation costs; hence, the routing assignment problem can be successfully optimized through a Predicting Particle Swarm Optimization algorithm.     

Sense abstractness, semantic activation, and word sense disambiguation

Concrete concepts are often easier to understand than abstract concepts. The notion of abstractness is thus closely tied to the organisation of our semantic memory, and more specifically our internal lexicon, which underlies our word sense disambiguation (WSD) mechanisms. State-of-the-art automatic WSD systems often draw on a variety of contextual cues and assign word senses by an optimal combination of statistical classifiers. The validity of various lexico-semantic resources as models of our internal lexicon and the cognitive aspects pertinent to the lexical sensitivity of WSD are seldom questioned. We attempt to address these issues by examining psychological evidence of the internal lexicon and its compatibility with the information available from computational lexicons. In particular, we compare the responses from a word association task against existing lexical resources, WordNet and SUMO, to explore the relation between sense abstractness and semantic activation, and thus the implications on semantic network models and the lexical sensitivity of WSD. Our results suggest that concrete senses are more readily activated than abstract senses, and broad associations are more easily triggered than narrow paradigmatic associations. The results are expected to inform the construction of lexico-semantic resources and WSD strategies.     

Structured population genetic algorithms: a literature survey

The Genetic Algorithm (GA) has been one of the most studied topics in evolutionary algorithm literature. Mimicking natural processes of inheritance, mutation, natural selection and genetic operators, GAs have been successful in solving various optimization problems. However, standard GA is often criticized as being too biased in candidate solutions due to genetic drift in search. As a result, GAs sometimes converge on premature solutions. In this paper, we survey the major advances in GA, particularly in relation to the class of structured population GAs, where better exploration and exploitation of the search space is accomplished by controlling interactions among individuals in the population pool. They can be classified as spatial segregation, spatial distance and heterogeneous population. Additionally, secondary factors such as aging, social behaviour, and so forth further guide and shape the reproduction process. Restricting randomness in reproduction has been seen to have positive effects on GAs. It is our hope that by reviewing the many existing algorithms, we shall see even better algorithms being developed.     

An Upper Bound on the Loss from Approximate Optimal-Value Functions

Many reinforcement learning approaches can be formulated using the theory of Markov decision processes and the associated method of dynamic programming (DP). The value of this theoretical understanding, however, is tempered by many practical concerns. One important question is whether DP-based approaches that use function approximation rather than lookup tables can avoid catastrophic effects on performance. This note presents a result of Bertsekas (1987) which guarantees that small errors in the approximation of a task's optimal value function cannot produce arbitrarily bad performance when actions are selected by a greedy policy. We derive an upper bound on performance loss that is slightly tighter than that in Bertsekas (1987), and we show the extension of the bound to Q-learning (Watkins, 1989). These results provide a partial theoretical rationale for the approximation of value functions, an issue of great practical importance in reinforcement learning.     

基于视觉系统的聚类算法

人类对于结构的感知方式和产生数据的物理系统原理对于聚类分析而言具有同等的重要性。因此,在聚类算法的设计和分析中,模拟人类的主要器官－视觉系统将帮助我们解决这一领域的一些基本问题。从这一观点出发,文中提出一类基于初级视觉系统尺度空间理论的聚类算法,并通过引入显著性假设,将生物物理学中的Weber定律与聚类结构的有效性联系起来。由此产生的聚类算法简洁有效,并可部分地回答那些与人类感知数据结构相关联的聚类有效性问题。我们的数值试验表明这一方法具有广泛的应用前景。     

Determination Of The Phase Current Waveform For A Disc‐Type Axial‐Flux Wheel Motor

An optimal design of the driving current pattern for a disc‐type axial‐flux brushless DC wheel motor of an electric vehicle is proposed in this paper. The electro‐magnetic dynamic model of the motor is established with magnetic circuits, describing the relationship between the output torque and excitation current. The optimal current pattern, in terms of magnitude and phase angle, is then obtained by maximizing the output torque with respect to the rotor shift. Compared with the traditional three‐phase‐on current pattern of fixed 120–degree phase shift, both the average torque and efficiency with the driving current of an optimal advanced switching angle are seen to be improved under various loading conditions. The motor performance with the optimal driving waveform is simulated and verified by experiments.     

Electrokinetically driven concentration of particles and cells by dielectrophoresis with DC-offset AC electric field

Insulator-based dielectrophoresis (iDEP) has been successfully used for on-chip manipulations of biological samples. Despite its effectiveness, iDEP typically requires high DC voltages to achieve sufficient electric field; this is mainly due to the coupled phenomena among linear electrokinetics: electroosmosis (EO) and electrophoresis (EP) and nonlinear electrokinetics: dielectrophoresis (DEP). This paper presents a microfluidic technique using DC-offset AC electric field for electrokinetic concentration of particles and cells by repulsive iDEP. This technique introduces AC electric field for producing iDEP which is decoupled from electroosmosis (EO) and electrophoresis (EP). The repulsive iDEP is generated in a PDMS tapered contraction channel that induces non-uniform electric field. The benefits of introducing AC electric field component are threefold: (i) it contributes to DEP force acting on particles, (ii) it suppresses EO flow and (iii) it does not cause any EP motion. As a result, the required DC field component that is mainly used to transport particles on the basis of EO and EP can be significantly reduced. Experimental results supported by numerical simulations showed that the total DC-offset AC electric field strength required to concentrate 15-μm particles is significantly reduced up to 85.9% as compared to using sole DC electric field. Parametric experimental studies showed that the higher buffer concentration, larger particle size and higher ratio of AC-to-DC electric field are favorable for particle concentration. In addition, the proposed technique was demonstrated for concentration of yeast cells.     

A Technical Word- and Term-Translation Aid Using Noisy Parallel Corpora across Language Groups

Technical-term translation represents one of the most difficult tasks for human translators since (1) most translators are not familiar with terms and domain-specific terminology and (2) such terms are not adequately covered by printed dictionaries. This paper describes an algorithm for translating technical words and terms from noisy parallel corpora across language groups. Given any word which is part of a technical term in the source language, the algorithm produces a ranked candidate match for it in the target language. Potential translations for the term are compiled from the matched words and are also ranked. We show how this ranked list helps translators in technical-term translation. Most algorithms for lexical and term translation focus on Indo-European language pairs, and most use a sentence-aligned clean parallel corpus without insertion, deletion or OCR noise. Our algorithm is language- and character-set-independent, and is robust to noise in the corpus. We show how our algorithm requires minimum preprocessing and is able to obtain technical-word translations without sentence-boundary identification or sentence alignment, from the English–Japanese awk manual corpus with noise arising from text insertions or deletions and on the English–Chinese HKUST bilingual corpus. We obtain a precision of 55.35% from the awk corpus for word translation including rare words, counting only the best candidate and direct translations. Translation precision of the best-candidate translation is 89.93% from the HKUST corpus. Potential term translations produced by the program help bilingual speakers to get a 47% improvement in translating technical terms.     

Adaptive weighted outer-product learning associative memory

Associative-memory neural networks with adaptive weighted outer-product learning are proposed in this paper. For the correct recall of a fundamental memory (FM), a corresponding learning weight is attached and a parameter called signal-to-noise-ratio-gain (SNRG) is devised. The sufficient conditions for the learning weights and the SNRG's are derived. It is found both empirically and theoretically that the SNRG's have their own threshold values for correct recalls of the corresponding FM's. Based on the gradient-descent approach, several algorithms are constructed to adaptively find the optimal learning weights with reference to global- or local-error measure.