Behavior of Pile Subject to Excavation-Induced Soil Movement

This paper presents the results of centrifuge model tests on unstrutted deep excavation in dense sand and its influence on an adjacent single pile foundation behind the retaining wall. It is found that, in the case of a stable wall, the induced pile bending moment and deflection decrease exponentially with increasing distance between the pile and the wall. Pile head boundary condition plays an important role in affecting the pile responses due to an adjacent excavation. In the case of retaining wall collapse, the failure pattern of the soil behind the wall features a slip plane projecting from near the wall toe to the ground surface. Soil within the failure zone demonstrates large lateral movement and induces significant bending moment and deflection on pile located within the zone. Soil movement and pile responses outside this zone are noted to be significantly less. A comparison between the experimental results and the theoretical predictions by an existing numerical method shows good agreement, provided that appropriate assumptions are made on the soil parameters and conditions, especially in the case of retaining wall collapse.     

Two Machine Scheduling under Disruptions with Transportation Considerations

Effective logistics scheduling requires synchronization of manufacturing and delivery to optimize customer service at minimum total cost. In this paper, we study a new scheduling problem that arises in a disruption environment. Such a problem occurs when a disruption unexpectedly happens, and consequently, some machines become unavailable for certain periods. Jobs that are assigned to the disrupted machines and have not yet been processed can either be moved to other available machines for processing, which may involve additional transportation time and cost, or can be processed by the same machine after the disruption. Our goal is to reschedule jobs so that an objective function, including the original cost function, and possibly transportation costs and disruption cost caused by deviating from the originally planned completion times, is minimized. In this paper, we focus on the two-machine case to demonstrate some major properties, and hope that these properties can provide insights for solving other general problems, such as multiple (more than two) machine scheduling and machine scheduling in other configurations (job shop or flow shop) under disruption. We study problems with different related costs. In each problem, we either provide a polynomial algorithm to solve the problem optimally, or show its NP-hardness. If the problem is NP-hard in the ordinary sense, we also present a pseudo-polynomial algorithm to solve the problem optimally. This research is supported in part by Hong Kong RGC grant HKUST 6145/03E and in part by NSF Grant DMI-0300156.     

Pilot mental workload: how well do pilots really perform?

The purpose of this study was to investigate the effects of increasing mental demands on various aspects of aircrew performance. In particular, the robustness of the prioritization and allocation hierarchy of aviate-navigate-communicate was examined, a hierarchy commonly used within the aviation industry. A total of 42 trainee pilots were divided into three workload groups (low, medium, high) to complete a desktop, computer-based exercise that simulated combinations of generic flight deck activities: flight control manipulation, rule-based actions and higher level cognitive processing, in addition to Air Traffic Control instructions that varied in length from one chunk of auditory information to seven chunks. It was found that as mental workload and auditory input increased, participants experienced considerable difficulty in carrying out the primary manipulation task. A similar decline in prioritization was also observed. Moreover, when pilots were under a high mental workload their ability to comprehend more than two chunks of auditory data deteriorated rapidly.     

Genetic Parallel Programming: design and implementation

This paper presents a novel Genetic Parallel Programming (GPP) paradigm for evolving parallel programs running on a Multi-Arithmetic-Logic-Unit (Multi-ALU) Processor (MAP). The MAP is a Multiple Instruction-streams, Multiple Data-streams (MIMD), general-purpose register machine that can be implemented on modern Very Large-Scale Integrated Circuits (VLSIs) in order to evaluate genetic programs at high speed. For human programmers, writing parallel programs is more difficult than writing sequential programs. However, experimental results show that GPP evolves parallel programs with less computational effort than that of their sequential counterparts. It creates a new approach to evolving a feasible problem solution in parallel program form and then serializes it into a sequential program if required. The effectiveness and efficiency of GPP are investigated using a suite of 14 well-studied benchmark problems. Experimental results show that GPP speeds up evolution substantially.     

CPE: a parallel library for financial engineering applications

The Clustertech parallel environment is an object-oriented C++ library that uses abstractions to simplify parallel programming for financial engineering applications. The message passing interface ensures CPE's portability and performance over a wide range of parallel cluster and symmetric multiprocessing machines.     

Order Scheduling in an Environment with Dedicated Resources in Parallel

We consider m machines in parallel with each machine capable of producing one specific product type. There are n orders with each one requesting specific quantities of the various different product types. Order j may have a release date r_j and a due date d_j. The different product types for order j can be produced at the same time. We consider the class of objectives ∑ f_j(C_j) that includes objectives such as the total weighted completion time ∑ w_j C_j and the total weighted tardiness ∑ w_j T_j of the n orders. We present structural properties of the various problems and a complexity result. In particular, we show that minimizing ∑ C_j when m ≥ 3 is strongly NP-hard. We introduce two new heuristics for the ∑ C_j objective. An empirical analysis shows that our heuristics outperform all heuristics that have been proposed for this problem in the literature.     

Articulatory-feature-based confidence measures

Confidence measures are computed to estimate the certainty that target acoustic units are spoken in specific speech segments. They are applied in tasks such as keyword verification or utterance verification. Because many of the confidence measures use the same set of models and features as in recognition, the resulting scores may not provide an independent measure of reliability. In this paper, we propose two articulatory feature (AF) based phoneme confidence measures that estimate the acoustic reliability based on the match in AF properties. While acoustic-based features, such as Mel-frequency cepstral coefficients (MFCC), are widely used in speech processing, some recent works have focus on linguistically based features, such as the articulatory features that relate directly to the human articulatory process which may better capture speech characteristics. The articulatory features can either replace or complement the acoustic-based features in speech processing. The proposed AF-based measures in this paper were evaluated, in comparison and in combination, with the HMM-based scores on phoneme and keyword verification tasks using children’s speech collected for a computer-based English pronunciation learning project. To fully evaluate their usefulness, the proposed measures and combinations were evaluated on both native and non-native data; and under field test conditions that mis-matches with the training condition. The experimental results show that under the different environments, combinations of the AF scores with the HMM-based scores outperforms HMM-based scores alone on phoneme and keyword verification.     

On Generalized Fuzzy Belief Functions in Infinite Spaces

Determined by a fuzzy implication operator, a general type of fuzzy belief structure and its induced dual pair of fuzzy belief and plausibility functions in infinite universes of discourse are first defined. Relationship between the belief-structure-based and the belief-space-based fuzzy Dempster-Shafer models is then established. It is shown that the lower and upper fuzzy probabilities induced by the fuzzy belief space yield a dual pair of fuzzy belief and plausibility functions. For any fuzzy belief structure, there must exist a fuzzy belief space such that the fuzzy belief and plausibility functions defined by the given fuzzy belief structure are just the lower and upper fuzzy probabilities induced by the fuzzy belief space, respectively. Essential properties of the fuzzy belief and plausibility functions are also examined. The fuzzy belief and plausibility functions are, respectively, a fuzzy monotone Choquet capacity and a fuzzy alternating Choquet capacity of infinite order.     

Examining the Potentially Confounding Effect of Class Size on the Associations between Object-Oriented Metrics and Change-Proneness

Previous research shows that class size can influence the associations between object-oriented (OO) metrics and fault-proneness and therefore proposes that it should be controlled as a confounding variable when validating OO metrics on fault-proneness. Otherwise, their true associations may be distorted. However, it has not been determined whether this practice is equally applicable to other external quality attributes. In this paper, we use three size metrics, two of which are available during the high-level design phase, to examine the potentially confounding effect of class size on the associations between OO metrics and change-proneness. The OO metrics that are investigated include cohesion, coupling, and inheritance metrics. Our results, based on Eclipse, indicate that: 1) The confounding effect of class size on the associations between OO metrics and change-proneness, in general, exists, regardless of whichever size metric is used; 2) the confounding effect of class size generally leads to an overestimate of the associations between OO metrics and change-proneness; and 3) for many OO metrics, the confounding effect of class size completely accounts for their associations with change-proneness or results in a change of the direction of the associations. These results strongly suggest that studies validating OO metrics on change-proneness should also consider class size as a confounding variable.