Case-based expert system using wavelet packet transform and kernel-based feature manipulation for engine ignition system diagnosis

Whenever there is any fault in an automotive engine ignition system or changes of an engine condition, an automotive mechanic can conventionally perform an analysis on the ignition pattern of the engine to examine symptoms, based on specific domain knowledge (domain features of an ignition pattern). In this paper, case-based reasoning (CBR) approach is presented to help solve human diagnosis problem using not only the domain features but also the extracted features of signals captured using a computer-linked automotive scope meter. CBR expert system has the advantage that it provides user with multiple possible diagnoses, instead of a single most probable diagnosis provided by traditional network-based classifiers such as multi-layer perceptions (MLP) and support vector machines (SVM). In addition, CBR overcomes the problem of incremental and decremental knowledge update as required by both MLP and SVM. Although CBR is effective, its application for high dimensional domains is inefficient because every instance in a case library must be compared during reasoning. To overcome this inefficiency, a combination of preprocessing methods, such as wavelet packet transforms (WPT), kernel principal component analysis (KPCA) and kernel K-means (KKM) is proposed. Considering the ignition signals captured by a scope meter are very similar, WPT is used for feature extraction so that the ignition signals can be compared with the extracted features. However, there exist many redundant points in the extracted features, which may degrade the diagnosis performance. Therefore, KPCA is employed to perform a dimension reduction. In addition, the number of cases in a case library can be controlled through clustering; KKM is adopted for this purpose. In this paper, several diagnosis methods are also used for comparison including MLP, SVM and CBR. Experimental results showed that CBR using WPT and KKM generated the highest accuracy and fitted better the requirements of the expert system.     

Service bid comparisons by fuzzy ranking in open railway market timetabling

In an open railway access market, the Infrastructure Provider (IP), upon the receipts of service bids from the Train Service Providers (TSPs), assigns track access rights according to its own business objectives and the merits of the bids; and produces the train service timetable through negotiations. In practice, IP chooses to negotiate with the TSPs one by one in such a sequence that IP optimizes its objectives. The TSP bids are usually very complicated, containing a large number of parameters in different natures. It is a difficult task even for an expert to give a priority sequence for negotiations from the contents of the bids. This study proposes the application of fuzzy ranking method to compare and prioritize the TSP bids in order to produce a negotiation sequence. The results of this study allow investigations on the behaviors of the stakeholders in bid preparation and negotiation, as well as evaluation of service quality in the open railway market.     

A mixture of generalized latent variable models for mixed mode and heterogeneous data

In the behavioral, biomedical, and social-psychological sciences, mixed data types such as continuous, ordinal, count, and nominal are common. Subpopulations also often exist and contribute to heterogeneity in the data. In this paper, we propose a mixture of generalized latent variable models (GLVMs) to handle mixed types of heterogeneous data. Different link functions are specified to model data of multiple types. A Bayesian approach, together with the Markov chain Monte Carlo (MCMC) method, is used to conduct the analysis. A modified DIC is used for model selection of mixture components in the GLVMs. A simulation study shows that our proposed methodology performs satisfactorily. An application of mixture GLVM to a data set from the National Longitudinal Surveys of Youth (NLSY) is presented.     

Heat transfer coefficient measurements for mixed gas working fluids at cryogenic temperatures

The use of mixed gas working fluids has become common in Joule-Thomson type cryocoolers for a variety of applications. However, there is very little data or theory currently available regarding the heat transfer coefficient associated with these multi-component, multi-phase mixtures at cryogenic temperatures. This paper describes an experimental test facility and procedure that has been used to make careful measurements of the horizontal, flow boiling heat transfer coefficient for several hydrocarbon mixtures that are nominally optimal for small, Joule-Thomson cryocoolers in the 80 K to 120 K operating range. Data are presented over a range of temperatures from 100 K to room temperature and for several pressures and mass flow rates. The results indicate that quality and mass flux are the most important parameters governing the heat transfer coefficient among those that were varied. The experiment is verified by carrying out tests using single-phase, pure nitrogen gas and comparing the results with the Dittus-Boelter equation. The experimental uncertainty of the measurements is estimated from 1st principles; additionally, the repeatability of the experimental measurements was investigated by replicating tests at a nominal set of operating conditions and composition on separate days. The measurements presented here are intended to aid in the design of small, mixed-gas Joule-Thomson cryocoolers.     

Hide-n-Sense: Preserving Privacy Efficiently in Wireless mHealth

As healthcare in many countries faces an aging population and rising costs, mobile sensing technologies promise a new opportunity. Using mobile health (mHealth) sensing, which uses medical sensors to collect data about the patients, and mobile phones to act as a gateway between sensors and electronic health record systems, caregivers can continuously monitor the patients and deliver better care. Furthermore, individuals can become better engaged in monitoring and managing their own health. Although some work on mHealth sensing has addressed security, achieving strong privacy for low-power sensors remains a challenge. We make three contributions. First, we propose an mHealth sensing protocol that provides strong security and privacy properties at the link layer, with low energy overhead, suitable for low-power sensors. The protocol uses three novel techniques: adaptive security, to dynamically modify transmission overhead; MAC striping, to make forgery difficult even for small-sized Message Authentication Codes; and asymmetric resource requirements, in recognition of the limited resources in tiny mHealth sensors. Second, we demonstrate its feasibility by implementing a prototype on a Chronos wrist device, and evaluating it experimentally. Third, we provide a security, privacy, and energy analysis of our system.     

Computational models of germanium point contact detectors

In the crystal bulk of group IV covalent semiconductors such as germanium (Ge), simple analytic models for the valence band structure can provide fast, accurate computations of hole mobility for moderate energy ranges up to a few eV. On the surfaces of these materials, such as on Ge-vacuum or Ge-GeO₂ interfaces, the transport rates differ significantly from the bulk. This can be problematic for both point contact and segmented Ge gamma ray detectors, that require accurate carrier drift rates for computing signal basis sets, which themselves are necessary for the precise determination of gamma-ray induced compton scattering events. While several techniques exist for computing surface hole mobilities, more often than not, these methods are complex to implement, require significant computational resources, and lack the simplicity of bulk models for interpreting results. This paper presents a new technique for computing Ge surface hole mobility that can give a first estimate for the surface transport rates after tuning a physically based computational parameter. This model is used in conjunction with particle-in-cell (PIC) simulations for modeling hole-dynamics inside a Ge p-type point contact detector. The results of our calculations agree with experimental data gathered from Ge p-type point contact detectors at Oak Ridge National Laboratory.     

Analytical models for understanding misbehavior and MAC friendliness in CSMA networks

Nodes using contention-based CSMA protocols are susceptible to the misbehavior of other nodes and also have little flexibility in controlling quality of service. To address the misbehavior problem, continuous-time protocols are proposed. The effects of “misbehavior” on the QoS of all nodes in the system caused by changing the cheater’s arrival rate and backoff rate are studied. The problem of flexibility in controlling QoS is addressed by introducing the concept of “MAC friendliness” where nodes can adjust arrival rates and backoff rates while maintaining a fixed share of the channel. The continuous-time system is modeled using an iterative method and matrix exponential distributions. Collision probabilities are determined both by the channel utilization of the entire system, as well as the actual stage within the backoff process. The model itself is a key contribution because it is accurate over all ranges of traffic loads and models both queueing within nodes and contention for the channel.     

Generating a multiplicity of policies for agent steering in crowd simulation

Pedestrian steering algorithms range from completely procedural to entirely data‐driven, but the former grossly generalize across possible human behaviors and suffer computationally, whereas the latter are limited by the burden of ever‐increasing data samples. Our approach seeks the balanced middle ground by deriving a collection of machine‐learned policies based on the behavior of a procedural steering algorithm through the decomposition of the space of possible steering scenarios into steering contexts. The resulting algorithm scales well in the number of contexts, the use of new data sets to create new policies, and in the number of controlled agents as the policies become a simple evaluation of the rules asserted by the machine‐learning process. We also explore the use of synthetic data from an “oracle algorithm” that serves as an as‐needed source of samples, which can be stochastically polled for effective coverage. We observe that our approach produces pedestrian steering similar to that of the oracle steering algorithm, but with a significant performance boost. Runtime was reduced from hours under the oracle algorithm with 10 agents to on the order of 10 frames per second (FPS) with 3000 agents. We also analyze the nature of collisions in such a framework with no explicit collision avoidance. Copyright © 2014 John Wiley & Sons, Ltd.