Anonymity and software agents: An interdisciplinary challenge

Software agents that play a role in E-commerce and E-government applications involving the Internet often contain information about the identity of their human user such as credit cards and bank accounts. This paper discusses whether this is necessary: whether human users and software agents are allowed to be anonymous under the relevant legal regimes and whether an adequate interaction and balance between law and anonymity can be realised from both the perspective of Computer Systems and the perspective of Law.This paper is an extended and fully revised version of Brazier, Kubbe, Oskamp and Wijngaards (2002) and Brazier, Oskamp, Prins, Schellekens and Wijngaards (2003).     

Robust fault detection of turbofan engines subject to adaptive controllers via a Total Measurable Fault Information Residual (ToMFIR) technique

This paper provides a new design of robust fault detection for turbofan engines with adaptive controllers. The critical issue is that the adaptive controllers can depress the faulty effects such that the actual system outputs remain the pre-specified values, making it difficult to detect faults/failures. To solve this problem, a Total Measurable Fault Information Residual (ToMFIR) technique with the aid of system transformation is adopted to detect faults in turbofan engines with adaptive controllers. This design is a ToMFIR-redundancy-based robust fault detection. The ToMFIR is first introduced and existing results are also summarized. The Detailed design process of the ToMFIRs is presented and a turbofan engine model is simulated to verify the effectiveness of the proposed ToMFIR-based fault-detection strategy.     

Wanted: institutions for balancing global food and energy markets

The increasing demand for biomass for energy use is further escalating existing food security risks. Managing these risks is a task for global institutions. These should ensure timely investment in the world’s capacity for producing biomass and balance the use of this biomass for foods and for non-foods. To achieve this, institutional arrangements for global food markets must fulfil two important goals: reduce the short-term price instability of food markets and prevent a structural scarcity of food in the long term. This paper analyses how agro-food markets, energy markets and biofuel markets are currently regulated. As this regulation is ill-suited to manage food price instabilities and balance food and non-food use of biomass, new institutions need to be put in place. A coordinated system of global commodity management — not unlike the Commodity Control Organization proposed by Keynes for the post-WWII era — is proposed to deal with these coming challenges.     

Lifetime commissioning as a tool to achieve energy‐efficient solutions

Quality control of the complete energy system is necessary if energy‐efficient solutions are to be met. To perform good building operation and quality control of a given system, it is necessary to have information about building systems and assessment tools. The paper presents Norwegian lifetime commissioning (LTC) procedures that are enabling follow‐up of the building performance during the building lifetime by establishing a generic framework on building performance data. Further, three developed assessment tools are presented: inspection algorithm for ventilation system, mass balance inspection algorithm for consumer substation, and advanced method for improved measurement of heat pump performance based on data fusion technique. The LTC procedures were tested on two case buildings. The results showed that 20% of all the defined building performance data can be monitored by BEMS. Using the mass balance inspection algorithm, it was found that fault in mass balance prevented implantation of desired temperature control for floor heating system. For heat pump performance, measurement of differential water temperature can be very erroneous. Hence, use of compressor electrical signal can give more precise data on heat pump performance. Comparative analysis showed that detailed monitoring system helps tracking energy use and fault detection in operation. Yearly and hourly profiles of energy consumption with separated use and energy carriers are given in the paper. Copyright © 2011 John Wiley & Sons, Ltd.     

Considerations for Synchronization in Body Area Networks for Human Activity Monitoring

In this paper, we highlight considerations for synchronization issues in body area networks. Requirements for the synchronization accuracy in body area networks depend on the application at hand. Synchronization may be needed for power management, sample ordering, calculation of stimulus responses and for sensor fusion. This paper provides a theoretical exercise to help understand the accuracy required for typical human motion sensing. It gives an overview of various synchronisation strategies used and implemented in prototype systems. Lessons learnt from practical implementations using Bluetooth, an IEEE 802.15.4 proprietary network and Nanonet are presented to illustrate the principles involved. The discussion provides some considerations and the requirements for typical WBAN applications.     

Direct estimation of voltage phasor, frequency and its rate of change using Newton''s iterative method

A new approach to the design of a digital algorithm for direct estimation of voltage phasor, frequency and its rate of change is presented. The algorithm derived is based on Newton's iterative method, very commonly used in the field of unconstrained optimization studies. A five-parameter voltage model was assumed, so the result of the estimation was a parameter vector, consisting of the following unknown parameters of the voltage signal processed: its DC component, magnitude, phase angle, frequency and its rate of change. To demonstrate the performance of the algorithm, off-line computer simulation results are presented. The algorithm showed high measurement accuracy over a wide range of frequency changes. The algorithm ‘order two convergence’ provided a very good dynamic response as well as a fast algorithm adaptability. The new algorithm seems to be a particularly useful tool in the field of frequency relaying as well as in various aspects of power engineering applications.     

Model order selection of damped sinusoids in noise by predictivedensities

We develop a procedure for the order selection of damped sinusoidal models based on the maximum a posteriori (MAP) criterion. The proposed method merges the concept of predictive densities with Bayesian inference to arrive at a complex multidimensional integral whose solution is achieved by way of the efficient Monte Carlo importance sampling technique. The importance function, a multivariate Cauchy probability density, is employed to produce stratified samples over the hypersurfaces support region. Centrality location parameters for the Cauchy are resolved by exploiting the special structure of the compressed likelihood function (CLF) and applying the fast maximum likelihood (FML) procedure of Umesh and Tufts. Simulation results allow for a comparison between our method and the singular value decomposition (SVD) based information theoretic criteria of Reddy and Biradar (see IEEE Trans. Signal Processing, vol.41, no.9, p.2872-81, 1993)     

Supervised clustering of label ranking data using label preference information

This paper studies supervised clustering in the context of label ranking data. The goal is to partition the feature space into K clusters, such that they are compact in both the feature and label ranking space. This type of clustering has many potential applications. For example, in target marketing we might want to come up with K different offers or marketing strategies for our target audience. Thus, we aim at clustering the customers’ feature space into K clusters by leveraging the revealed or stated, potentially incomplete customer preferences over products, such that the preferences of customers within one cluster are more similar to each other than to those of customers in other clusters. We establish several baseline algorithms and propose two principled algorithms for supervised clustering. In the first baseline, the clusters are created in an unsupervised manner, followed by assigning a representative label ranking to each cluster. In the second baseline, the label ranking space is clustered first, followed by partitioning the feature space based on the central rankings. In the third baseline, clustering is applied on a new feature space consisting of both features and label rankings, followed by mapping back to the original feature and ranking space. The RankTree principled approach is based on a Ranking Tree algorithm previously proposed for label ranking prediction. Our modification starts with K random label rankings and iteratively splits the feature space to minimize the ranking loss, followed by re-calculation of the K rankings based on cluster assignments. The MM-PL approach is a multi-prototype supervised clustering algorithm based on the Plackett-Luce (PL) probabilistic ranking model. It represents each cluster with a union of Voronoi cells that are defined by a set of prototypes, and assign each cluster with a set of PL label scores that determine the cluster central ranking. Cluster membership and ranking prediction for a new instance are determined by cluster membership of its nearest prototype. The unknown cluster PL parameters and prototype positions are learned by minimizing the ranking loss, based on two variants of the expectation-maximization algorithm. Evaluation of the proposed algorithms was conducted on synthetic and real-life label ranking data by considering several measures of cluster goodness: (1) cluster compactness in feature space, (2) cluster compactness in label ranking space and (3) label ranking prediction loss. Experimental results demonstrate that the proposed MM-PL and RankTree models are superior to the baseline models. Further, MM-PL is has shown to be much better than other algorithms at handling situations with significant fraction of missing label preferences.     

Drug release rate method for a liposome preparation

An in vitro method was developed to determine the rate of drug release from a liposome formulation. Liposome formulation containing metaproterenol sulfate was evaluated for release of the drug over a 24-hour period in an end-over-end tumbler device. Drug release appeared to be a function of all the operating parameters, such as rotational speed of the end-over-end tumbler, temperature of suspension, and the geometry of sample container that is placed in the end-over-end tumbler device.     

Distance protection and fault location utilizing only phase currentphasors

Two numerical algorithms for fault location and distance protection which use data from one end of a transmission line are presented. Both algorithms require only current signals as input data. Voltage signals are unnecessary for determining the unknown distance to the fault. The solution for the most frequent phase to ground fault is presented. The algorithms are relatively simple and easy to be implemented in the on-line application. The algorithms allow for accurate calculation of the fault location irrespective of the fault resistance and load. To illustrate the features of the new algorithms, steady-state and dynamic tests are presented