Robust parallel filtering design of a swarm tracking system

Swarming networks of mobile autonomous agents require inter-agent position information in order perform various tasks. The primary control input for the majority of current control strategies is inter-agent distance information. In this paper we provide a robust parallel filter based tracking scheme that allows a mobile agent to track other multiple mobile agents. The distance, angle, and relative position is given in a direct target tracking output. This allows the mobile agent to decide which information is best suited for the particular objective

Architecture of adaptive spam filtering based on machine learning algorithms

Spam is commonly defined as unsolicited email messages and the goal of spam filtering is to distinguish between spam and legitimate email messages. Much work has been done to filter spam from legitimate emails using machine learning algorithm and substantial performance has been achieved with some amount of false positive (FP) tradeoffs. In the case of spam detection FP problem is unacceptable sometimes. In this paper, an adaptive spam filtering model has been proposed based on Machine learning (ML) algorithms which will get better accuracy by reducing FP problems. This model consists of individual and combined filtering approach from existing well known ML algorithms. The proposed model considers both individual and collective output and analyzes them by an analyzer. A dynamic feature selection (DFS) technique also proposed in this paper for getting better accuracy.

Robust finite-horizon Kalman filtering for uncertain discrete-time systems

In this note, we propose a design for a robust finite-horizon Kalman filtering for discrete-time systems suffering from uncertainties in the modeling parameters and uncertainties in the observations process (missing measurements). The system parameter uncertainties are expected in the state, output and white noise covariance matrices. We find the upper-bound on the estimation error covariance and we minimize the proposed upper-bound.

Improved robust Kalman filtering for uncertain systems with missing measurements

In this paper, a novel robust finite-horizon Kalman filter is developed for discrete linear time-varying systems with missing measurements and normbounded parameter uncertainties. The missing measurements are modelled by a Bernoulli distributed sequence and the system parameter uncertainties are in the state and output matrices. A two stage recursive structure is considered for the Kalman filter and its parameters are determined guaranteeing that the covariances of the state estimation errorsare not more than the known upper bound. Finally, simulation results are presented to illustrate the outperformance of the proposed robust estimator compared with the previous results in the literature.

Privacy preserving collaborative filtering for KNN attack resisting

Privacy preserving is an essential aspect of modern recommender systems. However, the traditional approaches can hardly provide a rigid and provable privacy guarantee for recommender systems, especially for those systems based on collaborative filtering (CF) methods. Recent research revealed that by observing the public output of the CF, the adversary could infer the historical ratings of the particular user, which is known as the KNN attack and is considered a serious privacy violation for recommender systems. This paper addresses the privacy issue in CF by proposing a Private Neighbor Collaborative Filtering (PriCF) algorithm, which is constructed on the basis of the notion of differential privacy. PriCF contains an essential privacy operation, Private Neighbor Selection, in which the Laplace noise is added to hide the identity of neighbors and the ratings of each neighbor. To retain the utility, the Recommendation-Aware Sensitivity and a re-designed truncated similarity are introduced to enhance the performance of recommendations. A theoretical analysis shows that the proposed algorithm can resist the KNN attack while retaining the accuracy of recommendations. The experimental results on two real datasets show that the proposed PriCF algorithm retains most of the utility with a fixed privacy budget.

Using national input-output data for embodied energy analysis of individual residential buildings

Embodied energy (EE) analysis has become an important area of energy research, in attempting to trace the direct and indirect energy requirements of products and services throughout their supply chain. Typically, input-output (I-O) models have been used to calculate EE because they are considered to be comprehensive in their analysis. However, a major deficiency of using I-O models is that they have inherent errors and therefore cannot be reliably applied to individual cases. Thus, there is a need for the ability to disaggregate an I-O model into its most important 'energy paths', for the purpose of integrating case-specific data. This paper presents a new hybrid method for conducting EE analyses for individual buildings, which retains the completeness of the I-O model. This new method is demonstrated by application to an Australian residential building. Only 52% of the energy paths derived from the I-O model were substituted using case-specific data. This indicates that previous system boundaries for EE studies of individual residential buildings are less than optimal. It is envisaged that the proposed method will provide construction professionals with more accurate and reliable data for conducting life cycle energy analysis of buildings. Furthermore, by analysing the unmodified energy paths, further data collection can be prioritized effectively.

Reduced-order output feedback controllers for discrete-time systems

This article considers the stabilization by output feedback controllers for discrete-time systems. The controller can place all of the closed-loop poles within a specified disk D(-α, 1/β), centred at (-α,0) with radius 1/β, where | - α|  + 1/β < 1. The design method involves the decomposition of the system into two portions. The first portion comprises of all of the poles that are lying outside of the specified disk. A reduced-order model is constructed for this portion. The second portion comprises of all of the remaining poles of the system and is characterized by an H_∞-norm bound. The controller design is then accomplished by using H_∞-control theory. It is shown that, subject to the solvability of an algebraic Riccati equation, output feedback controllers can be systematically derived. The order of the controller is low, and can be as low as the number of the open-loop poles that are lying outside of the specified disk. A step-by-step design algorithm is provided. Numerical examples are given to illustrate the attractiveness of the design method.

Constrained pole placement for linear systems using low-order output feedback controllers

The paper presents a simple approach to the problem of designing low-order output feedback controllers for linear continuous systems. The controller can place all of the closed-loop poles within a circle, C(- , 1/ β) , with centre at - and radius of 1/ β in the left half s-plane. The design method is based on transformation of the original system and then applying the bounded-real-lemma to the transformed system. It is shown that subjected to the solvability of an algebraic Riccati equation (ARE), output feedback controllers can then be systematically derived. Furthermore, the order of the controller is low and equals only the number of the open-loop poles lying outside the circle. A step-by-step design algorithm is given. Numerical examples are given to illustrate the design method.

Comparison of multifactor productivity indicators for real estate sectors using the OECD input-output database

"Research on the international comparison of productivity has gained significant interest throughout several previous decades. Relatively little work has however been done in the real estate sector. This paper aims to develop a new productivity measurement framework for the international comparison of the real estate sector based on the newly-published OECD input-output database. Three multifactor productivity indicators are formulated using the ratio of the sectoral final demand to value added, the intermediate output to intermediate input and the total output to total input effect respectively in the input-output table. Historical analyses and comparisons are also carried out to indicate the differences of productivities of the real estate sectors in seven selected countries. Findings can improve the understanding of how technological, organisational and policy influences combine to affect productivity growth and aid the policy makers, real estate agencies and researchers in evaluating the competitive ability of the real estate sector."

Validation of the use of input-output data for embodied energy analysis of the Australian construction industry

This paper evaluates a recently developed hybrid method for the embodied energy analysis of the Australian construction industry. It was found that the truncation associated with process analysis can be up to 80%, whilst the use of input-output analysis alone does not always provide a perfect model for replacing process data. There is also a considerable lack in the quantity and possibly quality of process data currently available. These findings suggest that current best-practice methods are sufficiently accurate for most typical applications, but this is heavily dependant upon data quality and availability. The hybrid method evaluated can be used for the optimisation of embodied energy and for identifying opportunities for improvements in energy efficiency.

Economic indicator comparisons of international real estate sectors using the OECD input-output database

With its growing share in national economies, the real estate sector has been considered a vital contributor of economic development. Research efforts are needed in order to gain a better comprehension of the national specificities of the real estate sector and to identify its role in economic development. Due to limited comparable data, the economic indicators of real estate sectors are hard to compare between different countries. This paper aims to explore the quantitative interdependence amongst the real estate sector and other industries in developed economies using input-output analysis, and to investigate their significant linkages. Based on the recently published Organisation for Economic Co-operation and Development (OECD) input-output database at constant prices, the analysis focuses on the real estate's escalating role in terms ofshares in gross output, value added and gross national product. With emphasis on the relative role of manufacturing, construction and services inputs, this paper also highlights the strengths of the push and pull of the real estate sector.

Intelligent DDoS packet filtering in high-speed networks

Currently high-speed networks have been attacked by successive waves of Distributed Denial of Service (DDoS) attacks. There are two major challenges on DDoS defense in the high-speed networks. One is to sensitively and accurately detect attack traffic, and the other is to filter out the attack traffic quickly, which mainly depends on high-speed packet classification. Unfortunately most current defense approaches can not efficiently detect and quickly filter out attack traffic. Our approach is to find the network anomalies by using neural network, deploy the system at distributed routers, identify the attack packets, and then filter them quickly by a Bloom filter-based classifier. The evaluation results show that this approach can be used to defend against both intensive and subtle DDoS attacks, and can catch DDoS attacks’ characteristic of starting from multiple sources to a single victim. The simple complexity, high classification speed and low storage requirements make it especially suitable for DDoS defense in high-speed networks.

Economic performance analysis of the Australian property sector using input-output tables

The property sector has played an important role with its growing contribution in the national income and employment in the Australian economy. There is an increasing research need in measuring and analysing the economic performance of the Australian property sector at a country level and input-output tables are considered as an appropriate tool. This paper aims to analyse and measure the performance and sectoral linkages of the Australian property sector using the five latest input-output tables compiled by the Australian Bureau of Statistics. Findings suggested that the Australian residential property sector had played a more important role than the commercial sector in the economy. The backward linkage of the residential property sector showed a decreasing economic pull, while that of commercial property presented an upward pattern. Moreover. the Australian property sector showed a medium economic push to the national economy over the examined period. Findings can aid policy makers, the property sector and researchers in evaluating the competitive ability of the property sector in Australia.

Multifactor productivity measures of construction sectors using OECD input-output database

This research develops a new productivity measurement framework for the construction sector in the light of an input-output table. Three group multifactor productivity indicators are formulated based on the multiplier concepts in the input-output analysis. This measurement framework focuses on the intro-industry flows of products and considers the direct and indirect effects of input and output. Moreover, this framework enables us to measure the multifactor productivity of a specific sector systematically. Historical analyses and international comparisons are carried out to indicate the differences of the productivity of the construction sectors in seven selected countries, using the newly published OECD input-output database. Research findings are expected to clarify how technological, organizational and political factors affect the productivity growth, enabling the policy makers, construction businesses and researchers to quantify the competitive ability of the construction sector.