Indistinguishability operators in measurement theory,Part I: Conversions of indistinguishability operators with respect to scales

For two given ordinal scales in a measurement process, the present paper investigates how an indistinguishability operator evaluated according to one of these ordinal scales can be converted to another indistinguishability operator w.r.t. the other ordinal scale, and establishes the mathematical base of these conversions under the framework of measurement theory [Krantz, D.H., Luce, R.D., Suppes, P., Tversky, A. (1971) Foundations of Measurement, Vol. 1 (Academic Press, San Diego)]. Additionally, this work exposes the rudimentary facts behind the studies in [“Fuzzy Numbers and Equality Relations”, Proc. FUZZ'IEEE 93 (1993) 1298–1301; “Fuzzy Sets and Vague Environments”, Fuzzy Sets and Systems 66 (1994) 207–221; “Fuzzy Control on the Basis of Equality Relations-with an Example from Idle Speed Control”, IEEE Transactions on Fuzzy Systems 3 (1995) 336–350; and “T-partitions of the Real Line Generated by Idempotent Shapes”, Fuzzy Sets and Systems 91 (1997) 177–184], and points out the measurement theoretic derivations of the results in these studies.     

Smith Predictor Based Fractional‐Order‐Filter PID Controllers Design for Long Time Delay Systems

In this paper, an original model‐based analytical method is developed to design a fractional order controller combined with a Smith predictor and a modified Smith predictor that yield control systems which are robust to changes in the process parameters. This method can be applied for integer order systems and for fractional order ones. Based on the Bode's ideal transfer function, the fractional order controllers are designed via the internal model control principle. The simulation results demonstrate the successful performance of the proposed method for controlling integer as well as fractional order linear stable systems with long time delay.     

A new hyper-heuristic as a general problem solver: an implementation in HyFlex

This study provides a new hyper-heuristic design using a learning-based heuristic selection mechanism together with an adaptive move acceptance criterion. The selection process was supported by an online heuristic subset selection strategy. In addition, a pairwise heuristic hybridization method was designed. The motivation behind building an intelligent selection hyper-heuristic using these adaptive hyper-heuristic sub-mechanisms is to facilitate generality. Therefore, the designed hyper-heuristic was tested on a number of problem domains defined in a high-level framework, i.e., HyFlex. The framework provides a set of problems with a number of instances as well as a group of low-level heuristics. Thus, it can be considered a good environment to measure the generality level of selection hyper-heuristics. The computational results demonstrated the generic performance of the proposed strategy in comparison with other tested hyper-heuristics composed of the sub-mechanisms from the literature. Moreover, the performance and behavior analysis conducted for the hyper-heuristic clearly showed its adaptive characteristics under different search conditions. The principles comprising the here presented algorithm were at the heart of the algorithm that won the first international cross-domain heuristic search competition.     

Fall detection using single-tree complex wavelet transform

The goal of Ambient Assisted Living (AAL) research is to improve the quality of life of the elderly and handicapped people and help them maintain an independent lifestyle with the use of sensors, signal processing and telecommunications infrastructure. Unusual human activity detection such as fall detection has important applications. In this paper, a fall detection algorithm for a low cost AAL system using vibration and passive infrared (PIR) sensors is proposed. The single-tree complex wavelet transform (ST-C$\mathbb{C}$WT) is used for feature extraction from vibration sensor signal. The proposed feature extraction scheme is compared to discrete Fourier transform and mel-frequency cepstrum coefficients based feature extraction methods. Vibration signal features are classified into “fall” and “ordinary activity” classes using Euclidean distance, Mahalanobis distance, and support vector machine (SVM) classifiers, and they are compared to each other. The PIR sensor is used for the detection of a moving person in a region of interest. The proposed system works in real-time on a standard personal computer.     

Medical image security and EPR hiding using Shamir's secret sharing scheme

Medical applications such as telediagnosis require information exchange over insecure networks. Therefore, protection of the integrity and confidentiality of the medical images is an important issue. Another issue is to store electronic patient record (EPR) in the medical image by steganographic or watermarking techniques. Studies reported in the literature deal with some of these issues but not all of them are satisfied in a single method. A medical image is distributed among a number of clinicians in telediagnosis and each one of them has all the information about the patient's medical condition. However, disclosing all the information about an important patient's medical condition to each of the clinicians is a security issue. This paper proposes a (k, n) secret sharing scheme which shares medical images among a health team of n clinicians such that at least k of them must gather to reveal the medical image to diagnose. Shamir's secret sharing scheme is used to address all of these security issues in one method. The proposed method can store longer EPR strings along with better authenticity and confidentiality properties while satisfying all the requirements as shown in the results.     

A flexible and scalable audio information retrieval system for mixed‐type audio signals

The content‐based classification and retrieval of real‐world audio clips is one of the challenging tasks in multimedia information retrieval. Although the problem has been well studied in the last two decades, most of the current retrieval systems cannot provide flexible querying of audio clips due to the mixed‐type form (e.g., speech over music and speech over environmental sound) of audio information in real world. We present here a complete, scalable, and extensible content‐based classification and retrieval system for mixed‐type audio clips. The system gives users an opportunity for flexible querying of audio data semantically by providing four alternative ways, namely, querying by mixed‐type audio classes, querying by domain‐based fuzzy classes, querying by temporal information and temporal relationships, and querying by example (QBE). In order to reduce the retrieval time, a hash‐based indexing technique is introduced. Two kinds of experiments were conducted on the audio tracks of the TRECVID news broadcasts to evaluate the performance of the proposed system. The results obtained from our experiments demonstrate that the Audio Spectrum Flatness feature in MPEG‐7 standard performs better in music audio samples compared to other kinds of audio samples and the system is robust under different conditions. © 2011 Wiley Periodicals, Inc.     

Secure multidimensional range queries over outsourced data

In this paper, we study the problem of supporting multidimensional range queries on encrypted data. The problem is motivated by secure data outsourcing applications where a client may store his/her data on a remote server in encrypted form and want to execute queries using server??s computational capabilities. The solution approach is to compute a secure indexing tag of the data by applying bucketization (a generic form of data partitioning) which prevents the server from learning exact values but still allows it to check if a record satisfies the query predicate. Queries are evaluated in an approximate manner where the returned set of records may contain some false positives. These records then need to be weeded out by the client which comprises the computational overhead of our scheme. We develop a bucketization procedure for answering multidimensional range queries on multidimensional data. For a given bucketization scheme, we derive cost and disclosure-risk metrics that estimate client??s computational overhead and disclosure risk respectively. Given a multidimensional dataset, its bucketization is posed as an optimization problem where the goal is to minimize the risk of disclosure while keeping query cost (client??s computational overhead) below a certain user-specified threshold value. We provide a tunable data bucketization algorithm that allows the data owner to control the trade-off between disclosure risk and cost. We also study the trade-off characteristics through an extensive set of experiments on real and synthetic data.     

A reasoning method for a ship design expert system

Abstract: The ship design process is a highly data‐oriented, dynamic, iterative and multi‐stage algorithm. It utilizes multiple abstraction levels and concurrent engineering techniques. Specialized techniques for knowledge acquisition, knowledge representation and reasoning must be developed to solve these problems for a ship design expert system. Consequently, very few attempts have been made to model the ship design process using an expert system approach. The current work investigates a knowledge representation–reasoning technique for such a purpose. A knowledge‐based conceptual design was developed by utilizing a prototype approach and hierarchical decompositioning. An expert system program called ALDES (accommodation layout design expert system) was developed by using the CLIPS expert system shell and an object‐oriented user interface. The reasoning and knowledge representation methods of ALDES are explained in the paper. An application of the method is given for the general arrangement design of a containership.     

Fuzzy data envelopment analysis: A discrete approach

Data envelopment analysis (DEA) as introduced by Charnes, Cooper, and Rhodes (1978) is a linear programming technique that has widely been used to evaluate the relative efficiency of a set of homogenous decision making units (DMUs). In many real applications, the input-output variables cannot be precisely measured. This is particularly important in assessing efficiency of DMUs using DEA, since the efficiency score of inefficient DMUs are very sensitive to possible data errors. Hence, several approaches have been proposed to deal with imprecise data. Perhaps the most popular fuzzy DEA model is based on α-cut. One drawback of the α-cut approach is that it cannot include all information about uncertainty. This paper aims to introduce an alternative linear programming model that can include some uncertainty information from the intervals within the α-cut approach. We introduce the concept of “local α-level” to develop a multi-objective linear programming to measure the efficiency of DMUs under uncertainty. An example is given to illustrate the use of this method.