Automated construction of fuzzy event sets and its application toactive databases

Fuzzy sets and fuzzy logic research aims to bridge the gap between the crisp world of maths and the real world. Fuzzy set theory was applied to many different areas, from control to databases. Sometimes the number of events in an event-driven system may become very high and unmanageable. Therefore, it is very useful to organize the events into fuzzy event sets also introducing the benefits of fuzzy set theory. All the events that have occurred in a system can be stored in event histories which contain precious hidden information. We propose a method for automated construction of fuzzy event sets out of event histories via data mining techniques. The useful information hidden in the event history is extracted into a matrix called sequential proximity matrix. This matrix shows the proximities of events and it is used for fuzzy rule execution via similarity based event detection and construction of fuzzy event sets. Our application platform is active databases. We describe how fuzzy event sets can be exploited for similarity based event detection and fuzzy rule execution in active database systems     

Association rule hiding

Large repositories of data contain sensitive information that must be protected against unauthorized access. The protection of the confidentiality of this information has been a long-term goal for the database security research community and for the government statistical agencies. Recent advances in data mining and machine learning algorithms have increased the disclosure risks that one may encounter when releasing data to outside parties. A key problem, and still not sufficiently investigated, is the need to balance the confidentiality of the disclosed data with the legitimate needs of the data users. Every disclosure limitation method affects, in some way, and modifies true data values and relationships. We investigate confidentiality issues of a broad category of rules, the association rules. In particular, we present three strategies and five algorithms for hiding a group of association rules, which is characterized as sensitive. One rule is characterized as sensitive if its disclosure risk is above a certain privacy threshold. Sometimes, sensitive rules should not be disclosed to the public since, among other things, they may be used for inferring sensitive data, or they may provide business competitors with an advantage. We also perform an evaluation study of the hiding algorithms in order to analyze their time complexity and the impact that they have in the original database.     

Benchmarking the energy use of energy-intensive industries in industrialized and in developing countries

Improved energy efficiency is among the key measures for CO₂ emission abatement in the industry. Energy benchmark curves provide data measured at individual plants and they offer a basis to estimate the sectoral energy efficiency improvement potentials (IP) compared to a best practice technology (BPT) currently in operation worldwide. In this paper, we estimate the BPT energy use of 17 industry sectors based on such curves or energy indicators prepared at country-level. We compare BPT data with current energy use to estimate the IP. According to our analysis, BPT offers improvement potentials of 27 ± 8% worldwide. This is equivalent to 32.5 ± 9.6 EJ (exajoules) of final energy savings worldwide, of which three-quarters can be achieved in developing countries. Due to lack of benchmark curves and limited data availability for developing countries, our results include uncertainties. We used literature data at country-level and international energy statistics to fill data gaps and to develop energy indicators. Quality of these data should be improved and benchmark data needs to be collected for more sectors. By doing so, energy benchmarking could become a key tool to estimate energy saving potentials and energy indicators could serve as strong supplementary methodology.     

An integrated control framework for flexible manufacturing systems

This paper presents the design, development, and implementation of an integrated control framework that provides a real-time supervisory control model with limited look-ahead capability for flexible manufacturing systems. Control goals and policies are modeled and characterized by a fuzzy rule base, which is integrated with the control model. The framework consists of a finite state machine generator and a controller. The generator model is equipped with an output function and output sets. The controller model has a four-stage decision-making structure. The controller monitors performance measures of the manufacturing system and reacts according to the changes in the system states in order to keep the performance measures at desired levels. The integrated framework has been implemented on a software platform in order to validate its effectiveness. The performance of the framework has been tested on a hypothetical flexible manufacturing system using a simulation .     

Ensuring location diversity in privacy-preserving spatio-temporal data publishing

The rise of mobile technologies in the last decade has led to vast amounts of location information generated by individuals. From the knowledge discovery point of view, these data are quite valuable, but the inherent personal information in the data raises privacy concerns. There exists many algorithms in the literature to satisfy the privacy requirements of individuals, by generalizing, perturbing, and suppressing their data. Current techniques that try to ensure a level of indistinguishability between trajectories in a dataset are direct applications of \(k\) -anonymity, thus suffer from the shortcomings of \(k\) -anonymity such as the lack of diversity in sensitive regions. Moreover, these techniques fail to incorporate some common background knowledge, an adversary might have such as the underlying map, the traffic density, and the anonymization algorithm itself. We propose a new privacy metric \(p\) -confidentiality that ensures location diversity by bounding the probability of a user visiting a sensitive location with the \(p\) input parameter. We perform our probabilistic analysis based on the background knowledge of the adversary. Instead of grouping the trajectories, we anonymize the underlying map, that is, we group nodes (points of interest) to create obfuscation areas around sensitive locations. The groups are formed in such a way that the parts of trajectories entering the groups, coupled with the adversary background, do not increase the adversary’s belief in violating the \(p\) -confidentiality. We then use the map anonymization as a model to anonymize the trajectories. We prove that our algorithm is resistant to reverse-engineering attacks when the statistics required for map anonymization is publicly available. We empirically evaluate the performance of our algorithm and show that location diversity can be satisfied effectively.     

Linking historic developments and future scenarios of industrial energy use in the Netherlands between 1993 and 2040

Monitoring energy efficiency improvements is essential for policy evaluation and for future policy making. We estimate the annual energy efficiency improvements achieved in six Dutch industry sectors between 1993 and 2008 by using a bottom-up model. This model incorporates the production data and specific energy consumption values of 122 products. We estimate annual energy efficiency improvements of 1.0 % per annum (p.a.) for the total industry (excluding non-energy use); even though the results are subject to uncertainties due to errors in the energy statistics, we consider them as strong indication that Dutch industry needs to reinforce its efforts in energy efficiency. Based on historical achievements between 1989 and 2008, Business as Usual (BaU) scenarios project annual improvement potentials of 0.6–1.8 % p.a. until 2040. Based on literature review, this study estimates that implementing energy saving technologies can accelerate energy efficiency improvements to 2 % p.a. and beyond. Efficient combined heat and power technologies could increase these potentials further. These are beyond the historical achievements and BaU scenario projections. New policies will be required for technology development which ensures continuous energy efficiency improvements. The findings of this paper need to be extended by continuous monitoring and more scenario analyses with improved data.     

Tracklet clustering for robust multiple object tracking using distance dependent Chinese restaurant processes

To contrive an accurate and efficient strategy for object detection–object track assignment problem, we present a tracklet clustering approach using distance dependent Chinese restaurant processes (ddCRPs), which employ a two-level robust object tracker. The first level is an ordinary tracklet generator that obtains short yet reliable tracklets. In the second level, we cluster the tracklets over time based on color, spatial and temporal attributes, where the nonparametric process of clustering with ddCRPs allows us to maintain an unknown number of objects. Unlike the previously proposed Chinese restaurant processes and Dirichlet process mixture models, our ddCRPs method does not require prescribed complex cluster models to be initialized and updated, and thus, we can cluster complex tracklets by only computing similarities between them. Our comparative evaluations on tracking different object types demonstrate the generality of our approach.     

Real-time detection of grip length deviation during pull-type fastening: a Mahalanobis–Taguchi System (MTS)-based approach

In this study, a Mahalanobis–Taguchi system (MTS)-based diagnostic and root cause analysis scheme for monitoring grip length of pull-type fasteners in real-time is presented. The proposed approach is implemented on a fastening tool integrated with a strain gage, a linear variable differential transformer, a pressure sensor, and a mote for wireless communication. Experiments show that the process signature of strain-over-displacement ratio versus displacement has unique features that can be used to determine the grip length related deviations. The proposed approach takes as input various characteristics, such as peak strain, peak displacement, and depth and width of a bowl-shaped dip on the process signature in order to make real-time decisions. The experiments show that the proposed approach is effective in determining grip length deviations and in communicating the decision in real-time via a wireless network to a base station. Overall, the proposed architecture has merits to (1) detect quality problems in real-time during the fastening process and (2) reduce post-process inspection, thereby improving quality while reducing cost. In addition, the approach facilitates 100% data collection on each fastener as opposed to traditional statistical process control (SPC) techniques, which rely on sampling.     

SentiTurkNet: a Turkish polarity lexicon for sentiment analysis

Sentiment analysis aims to extract the sentiment polarity of given segment of text. Polarity resources that indicate the sentiment polarity of words are commonly used in different approaches. While English is the richest language in regard to having such resources, the majority of other languages, including Turkish, lack polarity resources. In this work we present the first comprehensive Turkish polarity resource, SentiTurkNet, where three polarity scores are assigned to each synset in the Turkish WordNet, indicating its positivity, negativity, and objectivity (neutrality) levels. Our method is general and applicable to other languages. Evaluation results for Turkish show that the polarity scores obtained through this method are more accurate compared to those obtained through direct translation (mapping) from SentiWordNet.     

A novel circularly polarized cavity backed SIW antenna with capability to change polarization diversity by a reconfigurable polarizer structure

This article examines a substrate integrated waveguide (SIW) cavity‐backed circularly polarized diversity antenna. In the proposed novel antenna, parasitic patch and reconfigurable diodes are used to change polarization diversity from left hand to right hand and vice versa, respectively. In addition to, what makes distinctive proposed antenna in compared with similar works, is ability to change linear polarization from a SIW slot to circularly polarization by a parasitic patch. Chopping off two diagonally opposite corners makes the resonance frequency of the mode along this diagonal to be higher than that for the mode along the unchopped diagonal. By exciting parasitic patch with a slot along axes of it the orthogonal modes are generated which causes to CP. The comparison between simulation and measurement results validate antenna design. The measured impedance BW (VSWR < 2) for state 1 and 2 are 22.18% (11.86‐14.82 GHz) and 21.68% (11.88‐14.77 GHz), and The measured 3‐dB AR BW for states 1 and 2 are about 11.46% (11.43‐12.82 GHz) and 11.23% (11.43‐12.79 GHz), respectively. Finally, the measured maximum gain is 9.81 dBic.