ppxscal/embedding_network huggingface dataset:
Neural-Network-Based Maintenance Decision Model for Diesel Engine
Authors: Yingkui Gu, Juanjuan Liu, Shuyun Tang
Venue: Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II (2008)

To decrease the fuzzy and uncertain factors in the maintenance decision models of diesel engine, a combination BP-neural-network-based maintenance decision model for diesel engine is presented in this paper. It can make the maintenance of diesel engine follow the prevention policy and take the technology and economy into account at the same time. In the presented model, the maintenance decision theory of diesel engine was analyzed in detail firstly. Then, the combination neural network model for maintenance decision was established, including an entire network and two modules sub-networks. Finally, an example was given to verify the effective feasibility of the proposed method. By training the network, the deterioration degree of the diesel engine and its parts can be obtained to make the right maintenance decision.
Solving Requirements Management Challenges in Product Line Development
Authors: Martin Sarabura, Paul Bowden
Venue: Proceedings of the 2008 12th International Software Product Line Conference (2008)

Requirements Management (RM) tools for software product lines have special requirements that until now have not been satisfied by existing tools. MKS Integrity for Requirements Management is a new product that satisfies all the major criteria outlined in Beuche et al [1]. It is an enterprise-scale product that allows analysts to efficiently share requirements amongst different documents and projects without compromising artifact traceability.
Capturing and Using QoS Relationships to Improve Service Selection
Authors: Caroline Herssens, Ivan J. Jureta, Stéphane Faulkner
Venue: Proceedings of the 20th international conference on Advanced Information Systems Engineering (2008)

In a Service-Oriented System (SOS), service requesters specify tasks that need to be executed and the quality levels to meet, whereas service providers advertise their services' capabilities and the quality levels they can reach. Service selectors then match to the relevant tasks, the candidate services that can perform these tasks to the most desirable quality levels. One of the key problems in QoS-aware service selection lies in managing tradeoffs among QoS expectations at runtime, that is, situations in which service requesters specify quality levels that cannot be simultaneously met. We propose a service selection approach that can deal with tradeoffs. The approach consists of: (i) rich QoS models to be used by service requesters when expressing QoS expectations and service providers when describing services' QoS, and for representing preference and priority relationships between QoS dimensions; and (ii) a multi-criteria decision making technique that uses the models for service selection.
Hopfield Neural Network for Sea Surface Current Tracking from Tiungsat-1 Data
Authors: Maged Marghany, Mazlan Hashim, Arthur P. Cracknell
Venue: Proceedings of the international conference on Computational Science and Its Applications, Part II (2008)

This paper introduces a new approach for neural network application to coastal studies. The method is based on the utilization of the Hopfield neural network to model sea surface current movements from single TiungSAT-1 image. In matching process using Hopfield neural network, identified features have to be mathematically compared to each other in order to build an energy function that will be minimized. In this context, the neuron network has been taken in two dimensions; raw and column in order to match between the similar features of surface pattern. It was required that the two features were extracted from the same location. The Euler method is used to minimized the energy function of neuron equation. The study shows that the surface current features such as structure morphology of water plume can be automatically detected. In TiungSAT-1 data, green and near-infrared bands were competent at sea surface current features detection with high accuracy speed of ±0.14 m/s. It can be said that, Hopfield neural network has highly promised feature enhancement and detection in optical satellite sensor such as TiungSAT-1 image. In conclusion, Hopfield neural network can be used advance computational tool for modeling the pattern movement of sea surface in satellite data.
An in silico approach to discover transcriptional regulatory elements
Authors: Weining Zhang
Venue:  (2008)

With the completion of the Human Genome Project (HGP), the significance of interpreting the biological implication of the sequences and annotating functional elements along the genome have been of great interest in the area of functional genomics. Therefore, to have a comprehensive understanding of the complex gene expression mechanisms, we need to identify the regulatory elements in the genomes and comprehend how the genes regulate and interact with each other. Although many computational approaches have been developed for lower eukaryotes and prokaryotes, most of these techniques do not generalize to vertebrates. This dissertation focuses on a number of fundamental issues within the same context of bioinformatics. Besides presenting a machine learning approach for data preprocessing, in this dissertation, we have modeled transcriptional regulatory relationships between transcription factors and genes in human genome using ensemble of complementary evidence. Based on the proposed model, we have illustrated several hypotheses to delineate putative regulatory elements that are building blocks of transcriptional regulatory networks. Specifically, this dissertation addresses the issues of discovering new transcription factors and potential cis-regulatory element of the existing transcription factors. Several applications of our approach to deal with biological problems are also discussed in the dissertation. Finally, a computational method to detect aberrant chromosomal regions in the genomes is presented.
On a Rough Sets Based Tool for Generating Rules from Data with Categorical and Numerical Values
Authors: Hiroshi Sakai, Kazuhiro Koba, Ryuji Ishibashi, Michinori Nakata
Venue: Proceedings of the 4th international conference on Modeling Decisions for Artificial Intelligence (2007)

Rough set theory has mainly been applied to data with categorical values. In order to handle data with numerical values, we have defined numerical patterns with two symbols # and @, and have proposed more flexible rough sets based rule generation. The concepts of `coarse' and `fine' for rules are explicitly defined according to numerical patterns. This paper focuses on the rough sets based method for rule generation, which is enhanced by numerical patterns, and refers to the tool programs. Tool programs are applied to data in UCI Machine Learning Repository, and some useful rules are obtained.
On Modeling and Analyzing Cost Factors in Information Systems Engineering
Authors: Bela Mutschler, Manfred Reichert
Venue: Proceedings of the 20th international conference on Advanced Information Systems Engineering (2008)

Introducing enterprise information systems (EIS) is usually associated with high costs. It is therefore crucial to understand those factors that determine or influence these costs. Though software cost estimation has received considerable attention during the last decades, it is difficult to apply existing approaches to EIS. This difficulty particularly stems from the inability of these methods to deal with the dynamic interactions of the many technological, organizational and project-driven cost factors which specifically arise in the context of EIS. Picking up this problem, we introduce the EcoPOST framework to investigate the complex cost structures of EIS engineering projects through qualitative cost evaluation models. This paper extends previously described concepts and introduces design rules and guidelines for cost evaluation models in order to enhance the development of meaningful and useful EcoPOST cost evaluation models. A case study illustrates the benefits of our approach. Most important, our EcoPOST framework is an important tool supporting EIS engineers in gaining a better understanding of the critical factors determining the costs of EIS engineering projects.
Quadratic optimization models and algorithms: one algorithm for linear bound constraints based on neural networks
Authors: Nicolae Popoviciu, Mioara Boncut
Venue: Proceedings of the 9th WSEAS International Conference on Neural Networks (2008)

This work contains a complete set of algorithms for several quadratic optimization problems. The problem constraints are very differently. For each type of constraint an appropriate algorithm is given. The algorithm for linear bound constraints is based on neural network [2] and uses a system of differential equations. In order to reduce the sensitivity and round off errors a preconditioning method is used. A great number of applications illustrate the algorithms.
Prototype of a Workers' Motion Trace System Using Terrestrial Magnetism and Acceleration Sensors
Authors: Nobuyoshi Sato, Shouichi Odashima, Jun Suzuki, Taiji Ishikawa, Yoshitoshi Murata
Venue: Proceedings of the 2nd international conference on Network-Based Information Systems (2008)

In quality control of industrial products, it is very important that not only qualities of materials and parts, design, but also a manufacturing work accords to instruction manuals for manufacturing process. Generally, most assembly process such as assembly line and cell production system have processes in everywhere to confirm quality and work in former processes. However, there are some cases that are difficult to be confirmed in later processes. For example, to fix a part by some screws, it is desirable to drive in diagonal order little by little. However, it is not easy to detect in appearance in case of this desirable procedure is violated. Therefore, we are developing a system to improve quality of industrial products. This system deals manufacturing processes as context, and grasp manufacturing process, recognizes context by tracing motions of worker's arm/hand by using terrestrial magnetism sensors and accelerometers. As a prototype of this system, we developed a system that judges attaching process of a particular part is correctly done or not in an accrual vehicle assemble factory. In this paper, we describe outline of the prototype of our workers' motion trace system and algorithms for judgment, and its evaluation.
Modified Lawn Weed Detection: Utilization of Edge-Color Based SVM and Grass-Model Based Blob Inspection Filterbank
Authors: Ukrit Watchareeruetai, Yoshinori Takeuchi, Tetsuya Matsumoto, Hiroaki Kudo, Noboru Ohnishi
Venue: Neural Information Processing: 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part II (2008)

We propose a lawn weed detection method modified from our previous work, i.e., Bayesian classifier based method. The proposed method employs features calculated from not only the edge-strength of weed/lawn textures but also color information of RGB. Instead of using Bayesian classifier, we exploit more sophisticated classifier, i.e., support-vector machine, for detecting weeds. After weed detection, the proposed method uses noise blob inspection for removing misclassified weed areas. The inspection process is based on a bank of directional filters modeled from characteristics of the edge of grass blade. Experimental results show that the performance of the proposed method outperforms the compared methods.
Some approaches concerning autonomous mobile robot control
Authors: Cononovici Boris-Sergiu
Venue: Proceedings of the 9th WSEAS International Conference on International Conference on Automation and Information (2008)

This paper tackles methods for building a reactive behaviour for navigation of a mobile robot in uncertain environment. Sensor-based navigation uses logical inference which represents the mapping between the sensor input space and the mobile robot action space. This mapping is obtained by a genetic algorithm or by learning. To the mobile robot equipped with this ability is attached the navigation skill. A control system based on two micro controllers running in parallel is an actual hardware on which are applied the control strategies. Simulations verify the effectiveness of the proposed methods.
A quasi-positive family of continuous Darcy-flux finite-volume schemes with full pressure support
Authors: Michael G. Edwards, Hongwen Zheng
Venue: Journal of Computational Physics (2008)

A new family of flux-continuous, locally conservative, finite-volume schemes is presented for solving the general tensor pressure equation of subsurface flow in porous media. The new schemes have full pressure continuity imposed across control-volume faces. Previous families of flux-continuous schemes are point-wise continuous in pressure and flux. When applying the earlier point-wise flux-continuous schemes to strongly anisotropic full-tensor fields their failure to satisfy a maximum principle (as with other FEM and finite-volume methods) can result in loss of local stability for high anisotropy ratios which can cause strong spurious oscillations in the numerical pressure solution. An M-matrix analysis reveals the upper limits for guaranteeing a maximum principle for general 9-point schemes and aids in the design of schemes that minimize the occurrence of spurious oscillations in the discrete pressure field. The full pressure continuity schemes are shown to possess a larger range of flux-continuous schemes, than the previous point-wise counter parts. For strongly anisotropic full-tensor cases it is shown that the full quadrature range possessed by the new schemes permits these schemes to exploit quadrature points (previously out of range) that are shown to minimize spurious oscillations in discrete pressure solutions. The new formulation leads to a more robust quasi-positive family of flux-continuous schemes applicable to general discontinuous full-tensor fields.
PORCE: An efficient power off recovery scheme for flash memory
Authors: Tae-Sun Chung, Myungho Lee, Yeonseung Ryu, Kangsun Lee
Venue: Journal of Systems Architecture: the EUROMICRO Journal (2008)

Flash memory is now replacing hard disk in many embedded applications including cellular phones, digital cameras, car navigation systems, and so on. However, because flash memory has its own characteristics such as ''erase-before-write'' and wear-leveling, a software layer called FTL (flash translation layer) should be provided. However, most FTL algorithms did not include the power off recovery module though it is very important in portable devices. In this paper, we suggest an efficient power off recovery scheme for flash memory called PORCE (Power Off Recovery sChEme for flash memory). PORCE is tightly coupled to FTL operations and minimizes performance degradation during normal operations by storing recovery information as small as possible. Additionally, PORCE provides cost-based reclamation protocols which include the wear-leveling module. Our empirical study shows that PORCE is an efficient recovery protocol.
Building an Ontological Base for Experimental Evaluation of Semantic Web Applications
Authors: Peter Bartalos, Michal Barla, György Frivolt, Michal Tvarožek, Anton Andrejko, Mária Bieliková, Pavol Návrat
Venue: Proceedings of the 33rd conference on Current Trends in Theory and Practice of Computer Science (2007)

The increasing number of Semantic Web applications that work with ontologies implies an increased need for building ontological knowledge bases. In order to improve ontologies during their development as well as to allow applications to be experimentally evaluated prior to their complete implementation and deployment, ontology bases must be filled with experimental data (i.e., instance ontologies), which can be used to evaluate methods used for information processing. We describe several approaches together with a method of building an ontological knowledge base for purposes of experimentation with Semantic Web applications. We also discuss characteristics and suitability of particular approaches to the development of experimental ontological knowledge bases.
Measures on wavelet segmentation of speech
Authors: Michał Dyrek, Jakub Gałka, Bartosz Ziółko
Venue: Proceedings of the 8th WSEAS International Conference on Multimedia systems and signal processing (2008)

Speech segmentation is widely used in many speech applications. We propose a new wavelet-based extension of the typical spectrum-based non-uniform speech segmentation methods. The use of wavelets improves computation performance and provides easy and flexible adjusting of algorithm parameters. Segmentation accuracy measures are introduced and applied for evaluation as well.
Learning Rules for Semantic Video Event Annotation
Authors: Marco Bertini, Alberto Bimbo, Giuseppe Serra
Venue: Proceedings of the 10th international conference on Visual Information Systems: Web-Based Visual Information Search and Management (2008)

Automatic semantic annotation of video events has received a large attention from the scientific community in the latest years, since event recognition is an important task in many applications. Events can be defined by spatio-temporal relations and properties of objects and entities, that change over time; some events can be described by a set of patterns.In this paper we present a framework for semantic video event annotation that exploits an ontology model, referred to as Pictorially Enriched Ontology, and ontology reasoning based on rules. The proposed ontology model includes: high-level concepts, concept properties and concept relations, used to define the semantic context of the examined domain; concept instances, with their visual descriptors, enrich the video semantic annotation. The ontology is defined using the Web Ontology Language (OWL) standard. Events are recognized using patterns defined using rules, that take into account high-level concepts and concept instances. In our approach we propose an adaptation of the First Order Inductive Learner (FOIL) technique to the Semantic Web Rule Language (SWRL) standard to learn rules. We validate our approach on the TRECVID 2005 broadcast news collection, to detect events related to airplanes, such as taxiing, flying, landing and taking off. The promising experimental performance demonstrates the effectiveness of the proposed framework.
Development of a Microscope Embedded Training System for Neurosurgery
Authors: Alessandro Mauro, Jörg Raczkowsky, Reiner Wirtz, Heinz Wörn
Venue: Proceedings of the 4th international symposium on Biomedical Simulation (2008)

Virtual reality based training systems for surgery, have recently shown great potential as an alternative to traditional training methods. In neurosurgery, state of art training devices is limited to a few examples. They are based either on traditional displays or head-mounted displays. The aim of this research is the development of the first virtual reality training system for neurosurgical interventions based on a real surgical microscope for a better visual and ergonomic realism. The simulation takes advantage of an accurate tissue modeling, a force feedback device and a rendering of the virtual scene directly to the oculars of the operating microscope. A prototype of a stereoscopic Augmented Reality microscope for intra-operative presentation of preoperative three-dimensional data has been realized in our laboratory. We are reusing the image injection component of this existing platform developing a training system for educational and preoperative purposes based on virtual organs reconstructed from real patients' images.
State Focusing: Lazy Abstraction for the Mu-Calculus
Authors: Harald Fecher, Sharon Shoham
Venue: Proceedings of the 15th international workshop on Model Checking Software (2008)

A key technique for the verification of programs is counterexample-guided abstraction refinement (CEGAR). In a previous approach, we developed a CEGAR-based algorithm for the modal μ-calculus, where refinement applies only locally, i.e. lazy abstraction techniques are used. Unfortunately, our previous algorithm was not completely lazy and had some further drawbacks, like a possible local state explosion. In this paper, we present an improved algorithm that maintains all advantages of our previous algorithm but eliminates all its drawbacks. The improvements were only possible by changing the philosophy of refinement from state splitting into the new philosophy of state focusing, where the states that are about to be split are not removed.
Spectroscopic imaging through magnetic resonance for brain tumour diagnostics: Recent achievements, dilemmas and potential solutions via advances in signal processing
Authors: Karen Belkić, Dževad Belkić
Venue: Journal of Computational Methods in Sciences and Engineering (2004)

In the very recent period Magnetic Resonance Spectroscopy (MRS) and Spectroscopic Imaging (MRSI) have become key diagnostic modalities for neuro-oncology. MRS and MRSI are now applied extensively for initial detection of brain tumours, for histopathologic classification, tumour localization and grading, as well as for assessment of response to therapy and for follow-up surveillance, striving, in particular, for earlier identification of recurrence. In this paper we review the current state of the art of MRS and MRSI for brain tumour diagnostics, highlighting the achievements, as well as remaining dilemmas. Clearly, MRS and MRSI represent an important advance for the detection and characterisation of tumours of the brain. However, there are still important shortcomings of the present applications of MRS and MRSI in neuro-oncology. First of all, very few of the currently assessed metabolite concentrations or ratios unequivocally distinguish intra-cerebral tumours from normal brain tissue. Moreover, changes in each of the metabolite concentrations and ratios are non-specific for cancer of the brain. In other words, non-neoplastic processes such as infection, stroke, demyelinating disorders, inter alia, frequently show spectral changes that are identical to those seen in brain tumours. Histopathological characterization and tumour grading, both crucial for clinical decision-making, have been greatly aided by MRS and MRSI. However, there are numerous contradictory findings in the literature. We demonstrate in this paper that many of the shortcomings of MRS and MRSI for neuro-oncology are directly related to the reliance upon the conventional Fourier-based framework for data analysis. We review the distinct advantages of the Fast Pad&eacute; Transform (FPT) relative to the Fast Fourier Transform (FFT). Our focus is on those salient features of the FPT that are of critical clinical relevance for achieving an overall improved diagnostic performance of MRS and MRSI. These features are extremely stable and rapid convergence, as well as highly accurate quantification estimates of spectra from in vivo MRS time signals, as presently illustrated for data from the brain of a healthy volunteer. The next, and urgently needed step, is to more widely apply the FPT to in vivo MRS and MRSI signals from patients with brain tumours with the aim of tackling, in actual practice, the diagnostic dilemmas still plaguing neuro-oncology.