Kamel BENACHENHOU1, Abdelmalik TALEB-AHMED2 and Mhamed HAMADOUCHE3 1Aeronautics and Space Studies Institute, Blida1 university, Algeria 2IEMN DOAE UMR CNRS 8520, Polytechnic university of Hauts de France, Valenciennes, France
This paper deals with the implementation of an adaptive acquisition stage in a global navigation satellite - GNSS - receiver with a pilot and data channel in case of GNSS L5 signal. Adaptive acquisition decides the presence or absence of GNSS signal by comparing a cell under test with adaptive threshold and provides a code delay and Doppler frequency estimation. Firstly, we introduce an adaptive acquisition with a cell- averaging-constant false alarm rate -CFAR- for pilot channel then we propose a data-pilot fusion. At a second level, the proposed schemes are implemented on FPGA by using system generator and Xilinx tools.
GPS L5, Acquisition, CFAR, FPGA, implementation
Christiana Panayiotou, Cyprus University of Technology, Cyprus
The purpose of the current paper is to present an ontological analysis to the identification of a particular type of prepositional natural language phrases called figures of speech  via the identification of inconsistencies in ontological concepts. Prepositional noun phrases are used widely in a multiplicity of domains to describe real world events and activities. However, one aspect that makes a prepositional noun phrase poetical is that the latter suggests a semantic relationship between concepts that does not exist in the real world. The current paper discusses how a set of rules based on Wordnet classes and an ontology representing human behavior and properties, can be used to identify figures of speech. It also addresses the problem of inconsistency resulting from the assertion of figures of speech at various levels identifying the problems involved in their representation. Finally, it discusses how a contextualized approach might help to resolve this problem.
ontologies, NLP, linguistic creativity
Dr.Mohammad Alodat, Department of Information Systems & Technology, Sur University College, Sur, Oman
The purpose this paper is to find a smart and effective tool for evaluating students and overcoming human defects, such as lack of expertise of Instructor, psychologists and over-trust when students. We have been providing Instructor Program for Student Assessment (PISA) because it has a positive impact on academic performance, and self-regulation and improved in their final exam scores. In order to test the efficiency of the prediction among the models used that give the closer expectation of the degree of the student in the final exam score of a course after the first exam, using four algorithms: Multiple Linear Regressions (MLP), K-mean cluster , Modular feed forward neural network and Radial basis Function (RBF). After comparing the four models, results show that RBF has the highest average classification rate, followed by neural network and K-mean cluster, while Multiple Linear Regressions was the worst at performance.
Euclidean Dissimilarity, Radial Basis Function, Neural Network, K Mean Cluster, Deep Learning
Rory Lewis, Department of Computer Science, University of Colorado Colorado Springs, Colorado, 80919, USA
This paper addresses what the role of artificial intelligence will be in space, and specifically, what China’s research in artificial intelligence for space war has been, is, and strives to become. The author first presents testimony from scholars and space research scientists from many countries who all categorically state, without a trace of doubt, that all future space warfare will rely heavily on artificial intelligence. This includes China’s strengths in Space artificial intelligence and, its weaknesses. The second portion of this research drills down into what are the specific mathematical theoretical research areas of artificial research for space wars in various countries, including China. The author concludes with research strategies that will combat China’s dominance of space wars.
Artificial Intelligence, Machine Learning, Deep Neural Networks, GPUs, Space War, Chinese artificial intelligence.
Rezvan Azimi Khojasteh1, Reza Rafeh2, Naji Alobaidi3, 1Department of Computer Engineering, Malayer Branch, Islamic Azad University, Hamedan, Iran, 2Centre for Information Technology, Waikato Institute of Technology, Hamilton, New Zealand and 3Department of Computer Engineering, Unitec Institute of Technology, Auckland, New Zealand
Emotion recognition has been a research topic in the field of Human Computer Interaction (HCI) during recent years. Computers have become an inseparable part of human life. Users need for human-like interaction to better communicate with computers. Many researches have become interested in emotion recognition and classification using different sources. A hybrid approach of audio and text has been recently introduced. All such approaches have been done to raise the accuracy and appropriateness of emotion classification. In this study, a hybrid approach of audio and video has been applied for emotion recognition. Innovation of this approach is selecting the characteristics of audio and video and their features as a unique specification for classification. In this research, the SVM method has been used for classifying the data in the SAVEE database. The experimental results show the maximum classification accuracy for audio data is 91.63% while by applying the hybrid approach the accuracy achieved is 99.26%.
Emotion Classification, Emotions Analysis, Emotion Detection, SVM, Speech Emotion Recognition
Sajib Sen1, Kishor Datta Gupta1, Subash Poudyal1 and Md ManjurulAhsan2, 1Department of Computer Engineering, University of Memphis, 2Department of Industrial Engineering, Lamar University 2MediDeniz Software, Old Street, New York, USA
Distributed network reconfiguration techniques are used widely to optimize power distribution systems. As renewable energy generation are very stochastic in nature, network reconfiguration with this stochastic nature does not provide the optimal solution. To address this problem a three-objective genetic algorithm approach has been taken in this project to find the optimal solution of energy scheduling throughout a day, simultaneously using the concept of network reconfiguration. In this research paper, we have applied a genetic algorithm approach, in order to optimize dispatching power with reconfiguring the network and scheduling the power sources. Our proposed methods shows that, it is possible to get 1MW less line lose compared to general condition.
Microgrid, Genetic algorithm, Power distribution, Network reconfiguration.
Marty Kelley, Whiting School of Engineering, Johns Hopkins University, Baltimore, Maryland, USA
The manufacturing industry is rapidly changing due in part to widespread adoption of information, communication and operational technologies. This new landscape, described as the fourth industrial revolution, will be characterized by highly complex and interdependent systems. One particular aspect of this industrial paradigm shift is horizontal integration, or the tight coupling of firms within a value chain. Highly interconnected and interdependent manufacturing systems will encounter new challenges associated with coordination and collaboration, specifically with regards to trust. Data was collected from manufacturing professionals to explore the nature of trust and the potential use of a Blockchain as a collaboration mechanism. Concepts from game theory, systems theory and organizational economics are used to augment research data and inform a collaborative manufacturing blockchain model and architecture.
Manufacturing, Collaboration, Industry 4.0, Horizontal integration, Trust, Blockchain, Smart Contracts, Provision Point, Payback Mechanism, User Boundaries, Resource Boundaries, Provision of Public Goods, Governance
Albert F. H. M. Lechner, Steve R. Gunn, School of Electronics and Computer Science University of Southampton, UK
Sales forecasts are essential to every business strategic plans and can both save the business money and increase its competitive advantage. However, many current businesses underestimate the op- portunities accurate forecasts provide and rely on judgemental forecasts from experts within the business. Machine learning and statistical forecasting methods are used by both academics and practitioners to increase the accuracy of these forecasting methods and can be further improved by applying the newly developed dynamic cluster based Markov model, presented in this work. This approach gathers global sales pipeline data to build a short-term sales forecast. The prediction of future sales for the next three months is improved over a regular Markov transition model. The new model can support short-term planning, thereby supporting regional and product-specific forecasting to steer business activities to their given targets and remain profitable.
Demand forecast, Time series data, Clustering, Markov model