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2018 IEEE International Workshop on Electromagnetics: Applications and Student Innovation Competition, iWEM 2018

作者： Peng, Kang-Chun Lin, Chiu-Chin Li, Cyuan-Fong Hung, Cheng-Yuan Hsieh, Yu-Sung Dep. of Computer and Communication Engineering National Kaohsiung Univ. of Science and Technology Kaohsiung Taiwan Opto-electronics Equipment Department Metal Industries Research Development Center Kaohsiung Taiwan

ISBN: (纸本)9781538648346

This work develops a compact 9 GHz microwave imaging system. The core of the system is a vector network analyzer (VNA) which utilizes range-correlation architecture to suppress the phase noise of the received signal. To overcome the bottleneck that conventional mixer-based phase detectors have limited phase detection range, a novel quadrature phase detection algorithm is proposed. The experimental results show that the microwave imaging system can well detect the bone image of a chicken wing. © 2018 IEEE.

关键词： Signal detection

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Effective Text Data Preprocessing Technique for Sentiment Analysis in Social Media Data

Effective Text Data Preprocessing Technique for Sentiment An...

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International Conference on Knowledge and Systems engineering

作者： Saurav Pradha Malka N. Halgamuge Nguyen Tran Quoc Vinh School of Computing and Mathematics Charles Sturt University Melbourne Victoria Australia Dep. of Electrical and Electronic Engineering The University of Melbourne Victoria Australia Faculty of Information Technology The University of Da Nang - University of Science and Education Vietnam

In the big data era, data is made in real-time or closer to real-time. Thus, businesses can utilize this evergrowing volume of data for the data-driven or information-driven decision-making process to improve their businesses. Social media, like Twitter, generates an enormous amount of such data. However, social media data are often unstructured and difficult to manage. Hence, this study proposes an effective text data preprocessing technique and develop an algorithm to train the Support Vector Machine (SVM), Deep Learning (DL) and Naive Bayes (NB) classifiers to process Twitter data. We develop an algorithm that weights the sentiment score in terms of weight of hashtag and cleaned text. In this study, we (i) compare different preprocessing techniques on the data collected from Twitter using various techniques such as (stemming, lemmatization and spelling correction) to obtain the efficient method (ii) develop an algorithm to weight the scores of the hashtag and cleaned text to obtain the sentiment. We retrieved N=1,314,000 Twitter data, and we compared the popularity of two products, Google Now and Amazon Alexa. Using our data preprocessing algorithm and sentiment weight score algorithm, we train SVM, DL, NB models. The results show that stemming technique performed best in terms of computational speed. Additionally, the accuracy of the algorithm was tested against manually sorted sentiments and sentiments produced before text data preprocessing. The result demonstrated that the impact produced by the algorithm was close to the manually annotated sentiments. In terms of model performance, the SVM performed better with the accuracy of 90.3%, perhaps, due to the unstructured nature of Twitter data. Previous studies used conventional techniques;hence, no precise methods were utilized on cleaning the text. Therefore, our approach confirms that proper text data preprocessing technique plays a significant role in the prediction accuracy and computational time of the clas

关键词： Twitter Tagging Classification algorithms Support vector machines Data preprocessing Google

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An Analysis on Use of Deep Learning and Lexical-Semantic Based Sentiment Analysis Method on Twitter Data to Understand the Demographic Trend of Telemedicine

An Analysis on Use of Deep Learning and Lexical-Semantic Bas...

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International Conference on Knowledge and Systems engineering

作者： Harshvadan Talpada Malka N. Halgamuge Nguyen Tran Quoc Vinh School of Computing and Mathematics Charles Sturt University Melbourne Victoria Australia Dep. of Electrical and Electronic Engineering The University of Melbourne Victoria Australia Faculty of Information Technology The University of Da Nang - University of Science and Education Vietnam

technology has turned into a fundamental piece of everybody's life. Social media technology is already used widely by the public to speak out once mind openly. This data can be leveraged to have a better understanding of the current state of decision making. However, Twitter data is highly unstructured. Sentiment analysis can be applied to such health-related data to extract useful information regarding public opinion. The aim of the research is to understand (i) the correlation between Deep Learning versus lexical and semantic-based sentiment prediction methods, (ii) the sentiment prediction accuracy of these methods on manually annotated sentiment dataset (iii) domain-specific knowledge on accuracy of the sentiment prediction methods, and (iv) to utilize Twitter-based sentiment to understand the influence of telemedicine in regards to heart attack and epilepsy. Four sentiment prediction methods are utilized for the research;Lexical and Semantic-based (Valence Aware Dictionary and Sentiment Reasoner (VADER) and TextBlob) and Deep Learning based (Long Short Term Memory (LSTM) and sentiment model from Stanford CoreNLP). The dataset that we retrieved consists of 1.84 million old health-related tweets. Our finding suggests that lexical and semantic-based methods for sentiment prediction offer better accuracy than Deep Learning methods;when a large enough and evenly distributed training dataset is not available. We observed that domain-specific knowledge affects the prediction accuracy of sentiment, mainly when the target text contains more domain-specific words. Sentiment prediction on Twitter data can be utilized to understand the demographic distribution of sentiment. In our case, we observed that telemedicine has a high number of positive sentiment. It is still in its infancy and has not spread to a broader demographic.

关键词： Training Machine learning Telemedicine Twitter Sentiment analysis Prediction methods

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Review: Data Security Models Developed by Blockchain technology for Different Business Domains

Review: Data Security Models Developed by Blockchain Technol...

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International Conference on Knowledge and Systems engineering (KSE)

作者： Mohib Hirani Malka N. Halgamuge Pham Duong Thu Hang School of Computing and Mathematics Charles Sturt University Melbourne Victoria Australia Dep. of Electrical and Electronic Engineering The University of Melbourne Victoria Australia Faculty of Information Technology The University of Da Nang - University of Science and Education Vietnam

This study investigates a range of data security models developed to achieve data security using blockchain technology for different industrial domains. Current industries utilize data-driven mechanisms for decision making with concern focussed on ensuring data security. Blockchain has the potential to secure data by integrating different information systems since data is decentralized, encrypted and validated by the whole network. This study includes an analysis of blockchain security models using data extracted from 30 peer-reviewed scientific publications over two years (2017-2019). This study analyzed three components of the publications, including the process involved in securing data, the stage of development for securing data and in which industry a model is best applied. Results of the research show that the majority of articles (51.11%) cited Blockchain as a key feature of data security for improvising the data sharing process in industries. This study also finds that the stage of implementation most commonly featured is the proposal stage with potential architectures yet to be implemented (30%). Finally, this study shows that models are applied in industrial domains such as enterprises using data analytics, finance, Internet of Things (IoT), healthcare, education, and cloud service providers. This study finds that security models are most often applied to industries and supply chain management models (28.13%). It is recommended that industry professionals conduct further research to customize the data security models in their own domain. This study gives clear guidelines to researchers of suitable frameworks, processes and consensus mechanism to utilize Blockchain in Industries for data security.

关键词： Data security Data models Industries Biological system modeling Contracts

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The price of interpretability

arXiv

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arXiv 2019年

作者： Bersimas, Dimitris Jaillet, Patrick Delarue, Arthur Martin, Sebastien Sloan School of Management Massachusetts Institute of Technology CambridgeMA02139 United States Dep. of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Operations Research Center Massachusetts Institute of Technology CambridgeMA02139 United States

When quantitative models are used to support decision-making on complex and important topics, understanding a model's "reasoning" can increase trust in its predictions, expose hidden biases, or reduce vulnerability to adversarial attacks. However, the concept of interpretability remains loosely defined and application-specific. In this paper, we introduce a mathematical framework in which machine learning models are constructed in a sequence of interpretable steps. We show that for a variety of models, a natural choice of interpretable steps recovers standard interpretability proxies (e.g., sparsity in linear models). We then generalize these proxies to yield a parametrized family of consistent measures of model interpretability. This formal definition allows us to quantify the "price" of interpretability, i.e., the tradeoff with predictive accuracy. We demonstrate practical algorithms to apply our framework on real and synthetic datasets. Copyright © 2019, The Authors. All rights reserved.

关键词： Decision making

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Efficient Finite-Position-Set MRAS Observer for Encoder-less Control of DFIGs

Efficient Finite-Position-Set MRAS Observer for Encoder-less...

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Workshop on Predictive Control of Electrical Drives and Power Electronics (PRECEDE)

作者： Mohamed Abdelrahem Ralph Kennel Christoph Hackl Mehmet Dal José Rodríguez Chair for Elec. Drive Sys. and Power Electronics Technische Universität München (TUM) München Deutschland Dep. of Elec. Eng. and Information Technology Hochschule München München Deutschland Dep. of Electricity and Energy Kocaeli VS Kocaeli University Kocaeli Turkey Faculty of Engineering Andrés Bello University Santiago Chile

This work introduces an efficient finite-position-set model-reference adaptive system (FPS-MRAS) estimator for sensor-less control of a doubly fed induction generator (DFIG) in a variable speed wind energy conversion system (WECS). The idea of the proposed FPS-MRAS is derived from the finite-control-set model predictive control (FCS-MPC) principle, i.e. a limited set of angles for the rotor position are employed to predict the rotor current of the DFIG. Subsequently, the optimal angle for the rotor position is the one that its prediction optimizes a pre-defined (novel) quality function. Accordingly, the constant-gain proportional-integral (CG-PI) regulator and its tedious tuning which is normally used in MRAS observers is not required. The estimation response of the suggested FPS-MRAS observer is investigated by experiments and compared with the performance of the classical MRAS under different operation conditions and at variations of the DFIG parameters.

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Reference measurement channel RMC parameters of LTE downlink waveforms

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IOP Conference Series: Materials Science and engineering 2020年第1期881卷

作者： Fatima Faydh Al Azzawi Zainab Faydhe Al Azzawi Sada Faydhe Al Azzawi Faeza Abbas Abid Electronic technical department Institute of technology Middle Technical University Iraq Dep. Computer Technology Engineering AL-Mansour university college Iraq Statistics department measurement and economic college university of kerbala

Long-Term Evolution (LTE) is a standard for high speed wireless communication for mobile devices and data terminals, It increases the capacity and speed using a different radio interface together with core network improvements. LTE employs SC-FDMA for uplink UL transmission and OFDM for downlink DL data transmission, in this paper LTE DL implemented with MATLAB programing and Simulink, Reference measurement channel RMC on DL waveform illustrated in different Transmission scheme of single, 4 antenna and cyclic delay diversity scheme CDD, duplexing mode, resources blocks and modulation approaches leadings to a various number of total information per frame, QPSK, 16 QAM and 64 QAM modulation approaches used in the proposed system are tested under AWGN Rayleigh fading channels.

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Using Scrum for Implementing IT Governance with COBIT 5

Using Scrum for Implementing IT Governance with COBIT 5

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International Conference on Enterprise Distributed Object Computing (EDOC)

作者： Ana Cláudia Amorim Miguel Mira da Silva Rúben Pereira Margarida Gonçalves Dep. of Computer Science and Engineering Instituto Superior Técnico Lisbon Portugal Dep. of Information Systems ISCTE Lisbon Portugal Quasinfalival Lisbon Portugal

COBIT 5 is a widely-used framework for implementing sound governance of enterprise IT (GEIT). Despite the existence of official guidance, there are still several challenges that we can encounter. Currently, the ISACA's official implementation solution follows a sequentially ordered process corresponding to a traditional approach, however, organizations are increasingly embracing more agile ones for managing projects where the solution is not clear from the beginning. Using the Design Science Research Methodology, the authors analyse the current state of art and provide a Scrum based methodology for managing at team level a COBIT 5 programme. With a hybrid agile-traditional approach, the authors aim to eliminate some known challenges of COBIT 5, such as lack of support from top management and misaligned scopes and solutions. Additionally, in this paper, the authors present the results obtained from applying the designed methodology in a COBIT 5 programme in the Portuguese Finance Ministry, as well as inspect two series of interviews: 10 performed with experts from both Scrum and COBIT 5 areas to evaluate the solution, and 6 others with the team involved in the demonstration programme, to understand if the objectives where achieved. The article ends with lessons learned, limitations and future work.

关键词： Organizations Software Stakeholders Resistance Interviews Finance

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Towards Optimized V2X Routing Protocol

Towards Optimized V2X Routing Protocol

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Scientific Conference of Electrical engineering (SCEE)

作者： Taqwa Oday Fahad Abduladhem A. Ali University of Technology Baghdad Baghdad IQ Dep. Of Computer Engineering University of Basra Iraq

ISBN: (纸本)9781728115887;9781728115870

Hybrid vehicular network communication (V2X) is a promising design technique that is emanated to offer the advantages of both Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication paradigms, aside from overcoming their shortcomings. However, designing a more reliable and effective routing protocol to assure utilizing the emerging advantages for such a paradigm is still a challenging task. In this context, two novel schemes are presented in this paper; the first one is expected to enhance the network performance by reducing route errors and overhead through considering multi-effective-criteria in selecting routes, as well as predicting their lifetimes, in addition, to control the dissemination-width of route requests (RREQs) during route discoveries. While the second scheme extends the first method to further argues the dissemination-dep.h control of RREQs aiming to reduce the overhead of unnecessarily repeated attempts of route discoveries. Furthermore, intelligent techniques of artificial bee colony (ABC) optimization and fuzzy logic are employed in the proposed schemes. The conducted results through simulation validate the investigated schemes.

关键词： fuzzy logic optimisation routing protocols vehicular ad hoc networks Routing protocols Fuzzy logic V2V design techniques Routes Scheme

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Ultra-low-power wireless anchor load monitoring system based on internet of things technology

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IOP Conference Series: Materials Science and engineering 2019年第1期615卷

作者： J Tong T H Lee Y Lin Dep. of Computer Science and Information Engineering Hungkuang University Taichung Taiwan Dep. of Computer Science National Taichung University Taichung Taiwan Dep. of Civil Engineering National Chung Hsing University Taichung Taiwan

Ground anchoring is a popular method to stabilize the slope. It is essential for stability evaluation to measure the tension value of anchor from installed load cell. In this paper, a wireless anchor load measuring system is proposed for continuously on-site slope stability monitoring. Each anchor load cell is connected to a MCU-based acquisition unit and become a wireless IoT end-device. This system is scalable so the sensor dep.oyment will be much more flexible than that in conventional data acquisition system. The excitation circuit, signal amplification and analog-to-digital conversion are completed by a small-size device next to the anchor so that very low-noise result can be obtained. Each measurement consumes less than 10mAs so this device can be drive only by a 55mm*70mm*40mm solar power module. Verified by almost 3-year's on-site test, it is confirmed the proposed monitoring system can continuously capture and send one measurement per minute to the cloud server without any other electric power. The experimental and on-site test results show the practicability of proposed system and its reliability of long-term measurement. It has very high application potential on real-time slope stability monitoring by collecting and analyzing the load values of all installed anchors.

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