Search results for: automatic assembly learning component and user control.

Authors: Pei-Chann Chang, Jui-Chien Hsieh, Jyun-Jie Lin, Feng-Ming Yeh

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Atrial Fibrillation is the most common sustained arrhythmia encountered by clinicians. Because of the invisible waveform of atrial fibrillation in atrial activation for human, it is necessary to develop an automatic diagnosis system. 12-Lead ECG now is available in hospital and is appropriate for using Independent Component Analysis to estimate the AA period. In this research, we also adopt a second-order blind identification approach to transform the sources extracted by ICA to more precise signal and then we use frequency domain algorithm to do the classification. In experiment, we gather a significant result of clinical data.

Keywords: 12-Lead ECG, Atrial Fibrillation, Blind SourceSeparation, Kurtosis

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Authors: T. Arudchelvam, W. W. E. N. Fernando

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Mobile phone has become as an essential thing in our life. Therefore, security is the most important thing to be considered in mobile communication. Short message service is the cheapest way of communication via the mobile phones. Therefore, security is very important in the short message service as well. This paper presents a method to maintain the security at user level. Different types of encryption methods are used to implement the user level security in mobile phones. Caesar cipher, Rail Fence, Vigenere cipher and RSA are used as encryption methods in this work. Caesar cipher and the Rail Fence methods are enhanced and implemented. The beauty in this work is that the user can select the encryption method and the key. Therefore, by changing the encryption method and the key time to time, the user can ensure the security of messages. By this work, while users can safely send/receive messages, they can save their information from unauthorised and unwanted people in their own mobile phone as well.

Keywords: SMS, user level security, encryption, mobile communication.

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Authors: Pham Luu Trung Duong, Moonyong Lee

Abstract:

To increase precision and reliability of automatic control systems, we have to take into account of random factors affecting the control system. Thus, operational matrix technique is used for statistical analysis of first order plus time delay system with uniform random parameter. Examples with deterministic and stochastic disturbance are considered to demonstrate the validity of the method. Comparison with Monte Carlo method is made to show the computational effectiveness of the method.

Keywords: First order plus dead-time, Operational matrix, Statistical analysis, Walsh function.

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Authors: H.Soussi, M.Hussain, H.Afifi, D.Seret

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Key management is a vital component in any modern security protocol. Due to scalability and practical implementation considerations automatic key management seems a natural choice in significantly large virtual private networks (VPNs). In this context IETF Internet Key Exchange (IKE) is the most promising protocol under permanent review. We have made a humble effort to pinpoint IKEv2 net gain over IKEv1 due to recent modifications in its original structure, along with a brief overview of salient improvements between the two versions. We have used US National Institute of Technology NIIST VPN simulator to get some comparisons of important performance metrics.

Keywords: Quantitative Analyses, IKEv1, IKEv2, NIIST.

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Authors: M. Rodionov, Z. Dedovets

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This article discusses ways to implement a differentiated approach to developing academic motivation for mathematical studies which relies on defining the primary structural characteristics of motivation. The following characteristics are considered: features of realization of cognitive activity, meaningmaking characteristics, level of generalization and consistency of knowledge acquired by personal experience. The assessment of the present level of individual student understanding of each component of academic motivation is the basis for defining the relevant educational strategy for its further development.

Keywords: Learning activity, mathematics, motivation, student.

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Authors: Carlos Machado, José A. Mendes

Abstract:

Home Automation is a field that, among other subjects, is concerned with the comfort, security and energy requirements of private homes. The configuration of automatic functions in this type of houses is not always simple to its inhabitants requiring the initial setup and regular adjustments. In this work, the ubiquitous computing system vision is used, where the users- action patterns are captured, recorded and used to create the contextawareness that allows the self-configuration of the home automation system. The system will try to free the users from setup adjustments as the home tries to adapt to its inhabitants- real habits. In this paper it is described a completely automated process to determine the light state and act on them, taking in account the users- daily habits. Artificial Neural Network (ANN) is used as a pattern recognition method, classifying for each moment the light state. The work presented uses data from a real house where a family is actually living.

Keywords: ANN, Home Automation, Neural Systems, PatternRecognition, Ubiquitous Computing.

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Authors: Igor Astrov, Natalya Berezovski

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This paper focuses on a critical component of the situational awareness (SA), the control of autonomous vertical flight for vectored thrust aerial vehicle (VTAV). With the SA strategy, we proposed a neural network motion control procedure to address the dynamics variation and performance requirement difference of flight trajectory for a VTAV. This control strategy with using of NARMAL2 neurocontroller for chosen model of VTAV has been verified by simulation of take-off and forward maneuvers using software package Simulink and demonstrated good performance for fast stabilization of motors, consequently, fast SA with economy in energy can be asserted during search-and-rescue operations.

Keywords: NARMA-L2 neurocontroller, situational awareness, vectored thrust aerial vehicle.

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Authors: D. S. A. Nanayakkara, K. J. P. G. Perera

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E-commerce platforms have revolutionized the shopping experience, offering convenient ways for consumers to make purchases. To improve interactions with customers and optimize marketing strategies, it is essential for businesses to understand user behavior, preferences, and needs on these platforms. This paper focuses on recommending businesses to customize interactions with users based on their behavioral patterns, leveraging data-driven analysis and machine learning techniques. Businesses can improve engagement and boost the adoption of e-commerce platforms by aligning behavioral patterns with user goals of usability and satisfaction. We propose TheAnalyzer, a clustering-based system designed to enhance business productivity by analyzing user-profiles and improving human-computer interaction. TheAnalyzer seamlessly integrates with business applications, collecting relevant data points based on users' natural interactions without additional burdens such as questionnaires or surveys. It defines five key user analytics as features for its dataset, which are easily captured through users' interactions with e-commerce platforms. This research presents a study demonstrating the successful distinction of users into specific groups based on the five key analytics considered by TheAnalyzer. With the assistance of domain experts, customized business rules can be attached to each group, enabling TheAnalyzer to influence business applications and provide an enhanced personalized user experience. The outcomes are evaluated quantitatively and qualitatively, demonstrating that utilizing TheAnalyzer’s capabilities can optimize business outcomes, enhance customer satisfaction, and drive sustainable growth. The findings of this research contribute to the advancement of personalized interactions in e-commerce platforms. By leveraging user behavioral patterns and analyzing both new and existing users, businesses can effectively tailor their interactions to improve customer satisfaction, loyalty and ultimately drive sales.

Keywords: Data clustering, data standardization, dimensionality reduction, human-computer interaction, user profiling.

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Authors: Jan Busch, Sebastian Schneider, Konja Knüppel, Peter Nyhuis

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In production processes, assembly conceals a considerable potential for increased efficiency in terms of lowering production costs. Due to the individualisation of customer requirements, product variants have increased in recent years. Simultaneously, the portion of automated production systems has increased. A challenge is to adapt the flexibility and adaptability of automated systems to these changes. The Institute for Production Systems and Logistics developed an aerodynamic orientation system for feeding technology. When changing to other components, only four parameters must be adjusted. The expenditure of time for setting parameters is high. An objective therefore is developing an optimisation algorithm for automatic parameter configuration. Know how regarding the interaction of the four parameters and their effect on the sizes to be optimised is required in order to be able to develop a more efficient algorithm. This article introduces an analysis of the interactions between parameters and their influence on the quality of feeding.

Keywords: Aerodynamic feeding system, design of experiments, interactions between parameters.

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Authors: Konstantinos Sofianos, Michail Stefanidakis

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Nowadays, despite the great advances in technology, most educational frameworks lack a strong educational design basis. E-learning has become prevalent, but it faces various challenges such as student isolation and lack of quality in the learning process. An intelligent learning system provides a student with educational material according to their learning background and learning preferences. It records full information about the student, such as demographic information, learning styles, and academic performance. This information allows the system to be fully adapted to the student’s needs. In this paper, we propose a framework and an ontology for music education, consisting of the learner model and all elements of the learning process (learning objects, teaching methods, learning activities, assessment). This framework can be integrated into an intelligent learning system and used for music education in schools for the development of professional skills and beyond.

Keywords: Intelligent learning systems, e-learning, music education, ontology, semantic web.

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Authors: T. Kamalakis, I. Neokosmidis, D. Varoutas, T. Sphicopoulos

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The photonic component industry is a highly innovative industry with a large value chain. In order to ensure the growth of the industry much effort must be devoted to road mapping activities. In such activities demand and price evolution forecasting tools can prove quite useful in order to help in the roadmap refinement and update process. This paper attempts to provide useful guidelines in roadmapping of optical components and considers two models based on diffusion theory and the extended learning curve for demand and price evolution forecasting.

Keywords: Roadmapping, Photonic Components, Forecasting, Diffusion Theory.

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Authors: Chinlun Lai, Lunjyh Jiang

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Due to the heavy burden and pressure of caring for infants, an integrated automatic baby watching system based on IoT smart sensing and deep learning machine vision techniques is proposed in this paper. By monitoring infant body conditions such as heartbeat, breathing, body temperature, sleeping posture, as well as the surrounding conditions such as dangerous/sharp objects, light, noise, humidity and temperature, the proposed system can analyze and predict the obvious/potential dangerous conditions according to observed data and then adopt suitable actions in real time to protect the infant from harm. Thus, reducing the burden of the caregiver and improving safety efficiency of the caring work. The experimental results show that the proposed system works successfully for the infant care work and thus can be implemented in various life fields practically.

Keywords: Baby care system, internet of things, deep learning, machine vision.

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Authors: Thi-Quynh Bui, Oum-El-Kheir Aktouf

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This paper studies the dependability of componentbased applications, especially embedded ones, from the diagnosis point of view. The principle of the diagnosis technique is to implement inter-component tests in order to detect and locate the faulty components without redundancy. The proposed approach for diagnosing faulty components consists of two main aspects. The first one concerns the execution of the inter-component tests which requires integrating test functionality within a component. This is the subject of this paper. The second one is the diagnosis process itself which consists of the analysis of inter-component test results to determine the fault-state of the whole system. Advantage of this diagnosis method when compared to classical redundancy faulttolerant techniques are application autonomy, cost-effectiveness and better usage of system resources. Such advantage is very important for many systems and especially for embedded ones.

Keywords: Dependability, diagnosis, middlewares, embeddedsystems, fault tolerance, inter-component testing.

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Authors: Berardina De Carolis, Giovanni Cozzolongo

Abstract:

In this paper, we present C@sa, a multiagent system aiming at modeling, controlling and simulating the behavior of an intelligent house. The developed system aims at providing to architects, designers and psychologists a simulation and control tool for understanding which is the impact of embedded and pervasive technology on people daily life. In this vision, the house is seen as an environment made up of independent and distributed devices, controlled by agents, interacting to support user's goals and tasks.

Keywords: Ambient intelligence, agent-based systems, influence diagrams.

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Authors: M. Pasbani Khiavi, M. A. Ghorbani

Abstract:

This paper presents an analytical solution to get a reliable estimation of the hydrodynamic pressure on gravity dams induced by vertical component earthquake when solving the fluid and dam interaction problem. Presented analytical technique is presented for calculation of earthquake-induced hydrodynamic pressure in the reservoir of gravity dams allowing for water compressibility and wave absorption at the reservoir bottom. This new analytical solution can take into account the effect of bottom material on seismic response of gravity dams. It is concluded that because the vertical component of ground motion causes significant hydrodynamic forces in the horizontal direction on a vertical upstream face, responses to the vertical component of ground motion are of special importance in analysis of concrete gravity dams subjected to earthquakes.

Keywords: Dam, Reservoir, Analytical solution, Vertical component, Earthquake

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Authors: V Krishnaveni, S Jayaraman, A Gunasekaran, K Ramadoss

Abstract:

The ElectroEncephaloGram (EEG) is useful for clinical diagnosis and biomedical research. EEG signals often contain strong ElectroOculoGram (EOG) artifacts produced by eye movements and eye blinks especially in EEG recorded from frontal channels. These artifacts obscure the underlying brain activity, making its visual or automated inspection difficult. The goal of ocular artifact removal is to remove ocular artifacts from the recorded EEG, leaving the underlying background signals due to brain activity. In recent times, Independent Component Analysis (ICA) algorithms have demonstrated superior potential in obtaining the least dependent source components. In this paper, the independent components are obtained by using the JADE algorithm (best separating algorithm) and are classified into either artifact component or neural component. Neural Network is used for the classification of the obtained independent components. Neural Network requires input features that exactly represent the true character of the input signals so that the neural network could classify the signals based on those key characters that differentiate between various signals. In this work, Auto Regressive (AR) coefficients are used as the input features for classification. Two neural network approaches are used to learn classification rules from EEG data. First, a Polynomial Neural Network (PNN) trained by GMDH (Group Method of Data Handling) algorithm is used and secondly, feed-forward neural network classifier trained by a standard back-propagation algorithm is used for classification and the results show that JADE-FNN performs better than JADEPNN.

Keywords: Auto Regressive (AR) Coefficients, Feed Forward Neural Network (FNN), Joint Approximation Diagonalisation of Eigen matrices (JADE) Algorithm, Polynomial Neural Network (PNN).

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Authors: N. Guru Prasath, Sangjin Ma, Chang-Wan Kim

Abstract:

A butterfly valve is a quarter turn valve which is used to control the flow of a fluid through a section of pipe. Generally, butterfly valve is used in wide range of applications such as water distribution, sewage, oil and gas plants. In particular, butterfly valve with larger diameter finds its immense applications in hydro power plants to control the fluid flow. In-lieu with the constraints in cost and size to run laboratory setup, analysis of large diameter values will be mostly studied by computational method which is the best and inexpensive solution. For fluid and structural analysis, CFD and FEM software is used to perform large scale valve analyses, respectively. In order to perform above analysis in butterfly valve, the CAD model has to recreate and perform mesh in conventional software’s for various dimensions of valve. Therefore, its limitation is time consuming process. In-order to overcome that issue, python code was created to outcome complete pre-processing setup automatically in Salome software. Applying dimensions of the model clearly in the python code makes the running time comparatively lower and easier way to perform analysis of the valve. Hence, in this paper, an attempt was made to study the fluid-structure interaction (FSI) of butterfly valves by varying the valve angles and dimensions using python code in pre-processing software, and results are produced.

Keywords: Butterfly valve, fluid-structure interaction, automatic CFD analysis, flow coefficient.

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Authors: Yang-Wai Chow

Abstract:

This research investigates the design of a low-cost 3D spatial interaction approach using the Wii Remote for immersive Head-Mounted Display (HMD) virtual reality. Current virtual reality applications that incorporate the Wii Remote are either desktop virtual reality applications or systems that use large screen displays. However, the requirements for an HMD virtual reality system differ from such systems. This is mainly because in HMD virtual reality, the display screen does not remain at a fixed location. The user views the virtual environment through display screens that are in front of the user-s eyes and when the user moves his/her head, these screens move as well. This means that the display has to be updated in realtime based on where the user is currently looking. Normal usage of the Wii Remote requires the controller to be pointed in a certain direction, typically towards the display. This is too restrictive for HMD virtual reality systems that ideally require the user to be able to turn around in the virtual environment. Previous work proposed a design to achieve this, however it suffered from a number of drawbacks. The aim of this study is to look into a suitable method of using the Wii Remote for 3D interaction in a space around the user for HMD virtual reality. This paper presents an overview of issues that had to be considered, the system design as well as experimental results.

Keywords: 3D interaction, head-mounted display, virtual reality, Wii remote

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Authors: Yuan J. Lui

Abstract:

Prospective readers can quickly determine whether a document is relevant to their information need if the significant phrases (or keyphrases) in this document are provided. Although keyphrases are useful, not many documents have keyphrases assigned to them, and manually assigning keyphrases to existing documents is costly. Therefore, there is a need for automatic keyphrase extraction. This paper introduces a new domain independent keyphrase extraction algorithm. The algorithm approaches the problem of keyphrase extraction as a classification task, and uses a combination of statistical and computational linguistics techniques, a new set of attributes, and a new machine learning method to distinguish keyphrases from non-keyphrases. The experiments indicate that this algorithm performs better than other keyphrase extraction tools and that it significantly outperforms Microsoft Word 2000-s AutoSummarize feature. The domain independence of this algorithm has also been confirmed in our experiments.

Keywords: classification, keyphrase extraction, machine learning, summarization

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Authors: Alexander A. Muravyov

Abstract:

A minimal complexity version of component mode synthesis is presented that requires simplified computer programming, but still provides adequate accuracy for modeling lower eigenproperties of large structures and their transient responses. The novelty is that a structural separation into components is done along a plane/surface that exhibits rigid-like behavior, thus only normal modes of each component is sufficient to use, without computing any constraint, attachment, or residual-attachment modes. The approach requires only such input information as a few (lower) natural frequencies and corresponding undamped normal modes of each component. A novel technique is shown for formulation of equations of motion, where a double transformation to generalized coordinates is employed and formulation of nonproportional damping matrix in generalized coordinates is shown.

Keywords: component mode synthesis, finite element models, transient response, nonproportional damping

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Authors: Kyoung-jae Kim

Abstract:

Recommender systems are usually regarded as an important marketing tool in the e-commerce. They use important information about users to facilitate accurate recommendation. The information includes user context such as location, time and interest for personalization of mobile users. We can easily collect information about location and time because mobile devices communicate with the base station of the service provider. However, information about user interest can-t be easily collected because user interest can not be captured automatically without user-s approval process. User interest usually represented as a need. In this study, we classify needs into two types according to prior research. This study investigates the usefulness of data mining techniques for classifying user need type for recommendation systems. We employ several data mining techniques including artificial neural networks, decision trees, case-based reasoning, and multivariate discriminant analysis. Experimental results show that CHAID algorithm outperforms other models for classifying user need type. This study performs McNemar test to examine the statistical significance of the differences of classification results. The results of McNemar test also show that CHAID performs better than the other models with statistical significance.

Keywords: Customer need type, Data mining techniques, Recommender system, Personalization, Mobile user.

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Authors: R. Usubamatov, A. Usubamatova, S. Hussain

Abstract:

Modern highly automated production systems faces problems of reliability. Machine function reliability results in changes of productivity rate and efficiency use of expensive industrial facilities. Predicting of reliability has become an important research and involves complex mathematical methods and calculation. The reliability of high productivity technological automatic machines that consists of complex mechanical, electrical and electronic components is important. The failure of these units results in major economic losses of production systems. The reliability of transport and feeding systems for automatic technological machines is also important, because failure of transport leads to stops of technological machines. This paper presents reliability engineering on the feeding system and its components for transporting a complex shape parts to automatic machines. It also discusses about the calculation of the reliability parameters of the feeding unit by applying the probability theory. Equations produced for calculating the limits of the geometrical sizes of feeders and the probability of sticking the transported parts into the chute represents the reliability of feeders as a function of its geometrical parameters.

Keywords: Chute-feeder, parts, reliability.

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Authors: Norazah Yusof, Andi Besse Firdausiah Mansur

Abstract:

Social learning network analysis has drawn attention for most researcher on e-learning research domain. This is due to the fact that it has the capability to identify the behavior of student during their social interaction inside e-learning. Normally, the social network analysis (SNA) is treating the students' interaction merely as node and edge with less meaning. This paper focuses on providing an ontology structure of e-learning Moodle that can enrich the relationships among students, as well as between the students and the teacher. This ontology structure brings great benefit to the future development of e-learning system.

Keywords: Ontology, e-learning, © Learning Network, Moodle.

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Authors: C. Wan, A. Mita

Abstract:

This paper proposes a novel system for monitoring the health of underground pipelines. Some of these pipelines transport dangerous contents and any damage incurred might have catastrophic consequences. However, most of these damage are unintentional and usually a result of surrounding construction activities. In order to prevent these potential damages, monitoring systems are indispensable. This paper focuses on acoustically recognizing road cutters since they prelude most construction activities in modern cities. Acoustic recognition can be easily achieved by installing a distributed computing sensor network along the pipelines and using smart sensors to “listen" for potential threat; if there is a real threat, raise some form of alarm. For efficient pipeline monitoring, a novel monitoring approach is proposed. Principal Component Analysis (PCA) was studied and applied. Eigenvalues were regarded as the special signature that could characterize a sound sample, and were thus used for the feature vector for sound recognition. The denoising ability of PCA could make it robust to noise interference. One class SVM was used for classifier. On-site experiment results show that the proposed PCA and SVM based acoustic recognition system will be very effective with a low tendency for raising false alarms.

Keywords: One class SVM, pipeline monitoring system, principal component analysis, sound recognition, third party damage.

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Authors: Çiğdem Bakır, Mecit Yüzkat

Abstract:

With the increasing emphasis on water quality worldwide, the search for and expanding the market for new and intelligent monitoring systems has increased. The current method is the laboratory process, where samples are taken from bodies of water, and tests are carried out in laboratories. This method is time-consuming, a waste of manpower and uneconomical. To solve this problem, we used machine learning methods to detect water pollution in our study. We created decision trees with the Orange3 software used in the study and tried to determine all the factors that cause water pollution. An automatic prediction model based on water quality was developed by taking many model inputs such as water temperature, pH, transparency, conductivity, dissolved oxygen, and ammonia nitrogen with machine learning methods. The proposed approach consists of three stages: Preprocessing of the data used, feature detection and classification. We tried to determine the success of our study with different accuracy metrics and the results were presented comparatively. In addition, we achieved approximately 98% success with the decision tree.

Keywords: Decision tree, water quality, water pollution, machine learning.

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Authors: Haiyan Wang

Abstract:

Constuctivism learning theory lays emphasis on the learners' active learning, such as learning initiative, sociality and context. By analyzing the relationship between constructivism learning theory and learner autonomy, this paper explores how to cultivate learners' learner autonomy under the guidance of constructivism learning theory.

Keywords: Constructivism learning theory, learner autonomy, relationship, cultivation.

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Authors: Heroe Wijanto, Sugihartono, Suhartono Tjondronegoro, Kuspriyanto

Abstract:

The use of High Order Statistics (HOS) analysis is expected to provide so many candidates of features that can be selected for pattern recognition. More candidates of the feature can be extracted using simple manipulation through a specific mathematical function prior to the HOS analysis. Feature extraction method using HOS analysis combined with Difference to the Nth-Power manipulation has been examined in application for Automatic Modulation Recognition (AMR) to perform scheme recognition of three digital modulation signal, i.e. QPSK-16QAM-64QAM in the AWGN transmission channel. The simulation results is reported when the analysis of HOS up to order-12 and the manipulation of Difference to the Nth-Power up to N = 4. The obtained accuracy rate of AMR using the method of Simple Decision obtained 90% in SNR > 10 dB in its classifier, while using the method of Voted Decision is 96% in SNR > 2 dB.

Keywords: modulation, automatic modulation recognition, feature analysis, feature manipulation.

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Authors: Hyoungseob Lee, Donghyun Choi, Yunho Lee, Dongho Won, Seungjoo Kim

Abstract:

Recently, with the appearance of smart cards, many user authentication protocols using smart card have been proposed to mitigate the vulnerabilities in user authentication process. In 2004, Das et al. proposed a ID-based user authentication protocol that is secure against ID-theft and replay attack using smart card. In 2009, Wang et al. showed that Das et al.-s protocol is not secure to randomly chosen password attack and impersonation attack, and proposed an improved protocol. Their protocol provided mutual authentication and efficient password management. In this paper, we analyze the security weaknesses and point out the vulnerabilities of Wang et al.-s protocol.

Keywords: Message Alteration Attack, Impersonation Attack

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Authors: Elisabeth Brandenburg, Robert Woll, Rainer Stark

Abstract:

This paper presents how smart glasses and voice commands can be used for improving the maintenance process of industrial gas turbines. It presents the process of inspecting a gas turbine’s combustion chamber and how it is currently performed using a set of paper-based documents. In order to improve this process, a portable hands-free process assistance system has been conceived. In the following, it will be presented how the approach of user-centered design and the method of paper prototyping have been successfully applied in order to design a user interface and a corresponding workflow model that describes the possible interaction patterns between the user and the interface. The presented evaluation of these results suggests that the assistance system could help the user by rendering multiple manual activities obsolete, thus allowing him to work hands-free and to save time for generating protocols.

Keywords: Paper prototyping, smart glasses, turbine maintenance, user centered design.

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Authors: Pham Thanh Trung, Huynh Quyet Thang

Abstract:

Reliability is one of the most important quality attributes of software. Based on the approach of Reussner and the approach of Cheung, we proposed the reliability prediction model of component-based software architectures. Also, the value of the model is shown through the experimental evaluation on a web server system.

Keywords: component-based architecture, reliability prediction model, software reliability engineering.

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