Search results for: structure learning

Authors: Hritwik Ghosh, Irfan Sadiq Rahat, Sachi Nandan Mohanty, J. V. R. Ravindra, Abdus Sobur

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In the rapidly evolving landscape of medical diagnostics, the early detection and accurate classification of skin cancer remain paramount for effective treatment outcomes. This research delves into the transformative potential of artificial intelligence (AI), specifically deep learning (DL), as a tool for discerning and categorizing various skin conditions. Utilizing a diverse dataset of 3,000 images, representing nine distinct skin conditions, we confront the inherent challenge of class imbalance. This imbalance, where conditions like melanomas are over-represented, is addressed by incorporating class weights during the model training phase, ensuring an equitable representation of all conditions in the learning process. Our approach presents a hybrid model, amalgamating the strengths of two renowned convolutional neural networks (CNNs), VGG16 and ResNet50. These networks, pre-trained on the ImageNet dataset, are adept at extracting intricate features from images. By synergizing these models, our research aims to capture a holistic set of features, thereby bolstering classification performance. Preliminary findings underscore the hybrid model's superiority over individual models, showcasing its prowess in feature extraction and classification. Moreover, the research emphasizes the significance of rigorous data pre-processing, including image resizing, color normalization, and segmentation, in ensuring data quality and model reliability. In essence, this study illuminates the promising role of AI and DL in revolutionizing skin cancer diagnostics, offering insights into its potential applications in broader medical domains.

Keywords: Artificial intelligence, machine learning, deep learning, skin cancer, dermatology, convolutional neural networks, image classification, computer vision, healthcare technology, cancer detection, medical imaging.

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Authors: S. B. Kotsiantis, P. E. Pintelas

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Bagging and boosting are among the most popular resampling ensemble methods that generate and combine a diversity of classifiers using the same learning algorithm for the base-classifiers. Boosting algorithms are considered stronger than bagging on noisefree data. However, there are strong empirical indications that bagging is much more robust than boosting in noisy settings. For this reason, in this work we built an ensemble using a voting methodology of bagging and boosting ensembles with 10 subclassifiers in each one. We performed a comparison with simple bagging and boosting ensembles with 25 sub-classifiers, as well as other well known combining methods, on standard benchmark datasets and the proposed technique was the most accurate.

Keywords: data mining, machine learning, pattern recognition.

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Authors: Libor Zachoval, Daire O Broin, Oisin Cawley

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E-learning platforms, such as Blackboard have two major shortcomings: limited data capture as a result of the limitations of SCORM (Shareable Content Object Reference Model), and lack of incorporation of Artificial Intelligence (AI) and machine learning algorithms which could lead to better course adaptations. With the recent development of Experience Application Programming Interface (xAPI), a large amount of additional types of data can be captured and that opens a window of possibilities from which online education can benefit. In a corporate setting, where companies invest billions on the learning and development of their employees, some learner behaviours can be troublesome for they can hinder the knowledge development of a learner. Behaviours that hinder the knowledge development also raise ambiguity about learner’s knowledge mastery, specifically those related to gaming the system. Furthermore, a company receives little benefit from their investment if employees are passing courses without possessing the required knowledge and potential compliance risks may arise. Using xAPI and rules derived from a state-of-the-art review, we identified three learner behaviours, primarily related to guessing, in a corporate compliance course. The identified behaviours are: trying each option for a question, specifically for multiple-choice questions; selecting a single option for all the questions on the test; and continuously repeating tests upon failing as opposed to going over the learning material. These behaviours were detected on learners who repeated the test at least 4 times before passing the course. These findings suggest that gauging the mastery of a learner from multiple-choice questions test scores alone is a naive approach. Thus, next steps will consider the incorporation of additional data points, knowledge estimation models to model knowledge mastery of a learner more accurately, and analysis of the data for correlations between knowledge development and identified learner behaviours. Additional work could explore how learner behaviours could be utilised to make changes to a course. For example, course content may require modifications (certain sections of learning material may be shown to not be helpful to many learners to master the learning outcomes aimed at) or course design (such as the type and duration of feedback).

Keywords: Compliance Course, Corporate Training, Learner Behaviours, xAPI.

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Authors: Oana Pop, Constantin Barbulescu, Mircea Nemes, Stefan Kilyeni

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The tracing methods determine the contribution the power system sources have in their supplying. The methods can be used to assess the transmission prices, but also to recover the transmission fixed cost. In this paper is presented the influence of the modification of commons structure has on the specific price of transfer. The operator must make use of a few basic principles about allocation. Most tracing methods are based on the proportional sharing principle. In this paper Kirschen method is used. In order to illustrate this method, the 25- bus test system is used, elaborated within the Electrical Power Engineering Department, from Timisoara, Romania.

Keywords: Power systems, P-U bus, P-Q bus, tracing methods.

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Authors: C. Iihoshi, G.A. MacRae, G.W. Rodgers, J.G. Chase

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Slab sliding system (SSS) with Coulomb friction  interface between slab and supporting frame is a passive structural  vibration control technology. The system can significantly reduce the  slab acceleration and accompanied lateral force of the frame. At the  same time it is expected to cause the slab displacement magnification  by sliding movement. To obtain the general comprehensive seismic  response of a single story structure, inelastic response spectra were  computed for a large ensemble of ground motions and a practical range  of structural periods and friction coefficient values. It was shown that  long period structures have no trade-off relation between force  reduction and displacement magnification with respect to elastic  response, unlike short period structures. For structures with the  majority of mass in the slab, the displacement magnification value can  be predicted according to simple inelastic displacement relation for  inelastically responding SDOF structures because the system behaves  elastically to a SDOF structure.

 

Keywords: Earthquake, Isolation, Slab, Sliding.

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Authors: Nitin Singh, Meng Ling, Talha Ahmed, Tianxia Zhao, Reinier van de Pol

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We propose the use of Reinforcement Learning (RL) as a viable alternative for optimizing supply chain management, particularly in scenarios with stochasticity in product demands. RL’s adaptability to changing conditions and its demonstrated success in diverse fields of sequential decision-making make it a promising candidate for addressing supply chain problems. We investigate the impact of demand fluctuations in a multi-product supply chain system and develop RL agents with learned generalizable policies. We provide experimentation details for training RL agents and a statistical analysis of the results. We study generalization ability of RL agents for different demand uncertainty scenarios and observe superior performance compared to the agents trained with fixed demand curves. The proposed methodology has the potential to lead to cost reduction and increased profit for companies dealing with frequent inventory movement between supply and demand nodes.

Keywords: Inventory Management, Reinforcement Learning, Supply Chain Optimization, Uncertainty.

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Authors: Adel Aloraini

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Financial forecasting using machine learning techniques has received great efforts in the last decide . In this ongoing work, we show how machine learning of graphical models will be able to infer a visualized causal interactions between different banks in the Saudi equities market. One important discovery from such learned causal graphs is how companies influence each other and to what extend. In this work, a set of graphical models named Gaussian graphical models with developed ensemble penalized feature selection methods that combine ; filtering method, wrapper method and a regularizer will be shown. A comparison between these different developed ensemble combinations will also be shown. The best ensemble method will be used to infer the causal relationships between banks in Saudi equities market.

Keywords: Causal interactions , banks, feature selection, regularizere,

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Authors: Pirada Techaratpong

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This qualitative research has 2 objectives: to study marketing management of the cultural learning center in Suansunandha Rajabhat University and to suggest guidelines to improve its marketing management. This research is based on a case study of the Arts and Culture Office in Suansunandha Rajabhat University, Bangkok. This research found the Art and Culture Office has no formal marketing management. However, the marketing management is partly covered in the overall business plan, strategic plan, and action plan. The process can be divided into 5 stages. The marketing concept has long been introduced to its policy but not apparently put into action due to inflexible system. Some gaps are found in the process. The research suggests the Art and Culture Office implement the concept of marketing orientation, meeting the needs and wants of its target customers and adapt to the changing situation. Minor guidelines for improvement are provided.

Keywords: Marketing, management, museum, cultural learning center.

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Authors: Κonstantinos Kalovrektis, Chryssanthi Palazi

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Schools today face ever-increasing demands in their attempts to ensure that students are well equipped to enter the workforce and navigate a complex world. Research indicates that computer technology can help support learning, implementation of various experiments or learning games, and that it is especially useful in developing the higher-order skills of critical thinking, observation, comprehension, implementation, comparison, analysis and active attention to activities such as research, field work, simulations and scientific inquiry. The ICT in education supports the learning procedure by enabling it to be more flexible and effective, create a rich and attractive training environment and equip the students with knowledge and potential useful for the competitive social environment in which they live. This paper presents the design, the development, and the results of the evaluation analysis of an interactive educational game which using real electric vehicles - toys (material) on a toy race track. When the game starts each student selects a specific vehicle toy. Then students are answering questionnaires in the computer. The vehicles' speed is related to the percentage of right answers in a multiple choice questionnaire (software). Every question has its own significant value depending of the different level of questionnaire. Via the developed software, each right or wrong answers in questionnaire increase or decrease the real time speed of their vehicle toys. Moreover the rate of vehicle's speed increase or decrease depends on the difficulty level of each question. The aim of the work is to attract the student’s interest in a learning process and also to improve their scores. The developed real time game was tested using independent populations of students of age groups: 8-10, 11-14, 15-18 years. Standard educational and statistical analysis tools were used for the evaluation analysis of the game. Results reveal that students using the developed real time control game scored much higher (60%) than students using a traditional simulation game on the same questionnaire. Results further indicate that student's interest in repeating the developed real time control gaming was far higher (70%) than the interest of students using a traditional simulation game.

Keywords: Real time game, sensor, learning games, LabVIEW

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Authors: Pan Long, Bi Dongjie, Li Xifeng, Xie Yongle

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The near-field synthetic aperture radar (SAR) imaging is an advanced nondestructive testing and evaluation (NDT&E) technique. This paper investigates the complex-valued signal processing related to the near-field SAR imaging system, where the measurement data turns out to be noncircular and improper, meaning that the complex-valued data is correlated to its complex conjugate. Furthermore, we discover that the degree of impropriety of the measurement data and that of the target image can be highly correlated in near-field SAR imaging. Based on these observations, A modified generalized sparse Bayesian learning algorithm is proposed, taking impropriety and noncircularity into account. Numerical results show that the proposed algorithm provides performance gain, with the help of noncircular assumption on the signals.

Keywords: Complex-valued signal processing, synthetic aperture radar (SAR), 2-D radar imaging, compressive sensing, Sparse Bayesian learning.

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Authors: M. Saeedi, R. Wuchner, K.-U. Bletzinger

Abstract:

In order to study the aerodynamic performance of a semi-flexible membrane wing, Fluid-Structure Interaction simulations have been performed. The fluid problem has been modeled using two different approaches which are the vortex panel method and the numerical solution of the Navier-Stokes equations. Nonlinear analysis of the structural problem is performed using the Finite Element Method. Comparison between the two fluid solvers has been made. Aerodynamic performance of the wing is discussed regarding its lift and drag coefficients and they are compared with those of the equivalent rigid wing.

Keywords: CFD, FSI, Membrane wing, Vortex panel method.

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Authors: T.C. Manjunath, B. Bandyopadhyay

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This paper features the proposed modeling and design of a Robust Decentralized Periodic Output Feedback (RDPOF) control technique for the active vibration control of smart flexible multimodel Euler-Bernoulli cantilever beams for a multivariable (MIMO) case by retaining the first 6 vibratory modes. The beam structure is modeled in state space form using the concept of piezoelectric theory, the Euler-Bernoulli beam theory and the Finite Element Method (FEM) technique by dividing the beam into 4 finite elements and placing the piezoelectric sensor / actuator at two finite element locations (positions 2 and 4) as collocated pairs, i.e., as surface mounted sensor / actuator, thus giving rise to a multivariable model of the smart structure plant with two inputs and two outputs. Five such multivariable models are obtained by varying the dimensions (aspect ratios) of the aluminum beam, thus giving rise to a multimodel of the smart structure system. Using model order reduction technique, the reduced order model of the higher order system is obtained based on dominant eigen value retention and the method of Davison. RDPOF controllers are designed for the above 5 multivariable-multimodel plant. The closed loop responses with the RDPOF feedback gain and the magnitudes of the control input are observed and the performance of the proposed multimodel smart structure system with the controller is evaluated for vibration control.

Keywords: Smart structure, Euler-Bernoulli beam theory, Periodic output feedback control, Finite Element Method, State space model, SISO, Embedded sensors and actuators, Vibration control, Reduced order model

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Authors: Nnamdi N. Omehe, Chibuzo Emeruwa

Abstract:

ZnIn2Se4 and ZnIn2Te4 are vacancy defect materials whose properties have been investigated using Density Functional Theory (DFT) framework. The pseudopotential method in conjunction with the LDA+U technique and the Projector Augmented Wave (PAW) was used to calculate the electronic band structure, total density of state, and the partial density of state; while the norm-conserving pseudopotential was used to calculate the dielectric response function with scissors shift. Both ZnIn2Se4 and ZnIn2Te4 were predicted to be semiconductors with energy band gap of 1.66 eV and 1.33 eV respectively, and they both have direct energy band gap at the gamma point of high symmetry. The topmost valence subband for ZnIn2Se4 and ZnIn2Te4 has an energy width of 5.7 eV and 6.0 eV respectively. The calculations of partial density of state (PDOS) show that for ZnIn2Se4, the top of the valence band is dominated by Se-4p orbital, while the bottom of the conduction band is composed of In-5p, In-5s, and Zn-4s states. PDOS for ZnIn2Te4, shows that the top of the valence band is mostly of Te-5p states, while its conduction band bottom is composed mainly of Zn-4s, Te-5p, Te-5s, and In-5s states. Dielectric response function calculation yielded (0) of 11.9 and 36 for ZnIn2Se4 and ZnIn2Te4 respectively.

Keywords: Optoelectronic, Dielectric Response Function, LDA+U, band structure calculation.

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Authors: Sh. Yasini, M. B. Naghibi-Sistani, A. Karimpour

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This paper employs a new approach to regulate the blood glucose level of type I diabetic patient under an intensive insulin treatment. The closed-loop control scheme incorporates expert knowledge about treatment by using reinforcement learning theory to maintain the normoglycemic average of 80 mg/dl and the normal condition for free plasma insulin concentration in severe initial state. The insulin delivery rate is obtained off-line by using Qlearning algorithm, without requiring an explicit model of the environment dynamics. The implementation of the insulin delivery rate, therefore, requires simple function evaluation and minimal online computations. Controller performance is assessed in terms of its ability to reject the effect of meal disturbance and to overcome the variability in the glucose-insulin dynamics from patient to patient. Computer simulations are used to evaluate the effectiveness of the proposed technique and to show its superiority in controlling hyperglycemia over other existing algorithms

Keywords: Insulin Delivery rate, Q-learning algorithm, Reinforcement learning, Type I diabetes.

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Authors: Ana Cázares

Abstract:

Aims for this study: first, to compare the expertise level in data analysis, communication and information technologies in undergraduate psychology students. Second, to verify the factor structure of E-ETICA (Escala de Experticia en Tecnologias de la Informacion, la Comunicacion y el Análisis or Data Analysis, Communication and Information'Expertise Scale) which had shown an excellent internal consistency (α= 0.92) as well as a simple factor structure. Three factors, Complex, Basic Information and Communications Technologies and E-Searching and Download Abilities, explains 63% of variance. In the present study, 260 students (119 juniors and 141 seniors) were asked to respond to ETICA (16 items Likert scale of five points 1: null domain to 5: total domain). The results show that both junior and senior students report having very similar expertise level; however, E-ETICA presents a different factor structure for juniors and four factors explained also 63% of variance: Information E-Searching, Download and Process; Data analysis; Organization; and Communication technologies.

Keywords: Data analysis, Information, Communications Technologies, Expertise'Levels.

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Authors: Sivapalan, S., Subramaniam, G., Clifford, M.J., Balbir Singh, M.S., Abdullah, A

Abstract:

Higher education has an important role to play in advocating environmentalism. Given this responsibility, the goal of higher education should therefore be to develop graduates with the knowledge, skills and values related to environmentalism. However, research indicates that there is a lack of consciousness amongst graduates on the need to be more environmentally aware, especially when it comes to applying the appropriate knowledge and skills related to environmentalism. Although institutions of higher learning do include environmental parameters within their undergraduate and postgraduate academic programme structures, the environmental boundaries are usually confined to specific engineering majors within an engineering programme. This makes environmental knowledge, skills and values exclusive to certain quarters of the higher education system. The incorporation of environmental literacy within higher education institutions as a whole is of utmost pertinence if a nation-s human capital is to be nurtured to become change agents for the preservation of environment. This paper discusses approaches that can be adapted by institutions of higher learning to include environmental literacy within the graduate-s higher learning experience.

Keywords: Higher education, engineering education, environmental literacy, Malaysia.

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Authors: Anne-Lena Kampen, Øivind Kure

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Machine learning (ML) can be implemented in Wireless Sensor Networks (WSNs) as a central solution or distributed solution where the ML is embedded in the nodes. Embedding improves privacy and may reduce prediction delay. In addition, the number of transmissions is reduced. However, quality factors such as prediction accuracy, fault detection efficiency and coordinated control of the overall system suffer. Here, we discuss and highlight the trade-offs that should be considered when choosing between embedding and centralized ML, especially for multihop networks. In addition, we present estimations that demonstrate the energy trade-offs between embedded and centralized ML. Although the total network energy consumption is lower with central prediction, it makes the network more prone for partitioning due to the high forwarding load on the one-hop nodes. Moreover, the continuous improvements in the number of operations per joule for embedded devices will move the energy balance toward embedded prediction.

Keywords: Central ML, embedded machine learning, energy consumption, local ML, Wireless Sensor Networks, WSN.

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Authors: Almutasim Billa A. Alanazi, Hal S. Tharp

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Safe performance and efficient energy consumption are essential factors for designing a control system. This paper presents a reinforcement learning (RL) model that can be applied to control applications to improve safety and reduce energy consumption. As hardware constraints and environmental disturbances are imprecise and unpredictable, conventional control methods may not always be effective in optimizing control designs. However, RL has demonstrated its value in several artificial intelligence (AI) applications, especially in the field of control systems. The proposed model intelligently monitors a system's success by observing the rewards from the environment, with positive rewards counting as a success when the controlled reference is within the desired operating zone. Thus, the model can determine whether the system is safe to continue operating based on the designer/user specifications, which can be adjusted as needed. Additionally, the controller keeps track of energy consumption to improve energy efficiency by enabling the idle mode when the controlled reference is within the desired operating zone, thus reducing the system energy consumption during the controlling operation. Water temperature control for a hydroponic system is taken as a case study for the RL model, adjusting the variance of disturbances to show the model’s robustness and efficiency. On average, the model showed safety improvement by up to 15% and energy efficiency improvements by 35%-40% compared to a traditional RL model.

Keywords: Control system, hydroponics, machine learning, reinforcement learning.

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Authors: S. Hong, H. Kim, D. Cho, S. Park

Abstract:

Existing underground pipe jacking methods use a reinforcing rod in a steel tube to obtain structural stiffness. However, some problems such as inconvenience of works and expensive materials resulted from limited working space and reinforcing works are existed. To resolve these problems, a new pipe jacking method, namely PST (Prestressed Segment Tunnel) method, was developed which used joint to connect the steel segment and form erection structure. For evaluating the flexural capacity of the PST method structure, a experimental test was conducted. The parameters considered in the test were span-to-depth ratio of segment, diameter of steel tube at the corner, prestressing force, and welding of joint. The flexural behaviours with the effect of load capacity in serviceability state according to different parameters were examined.. The frame with long segments could increase flexural stiffness and the specimen with large diameter of concave corner showed excellent resistance ability to the negative moment. In addition, welding of joints increased the flexural capacity.

Keywords: PST method, Pipe jacking method, Flexural behavior, Prestressed concrete.

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Authors: Lele Zhang, HuiLeng Choo, Alexander Konyukhov, Shuguang Li

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China is currently the world's largest producer and distributor of electric bicycle (e-bike). The increasing number of e-bikes on the road is accompanied by rising injuries and even deaths of e-bike drivers. Therefore, there is a growing need to improve the safety structure of e-bikes. This 3D frictionless contact analysis is a preliminary, but necessary work for further structural design improvement of an e-bike. The contact analysis between e-bike and the ground was carried out as follows: firstly, the Penalty method was illustrated and derived from the simplest spring-mass system. This is one of the most common methods to satisfy the frictionless contact case; secondly, ANSYS static analysis was carried out to verify finite element (FE) models with contact pair (without friction) between e-bike and the ground; finally, ANSYS transient analysis was used to obtain the data of the penetration p(u) of e-bike with respect to the ground. Results obtained from the simulation are as estimated by comparing with that from theoretical method. In the future, protective shell will be designed following the stability criteria and added to the frame of e-bike. Simulation of side falling of the improvedsafety structure of e-bike will be confirmed with experimental data.

Keywords: Frictionless contact, penalty method, e-bike, finite element.

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Authors: F. Salehuddin, I. Ahmad, F. A. Hamid, A. Zaharim, H. A. Elgomati, B. Y. Majlis, P. R. Apte

Abstract:

In this study, the Taguchi method was used to optimize the effect of HALO structure or halo implant variations on threshold voltage (VTH) and leakage current (ILeak) in 45nm p-type Metal Oxide Semiconductor Field Effect Transistors (MOSFETs) device. Besides halo implant dose, the other process parameters which used were Source/Drain (S/D) implant dose, oxide growth temperature and silicide anneal temperature. This work was done using TCAD simulator, consisting of a process simulator, ATHENA and device simulator, ATLAS. These two simulators were combined with Taguchi method to aid in design and optimize the process parameters. In this research, the most effective process parameters with respect to VTH and ILeak are halo implant dose (40%) and S/D implant dose (52%) respectively. Whereas the second ranking factor affecting VTH and ILeak are oxide growth temperature (32%) and halo implant dose (34%) respectively. The results show that after optimizations approaches is -0.157V at ILeak=0.195mA/μm.

Keywords: Optimization, p-type MOSFETs device, HALO Structure, Taguchi Method.

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Authors: Fotis N. Koumboulis, Michael G. Skarpetis, Maria P. Tzamtzi

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The problem of robust disturbance rejection (RDR) using a proportional state feedback controller is studied for the case of Left Invertible MIMO generalized state space linear systems with nonlinear uncertain structure. Sufficient conditions for the problem to have a solution are established. The set of all proportional feedback controllers solving the problem subject to these conditions is analytically determined.

Keywords: System theory, uncertain systems, robust control, singular systems.

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Authors: Chad Goldsworthy, B. Rajeswari Matam

Abstract:

The use of deep learning for species identification in camera trap images has revolutionised our ability to study, conserve and monitor species in a highly efficient and unobtrusive manner, with state-of-the-art models achieving accuracies surpassing the accuracy of manual human classification. The high imbalance of camera trap datasets, however, results in poor accuracies for minority (rare or endangered) species due to their relative insignificance to the overall model accuracy. This paper investigates the use of Focal Loss, in comparison to the traditional Cross Entropy Loss function, to improve the identification of minority species in the “255 Bird Species” dataset from Kaggle. The results show that, although Focal Loss slightly decreased the accuracy of the majority species, it was able to increase the F1-score by 0.06 and improve the identification of the bottom two, five and ten (minority) species by 37.5%, 15.7% and 10.8%, respectively, as well as resulting in an improved overall accuracy of 2.96%.

Keywords: Convolutional neural networks, data imbalance, deep learning, focal loss, species classification, wildlife conservation.

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Authors: Joel S. Mtebe, Aron Kondoro, Mussa M. Kissaka, Elia Kibga

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Sub-Saharan Africa is described as the second fastest growing in mobile phone penetration in the world more than in the United States or the European Union. Mobile phones have been used to provide a lot of opportunities to improve people’s lives in the region such as in banking, marketing, entertainment, and paying for various bills such as water, TV, and electricity. However, the potential of mobile phones to enhance teaching and learning has not been explored. This study presents an experience of developing and delivering SMS based quiz questions used to assess mastery of subject content knowledge of science and mathematics secondary school teachers in Tanzania. The SMS quizzes were used as a follow up support mechanism to 500 teachers who participated in a project to upgrade subject content knowledge of teachers in science and mathematics subjects in Tanzania. Quizzes of 10-15 questions were sent to teachers each week for 8 weeks and the results were analyzed using SPSS. Results show that teachers who participated in chemistry and biology subjects have better performance compared to those who participated in mathematics and physics subjects. Teachers reported some challenges that led to poor performance, This research has several practical implications for those who are implementing or planning to use mobile phones in teaching and learning especially in rural secondary schools in sub-Saharan Africa.

Keywords: Mobile learning, e-learning, educational technologies, SMS, secondary education, assessment.

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Authors: Mario Gianni, Manuel A. Ruiz Garcia, Fiora Pirri

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In this work, we present a Bayesian non-parametric approach to model the motion control of ATVs. The motion control model is based on a Dirichlet Process-Gaussian Process (DP-GP) mixture model. The DP-GP mixture model provides a flexible representation of patterns of control manoeuvres along trajectories of different lengths and discretizations. The model also estimates the number of patterns, sufficient for modeling the dynamics of the ATV.

Keywords: Dirichlet processes, Gaussian processes, robot control learning, tracked vehicles.

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Authors: Javid Iqbal, Zhu Shifan

Abstract:

The development in the construction of unconventional ships and the implementation of lightweight materials have shown a large impulse towards finite element (FE) method, making it a general tool for ship design. This paper briefly presents the modeling and analysis techniques of ship structures using FE method for complex boundary conditions which are difficult to analyze by existing Ship Classification Societies rules. During operation, all ships experience complex loading conditions. These loads are general categories into thermal loads, linear static, dynamic and non-linear loads. General strength of the ship structure is analyzed using static FE analysis. FE method is also suitable to consider the local loads generated by ballast tanks and cargo in addition to hydrostatic and hydrodynamic loads. Vibration analysis of a ship structure and its components can be performed using FE method which helps in obtaining the dynamic stability of the ship. FE method has developed better techniques for calculation of natural frequencies and different mode shapes of ship structure to avoid resonance both globally and locally. There is a lot of development towards the ideal design in ship industry over the past few years for solving complex engineering problems by employing the data stored in the FE model. This paper provides an overview of ship modeling methodology for FE analysis and its general application. Historical background, the basic concept of FE, advantages, and disadvantages of FE analysis are also reported along with examples related to hull strength and structural components.

Keywords: Dynamic analysis, finite element methods, ship structure, vibration analysis.

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Authors: Bingchun Liu, Pei-Chann Chang, Natasha Huang, Dun Li

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Machine Learning and Data Mining are the two important tools for extracting useful information and knowledge from large datasets. In machine learning, classification is a wildly used technique to predict qualitative variables and is generally preferred over regression from an operational point of view. Due to the enormous increase in air pollution in various countries especially China, Air Quality Classification has become one of the most important topics in air quality research and modelling. This study aims at introducing a hybrid classification model based on information theory and Support Vector Machine (SVM) using the air quality data of four cities in China namely Beijing, Guangzhou, Shanghai and Tianjin from Jan 1, 2014 to April 30, 2016. China's Ministry of Environmental Protection has classified the daily air quality into 6 levels namely Serious Pollution, Severe Pollution, Moderate Pollution, Light Pollution, Good and Excellent based on their respective Air Quality Index (AQI) values. Using the information theory, information gain (IG) is calculated and feature selection is done for both categorical features and continuous numeric features. Then SVM Machine Learning algorithm is implemented on the selected features with cross-validation. The final evaluation reveals that the IG and SVM hybrid model performs better than SVM (alone), Artificial Neural Network (ANN) and K-Nearest Neighbours (KNN) models in terms of accuracy as well as complexity.

Keywords: Machine learning, air quality classification, air quality index, information gain, support vector machine, cross-validation.

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Authors: Mohamed M. El-Gendy, Ibrahim A. El-Arabi, Rafik W. Abdel-Missih, Omar A. Kandil

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In the present research, a finite element model is presented to study the geometrical and material nonlinear behavior of reinforced concrete plane frames considering soil-structure interaction. The nonlinear behaviors of concrete and reinforcing steel are considered both in compression and tension up to failure. The model takes account also for the number, diameter, and distribution of rebar along every cross section. Soil behavior is taken into consideration using four different models; namely: linear-, nonlinear Winkler's model, and linear-, nonlinear continuum model. A computer program (NARC) is specially developed in order to perform the analysis. The results achieved by the present model show good agreement with both theoretical and experimental published literature. The nonlinear behavior of a rectangular frame resting on soft soil up to failure using the proposed model is introduced for demonstration.

Keywords: Nonlinear analysis, Geometric nonlinearity, Material nonlinearity, Reinforced concrete, Finite element method, Soilstructure interaction, Winkler's soil model, Continuum soil model

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Authors: Torsti Rantapuska, Sariseelia Sore

Abstract:

Acquiring commercial off-the-shelf (COTS) software applications is becoming routine in organizations. However, eliciting user requirements, finding the candidate COTS products and making the decision is a complex task, especially for SMEs who do not have the time and knowledge needed to do the task properly. The existing models intended to help the decision makers are originally designed for professional use. SMEs are obligated to rely on the software vendor’s ability to solve the problem with the systems provided.  In this paper, we develop a model for SMEs for the acquisition of Commercial Off-The-Shelf (COTS) software products. A leading idea of the model is that the ICT investment is basically a change initiative and therefore it should also be taken as a process of organizational learning. The model is designed bearing three objectives in mind: 1) business orientation, 2) agility, and 3) Learning and knowledge management orientation. The model can be applied to ICT investments in SMEs which have a professional team leader with basic business and IT knowledge. 

 

Keywords: COTS acquisition, ICT investment, organizational learning, ICT adoption.

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Authors: Linnea Stenliden

Abstract:

The aim of this paper is to understand emerging learning conditions, when a visual analytics is implemented and used in K 12 (education). To date, little attention has been paid to the role visual analytics (digital media and technology that highlight visual data communication in order to support analytical tasks) can play in education, and to the extent to which these tools can process actionable data for young students. This study was conducted in three public K 12 schools, in four social science classes with students aged 10 to 13 years, over a period of two to four weeks at each school. Empirical data were generated using video observations and analyzed with help of metaphors within Actor-network theory (ANT). The learning conditions are found to be distinguished by broad complexity, characterized by four dimensions. These emerge from the actors’ deeply intertwined relations in the activities. The paper argues in relation to the found dimensions that novel approaches to teaching and learning could benefit students’ knowledge building as they work with visual analytics, analyzing visualized data.

Keywords: Analytical reasoning, complexity, data use, problem space, visual analytics, visual storytelling, translation.

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