Search results for: algorithm techniques

Authors: Ethan James

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Over one billion people worldwide suffer from some level of vision loss or blindness as a result of progressive retinal diseases. Many patients, particularly in developing areas, are incorrectly diagnosed or undiagnosed whatsoever due to unconventional diagnostic tools and screening methods. Artificial intelligence (AI) based on deep learning (DL) convolutional neural networks (CNN) have recently gained a high interest in ophthalmology for its computer-imaging diagnosis, disease prognosis, and risk assessment. Optical coherence tomography (OCT) is a popular imaging technique used to capture high-resolution cross-sections of retinas. In ophthalmology, DL has been applied to fundus photographs, optical coherence tomography, and visual fields, achieving robust classification performance in the detection of various retinal diseases including macular degeneration, diabetic retinopathy, and retinitis pigmentosa. However, there is no complete diagnostic model to analyze these retinal images that provide a diagnostic accuracy above 90%. Thus, the purpose of this project was to develop an AI model that utilizes machine learning techniques to automatically diagnose specific retinal diseases from OCT scans. The algorithm consists of neural network architecture that was trained from a dataset of over 20,000 real-world OCT images to train the robust model to utilize residual neural networks with cyclic pooling. This DL model can ultimately aid ophthalmologists in diagnosing patients with these retinal diseases more quickly and more accurately, therefore facilitating earlier treatment, which results in improved post-treatment outcomes.

Keywords: artificial intelligence, deep learning, imaging, medical devices, ophthalmic devices, ophthalmology, retina

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Authors: Maha Mourad

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Higher education in any competitive European economy should serve the new information society by increasing the supply of good quality education services and by creating good international brands in the international higher education market. Hence, continuous risk management techniques through higher educational reforms programs became one of the top priorities within the European Union to control the quality of higher education. Risk is higher education is studies by several researchers who agreed that the risk in higher education has a direct influence on continuity of quality education and research contribution. The focus of this research is to highlights the Internal Quality Assurance (IQA) activities in the Polish higher education system as a risk management tool used to control the quality of education. This paper presents a qualitative empirical analysis in 5 different universities in Poland. In addition, it aims to help in finding global practical and create benchmark for policy makers concerning the risk management techniques based on the Polish experience.

Keywords: education development, quality assurance, sustainability, european higher education

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Authors: Palash Dey, Sudip Talukdar

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In this work, a cross-breed approach is presented for obtaining both the amount of the damage intensity and location of damage existing in thin-walled members. This cross-breed approach is developed based on response surface methodology (RSM) and genetic algorithm (GA). Theoretical finite element (FE) model of cracked angle section thin walled curved beam has been linked to the developed approach to carry out trial experiments to generate response surface functions (RSFs) of free, forced and heterogeneous dynamic response data. Subsequently, the error between the computed response surface functions and measured dynamic response data has been minimized using GA to find out the optimum damage parameters (amount of the damage intensity and location). A single crack of varying location and depth has been considered in this study. The presented approach has been found to reveal good accuracy in prediction of crack parameters and possess great potential in crack detection as it requires only the current response of a cracked beam.

Keywords: damage parameters, finite element, genetic algorithm, response surface methodology, thin walled curved beam

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Authors: Farhan A. Alenizi

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Digital watermarking has evolved in the past years as an important means for data authentication and ownership protection. The images and video watermarking was well known in the field of multimedia processing; however, 3D objects' watermarking techniques have emerged as an important means for the same purposes, as 3D mesh models are in increasing use in different areas of scientific, industrial, and medical applications. Like the image watermarking techniques, 3D watermarking can take place in either space or transform domains. Unlike images and video watermarking, where the frames have regular structures in both space and temporal domains, 3D objects are represented in different ways as meshes that are basically irregular samplings of surfaces; moreover, meshes can undergo a large variety of alterations which may be hard to tackle. This makes the watermarking process more challenging. While the transform domain watermarking is preferable in images and videos, they are still difficult to implement in 3d meshes due to the huge number of vertices involved and the complicated topology and geometry, and hence the difficulty to perform the spectral decomposition, even though significant work was done in the field. Spatial domain watermarking has attracted significant attention in the past years; they can either act on the topology or on the geometry of the model. Exploiting the statistical characteristics in the 3D mesh models from both geometrical and topological aspects was useful in hiding data. However, doing that with minimal surface distortions to the mesh attracted significant research in the field. A 3D mesh blind watermarking technique is proposed in this research. The watermarking method depends on modifying the vertices' positions with respect to the center of the object. An optimal method will be developed to reduce the errors, minimizing the distortions that the 3d object may experience due to the watermarking process, and reducing the computational complexity due to the iterations and other factors. The technique relies on the displacement process of the vertices' locations depending on the modification of the variances of the vertices’ norms. Statistical analyses were performed to establish the proper distributions that best fit each mesh, and hence establishing the bins sizes. Several optimizing approaches were introduced in the realms of mesh local roughness, the statistical distributions of the norms, and the displacements in the mesh centers. To evaluate the algorithm's robustness against other common geometry and connectivity attacks, the watermarked objects were subjected to uniform noise, Laplacian smoothing, vertices quantization, simplification, and cropping. Experimental results showed that the approach is robust in terms of both perceptual and quantitative qualities. It was also robust against both geometry and connectivity attacks. Moreover, the probability of true positive detection versus the probability of false-positive detection was evaluated. To validate the accuracy of the test cases, the receiver operating characteristics (ROC) curves were drawn, and they’ve shown robustness from this aspect. 3D watermarking is still a new field but still a promising one.

Keywords: watermarking, mesh objects, local roughness, Laplacian Smoothing

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Authors: Zeineb Yousse, Fakher Kharrat

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Mosaic is a part of a broad universal cultural heritage; sometimes it represents a rather essential source for the researches on the everyday life of some of the previous civilizations. Tunisia has one of the finest and largest collections of mosaics in the world, which is essentially exhibited in the Museums of Bardo and Sousse. Restored and reconstituted, they bear witnesses to hard work. Our paper deals with the discipline of conservation of Roman mosaics based on the proceedings of the workshop of the Museum of Sousse. Thus, we highlight two main objectives. In the first place, it is a question of revealing the techniques adopted by professionals to handle mosaics and to which school of conservation these techniques belong. In the second place, we are going to interpret the works initiated to preserve the archaeological heritage in order to protect it in present time and transmit it to future generations. To this end, we paid attention to four Roman mosaics currently exhibited in the Museum of Sousse. These Mosaics show different voids or gaps at the level of their surfaces and the method used to fill these gaps seems to be interesting to analyze. These mosaics are known under the names of: Orpheus Charming the Animals, Gladiator and Bears, Stud farm of Sorothus and finally Head of Medusa. The study on the conservation passes through two chained phases. We start with a small historical overview in order to gather information related to the original location, the date of its composition as well as the description of its image. Afterward, the intervention process is analyzed by handling three complementary elements which are: diagnosis of the existing state, the study of the medium processing and the study of the processing of the tesselatum surface which includes the pictorial composition of the mosaic. Furthermore, we have implemented an evaluation matrix with six operating principles allowing the assessment of the appropriateness of the intervention. These principles are the following: minimal intervention, reversibility, compatibility, visibility, durability, authenticity and enhancement. Various accumulated outcomes are pointing out the techniques used to fill the gaps as well as the level of compliance with the principles of conservation. Accordingly, the conservation of mosaics in Tunisia is a practice that combines various techniques without really arguing about the choice of a particular theory.

Keywords: conservation, matrix, museum of Sousse, operating particular theory, principles, Roman mosaics

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Authors: Golovchun Aleftina, Dabyltayeva Raikhan

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Nowadays, in the world of widely developing cross-cultural interactions and rapidly changing demands of the global labor market, foreign language teaching and learning has taken a special role not only in school education but also in everyday life. Cognitive Lingua-Cultural Methodology of Foreign Language Teaching originated in Kazakhstan brings a communicative approach to the forefront in foreign language teaching that gives raise a variety of techniques to make the language learning a real communication. One of these techniques is Computer Assisted Language Learning. In our article, we aim to: demonstrate what learning benefits students are likely to get by teachers having implemented computer-based technologies into foreign language teaching process; prove that technology-based classroom serves as the best tool for interactive and efficient language learning; give examples of classroom sufficient organization with computer-based activities.

Keywords: computer assisted language learning, learning benefits, foreign language teaching process, implementation, communicative approach

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Authors: Ayodele Odularu, Peter Ajibade, Albert Bolhuis

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This study aimed at assessing the antibacterial activities of four zinc (II) complexes. Zinc (II) complexes of nitrogen, oxygen and sulfur coordination modes were synthesized using direct substitution reaction. The characterization techniques involved physicochemical properties (molar conductivity) and spectroscopic techniques. The molar conductivity gave the non-electrolytic nature of zinc (II) complexes. The spectral studies of zinc (II) complexes were done using electronic spectra (UV-Vis) and Fourier Transform Infra-red Spectroscopy (FT-IR). Spectral data from the spectroscopic studies confirmed the coordination of the mixed ligands with zinc (II) ion. The antibacterial activities of zinc(II) complexes of were all in supportive of Overtone’s concept and Tweedy’s theory of chelation for bacterial strains of S. aureus MRSA252 and E coli MC4100 because the zones of inhibition were greater than the corresponding ligands. In summary, all zinc (II) complexes of ZEPY, ZE1PH, ZE1PY and ZE135PY all have potentials for antibacterial activities.

Keywords: antibacterial activities, spectral studies, syntheses, zinc(II) complexes

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Authors: Ginevra Addis

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The possibility of reproducing physical artifacts in a digital format is one of the opportunities offered by the technological advancements in information and communication most frequently promoted by museums. Indeed, the study and conservation of our cultural heritage have seen significant advancement due to the three-dimensional acquisition and modeling technology. A variety of laser scanning systems has been developed, based either on optical triangulation or on time-of-flight measurement, capable of producing digital 3D images of complex structures with high resolution and accuracy. It is necessary, however, to explore the challenges and opportunities that this practice brings within museums. The purpose of this paper is to understand what change is introduced by digital techniques in those museums that are hosting digital masterpieces. The methodology used will investigate three distinguished Italian exhibitions, related to the territory of Milan, trying to analyze the following issues about museum practices: 1) how digitizing art masterpieces increases the number of visitors; 2) what the need that calls for the digitization of artworks; 3) which techniques are most used; 4) what the setting is; 5) the consequences of a non-publication of hard copies of catalogues; 6) envision of these practices in the future. Findings will show how interconnection plays an important role in rebuilding a collection spread all over the world. Secondly how digital artwork duplication and extension of reality entail new forms of accessibility. Thirdly, that collection and preservation through digitization of images have both a social and educational mission. Fourthly, that convergence of the properties of different media (such as web, radio) is key to encourage people to get actively involved in digital exhibitions. The present analysis will suggest further research that should create museum models and interaction spaces that act as catalysts for innovation.

Keywords: digital masterpieces, education, interconnection, Italian museums, preservation

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Authors: Tayyaba Hussain, Sobia Jangsher, Saqib Ali, Saqib Ejaz

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Unmanned Aerial vehicles (UAV’s) have gained great popularity due to their remoteness, ease of deployment and high maneuverability in different applications like real-time surveillance, image capturing, weather atmospheric studies, disaster site monitoring and mapping. These applications can involve a real-time communication with the ground station. However, altitude and mobility possess a few challenges for the communication. UAV’s at high altitude usually require more transmit power. One possible solution can be with the use of multi hops (UAV’s acting as relays) and exploiting the mobility pattern of the UAV’s. In this paper, we studied a relay (UAV’s acting as relays) selection for a reliable transmission to a destination UAV. We exploit the mobility information of the UAV’s to propose a Mobility-Aware Relay Selection (MARS) algorithm with the objective of giving improved data rates. The results are compared with Non Mobility-Aware relay selection scheme and optimal values. Numerical results show that our proposed MARS algorithm gives 6% better achievable data rates for the mobile UAV’s as compared with Non MobilityAware relay selection scheme. On average a decrease of 20.2% in data rate is achieved with MARS as compared with SDP solver in Yalmip.

Keywords: mobility aware, relay selection, time division multiple acess, unmanned aerial vehicle

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Authors: Jatinderkumar R. Saini, Nikita. R. Sonthalia, Khushbu. A. Dodiya

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Research leads to development of science and technology and hence to the betterment of humankind. Journals and conferences provide a platform to receive large number of research papers for publications and presentations before the expert and scientific community. In order to assure quality of such papers, they are also sent to reviewers for their comments. In order to maintain good ethical standards, the research papers are sent to reviewers in such a way that they do not know each other’s identity. This technique is called double-blind review process. It is called single-blind review process, if identity of any one party (generally authors) is disclosed to the other. This paper presents the techniques by which identity of author as well as reviewer could be made out even through double-blind review process. It is proposed that the characteristics and techniques presented here will help journals and conferences in assuring intentional or unintentional disclosure of identity revealing information by either party to the other.

Keywords: author, conference, double blind paper, journal, reviewer, single blind paper

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Authors: Mohammad Tarek Al Muallim, Ozhan Duzenli, Ceyhun Ilguy

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Harbor protection systems are so important. The need for automatic protection systems has increased over the last years. Diver detection active sonar has great significance. It used to detect underwater threats such as divers and autonomous underwater vehicle. To automatically detect such threats the sonar image is processed by algorithms. These algorithms used to detect, track and classify of underwater objects. In this work, divers tracking and classification algorithm is improved be proposing a robust wake detection method. To detect objects the sonar images is normalized then segmented based on fixed threshold. Next, the centroids of the segments are found and clustered based on distance metric. Then to track the objects linear Kalman filter is applied. To reduce effect of noise and creation of false tracks, the Kalman tracker is fine tuned. The tuning is done based on our active sonar specifications. After the tracks are initialed and updated they are subjected to a filtering stage to eliminate the noisy and unstable tracks. Also to eliminate object with a speed out of the diver speed range such as buoys and fast boats. Afterwards the result tracks are subjected to a classification stage to deiced the type of the object been tracked. Here the classification stage is to deice wither if the tracked object is an open circuit diver or a close circuit diver. At the classification stage, a small area around the object is extracted and a novel wake detection method is applied. The morphological features of the object with his wake is extracted. We used support vector machine to find the best classifier. The sonar training images and the test images are collected by ARMELSAN Defense Technologies Company using the portable diver detection sonar ARAS-2023. After applying the algorithm to the test sonar data, we get fine and stable tracks of the divers. The total classification accuracy achieved with the diver type is 97%.

Keywords: harbor protection, diver detection, active sonar, wake detection, diver classification

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Authors: S. Pirrello

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Sydney’s booming real estate market has pushed property developers to invest in historically “no-go” areas, which were previously too expensive to develop. These areas are usually near rivers where the sites are underlain by deep alluvial and estuarine sediments. In these ground conditions, conventional bored pile techniques are often not competitive. Contiguous Flight Auger (CFA) and Drilled Displacement (DD) Piles techniques are on the other hand suitable for these ground conditions. This paper deals with the design and construction challenges encountered with these piling techniques for a series of high-rise towers in Sydney’s West. The advantages of DD over CFA piles such as reduced overall spoil with substantial cost savings and achievable rock sockets in medium strength bedrock are discussed. Design performances were assessed with PIGLET. Pile performances are validated in two stages, during constructions with the interpretation of real-time data from the piling rigs’ on-board computer data, and after construction with analyses of results from high strain pile dynamic testing (PDA). Results are then presented and discussed. High Strain testing data are presented as Case Pile Wave Analysis Program (CAPWAP) analyses.

Keywords: contiguous flight auger (CFA) , DEFPIG, case pile wave analysis program (CAPWAP), drilled displacement piles (DD), pile dynamic testing (PDA), PIGLET, PLAXIS, repute, pile performance

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Authors: Bernardo Cedano, Ahmed H. Eltom, Bob Hay, Jim Glass, Raga Ahmed

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The deployment of smart grid devices such as smart meters and smart switches (SS) supported by a reliable and fast communications system makes automated distribution possible, and thus, provides great benefits to electric power consumers and providers alike. However, more research is needed before the full utility of smart switch data is realized. This paper presents new automated switching techniques using SS within the electric power grid. A concise background of the SS is provided, and operational examples are shown. Organization and presentation of data obtained from SS are shown in the context of the future goal of total automation of the distribution network. The description of application techniques, the examples of success with SS, and the vision outlined in this paper serve to motivate future research pertinent to disturbance analysis automation.

Keywords: disturbance automation, electric power grid, smart grid, smart switches

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Authors: Latika Choudhary

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“The potential benefits of Artificial Intelligence are huge, So are the dangers.” - Dave Water. Artificial intelligence is one of the facet of Information technology domain which despite several attempts does not have a clear definition or ambit. However it can be understood as technology to solve problems via automated decisions and predictions. Artificial intelligence is essentially an algorithm based technology which analyses the large amounts of data and then solves problems by detecting useful patterns. Owing to its automated feature it will not be wrong to say that humans & AI have more utility than humans alone or computers alone.1 For many decades AI experienced enthusiasm as well as setbacks, yet it has today become part and parcel of our everyday life, making it convenient or at times problematic. AI and related technology encompass Intellectual Property in multiple ways, the most important being AI technology for management of Intellectual Property, IP for protecting AI and IP as a hindrance to the transparency of AI systems. Thus the relationship between the two is of reciprocity as IP influences AI and vice versa. While AI is a recent concept, the IP laws for protection or even dealing with its challenges are relatively older, raising the need for revision to keep up with the pace of technological advancements. This paper will analyze the relationship between AI and IP to determine how beneficial or conflictual the same is, address how the old concepts of IP are being stretched to its maximum limits so as to accommodate the unwanted consequences of the Artificial Intelligence and propose ways to mitigate the situation so that AI becomes the friend it is and not turn into a potential foe it appears to be.

Keywords: intellectual property rights, information technology, algorithm, artificial intelligence

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Authors: Hanqing Yang, Xiang Meng, Qi Li, Weirong Chen

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The SOC (state of charge) based droop control has limitations on the load power sharing among different energy storage units, due to the line impedance. In this paper, a distributed control strategy for dispersed energy storage units in the DC microgrid based on discrete consensus is proposed. Firstly, a sparse information communication network is built. Thus, local controllers can communicate with its neighbors using voltage, current and SOC information. An average voltage of grid can be evaluated to compensate voltage offset by droop control, and an objective virtual resistance fulfilling above requirement can be dynamically calculated to distribute load power according to the SOC of the energy storage units. Then, the stability of the whole system and influence of communication delay are analyzed. It can be concluded that this control strategy can improve the robustness and flexibility, because of having no center controller. Finally, a model of DC microgrid with dispersed energy storage units and loads is built, the discrete distributed algorithm is established and communication protocol is developed. The co-simulation between Matlab/Simulink and JADE (Java agent development framework) has verified the effectiveness of proposed control strategy.

Keywords: dispersed energy storage units, discrete consensus algorithm, state of charge, communication delay

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Authors: Gautam Kumar Saharia, Sagardeep Talukdar, Riki Dutta, Sudipta Nandy

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The physics-informed neural network (PINN) method opens up an approach for numerically solving nonlinear partial differential equations leveraging fast calculating speed and high precession of modern computing systems. We construct the PINN based on a strong universal approximation theorem and apply the initial-boundary value data and residual collocation points to weekly impose initial and boundary conditions to the neural network and choose the optimization algorithms adaptive moment estimation (ADAM) and Limited-memory Broyden-Fletcher-Golfard-Shanno (L-BFGS) algorithm to optimize learnable parameter of the neural network. Next, we improve the PINN with a weighted loss function to obtain both the bright and dark soliton solutions of the Fokas-Lenells equation (FLE). We find the proposed scheme of adjustable weight coefficients into PINN has a better convergence rate and generalizability than the basic PINN algorithm. We believe that the PINN approach to solve the partial differential equation appearing in nonlinear optics would be useful in studying various optical phenomena.

Keywords: deep learning, optical soliton, physics informed neural network, partial differential equation

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Authors: Dawei Li, Cheng Liu, Yiteng Liu

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This paper introduces an embedded digital image system for Chinese space environment vertical exploration sounding rocket. In order to record the flight status of the sounding rocket as well as the payloads, an onboard embedded image processing system based on ADV212, a JPEG2000 compression chip, is designed in this paper. Since the sounding rocket is not designed to be recovered, all image data should be transmitted to the ground station before the re-entry while the downlink band used for the image transmission is only about 600 kbps. Under the same condition of compression ratio compared with other algorithm, JPEG2000 standard algorithm can achieve better image quality. So JPEG2000 image compression is applied under this condition with a limited downlink data band. This embedded image system supports lossless to 200:1 real time compression, with two cameras to monitor nose ejection and motor separation, and two cameras to monitor boom deployment. The encoder, ADV7182, receives PAL signal from the camera, then output the ITU-R BT.656 signal to ADV212. ADV7182 switches between four input video channels as the program sequence. Two SRAMs are used for Ping-pong operation and one 512 Mb SDRAM for buffering high frame-rate images. The whole image system has the characteristics of low power dissipation, low cost, small size and high reliability, which is rather suitable for this sounding rocket application.

Keywords: ADV212, image system, JPEG2000, sounding rocket

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Authors: Xiaolei Hu, Enrico Ferrera, Riccardo Tomasi, Claudio Pastrone

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Load Forecasting plays a key role in making today's and future's Smart Energy Grids sustainable and reliable. Accurate power consumption prediction allows utilities to organize in advance their resources or to execute Demand Response strategies more effectively, which enables several features such as higher sustainability, better quality of service, and affordable electricity tariffs. It is easy yet effective to apply Load Forecasting at larger geographic scale, i.e. Smart Micro Grids, wherein the lower available grid flexibility makes accurate prediction more critical in Demand Response applications. This paper analyses the application of short-term load forecasting in a concrete scenario, proposed within the EU-funded GreenCom project, which collect load data from single loads and households belonging to a Smart Micro Grid. Three short-term load forecasting techniques, i.e. linear regression, artificial neural networks, and radial basis function network, are considered, compared, and evaluated through absolute forecast errors and training time. The influence of weather conditions in Load Forecasting is also evaluated. A new definition of Gain is introduced in this paper, which innovatively serves as an indicator of short-term prediction capabilities of time spam consistency. Two models, 24- and 1-hour-ahead forecasting, are built to comprehensively compare these three techniques.

Keywords: short-term load forecasting, smart micro grid, linear regression, artificial neural networks, radial basis function network, gain

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Authors: Antoni Ivanov, Nikolay Dandanov, Nicole Christoff, Vladimir Poulkov

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Spectrum underutilization has made cognitive radio a promising technology both for current and future telecommunications. This is due to the ability to exploit the unused spectrum in the bands dedicated to other wireless communication systems, and thus, increase their occupancy. The essential function, which allows the cognitive radio device to perceive the occupancy of the spectrum, is spectrum sensing. In this paper, the performance of modern adaptations of the four most widely used spectrum sensing techniques namely, energy detection (ED), cyclostationary feature detection (CSFD), matched filter (MF) and eigenvalues-based detection (EBD) is compared. The implementation has been accomplished through the PlutoSDR hardware platform and the GNU Radio software package in very low Signal-to-Noise Ratio (SNR) conditions. The optimal detection performance of the examined methods in a realistic implementation-oriented model is found for the common relevant parameters (number of observed samples, sensing time and required probability of false alarm).

Keywords: cognitive radio, dynamic spectrum access, GNU Radio, spectrum sensing

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Authors: M. Mansouri, P. Jaklitsch, E. Teiniker

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SQL injection on web applications is a very popular kind of attack. There are mechanisms such as intrusion detection systems in order to detect this attack. These strategies often rely on techniques implemented at high layers of the application but do not consider the low level of system calls. The problem of only considering the high level perspective is that an attacker can circumvent the detection tools using certain techniques such as URL encoding. One technique currently used for detecting low-level attacks on privileged processes is the tracing of system calls. System calls act as a single gate to the Operating System (OS) kernel; they allow catching the critical data at an appropriate level of detail. Our basic assumption is that any type of application, be it a system service, utility program or Web application, “speaks” the language of system calls when having a conversation with the OS kernel. At this level we can see the actual attack while it is happening. We conduct an experiment in order to demonstrate the suitability of system call analysis for detecting SQL injection. We are able to detect the attack. Therefore we conclude that system calls are not only powerful in detecting low-level attacks but that they also enable us to detect high-level attacks such as SQL injection.

Keywords: Linux system calls, web attack detection, interception, SQL

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Authors: M. V. Manoj Kumar, Likewin Thomas, Annappa

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Process mining provides methods and techniques for analyzing event logs recorded in modern information systems that support real-world operations. While analyzing an event-log, state-of-the-art techniques available in process mining believe that the operational process as a static entity (stationary). This is not often the case due to the possibility of occurrence of a phenomenon called concept drift. During the period of execution, the process can experience concept drift and can evolve with respect to any of its associated perspectives exhibiting various patterns-of-change with a different pace. Work presented in this paper discusses the main aspects to consider while addressing concept drift phenomenon and proposes a method for detecting and localizing the sudden concept drifts in control-flow perspective of the process by using features extracted by processing the traces in the process log. Our experimental results are promising in the direction of efficiently detecting and localizing concept drift in the context of process mining research discipline.

Keywords: abrupt drift, concept drift, sudden drift, control-flow perspective, detection and localization, process mining

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Authors: Evan Lowhorn, Rocio Alba-Flores

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The purpose of this work is to develop a method for classifying hand signals and using the output in a drone control algorithm. To achieve this, methods based on Convolutional Neural Networks (CNN) were applied. CNN's are a subset of deep learning, which allows grid-like inputs to be processed and passed through a neural network to be trained for classification. This type of neural network allows for classification via imaging, which is less intrusive than previous methods using biosensors, such as EMG sensors. Classification CNN's operate purely from the pixel values in an image; therefore they can be used without additional exteroceptive sensors. A development bench was constructed using a desktop computer connected to a high-definition webcam mounted on a scissor arm. This allowed the camera to be pointed downwards at the desk to provide a constant solid background for the dataset and a clear detection area for the user. A MATLAB script was created to automate dataset image capture at the development bench and save the images to the desktop. This allowed the user to create their own dataset of 12,000 images within three hours. These images were evenly distributed among seven classes. The defined classes include forward, backward, left, right, idle, and land. The drone has a popular flip function which was also included as an additional class. To simplify control, the corresponding hand signals chosen were the numerical hand signs for one through five for movements, a fist for land, and the universal “ok” sign for the flip command. Transfer learning with PyTorch (Python) was performed using a pre-trained 18-layer residual learning network (ResNet-18) to retrain the network for custom classification. An algorithm was created to interpret the classification and send encoded messages to a Ryze Tello drone over its 2.4 GHz Wi-Fi connection. The drone’s movements were performed in half-meter distance increments at a constant speed. When combined with the drone control algorithm, the classification performed as desired with negligible latency when compared to the delay in the drone’s movement commands.

Keywords: classification, computer vision, convolutional neural networks, drone control

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Authors: Hassan Qandil

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Using a statistical algorithm incorporated in MATLAB, four types of non-imaging Fresnel lenses are designed; spot-flat, linear-flat, dome-shaped and semi-cylindrical-shaped. The optimization employs a statistical ray-tracing methodology of the incident light, mainly considering effects of chromatic aberration, varying focal lengths, solar inclination and azimuth angles, lens and receiver apertures, and the optimum number of prism grooves. While adopting an equal-groove-width assumption of the Poly-methyl-methacrylate (PMMA) prisms, the main target is to maximize the ray intensity on the receiver’s aperture and therefore achieving higher values of heat flux. The algorithm outputs prism angles and 2D sketches. 3D drawings are then generated via AutoCAD and linked to COMSOL Multiphysics software to simulate the lenses under solar ray conditions, which provides optical and thermal analysis at both the lens’ and the receiver’s apertures while setting conditions as per the Dallas-TX weather data. Once the lenses’ characterization is finalized, receivers are designed based on its optimized aperture size. Several cavity shapes; including triangular, arc-shaped and trapezoidal, are tested while coupled with a variety of receiver materials, working fluids, heat transfer mechanisms, and enclosure designs. A vacuum-reflective enclosure is also simulated for an enhanced thermal absorption efficiency. Each receiver type is simulated via COMSOL while coupled with the optimized lens. A lab-scale prototype for the optimum lens-receiver configuration is then fabricated for experimental evaluation. Application-based testing is also performed for the selected configuration, including that of a photovoltaic-thermal cogeneration system and solar furnace system. Finally, some future research work is pointed out, including the coupling of the collector-receiver system with an end-user power generator, and the use of a multi-layered genetic algorithm for comparative studies.

Keywords: COMSOL, concentrator, energy, fresnel, optics, renewable, solar

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Authors: Mahesh K. Jat, Ankan Jana, Mahender Choudhary

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Rapid population and economic growth resulted in changes in large-scale land use land cover (LULC) changes. Changes in the biophysical properties of the Earth's surface and its impact on climate are of primary concern nowadays. Different approaches, ranging from location-based relationships or modelling earth surface - atmospheric interaction through modelling techniques like surface energy balance (SEB) are used in the recent past to examine the relationship between changes in Earth surface land cover and climatic characteristics like temperature and precipitation. A remote sensing-based model i.e., Surface Energy Balance Algorithm for Land (SEBAL), has been used to estimate the surface heat fluxes over Mahi Bajaj Sagar catchment (India) from 2001 to 2020. Landsat ETM and OLI satellite data are used to model the SEB of the area. Changes in observed precipitation and temperature, obtained from India Meteorological Department (IMD) have been correlated with changes in surface heat fluxes to understand the relative contributions of LULC change in changing these climatic variables. Results indicate a noticeable impact of LULC changes on climatic variables, which are aligned with respective changes in SEB components. Results suggest that precipitation increases at a rate of 20 mm/year. The maximum and minimum temperature decreases and increases at 0.007 ℃ /year and 0.02 ℃ /year, respectively. The average temperature increases at 0.009 ℃ /year. Changes in latent heat flux and sensible heat flux positively correlate with precipitation and temperature, respectively. Variation in surface heat fluxes influences the climate parameters and is an adequate reason for climate change. So, SEB modelling is helpful to understand the LULC change and its impact on climate.

Keywords: LULC, sensible heat flux, latent heat flux, SEBAL, landsat, precipitation, temperature

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Authors: Sai Hung Cheung, Zhe Shao

Abstract:

Recent earthquake-induced tsunamis in Padang, 2004 and Tohoku, 2011 brought huge losses of lives and properties. Maintaining vertical evacuation systems is the most crucial strategy to effectively reduce casualty during the tsunami event. Thus, it is of our great interest to quantify the risk to structural dynamic systems due to earthquake-induced tsunamis. Despite continuous advancement in computational simulation of the tsunami and wave-structure interaction modeling, it still remains computationally challenging to evaluate the reliability (or its complement failure probability) of a structural dynamic system when uncertainties related to the system and its modeling are taken into account. The failure of the structure in a tsunami-wave-structural system is defined as any response quantities of the system exceeding specified thresholds during the time when the structure is subjected to dynamic wave impact due to earthquake-induced tsunamis. In this paper, an approach based on a novel integration of the Subset Simulation algorithm and a recently proposed moving least squares response surface approach for stochastic sampling is proposed. The effectiveness of the proposed approach is discussed by comparing its results with those obtained from the Subset Simulation algorithm without using the response surface approach.

Keywords: response surface model, subset simulation, structural reliability, Tsunami risk

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Authors: Steven Woods

Abstract:

The use of Corporate Social Responsibility Statements has become ubiquitous in society. The appeal to consumers by being a well-behaved social entity has become a strategy not just to ensure brand loyalty but also to further larger scale projects of corporate interests. Specifically, the use of CSR to position corporations as good planetary citizens involves not just self-promotion but also a way of transferring responsibility from systems to individuals. By using techniques labeled as “greenwashing” and emphasizing ethical consumption choices as the solution, corporations present themselves as good members of the community and pursuing sustainability. Ultimately, the primary function of Corporate Social Responsibility statements is to maintain the economic status quo of ongoing growth and consumption while presenting and environmentally progressive image to the public, as well as reassuring them corporate behavior is superior to government intervention. By analyzing the communication techniques utilized through content analysis of specific examples, along with an analysis of the frames of meaning constructed in the CSR statements, the practices of Corporate Responsibility and Sustainability will be addressed from an ethical perspective.

Keywords: corporate social responsibility, ethics, greenwashing, sustainability

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Authors: Feras Hanandeh, Majdi Shannag

Abstract:

This research studies the different factors that could affect the Faculty of Information Technology in Hashemite University students’ accumulative average. The research paper verifies the student information, background, their academic records, and how this information will affect the student to get high grades. The student information used in the study is extracted from the student’s academic records. The data mining tools and techniques are used to decide which attribute(s) will affect the student’s accumulative average. The results show that the most important factor which affects the students’ accumulative average is the student Acceptance Type. And we built a decision tree model and rules to determine how the student can get high grades in their courses. The overall accuracy of the model is 44% which is accepted rate.

Keywords: data mining, classification, extracting rules, decision tree

Procedia PDF Downloads 416

Authors: Indri Mahadiraka Rumamby, R. R. Dwinanti Rika Marthanty, Jessica Sjah

Abstract:

Smoothed particle hydrodynamics (SPH) was originally created to simulate nonaxisymmetric phenomena in astrophysics. However, this method still has several shortcomings, namely the high computational cost required to model values with high resolution and problems with boundary conditions. The difficulty of modeling boundary conditions occurs because the SPH method is influenced by particle deficiency due to the integral of the kernel function being truncated by boundary conditions. This research aims to answer if SPH modeling with a focus on boundary layer interactions and continuous flow can produce quantifiably accurate values with low computational cost. This research will combine algorithms and coding in the main program of meandering river, continuous flow algorithm, and solid-fluid algorithm with the aim of obtaining quantitatively accurate results on solid-fluid interactions with the continuous flow on a meandering channel using the SPH method. This study uses the Fortran programming language for modeling the SPH (Smoothed Particle Hydrodynamics) numerical method; the model is conducted in the form of a U-shaped meandering open channel in 3D, where the channel walls are soil particles and uses a continuous flow with a limited number of particles.

Keywords: smoothed particle hydrodynamics, computational fluid dynamics, numerical simulation, fluid mechanics

Procedia PDF Downloads 132

Authors: T. Gayen

Abstract:

Although, there are several software reliability models existing today there does not exist any versatile model even today which can be used for the reliability assessment of software. Complex software has a large number of states (unlike the hardware) so it becomes practically difficult to completely test the software. Irrespective of the amount of testing one does, sometimes it becomes extremely difficult to assure that the final software product is fault free. The Black Box Software Reliability models are found be quite uncertain for the reliability assessment of various systems. As mission critical applications need to be highly reliable and since it is not always possible to ensure the development of highly reliable system. Hence, in order to achieve fault-free operation of software one develops some mechanism to handle faults remaining in the system even after the development. Although, several such techniques are currently in use to achieve fault tolerance, yet these mechanisms may not always be very suitable for various systems. Hence, this discussion is focused on analyzing the issues and challenges faced with the existing techniques for reliability assessment and fault tolerance of various software systems.

Keywords: black box, fault tolerance, failure, software reliability

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Authors: Ganesh B. Shinde, Vijaya B. Musande

Abstract:

Cotton crop identification based on the timely information has significant advantage to the different implications of food, economic and environment. Due to the significant advantages, the accurate detection of cotton crop regions using supervised learning procedure is challenging problem in remote sensing. Here, classifiers on the direct image are played a major role but the results are not much satisfactorily. In order to further improve the effectiveness, variety of vegetation indices are proposed in the literature. But, recently, the major challenge is to find the better vegetation indices for the cotton crop identification through the proposed methodology. Accordingly, fuzzy c-means clustering is combined with neural network algorithm, trained by Levenberg-Marquardt for cotton crop classification. To experiment the proposed method, five LISS-III satellite images was taken and the experimentation was done with six vegetation indices such as Simple Ratio, Normalized Difference Vegetation Index, Enhanced Vegetation Index, Green Atmospherically Resistant Vegetation Index, Wide-Dynamic Range Vegetation Index, Green Chlorophyll Index. Along with these indices, Green Leaf Area Index is also considered for investigation. From the research outcome, Green Atmospherically Resistant Vegetation Index outperformed with all other indices by reaching the average accuracy value of 95.21%.

Keywords: Fuzzy C-Means clustering (FCM), neural network, Levenberg-Marquardt (LM) algorithm, vegetation indices

Procedia PDF Downloads 318