Search results for: evolutionary algorithm

Authors: Maria E. Fortună, Elena Ungureanu, Răzvan Rotaru, Valeria Harabagiu

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Polymers are used in a variety of areas due to their unique mechanical and chemical properties. Natural polymers are biodegradable, whereas synthetic polymers are rarely biodegradable but can be modified. As a result, by combining the benefits of natural and synthetic polymers, composite materials that are biodegradable can be obtained with potential for biomedical and environmental applications. However, because of their strong resistance to degradation, it may be difficult to eliminate waste. As a result, interest in developing biodegradable polymers has risen significantly. This research involves obtaining and characterizing two biodegradable poly-ε-caprolactone-polydimethylsiloxane copolymers. A comparison study was conducted using an aminopropyl-terminated polydimethylsiloxane macroinitiator with two distinct molecular weights. The copolymers were obtained by ring-opening polymerization of poly (ɛ-caprolactone) in the presence of aminopropyl-terminated polydimethylsiloxane as initiator and comonomers and stannous 2-ethylhexanoate as a catalyst. The materials were characterized using a number of techniques, including NMR, FTIR, EDX, SEM, AFM, and DSC. Additionally, the water contact angle and water vapor sorption capacity were assessed. Furthermore, the copolymers were examined for environmental susceptibility by conducting biological tests on tomato plants (Lypercosium esculentum), with an accent on biological stability and metabolism. Subsequent to the copolymer's degradation, the dynamics of nitrogen experience evolutionary alterations, validating the progression of the process accompanied by the liberation of organic nitrogen. The biological tests performed (germination index, average seedling height, green and dry biomass) on Lypercosium esculentum, San Marzano variety tomato plants in direct contact with the copolymer indicated normal growth and development, suggesting a minimal toxic effect and, by extension, compatibility of the copolymer with the environment. The total chlorophyll concentration of plant leaves in contact with copolymers was determined, considering the pigment's critical role in photosynthesis and, implicitly, plant metabolism and physiological state.

Keywords: biodegradable, biological stability, copolymers, polydimethylsiloxane

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Authors: Yashas Bedre Raghavendra, Pim Vullers

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This study presents the design and implementation of an abstract cryptographic coprocessor, leveraging AMBA(Advanced Microcontroller Bus Architecture) protocols - APB (Advanced Peripheral Bus) and AHB (Advanced High-performance Bus), to enable seamless integration with the main CPU(Central processing unit) and enhance the coprocessor’s algorithm flexibility. The primary objective is to create a versatile coprocessor that can execute various cryptographic algorithms, including ECC(Elliptic-curve cryptography), RSA(Rivest–Shamir–Adleman), and AES (Advanced Encryption Standard) while providing a robust and secure solution for modern secure embedded systems. To achieve this goal, the coprocessor is equipped with a tightly coupled memory (TCM) for rapid data access during cryptographic operations. The TCM is placed within the coprocessor, ensuring quick retrieval of critical data and optimizing overall performance. Additionally, the program memory is positioned outside the coprocessor, allowing for easy updates and reconfiguration, which enhances adaptability to future algorithm implementations. Direct links are employed instead of DMA(Direct memory access) for data transfer, ensuring faster communication and reducing complexity. The AMBA-based communication architecture facilitates seamless interaction between the coprocessor and the main CPU, streamlining data flow and ensuring efficient utilization of system resources. The abstract nature of the coprocessor allows for easy integration of new cryptographic algorithms in the future. As the security landscape continues to evolve, the coprocessor can adapt and incorporate emerging algorithms, making it a future-proof solution for cryptographic processing. Furthermore, this study explores the addition of custom instructions into RISC-V ISE (Instruction Set Extension) to enhance cryptographic operations. By incorporating custom instructions specifically tailored for cryptographic algorithms, the coprocessor achieves higher efficiency and reduced cycles per instruction (CPI) compared to traditional instruction sets. The adoption of RISC-V 128-bit architecture significantly reduces the total number of instructions required for complex cryptographic tasks, leading to faster execution times and improved overall performance. Comparisons are made with 32-bit and 64-bit architectures, highlighting the advantages of the 128-bit architecture in terms of reduced instruction count and CPI. In conclusion, the abstract cryptographic coprocessor presented in this study offers significant advantages in terms of algorithm flexibility, security, and integration with the main CPU. By leveraging AMBA protocols and employing direct links for data transfer, the coprocessor achieves high-performance cryptographic operations without compromising system efficiency. With its TCM and external program memory, the coprocessor is capable of securely executing a wide range of cryptographic algorithms. This versatility and adaptability, coupled with the benefits of custom instructions and the 128-bit architecture, make it an invaluable asset for secure embedded systems, meeting the demands of modern cryptographic applications.

Keywords: abstract cryptographic coprocessor, AMBA protocols, ECC, RSA, AES, tightly coupled memory, secure embedded systems, RISC-V ISE, custom instructions, instruction count, cycles per instruction

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Authors: Mahtab Makaremi Masouleh, Günter Wozniak

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This research is part of an interdisciplinary project, promoting the design of a light temporary installable textile defence system against flood. In case river water levels increase abruptly especially in winter time, one can expect massive extra load on a textile protective structure in term of impact as a result of floating debris and even tree trunks. Estimation of this impulsive force on such structures is of a great importance, as it can ensure the reliability of the design in critical cases. This fact provides the motivation for the numerical analysis of a fluid structure interaction application, comprising flexible slender shaped and free-surface water flow, where an accelerated heavy flotsam tends to approach the membrane. In this context, the analysis on both the behavior of the flexible membrane and its interaction with moving flotsam is conducted by finite elements based solvers of the explicit solver and implicit Abacus solver available as products of SIMULIA software. On the other hand, a study on how free surface water flow behaves in response to moving structures, has been investigated using the finite volume solver of Star CCM+ from Siemens PLM Software. An automatic communication tool (CSE, SIMULIA Co-Simulation Engine) and the implementation of an effective partitioned strategy in form of an implicit coupling algorithm makes it possible for partitioned domains to be interconnected powerfully. The applied procedure ensures stability and convergence in the solution of these complicated issues, albeit with high computational cost; however, the other complexity of this study stems from mesh criterion in the fluid domain, where the two structures approach each other. This contribution presents the approaches for the establishment of a convergent numerical solution and compares the results with experimental findings.

Keywords: co-simulation, flexible thin structure, fluid-structure interaction, implicit coupling algorithm, moving flotsam

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Authors: Emhimed Saffor

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In this paper, the problem of edge detection in digital images is considered. Three methods of edge detection based on mathematical morphology algorithm were applied on two sets (Brain and Chest) CT images. 3x3 filter for first method, 5x5 filter for second method and 7x7 filter for third method under MATLAB programming environment. The results of the above-mentioned methods are subjectively evaluated. The results show these methods are more efficient and satiable for medical images, and they can be used for different other applications.

Keywords: CT images, Matlab, medical images, edge detection

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Authors: Mohammad Reza Esmaili

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One of the most important parts of the restoration process of a power network is the synchronizing of its subsystems. In this situation, the biggest concern of the system operators will be the reduction of the standing phase angle (SPA) between the endpoints of the two islands. In this regard, the system operators perform various actions and maneuvers so that the synchronization operation of the subsystems is successfully carried out and the system finally reaches acceptable stability. The most common of these actions include load control, generation control and, in some cases, changing the network topology. Although these maneuvers are simple and common, due to the weak network and extreme load changes, the restoration will be associated with low speed. One of the best ways to control the SPA is to use FACTS devices. By applying a soft control signal, these tools can reduce the SPA between two subsystems with more speed and accuracy, and the synchronization process can be done in less time. Meanwhile, the unified power controller (UPFC), a series-parallel compensator device with the change of transmission line power and proper adjustment of the phase angle, will be the proposed option in order to realize the subject of this research. Therefore, with the optimal placement of UPFC in a power system, in addition to improving the normal conditions of the system, it is expected to be effective in reducing the SPA during power system restoration. Therefore, the presented paper provides an optimal structure to coordinate the three problems of improving the division of subsystems, reducing the SPA and optimal power flow with the aim of determining the optimal location of UPFC and optimal subsystems. The proposed objective functions in this paper include maximizing the quality of the subsystems, reducing the SPA at the endpoints of the subsystems, and reducing the losses of the power system. Since there will be a possibility of creating contradictions in the simultaneous optimization of the proposed objective functions, the structure of the proposed optimization problem is introduced as a non-linear multi-objective problem, and the Pareto optimization method is used to solve it. The innovative technique proposed to implement the optimization process of the mentioned problem is an optimization algorithm called the water cycle (WCA). To evaluate the proposed method, the IEEE 39 bus power system will be used.

Keywords: UPFC, SPA, water cycle algorithm, multi-objective problem, pareto

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Authors: Giovanna Borges Bottino, Luis Felipe Freitas do Nascimento Alves Teixeira

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This article aims to present a case study of the National Civil Aviation Agency (ANAC) in Brazil, ANAC-id. ANAC-id is the artificial intelligence algorithm developed for image analysis that recognizes standard images of unobstructed and uprighted face without sunglasses, allowing to identify potential inconsistencies. It combines YOLO architecture and 3 libraries in python - face recognition, face comparison, and deep face, providing robust analysis with high level of accuracy.

Keywords: artificial intelligence, deepface, face compare, face recognition, YOLO, computer vision

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Authors: Insaf Ajili, Malik Mallem, Jean-Yves Didier

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Motion recognition from videos is actually a very complex task due to the high variability of motions. This paper describes the challenges of human motion recognition, especially motion representation step with relevant features. Our descriptor vector is inspired from Laban Movement Analysis method. We propose discriminative features using the Random Forest algorithm in order to remove redundant features and make learning algorithms operate faster and more effectively. We validate our method on MSRC-12 and UTKinect datasets.

Keywords: discriminative LMA features, features reduction, human motion recognition, random forest

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Authors: S. Delgado, C. Cerrada, R. S. Gómez

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This research introduces an approach to voxelizing the surfaces of triangular meshes with efficiency and accuracy. Our method leverages parallel equidistant scan-lines and introduces a Gap Detection technique to address the limitations of existing approaches. We present a comprehensive study showcasing the method's effectiveness, scalability, and versatility in different scenarios. Voxelization is a fundamental process in computer graphics and simulations, playing a pivotal role in applications ranging from scientific visualization to virtual reality. Our algorithm focuses on enhancing the voxelization process, especially for complex models and high resolutions. One of the major challenges in voxelization in the Graphics Processing Unit (GPU) is the high cost of discovering the same voxels multiple times. These repeated voxels incur in costly memory operations with no useful information. Our scan-line-based method ensures that each voxel is detected exactly once when processing the triangle, enhancing performance without compromising the quality of the voxelization. The heart of our approach lies in the use of parallel, equidistant scan-lines to traverse the interiors of triangles. This minimizes redundant memory operations and avoids revisiting the same voxels, resulting in a significant performance boost. Moreover, our method's computational efficiency is complemented by its simplicity and portability. Written as a single compute shader in Graphics Library Shader Language (GLSL), it is highly adaptable to various rendering pipelines and hardware configurations. To validate our method, we conducted extensive experiments on a diverse set of models from the Stanford repository. Our results demonstrate not only the algorithm's efficiency, but also its ability to produce 26 tunnel free accurate voxelizations. The Gap Detection technique successfully identifies and addresses gaps, ensuring consistent and visually pleasing voxelized surfaces. Furthermore, we introduce the Slope Consistency Value metric, quantifying the alignment of each triangle with its primary axis. This metric provides insights into the impact of triangle orientation on scan-line based voxelization methods. It also aids in understanding how the Gap Detection technique effectively improves results by targeting specific areas where simple scan-line-based methods might fail. Our research contributes to the field of voxelization by offering a robust and efficient approach that overcomes the limitations of existing methods. The Gap Detection technique fills a critical gap in the voxelization process. By addressing these gaps, our algorithm enhances the visual quality and accuracy of voxelized models, making it valuable for a wide range of applications. In conclusion, "Closing the Gap: Efficient Voxelization with Equidistant Scan-lines and Gap Detection" presents an effective solution to the challenges of voxelization. Our research combines computational efficiency, accuracy, and innovative techniques to elevate the quality of voxelized surfaces. With its adaptable nature and valuable innovations, this technique could have a positive influence on computer graphics and visualization.

Keywords: voxelization, GPU acceleration, computer graphics, compute shaders

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Authors: Wang Xingbo

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Computation of determinant in the form |I-X| is primary and fundamental because it can help to compute many other determinants. This article puts forward a time-reducible approach to compute determinant |I-X|. The approach is derived from the Newton’s identity and its time complexity is no more than that to compute the eigenvalues of the square matrix X. Mathematical deductions and numerical example are presented in detail for the approach. By comparison with classical approaches the new approach is proved to be superior to the classical ones and it can naturally reduce the computational time with the improvement of efficiency to compute eigenvalues of the square matrix.

Keywords: algorithm, determinant, computation, eigenvalue, time complexity

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Authors: Ben Soltane Cheima, Ittansa Yonas Kelbesa

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Speaker Identification (SI) is the task of establishing identity of an individual based on his/her voice characteristics. The SI task is typically achieved by two-stage signal processing: training and testing. The training process calculates speaker specific feature parameters from the speech and generates speaker models accordingly. In the testing phase, speech samples from unknown speakers are compared with the models and classified. Even though performance of speaker identification systems has improved due to recent advances in speech processing techniques, there is still need of improvement. In this paper, a Closed-Set Tex-Independent Speaker Identification System (CISI) based on a Multiple Classifier System (MCS) is proposed, using Mel Frequency Cepstrum Coefficient (MFCC) as feature extraction and suitable combination of vector quantization (VQ) and Gaussian Mixture Model (GMM) together with Expectation Maximization algorithm (EM) for speaker modeling. The use of Voice Activity Detector (VAD) with a hybrid approach based on Short Time Energy (STE) and Statistical Modeling of Background Noise in the pre-processing step of the feature extraction yields a better and more robust automatic speaker identification system. Also investigation of Linde-Buzo-Gray (LBG) clustering algorithm for initialization of GMM, for estimating the underlying parameters, in the EM step improved the convergence rate and systems performance. It also uses relative index as confidence measures in case of contradiction in identification process by GMM and VQ as well. Simulation results carried out on voxforge.org speech database using MATLAB highlight the efficacy of the proposed method compared to earlier work.

Keywords: feature extraction, speaker modeling, feature matching, Mel frequency cepstrum coefficient (MFCC), Gaussian mixture model (GMM), vector quantization (VQ), Linde-Buzo-Gray (LBG), expectation maximization (EM), pre-processing, voice activity detection (VAD), short time energy (STE), background noise statistical modeling, closed-set tex-independent speaker identification system (CISI)

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Authors: Mohammed Arif, Abdessalam Kifouche

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Optical character recognition (OCR) has a main role in the present time. It's capable to solve many serious problems and simplify human activities. The OCR yields to 70's, since many solutions has been proposed, but unfortunately, it was supportive to nothing but Latin languages. This work proposes a system of recognition of an off-line Arabic handwriting. This system is based on a structural segmentation method and uses support vector machines (SVM) in the classification phase. We have presented a state of art of the characters segmentation methods, after that a view of the OCR area, also we will address the normalization problems we went through. After a comparison between the Arabic handwritten characters & the segmentation methods, we had introduced a contribution through a segmentation algorithm.

Keywords: OCR, segmentation, Arabic characters, PAW, post-processing, SVM

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Authors: Amulya Srivatsa, Gayatri Prakash, Deeksha Sarda, Varshni Nandakumar, Duncan Salmon

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Delivery of Patient-Directed Wound Care Via Mobile Application-Based Qualitative Analysis Chronic wounds are difficult for patients to manage at-home due to their unpredictable healing process. These wounds are associated with increased morbidity and negatively affect physical and mental health. The solution is a mobile application that will have an algorithm-based checklist to determine the state of the wound based on different factors that vary from person to person. Once this information is gathered, the application will recommend a plan of care to the user and subsequent steps to be taken. The mobile application will allow users to perform a digital scan of the wound to extract quantitative information regarding wound width, length, and depth, which will then be uploaded to the EHR to notify the patient’s provider. This scan utilizes a photo taken by the user, who is prompted appropriately. Furthermore, users will enter demographic information and answer multiple choice and drop-down menus describing the wound state. The proposed solution can save patients from unnecessary trips to the hospital for chronic wound care. The next iteration of the application can incorporate AI to allow users to perform a digital scan of the wound to extract quantitative information regarding wound width, length, and depth, which can be shared with the patient’s provider to allow for more efficient treatment. Ultimately, this product can provide immediate and economical medical advice for patients that suffer from chronic wounds. Research Objectives: The application should be capable of qualitative analysis of a wound and recommend a plan of care to the user. Additionally, the results of the wound analysis should automatically upload to the patient’s EMR. Research Methodologies: The app has two components: the first is a checklist with tabs for varying factors that assists users in the assessment of their skin. Subsequently, the algorithm will create an at-home regimen for patients to follow to manage their wounds. Research Contributions: The app aims to return autonomy back to the patient and reduce the number of visits to a physician for chronic wound care. The app also serves to educate the patient on how best to care for their wounds.

Keywords: wound, app, qualitative, analysis, home, chronic

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Authors: Thomas Bryan, Veton Kepuska, Ivica Kostanic

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Speaker recognition is performed in high Additive White Gaussian Noise (AWGN) environments using principals of Computational Auditory Scene Analysis (CASA). CASA methods often classify sounds from images in the time-frequency (T-F) plane using spectrograms or cochleargrams as the image. In this paper atomic decomposition implemented by matching pursuit performs a transform from time series speech signals to the T-F plane. The atomic decomposition creates a sparsely populated T-F vector in “weight space” where each populated T-F position contains an amplitude weight. The weight space vector along with the atomic dictionary represents a denoised, compressed version of the original signal. The arraignment or of the atomic indices in the T-F vector are used for classification. Unsupervised feature learning implemented by a sparse autoencoder learns a single dictionary of basis features from a collection of envelope samples from all speakers. The approach is demonstrated using pairs of speakers from the TIMIT data set. Pairs of speakers are selected randomly from a single district. Each speak has 10 sentences. Two are used for training and 8 for testing. Atomic index probabilities are created for each training sentence and also for each test sentence. Classification is performed by finding the lowest Euclidean distance between then probabilities from the training sentences and the test sentences. Training is done at a 30dB Signal-to-Noise Ratio (SNR). Testing is performed at SNR’s of 0 dB, 5 dB, 10 dB and 30dB. The algorithm has a baseline classification accuracy of ~93% averaged over 10 pairs of speakers from the TIMIT data set. The baseline accuracy is attributable to short sequences of training and test data as well as the overall simplicity of the classification algorithm. The accuracy is not affected by AWGN and produces ~93% accuracy at 0dB SNR.

Keywords: time-frequency plane, atomic decomposition, envelope sampling, Gabor atoms, matching pursuit, sparse dictionary learning, sparse autoencoder

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Authors: Babesse Saad, Ameddah Djemeleddine

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In this paper, a learning algorithm using neuronal networks to improve the roll stability and prevent the rollover in a single unit heavy vehicle is proposed. First, LQR control to keep balanced normalized rollovers, between front and rear axles, below the unity, then a data collected from this controller is used as a training basis of a neuronal regulator. The ANN controller is thereafter applied for the nonlinear side force model, and gives satisfactory results than the LQR one.

Keywords: rollover, single unit heavy vehicle, neural networks, nonlinear side force

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Authors: Ahmed Machmoum, Malika El Kyal

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We consider iterative parallel multi-splitting method for differential algebraic equations. The main feature of the proposed idea is to use the asynchronous form. We prove that the multi-splitting technique can effectively accelerate the convergent performance of the iterative process. The main characteristic of an asynchronous mode is that the local algorithm not have to wait at predetermined messages to become available. We allow some processors to communicate more frequently than others, and we allow the communication delays tobe substantial and unpredictable. Note that synchronous algorithms in the computer science sense are particular cases of our formulation of asynchronous one.

Keywords: computer, multi-splitting methods, asynchronous mode, differential algebraic systems

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Authors: R. L. Ujjwal, Abhishek Jain

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In the field of computer vision, most facial variations such as identity, expression, emotions and gender have been extensively studied. Automatic age estimation has been rarely explored. With age progression of a human, the features of the face changes. This paper is providing a new comparable study of different type of algorithm to feature extraction [Hybrid features using HAAR cascade & HOG features] & classification [KNN & SVM] training dataset. By using these algorithms we are trying to find out one of the best classification algorithms. Same thing we have done on the feature selection part, we extract the feature by using HAAR cascade and HOG. This work will be done in context of age group classification model.

Keywords: computer vision, age group, face detection

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Authors: A. Giniatoulline

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A nonlinear model of the mathematical fluid dynamics which describes the motion of an incompressible viscous rotating fluid in a homogeneous gravitational field is considered. The model is a generalization of the known Navier-Stokes system with the addition of the Coriolis parameter and the equations for changeable density. An explicit algorithm for the solution is constructed, and the proof of the existence and uniqueness theorems for the strong solution of the nonlinear problem is given. For the linear case, the localization and the structure of the spectrum of inner waves are also investigated.

Keywords: Galerkin method, Navier-Stokes equations, nonlinear partial differential equations, Sobolev spaces, stratified fluid

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Authors: B. Selma

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The aim of this study is to test the feasibility of SIPINA method to predict the harmfulness parameters controlling the seismic response. The approach developed takes into consideration both the focal depth and the peak ground acceleration. The parameter to determine is displacement. The data used for the learning of this method and analysis nonlinear seismic are described and applied to a class of models damaged to some typical structures of the existing urban infrastructure of Jassy, Romania. The results obtained indicate an influence of the focal depth and the peak ground acceleration on the displacement.

Keywords: SIPINA algorithm, seism, focal depth, peak ground acceleration, displacement

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Authors: Fola John Adeyeye

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In this paper, we propose a cryptosystem private and public key base on symmetric group Pn and validates its theoretical formulation. This proposed system benefits from the algebraic properties of Pn such as noncommutative high logical, computational speed and high flexibility in selecting key which makes the discrete permutation multiplier logic (DPML) resist to attack by any algorithm such as Pohlig-Hellman. One of the advantages of this scheme is that it explore all the possible triangular symmetries. Against these properties, the only disadvantage is that the law of permutation multiplicity only allow an operation from left to right. Many other cryptosystems can be transformed into their symmetric group.

Keywords: cryptosystem, private and public key, DPML, symmetric group Pn

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Authors: Roghieh A. Biroon, Zoleikha Abdollahi

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The recent growth of renewables, e.g., solar panels, batteries, and electric vehicles (EVs) in residential and small commercial sectors, has potential impacts on the stability and operation of power grids. Considering approximately 50 percent share of the residential and the commercial sectors in the electricity demand market, the significance of these impacts, and the necessity of addressing them are more highlighted. Utilities and power system operators should manage the renewable electricity sources integration with power systems in such a way to extract the most possible advantages for the power systems. The most common effect of high penetration level of the renewables is the reverse power flow in the distribution feeders when the customers generate more power than their needs. The reverse power flow causes voltage rise and thermal issues in the power grids. To overcome the voltage rise issues in the distribution system, several techniques have been proposed including reducing transformers short circuit resistance and feeder impedance, installing autotransformers/voltage regulators along the line, absorbing the reactive power by distributed generators (DGs), and limiting the PV and battery sizes. In this study, we consider a medium-scale battery energy storage to manage the power energy and address the aforementioned issues on voltage deviation and power loss increase. We propose an optimization algorithm to find the optimum size and location for the battery. The optimization for the battery location and size is so that the battery maintains the feeder voltage deviation and power loss at a certain desired level. Moreover, the proposed optimization algorithm controls the charging/discharging profile of the battery to absorb the negative power flow from residential and commercial customers in the feeder during the peak time and sell the power back to the system during the off-peak time. The proposed battery regulates the voltage problem in the distribution system while it also can play frequency regulation role in islanded microgrids. This battery can be regulated and controlled by the utilities or a third-party ancillary service provider for the utilities to reduce the power system loss and regulate the distribution feeder voltage and frequency in standard level.

Keywords: ancillary services, battery, distribution system and optimization

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Authors: Mounir Bekaik, Messaoud Ramdani

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We present in this work a new technique of stabilization for fault nonlinear systems. The approach we adopt focus on a fuzzy Luenverger observer. The T-S approximation of the nonlinear observer is based on fuzzy C-Means clustering algorithm to find local linear subsystems. The MOESP identification approach was applied to design an empirical model describing the subsystems state variables. The gain of the observer is given by the minimization of the estimation error through Lyapunov-krasovskii functional and LMI approach. We consider a three tank hydraulic system for an illustrative example.

Keywords: nonlinear system, fuzzy, faults, TS, Lyapunov-Krasovskii, observer

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Authors: Zachary Huffman, Joana Rocha

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Wall-pressure fluctuations induced by the turbulent boundary layer (TBL) developed over aircraft are a significant source of aircraft cabin noise. Since the power spectral density (PSD) of these pressure fluctuations is directly correlated with the amount of sound radiated into the cabin, the development of accurate empirical models that predict the PSD has been an important ongoing research topic. The sound emitted can be represented from the pressure fluctuations term in the Reynoldsaveraged Navier-Stokes equations (RANS). Therefore, early TBL empirical models (including those from Lowson, Robertson, Chase, and Howe) were primarily derived by simplifying and solving the RANS for pressure fluctuation and adding appropriate scales. Most subsequent models (including Goody, Efimtsov, Laganelli, Smol’yakov, and Rackl and Weston models) were derived by making modifications to these early models or by physical principles. Overall, these models have had varying levels of accuracy, but, in general, they are most accurate under the specific Reynolds and Mach numbers they were developed for, while being less accurate under other flow conditions. Despite this, recent research into the possibility of using alternative methods for deriving the models has been rather limited. More recent studies have demonstrated that an artificial neural network model was more accurate than traditional models and could be applied more generally, but the accuracy of other machine learning techniques has not been explored. In the current study, an original model is derived using a stepwise regression algorithm in the statistical programming language R, and TBL wall-pressure fluctuations PSD data gathered at the Carleton University wind tunnel. The theoretical advantage of a stepwise regression approach is that it will automatically filter out redundant or uncorrelated input variables (through the process of feature selection), and it is computationally faster than machine learning. The main disadvantage is the potential risk of overfitting. The accuracy of the developed model is assessed by comparing it to independently sourced datasets.

Keywords: aircraft noise, machine learning, power spectral density models, regression models, turbulent boundary layer wall-pressure fluctuations

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Authors: Dawei Cai

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In this paper, we present an autonomous guidance service by combining the position information from NFC and the orientation information from a 6 axis acceleration and terrestrial magnetism sensor. We developed an algorithm to calculate the device orientation based on the data from acceleration and terrestrial magnetism sensor. If visitors want to know some explanation about an exhibit in front of him, what he has to do is just lift up his mobile device. The identification program will automatically identify the status based on the information from NFC and MEMS, and start playing explanation content for him. This service may be convenient for old people or disables or children.

Keywords: NFC, ubiquitous computing, guide sysem, MEMS

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Authors: Khaled Moh. Alhamad

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This paper describes a tabu search heuristic for a ship routing and scheduling problem (SRSP). The method was developed to address the problem of loading cargos for many customers using heterogeneous vessels. Constraints relate to delivery time windows imposed by customers, the time horizon by which all deliveries must be made and vessel capacities. The results of a computational investigation are presented. Solution quality and execution time are explored with respect to problem size and parameters controlling the tabu search such as tenure and neighbourhood size.

Keywords: heuristic, scheduling, tabu search, transportation

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Authors: Benjamin A. Ha, Marco Morselli, Xinhui Paige Zhang, Elizabeth A. C. Heath-Heckman, Jonathan B. Puritz, David K. Jacobs

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Next-generation sequencing has enhanced the ability to acquire massive amounts of sequence data to address classic population genetic questions for non-model organisms. Targeted approaches allow for cost effective or more precise analyses of relevant sequences; although, many such techniques require a known genome and it can be costly to purchase probes from a company. This is challenging for non-model organisms with no published genome and can be expensive for large population genetic studies. Expressed exome capture sequencing (EecSeq) synthesizes probes in the lab from expressed mRNA, which is used to capture and sequence the coding regions of genomic DNA from a pooled suite of samples. A normalization step produces probes to recover transcripts from a wide range of expression levels. This approach offers low cost recovery of a broad range of genes in the genome. This research project expands on EecSeq to investigate if mRNA from one taxon may be used to capture relevant sequences from a series of increasingly less closely related taxa. For this purpose, we propose to use the endangered Northern Tidewater goby, Eucyclogobius newberryi, a non-model organism that inhabits California coastal lagoons. mRNA will be extracted from E. newberryi to create probes and capture exomes from eight other taxa, including the more at-risk Southern Tidewater goby, E. kristinae, and more divergent species. Captured exomes will be sequenced, analyzed bioinformatically and phylogenetically, then compared to previously generated phylogenies across this group of gobies. This will provide an assessment of the utility of the technique in cross-species studies and for analyzing low genetic variation within species as is the case for E. kristinae. This method has potential applications to provide economical ways to expand population genetic and evolutionary biology studies for non-model organisms.

Keywords: coastal lagoons, endangered species, non-model organism, target capture method

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Authors: Huey-Kuo Chen

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Travel demand forecasting including four travel choices, i.e., trip generation, trip distribution, modal split and traffic assignment constructs the core of transportation planning. In its current application, travel demand forecasting has associated with three important issues, i.e., interface inconsistencies among four travel choices, inefficiency of commonly used solution algorithms, and undesirable multiple path solutions. In this paper, each of the three issues is extensively elaborated. An ideal unified framework for the combined model consisting of the four travel choices and variable demand functions is also suggested. Then, a few remarks are provided in the end of the paper.

Keywords: travel choices, B algorithm, entropy maximization, dynamic traffic assignment

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Authors: S. J. Bindhu, Edwina G. Rodrigues, Jijo Balakrishnan

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A grid-connected photovoltaic (PV) micro inverter with good performance properties is proposed in this paper. The proposed inverter with a quadrupler, having more efficiency and less voltage stress across the diodes. The stress that come across the diodes that use in the inverter section is considerably low in the proposed converter, also the protection scheme that we provided can eliminate the chances of the error due to fault. The proposed converter is implemented using perturb and observe algorithm so that the fluctuation in the voltage can be reduce and can attain maximum power point. Finally, some simulation and experimental results are also presented to demonstrate the effectiveness of the proposed converter.

Keywords: DC-DC converter, MPPT, quadrupler, PV panel

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Authors: G. V. Siva Krishna Rao, B. Srinivasa Rao

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The main aim of the paper is to implement a technique using distributed generation in distribution systems to reduce the distribution system losses and to improve voltage profiles. The fuzzy logic technique is used to select the proper location of DG and an analytical method is proposed to calculate the size of DG unit at any power factor. The optimal sizes of DG units are compared with optimal sizes obtained using the genetic algorithm. The suggested method is programmed under Matlab software and is tested on IEEE 33 bus system and the results are presented.

Keywords: DG Units, sizing of DG units, analytical methods, optimum size

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Authors: Carla Idely Palencia-Aguilar

Abstract:

Artificial wetlands play an important role at Guasca Municipality in Colombia, not only because they are used for the agroindustry, but also because more than 45 species were found, some of which are endemic and migratory birds. Remote sensing was used to determine the changes in the area occupied by water of artificial wetlands by means of Aster and Modis images for different time periods. Evapotranspiration was also determined by three methods: Surface Energy Balance System-Su (SEBS) algorithm, Surface Energy Balance- Bastiaanssen (SEBAL) algorithm, and Potential Evapotranspiration- FAO. Empirical equations were also developed to determine the relationship between Normalized Difference Vegetation Index (NDVI) versus net radiation, ambient temperature and rain with an obtained R2 of 0.83. Groundwater level fluctuations on a daily basis were studied as well. Data from a piezometer placed next to the wetland were fitted with rain changes (with two weather stations located at the proximities of the wetlands) by means of multiple regression and time series analysis, the R2 from the calculated and measured values resulted was higher than 0.98. Information from nearby weather stations provided information for ordinary kriging as well as the results for the Digital Elevation Model (DEM) developed by using PCI software. Standard models (exponential, spherical, circular, gaussian, linear) to describe spatial variation were tested. Ordinary Cokriging between height and rain variables were also tested, to determine if the accuracy of the interpolation would increase. The results showed no significant differences giving the fact that the mean result of the spherical function for the rain samples after ordinary kriging was 58.06 and a standard deviation of 18.06. The cokriging using for the variable rain, a spherical function; for height variable, the power function and for the cross variable (rain and height), the spherical function had a mean of 57.58 and a standard deviation of 18.36. Threatens of eutrophication were also studied, given the unconsciousness of neighbours and government deficiency. Water quality was determined over the years; different parameters were studied to determine the chemical characteristics of water. In addition, 600 pesticides were studied by gas and liquid chromatography. Results showed that coliforms, nitrogen, phosphorous and prochloraz were the most significant contaminants.

Keywords: DEM, evapotranspiration, geostatistics, NDVI

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Authors: Bilal Saoud

Abstract:

In this paper, we propose a new method to find the community structure in networks. Our method is based on bee colony and the maximization of modularity to find the community structure. We use a bee colony algorithm to find the first community structure that has a good value of modularity. To improve the community structure, that was found, we merge communities until we get a community structure that has a high value of modularity. We provide a general framework for implementing our approach. We tested our method on computer-generated and real-world networks with a comparison to very known community detection methods. The obtained results show the effectiveness of our proposition.

Keywords: bee colony, networks, modularity, normalized mutual information

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