Search results for: hybrid genetic algorithm.

Authors: Chih-Hao Chen, Hsing-Chung Lee, Qingdong Ling, Hsiao-Jung Chen, Sun-Chong Wang, Li-Ching Wu, H.C. Lee

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Segmentation, filtering out of measurement errors and identification of breakpoints are integral parts of any analysis of microarray data for the detection of copy number variation (CNV). Existing algorithms designed for these tasks have had some successes in the past, but they tend to be O(N2) in either computation time or memory requirement, or both, and the rapid advance of microarray resolution has practically rendered such algorithms useless. Here we propose an algorithm, SAD, that is much faster and much less thirsty for memory – O(N) in both computation time and memory requirement -- and offers higher accuracy. The two key ingredients of SAD are the fundamental assumption in statistics that measurement errors are normally distributed and the mathematical relation that the product of two Gaussians is another Gaussian (function). We have produced a computer program for analyzing CNV based on SAD. In addition to being fast and small it offers two important features: quantitative statistics for predictions and, with only two user-decided parameters, ease of use. Its speed shows little dependence on genomic profile. Running on an average modern computer, it completes CNV analyses for a 262 thousand-probe array in ~1 second and a 1.8 million-probe array in 9 seconds

Keywords: Cancer, pathogenesis, chromosomal aberration, copy number variation, segmentation analysis.

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Authors: Mohanad Alata , Moh'd Al-Nimr, Rami Al-Jarrah

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The present work demonstrates the design and simulation of a fuzzy control of an air conditioning system at different pressures. The first order Sugeno fuzzy inference system is utilized to model the system and create the controller. In addition, an estimation of the heat transfer rate and water mass flow rate injection into or withdraw from the air conditioning system is determined by the fuzzy IF-THEN rules. The approach starts by generating the input/output data. Then, the subtractive clustering algorithm along with least square estimation (LSE) generates the fuzzy rules that describe the relationship between input/output data. The fuzzy rules are tuned by Adaptive Neuro-Fuzzy Inference System (ANFIS). The results show that when the pressure increases the amount of water flow rate and heat transfer rate decrease within the lower ranges of inlet dry bulb temperatures. On the other hand, and as pressure increases the amount of water flow rate and heat transfer rate increases within the higher ranges of inlet dry bulb temperatures. The inflection in the pressure effect trend occurs at lower temperatures as the inlet air humidity increases.

Keywords: Air Conditioning, ANFIS, Fuzzy Control, Sugeno System.

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Authors: Youcef Bekakra, Djilani Ben attous

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This paper presents the study of a variable speed wind energy conversion system based on a Doubly Fed Induction Generator (DFIG) based on a sliding mode control applied to achieve control of active and reactive powers exchanged between the stator of the DFIG and the grid to ensure a Maximum Power Point Tracking (MPPT) of a wind energy conversion system. The proposed control algorithm is applied to a DFIG whose stator is directly connected to the grid and the rotor is connected to the PWM converter. To extract a maximum of power, the rotor side converter is controlled by using a stator flux-oriented strategy. The created decoupling control between active and reactive stator power allows keeping the power factor close to unity. Simulation results show that the wind turbine can operate at its optimum energy for a wide range of wind speed.

Keywords: Doubly fed induction generator, wind energy, wind turbine, sliding mode control, maximum power point tracking (MPPT).

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Authors: M. Govindarajan, R. M. Chandrasekaran

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Text Mining is around applying knowledge discovery techniques to unstructured text is termed knowledge discovery in text (KDT), or Text data mining or Text Mining. In Neural Network that address classification problems, training set, testing set, learning rate are considered as key tasks. That is collection of input/output patterns that are used to train the network and used to assess the network performance, set the rate of adjustments. This paper describes a proposed back propagation neural net classifier that performs cross validation for original Neural Network. In order to reduce the optimization of classification accuracy, training time. The feasibility the benefits of the proposed approach are demonstrated by means of five data sets like contact-lenses, cpu, weather symbolic, Weather, labor-nega-data. It is shown that , compared to exiting neural network, the training time is reduced by more than 10 times faster when the dataset is larger than CPU or the network has many hidden units while accuracy ('percent correct') was the same for all datasets but contact-lences, which is the only one with missing attributes. For contact-lences the accuracy with Proposed Neural Network was in average around 0.3 % less than with the original Neural Network. This algorithm is independent of specify data sets so that many ideas and solutions can be transferred to other classifier paradigms.

Keywords: Back propagation, classification accuracy, textmining, time complexity.

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Authors: Leonardo A. Ambrosio, Carlos. H. Silva Santos, Ivan E. L. Rodrigues, Ayumi K. de Campos, Leandro A. Machado

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In this work, we propose an algorithm developed under Python language for the modeling of ordinary scalar Bessel beams and their discrete superpositions and subsequent calculation of optical forces exerted over dielectric spherical particles. The mathematical formalism, based on the generalized Lorenz-Mie theory, is implemented in Python for its large number of free mathematical (as SciPy and NumPy), data visualization (Matplotlib and PyJamas) and multiprocessing libraries. We also propose an approach, provided by a synchronized Software as Service (SaaS) in cloud computing, to develop a user interface embedded on a mobile application, thus providing users with the necessary means to easily introduce desired unknowns and parameters and see the graphical outcomes of the simulations right at their mobile devices. Initially proposed as a free Android-based application, such an App enables data post-processing in cloud-based architectures and visualization of results, figures and numerical tables.

Keywords: Bessel Beams and Frozen Waves, Generalized Lorenz-Mie Theory, Numerical Methods, Optical Forces.

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Authors: K. Kouzi

Abstract:

In order to have stable and high performance of direct torque and flux control (DTFC) of double star induction motor drive (DSIM), proper on-line adaptation of the stator resistance is very important. This is inevitably due to the variation of the stator resistance during operating conditions, which introduces error in estimated flux position and the magnitude of the stator flux. Error in the estimated stator flux deteriorates the performance of the DTFC drive. Also, the effect of error in estimation is very important especially at low speed. Due to this, our aim is to overcome the sensitivity of the DTFC to the stator resistance variation by proposing on-line fuzzy estimation stator resistance. The fuzzy estimation method is based on an on-line stator resistance correction through the variations of the stator current estimation error and its variations. The fuzzy logic controller gives the future stator resistance increment at the output. The main advantage of the suggested algorithm control is to avoid the drive instability that may occur in certain situations and ensure the tracking of the actual stator resistance. The validity of the technique and the improvement of the whole system performance are proved by the results.

Keywords: Direct torque control, dual stator induction motor, fuzzy logic estimation, stator resistance adaptation.

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Authors: Herlandson C. Moura, Jorge H. Bidinotto, Eduardo M. Belo

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The present work proposes the development of an adaptive control system which enables the suppression of Pilot Induced Oscillations (PIO) in Digital Fly-By-Wire (DFBW) aircrafts. The proposed system consists of a Modified Model Reference Adaptive Control (M-MRAC) integrated with the Gain Scheduling technique. The PIO oscillations are detected using a Real Time Oscillation Verifier (ROVER) algorithm, which then enables the system to switch between two reference models; one in PIO condition, with low proneness to the phenomenon and another one in normal condition, with high (or medium) proneness. The reference models are defined in a closed loop condition using the Linear Quadratic Regulator (LQR) control methodology for Multiple-Input-Multiple-Output (MIMO) systems. The implemented algorithms are simulated in software implementations with state space models and commercial flight simulators as the controlled elements and with pilot dynamics models. A sequence of pitch angles is considered as the reference signal, named as Synthetic Task (Syntask), which must be tracked by the pilot models. The initial outcomes show that the proposed system can detect and suppress (or mitigate) the PIO oscillations in real time before it reaches high amplitudes.

Keywords: Adaptive control, digital fly-by-wire, oscillations suppression, PIO.

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Authors: Sidhartha Panda, N. P. Padhy

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This paper investigates the application of Particle Swarm Optimization (PSO) technique for coordinated design of a Power System Stabilizer (PSS) and a Thyristor Controlled Series Compensator (TCSC)-based controller to enhance the power system stability. The design problem of PSS and TCSC-based controllers is formulated as a time domain based optimization problem. PSO algorithm is employed to search for optimal controller parameters. By minimizing the time-domain based objective function, in which the deviation in the oscillatory rotor speed of the generator is involved; stability performance of the system is improved. To compare the capability of PSS and TCSC-based controller, both are designed independently first and then in a coordinated manner for individual and coordinated application. The proposed controllers are tested on a weakly connected power system. The eigenvalue analysis and non-linear simulation results are presented to show the effectiveness of the coordinated design approach over individual design. The simulation results show that the proposed controllers are effective in damping low frequency oscillations resulting from various small disturbances like change in mechanical power input and reference voltage setting.

Keywords: Particle swarm optimization, Phillips-Heffron model, power system stability, PSS, TCSC.

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Authors: Shengbiao Wu, Weihua Cao, Min Wu, Can Liu

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This paper presents an intelligent tuning method of microwave filter based on complex neural network and improved space mapping. The tuning process consists of two stages: the initial tuning and the fine tuning. At the beginning of the tuning, the return loss of the filter is transferred to the passband via the error of phase. During the fine tuning, the phase shift caused by the transmission line and the higher order mode is removed by the curve fitting. Then, an Cauchy method based on the admittance parameter (Y-parameter) is used to extract the coupling matrix. The influence of the resonant cavity loss is eliminated during the parameter extraction process. By using processed data pairs (the amount of screw variation and the variation of the coupling matrix), a tuning model is established by the complex neural network. In view of the improved space mapping algorithm, the mapping relationship between the actual model and the ideal model is established, and the amplitude and direction of the tuning is constantly updated. Finally, the tuning experiment of the eight order coaxial cavity filter shows that the proposed method has a good effect in tuning time and tuning precision.

Keywords: Microwave filter, scattering parameter (s-parameter), coupling matrix, intelligent tuning.

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Authors: Mochammad Dewo, Sumarsono Sudarto

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The Exponential Triple Smoothing Algorithm approach nowadays, which is used to anticipate the export value of Indonesia's two major commodities, coal and palm oil, has a Mean Percentage Absolute Error (MAPE) value of 30-50%, which may be considered as a "reasonable" forecasting mistake. Forecasting errors of more than 30% shall have a domino effect on industrial output, as extra production adds to raw material, manufacturing and storage expenses. Whereas, reaching an "excellent" classification with an error value of less than 10% will provide new investors and exporters with confidence in the commercial development of related sectors. Industrial growth will bring out a positive impact on economic development. It can be applied for other commodities if the forecast error is less than 10%. The purpose of this project is to create a forecasting technique that can produce precise forecasting results with an error of less than 10%. This research analyzes forecasting methods such as ARIMA (Autoregressive Integrated Moving Average), ANN (Artificial Neural Network) and LSTM (Long-Short Term Memory). By providing a MAPE of 1%, this study reveals that ANN is the most successful strategy for forecasting coal and palm oil commodities in Indonesia.

Keywords: ANN, Artificial Neural Network, ARIMA, Autoregressive Integrated Moving Average, export value, forecast, LSTM, Long Short Term Memory.

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Authors: Partha Sarathi Majee, S. Bhattacharyya

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Migration of a core-shell soft particle under the influence of an external electric field in an electrolyte solution is studied numerically. The soft particle is coated with a positively charged polyelectrolyte layer (PEL) and the rigid core is having a uniform surface charge density. The Darcy-Brinkman extended Navier-Stokes equations are solved for the motion of the ionized fluid, the non-linear Nernst-Planck equations for the ion transport and the Poisson equation for the electric potential. A pressure correction based iterative algorithm is adopted for numerical computations. The effects of convection on double layer polarization (DLP) and diffusion dominated counter ions penetration are investigated for a wide range of Debye layer thickness, PEL fixed surface charge density, and permeability of the PEL. Our results show that when the Debye layer is in order of the particle size, the DLP effect is significant and produces a reduction in electrophoretic mobility. However, the double layer polarization effect is negligible for a thin Debye layer or low permeable cases. The point of zero mobility and the existence of mobility reversal depending on the electrolyte concentration are also presented.

Keywords: Debye length, double layer polarization, electrophoresis, mobility reversal, soft particle.

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Authors: Endri Rama, Genci Capi, Shigenori Kawahara

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The mobile robot ability to navigate autonomously in its environment is very important. Even though the advances in technology, robot self-localization and goal directed navigation in complex environments are still challenging tasks. In this article, we propose a novel method for robot navigation based on rat’s brain signals (Local Field Potentials). It has been well known that rats accurately and rapidly navigate in a complex space by localizing themselves in reference to the surrounding environmental cues. As the first step to incorporate the rat’s navigation strategy into the robot control, we analyzed the rats’ strategies while it navigates in a multiple Y-maze, and recorded Local Field Potentials (LFPs) simultaneously from three brain regions. Next, we processed the LFPs, and the extracted features were used as an input in the artificial neural network to predict the rat’s next location, especially in the decision-making moment, in Y-junctions. We developed an algorithm by which the robot learned to imitate the rat’s decision-making by mapping the rat’s brain signals into its own actions. Finally, the robot learned to integrate the internal states as well as external sensors in order to localize and navigate in the complex environment.

Keywords: Brain machine interface, decision-making, local field potentials, mobile robot, navigation, neural network, rat, signal processing.

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Authors: Mahtab Davari, Charles Edward Okon, Somayeh Aghanavesi

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Every institute of learning is usually interested in the performance of enrolled students. The level of these performances determines the approach an institute of study may adopt in rendering academic services. The focus of this paper is to evaluate students' academic performance in given courses of study using machine learning methods. This study evaluated various supervised machine learning classification algorithms such as Logistic Regression (LR), Support Vector Machine (SVM), Random Forest, Decision Tree, K-Nearest Neighbors, Linear Discriminant Analysis (LDA), and Quadratic Discriminant Analysis, using selected features to predict study performance. The accuracy, precision, recall, and F1 score obtained from a 5-Fold Cross-Validation were used to determine the best classification algorithm to predict students’ performances. SVM (using a linear kernel), LDA, and LR were identified as the best-performing machine learning methods. Also, using the LR model, this study identified students' educational habits such as reading and paying attention in class as strong determinants for a student to have an above-average performance. Other important features include the academic history of the student and work. Demographic factors such as age, gender, high school graduation, etc., had no significant effect on a student's performance.

Keywords: Student performance, supervised machine learning, prediction, classification, cross-validation.

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Authors: P. Priakanth, P. Thangaraj

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Due to the complex network architecture, the mobile adhoc network-s multihop feature gives additional problems to the users. When the traffic load at each node gets increased, the additional contention due its traffic pattern might cause the nodes which are close to destination to starve the nodes more away from the destination and also the capacity of network is unable to satisfy the total user-s demand which results in an unfairness problem. In this paper, we propose to create an algorithm to compute the optimal MAC-layer bandwidth assigned to each flow in the network. The bottleneck links contention area determines the fair time share which is necessary to calculate the maximum allowed transmission rate used by each flow. To completely utilize the network resources, we compute two optimal rates namely, the maximum fair share and minimum fair share. We use the maximum fair share achieved in order to limit the input rate of those flows which crosses the bottleneck links contention area when the flows that are not allocated to the optimal transmission rate and calculate the following highest fair share. Through simulation results, we show that the proposed protocol achieves improved fair share and throughput with reduced delay.

Keywords: MAC-layer, MANETs, Multihop, optimal rate, Transmission.

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Authors: Faith-Michael E. Uzoka, Boluwaji A. Akinnuwesi, Taiwo Amoo, Flora Aladi, Stephen Fashoto, Moses Olaniyan, Joseph Osuji

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The overarching aim of this study is to develop a soft-computing system for the differential diagnosis of tropical diseases. These conditions are of concern to health bodies, physicians, and the community at large because of their mortality rates, and difficulties in early diagnosis due to the fact that they present with symptoms that overlap, and thus become ‘confusable’. We report on the first phase of our study, which focuses on the development of a fuzzy cognitive map model for early differential diagnosis of tropical diseases. We used malaria as a case disease to show the effectiveness of the FCM technology as an aid to the medical practitioner in the diagnosis of tropical diseases. Our model takes cognizance of manifested symptoms and other non-clinical factors that could contribute to symptoms manifestations. Our model showed 85% accuracy in diagnosis, as against the physicians’ initial hypothesis, which stood at 55% accuracy. It is expected that the next stage of our study will provide a multi-disease, multi-symptom model that also improves efficiency by utilizing a decision support filter that works on an algorithm, which mimics the physician’s diagnosis process.

Keywords: Medical diagnosis, tropical diseases, fuzzy cognitive map, decision support filters, malaria differential diagnosis.

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Authors: Hsin-Chang Yang, Chung-Hong Lee, Kuo-Lung Ke

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The self-organizing map (SOM) model is a well-known neural network model with wide spread of applications. The main characteristics of SOM are two-fold, namely dimension reduction and topology preservation. Using SOM, a high-dimensional data space will be mapped to some low-dimensional space. Meanwhile, the topological relations among data will be preserved. With such characteristics, the SOM was usually applied on data clustering and visualization tasks. However, the SOM has main disadvantage of the need to know the number and structure of neurons prior to training, which are difficult to be determined. Several schemes have been proposed to tackle such deficiency. Examples are growing/expandable SOM, hierarchical SOM, and growing hierarchical SOM. These schemes could dynamically expand the map, even generate hierarchical maps, during training. Encouraging results were reported. Basically, these schemes adapt the size and structure of the map according to the distribution of training data. That is, they are data-driven or dataoriented SOM schemes. In this work, a topic-oriented SOM scheme which is suitable for document clustering and organization will be developed. The proposed SOM will automatically adapt the number as well as the structure of the map according to identified topics. Unlike other data-oriented SOMs, our approach expands the map and generates the hierarchies both according to the topics and their characteristics of the neurons. The preliminary experiments give promising result and demonstrate the plausibility of the method.

Keywords: Self-organizing map, topic identification, learning algorithm, text clustering.

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Authors: R. Kamali, A. R. Binesh, S. Hossainpour

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To understand working features of a micro combustor, a computer code has been developed to study combustion of hydrogen–air mixture in a series of chambers with same shape aspect ratio but various dimensions from millimeter to micrometer level. The prepared algorithm and the computer code are capable of modeling mixture effects in different fluid flows including chemical reactions, viscous and mass diffusion effects. The effect of various heat transfer conditions at chamber wall, e.g. adiabatic wall, with heat loss and heat conduction within the wall, on the combustion is analyzed. These thermal conditions have strong effects on the combustion especially when the chamber dimension goes smaller and the ratio of surface area to volume becomes larger. Both factors, such as larger heat loss through the chamber wall and smaller chamber dimension size, may lead to the thermal quenching of micro-scale combustion. Through such systematic numerical analysis, a proper operation space for the micro-combustor is suggested, which may be used as the guideline for microcombustor design. In addition, the results reported in this paper illustrate that the numerical simulation can be one of the most powerful and beneficial tools for the micro-combustor design, optimization and performance analysis.

Keywords: Numerical simulation, Micro-combustion, MEMS, CFD, Chemical reaction.

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Authors: Erika Pigliapoco, Valerio Freschi, Alessandro Bogliolo

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This work presents a new phonetic transcription system based on a tree of hierarchical pronunciation rules expressed as context-specific grapheme-phoneme correspondences. The tree is automatically inferred from a phonetic dictionary by incrementally analyzing deeper context levels, eventually representing a minimum set of exhaustive rules that pronounce without errors all the words in the training dictionary and that can be applied to out-of-vocabulary words. The proposed approach improves upon existing rule-tree-based techniques in that it makes use of graphemes, rather than letters, as elementary orthographic units. A new linear algorithm for the segmentation of a word in graphemes is introduced to enable outof- vocabulary grapheme-based phonetic transcription. Exhaustive rule trees provide a canonical representation of the pronunciation rules of a language that can be used not only to pronounce out-of-vocabulary words, but also to analyze and compare the pronunciation rules inferred from different dictionaries. The proposed approach has been implemented in C and tested on Oxford British English and Basic English. Experimental results show that grapheme-based rule trees represent phonetically sound rules and provide better performance than letter-based rule trees.

Keywords: Automatic phonetic transcription, pronunciation rules, hierarchical tree inference.

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Authors: Farhad Kolahan, A. Hamid Khajavi

Abstract:

Abrasive waterjet is a novel machining process capable of processing wide range of hard-to-machine materials. This research addresses modeling and optimization of the process parameters for this machining technique. To model the process a set of experimental data has been used to evaluate the effects of various parameter settings in cutting 6063-T6 aluminum alloy. The process variables considered here include nozzle diameter, jet traverse rate, jet pressure and abrasive flow rate. Depth of cut, as one of the most important output characteristics, has been evaluated based on different parameter settings. The Taguchi method and regression modeling are used in order to establish the relationships between input and output parameters. The adequacy of the model is evaluated using analysis of variance (ANOVA) technique. The pairwise effects of process parameters settings on process response outputs are also shown graphically. The proposed model is then embedded into a Simulated Annealing algorithm to optimize the process parameters. The optimization is carried out for any desired values of depth of cut. The objective is to determine proper levels of process parameters in order to obtain a certain level of depth of cut. Computational results demonstrate that the proposed solution procedure is quite effective in solving such multi-variable problems.

Keywords: AWJ cutting, Mathematical modeling, Simulated Annealing, Optimization

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Authors: Yun-Tao Zhang, Jong-Yeop Bae, Whoi-Yul Kim

Abstract:

Background modeling and subtraction in video analysis has been widely used as an effective method for moving objects detection in many computer vision applications. Recently, a large number of approaches have been developed to tackle different types of challenges in this field. However, the dynamic background and illumination variations are the most frequently occurred problems in the practical situation. This paper presents a favorable two-layer model based on codebook algorithm incorporated with local binary pattern (LBP) texture measure, targeted for handling dynamic background and illumination variation problems. More specifically, the first layer is designed by block-based codebook combining with LBP histogram and mean value of each RGB color channel. Because of the invariance of the LBP features with respect to monotonic gray-scale changes, this layer can produce block wise detection results with considerable tolerance of illumination variations. The pixel-based codebook is employed to reinforce the precision from the output of the first layer which is to eliminate false positives further. As a result, the proposed approach can greatly promote the accuracy under the circumstances of dynamic background and illumination changes. Experimental results on several popular background subtraction datasets demonstrate very competitive performance compared to previous models.

Keywords: Background subtraction, codebook model, local binary pattern, dynamic background, illumination changes.

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Authors: R.V Pawar, P.P.Kajave, S.N.Mali

Abstract:

The speech signal conveys information about the identity of the speaker. The area of speaker identification is concerned with extracting the identity of the person speaking the utterance. As speech interaction with computers becomes more pervasive in activities such as the telephone, financial transactions and information retrieval from speech databases, the utility of automatically identifying a speaker is based solely on vocal characteristic. This paper emphasizes on text dependent speaker identification, which deals with detecting a particular speaker from a known population. The system prompts the user to provide speech utterance. System identifies the user by comparing the codebook of speech utterance with those of the stored in the database and lists, which contain the most likely speakers, could have given that speech utterance. The speech signal is recorded for N speakers further the features are extracted. Feature extraction is done by means of LPC coefficients, calculating AMDF, and DFT. The neural network is trained by applying these features as input parameters. The features are stored in templates for further comparison. The features for the speaker who has to be identified are extracted and compared with the stored templates using Back Propogation Algorithm. Here, the trained network corresponds to the output; the input is the extracted features of the speaker to be identified. The network does the weight adjustment and the best match is found to identify the speaker. The number of epochs required to get the target decides the network performance.

Keywords: Average Mean Distance function, Backpropogation, Linear Predictive Coding, MultilayeredPerceptron,

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Authors: Faizal A. Samman, Thomas Hollstein, Manfred Glesner

Abstract:

A synchronous network-on-chip using wormhole packet switching and supporting guaranteed-completion best-effort with low-priority (LP) and high-priority (HP) wormhole packet delivery service is presented in this paper. Both our proposed LP and HP message services deliver a good quality of service in term of lossless packet completion and in-order message data delivery. However, the LP message service does not guarantee minimal completion bound. The HP packets will absolutely use 100% bandwidth of their reserved links if the HP packets are injected from the source node with maximum injection. Hence, the service are suitable for small size messages (less than hundred bytes). Otherwise the other HP and LP messages, which require also the links, will experience relatively high latency depending on the size of the HP message. The LP packets are routed using a minimal adaptive routing, while the HP packets are routed using a non-minimal adaptive routing algorithm. Therefore, an additional 3-bit field, identifying the packet type, is introduced in their packet headers to classify and to determine the type of service committed to the packet. Our NoC prototypes have been also synthesized using a 180-nm CMOS standard-cell technology to evaluate the cost of implementing the combination of both services.

Keywords: Network-on-Chip, Parallel Pipeline Router Architecture, Wormhole Switching, Two-Level Priority Service.

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Authors: Talal Alshammari, Nasser Alshammari, Mohamed Sedky, Chris Howard

Abstract:

With the widespread adoption of the Internet-connected devices, and with the prevalence of the Internet of Things (IoT) applications, there is an increased interest in machine learning techniques that can provide useful and interesting services in the smart home domain. The areas that machine learning techniques can help advance are varied and ever-evolving. Classifying smart home inhabitants’ Activities of Daily Living (ADLs), is one prominent example. The ability of machine learning technique to find meaningful spatio-temporal relations of high-dimensional data is an important requirement as well. This paper presents a comparative evaluation of state-of-the-art machine learning techniques to classify ADLs in the smart home domain. Forty-two synthetic datasets and two real-world datasets with multiple inhabitants are used to evaluate and compare the performance of the identified machine learning techniques. Our results show significant performance differences between the evaluated techniques. Such as AdaBoost, Cortical Learning Algorithm (CLA), Decision Trees, Hidden Markov Model (HMM), Multi-layer Perceptron (MLP), Structured Perceptron and Support Vector Machines (SVM). Overall, neural network based techniques have shown superiority over the other tested techniques.

Keywords: Activities of daily living, classification, internet of things, machine learning, smart home.

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Authors: Cherry Dhiman, Ayushi Paliwal, Mohd. Shahid Khan, M. N. Reddy, Vinay Gupta, Monika Tomar

Abstract:

The laser based high resolution spectroscopic experimental techniques such as Laser Induced Breakdown Spectroscopy (LIBS), Rotating Disk Electrode Optical Emission spectroscopy (RDE-OES) and Surface Plasmon Resonance (SPR) have been used for the study of composition and degradation analysis of used engine oils. Engine oils are mainly composed of aliphatic and aromatics compounds and its soot contains hazardous components in the form of fine, coarse and ultrafine particles consisting of wear metal elements. Such coarse particulates matter (PM) and toxic elements are extremely dangerous for human health that can cause respiratory and genetic disorder in humans. The combustible soot from thermal power plants, industry, aircrafts, ships and vehicles can lead to the environmental and climate destabilization. It contributes towards global pollution for land, water, air and global warming for environment. The detection of such toxicants in the form of elemental analysis is a very serious issue for the waste material management of various organic, inorganic hydrocarbons and radioactive waste elements. In view of such important points, the current study on used engine oils was performed. The fundamental characterization of engine oils was conducted by measuring water content and kinematic viscosity test that proves the crude analysis of the degradation of used engine oils samples. The microscopic quantitative and qualitative analysis was presented by RDE-OES technique which confirms the presence of elemental impurities of Pb, Al, Cu, Si, Fe, Cr, Na and Ba lines for used waste engine oil samples in few ppm. The presence of such elemental impurities was confirmed by LIBS spectral analysis at various transition levels of atomic line. The recorded transition line of Pb confirms the maximum degradation which was found in used engine oil sample no. 3 and 4. Apart from the basic tests, the calculations for dielectric constants and refractive index of the engine oils were performed via SPR analysis.

Keywords: Laser induced breakdown spectroscopy, rotating disk electrode optical emission spectroscopy, surface plasmon resonance, ICCD spectrometer, Nd:YAG laser, engine oil.

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Authors: Brennan Lodge

Abstract:

Amidst a complex regulatory landscape, Retrieval Augmented Generation (RAG) emerges as a transformative tool for Governance Risk and Compliance (GRC) officers. This paper details the application of RAG in synthesizing Large Language Models (LLMs) with external knowledge bases, offering GRC professionals an advanced means to adapt to rapid changes in compliance requirements. While the development for standalone LLMs is exciting, such models do have their downsides. LLMs cannot easily expand or revise their memory, and they cannot straightforwardly provide insight into their predictions, and may produce “hallucinations.” Leveraging a pre-trained seq2seq transformer and a dense vector index of domain-specific data, this approach integrates real-time data retrieval into the generative process, enabling gap analysis and the dynamic generation of compliance and risk management content. We delve into the mechanics of RAG, focusing on its dual structure that pairs parametric knowledge contained within the transformer model with non-parametric data extracted from an updatable corpus. This hybrid model enhances decision-making through context-rich insights, drawing from the most current and relevant information, thereby enabling GRC officers to maintain a proactive compliance stance. Our methodology aligns with the latest advances in neural network fine-tuning, providing a granular, token-level application of retrieved information to inform and generate compliance narratives. By employing RAG, we exhibit a scalable solution that can adapt to novel regulatory challenges and cybersecurity threats, offering GRC officers a robust, predictive tool that augments their expertise. The granular application of RAG’s dual structure not only improves compliance and risk management protocols but also informs the development of compliance narratives with pinpoint accuracy. It underscores AI’s emerging role in strategic risk mitigation and proactive policy formation, positioning GRC officers to anticipate and navigate the complexities of regulatory evolution confidently.

Keywords: Retrieval Augmented Generation, Governance Risk and Compliance, Cybersecurity, AI-driven Compliance, Risk Management, Generative AI.

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Authors: Cameron Hamilton, Walter Potter, Gerrit Hoogenboom, Ronald McClendon, Will Hobbs

Abstract:

A model was constructed to predict the amount of solar radiation that will make contact with the surface of the earth in a given location an hour into the future. This project was supported by the Southern Company to determine at what specific times during a given day of the year solar panels could be relied upon to produce energy in sufficient quantities. Due to their ability as universal function approximators, an artificial neural network was used to estimate the nonlinear pattern of solar radiation, which utilized measurements of weather conditions collected at the Griffin, Georgia weather station as inputs. A number of network configurations and training strategies were utilized, though a multilayer perceptron with a variety of hidden nodes trained with the resilient propagation algorithm consistently yielded the most accurate predictions. In addition, a modeled direct normal irradiance field and adjacent weather station data were used to bolster prediction accuracy. In later trials, the solar radiation field was preprocessed with a discrete wavelet transform with the aim of removing noise from the measurements. The current model provides predictions of solar radiation with a mean square error of 0.0042, though ongoing efforts are being made to further improve the model’s accuracy.

Keywords: Artificial Neural Networks, Resilient Propagation, Solar Radiation, Time Series Forecasting.

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Authors: J.V.R.Ravindra, M.B.Srinivas,

Abstract:

Accurate modeling of high speed RLC interconnects has become a necessity to address signal integrity issues in current VLSI design. To accurately model a dispersive system of interconnects at higher frequencies; a full-wave analysis is required. However, conventional circuit simulation of interconnects with full wave models is extremely CPU expensive. We present an algorithm for reducing large VLSI circuits to much smaller ones with similar input-output behavior. A key feature of our method, called Frequency Shift Technique, is that it is capable of reducing linear time-varying systems. This enables it to capture frequency-translation and sampling behavior, important in communication subsystems such as mixers, RF components and switched-capacitor filters. Reduction is obtained by projecting the original system described by linear differential equations into a lower dimension. Experiments have been carried out using Cadence Design Simulator cwhich indicates that the proposed technique achieves more % reduction with less CPU time than the other model order reduction techniques existing in literature. We also present applications to RF circuit subsystems, obtaining size reductions and evaluation speedups of orders of magnitude with insignificant loss of accuracy.

Keywords: Model order Reduction, RLC, crosstalk

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Authors: Adrienne Kline, Jaydip Desai

Abstract:

Electroencephalogram (EEG) is a noninvasive technique that registers signals originating from the firing of neurons in the brain. The Emotiv EEG Neuroheadset is a consumer product comprised of 14 EEG channels and was used to record the reactions of the neurons within the brain to two forms of stimuli in 10 participants. These stimuli consisted of auditory and visual formats that provided directions of ‘right’ or ‘left.’ Participants were instructed to raise their right or left arm in accordance with the instruction given. A scenario in OpenViBE was generated to both stimulate the participants while recording their data. In OpenViBE, the Graz Motor BCI Stimulator algorithm was configured to govern the duration and number of visual stimuli. Utilizing EEGLAB under the cross platform MATLAB®, the electrodes most stimulated during the study were defined. Data outputs from EEGLAB were analyzed using IBM SPSS Statistics® Version 20. This aided in determining the electrodes to use in the development of a brain-machine interface (BMI) using real-time EEG signals from the Emotiv EEG Neuroheadset. Signal processing and feature extraction were accomplished via the Simulink® signal processing toolbox. An Arduino™ Duemilanove microcontroller was used to link the Emotiv EEG Neuroheadset and the right and left Mecha TE™ Hands.

Keywords: Brain-machine interface, EEGLAB, emotiv EEG neuroheadset, openViBE, simulink.

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Authors: Yao-Hong Tsai

Abstract:

Due to the sensor technology, video surveillance has become the main way for security control in every big city in the world. Surveillance is usually used by governments for intelligence gathering, the prevention of crime, the protection of a process, person, group or object, or the investigation of crime. Many surveillance systems based on computer vision technology have been developed in recent years. Moving target tracking is the most common task for Unmanned Aerial Vehicle (UAV) to find and track objects of interest in mobile aerial surveillance for civilian applications. The paper is focused on vision-based collision avoidance for UAVs by recurrent neural networks. First, images from cameras on UAV were fused based on deep convolutional neural network. Then, a recurrent neural network was constructed to obtain high-level image features for object tracking and extracting low-level image features for noise reducing. The system distributed the calculation of the whole system to local and cloud platform to efficiently perform object detection, tracking and collision avoidance based on multiple UAVs. The experiments on several challenging datasets showed that the proposed algorithm outperforms the state-of-the-art methods.

Keywords: Unmanned aerial vehicle, object tracking, deep learning, collision avoidance.

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Authors: Alena Zemanová, Jan Zeman, Michal Šejnoha

Abstract:

The polymer foil used for manufacturing of laminated glass members behaves in a viscoelastic manner with temperature dependance. This contribution aims at incorporating the time/temperature-dependent behavior of interlayer to our earlier elastic finite element model for laminated glass beams. The model is based on a refined beam theory: each layer behaves according to the finite-strain shear deformable formulation by Reissner and the adjacent layers are connected via the Lagrange multipliers ensuring the inter-layer compatibility of a laminated unit. The time/temperature-dependent behavior of the interlayer is accounted for by the generalized Maxwell model and by the time-temperature superposition principle due to the Williams, Landel, and Ferry. The resulting system is solved by the Newton method with consistent linearization and the viscoelastic response is determined incrementally by the exponential algorithm. By comparing the model predictions against available experimental data, we demonstrate that the proposed formulation is reliable and accurately reproduces the behavior of the laminated glass units.

Keywords: Laminated glass, finite element method, finite-strain Reissner model, Lagrange multipliers, generalized Maxwell model, Williams-Landel-Ferry equation, Newton method.

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