2,151 research outputs found

    Positional multi-length and mutual-attention network for epileptic seizure classification

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    The automatic classification of epilepsy electroencephalogram (EEG) signals plays a crucial role in diagnosing neurological diseases. Although promising results have been achieved by deep learning methods in this task, capturing the minute abnormal characteristics, contextual information, and long dependencies of EEG signals remains a challenge. To address this challenge, a positional multi-length and mutual-attention (PMM) network is proposed for the automatic classification of epilepsy EEG signals. The PMM network incorporates a positional feature encoding process that extracts minute abnormal characteristics from the EEG signal and utilizes a multi-length feature learning process with a hierarchy residual dilated LSTM (RDLSTM) to capture long contextual dependencies. Furthermore, a mutual-attention feature reinforcement process is employed to learn the global and relative feature dependencies and enhance the discriminative abilities of the network. To validate the effectiveness PMM network, we conduct extensive experiments on the public dataset and the experimental results demonstrate the superior performance of the PMM network compared to state-of-the-art methods

    XgBoost Hyper-Parameter Tuning Using Particle Swarm Optimization for Stock Price Forecasting

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    Investment in the capital market has become a lifestyle for millennials in Indonesia as seen from the increasing number of SID (Single Investor Identification) from 2.4 million in 2019 to 10.3 million in December 2022. The increase is due to various reasons, starting from the Covid-19 pandemic, which limited the space for social interaction and the easy way to invest in the capital market through various e-commerce platforms. These investors generally use fundamental and technical analysis to maximize profits and minimize the risk of loss in stock investment. These methods may lead to problem where subjectivity and different interpretation may appear in the process. Additionally, these methods are time consuming due to the need in the deep research on the financial statements, economic conditions and company reports. Machine learning by utilizing historical stock price data which is time-series data is one of the methods that can be used for the stock price forecasting. This paper proposed XGBoost optimized by Particle Swarm Optimization (PSO) for stock price forecasting. XGBoost is known for its ability to make predictions accurately and efficiently. PSO is used to optimize the hyper-parameter values of XGBoost. The results of optimizing the hyper-parameter of the XGBoost algorithm using the Particle Swarm Optimization (PSO) method achieved the best performance when compared with standard XGBoost, Long Short-Term Memory (LSTM), Support Vector Regression (SVR) and Random Forest. The results in RSME, MAE and MAPE shows the lowest values in the proposed method, which are, 0.0011, 0.0008, and 0.0772%, respectively. Meanwhile, the  reaches the highest value. It is seen that the PSO-optimized XGBoost is able to predict the stock price with a low error rate, and can be a promising model to be implemented for the stock price forecasting. This result shows the contribution of the proposed method

    Characteristic and Allowable Compressive Strengths of Dendrocalamus Sericeus Bamboo Culms with/without Node Using Artificial Neural Networks

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    The strength of construction material is a crucial consideration in the process of structural design and construction. Conventional materials such as concrete or steel have been widely utilized due to their predictable material performance. However, a significant obstacle to the widespread use of bamboo in structural elements lies in the challenge of its standardization. Many previous research studies have explored bamboo’s load bearing capacity, but the information remains limited due to variations in species, size, age, physical properties, moisture content, and other factors, making it difficult to predict their load-bearing capacity. This study aims to propose Artificial Neural Network (ANN) models to predict ultimate compressive load and compressive strength of Dendrocalamus Sericeus bamboo culm. Additionally, for structural design purposes, the proposed ANN models were employed to determine the characteristic and allowable compressive strengths. As a first step, experimental data from compressive tests in the literature were used for training and developing the ANN model. To investigate the effect of the node on compressive loading capacities, the test data were separated into two datasets, “Node” samples and “Internode” samples. Through the training process, ANN models were finally proposed, and the R-square values for the prediction of ultimate compressive load and compressive strength from the proposed ANN models were significantly higher than those obtained from the linear regression analyses used in the literature. Subsequently, the characteristic and allowable compressive strengths were calculated and compared to the strengths obtained from the experiment data, revealing a difference of approximately only 8.0%. Overall, the ANN models presented in this study offer promising predictive ability for both ultimate compressive load and compressive strength of Dendrocalamus Sericeus bamboo culm, as well as for determining characteristic and allowable strengths. Hence, ANN models are suggested to be adopted as a tool for the design and construction of bamboo buildings

    MECHANICAL ENERGY HARVESTER FOR POWERING RFID SYSTEMS COMPONENTS: MODELING, ANALYSIS, OPTIMIZATION AND DESIGN

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    Finding alternative power sources has been an important topic of study worldwide. It is vital to find substitutes for finite fossil fuels. Such substitutes may be termed renewable energy sources and infinite supplies. Such limitless sources are derived from ambient energy like wind energy, solar energy, sea waves energy; on the other hand, smart cities megaprojects have been receiving enormous amounts of funding to transition our lives into smart lives. Smart cities heavily rely on smart devices and electronics, which utilize small amounts of energy to run. Using batteries as the power source for such smart devices imposes environmental and labor cost issues. Moreover, in many cases, smart devices are in hard-to-access places, making accessibility for disposal and replacement difficult. Finally, battery waste harms the environment. To overcome these issues, vibration-based energy harvesters have been proposed and implemented. Vibration-based energy harvesters convert the dynamic or kinetic energy which is generated due to the motion of an object into electric energy. Energy transduction mechanisms can be delivered based on piezoelectric, electromagnetic, or electrostatic methods; the piezoelectric method is generally preferred to the other methods, particularly if the frequency fluctuations are considerable. In response, piezoelectric vibration-based energy harvesters (PVEHs), have been modeled and analyzed widely. However, there are two challenges with PVEH: the maximum amount of extractable voltage and the effective (operational) frequency bandwidth are often insufficient. In this dissertation, a new type of integrated multiple system comprised of a cantilever and spring-oscillator is proposed to improve and develop the performance of the energy harvester in terms of extractable voltage and effective frequency bandwidth. The new energy harvester model is proposed to supply sufficient energy to power low-power electronic devices like RFID components. Due to the temperature fluctuations, the thermal effect over the performance of the harvester is initially studied. To alter the resonance frequency of the harvester structure, a rotating element system is considered and analyzed. In the analytical-numerical analysis, Hamilton’s principle along with Galerkin’s decomposition approach are adopted to derive the governing equations of the harvester motion and corresponding electric circuit. It is observed that integration of the spring-oscillator subsystem alters the boundary condition of the cantilever and subsequently reforms the resulting characteristic equation into a more complicated nonlinear transcendental equation. To find the resonance frequencies, this equation is solved numerically in MATLAB. It is observed that the inertial effects of the oscillator rendered to the cantilever via the restoring force effects of the spring significantly alter vibrational features of the harvester. Finally, the voltage frequency response function is analytically and numerically derived in a closed-from expression. Variations in parameter values enable the designer to mutate resonance frequencies and mode shape functions as desired. This is particularly important, since the generated energy from a PVEH is significant only if the excitation frequency coming from an external source matches the resonance (natural) frequency of the harvester structure. In subsequent sections of this work, the oscillator mass and spring stiffness are considered as the design parameters to maximize the harvestable voltage and effective frequency bandwidth, respectively. For the optimization, a genetic algorithm is adopted to find the optimal values. Since the voltage frequency response function cannot be implemented in a computer algorithm script, a suitable function approximator (regressor) is designed using fuzzy logic and neural networks. The voltage function requires manual assistance to find the resonance frequency and cannot be done automatically using computer algorithms. Specifically, to apply the numerical root-solver, one needs to manually provide the solver with an initial guess. Such an estimation is accomplished using a plot of the characteristic equation along with human visual inference. Thus, the entire process cannot be automated. Moreover, the voltage function encompasses several coefficients making the process computationally expensive. Thus, training a supervised machine learning regressor is essential. The trained regressor using adaptive-neuro-fuzzy-inference-system (ANFIS) is utilized in the genetic optimization procedure. The optimization problem is implemented, first to find the maximum voltage and second to find the maximum widened effective frequency bandwidth, which yields the optimal oscillator mass value along with the optimal spring stiffness value. As there is often no control over the external excitation frequency, it is helpful to design an adaptive energy harvester. This means that, considering a specific given value of the excitation frequency, energy harvester system parameters (oscillator mass and spring stiffness) need to be adjusted so that the resulting natural (resonance) frequency of the system aligns with the given excitation frequency. To do so, the given excitation frequency value is considered as the input and the system parameters are assumed as outputs which are estimated via the neural network fuzzy logic regressor. Finally, an experimental setup is implemented for a simple pure cantilever energy harvester triggered by impact excitations. Unlike the theoretical section, the experimental excitation is considered to be an impact excitation, which is a random process. The rationale for this is that, in the real world, the external source is a random trigger. Harmonic base excitations used in the theoretical chapters are to assess the performance of the energy harvester per standard criteria. To evaluate the performance of a proposed energy harvester model, the input excitation type consists of harmonic base triggers. In summary, this dissertation discusses several case studies and addresses key issues in the design of optimized piezoelectric vibration-based energy harvesters (PVEHs). First, an advanced model of the integrated systems is presented with equation derivations. Second, the proposed model is decomposed and analyzed in terms of mechanical and electrical frequency response functions. To do so, analytic-numeric methods are adopted. Later, influential parameters of the integrated system are detected. Then the proposed model is optimized with respect to the two vital criteria of maximum amount of extractable voltage and widened effective (operational) frequency bandwidth. Corresponding design (influential) parameters are found using neural network fuzzy logic along with genetic optimization algorithms, i.e., a soft computing method. The accuracy of the trained integrated algorithms is verified using the analytical-numerical closed-form expression of the voltage function. Then, an adaptive piezoelectric vibration-based energy harvester (PVEH) is designed. This final design pertains to the cases where the excitation (driving) frequency is given and constant, so the desired goal is to match the natural frequency of the system with the given driving frequency. In this response, a regressor using neural network fuzzy logic is designed to find the proper design parameters. Finally, the experimental setup is implemented and tested to report the maximum voltage harvested in each test execution

    An Enhanced Automated Epileptic Seizure Detection Using ANFIS, FFA and EPSO Algorithms

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    Objectives: Electroencephalogram (EEG) signal   gives   a   viable perception about the neurological action of the human brain that aids the detection of epilepsy. The objective of this study is to build an accurate automated hybrid model for epileptic seizure detection. Methods: This work develops a computer-aided diagnosis (CAD) machine learning model which can spontaneously classify pre-ictal and ictal EEG signals. In the proposed method two most effective nature inspired algorithms, Firefly algorithm (FFA) and Efficient Particle Swarm Optimization (EPSO) are used to determine the optimum parameters of Adaptive Neuro Fuzzy Inference System (ANFIS) network. Results: Compared to the FFA and EPSO algorithm separately, the composite (ANFIS+FFA+EPSO) optimization algorithm outperforms in all respects. The proposed technique achieved accuracy, specificity, and sensitivity of 99.87%, 98.71% and 100% respectively. Conclusion: The ANFIS-FFA-EPSO method is able to enhance the seizure detection outcomes for demand forecast in hospital

    Multi-Objective Density Diagrams Developed With Machine Learning Models to Optimize Sustainability and Cost-Efficiency of UHPC Mix Design

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    The emergence of ultra-high-performance concrete (UHPC) as an attractive solution for precast and prestressed applications has coincided with global efforts towards sustainable construction. The increasing need for tools capable of intuitively demonstrating the effect of concrete mixture composition on mechanical performance, cost and eco-efficiency concurrently has motivated this work in an effort to promote design of more sustainable solutions to help meet environmental goals. Such tools are needed to effectively evaluate the environmental impact of UHPC given the outstanding mechanical properties of the material coupled with high volumetric embodied CO2. Meanwhile, artificial intelligence (AI) techniques have emerged as a great opportunity for game-changing tools capable of effectively modeling the synergistic relationships between mix proportions and material performance. This work couples machine learning models with orthogonal arrays to generate machine-learning-based tools to evaluate the tradeoffs between emissions, cost and mechanical performance concurrently. Random forest and k-nearest neighbors’ models are ensembled to predict the compressive strength of UHPC mixtures and generate Performance Density Diagrams (PDDs). These predicted strengths are then coupled with volumetric environmental factors and unit costs to generate eco- and cost-efficiency density diagrams. The makeup of these tools facilitates the evaluation of rather complicated trends associated with mix proportions and multi-objective outcomes, allowing AI-based tools to be of easy use by industry personnel on a daily basis, while serving as decision-making aids during mix design stages and provide proof of mixture optimization that could be introduced in Environmental Product Declarations. The PDD developed herein enabled the design of a mix with compressive strength of 155 MPa, while keeping the aggregate-to-cementitious ratio above unit. Other mixtures were developed from these models and compared to several different concretes from the literature. Results show that high paste content, high strength (and ultra-high strength) concrete technologies are not necessarily detrimental to cost or eco efficiencies. For the different indices evaluated, optimum solutions were mostly obtained with these types of concrete, which means that industry trends toward requiring minimization of embodied CO2 in concrete on a per volume basis are misguided and do not minimize the embodied CO2 in concrete structures

    Practical Inherently Safer Design Approaches During Early Process Design Stages Aiming for Sustainability

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    In traditional industrial process design approaches, techno-economic criteria have been the primary objectives in the early process design stages. Safety is often considered only in the later design stage (e.g., detailed engineering stage). Such a traditional approach is that most of the design degrees of freedom, including technology and configuration issues, have already been determined when considering safety. Modifying a process is costly or unreliable at later stages. To solve this issue, there have been numerous attempts to consider process safety during the early design stages in safety engineers and researchers. In particular, special attention to adopting inherently safer design (ISD) has been made because ISD is deemed the most cost-effective risk reduction strategy at early design stages. However, it is still challenging to adopt ISD for process engineers at the early design stages because of the lack of guidance and insufficient information on upcoming process facilities. To address this challenge, this dissertation consists of three peer-reviewed journal papers [Articles #1 - #3]. With respect to the progress of inherently safer design (in particular, during the early design stage) over the last three decades, Article #1 selects 73 inherent safety assessment tools, which can be utilized during the early design stages, and categorized into three groups: hazard-based inherent safety assessment tools (H-ISATs) for 22 tools, risk-based inherent safety assessment tools (R-ISATs) for 33 tools, and cost-optimal inherent safety assessment tools (CO-ISATs) for 18 tools. The goal of this article is to enable process engineers to use all the available design degrees of freedom to mitigate risk early enough in the design process. Article #2 analyzes 94 chemical process incidents investigated by the U.S. Chemical Safety and Hazard Investigation Board (CSB) reports. To analyze in a systematic approach, this article proposes 17 incident cause factors, 6 scenario factors, and 6 consequence factors to find out whether ISD would have helped to prevent these incidents. Article #3 proposes hands-on predictive models of the flash point, the heat of combustion, lower flammability limit (LFL), and upper flammability limit (UFL). By incorporating the nonlinearity and transformation along with linearity of variables, this article constructed practical, reliable regression models thoroughly with readily available variables—the number of all atoms, molecular weights, and boiling points. The purpose is to enable a process engineer to quickly obtain hazardous properties of intended process materials

    Using machine learning to predict the performance of a cross-flow ultraïŹltration membrane in xylose reductase separation

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    This study provides a new perspective for xylose reductase enzyme separation from the reaction mixtures—obtained in the production of xylitol—by means of machine learning technique for large-scale production. Two types of machine learning models, including an adaptive neuro-fuzzy inference system based on grid partitioning of the input space and a boosted regression tree were developed, validated, and tested. The models’ inputs were cross-flow velocity, transmembrane pressure, and filtration time, whereas the membrane permeability (called membrane flux) and xylitol concentration were considered as the outputs. According to the results, the boosted regression tree model demonstrated the highest predictive performance in forecasting the membrane flux and the amount of xylitol produced with a coefficient of determination of 0.994 and 0.967, respectively, against 0.985 and 0.946 for the grid partitioning-based adaptive neuro-fuzzy inference system, 0.865 and 0.820 for the best nonlinear regression picked from among 143 different equations, and 0.815 and 0.752 for the linear regression. The boosted regression tree modeling approach demonstrated a superior capability of predictive accuracy of the critical separation performances in the enzymatic-based cross-flow ultrafiltration membrane for xylitol synthesis

    Methods for Improving Potassium Fertilizer Recommendations for Corn in South Dakota

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    Corn (Zea mays L.) is a vital commodity in South Dakota’s agricultural sector. Optimal corn production occurs when there are sufficient mineral nutrients in the soil, especially potassium (K). Applications of K fertilizer are used when soil test K (STK) levels are deficient. Therefore, producers need reliable, thoroughly tested fertilizer recommendations to make profitable decisions and maintain environmental stewardship. South Dakota K fertilizer recommendations have not been updated in nearly 20 years. Simultaneously, changes in corn genetics, management practices, and climate patterns suggest that the critical soil test value (CSTV) for STK may have shifted in that same time frame. Furthermore, the addition of other variables, notably clay mineralogy, could improve the accuracy of K fertilizer recommendations. Therefore, the objectives of this study were to 1) evaluate relationships among clay mineralogy, STK, and other common soil test parameters, and 2) use those relationships to improve K fertilizer recommendations for South Dakota. From 2019 to 2022, soil samples were collected from 43 locations, and field trials were conducted at 35 locations throughout central and eastern South Dakota. A correlation matrix and nonlinear regressions demonstrated significant relationships between STK and the smectite:illite ratio. Linear regressions between STK and several other soil parameters were influenced by smectite:illite ratio groupings: (illitic [1 but4.5]). Soil test K and several other soil test variables (water-soluble K, total K, soil organic matter [SOM], and clay content) were all positively related regardless of clay mineralogy, but STK was predicted to be lower by all soil test variables in highly smectitic soils as opposed to illitic and smectitic soils. Moreover, STK decreased as pH increased in highly smectitic soils. Random forest modeling identified STK as the most important variable for predicting the smectite:illite ratio. Therefore, the interactions between STK, the smectite:illite ratio, and other soil parameters should be further investigated for implementation in K fertilizer recommendations. Using soil test correlation techniques, seven nonlinear regression models displayed a wide range of CSTVs (111-196 mg kg-1 STK). Using model averaging, the optimal CSTV for improved corn yield response predictions was 144 mg kg-1, which was lower than the current South Dakota CSTV of 160 mg kg-1. While clay mineralogy variables were not identified as important predictors of yield responsiveness using random forest modeling, CEC, SOM, and permanganate oxidizable carbon (POXC), along with STK (CSTV = 144 mg kg-1) were important. Using these variables in a decision tree improved prediction accuracy from 62% to 72% compared to using STK alone (CSTV = 160 mg kg-1). Overall, these results demonstrated that there were significant relationships among STK, clay mineralogy, and other soil parameters, but clay mineralogy could not confidently be incorporated into K fertilizer recommendations. Rather, lowering the CSTV from 160 to 144 mg kg-1 STK and inclusion of CEC, SOM, and POXC resulted in improved accuracy of corn yield responsiveness to K fertilization. These results will help corn producers in South Dakota and abroad to improve farm profitability and reduce misapplications of fertilizer
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