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31,456 Article Results

Deep-SFER: deep convolutional neural network and MFCC an effective speech and face emotion recognition

10.11591/ijeecs.v36.i3.pp1448-1459
Ravi Gummula , Vinothkumar Arumugam , Abilasha Aranganathan
There has been a lot of progress in recent years in the fields of expert systems, artificial intelligence (AI) and human machine interface (HMI). The use of voice commands to engage with machinery or instruct it to do a certain task is becoming more common. Numerous consumer electronics have SIRI, Alexa, Cortana, and Google Assistant built in. In the field of human-device interaction, emotion recognition from speech is a complex research subject. We can't imagine modern life without machines, so naturally there's a need to create a more robust framework for human-machine communication. A number of academics are now working on speech emotion recognition (SER) in an effort to improve the interaction between humans and machines. We aimed to identify four fundamental emotions: angry, unhappy, neutral and joyful from speech in our experiment. As you can hear below, we trained and tested our model using audio data of brief Manipuri speeches taken from films. This task makes use of convolutional neural networks (CNNs) to extract functions from speech in order to recognize different moods using the Mel-frequency cepstral coefficient (MFCC).
Volume: 36
Issue: 3
Page: 1448-1459
Publish at: 2024-12-01

Information system architecture for healthcare company based on TOGAF

10.11591/ijaas.v13.i4.pp810-817
Vania Christy , Johanes Fernandes Andry , Ahya Radiatul Kamila , Francka Sakti Lee
In 2020 COVID-19 cases have entered Indonesia causing public health problems and millions of deaths. To prevent transmission of COVID-19, an air purifier is needed whose function is to remove small droplets that can carry the virus. One of them is a medical device company located in Jakarta. The purpose of this research is to produce a design that can improve business processes in the health sector and achieve company goals. The current business process is not very optimal because it is still done conventionally and the existing system has not been integrated with other divisions. To achieve business goals, it is necessary to integrate business processes with information technology (IT) and technology development that will be proposed based on the design of information system architecture that will produce a blueprint and assisted by the open group architecture framework (TOGAF) framework which is very helpful in the process of analyzing company needs. In this research, data collection through interviews with directors and direct observation of health service companies. The results of this study are recommendations given to help health.
Volume: 13
Issue: 4
Page: 810-817
Publish at: 2024-12-01

Machine learning based optimal control of modular converter for PV assisted power supply systems

10.11591/ijpeds.v15.i4.pp2570-2579
Srungaram Ravi Teja , Kishore Yadlapati
This paper presents the topology and machine learning-based intelligent control of a single-stage grid-connected high-power photovoltaic (PV) system for quality power export to the grid and optimal net energy utilization. A nineteen-level bi-modular inverter is proposed for efficient single-stage PV power conversion. The proposed integrated intelligent machine learning-based control serves for power conversion control as well as supervisory control for hourly PV energy estimation and load demand control for optimal energy consumption. The objectives of power control are extracting maximum power from PV sources and exporting power to the grid at unity power factor. While the objectives for supervisory control are local load demand control for exporting power at higher export prices. The proposed system is implemented using MATLAB/Simulink to validate the efficiency of power conversion, effectiveness of machine learning for energy estimation, and load relay control for optimal energy pricing. The results proved efficient tracking of maximum power, unity power factor at grid terminals, and load relay control for PV energy availability and export cost function.
Volume: 15
Issue: 4
Page: 2570-2579
Publish at: 2024-12-01

Leveraging machine learning for sustainable integration of renewable energy generation

10.11591/ijeecs.v36.i3.pp1347-1355
Pushpa Sreenivasan , Keerthiga Ganesan , Iffath Fawad , Sathya Sureshkumar , Kirubakaran Dhandapani
Long-term economic benefits and sustainability are provided by the integration of renewable energy sources (RESs) into electrical networks. However, because of their intermittent nature and reliance on environmental factors, RESs pose issues in production and consumption balance. Because renewable energy sources like wind and solar are unpredictable, forecasting their output is essential for planning purposes and maintaining grid stability. This thesis focuses on developing effective instruments and algorithms to improve renewable energy generation estimates and handle abnormalities in consumption. These tools and algorithms include maximum power point tracking and machine learning models like random forest (RF), adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost). The methods' effectiveness is confirmed by accuracies higher than 80%, which provides speedier and more user-friendly solutions in comparison to the traditional ways. In the end, our effort seeks to offer practical instruments for anticipatory modelling and mitigating intermittentness in renewable energy sources, enabling their assimilation into current power structures to adequately supply energy requirements in a sustainable manner.
Volume: 36
Issue: 3
Page: 1347-1355
Publish at: 2024-12-01

A holistic approach of stability using material parameters of manipulators

10.11591/ijra.v13i4.pp380-390
Shabnom Mustary , Mohammod Abul Kashem , Mohammad Asaduzzaman Chowdhury , Jia Uddin
The demand for a comprehensive method to assess stability using manipulator material parameters is high. Various material parameters, such as the Young modulus, which represents stiffness, damping, and deflection, influence the material of the robot manipulator. The correlation between robot stability and these characteristics remains unclear, as prior studies have not yet examined the collective impact of these parameters on robot manipulators. This work considers two sophisticated manipulators, namely ABB and FANUC. The main objective of this research is to construct a stability model that considers the material properties of stiffness, damping, and deflection to assess the manipulator’s stability level, which may be categorized as low, medium, or high. Furthermore, the presented stability model examines and employs numerous modified and conventional formulas for material properties to determine the level of stability. The findings show that stiffness significantly influences the stability of robot manipulators, a relationship that applies to all the examined manipulators. We also emphasize that the choice of manipulator materials significantly impacts stability maintenance. These findings are expected to enhance the design and advancement of novel robot manipulators within the industry.
Volume: 13
Issue: 4
Page: 380-390
Publish at: 2024-12-01

Preparation and characterization of cellulose acetate propionate membrane

10.11591/ijaas.v13.i4.pp891-899
Maryudi Maryudi , Dhias Cahya Hakika , Firda Mahira Alfiata Chusna , Amillia Amillia
Membrane technology is a technique in water and wastewater treatment that has many advantages. This study focuses on the manufacture and characterization of cellulose acetate propionate (CAP) membranes using the phase inversion method. The CAP is preferable since it is a biopolymer that is environmentally friendly and cheap. The production of CAP membrane was carried out using phase inversion method, and varied in 3 different concentrations of CAP i.e., 13, 14, and 15% wt., with additional material of polyethylene glycol (PEG). The characterization was conducted using scanning electron microscopy (SEM) and Fourier transform infrared (FTIR) analysis to determine its morphology, pores, and functional groups. The results show that the membrane containing 13% wt. CAP exhibits higher porosity with more macropores than a membrane with 14 and 15% wt. CAP. However, membranes with higher concentrations of CAP show more uniform pores and fewer macropores. FTIR analysis confirmed the presence of functional groups in the membrane such as C-O, -CH3, C=O, C-H, and –OH. It is also found that there is a shift in Wavenumber due to the increase in the concentration of CAP in each membrane.
Volume: 13
Issue: 4
Page: 891-899
Publish at: 2024-12-01

Low-voltage DC-DC off-grid PV system: various irradiance studies

10.11591/ijpeds.v15.i4.pp2651-2659
A. Razi , Syahar Azalia Ab Shukor , Wei Wen Lee , M. Nabil Hidayat
The off-grid photovoltaic (OGPV) system, also referred to as a stand-alone power system, generates electricity from solar energy independently of the main electricity grid. However, photovoltaic (PV) modules face limitations in consistently providing the necessary voltage. Thus, a DC-DC converter is employed to adjust the voltage to match the requirements of the load. This paper proposes a detailed analysis of irradiance patterns resembling in-lab and real-world conditions to address the challenge of selecting the optimal DC-DC OGPV system capable of functioning effectively under standard test condition (STC) and dynamic test condition (DTC), respectively. Three proposed irradiance analyses, tailored to different environmental scenarios, aim to identify output performances for DC-DC OGPV system which are aligning with standards set by the International Electrotechnical Commission (IEC) 61727. The DC-DC OGPV system is simulated using MATLAB Simulink. Furthermore, comparative studies involving different PV module types and several switching techniques (pulse generator and maximum power point tracking or MPPT implementations), where findings are instrumental in advancing the development and projection planning for medium to high-voltage OGPV systems, contributing to the global initiative for accessible and sustainable energy solutions aligned with sustainable development goal (SDG 7).
Volume: 15
Issue: 4
Page: 2651-2659
Publish at: 2024-12-01

Fuzzy logic assessment of X-ray tube risks in robotic C-arm angiography: a failure mode and effect analysis study

10.11591/ijra.v13i4.pp506-514
Ade Firdaus , Andi Adriansyah , Nanda Ferdana , Rahmalisa Suhartina , Rino Ferdian Surakusumah , Jakfat Haekal , Zulhamidi Zulhamidi , Abu Ubaidillah Shamsudin
This research examines the integration of robotic C-arm technology in angiography, a critical tool for treating cardiac conditions. The robotic C-arm, which includes an X-ray tube, is essential for scanning patients during procedures. The study also investigates the associated risks, specifically in Indonesian hospitals with cardiac facilities. Angiography is used to diagnose and treat heart disease by visualizing blood vessels and facilitating catheterization procedures. However, its mobility poses hazards and can impact the process. To address potential risks, failure mode and effect analysis (FMEA) is utilized. Traditionally, risk assessment using risk priority numbers (RPN) is conducted, but these may not accurately reflect failures due to complex evaluating processes. To overcome this limitation, fuzzy logic is employed, enhancing risk assessment accuracy. Through this approach, twenty-seven failure modes are identified across two brands, with ten major ones prioritized using fuzzy logic. These findings facilitate the development of preventive measures to mitigate future failures and enhance patient safety during angiography in hospitals. In conclusion, the study underscores the importance of robust risk management in medical equipment, particularly in dynamic environments. By integrating fuzzy logic into risk assessment, the study improves prioritization accuracy, enabling effective allocation of resources for preventive actions.
Volume: 13
Issue: 4
Page: 506-514
Publish at: 2024-12-01

Experimental results on position and path control of an automated guided vehicle using fixed camera at ceiling and color markers

10.11591/ijra.v13i4.pp391-400
Ramesh Pungle , Atul Andhare , Durva Pungle
This article presents the results of experiments on path planning and control of automated guided vehicles (AGV) using single, fixed ceiling mounted, monocular cameras and colored markers. The camera employed in the system serves as both a sensor and controller. Initially, the working environment is structured using colored markers for given applications. For every new setup, structuring the environment is essential. The image processing algorithm identifies the colored markers and their positions, which are then utilized for path planning and segmentation. The actuation time required to transverse each segment is calculated and then AGV is actuated accordingly. A transformation or inverse mapping matrix (M), predetermined, is employed for calculating world coordinates from given image coordinates. Path planning and AGV control are across various paths, both with and without static obstacles, in real-time applications. The colored marker detection and recognition accuracy for the given setup have been found cent percentage while the AGV reaches the goal point with an error margin of around 3.9% on straight paths, both with and without obstacles.
Volume: 13
Issue: 4
Page: 391-400
Publish at: 2024-12-01

Forecasting hourly short-term solar photovoltaic power using machine learning models

10.11591/ijpeds.v15.i4.pp2553-2569
Sravankumar Jogunuri , F. T. Josh , J. Jency Joseph , R. Meenal , R. Mohan Das , S. Kannadhasan
Forecasting solar photovoltaic power ensures a stable and dependable power grid. Given its dependence on stochastic weather conditions, predicting solar photovoltaic power accurately demands applying intelligent and sophisticated techniques capable of handling its inherent nonlinearity and volatility. Controlling electrical energy sources is an important strategy for reaching this energy balance because grid operators often have no control over use patterns. Accurately forecasting photovoltaic (PV) power generation from highly integrated solar plants to the grid is essential for grid stability. This study aims to improve forecasting accuracy and make accurate predictions of solar power output from the selected grid-connected PV system. In this study, the weather data was collected on-site and recorded PV power from a 20 kW on-grid system for one year, and different machine learning techniques like deep neural networks, random forests, and artificial neural networks were evaluated and benchmarked against reference support vector regression model. With improvements in forecasting accuracy of 2 to 37% over the reference model at study location (22.780 N, 73.650 E), College of Agricultural Engineering and Technology, Anand Agricultural University, Godhra, India, simulation results showed that the random forest technique is effective for the forecasting horizons of 1 to 4 hours.
Volume: 15
Issue: 4
Page: 2553-2569
Publish at: 2024-12-01

Hybrid improved fuzzy C-means and watershed segmentation to classify Alzheimer’s using deep learning

10.11591/ijai.v13.i4.pp4080-4094
Esraa H. Ali , Sawsan Sadek , Zaid F. Makki
Brain damage and deficits in interactions among brain cells are the primary causes of dementia and Alzheimer’s disease (AD). Despite ongoing research, no effective medications have yet been developed for these conditions. Therefore, early detection is crucial for managing the progression of these disorders. In this study, we introduce a novel tool for detecting AD using non invasive medical tests, such as magnetic resonance imaging (MRI). Our method employs fuzzy C-means clustering to identify features that enhance image accuracy. The standard fuzzy C-means algorithm has been augmented with fuzzy components to improve clustering performance. This enhanced approach optimizes segmentation by extracting image information and utilizing a sliding window to calculate center coordinates and establish a stable group matrix. These critical features are subsequently integrated with a two-phase watershed segmentation process. The resulting segmented images are then used to train an optimal convolutional neural network (CNN) for AD classification. Our methodology demonstrated a 98.20% accuracy rate in the detection and classification of segmented MRI brain images, highlighting its efficacy in identifying disease types.
Volume: 13
Issue: 4
Page: 4080-4094
Publish at: 2024-12-01

A review of the literature on "determinants of insurers' capital structure"

10.11591/ijaas.v13.i4.pp1050-1058
Ashwag Alsofiani , Maizaitulaidawati Md Husin
Capital structure plays an essential role in the financial decision-making of a company by strengthening financial performance and worth. This research aims to provide a literature review to identify the factors that affect insurance companies’ capital structure. The paper focuses on articles published from 2010 to 2021 on insurance companies’ capital structure in developing countries were reviewed. Three theories were identified as having common determinants: trade-off, pecking order, and agency cost. These independent determinants include seven firm-specific determinants: company size, age, profitability, growth, liquidity, tangibility, and risk along with two macroeconomic determinants: economic growth and inflation rate. The research found that the leverage ratio is the primary measurement of capital structure used as a dependent variable. Furthermore, previous studies have shown that the static data model was the most appropriate framework in most research. This research provides future researchers with information on understanding the determinants of capital structure in the insurance field.
Volume: 13
Issue: 4
Page: 1050-1058
Publish at: 2024-12-01

A multi-core makespan model for parallel scientific workflow execution in cloud computational framework

10.11591/ijai.v13.i4.pp3849-3857
Farha Naaz , Sameena Banu
Researchers have shown a lot of potential in optimizing cloud-based workload-scheduling over the past few years. However, executing scientific workloads inside the cloud is time-consuming and costly, making it inefficient from both a financial and productivity standpoint. As a result, there are many investigations conducted, with the general trend being to speed up the rate of processing and establish a cost-effective system, whereby customers are billed according to their actual use. In addition, energy-consumption is capable of being reduced, especially if the available resources are heterogeneous; however, few investigations have optimized multi-core with analyzing makespan parameters collectively to fulfill the quality of service (QoS) and service level agreement (SLA) of the workload task. In this research, we introduce an optimal scheduling for a heterogeneous distributed cloud computing environment called task aware makespan optimized scheduler (TAMOS) that guarantees requirements across the task levels of scientific workflows. The energy and time required to carry out specific workflows are significantly reduced by using this TAMOS strategy. The TAMOS framework was studied using the scientific workflows namely, inspiral and sipht. When compared to the conventional method of scheduling work, our methodology used less energy and makespan.
Volume: 13
Issue: 4
Page: 3849-3857
Publish at: 2024-12-01

Digitally fast synchronization of single-phase grid-tied inverter using FPGA

10.11591/ijpeds.v15.i4.pp2452-2461
Afarulrazi Abu Bakar , Balarajan Sannasy , Hazwaj Mhd Poad , Tharnisha Sithananthan , Wahyu Mulyo Utomo , Nor Farisha Diana Rosli
As interest in alternative energy sources grows, grid-connected inverters are getting more advanced. Thus, to synchronize the output waveform of an inverter with the grid supply system, the frequency and phase angle ought to be consistent. This paper presents an enhanced digital implementation controller for a grid-connected inverter using the sinusoidal pulse-width modulation (SPWM) switching technique via an appropriately designed low-pass filter. The main contribution of the proposed digital controller algorithm is to synchronize with the grid for the next half-cycle. The proposed control technique used the integrated response from the zero-crossing detector (ZCD) circuit for every half-cycle to trigger the digital phase-locked loop (PLL) implemented using field-programmable gate array (FPGA). An experimental 100 W single-phase full-bridge inverter prototype tested and validated the proposed control algorithm to prove the switching approach works. From the experimental results, the proposed control algorithm demonstrates synchronization with the grid voltage within 8 ms during startup. Furthermore, it exhibits the ability to adapt to phase changes when subjected to a distorted grid, achieving synchronization within 42 ms. This research also emphasizes synchronization between the grid waveform and the inverter output via the phase angle difference.
Volume: 15
Issue: 4
Page: 2452-2461
Publish at: 2024-12-01

Application of inverter input rating method and standard AC voltage drop/over method on automatic transfer switch for hybrid powered e-bike charging station

10.11591/ijpeds.v15.i4.pp2480-2492
Mohammad Noor Hidayat , Azam Muzakhim Imammuddin , Abdullah Faiq Munir , Aji Nugroho
Automatic transfer switch (ATS) is useful for integrating two different energy sources can be applied to e-bike charging stations. Renewable energy sources from sunlight using 2×100 Wp photovoltaic (PV) modules as the main source in this study, as well as grid energy sources functioned as backup energy. This ATS uses inverter input rating methods and standard AC voltage drop/over, a novelty of this study. The amount of current during testing is up to 14.09 A. The value of the inverter input voltage affects the quality of the inverter output voltage, there is a stability of the inverter output voltage from the AC voltage standard ±5% of the, lowest voltage value of (Vo_inv) = 223.9 V and the highest value (Vo_inv) = 226.1 V, at the time of (Vi_inv) max = 14.15 V and (Vi_inv) min = 12.66 V. It can be concluded that the ATS works in the PV position of the supply to the e-bike load, in the PV state it can produce power of a maximum value of 135.9 W at 11:25 am and is at the lowest power of 31 W at 03:31 pm. This ATS device in the switch mode successfully implemented a duration range of 10-21 ms, during the transfer of energy.
Volume: 15
Issue: 4
Page: 2480-2492
Publish at: 2024-12-01
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