International Journal on Robotics, Automation and Sciences

Review on Development of Digital Twins for Predicting, Mitigating Faults and Defects in Solar Plants

2024-03-04T12:59:20+08:00

Abstract – The thought of digital twins has gained substantial attention in recent years due to its potential to transform various industries, including renewable energy. Digital twins involve the creation of virtual models that mirror the behaviour and characteristics of real-world physical systems. In the perspective of solar plants, digital twins have emerged as a promising tool to enhance performance monitoring, predictive maintenance, and overall operational efficiency. Digital twin engineering, characterized by its dynamic data modelling of industrial assets, offers a disruptive technology capable of adapting to real-time changes in the environment and operations. This living model can predict future infrastructure behaviour and proactively identify potential issues within the physical system. The article highlights the essential components of the digital twin ecosystem, such as sensor technologies, the Industrial Internet of Things, simulation, modelling, and machine learning, underscoring their relevance in predictive maintenance applications. This review provides an in-extensive review of the development and application of digital twins for predicting and mitigating faults and defects in solar power plants. It opens with a look at current developments, underlining the rising focus on digital twins for optimizing solar farms. It begins with an overview of existing solutions in the field, highlighting the growing interest in leveraging digital twin technology to enhance solar plant operations. Additionally, the article outlines the implementation stage of a prototype digital twin for a solar power plant.

[Manuscript received: 17 Feb 2024 | Accepted: 13 Mar 2024 | Published: : 30 Sep 2024]

Exploring Recommender Systems in the Healthcare: A Review on Methods, Applications and Evaluations

2024-04-07T11:42:48+08:00

Due to the vast amount of publicly available online data, people may find it difficult to obtain relevant information to find food or meals that match their taste and health while maintaining a healthy lifestyle. The overload of information makes it difficult to separate relevant, personalized information from massive volumes of data. Recommendation systems (RS) are suggestion system that provides users with information that they may be interested in. With RS, this enormous amount of information is filtered and analyzed for further insights. This paper will explore several generations of recommender systems in the healthcare industry. This paper offers a thorough analysis of the current state-of-the-art recommender systems focusing on the grouping, methods, application and evaluation metrics. In addition, several challenges for further research and improvement in this domain are also outlined in the paper.

[Manuscript received: 2 Apr 2024 | Accepted: 5 Jun 2024 | Published: : 30 Sep 2024]

Automatic Filling Machine for Metracide 1 Liter Product Variant at Global Medipro Investama LLC

2024-04-15T11:17:01+08:00

Abstract – Global Medipro Investama LLC previously used a CNC liquid filling machine to fill the Metracide 1 liter variant product into bottles. This machine very often produces output that was not accordance with the company's specifications. Based on data from 3 production batches, the average yield rate score obtained was 51.96%. The low-yield rate score indicates that the production process is ineffective and inefficient because of the unstable filling process. This research aims to design and manufacture an automatic filling machine with four nozzles and use PLC as its controller so that the production process becomes more ef-fective and efficient by increasing the rated yield and quantity of output and speeding up the cycle time by eliminating the manual weighing process using loadcell and weighing indicator. Based on data from 3 batches of production using the new machine, the average yield rate score obtained was 98.51%, which increased by 46.3%, significantly more than the old machine of 51.96%. The machine also managed to speed up the production cycle time at the filling station. To produce 495 bottles only takes 33 minutes, making the production process 75.49% faster than the old machine of 134 minutes. The increase in output yield and quantity, and the reduction in cycle time show that the production process has become more efficient and effective.

[Manuscript received: 8 Apr 2024 | Accepted: 4 Jul 2024 | Published: : 30 Sep 2024]

Susceptibility Inference and Response on Transmission Dynamics of Ebola Virus in Fuzzy Environment

2024-04-15T11:26:34+08:00

This article uses fuzzy parameters to develop a susceptibility inference and response (SIR) model for the Ebola virus. The construction of the SIR model involves considering several aspects, including immunization, therapy, compliance with medical protocols, and Ebola virus load. The parameters representing the infection, mortality, and recovery rates caused by the Ebola virus are expressed as fuzzy numbers. These parameters are then employed as fuzzy parameters in the model. The study of the model uses the generation matrix approach to get the fundamental reproduction number and assess the stability of the equilibrium point inside the model. The findings from the simulation indicate that the variation in the Ebola virus load is associated with disparities in the transmission patterns of the Ebola virus. Also, we compare the impact of the variables of vaccination and following the medical guidelines in reducing the spread of the Ebola virus. Using Matlab software, the numerical simulation for this model is carried out, and the analysis of Ebola virus transmission is investigated in the fuzzy environment.

[Manuscript received: 14 Apr 2024 | Accepted: 5 Jun 2024 | Published: : 30 Sep 2024]

The Neutrosophic Economic Order Quantity: Backlogged Shortages and Quality Issues

2024-05-13T10:14:45+08:00

This paper investigates an economic order quantity with imperfect qualityitems that are backlogged in the neutrosophic sense.Defuzzification is done by implementingthe signed-distance approach.The objective is to determine the optimal inventory level and optimal backorder quantity that reduces the yearly total cost of the neutrosophic type.Numerical examples are produced to justify the output of the suggested models.

[Manuscript received: 14 Apr 2024 | Accepted: 12 Jun 2024 | Published: : 30 Sep 2024]

Forecasting PM2.5 Concentrations in Chiang Mai using Machine Learning Models

2024-04-15T11:16:07+08:00

Particulate matter 2.5 poses a significant threat to human life. Over the past decade, there has been a significant increase in the number of articles dedicated to studying and forecasting PM2.5 concentrations. Thailand, particularly Chiang Mai, has elevated levels of dangerous PM2.5 throughout the hot season. The primary objective of this study is to evaluate the efficacy of three widely used machine learning models, namely artificial neural network (ANN), long short-term memory network (LSTM), and convolutional neural network (CNN), in predicting the levels of PM2.5 particles in Chiang Mai. The raw data are obtained from the Pollution Control Department, Ministry of Natural Resources and Environment Thailand between January 2014 and June 2023, a total of 3,468 observations. We split the data into three sets namely, training, validation, and test sets. The criterion to evaluate three machine learning techniques is the median absolute error. The experimental results confirm that all three machine learning models provide similar movements of PM2.5 dust pollution. Moreover, the artificial neural network technique provides better results than the others regarding error measurement.

[Manuscript received: 14 Apr 2024 | Accepted: 12 Jun 2024 | Published: : 30 Sep 2024]

Complement Properties of Pythagorean Co-Neutrosophic Graphs

2024-05-13T10:18:01+08:00

The origination of graphs with neutrosophic type where membership of indeterminacy expels the vague results, by increasing the accuracy is used to extend application through the graphical environment. Since it is an extension of the intuitionistic type, there comes an immediate need to extend its findings and application to the neutrosophic type. Reversing the conditions of neutrosophic graphs by introducing the anti-behavior properties will produce an adequate number of new results and data, breaking the backlog in approaching decision-making problems and other real-world applications. This research aims to recognize the complementation concept in the Pythagorean co-neutrosophic graph, which has not been dealt with yet. The co-neutrosophic graph is the reversal concept of neutrosophic graphs, where the vertex and edge membership conditions are reversed, but the total sum of these memberships remains the same. Here, the discussion about complementation, co-complementation, and its properties are carried out on a Pythagorean co-neutrosophic Graph. As a result, an application with improved accuracy result will be obtained as an outcome.

[Manuscript received: 15 Apr 2024 | Accepted: 5 Jun 2024 | Published: : 30 Sep 2024]

Human Fall Motion Prediction – A Review

2024-05-13T12:30:08+08:00

Abstract – In predicting human fall motion, focused on enhancing safety and quality of life for the elderly and individuals at risk of falls. By highlighting the critical role of Human Pose Estimation, advancements in human motion forecasting, and fall prediction. It explores the continuous efforts to improve fall detection systems using innovative technologies, such as wearable sensors and IoT devices to implement deep learning models and analyze human poses and gestures. Various methods show promise in accurately predicting human fall motion by capturing complex patterns and relationships in the data. For instance, self-attention mechanisms can revolutionize human motion prediction by effectively capturing these intricate patterns, leading to more accurate predictions. Future research directions should focus on enhancing model accuracy, exploring new techniques for capturing complex patterns, and enabling real-time implementation in wearable devices or smart environments. By addressing these areas, fall detection systems can be significantly improved, benefiting individuals and healthcare systems worldwide.

[Manuscript received: 15 Apr 2024 | Accepted: 12 Jun 2024 | Published: : 30 Sep 2024]

Performance Evaluation of Machine Learning Techniques on Resolution Time Prediction in Helpdesk Support System

2024-06-04T16:27:55+08:00

Estimating incident resolution times accurately is critical to maintaining an effective resource allocation for customer service. In order to meet this need, this paper explores machine learning techniques widely applied in the Resolution Time Prediction and identify the performance of chosen approaches via benchmarking dataset. The proposed method starts with data preprocessing, such as removing outliers and missing values and determining any irregularities in the resolution times distribution. Subsequently, we automatically choose the most relevant features using various statistical techniques. As the last stage of our prediction pipeline, we will apply different machine learning approaches the dataset to find the effectiveness of model and conclude the best technique based on the model accuracy and model fitting time. By applying this strategy, we hope to gain a better understanding of the factors affecting incident resolution times, which will eventually result in better resource allocation and planning for customer support operations.

[Manuscript received: 19 Apr 2024 | Accepted: 24 Jun 2024 | Published: : 30 Sep 2024]

Design and Development of Electrical Go Kart

2024-05-13T13:36:02+08:00

This study explores the complex process of designing, developing, and building an electric go-kart with a focus on performance and sustainability. Using a multidisciplinary methodology, the research maximizes the vehicle's efficiency and environmental friendliness by integrating the principles of mechanical engineering, electrical engineering, and sustainable design. The study assesses many design factors, including motor power, battery capacity, and chassis materials, to find an ideal balance between performance and environmental impact through methodical experimentation and analysis. In order to improve the kart's energy economy and agility, the project also investigates cutting-edge technologies including lightweight composite materials and regenerative braking systems. The results of this study offer significant contributions to the subject of sustainable transportation, as well as to the development of electric car technology. Through the demonstration of the viability and efficiency of electric go-karts in comparison to their conventional gasoline-powered equivalents, this study highlights the significance of adopting renewable energy solutions within the automotive sector. In the end, the journal clarifies how electric go-karts can transform both competitive and recreational racing, making a strong argument for the broad use of clean energy technology in the quest for a more sustainable and environmentally friendly future.

[Manuscript received: 6 May 2024 | Accepted: 3 Sep 2024 | Published: : 30 Sep 2024]

A Review on Mechanical Fuzzy Logic Control Cutters for Latex Glove

2024-05-13T12:43:57+08:00

Latex gloves are widely used in various industries, such as healthcare, laboratories, and manufacturing. Especially in the healthcare industry, it provides protection for doctors and nurses so that they will not get infected by viruses. The latex gloves contain some types of proteins that will trigger the allergic reactions of people with latex allergies. Therefore, before the latex gloves are sold on the market, protein concentration tests need to be done. In order to do the protein concentration tests, 2 cm by 2 cm samples of the latex gloves are needed. A cutter machine is needed in order to increase efficiency, save time, and also precisely cut. The samples can be obtained by cutting or stamping the latex gloves. In this paper, research in the literature that attempted to identify the mechanical cutters for latex gloves is reviewed. Furthermore, considering the ambiguity and variability in glove materials and cutting requirements, this paper explores the integration of fuzzy logic into cutter selection processes to accommodate uncertain criteria and optimize cutter performance in diverse operating conditions.

[Manuscript received: 6 May 2024 | Accepted: 3 Sep 2024 | Published: : 30 Sep 2024]

Review and Analysis of Mechanical Cutting Tools for Rubber Stamping

2024-05-27T10:36:10+08:00

Rubber industry is one of the major industries in Malaysia. Rubber stamping machine is a machine that cuts rubber sheets into desired shapes and dimensions, facing challenges due to the elastic properties of rubber sheets. These challenges include long process time and non-identical dimension. This review paper focuses on the design of the rubber-stamping machine to address the challenges by reducing process time and producing identical dimensions products. The rubber-stamping machine was fabricated, and analysis was performed to verify its efficiency. The core of the designs is to ensure the user safety, friendliness, and increase productivity and product consistency. Based on the findings from existing studies, the review highlights significant improvements in machine design and operational efficiency. The paper also discusses the impact of these innovations on the competitiveness of rubber stamping operations and provides insights into future directions for research in mechanical design and automation systems. This review can be a crucial resource for developers and manufacturers looking to enhance the efficiency and product quality of rubber-stamping machines, contributing to the advancement of manufacturing practices in the rubber industry.

[Manuscript received: 6 May 2024 | Accepted: 3 Sep 2024 | Published: : 30 Sep 2024]

Hypertension Diagnosis: A Review on Techniques to Measure Blood Pressure

2024-07-31T17:10:58+08:00

Hypertension is both a symptom and a cause of health complications. The consequences of long-term hypertension are more documented than short-term hypertension, so detection methods emphasize the presence of long-term hypertension. These methods require confirmation of consistently high blood pressure. Thus, established methods require long-term observation of the patient, which poses the risk of starting treatment too late for effective mitigation. These methods are also not portable enough for long-term observation to be comfortable. The goals of on-going research into detection of hypertension are the confirmation of hypertension with shorter durations of observation, and comfortable and convenient methods of frequent blood pressure checks. Future methods that are promising include wearable non-auscultatory sensors, AI-assisted comparisons of short-term observation data against databases of readings, and small implants.

[Manuscript received: 14 Jun 2024 | Accepted: 3 Sep 2024 | Published: : 30 Sep 2024]