Scholars worldwide have shown considerable interest in the industrial sector, mainly due to its abundant resources, which have facilitated the adoption of conveyor belt technologies like Robotic Arm-Based Conveyor Belts (RACBs). RACBs rely on four primary movements: (i.e., joint, motor, gear, and sensor), which can have a significant impact on the overall motions and motion estimation. To optimize these operations, an assistive algorithm has been developed to enhance the effectiveness of motion by achieving favorable gains. However, each motion requires specific criteria for Fractional Order Proportional Integral Derivative (FOPID) controller gains, leading to various challenges. These challenges include the existence of multiple evaluation and selection criteria, the significance of these criteria for each motion, the trade-off between criterion performance for each motion, and determining critical values for the criteria. As a result, the evaluation and selection of the Proposed Jaya optimization algorithm for RACB motion control becomes a complex problem. To address these challenges, this study proposes a novel integrated approach for selecting the Jaya optimization algorithm in different RACB motions. The approach incorporates two evaluation methods: the Nonlinear Autoregressive Moving Average with exogenous inputs (NARMA-L2) controller for Neural Network (NN) weighting of the criteria, and the Adaptive Neuro-Fuzzy Inference System (ANFIS) for selecting the Jaya optimization algorithm. The approach consists of three main phases: RACB-based NARMA-L2 Controller Identification and Pre-processing, Development of NARMA-L2 controller-based NARMA(L2)-FO(ANFIS)PD-I, and Evaluation of FOPID criteria based on JOA. The proposed approach is evaluated based on NARMA(L2)-FO(ANFIS)PD-I that given 0.4074, 0.3156, 0.3724, 0.1898 and 0.2135 for K_p_joint, K_i_motor, K_d_sensor, λ_gear, and µ_N respectively, which verifies the soundness of the proposed methodology.

The issue of proper modeling and control for industrial systems is one of the challenging issues in the industry. In addition, in recent years, PID controller design for linear systems has been widely considered. The topic discussed in some of the articles is mostly speed control in the field of electric machines, where various algorithms have been used to optimize the considered controller, and always one of the most important challenges in this field is designing a controller with a high degree of freedom. In these researches, the focus is more on searching for an algorithm with more optimal results than others in order to estimate the parameters in a more appropriate way. There are many techniques for designing a PID controller. Among these methods, meta-innovative methods have been widely studied. In addition, the effectiveness of these methods in controlling systems has been proven. In this paper, a new method for grid control is discussed. In this method, the PID controller is used to control the power systems, which can be controlled more effectively, so that this controller has four parameters, and to determine these parameters, the optimization method and evolutionary algorithms of genetics (EGA) and PSO are used. One of the most important advantages of these algorithms is their high speed and accuracy. In this article, these algorithms have been tested on a single-machine system, so that the single-machine system model is presented first, then the PID controller components will be examined. In the following, according to the transformation function matrix and the relative gain matrix, suitable inputs for each of the outputs are determined. At the end, an algorithm for designing PID controller for multivariable MIMO systems is presented. To show the effectiveness of the proposed controller, a simulation was performed in the MATLAB environment and the results of the simulations show the effectiveness of the proposed controller.

Temperature forecasting is a crucial aspect of meteorology and climate change studies, but challenges arise due to the complexity of time series data involving seasonal patterns and long-term trends. Traditional methods often fall short in handling this variability, necessitating more advanced solutions to enhance prediction accuracy. LSTM and GRU models have emerged as promising alternatives for modeling temperature data. This study is a literature review comparing the effectiveness of LSTM and GRU based on previous research in temperature forecasting. The goal of this review is to evaluate the performance of both models using various evaluation metrics such as MSE, RMSE, and MAE to identify gaps in previous research and suggest improvements for future studies. The method involves a comprehensive analysis of previous studies using LSTM and GRU for temperature forecasting. Assessment is based on RMSE values and other metrics to compare the accuracy and consistency of both models across different conditions and temperature datasets. The analysis results show that LSTM has an RMSE range of 0.37 to 2.28. While LSTM demonstrates good performance in handling long-term dependencies, GRU provides more stable and accurate performance with an RMSE range of 0.03 to 2.00. This review underscores the importance of selecting the appropriate model based on data characteristics to improve the reliability of temperature forecasting.

Robotics forms an integral part of industry 4.0, the industrial revolution of the 21st century. This paper presents a bibliometric analysis of Web of Science (WoS) indexed publications addressing this emerging field from 2011 till June 2022. WoS research publications were firstly analysed along multiple verticals such as annual counts, types, publishing sources, research directions, researchers, organizations, and countries. Next, co-authorship collaborations among authors, organizations, and countries were discovered. This was followed by an analysis of co-occurring keywords related to robotics in industry 4.0. Finally, a detailed citation analysis was carried out to unearth citation linkages among authors, institutions, documents, nations, and journals. Latest trends, under-investigated topics, and future directions are also discussed. Primary results indicate that more than 3000 articles are being published annually in this emerging field, with a total of 18,893 documents published in WoS during the last decade. The 'IEEE Access', Chinese Academy of Science, Wang Y. (USA), and the USA emerged as the topmost productive journal, institution, author, and nation. Porpiglia Francesco (Italy), Chinese Academy Science and USA obtained the highest co-authorship total link strength (TLS); whereas Lee Chengkuo (Singapore), China, Chinese Academy Science, and the IEEE Access scored the highest citation TLS among authors, countries, organizations, and sources respectively. Machine learning (ML) emerged as the highest co-occurring keyword, followed by artificial intelligence (AI). Computer Science emerged as the most trending research domain, followed by general applications. In the future, ML and AI will advance more sophisticated robots in industry 4.0 systems.

Meat may provide not only essential nutritional content but also possible harmful effects on human bodies. Unsafe consumption of meat potentially triggers colorectal cancer risks. Grilling is the most popular way to consume meat. However, meat grilling triggers the formation of hazardous chemical substances such as poisonous Polycyclic Aromatic Hydrocarbons (PAHs). This study conducted experiments using hardware engineering with microcontrollers and different gas sensors, aiming to identify gas substances produced by meat during grilling. The hardware prototype for the test simulation tool was assembled with integrated block systems and circuits. Evaluations were conducted on the direct grilling of three different types of meat. The data results were then utilized to analyze gas substances produced by meat during direct grilling. Based on the results, only five of the seven MQ-type gas sensors used in the research reacted to gas substances produced by all types of meat: LPG, alcohol, carbon monoxide, methane, and carbon dioxide, which were successfully detected in meat during grilling. Our research contributes to discovering a potential prevalence of increased alcoholic content in meat that has been grilled for five minutes. This finding is especially crucial for Muslims since it is highly correlated with halal certification of meat consumption. According to the results, Muslims should wait at least seven minutes or more after direct grilling to let the alcoholic content in meat thoroughly decrease so that it can be safely certified as halal to be consumed according to Islamic laws. Doi: 10.28991/HIJ-2023-04-03-01 Full Text: PDF

This paper purposes to demonstrate the effectiveness of fuzzy logic controller (FLC) over proportional integral (PI) controller for reducing the fault current and maintaining the voltage profile at different faults conditions using Static Synchronous Compensator (STATCOM) which is considered an effective FACTS (Flexible Alternating Current Transmission System) device. The study evaluates the performance of a power system equipped with STATCOM which is connected in shunt with bus B1 under various faults conditions, including single-phase and three-phase faults. The performance of the STATCOM is evaluated by using two different controllers: PI controllers and FLCs. A comparative analysis is done between the performances of the two different controllers using Matlab/Simulink software package. The results obtained conclude that the presented system gives better performance with STATCOM as compared to not using it under several faults conditions besides, the STATCOM gives better response with FLC as compared to PI controller. It is demonstrated that STATCOM with FLC can reduce the positive sequence fault current at bus B1 ‎to 96.49% of its value without ‎using STATCOM under line to ground fault and 98.17% under three line to ‎ground fault whereas STATCOM with PI controller can reduce it ‎to (99.57, 99.05%), respectively. Also, the bus voltage B1 is improved to 102.19% by using STATCOM with fuzzy controller under line to ground fault and 101.86% under three line to ‎ground fault whereas STATCOM with PI controller can improve it ‎to (100.21, 100.93%), respectively.