Shunt Adaptive Power Filter (SAPF) is widely used in the performance of power quality improvement activities in the power supply industry for processing industries or civil power sources in the world today based on its simplicity, transparency, high reliability, efficiency, and reliability, and their powerful compensating current-providing nature. The PI controller integrated into the SAPF operation mechanism works with extra high efficiency in selecting the current to compensate for the lost current generated in the power supply due to harmonics generated by the $K_p, K_i$ parameter values. The system operates by the PWM method for bridge rectifier circuits that perform the function of selecting the appropriate compensating current, providing correct compensation for the amount of current loss in the power supply. Adjusting the $K_p, K_i$ parameter to reach the optimal value by different methods is a promising and popular research direction at present. The $K_p, K_i$ parameter serves the right purpose for the PI controller to generate enough PWM pulses to excite the bridge rectifiers to generate just the right amount of compensating current and enough current to be compensated on the power supply. The commonly used $K_p, K_i$ parameter adjustment methods include the Ziegler Nichols closed-loop vibration method, the P-Q theoretical method, and several other methods. This study conducts a comprehensive review of the literature on modern strategies for adjusting the $K_p, K_i$ parameters in the PI controller in the SAPF suite by using the meta-heuristic optimization method. This study performs classification according to the operation mode of meta-heuristic optimization methods to adjust the $K_p, K_i$ parameter to control the PI to select the correct PWM frequency to activate bridge rectifiers to select the most optimal compensation current to compensate for the loss of current on the power supply to meet the goal of improving power quality in accordance with IEEE 519-2022 standard, leading to the total harmonic distortion (THD) value is below 5%. The study presents in detail some meta-heuristic optimization algorithms, including applications, mathematical equations, and implementation of flow charts for SAPF and provides some open problems for future research. The main objective of this study is to provide an overview of applying meta-heuristic optimization algorithms to the $K_p, K_i$ parameter tuning of PI controllers.

Customers’ requirements for product quality are increasing and more and more manufacturing companies are improving the quality of processing conditions at each production process by applying advances in Internet Technologies, Internet of Things, Big Data, Industry 4.0, Industrial Engineering Tools, Multi-objective optimization algorithms, Digital Numerical Control (DNC) method, and Six Sigma tools to improve productivity and quality of product. Continuous improvement of the manufacturing process presents a huge opportunity for the transformation of the normal production model to smart manufacturing. Manufacturing condition data, production data, quality of product data, and power quality data are measured and collected in real-time forming big data fed into operation analysis of the development strategy of the manufacturing company. In this study, the implementation of the Hybrid DMAIC (Define-Measure-Analyze-Improve-Control) method in six sigma is presented in detail based on the statistical hypothesis testing techniques for analyzing production data applied at the measurement phase, Multi-objective decision Fuzzy TOPSIS techniques applied to choose the best improvement solution applied at the analysis phase, Multi-objective optimization technique optimizes production conditions, and the DNC method performs automatically program call by RFID system applied at improvement phase, measurement technique, real-time measurement result collection and analysis by Industry 4.0 system and the method of phase shift is used to keep Total Harmonic Distortion (THD) and Total Demand Distortion (TDD) less than 5% within the international standard limits defined by IEEE 519:2022 and IEC 61000 applied at control phase. A common data system linking all activities at a particular manufacturing process and hybrid DMAIC in Six Sigma continuous improvement model is proposed in this study. This research’s results, the rate of occurrence of smaller-than-standard outer diameter defects decreased from 31.20% to 4.5% per month, the grinding process productivity achieved 610 pcs per day while the number of operators decreased from 4 people to 2 people per day, and productivity per person per hour increased from 15 PCS to 30 PCS. The profit is achieved at 11,184 USD per year. The Hybrid DMAIC method in Six Sigma is considered a model of continuous improvement that can be applied to production processes in manufacturing companies. The Hybrid DMAIC method in Six Sigma is simple to implement, easy to deploy into the real environment at production processes, and uses commercial software such as Minitab, LABVIEW, and SPSS. Manufacturing companies apply this research model to help improve competitiveness, improve business performance, and improve customer satisfaction.

This article presents a comprehensive system for testing and verifying shunt active power filter control methods. The aim of this experimental platform is to provide tools to a user to objectively compare the individual control methods. The functionality of the system was verified on a hardware platform using least mean squares and recursive least squares algorithms. In the experiments, an average relative suppression of the total harmonic distortion of 22% was achieved. This article describes the principle of the shunt active power filter, the used experimental platform of the controlled current injection source, its control system based on virtual instrumentation and control software and ends with experimental verification. The discussion of the paper outlines the extension of the experimental platform with the cRIO RTOS control system to reduce the latency of reference current generation and further planned research including motivation.

The mechanical components manufacturing industry is carried out through turning processes using CNC machines. Improving productivity and improving product quality in the CNC process is necessary. Automating operations at the CNC process with Industry 4.0 technology at low cost is a direction in the right direction with the development trend of Industry 4.0 technology. Making decisions to improve CNC processes at operations and implementing continuous improvement is an urgent activity. Industry 4.0 technology helps connect data between customer companies, business sectors, and supply chain partners in a more specific and simple way. Commonly connected data between companies helps decision-makers at each company have a common source of analytical data to make more effective production and business development decisions. Industry 4.0 technologies are the subject of much research, but it is important to consider how to integrate them into the manufacturing world in the most efficient and effective way possible. Manufacturers will benefit from this article’s additional insights to solve their problems more effectively. This paper proposes a Hybrid Six Sigma methodology based on Fuzzy TOPSIS and PLS-SEM methods. First, the Fuzzy TOPSIS method helps decision-makers to select problem points for improvement in an unknown environment to implement continuous improvement. Second, the PLS-SEM method evaluates the impact factors of the results of continuous improvement at the mechanical component factory. The Industry 4.0 technology is proposed to be designed, tested, and installed in the CNC manufacturing process. As a result of this study, the product length dimensional error rate decreased from 54.90% per month to zero defects, saving $9593 in annual production costs. Research machines are semi-automatic and cannot increase or digitize their performance because of studies on the application of low-cost Industry 4.0 technology systems to increase the capacity of industrial processes. This study contributes by using Industry 4.0 technology’s low-cost way to improve manufacturing processes using the Hybrid DMAIC method in Six Sigma methodology. This hybrid approach is adaptable and can be used with different business process improvement models.

Underwater visible light communication (UVLC) offers a high-speed and low-latency alternative to traditional underwater data transmission methods such as acoustic and radio frequency systems, which face inherent limitations in aquatic environments. This letter presents the design and implementation of an orthogonal frequency-division multiplexing (OFDM)-based UVLC system using multi-order quadrature amplitude modulation (M-QAM) with a focus on reliable performance in real-world conditions. Experiments were conducted in three water conditions—clear, murky, and dynamically turbid—using a 545 nm LED. The results show that 64-QAM provides stable performance across all conditions, while higher-order schemes, such as 128-QAM and 256-QAM, offer increased data rates but suffer from higher bit error rates under turbidity. The study demonstrates that the proposed system can maintain short-range high-throughput communication in variable underwater environments.