Kongzhi yu Juece/Control and Decision (ISSN:1001-0920) is a monthly peer-reviewed scopus indexed journal originally founded in 1986. It is sponsored by the Ministry of Education, china and Northeastern University, china. Kongzhi yu Juece/Control and Decision (ISSN:1001-0920) has always adhered to the correct purpose of running the journal, and has been committed to gathering and disseminating excellent academic achievements, inspiring technological innovation, and promoting the development of disciplines in my country.Aiming at major national needs and international frontiers, this journal has published a large number of original and high-level research result. The journal was selected into the "China Science and Technology Journal Excellence Action Plan Project" in December 2019.In the future, it will strive to build an open innovation, collaborative integration.
Activated carbon was produced from Arenga pinnata peel using chemical activation with 2M HCl followed by pyrolysis at temperatures ranging from 500°C to 700°C in a nitrogen environment. X-ray diffraction results confirmed that increasing the pyrolysis temperature enhanced crystallinity, as shown by sharper (002) and (100) peaks and a reduction in interlayer spacing from 0.381nm to 0.369nm. FTIR analysis revealed a decline in O–H absorption and a rise in C=C bond intensity, pointing to greater graphitization with elevated temperatures. SEM images illustrated the transition from dense, low-p
Wind energy is one of the forms of renewable energy being developed to convert mechanical energy into electrical energy. In the Aceh Island region, wind speeds range from 4 to 6 m/s for about 4 to 5 hours per day, making it a potential source for power generation. The electrical energy produced by a wind turbine is influenced by the weight and number of blades. This study examines the effect of variations in blade weight and quantity on wind turbine performance. The blades used have a length of 1 meter, with weight variations of 0.75 kg, 1 kg, 1.25 kg, and 1.5 kg, and blade quantities of 4, 5,
The efficiency of photovoltaic (PV) systems depends on accurately and rapidly tracking the maximum power point (MPP) under dynamic conditions. Conventional maximum power point tracking (MPPT) methods like Perturb and Observe (P&O) suffer from slow convergence, steady-state oscillations, and poor adaptability. This study proposes an artificial neural network (ANN)-based MPPT to improve tracking performance by leveraging key electrical characteristics of PV systems and DC-DC boost converters. The ANN model is trained using critical electrical parameters, including maximum current, voltage at MPP
Artificial Intelligence (AI) is rapidly transforming healthcare delivery through automation of diagnostic, clinical, and administrative processes. Intelligent systems such as machine learning algorithms, decision support tools, and automated diagnostics are increasingly adopted to improve accuracy, efficiency, and patient-centered care. However, the effectiveness of AI in real-world clinical settings requires empirical validation. This study aimed to evaluate the impact of AI-based automation on clinical efficiency, diagnostic accuracy, patient satisfaction, system usability, and acceptance of
This study discussed the accuracy of wavelet coefficients in identifying fatigue features. The accuracy was based on the correlation coefficient and coefficient of determination obtained from the scattering of wavelet coefficients and the fatigue life developed using Fuzzy C-Means-based integrated clustering algorithm. During the analysis, fatigue-based strain data were simulated within a range of 200 to 2,000 µε of using both constant and variable amplitudes, with mean values set to negative, zero, and positive. Wavelet transforms included were Morlet wavelet and the 4th, 12th, 20th, and 30