Electrical energy is energy that can be generated from the conversion of various types of energy, one of which is wind energy. The average wind speed in Indonesia is classified as low speed, only certain areas have moderate to high wind speeds. Low wind speed does not mean that the potential energy contained in it cannot be utilized or converted into electrical energy, it can still be utilized but a generator is needed that matches the characteristics of the wind speed. Therefore, the turbine used is the Savonius type, which can work in low wind speeds. The purpose of this research is to measure output power against wind speed. With experimental methods using an anemometer and measuring voltage, current and output power. From the results of this study, the maximum power generated by the wind power training module is 0.008 Watt with a wind speed of 4.31 m/s and the minimum power generated is 0.001 Watt with a wind speed of 1.17 m/s. In a day, the average wind speed is 2.94 m/s so from the wind speed the wind power training module is able to produce an average power of 0.001 Watt.

Ability For analyze prediction mark something share going up or down as well as level possible risk _ taken into account , much needed stock investors . In study this done analysis risk and correlation between share with calculate daily returns use method moving averages (MA). Besides that with a dataset of 4 stocks (Apple, Google, Microsoft and Amazon) also performed prediction mark stock in period time next (future), with use neural network method (deep learning) Long Short Term Memory (LSTM) model. Result of programming in python language in the form of a number of visualization easy graph _ reading information . Changes in sales volume share No happen in a manner significant , though from term MA chart short and long to four share tend experience decline price since month January to May 2022. Correlation highest 75% between shares of Google and Microsoft, and the lowest 60% between Apple and Google shares. From analysis risk and expected return, obtained results shares Amazon has risk the highest (0.022059) and the lowest expected return (-0.000003). Apple shares with medium risk ( 0.019926 ) and the highest expected return (0.001283). While 2 shares finally Google and Microsoft have risk small below 0.018 with expected return at the level are respectively 0.001019 and 0.000784. predictions time future with the LSTM model, indicating Apple , Google and Amazon will experience increase price stock , meanwhile decline price share will happened at Microsoft.

This study addresses the critical challenge of Diabetic Retinopathy (DR) detection and severity grading, aiming to advance the field of medical image analysis. The research problem focuses on the need for an accurate and efficient model to discern DR conditions, thereby facilitating early diagnosis and intervention. Employing a Convolutional Neural Network (CNN), our methodology is developed to strike a balance between precision and computational efficiency, a pivotal aspect in the context of healthcare applications. The research leverages the APTOS 2019 dataset, a comprehensive collection of fundus photographs, to evaluate the efficacy of our proposed model. The dataset allows for a thorough investigation into the model's performance in binary-class and multi-class classifications, providing a robust foundation for analysis. The most important result of our study manifests in the achieved accuracy rates of 98.67% and 87.81% for binary-class and multi-class classifications, respectively. These outcomes underscore the model's reliability and innovation, surpassing established machine learning algorithms and affirming its potential as a valuable tool for early DR detection and severity assessment. In conclusion, the study marks a significant advancement in leveraging deep learning for ophthalmic diagnoses, particularly in the nuanced landscape of Diabetic Retinopathy. The implications of our findings extend to the broader realm of AI-driven healthcare solutions, presenting opportunities for enhanced clinical practices and early intervention strategies. Future research endeavors could explore further refinements to the model, considering additional datasets and collaborating with healthcare professionals for real-world validation, ensuring the continued progress of AI applications in the medical domain.

Sequential analysis has been used in many cases when the decision is supposed to be taken quickly such as for signal detection in statistical signal processing, namely sequential detector. For identical error probabilities, a sequential detector needs a smaller average sample number (ASN) than its counterpart of a fixed sample number quadrature detector based on Neyman-Pearson criteria. The optimum sequential detector was derived based on the assumption that the observations are uncorrelated (independent). However, in realistic scenario, such as in radar, the assumption is commonly violated. Using a sequential detector under correlated observations is sub-optimal and it poses a problem. It demands a high computational complexity, since it needs to recalculate the inverse and the determinant of the signal covariance matrix for each new sample taken. This paper presents a technique for reducing the computational complexity, which involves using recursive matrix inverse to subsequently calculate conditional probability density functions (pdf). This eliminates the need to recalculate the inverse and determinant, leading to a more reasonable solution in real-world scenario. We evaluate the performance of the proposed (recursive) sequential detector by using Monte-Carlo simulations and we use the conventional and non-recursive sequential detectors for comparisons. The results show that the recursive sequential detector has equal probabilities of false alarm and miss-detection with the conventional sequential detector and performs better than the non-recursive sequential detector. In terms of ASN, it maintains comparable results to the two conventional detectors. The recursive approach has reduced the computational complexity for matrix multiplication to from and it also has rendered the calculation of matrix determinant to be unnecessary. Therefore, by having better probabilities of error and reduced computational complexities under correlated observations, the proposed recursive sequential detector may become a viable alternative to obtain a more agile detection system as required in future applications, such as in radar and cognitive radio.

Microwave imaging has become an active research area in recent years, owing primarily to advancements in detecting the early stages of cancer. The study aimed to create a high-gain compact Vivaldi Tapered Slot antenna (VTSA) for microwave imaging in medical applications and also aims to address several challenges in the development of microwave imaging (MWI) technology for medical applications. These challenges include the ability to detect and identify abnormalities in human tissue and considering safe Specific Absorption Rate (SAR) limits for patients, the approach of balancing of penetration and resolution can be done on the design. The antenna operates at frequencies ranging from 1.7 to 3.1 GHz and is built on a low-cost Flame Retardant-4 (FR-4) substrate with a thickness of 1.6 mm. A compact exponential VTSA is initially presented while designing the proposed antenna for broad impedance bandwidth performances. The simulation used a back-to-back linear array of antennas with or without a phantom, specifically a without phantom (only antennas), a water phantom (cube shape), and an anomaly inside the water phantom. The results revealed a significant shift in the signal graph between the three results, indicating a difference in values between the three simulations. A transient domain solver calculation was used in the simulation. The designed antenna improved a gain of 6.09 dBi and a SAR of 0.326 W/kg by maximizing the edges of the exponential in the tapered section and the feedline slot area. The antenna exhibits differences in scattering parameters on each simulation of anomalies across the required frequency range. The result finds suitability of the experiment and simulation in assessing the microwave imaging capabilities. With the data presented, simulated antennas can be used for microwave imaging. The next study should aim on making a suitable imaging system with dimensions that supported in the antenna range and specifications.

Blended learning has recently acquired popularity in a variety of educational settings. This approach has the advantage of being able to autonomously monitor students' knowledge states using the collected learning data. Moodle is the most widely used learning management system in blended learning environments. Students can access Moodle to obtain supplementary materials, exercises, and assessments to complement their face-to-face meetings. However, its performance can be improved by more effectively tailoring students' skills and pace of learning. Several studies have been conducted on knowledge tracing; however, we have not discovered any studies that particularly investigate knowledge tracing in a blended learning setting with Moodle as a component. This study proposes a scheme for assessment using the features of the Moodle quiz platform. The assessment data is used to conduct knowledge tracing with the Bayesian Knowledge Tracing (BKT) model, which improves interpretability. The aforementioned data were collected from information engineering undergraduate students who completed 88 exercises that assessed 23 knowledge components within the course. We measure RMSE and MAE to evaluate the performance of the BKT model on our dataset. Furthermore, we compare the knowledge tracing performance to other well-known datasets. Our results show that the BKT model performed better with our dataset, with an RMSE of 0.314 and an MAE of 0.197. Moreover, the BKT model can be used to assess student performance and determine the level of mastery for each knowledge component. Thus, the outcomes can be applied to personalized learning in the future.

One of solutions to address the increasing demands for data traffic by mobile users is to implement ultra dense network (UDN). UDN consists of many femtocells that are densely deployed on macrocellular communication networks. Since femtocells radius is very short and they are tightly packed, it faces interferences problem i.e., co-tier and cross-tier interferences. This paper proposes interference management technique based on clustering method for UDN multicellular communication networks at downlink transmission. It is purposed to reduce interference effects. To evaluate the proposed clustering method, two simulation scenarios have been designed; namely baseline system and system with the proposed clustering method. The scenarios applied is to randomly distribute femtocells following a uniform distribution in three macrocells areas. Through a clustering algorithm, adjacent femtocells are grouped into one cluster and assigned different frequency channels for each femtocell in that cluster. The same cluster pattern is repeated for all femtocells. Thus, interference effect is canceled within one cluster and reduced among clusters by widening the distance among femtocells that use the same frequency channels. Through simulation experiment, the proposed clustering method is evaluated and compared to the baseline system. The simulation results show that Signal to Interference plus Noise Ratio (SINR) of femtocells increases 0.44 dB, throughput increases 1.67%, Bit Error Rate (BER) reduces, and other parameters improve as well which include spectral efficiency, network energy, and average network energy. The proposed clustering method increases performances of the networks and provides better solution for transmission data speed in densely femtocell network.

The increasing demand for electrical energy must be balanced with using renewable energy. However, the use of renewable energy requires a system to maximize the conversion of renewable energy into electrical energy. Various topologies have been studied to achieve maximum energy conversion. Two types of inverters, namely step up-down, have been widely used but have their respective limitations. Step-down inverters can only be used in lower output voltage than input voltage conditions with simple control, while step-up inverters can only operate in higher output voltage than input voltage conditions. This paper aims to combine these two converters to have both step-up-down voltage functions, with the goal of expanding the operating range and maintaining the advantages of each type. Thus, a topology called a step-up-down five-level inverter with a simple voltage-controlled capacitor balancing system is proposed in this paper. Finally, the simulation and laboratory tests were done. This inverter operates in step-up-down voltage, and the voltage in capacitors is always balanced to reach the desired level. The voltage control was done to get the constant voltage event the load was changed.

With current developments, underwater communication using acoustic signals is widely used. Many things need to be prepared to support a reliable underwater communication system, such as taking measurements in a test tank to find out the correct measurement configuration. Underwater acoustic intensity measurements, which are detailed in this paper, are performed in the test tank using distance variation schemes. Measurements were made at various distances of 4, 10, 20, and 50 meters from the signal source. The hydrophone that was used has a sensitivity of -180 dB re 1V/µPa. The hydrophone was placed at a depth of 2 meters below the surface of the water in the test tank, which divided the test tank depth in half to ensure that reflections from the bottom and the surface were kept to a minimum. However, the problem is that there are noisy signals at different frequencies. This paper proposes a method using Noise Assisted - Multivariate Empirical Mode Decomposition (NA-MEMD) to decompose the signal and then calculate the sound intensity. The result shows that an increase in the distance between the transmitter and receiver, also causes a change in the intensity with an average change of 0.467 dB/meter. It is concluded that the NA-MEMD approach was shown to be successful in decomposing the intended signal from the noise to equalize the quality of the signal received at different distances, and the correlation between intensity value and change in distance is resilient, with a correlation value of 0.98, indicating a very strong correlation.

The Distributed Generator of a Photovoltaic System (DGPVS) is an essential factor for future power plant generation, and it can be created by connecting multiple small power plant generators in a microgrid system. This paper focuses on the overcurrent protection of a three-phase grid-connected inverter (3P-GCI) under voltage sag conditions in sustaining connection loss between the 3P-GCI and the primary grid, which involves voltage instability. The ancillary service shows more advantage in overcurrent protection during voltage sags, which limits the generated current under sag duration. Its service can protect the inverter and avoid more disturbances to the primary grid because the 3P-GCI remains connected. Proposed LVRT strategy with limit current feature play the role to protect 3P-GCI under voltage sag. In the normal grid, the 3P-GCI can inject 302W of active power with a power factor (PF) equal to one. 1.4% of VTHD and 4.3% of ITHD shows the performance of the proposed system. Meanwhile, the 3P-GCI injects 239VAr reactive power and reduces injected active power to 135W which is essential to remain connected to the primary grid during voltage sags and limit the generated current. The validation results show that this prototype successfully compensates for the grid voltage drops by injecting 239Var of reactive power and limiting its generated current to 1.592A.