This paper introduces a novel master selector for master-slave technique to synchronize the rotational speeds of multiple motors. There are several well-established speed synchronization techniques for multiple electric motor systems. Master reference and master-slave are simpler techniques for more than two motors with some drawbacks. Master reference works for uncoupled motors and in master-slave technique slave's performance is highly dependent on the master. The proposed speed synchronization technique is offered for system with two or more by way of modifying the master-slave technique to its simpler form to improve the synchronization performance of Permanent Magnet Synchronous Motors (PMSM). It works by generating a dynamic master motor that allows a dynamic switch among motors to take turn to be the master based on the applied load. Evaluation of this technique is done through Matlab-Simulink® environment and hardware simulation with various load scenarios. The performance of the new technique is then compared against two existing techniques: master reference and conventional +master-slave without a master selector. The computer and hardware simulation results demonstrate that the proposed technique achieves lower unsynchronized speeds compared to the other two methods. Furthermore, this study also infers that simplifying the synchronization structure does not compromise synchronization quality.

Condensers are vital components in a steam power plant that condense the exhaust steam from steam turbines. However, the performance of the condensers often decreases due to the flow reaching the critical pipe area on the condenser tube surface, which can cause corrosion. This study aims to analyze the performance of a condenser in a steam power plant by adding an airfoil guide vane as an impingement plate. Computational Fluid Dynamics (CFD) simulation analyzed the flow characteristics in critical pipes (P_1, P_3, P_4, P_6) and examined the effect of adding a shape airfoil guide vane as an impingement plate at variations of 15°, 30°, and 45° Angle of Attack (AoA). The best results were obtained in the 45° variation by significantly reducing the drag coefficient (Cd) up to 56.73%, and the pressure coefficient (Cp) is 3.21%. Furthermore, adding an airfoil guide vane as impingement plates reduces Cp by 25.47%, Cd by 16.57%, and the heat transfer rate by 28.81%. Adding an airfoil guide vane directs the flow distribution better, maintains turbulence intensity, and improves condenser performance.

Indonesia has 17,503 islands, with most being isolated islands, causing limited access to electricity supplies. As a tangible action to address the issue, the Central Government has implemented the LTSHE program. PLN is mandated to replace the program after a maximum installation period of three years. PLN is currently developing methods that can be employed to electrify regions in isolated islands. Electrical system planning can commence with load forecasting to assist in determining the required power plant capacity. In forecasting electrical load on limited isolated islands, the use of spatial methods is proposed. The advantage of this method lies in considering the direction of spatial development patterns in a region in accordance with local regulations. The direction of the load projection results will be compared with the ARIMA method. Significant differences are evident in the presentation of load projection results, where the Spatial Demand Forecasting method uses a 90% confidence level in the form of a range. In contrast, the ARIMA method has a MAPE of 2.953% in the form of a single number. The findings from this comparative are crucial for evaluating trade-offs in electrical system planning when selecting a relevant electric load projection method for isolated islands.

Sentiment Analysis (SA) involves collecting opinions regarding a particular subject and classifying them based on emotional content; it refers to opinion mining, which examines product reviews and online feedback. Capturing relationships of long sequences, contextual meaning, and effectively achieving Cross-Lingual Sentiment Classification can be considered key challenges of the SA. This paper performs a comparative analysis of traditional sequence models, such as Gated Recurrent Unit (GRU) and Bidirectional GRU (Bi-GRU), and state-of-the-art transformer-based models, including Bidirectional Encoder Representations from Transformers (BERT) and XLM-RoBERTa (XLM-R). The traditional sequence models are enhanced with pre-trained Word2Vec embeddings. Firstly, all the models are trained and evaluated using the English IMDB dataset. Then, the XLM-R is assessed on the Spanish IMDB dataset without further fine-tuning. The results indicate that both BERT and XLM-R surpass GRU and Bi-GRU in sentiment classification accuracy. XLM-R obtained the best results, with an accuracy of 93.21%, slightly outperforming BERT, which achieved an accuracy of 93.11% in English. XLM-R was also utilized for zero-shot cross-lingual sentiment classification, achieving a sentiment accuracy of 93.12% in Spanish, close to its performance in English.

This paper addresses the blow-up behavior of a degenerate and singular nonlinear parabolic problem governed by a second-type integral condition. First, the existence and the uniqueness of solutions to this complex problem, highlighting its mathematical originality, are established. Subsequently, the conditions under which blow-up occurs within a finite time frame, a critical aspect of the study that contributes to the understanding of nonlinear dynamics in parabolic equations, is analyzed. In order to enhance the theoretical insights, various numerical approximations that illustrate the finite-time blow-up behavior of the solutions are employed. The paper provides a detailed exploration of the interplay between analytical findings and numerical simulations, offering valuable contributions to the field of nonlinear parabolic problems.

Effective gait rehabilitation is crucial for children patients with gait impairment due to neurological disorders to keep their mobility while growing up. Children's lower-limb movements (gait cycle) can be enhanced by using wearable robotic systems or Lower-Limb Exoskeleton (LLE) systems. However, most prototyped double-leg exoskeletons have been developed for elderly rather than children (pediatric) patients (i.e., exoskeletons for children rehabilitation are still limited). As a result, there are minimal studies available on existing children's exoskeletons. However, there is still a need for enhancement in the design, the actuation, and the control of these devices to make them cost-effective in the worldwide shop. This paper presents a review of different exoskeleton systems in the form of the design of mechanical structures, actuation approaches, and control schemes for Children's Gait Rehabilitation (CGR). It is noted from this review that the most prototyped exoskeleton for children is an active (powered) double-limb exoskeleton, and not even a study has been found on developing a semi-active, single-limb exoskeleton for children with muscular weakness. In addition, it is noted there does not appear to be an appropriate LLE device that inserts the requirements of the children population, such as lightweight, rapidly adjustable, and easy to doff and don. Lastly, the main concerns hampering the improvement of children training exoskeletons have been discussed.

This paper proposes a novel hybrid methodology for short-term forecasting of hourly. The F-Multi Criteria Decision Making-Neural Network (F-MCDM-NN) hybrid model is designed to forecast Global Horizontal Irradiance (GHI) 1 day ahead based on Meteorological data for the target photovoltaic generation. The proposed hybrid modeling is based on meteorology data i.e. wind speed, wind direction, pressure, rainfall, temperature, humidity, gust, and visibility, data especially for optimizing the operation of power generating electricity from photovoltaic (PV) generation system. This modeling is a hybrid of Fuzzy-Multi Criteria Decision Making (F-MCDM) algorithm modeling and Neural Network (NN) model. The novelty of this combination model is taking into account the meteorology data. A set of data on GHI was from the PV station in Indonesia, which is used as research test data. The first hybrid model implements F-MCDM as an input data preprocessing technique prior to the NN method. The error statistical indicators of the F-MCDM-NN hybrid model using the Mean Absolute Percentage Error (MAPE) is 7.5% and the Root-Mean-Square Error (RMSE) is 73.7 W/m2. The hybrid model prediction is then compared to measured data and simulation results indicate that the F-MCDM-NN-based hybrid model presented in this research can calculate hourly GHI with satisfactory accuracy with better result compared to the Neural Network method.

The call for improvement in ship energy efficiency for sustainable shipping leads to emerging development of energy saving devices. Amongst all devices, propeller boss cap fins has gained much interest due to their benefit in improving the propulsive efficiency and ease of installation. However, past researches in the field have disclosed that each design parameter behaves differently in sharing the increment of overall propulsion performance. This study presents the optimization of propeller boss cap fins design parameter by using CFD analysis. The initial main geometrical parameters opted for the study are the significant ones suggested by previous researches. Various design alternatives using the parameters have been simulated and the influences on the propulsion performance have been analyzed. The examination carried out revealed that each design configuration contributes to the net thrust and torque qualities in various ways. The optimum configuration achieves an efficiency up to 3.23% compared to the conventional propulsion system. Observation on the wake flow downstream the device indicates that the additional fins from the device restrained the flow downforce, weakened the hub vortex, and in return boosted up the propulsion system efficiency. As a standalone retrofit using the device is proven effective, a combined retrofit with other devices would be more fruitful.

This work investigated the gradient performances during Neural Network (NN) model training: the Vanilla NN (VNN) model and the designed hybrid adaptive NN model using measurement datasets from a Line-of-Sight (LOS) area 1 and a Non-Line-of-Sight (NLOS) area 2 from a Long-Term Evolution (LTE) micro-cell environment. The NN model training results from tables 1 to 4 for training with the dataset from LOS Area 1 show that the gradient stopped value after the NN model training of the VNN model using the Levenberg Marquardt (LM) training algorithm gave 1.29 and trained 13 epochs, while training the same VNN model with the Bayesian Regularization (BR) training algorithm gave a gradient stopped value of 0.084 with 32 epochs. Training the NN model with a hybrid adaptive NN model using the LM training algorithm gave a stopped gradient value of 3.23 with 11 epochs, while training the same hybrid adaptive NN model using the BR training algorithm gave a stopped gradient value of 0.055 with an epoch of 20. The same pattern of results was seen when training the NN models with datasets from NLOS Area 2. The training results showed effective training of the NN with a low gradient stopped value using a designed hybrid adaptive NN model as well as the exertion of the BR training algorithm over the VNN model and the LM training algorithm. This is because the hybrid adaptive NN model combines both the merits of ADALINE and VNN models for optimal performance, and also because the BR training algorithm serves as an efficient training technique that compensates for the exaggerated amount of the network parameters during network training, thereby avoiding over-fitting.

This paper considers indoor Optical Wireless Communication (OWC) channel modeling. Time delay spread, rms delay spread along with the channel impulse response, and channel frequency response are evaluated and presented. The presence of multiple reflections from walls, doors, and room furniture in the channel model leads to multipath fading. As a consequence, this multipath fading causes the signal to become time-dispersed, eventually resulting in a deterioration of the Bit Error Ratio (BER). To overcome this challenge, a Low-Density Parity-Check coding (LDPC), as standardized in the Fifth Generation (5G) wireless system, is implemented for Differential Phase-Shift Keying (DPSK) with various code rates. The performance of transmission of 4 DPSK and 8 DPSK coded with LDPC over an indoor OWC was evaluated for the first time. Bit Error Ratio (BER) results show that, even in differential encoding, LDPC performs very well in indoor OWC, providing low cost and less complexity. The proposed approach achieves a gain of 7.75 dB of SNR at a BER of 10-5 using ½ rate LDPC and 4 DPSK. ½ code rate offers superior results for both 4 DPSK and 8 DPSK.