The frequency response of the power system is changing because of the inverter-based generator connection. To depict a more realistic frequency characteristic, an adaptive virtual synchronous generator (VSG) control strategy including the inertia supporting assessment index is proposed. The virtual inertia in VSG can be adjusted according to the frequency response process. And the droop coefficient is changed proportionally. Besides, the inertia supporting assessment index F d is introduced to judge the FR results so that the operator can choose the optimal control parameter sets. The simulation results verified the accuracy and applicability of the proposed method. The method can also help to research on the frequency response and inertia estimation in the power system with high proportion power electronics.
Topic modeling is an important and interesting research area that can assist in discovering patterns and underlying themes in large datasets. This research aims to identify commonly used topics in previous undergraduate thesis research through text mining using Latent Dirichlet Allocation (LDA) as the topic modeling method. The study utilizes abstracts as the primary source to identify the content of the research documents accurately and quickly. We collected a dataset of 666 abstract theses and utilized LDA to identify the dominant themes and topics within the corpus. Our results indicate that the LDA model achieved a coherence score of up to 0.448, indicating a reasonable level of coherence in the identified topics. We identified six main topics within the dataset, including system analysis and design, data mining, computer networks, decision support systems, software testing, and computer security. The result of this study is expected to serve as a useful reference for students and thesis supervisors in selecting future thesis topics and identifying novel and underexplored topics.
Semantic segmentation can help in everyday life, especially in the medical field, to help detect cancer metastasis at an early stage. In semantic segmentation, CNN-based approaches have been known to dominate the semantic segmentation field, such as FCN and DeepLabv3+. The success of the Transformer approach in the Nature Language Processing (NLP) area triggered many researchers to use the Transformer approach in solving semantic segmentation problems, so the Vision Transformer (ViT) was born. ViT accepts images as patches to produce local and global attention, unlike the convolution approach. One ViT-inspired model, SegFormer, combines a Hierarchical Transformer encoder component to generate low-resolution fine features that focus on capturing small detailed, fine-grained information such as edges, corners, and local patterns, and high-resolution coarse features that focus on capturing more general and global characteristics of a scene, and a lightweight All-MLP decoder to combine those multi-level features to produce the final semantic segmentation mask. This allows SegFormer to capture local and global contextual information from an image. This paper proposed the SegFormer model to perform semantic segmentation of bone scan images into 12 classes based on bone regions. As a result, by comparing FCN and DeepLabv3+ convolution approaches, SegFormer outperforms both models with the highest mIoU value of 77.86%.
Electricity consumption forecasting is vital to policymakers and electricity providers for power generation planning and management. Over-generating electricity results in high cost and resource wastage. Conversely, electricity underproduction may cause a productivity loss to customers. Therefore, it is important to accurately predict electricity demand to effectively plan and deliver electricity production. This paper offers a review of methods employed to model and predict electricity consumption and demand in Indonesia. This paper also investigated what factors are taken into account to project trends in electricity consumption and demand. Finally, this paper lists evaluation metrics that indicate the accuracy of each prediction method.
The pandemic has impacted how people approach their local clinics, and researchers have developed a telemedicine system to address the resulting challenges. The CheckApp system was developed in five phases: research and analysis, CheckApp development, feature integration, clinic, and patient portal development, and was evaluated using ISO-certified testing tools. The system has a completion rate of 95%, and researchers tested it with five respondents: Quality Assurance, Clinic/Doctor, IT Professional, Juvenile, and Adult Patient. The respondents’ feedback was calculated using the SUS testing tool, and the overall weighted average score was 76.5, indicating that the system is acceptable, feasible, and qualified for deployment. The CheckApp system provides a seamless workflow process that can organize the clinic, patient, and doctor’s schedules while adhering to government-mandated health and safety protocols due to the pandemic. This system is significant and valuable, especially to the medical sector of society, in the current state of the Philippines.
