Community Battery System Sizing To Maximize Financial Returns to the Prosumers in PV-Rich Neighborhood
The paper extends the knowledge on Community battery systems and sustainable energy.
Utilizing Caesium-based Vacancy-formed Materials For All-Perovskite Tandem Solar Cells: Photovoltaic Evaluation Using SCAPS 1-D
In this study, we reported the simulation study of lead-free all-perovskite tandem solar cell comprised of varied vacancy formation perovskite structure as wide bandgap absorber layer for FAMASnGeI3. The observations revealed that all-perovskite tandem solar cell of FTO/ZnO; 400 nm/Cs2AgBiBr6; 600 nm/FAMASnGeI3; 200 nm/Cu2O; 100 nm/Au achieved a notable PCE of 22.63 % at an operating temperature of 300 K with Jsc; 27.09 mA/cm2 Voc; 1.10 V and FF; 75.68 %. These findings suggests that this cell has strong potential in converting sunlight into electrical energy and indirectly will contribute to the advancement of environmentally friendly and high-performance solar cells, promoting the broader adoption of renewable energy technologies.
Impacts of Uncoordinated Electric Ferry Charging on Distribution Network
This study examines the potential effects of uncoordinated EF (electric ferry) charging on local distribution networks, focusing on Gladstone Marina in Queensland, Australia. Using OpenDSS software, power flow analysis assesses the simulated network with BESSs (Battery Energy Storage Systems) which represent proposed charging stations.
A Channel Selection Strategy for Energy Harvesting in Cognitive Radio IoT Networks
formulated an optimal channel selection strategy based on the combination of reliable reputation model and multiarmed bandit (MAB) problem to determine an optimal channel selection policy for the SU’s. With the main goal to maximize the SUs harvested RF energy from the PUs channels during transmission.
Automated Leak Detection in Drip Irrigation Systems using RGB and Thermal Sensor Fusion
Water leaks are a common issue in surface drip irrigation systems. Visual inspection of irrigation pipelines by humans is the most prevalent method for leak detection. However, this approach is costly and labour-intensive due to the need for frequent on-site visits. This paper describes an AI based sensor fusion algorithm to automatically detect leaks along the drip lines using RGB and thermal images collected from a low-cost ground vision system. The proposed algorithm was tested using images collected from vineyard under various light conditions. Results indicated that proposed sensor fusion detection algorithm is accurate and efficient.
Is Benford’s Law Based Detectors Effective for GAI Generated Images?
Benford’s Law is used in image forensics. We tested GAI generated images to see whether BL is able to detect artificially generated images. The experiments show that only about 60\% of the images can be detected using a simple similarity threshold.
ActJOLO: Action Recognition Guided by Actionlets Using Joint Lightweight Optical Flow Information
In this study, we propose a novel method, named ActJOLO, which builds upon the existing JOLO model by incorporating an advanced self-supervised learning technique as an upstream guide for posture recognition. Our approach emphasizes the analysis of high-intensity motion features within the human body, thereby enhancing the efficiency of action modeling.
Experimental results on the NTU RGB+D dataset demonstrate that our framework improves processing speed compared to the original model, while maintaining high ccuracy. This work offers a new perspective on skeleton-based human action recognition and highlights its potential for deployment on low-performance processors.
YuihaFS: Creating Versions for Each File in the File System
We propose a file system with novel snapshot function, called YuihaFS. The proposed file system reduces the disk usage for the differential data by allowing users and applications to select a file for creating snapshots. With this new property, YuihaFS can reduce the amount of differential data for snapshots by avoiding creating unnecessary snapshots.
Incorporating Human Intuitions into Data Augmentation to Detect Concentration on Conversation
This study proposes a data expansion method to classify the excitement of conversation. This study incorporates human intuitions for conversation excitement into data augmentation. Quantification of the human intuitions would efficiently assign correct labels to the data set generated by data augmentation. The pandemic of the new coronavirus has resulted in a loss of communication opportunities. We have lost opportunities that are important to form good relationships. A deep learning model to discriminate conversation excitement would contribute to increasing such important opportunities. However, training and using models to solve real-world problems requires a lot of data. There are many cases where sufficient data cannot be collected to train a model. In such cases, data augmentation is the most promising solution. We should pay attention to the point that effective data augmentation methods vary depending on the type and characteristics of the data. This study experimentally collects conversational data. It performs data augmentation on the conversational data. It creates datasets by similarity and trains multiple models. Comparing the accuracy of these models verifies the effectiveness of incorporating human intuitions into data augmentation. The paper discusses what kind of data augmentation technique works well to generate realistic conversation data with augmentation.
Enhancing Interpretability of Skin Lesion Classification using Grad-CAM and Weighted Grad-CAM
Introduced a new novel weighted Grad-CAM to give more insights into how the CNN Models give their result, based on the HAM10000 dataset for skin cancer classification and Interpretability.