This paper addresses the challenge of accurately forecasting solar power generation (SPG) across multiple sites using a single common model. The proposed deep learning-based model is designed to predict SPG for various locations by leveraging a comprehensive dataset from multiple sites in the Republic of Korea. By incorporating common meteorological elements such as temperature, humidity, and cloud cover into its framework, the model uniquely identifies site-specific features to enhance the forecasting accuracy. The key innovation of this model is the integration of a classifier module within the common model framework, enabling it to adapt and predict SPG for both known and unknown sites based on site similarities. This approach allows for the extraction and utilization of site-specific characteristics from shared meteorological data, significantly improving the model’s adaptability and generalization across diverse environmental conditions. The evaluation results demonstrate that the model maintains high performance levels across different SPG sites with minimal performance degradation compared to site-specific models. Notably, the model shows robust forecasting capabilities, even in the absence of target SPG data, highlighting its potential to enhance operational efficiency and support the integration of renewable energy into the power grid, thereby contributing to the global transition towards sustainable energy sources.

In recent times, digital image forensics is gaining increased attention in multimedia forensics owing to the widespread scam alertness. Several forensic methods have been studied to establish the integrity of digital images by disclosing manipulation fingerprints. Anti-forensic (AF) attacks on manipulated images, particularly deep learning-based adversarial attacks using generative adversarial network (GAN), have been successfully applied to delude forensic methods. Consequently, an efficacious, efficient, and robust counter-AF (CAF) method is required to secure the integrity of digital images. In this study, we propose a robust open-set multi-instance learning approach for exposing GAN-based AF on manipulated images by introducing additional GAN-based operations. First, we generate multiple real instances from real images using multiple additional generators. Then we train an embedding network collaboratively with multiple real instances in an open-set fashion. During training, the embedding network learns only real images and has no prior knowledge regarding AF images. In the testing phase, real and AF images are processed for detection. The proposed open-set CAF method can effectively detect AF images and is more robust against transferable updating.

Owing to imperfect scans, occlusions, low reflectance of the scanned surface, and packet loss, there may be several incomplete regions in the 3D point cloud dataset. These missing regions can degrade the performance of recognition, classification, segmentation, or upsampling methods in point cloud datasets. In this study, we propose a new approach to estimate the incomplete regions of 3D point cloud human face datasets using the masking method. First, we perform some preprocessing on the input point cloud, such as rotation in the left and right angles. Then, we project the preprocessed point cloud onto a 2D surface and generate masks. Finally, we interpolate the 2D projection and the mask to produce the estimated point cloud. We also designed a deep learning model to restore the estimated point cloud to improve its quality. We use chamfer distance (CD) and hausdorff distance (HD) to evaluate the proposed method on our own human face and large-scale facial model (LSFM) datasets. The proposed method achieves an average CD and HD results of 1.30 and 21.46 for our own and 1.35 and 9.08 for the LSFM datasets, respectively. The proposed method shows better results than the existing methods.

Machine vision-based intelligent applications that analyze video data collected by machines are rapidly increasing. Therefore, it is essential to efficiently compress a large volume of video data for machine consumption. Accordingly, the Moving Picture Experts Group (MPEG) has been developing a new video coding standard called Video Coding for Machines (VCM), aimed at video consumed by machines rather than humans. Recently, studies have demonstrated that multi-scale feature compression (MSFC)-based feature compression methods significantly improve the performance of MPEG-VCM. This paper proposes an efficient MSFC (eMSFC) method with quantization parameter (QP)-adaptive feature channel truncation. The proposed eMSFC incorporates an MSFC network with a selective learning strategy (SLS) and Versatile Video Coding (VVC)-based compression. The SLS extracts a single-scale feature from the input image, arranged in order of channel-wise importance. The size of the single-scale feature is adaptively adjusted by truncating the feature channels according to the QP. The truncated feature is efficiently compressed using VVC. Compared to the VCM feature anchor, the experimental results reveal that the proposed method provides a 98.72%, 98.34%, and 98.04% Bjontegaard delta rate gain for machine vision tasks of instance segmentation, object detection, and object tracking, respectively. The proposed method performed best among the “Call for Evidence” response technologies in MPEG-VCM.

With the great flexibility and performance of deep learning technology, there have been many attempts to replace existing functions inside video codecs such as High-Efficiency Video Coding (HEVC) with deep-learning-based solutions. One of the most researched approaches is adopting a deep network as an image restoration filter to recover distorted compressed frames. In this paper, instead, we introduce a novel idea for using a deep network, in which it chooses and transmits the side information according to the type of errors and contents, inspired by the sample adaptive offset filter in HEVC. A part of the network computes the optimal offset values while another part estimates the type of error and contents simultaneously. The combination of two subnetworks can address the estimation of highly nonlinear and complicated errors compared to conventional deep-learning-based schemes. Experimental results show that the proposed system yields an average bit-rate saving of 4.2% and 2.8% for the low-delay P and random access modes, respectively, compared to the conventional HEVC. Moreover, the performance improvement is up to 6.3% and 3.9% for higher-resolution sequences.