Earticle

Recent advancements in data-driven methodologies have brought significant attention to the computational prediction of material properties. Traditional machine learning (ML) approaches have struggled to achieve high accuracy due to the complex relationships between a material's structure and its properties. To address this challenge, in this work, we present an ML framework for predicting the stability of silicon (Si) and Si-based alkaline metal alloys with reduced error. This emphasizes the model transferability to discover new silicon alloys with diverse electronic configurations and structures. We explore the effectiveness of two atomic structural descriptors including X-ray diffraction (XRD) and sine coulomb matrix (SCM). The dynamic ensemble learning (DEL) model is trained and evaluated using 750 Si alloys from the materials project database (MPD) and optimized via ensemble learning. The results indicate that the XRD descriptor with DEL performs most reliably for formation energy, total energy and packaging fraction prediction, showing the model robustness and transferability for ultimate efficient silicon anode’s material synthesis.

Recognizing anomalies in surveillance is crucial for public safety to identify events that deviate from normal patterns. Visual information is essential for effective anomaly recognition; however, audio data can enhance recognition accuracy by providing additional context. Despite this, existing systems only utilize visual information, overlooking the potential of audio modalities in anomaly recognition. This paper introduces a multi-modal framework for anomaly recognition through active learning, integrating audio and visual modalities to enhance anomaly prediction. The framework extracts features using a pretrained ResNet-50 convolutional neural network (CNN) model from the visual and audio data. The extracted features are then forwarded to the Bi-Directional Long Short-Term Memory (Bi-LSTM) network for temporal feature learning. These features are then fused and fed into a classification layer for final prediction. The proposed framework's performance is assessed on a benchmark dataset and yields promising results.

In recent years, anomaly recognition using audio has attracted the attention of the research community, due to the increasing number of abnormal situations day by day. In the past, researchers have mainly focused on video-based anomaly recognition. However, occlusion is one of the most important factors due to which the anomalous object is unidentifiable. Therefore, in this paper, we proposed a modified vision transformer that utilized the Shifted Patch Tokenization (SPT), and Local Self-Attention (LSA) mechanism and reduced the number of multilayer perceptrons in the head, enabling the model to capture rich spatial information within the spectrogram of anomalous data. The proposed model is implemented using the Sound Events for Surveillance Applications (SESA) dataset and obtained 87% testing accuracy. Thus, the proposed model is an efficient and effective solution for audio-based anomaly recognition.

This paper introduces a comprehensive approach to dataset standardization aimed at enhancing the effectiveness and reliability of solar power forecasting models. Leveraging multiple datasets, this study incorporates additional attributes such as atmospheric pressure and sunshine duration. These enrichments bridge critical gaps in meteorological and environmental data, facilitating more robust and precise solar power forecasting. The paper underscores the significance of these attributes, furnishes detailed equations for their computation, and presents the outcomes of their integration. It underscores their pivotal role in enabling solar energy stakeholders to make informed decisions and optimize energy production effectively.

Sustainable power systems should include solar energy generation. However, for effective grid management and the integration of renewable energy sources, accurate solar power generation predictions are essential. Therefore, this study compares the prediction of solar power forecasting in Italy and Bulgaria. These are two countries that have alike latitudes but different populations and solar energy production. The historical solar power generation and meteorological data from these countries are preprocessed and then used to apply four different deep learning models including Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The results are analyzed to gain insights into how the proximity of geographical locations and the quality and quantity of data impact the precision of prediction algorithms.

Accurate detection of small targets in aerial images is crucial but challenging due to the limited computational resources of UAVs. This paper presents an efficient approach based on YOLO-V5S for detecting and classifying distant vehicles in aerial scenes. Extensive ablation study is conducted to find the optimal YOLO architecture. The proposed method is efficient and effective, making it applicable for real-time deployment. A dataset of 1000 annotated images are developed to validate the proposed method's effectiveness. The proposed network outperforms existing state-of-the-art methods in accuracy, speed, and resource efficiency, making it a promising solution for aerial vision-based applications.

Nowadays, renewable energy resources such as Photovoltaic (PV) is one of the convenient ways to integrate it into the distributed grid to fulfill the huge energy demands without burning costly and pollutant fossil fuels. Researchers have been contributing from various aspects to develop accurate PV-power forecasting methods however further improvements are needed for an effective power management system. Therefore, in this work, we propose an attention-based deep learning (DL) model (PV-ANet) for short-term PV-power forecasting. The proposed system mainly consists of three modules. First, data from an actual PV power plant is acquired and preprocessed to remove outliers and normalized for efficient processing. Next, the PV-ANet model is developed, which is consisting of an encoder and decoder modules. The encoder encodes the input attributes via stack conventional and attention layer. While the decoder part contains the normalization and series of the dense layers to expends the encoded features into optimal features and generate one hour ahead forecast. Finally, the proposed model is evaluated via standard error metrics including MSE, MAE, and RMSE and achieved the lowest errors rates compared to state-of-the-art methods.

The integration of solar energy with a power system brings great economic and environmental benefits. However, the high penetration of solar power challenges the operation and planning of the existing power system owing to the intermittence and randomicity of solar power generation. Achieving accurate prediction for power generation is important to provide balanced electric energy for end-users. Therefore, in this paper, we introduce a deep learning-based dual stream Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network to learn spatial patterns using CNN and temporal features via the LSTM network. These features are then fused via a concatenation layer and then feed forward to Dense layers for optimal features selection and future solar power prediction. The performance of the proposed model is evaluated on benchmark datasets and achieved a new state-of-the-art on these datasets.

Nowadays, due to natural disasters the world is facing huge challenges such as economical, climatic, and losses a lot of precious human life. The traditional emergency response and rescue teams are physically visit different affected areas for inspection and save human lives. In this manual monitoring system created various problems such as human resources, time-consuming, and in real-time unable to accurately analyze the nature of the disaster. Therefore, there is an urgent need for an automatic real-time system to intelligently identified different disaster scenes and analyze the affected areas for quick response. Therefore, in this paper, an Unmanned Aerial Vehicles (UAVs) inspired framework is proposed for disaster scenes classification using a lightweight Convolution Neural Network (CNN). To validate the strength of the proposed framework a comparative analysis is conducted to show its superiority against different state-of-the-art models in terms of computational complexity and performance.

오늘날 현대사회에서 스마트하고 안전한 도시는 연구 커뮤니티의 주요 관심사 중 하나이다. 도시들은 개방된 지역, 농경지, 숲으로 둘러싸여 있으며, 화재 발생은 인간의 삶을 위협하고 그들의 재산도 손상시킬 수 있다. 최근 비전 센서 기반 화재 감지 기술은 컴퓨터 비전 분야의 전문가들을 통해, 최신 문헌에서 다양한 컨볼루션 신경 네트워크 (CNN)을 대한 최고의 성능을 달성하고 있다. 그러나 이러한 기술은 변환 불변이고, 지역성에 민감하며, 이미지에 대한 전체적인 이해가 부족하다. 또한 CNN 기반 모델은 계산 비용을 줄이기 위해 차원 축소를 위한 풀링 레이어 전략을 사용했지만, 가장 활동적인 특징 검출기의 정확한 위치와 같은 많은 의미 있는 정보를 손실한다. 이러한 문 제를 극복하기 위해 본 연구에서는 비전 트랜스포머(ViT)기반 화재 감지 모델을 개발하였다. ViT는 입력 이미지를 이미지 패치로 분할한 다음 워드 임베딩과 유사한 시퀀스 구조로 트랜스포머에 제공한다. 우리는 벤치마크 화재 데 이터 세트에서 제안된 작업의 성능을 평가하고 최신(SOTA) CNN 방법과 비교할 때 좋은 결과를 달성한다.

In today's modern age, smart and safe cities are one of the major concerns of the research community. The cities are surrounded by open areas, agricultural land, and forests, where fire incidence can make human lives threatening, damaging their properties as well. Recently, vision sensors-based fire detection has attracted computer vision domain experts, where the leading performance is achieved by a variety of convolution neural networks(CNN) in the recent literature. However, these techniques are translation invariant, locality-sensitive, and lacking a global understanding of images. Furthermore, CNN-based models use the pooling layers strategy for dimensionality reduction to reduce the computational cost but it also loses a lot of meaningful information such as the precise location of the most active feature detector. To overcome these problems, in this work, we developed Vision Transformers(ViT) based model for fire detection. The ViT split the input image into image patches and then feed these patches to the transformer in a sequence structure similar to word embeddings. We evaluate the performance of the proposed work on the benchmark fire dataset and achieve good results when compared to state-of-the-art(SOTA) fire detection CNN models.

인공지능을 활용한 사업이 활발히 진행되면서 범죄 예방 및 안전분야와 관련하여 이상행동 및 행동 인식에 대한 연구와 관심이 높아지고 있다. 하지만 딥러닝 등 인공지능 모델을 생성하는 것은 전문 지식이 없는 경우 많은 어려움이 따른다. 본 논문에서는 사용자가 편리하게 딥러닝 모델을 생성할 수 있도록 데이터셋을 제공하고 이상행동 및 행동 인식 기술을 API화하여 인터페이스에서 호출하는 방식을 사용하는 사용자 친화적인 모델 학습 및 테스트를 위한 시스템 UI를 제안하였다. 본 논문에서 제안한 시스템은 딥러닝에 대한 사전 지식이 없는 사용자가 편리하게 딥러닝 모델을 생성할 수 있을 것으로 기대된다.