Earticle

This survey explores electricity theft detection in smart grids, where traditional power systems meet modern technology. Smart grids, designed for efficient energy management and continuous integration of renewables, face a pressing challenge electricity theft, costing utility companies over $96 billion annually. The survey traces the evolution from conventional to smart grids, emphasizing their core components. It underscores the economic impact of theft, driving researchers to explore Artificial Intelligence (AI) and Deep Learning (DL) techniques for detection. A comprehensive literature review reveals various approaches, with a focus on DL's growing influence. Public datasets are explored as invaluable resources, and methods for theft detection, including advanced AI and DL, are dissected. Performance metrics like accuracy and precision are discussed, and challenges, including imbalanced data and privacy concerns, are highlighted. In conclusion, the survey emphasizes the need for diverse AI and DL approaches, data sources, and features to create robust theft detection systems for smart grids, ensuring their secure and efficient operation.

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.

The analysis, recognition and perception of behavior has usually been a crucial task for researchers. The goal of this paper is to address the problem to recognize animal species, which has numerous applications in zoology, ecology, biology, and entertainment. Researchers used different machine learning approach for animal species recognition, however the researchers mostly used image data for this purpose and ignore the importance of audio data. In this work, our focus is to process multi modality (image and voice) dataset for animal species recognition. We proposed two different networks for animals’ audio and visual representation to recongize animals’ species. First network for animals’ audios classification that extract MFCC features, and these features is passed from four VGG style blocks while the second network extract visual features from images to classify according to their species. The experimental results demonstrated the effectiveness of the proposed model of achieved better performance in terms of classification accuracies.

This research work demonstrates surveillance of traffic on roads and streets which is used by private companies and public organizations and government institutions. The primary purpose is the well-organized management of the transport system and public safety on highways and in civil areas. This paper used the technique to well-structured localize the LP and segmentation of captured images is done by the ALPR system. We explained the localization of license plates by using the integrated segmentation method. ALPR system contains several well-observed skeletons like security administration, parking, vehicle identification, streets and road activity management, schedule of toll collecting framework, and so forth. There are various frameworks are present which are used for License plate capturing. The most important part of the ALPR framework is the accurate confinement of different number plates, recognition, and segmentation. By ALPR systems we can easily identify the number of vehicle plates. ANPR system also plays a crucial part in vehicle plate capturing and identification. This system helps in monitoring and tracking automobiles. In this paper, we have tried numerous techniques for traffic control and monitoring purposes which are works based on various techniques and methodologies. But ANPR primarily did their work for accuracy and template matching of vehicle number plates.

인간 상호 작용 인식(HIR)은 이미 인간의 행동 및 활동 인식과 동일하게 급속도로 발전하고 있다. HIR에서 우리는 여러 인간 간의 장기적인 상호 관련된 동력을 탐구하여 비디오에서 인간과 인간의 상호 작용 인식 문제를 강조하고 자 한다. HIR 시스템은 인간과 인간의 상호 작용을 정확하게 이해하기 위해서는 동영상을 기반으로 한 강력한 특징 추출 및 선택 방법이 필요하다. 이 논문에서는 비디오에서 인간과 인간의 상호 작용을 현명하게 추적하기 위해 완전 히 연결된 블록에 이어 새로운 3D 컨볼루션 신경망(3D CNN)을 제안한다. 우리는 제안된 15개의 비디오 프레임 시퀀스를 새로운 3D CNN 아키텍처에 공급하여 모든 시퀀스에서 심층 특징을 추출한 다음 해당 시퀀스를 완전히 연결된 블록으로 전달하여 효율성을 높인다. 우리가 제안한 네트워크는 두 개의 벤치마크 데이터 셋인 UT-I와 TV Human Interaction 데이터 셋에서 전체적으로 84% 와 74%의 탁월한 인식 정확도를 달성하고 최신기술을 개선 함으로써 기존 최첨단 방식을 능가하였다. 우리가 제안한 네트워크는 비디오 기반 학습, 서비스 전투, 의료 미래학 자, 대화형 게임 및 감시 시스템과 같은 다양한멀티미디어 콘텐츠 및 보안 응용 프로그램에도 적용할 수 있다.

Human Interaction Recognition(HIR) has already been perceived rapid progress same as human action and activity recognition. In HIR, we intend to highlight the problem of human-to-human interaction recognition in videos by exploring the long term inter-related dynamics between multiple humans. In order to understand the human-to-human interaction precisely, HIR system requires a robust feature extraction and selection method based on videos. In this paper, we propose a novel 3D convolutional neural network(3D CNN) followed by a fully connected block, to wisely trace human to human interactions in videos. We feed our proposed model with 15 sequence of video frames to our novel 3D CNN architecture which extracts deep features from all the sequences and then pass those sequences to the fully connected block to boost our efficiency. Our proposed network outperformed the existing state-of-the-art methods by accomplishing extraordinary recognition accuracy on two benchmark datasets, UT-I and TV Human Interaction dataset i.e., 84% and 74% overall and improved from the state-of-the-art techniques. Our proposed network can also be applicable to other numerous multimedia contents and security applications such as video-based learning, service combats, medical futurists, interactive gaming, and surveillance systems.

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.

감시 영상에서 군중 행동의 자동 모니터링 및 감지는 보안, 안전 및 자산 보호와 같은 방대한 응용 프로그램으로 인 해 컴퓨터 비전 분야에서 중요한 관심을 받고 있다. 또한 연구 커뮤니티에서 군중 분석 분야가 점차 증가하고 있다. 이를 위해서는 군중들의 행동을 감지하고 분석하는 것이 매우 필요하다. 본 논문에서는 스마트 시티에 설치된 감시 카메라의 비정상적인 활동을 감지하는 딥러닝 기반 방법을 제안하였다. 미세 조정된 VGG-16모델은 트레이닝된 공 개적으로 사용 가능한 벤치마크 군중 데이터 셋을 실시간 스트리밍으로 테스트한다. CCTV카메라는 비디오 스트림 을 캡쳐하는데, 비정상적인 활동이 감지되면 경보가 발생하여 추가 손실 전에 즉각적인 조치가 이루어지도록 가장 가까운 경찰서로 전송된다. 우리는 제안된 방법이 기존의 첨단 기술 보다 성능이 뛰어남을 실험으로 입증하였다.

The automatic monitoring and detection of crowd behavior in the surveillance videos has obtained significant attention in the field of computer vision due to its vast applications such as security, safety and protection of assets etc. Also, the field of crowd analysis is growing upwards in the research community. For this purpose, it is very necessary to detect and analyze the crowd behavior. In this paper, we proposed a deep learning-based method which detects abnormal activities in surveillance cameras installed in a smart city. A fine-tuned VGG-16 model is trained on publicly available benchmark crowd dataset and is tested on real-time streaming. The CCTV camera captures the video stream, when abnormal activity is detected, an alert is generated and is sent to the nearest police station to take immediate action before further loss. We experimentally have proven that the proposed method outperforms over the existing state-of-the-art techniques.