스마트 감시 시스템을 이용한 효율적인 화재 감지

Samee Ullah Khan,Hikmat Yar,Habib Khan,Sumin Lee,Mi Young Lee,Sung Wook Baik 한국차세대컴퓨팅학회 2023 한국차세대컴퓨팅학회 학술대회 Vol.2023 No.06

Fire detection is a significant attempt for preserving public safety in complex surveillance environments. Although advances in deep learning for fire detection, the task remains challenging due to the natural irregularity in fire images, including differences in lighting conditions, occlusions, and background complexity. To address these challenges, we present a novel framework for fire detection named fire channel attention network (FCAN), which is capable of differentiating challenging fire scenes. Our approach is motivated by the need to enhance the accuracy of fire detection by selectively emphasizing the most informative channels of the input image through a channel attention (CA). Furthermore, our model captures the salient features from the input image and suppresses the irrelevant ones, thereby overcoming the aforementioned challenges of fire detection. The FCAN is evaluated on two benchmark datasets and surpassed existing methods in terms of accuracy and F1 score. The proposed model demonstrates the effectiveness of fire detection, highlighting its potential for practical applications in fire safety and prevention.

이상행동 및 행동 인식 모델 학습 및 테스트를 위한 시스템 UI 설계에 대한 연구

이수민,권찬민,Tanveer Hussain,Samee Ullah Khan,Waseem Ullah,Noman Khan,Zulfiqar Ahmad Khan,이미영,백성욱 한국차세대컴퓨팅학회 2021 한국차세대컴퓨팅학회 학술대회 Vol.2021 No.05

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.

건물의 전력 소비 예측을 위한 어텐션 기반 이중 스트림 딥러닝 네트워크를 활용한 개선된 전력 소비 예측

Noman Khan,Samee Ullah Khan,Altaf Hussain,Sumin Lee,Mi Young Lee,Sung Wook Baik 한국차세대컴퓨팅학회 2023 한국차세대컴퓨팅학회 학술대회 Vol.2023 No.06

A crucial component of designing intelligent and ecologically friendly environments nowadays is electricity consumption forecasting. The generation of energy can be enhanced to effectively meet the population's rising requirements by using the prediction of future electricity consumption. Due to the broad variety of consumption patterns, it is difficult to anticipate the energy requirements of buildings. Therefore, this work uses a dual-steam approach with multi-head attention to anticipate the power consumption of the building to address this issue and produce precise predictions. The proposed network concurrently learns temporal representations through a Bidirectional Gated Recurrent Unit (BGRU) and spatial patterns through Atrous Convolutional Neural Network (ACNN). The obtained features are combined to create a single feature vector that is used as the input for the multi-head attention, which finds the features that are most suited to forecasting the electricity consumption of a building. Finally, the dense layer receives the effective features and uses them to forecast short-term power consumption. In this paper, the proposed dual-stream network with attention outperforms competing models, achieving the lowest error value for hourly building power consumption prediction, according to experimentation on the household electricity consumption dataset.

Detecting Natural Disasters with Unmanned Aerial Vehicles

Noman Khan,Samee Ullah Khan,Mi Young Lee,Sung Wook Baik 한국차세대컴퓨팅학회 2021 한국차세대컴퓨팅학회 학술대회 Vol.2021 No.11

Unmanned aerial vehicles (UAVs) or drones are versatile innovations that can capture pictures and videos and even collect air or soil samples. Natural disaster drones are especially critical, which help with understanding the damage after a disaster, locating people who need help, distributing resources and preparing for the next event. Computer vision, deep learning (DL), and drones can augment the existing sensors, thereby increasing the accuracy of natural disasters detector, and most importantly, allow people to take precautions, stay safe, and reduce the number of deaths and injuries that happens due to these disasters. Therefore, in this paper we propose a novel lightweight convolutional neural network (CNN) based framework to detect natural disasters including cyclone, flood, earthquake, and wildfire. The proposed CNN model is obtained by fine-tuning the MobileNetV2 that can be deployed on drones. Furthermore, the model is trained and evaluated using a publicly available natural disasters dataset by obtaining 83.4% accuracy. Similarly, the framework has ability to broad cast the notification in alarming situations, which makes our proposed framework a best fit for natural disasters detection in realworld surveillance settings.

데이터 주석 및 모델 성능 향상을 위한 능동적 학습 접근

Hikmat Yar,Samee Ullah Khan,Tanveer Hussain,Min Je Kim,Mi Young Lee,Sung Wook Baik 한국차세대컴퓨팅학회 2022 한국차세대컴퓨팅학회 학술대회 Vol.2022 No.05

Deep learning models achieved a lot of success due to the availability of labeled training data. In contrast, labeling a huge amount of data by a human is a time-consuming and expensive solution. Active Learning (AL) efficiently addresses the issue of labeled data collection at a low cost by picking the most useful samples from a large number of unlabeled datasets However, current AL techniques largely depend on regular human involvement to annotate the most uncertain/informative samples in the collection. Therefore, a novel AL-based framework is proposed comprised of proxy and active models to reduce the manual labeling costs. In the proxy model, VGG-16 is trained on chunks of labeled data that later act as an annotator decision. On the other hand, in the active model, unlabeled is passed to Inception-V3 using the sampling strategy. The uncorrected predicted samples are then forwarded to the proxy model for annotation and considered those data have a high confidence score. The empirical results verify that our proposed model is the best in terms of annotation and accuracy.

A Lightweight Deep Learning Model for Early Fire Detection using UAV Imagery

Hikmat Yar,Samee Ullah Khan,Noman Khan,Min Je Kim,Mi Young Lee,Sung Wook Baik 한국차세대컴퓨팅학회 2021 한국차세대컴퓨팅학회 학술대회 Vol.2021 No.11

Fire is an extremely catastrophic disaster that leads to the destruction of forests, human assets, reduced soil fertility, land resources, and the cause of global warming. In the current decade, fire detection and its management are the major concern of several researchers to prevent social, ecological, and economic damages. To overcome such kind of losses, early fire detection, and the automatic response is very significant. Moreover, achieving high accuracy with reducing inference time and model size is also challenging for the Unmanned Aerial Vehicle (UAVs). Therefore, in this work, we enabled the VGG16 architecture for UAV in terms of reducing its learning parameters from 138 million to 11.4 million for early fire detection. The proposed system is inexpensive in terms of computation and size. The performance of our proposed work is evaluated over the custom dataset. We performed comprehensive experiments using various deep learning architectures such as VGG16, ResNet50, and the proposed CNN model. The experimental results based on the proposed model achieved an accuracy of 98% on 50 epochs.

Disasters Scenes Classification Based on Unmanned Aerial Vehicles Using Lightweight CNN

Altaf Hussain,Samee Ullah Khan,Fath U Min Ullah,Zulfiqar Ahmad Khan,Mi Young Lee,Sung Wook Baik 한국차세대컴퓨팅학회 2021 한국차세대컴퓨팅학회 학술대회 Vol.2021 No.11

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.

머신 러닝 기법을 활용한 무인 항공기 기반 재난 영상 분류

Altaf Hussain,Samee Ullah Khan,Fath U Min Ullah,Mi Young Lee,Sung Wook Baik 한국차세대컴퓨팅학회 2021 한국차세대컴퓨팅학회 학술대회 Vol.2021 No.05

Recently due to natural disasters, the world is facing huge ecological, social, economic, and loss of precious lives. Traditionally during natural disasters, emergency response teams are physically visiting different areas to inspect and stop their further damages. Therefore, the existing monitoring system is facing issues such as human accessibility and unable to analyze disaster in real-time. To address these issues, we propose a machine learning inspired framework for automatically recognized disaster scenes that contains three main steps. In the first step preprocessing is applied for condense and normalize the image dimension. Next, histogram of oriented gradient (HOG) descriptor is utilize to extract discriminative features and extracted features are classified through SVM. Finally in testing step, in case of disaster scenes our system trigger notification to nearby disaster management centers to take an appropriate action. We provide comprehensive experiments on various machine learning approaches among them we obtain 64% accuracy on HOG with SVM.

Towards Autonomous Grid : Solar, Wind, and Weather Data for Renewable Energy Production

Sang ll Yoon,Noman Khan,Samee Ullah Khan,Fath U Min Ullah,Su Min Lee,Mi Young Lee,Sung Wook Baik 한국차세대컴퓨팅학회 2022 한국차세대컴퓨팅학회 학술대회 Vol.2022 No.10

Nowadays, energy management and its optimization using smart devices are getting more attention due to their significant applications. Moreover, the applications used in these devices play a key role in developing smart cities that is only the way to solve urban problems. The potential of renewable energy sources like solar and wind power has been integrated in the smart grids to overcome the lack of supply via conventional fossil fuels and their environmental disputes that reduce operational cost. This review paper describes the significance of renewable power data that directly assists all the functions in smart cities such as the evolution of microgrids, renewable resources, energy forecasting, and power storage technologies. Furthermore, solar and wind power plants’ data with weather information as an additional cue is collected from different companies in South Korea. We aim to assist the researchers to develop artificial intelligence (AI)-based algorithms for power forecasting and establish its efficient management between suppliers and consumers.

Deep Learning framework for intelligent surveillance video analytics

Su Min Lee,Min Je Kim,Khan Samee Ullah,Khan Zulfiqar Ahmad,Khan Noman,Mi Young Lee,Sung Wook Baik 한국차세대컴퓨팅학회 2021 한국차세대컴퓨팅학회 학술대회 Vol.2021 No.11

Recently, in computer vision behavior recognition is an active research area that plays a significant role in smart cities for crime prevention and urban safety. However, without base knowledge of Artificial Intelligence (AI) designing an efficient model is very difficult because we need data and programing skills for implementing the system. To tackle this problem, we designed and implemented a system that allows a user having no professional knowledge to easily and conveniently create a deep learning model. The interface of this system consists of Data Selection, Model Training and Testing, and Model Parameter values according to domains and categories. In addition, we designed a function to check the test results for the model selected by the user. This system allows users to quickly and easily create and test models.