Mi Young Lee

Chung-Ang University

The growing demand for high-quality industrial products has led to a significant emphasis on image anomaly detection (AD). Anomaly detection in industrial goods presents a formidable research challenge that demands the application of sophisticated techniques to identify and address deviations from the expected norm accurately. Manufacturers increas...

In order to predict and fill in the gaps in categorical datasets, this research looked into the use of machine learning algorithms. The emphasis was on ensemble models constructed using the Error Correction Output Codes (ECOC) framework, including models based on SVM and KNN as well as a hybrid classifier that combines models based on SVM, KNN, and...

Power Generation (PG) prediction from Renewable Energy Sources (RESs) plays a vital role in effective energy management in smart cities. However, harnessing the potential of edge intelligence in well-controlled Internet of Things (IoT) networks poses significant challenges. To address this, we propose an IoT-based framework for intelligent and effi...

Extensive surveillance systems’ usage, particularly installed in IoT environments crops tremendous amount of video data continuously, where its effective analysis and management is a challenging task for surveillance experts due to unstructured storage, and variability. We propose an intelligent modeling framework, offering a convenient representat...

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 diff...

The aerial view diverse action recognition (AR) benchmark provides a valuable resource for researchers and developers in computer vision (CV) for human actions recognition (HAR) from an aerial perspective. With the increasing use of unmanned aerial vehicles (UAVs) for surveillance, delivery, search, and rescue, a robust understanding of human actio...

The identification of anomalies in industrial settings poses a significant challenge, especially when there is a lack of negative samples and when the anomalous regions are small. Although existing computer vision methods have automated this task to some extent, these approaches struggle to extract salient features for inspecting defective chips. T...

Animal detection and classification are crucial for effective wildlife management (WM) and reducing risks associated with animals related road accidents and attacks. Previous attempts trained the models using imbalanced data with fewer representative features and baseline models without improvement. This paper presents a new dataset of five animal...

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 me...

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 challenge...

언어 번역, 음성 인식 등의 기술이 발전하면서 음성을 이용한 실시간 언어 번역, AI 음성 인식 시스템, 음악 인식 등의 기술이 생겨났다. 이 기술들은 입력되는 음성, 대사, 대화의 질에 대한 의존도가 높아서 주변 대화 소리, 기계 소음 등의 잡음이 있는 환경에서는 좋은 성능을 내기 어렵다. 따라서 본 논문에서는 Kalman 필터와 딥러닝을 이용한 음성, 대사, 대화 복원 기법을 제안한다. 제안된 기법은 필터링을 통해 잡음을 최소화 하고, 손실 또는 왜곡된 음성을 복원하여 기술의 최대 성능을 낼 수 있게 할 수 있을 것으로 기대된다.

The analysis of overcrowded areas is essential for flow monitoring, assembly control, and security. Crowd counting's primary goal is to calculate the population in a given region, which requires real-time analysis of congested scenes for prompt reactionary actions. The crowd is always unexpected, and the benchmarked available datasets have a lot of...

Due to global warming and climate change rising issues, buildings, including residential and commercial ones, are major contributors to energy consumption. To this end, net zero energy building (NZEB) has become a progressively popular concept where the annual sum of power generation and consumption is zero. However, occasionally, there exists a mi...

Vision sensors-based fire detection is an interesting and useful research domain with significant alleviated attention from computer vision experts. The baseline research is based on low-level color features, lately replaced by the effective representation of deep models, achieving better accuracy, but higher false alarm rates still exist with expe...

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 improvem...

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...

Over the decades, a rapid upsurge in electricity demand has been observed due to overpopulation and technological growth. The optimum production of energy is mandatory to preserve it and improve the energy infrastructure using the power load forecasting (PLF) method. However, the complex energy systems’ transition towards more robust and intelligen...

Around the world, agriculture is one of the important sectors of human life in terms of food, business, and employment opportunities. In the farming field, wheat is the most farmed crop but every year, its ultimate production is badly influenced by various diseases. On the other hand, early and precise recognition of wheat plant diseases can decrea...

Human interaction recognition (HIR) is challenging due to multiple humans’ involvement and their mutual interaction in a single frame, generated from their movements. Mainstream literature is based on three-dimensional (3-D) convolutional neural networks (CNNs), processing only visual frames, where human joints data play a vital role in accurate in...

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...

The online retrieval of images related to clothes is a crucial task because finding the exact items like the query image from a large amount of data is extremely challenging. However, large variations in clothes images degrade the retrieval accuracy of visual searches. Another problem with retrieval accuracy is high dimensions of feature vectors ob...

Fire detection and management is very important to prevent social, ecological, and economic damages. However, achieving real-time fire detection with higher accuracy in an IoT environment is a challenging task due to limited storage, transmission, and computation resources. To overcome these challenges, early fire detection and automatic response a...

Traditional power generating technologies rely on fossil fuels, which contribute to worldwide environmental issues such as global warming and climate change. As a result, renewable energy sources (RESs) are used for power generation where battery energy storage systems (BESSs) are widely used to store electrical energy for backup, match power consu...

In the era of cutting edge technology, excessive demand for electricity is rising day by day, due to the exponential growth of population, electricity reliant vehicles, and home appliances. Precise energy consumption prediction (ECP) and integrated local energy systems (ILES) are critical to boost clean energy management systems between consumers a...

Renewable energies use clean sources for energy generation and have the potential to balance the supply and demand of power. One of the best ways to save energy for high-demand time is to preserve it in a battery energy storage system (BESS). Various methods are presented in the last two decades for battery state of charge (SOC) estimation, however...

Nowadays, for efficient energy management, local demand-supply matching in power grid is emerging research domain. However, energy demand is increasing day by day in many countries due to rapid growth of the population and most of their work being reliant on electronic devices. This problem has highlighted the significance of effectively matching p...

Renewable energy (RE) power plants are deployed globally because renewable energy sources (RESs) are sustainable, clean, and environmentally friendly. However, the demand for power increases on a daily basis due to population growth, technology, marketing, and the number of installed industries. This challenge has raised a critical issue of how to...

Recently, surveillance systems are globally installed for crime prevention by monitoring both private and public places which generate a massive amount of video data. This setup requires human experts to observe and monitor the ongoing activities continuously. To handle this tedious task, an automatic technique workable in real-time for violent act...

Digital surveillance systems are ubiquitous and continuously generate massive amounts of data, and manual monitoring is required in order to recognise human activities in public areas. Intelligent surveillance systems that can automatically identify normal and abnormal activities are highly desirable, as these would allow for efficient monitoring b...

With the emerging technologies of augmented reality (AR) and virtual reality (VR), the learning process in today’s classroom is much more effective and motivational. Overlaying virtual content into the real world makes learning methods attractive and entertaining for students while performing activities. AR techniques make the learning process easy...

This paper proposes an action recognition framework for depth map sequences using the 3D Space-Time Auto-Correlation of Gradients (STACOG) algorithm. First, each depth map sequence is split into two sets of sub-sequences of two different frame lengths individually. Second, a number of Depth Motion Maps (DMMs) sequences from every set are generated...

Computer vision is an interdisciplinary domain for object detection. Object detection relay is a vital part in assisting surveillance, vehicle detection and pose estimation. In this work, we proposed a novel deep you only look once (deep YOLO V3) approach to detect the multi-object. This approach looks at the entire frame during the training and te...

Forest fire is one of the most dangerous disasters worldwide, due to which its management is a key concern of the research community to prevent social, ecological, and economic damages. Wildfires are extremely catastrophic disasters that lead to the destruction of forests, and human assets, reduction of soil fertility, and cause global warming. To...

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.

Healthcare practices include collecting all kinds of patient data which would help the doctor correctly diagnose the health condition of the patient. These data could be simple symptoms observed by the subject, initial diagnosis by a physician or a detailed test result from a laboratory. Thus, these data are only utilized for analysis by a doctor w...

In this article, we present an in-depth comparative analysis of the conventional and sequential learning algorithms for electricity load forecasting and optimally select the most appropriate algorithm for energy consumption prediction (ECP). ECP reduces the misusage and wastage of energy using mathematical modeling and supervised learning algorithm...

The use of electrical energy is directly proportional to the increase in global population, both concerning growing industrialization and rising residential demand. The need to achieve a balance between electrical energy production and consumption inspires researchers to develop forecasting models for optimal and economical energy use. Mostly, the...

The reliability, scalability, and stability of routing schemes are open challenges in highly evolving vehicular ad hoc networks (VANETs). Cluster-based routing is an efficient solution to cope with the dynamic and inconsistent structure of VANETs. In this paper, we propose a cluster-based routing scheme (hereinafter referred to as connectivity-base...

This paper portrays the application of a Partial Discharge (PD) signal combined with the dual-input VGG Convolution Neural Network (CNN) to predict the location of the pollution layer on 11 kV polymer insulators subjected to alternating current for smart grid applications. First, a non-uniform pollution layer artificially created for HV insulator w...

The prognostics and health management (PHM) plays the main role to handle the risk of failure before its occurrence. Next, it has a broad spectrum of applications including utility networks, energy storage systems (ESS), etc. However, an accurate capacity estimation of batteries in ESS is mandatory for their safe operations and decision making poli...

Convolutional Neural Networks(CNNs) have shown encouraging results for image classification, recognition, and retrieval tasks. In this perspective, sports video classification remains an active and challenging area where CNNs are less explored. Encouraged by this, we extensively provide an empirical evaluation of CNNs on sports video classification...

In the current technological era, energy-efficient buildings have a significant research body due to increasing concerns about energy consumption and its environmental impact. Designing an appropriate energy-efficient building depends on its layout, such as relative compactness, overall area, height, orientation, and distribution of the glazing are...

Distributed intrusion detection systems (IDS) are primarily deployed across the network to monitor, detect, and report anomalies, as well as to respond in real-time. Predominantly, an IDS is equipped with a set of rules that it needs to infer to be able to perform efficient detection. However, generating false alarms is a major challenge in any IDS...

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 necess...

The recent improvements in traditional object detection techniques is transformed into efficient and robust deep learning based object detectors. Majority of these models are limited only to natural scenes, objects detection on ground images, with no focus on surveillance. In distinction to ground images, aerial images pose several challenges to ob...

Movies have become one of the major sources of entertainment in the current era, which are based on diverse ideas. Action movies have received the most attention in last few years, which contain violent scenes, because it is one of the undesirable features for some individuals that is used to create charm and fantasy. However, these violent scenes...

The diagnosis of bankruptcy companies becomes extremely important for business owners, banks, governments, securities investors, and economic stakeholders to optimize the profitability as well as to minimize risks of investments. Many studies have been developed for bankruptcy prediction utilizing different machine learning approaches on various da...

Due to the recent developments in computer vision technology, complex actions can be recognized with reasonable accuracy in smart cities. In contrast, violence recognition such as events related to fight and knife, has gained less attention. The capability of visual surveillance can be used for detecting fight in streets or in prison centers. In th...

Movie data has a prominent role in the exponential growth of multimedia data over the Internet, and its analysis has become a hot topic with computer vision. The initial step towards movie analysis is scene segmentation. In this article, we investigated this problem through a novel intelligent Convolutional Neural Network (CNN) based three folded f...

Personalized movie summarization is demand of the current era due to an exponential growth in movies production. The employed methods for movies summarization fail to satisfy the user’s requirements due to the subjective nature of movies data. Therefore, in this paper, we present a user-preference based movie summarization scheme. First, we segment...

Emotion recognition from speech signals is an interesting research with several applications like smart healthcare, autonomous voice response systems, assessing situational seriousness by caller affective state analysis in emergency centers, and other smart affective services. In this paper, we present a study of speech emotion recognition based on...

Discovering Erasable Patterns (EPs) consists of identifying product parts that will produce a small profit loss if their production is stopped. It is a data mining problem that has attracted the attention of numerous researchers in recent years due to the possibility of using EPs to reduce profit loss of manufacturers. Though, many algorithms have...

Mainstream Internet of Things (IoT) techniques for smart homes focus on appliances and surveillance in smart cities. Most of the researchers utilize vision sensors in IoT environment targeting only adult users for various applications such as abnormal activity recognition. This paper introduces a new paradigm in vision sensor IoT technologies by an...

Salient information extraction from multi-view videos is a very challenging area because of interview , intra-view correlations, and computational complexity. There are several techniques developed for keyframes extraction from multi-view videos with very high computational complexities. In this paper, we present a keyframes extraction approach fro...

Visual surveillance has become an undeniable necessity of the recent smart cities infrastructures. Consequently, widespread deployment of surveillance cameras to monitor areas of interest generates huge volumes of multimedia data. This rapid growth give rise to problems pertaining to their transmission, storage, and retrieval. Typically, the object...

Bankruptcy prediction has been a popular and challenging research topic in both computer science and economics due to its importance to financial institutions, fund managers, lenders, governments, as well as economic stakeholders in recent years. In a bankruptcy dataset, the problem of class imbalance, in which the number of bankruptcy companies is...

In recent years, weakened by the fall of economic growth, many enterprises fell into the crisis caused by financial difficulties. Bankruptcy prediction, a machine learning model, is a great utility for financial institutions, fund managers, lenders, governments, and economic stakeholders. Due to the number of bankrupt companies compared to that of...

The parking management system(PMS)using the drone camera is one of the challenging task and trending technologies of computer vision that can help to solve the parking problem. In this paper, we proposed anew PMS and a method to improve the performance of license plate recognition (LPR)in existing PMS based on drone camera. The proposed method is e...

In the millions of emergency reporting calls made each year, about a quarter are non-emergencies. To avoid responding to such situations, forensic examination of the reported situation in the presence of speech as evidence has become an indispensable requirement for emergency response centers. Caller profile information like gender, age, emotional...

Face detection is one of the key visual information analysis tasks in Computer vision. In this paper, we present an efficient and light weight face detection technique based on spatial activations in the intermediate feature maps of a pre-trained convolutional neural network (CNN). Feature maps which detect various facial features are first identif...

With the growing use of minimally invasive surgical procedures, endoscopic video archives are growing at a rapid pace. Efficient access to relevant content in such huge multimedia archives require compact and discriminative visual features for indexing and matching. In this paper, we present an effective method to represent images using salient con...

Recently, researchers of computer vision have focused on human action recognition in video clips and have used it for applications in various domains such as surveillance and sports. In this paper, we have recognized human action in movies clips using deep features of keyframes. Firstly, k-mean clustering is used to achieve representative frames (k...

In computer vision, person re-identification is a challenging task in the context of making intelligent surveillance system. State-of-the-art techniques in person re-identification systems still rely on traditional features extraction such as HOG, SIFT, SURF, and LBP etc. In this study we present convolution neural network (CNN) features to represe...

Recent years have shown enthusiastic research interests in diagnostic hysteroscopy (DH), where various regions of the female reproductive system are visualized for diagnosing uterine disorders. Currently, the hysteroscopy videos produced during various sessions of patients are stored in medical libraries, which are usually browsed by medical specia...

The world is moving towards automation in every field, which is the main motivational reason of recent researches. Automated CCTV surveillance systems have also drawn the attention of researchers since the last decade. In CCTV systems, data is collected from multiple sources with overlapping contents, which is mostly redundant and containing both i...

Besides spoken words, speech signals also carry information about speaker gender, age, and emotional state which can be used in a variety of speech analysis applications. In this paper, a divide and conquer strategy for ensemble classification has been proposed to recognize emotions in speech. Intrinsic hierarchy in emotions has been utilized to co...

A reasoning process governed by Dempster-Shafer theory (DST) is presented to boost the performance of a statistical classifier for speaker-independent gender recognition from telephone calls in real-time. Statistical classifiers attempt to determine gender using mel-frequency cepstral coefficients (MFCC) extracted from speech signals. The probabili...

In the realm of social media, contents creation is getting significantly popular. Developers are interested in contents authoring tools to efficiently fulfill their requirements regarding multimedia authoring. These proprietary contents are created according to the needs of their potential users and then published. Several online services are avail...

Obesity is the most crucial condition that affects life quality and proliferate various serious chronic risky diseases causing premature death due to lack of interest to perform physical activities. Virtual fitness (ViFi) platform is an attempt to bring virtual reality (VR) into physical fitness centers. This paper introduces the ViFi platform wher...

Image classification is an enthusiastic research field where large amount of image data is classified into various classes based on their visual contents. Researchers have presented various low-level features-based techniques for classifying images into different categories. However, efficient and effective classification and retrieval is still a c...

This paper presents a flowchart based storyboard system that facilitates user in creation, systemization and analysis of story visualization. The proposed storyboard system has the ability to display complete story in a single view by connecting various scenes of the story. It can handle complex scene using scene inside scene. The proposed system h...

In recent years, various systems in different fields of life have been developed to simulate real world scenarios with interactive virtual environments. In the mixed reality (MR) space, several objects are involved to interact in numerous ways which may lead to inconsistencies. Hence, we designed a cost-effective and robust framework that allows sy...