Youngdoo Son
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Mixing High-Frequency Bands Based on Wavelet Decomposition for Long-Term State-of-Charge Forecasting of Lithium-Ion Batteries Open
Although state-of-charge (SoC) forecasting has received considerable attention, long-term prediction remains a challenging task due to disrupted temporal dependencies and the neglect of battery signal characteristics. In this study, we pro…
Artificial intelligence-enhanced diagnosis of temporomandibular joint osteoarthritis using temporomandibular joint panoramic radiography and joint noise data Open
This study aimed to develop an artificial intelligence (AI) model for the screening of temporomandibular joint osteoarthritis (TMJ OA) using temporomandibular joint (TMJ) panoramic radiography and joint noise data. A total of 2,631 TMJ pan…
Learning Representation for Multitask learning through Self Supervised Auxiliary learning Open
Multi-task learning is a popular machine learning approach that enables simultaneous learning of multiple related tasks, improving algorithmic efficiency and effectiveness. In the hard parameter sharing approach, an encoder shared through …
Deep artificial intelligence applications for natural disaster management systems: A methodological review Open
Deep learning techniques through semantic segmentation networks have been widely used for natural disaster analysis and response. The underlying base of these implementations relies on convolutional neural networks (CNNs) that can accurate…
Spatio-Temporal Consistency for Multivariate Time-Series Representation Learning Open
Label sparsity in multivariate time series (MTS) makes using label information for practical applications challenging. Thus, unsupervised representation learning methods have gained attention to learn effective representations suitable for…
Machine Learning for the Expedited Screening of Hydrogen Evolution Catalysts for Transition Metal-Doped Transition Metal Dichalcogenides Open
Two-dimensional transition metal dichalcogenides (TMDs) have gained attention as potent catalysts for the hydrogen evolution reaction (HER). The traditional trial-and-error methodology for catalyst development has proven inefficient due to…
Genetic descriptor search algorithm for predicting hydrogen adsorption free energy of 2D material Open
Transition metal dichalcogenides (TMDs) have emerged as a promising alternative to noble metals in the field of electrocatalysts for the hydrogen evolution reaction. However, previous attempts using machine learning to predict TMD properti…
A 0.57 mW@1 FPS In-Column Analog CNN Processor Integrated Into CMOS Image Sensor Open
This article presents a high-performance, low-power analog convolutional neural network (CNN) circuit integrated into a CMOS image sensor (CIS) for face detection applications. The main block of the proposed in-column analog CNN circuits i…
View article: Scalable Synthesis of Pt Nanoflowers on Solution‐Processed MoS<sub>2</sub> Thin Film for Efficient Hydrogen Evolution Reaction
Scalable Synthesis of Pt Nanoflowers on Solution‐Processed MoS<sub>2</sub> Thin Film for Efficient Hydrogen Evolution Reaction Open
Platinum Nanocatalysts In article number 2200043, Joohoon Kang and co-workers demonstrate precisely controllable nanostructuring of platinum on various solution-processed 2D nanomaterials in large scale. This approach allows mass productio…
View article: Scalable Synthesis of Pt Nanoflowers on Solution‐Processed MoS<sub>2</sub> Thin Film for Efficient Hydrogen Evolution Reaction
Scalable Synthesis of Pt Nanoflowers on Solution‐Processed MoS<sub>2</sub> Thin Film for Efficient Hydrogen Evolution Reaction Open
Nanostructuring of Pt nanocatalysts increases the surface‐to‐volume ratio, thus enabling efficient usage of Pt for hydrogen evolution reaction (HER). Direct electrochemical reduction of Pt on the electrode can produce nanostructured Pt cat…
Novel Graph-Based Features for Bearing Fault Diagnosis: Two Aspects of Time Series Structure Open
The feature-based methods for bearing fault diagnosis in prognostics and health management have been achieved satisfactory performances because of their robustness to noise and reduced dimension by pre-defined features. However, widely emp…
Artificial Intelligence in Positioning Between Tooth and Nerve on Panoramic Radiography Open
Determining the exact positional relationship between mandibular third molar (M3) and inferior alveolar nerve (IAN) is important for surgical extractions. Panoramic radiography is the most common dental imaging test. The purposes of this s…
Motor Load Balancing with Roll Force Prediction for a Cold-Rolling Setup with Neural Networks Open
The use of machine learning algorithms to improve productivity and quality and to maximize efficiency in the steel industry has recently become a major trend. In this paper, we propose an algorithm that automates the setup in the cold-roll…
An Optimized Framework for Energy-Resource Allocation in a Cloud Environment based on the Whale Optimization Algorithm Open
Cloud computing offers the services to access, manipulate and configure data online over the web. The cloud term refers to an internet network which is remotely available and accessible at anytime from anywhere. Cloud computing is undoubte…
Dynamical Properties of Ion-Acoustic Waves in Space Plasma and Its Application to Image Encryption Open
The nonlinear ion-acoustic waves (IAWs) in a space plasma are capable of exhibiting chaotic dynamics which can be applied to cryptography. Dynamical properties of IAWs are examined using the direct method in plasmas composed of positive an…
Missing Value Imputation in Stature Estimation by Learning Algorithms Using Anthropometric Data: A Comparative Study Open
Estimating stature is essential in the process of personal identification. Because it is difficult to find human remains intact at crime scenes and disaster sites, for instance, methods are needed for estimating stature based on different …
Design of an Always-On Image Sensor Using an Analog Lightweight Convolutional Neural Network Open
This paper presents an always-on Complementary Metal Oxide Semiconductor (CMOS) image sensor (CIS) using an analog convolutional neural network for image classification in mobile applications. To reduce the power consumption as well as the…
Multitask Learning with Single Gradient Step Update for Task Balancing Open
Multitask learning is a methodology to boost generalization performance and also reduce computational intensity and memory usage. However, learning multiple tasks simultaneously can be more difficult than learning a single task because it …
Data-Driven Cervical Cancer Prediction Model with Outlier Detection and Over-Sampling Methods Open
Globally, cervical cancer remains as the foremost prevailing cancer in females. Hence, it is necessary to distinguish the importance of risk factors of cervical cancer to classify potential patients. The present work proposes a cervical ca…
Forecasting Daily Temperatures with Different Time Interval Data Using Deep Neural Networks Open
Temperature forecasting has been a consistent research topic owing to its significant effect on daily lives and various industries. However, it is an ever-challenging task because temperature is affected by various climate factors. Researc…
Chronic Disease Prediction Using Character-Recurrent Neural Network in The Presence of Missing Information Open
The aim of this study was to predict chronic diseases in individual patients using a character-recurrent neural network (Char-RNN), which is a deep learning model that treats data in each class as a word when a large portion of its input v…
Classification of Children’s Sitting Postures Using Machine Learning Algorithms Open
Sitting on a chair in an awkward posture or sitting for a long period of time is a risk factor for musculoskeletal disorders. A postural habit that has been formed cannot be changed easily. It is important to form a proper postural habit f…
Deep Learning Based Virtual Metrology and Yield Prediction in Semiconductor Manufacturing Processes Open
We present a deep learning based supervised autoencoder to extract meaningful features from massive in-line sensor functional signals of semiconductor manufacturing processes. Based on those extract features, we build thevirtual metrology …
Predicting Market Impact Costs Using Nonparametric Machine Learning Models Open
Market impact cost is the most significant portion of implicit transaction costs that can reduce the overall transaction cost, although it cannot be measured directly. In this paper, we employed the state-of-the-art nonparametric machine l…