Time-Series Change Detection Using KOMPSAT-5 Data with Statistical Homogeneous Pixel Selection Algorithm.

For change detection in synthetic aperture radar (SAR) imagery, amplitude change detection (ACD) and coherent change detection (CCD) are widely employed. However, time-series SAR data often contain noise and variability introduced by system and environmental factors, requiring mitigation. Additionally, the stability of SAR signals is preserved when calibration accounts for temporal and environmental variations.

Time-Series Image-Based Automated Monitoring Framework for Visible Facilities: Focusing on Installation and Retention Period.

In the construction industry, ensuring the proper installation, retention, and dismantling of temporary structures, such as jack supports, is critical to maintaining safety and project timelines. However, inconsistencies between on-site data and construction documentation remain a significant challenge. To address this, this study proposes an integrated monitoring framework that combines computer vision-based object detection and document recognition techniques.

Development of a Sustainable Flexible Humidity Sensor Based on Larvae Biomass-Derived Chitosan.

This study presents the fabrication of a sustainable flexible humidity sensor utilizing chitosan derived from mealworm biomass as the primary sensing material. The chitosan-based humidity sensor was fabricated by casting chitosan and polyvinyl alcohol (PVA) films with interdigitated copper electrodes, forming a laminate composite suitable for real-time, resistive-type humidity detection. Comprehensive characterization of the chitosan film was performed using Fourier-transform infrared (FTIR) spectroscopy, contact angle measurements, and tensile testing, which confirmed its chemical structure, wettability, and mechanical stability.

Optimal Design and Operation of an Ultrasonic Driving System for Algae Removal Considering Underwater Environment Load.

This study investigates the optimal design and operation of an underwater ultrasonic system for algae removal, focusing on the electromechanical load of Langevin-type piezoelectric transducers. These piezoelectric transducers, which operate in underwater environments, exhibit variations in electrical-mechanical impedance due to practical environmental factors, such as waterproof molding structures or variations in pressure and flow rates depending on the water depth. To address these challenges, we modeled the underwater load conditions using the finite element method and analyzed the impedance characteristics of the piezoelectric transducer under realistic environmental conditions.

Trends of Soil and Solution Nutrient Sensing for Open Field and Hydroponic Cultivation in Facilitated Smart Agriculture.

Efficient management of soil nutrients is essential for optimizing crop production, ensuring sustainable agricultural practices, and addressing the challenges posed by population growth and environmental degradation. Smart agriculture, using advanced technologies, plays an important role in achieving these goals by enabling real-time monitoring and precision management of nutrients. In open-field soil cultivation, spatial variability in soil properties demands site-specific nutrient management and integration with variable-rate technology (VRT) to optimize fertilizer application, reduce nutrient losses, and enhance crop yields.

Pic2Plate: A Vision-Language and Retrieval-Augmented Framework for Personalized Recipe Recommendations.

Choosing nutritious foods is essential for daily health, but finding recipes that match available ingredients and dietary preferences can be challenging. Traditional recommendation methods often lack personalization and accurate ingredient recognition. Personalized systems address this by integrating user preferences, dietary needs, and ingredient availability.

IMU Sensor-Based Worker Behavior Recognition and Construction of a Cyber-Physical System Environment.

According to South Korea's Ministry of Employment and Labor, approximately 25,000 construction workers suffered from various injuries between 2015 and 2019. Additionally, about 500 fatalities occur annually, and multiple studies are being conducted to prevent these accidents and quickly identify their occurrence to secure the golden time for the injured. Recently, AI-based video analysis systems for detecting safety accidents have been introduced.

A Hybrid Approach for Sports Activity Recognition Using Key Body Descriptors and Hybrid Deep Learning Classifier.

This paper presents an approach for event recognition in sequential images using human body part features and their surrounding context. Key body points were approximated to track and monitor their presence in complex scenarios. Various feature descriptors, including MSER (Maximally Stable Extremal Regions), SURF (Speeded-Up Robust Features), distance transform, and DOF (Degrees of Freedom), were applied to skeleton points, while BRIEF (Binary Robust Independent Elementary Features), HOG (Histogram of Oriented Gradients), FAST (Features from Accelerated Segment Test), and Optical Flow were used on silhouettes or full-body points to capture both geometric and motion-based features.

Real-Time On-Device Continual Learning Based on a Combined Nearest Class Mean and Replay Method for Smartphone Gesture Recognition.

Sensor-based gesture recognition on mobile devices is critical to human-computer interaction, enabling intuitive user input for various applications. However, current approaches often rely on server-based retraining whenever new gestures are introduced, incurring substantial energy consumption and latency due to frequent data transmission. To address these limitations, we present the first on-device continual learning framework for gesture recognition.

Image-Driven Hybrid Structural Analysis Based on Continuum Point Cloud Method with Boundary Capturing Technique.

Conventional approaches for the structural health monitoring of infrastructures often rely on physical sensors or targets attached to structural members, which require considerable preparation, maintenance, and operational effort, including continuous on-site adjustments. This paper presents an image-driven hybrid structural analysis technique that combines digital image processing (DIP) and regression analysis with a continuum point cloud method (CPCM) built on a particle-based strong formulation. Polynomial regressions capture the boundary shape change due to the structural loading and precisely identify the edge and corner coordinates of the deformed structure.

An Enhanced Approach Using AGS Network for Skin Cancer Classification.

Skin cancer accounts for over 40% of all cancer diagnoses worldwide. However, accurately diagnosing skin cancer remains challenging for dermatologists, as multiple types of skin cancer often appear visually similar. The diagnostic accuracy of dermatologists ranges between 62% and 80%.

Mounting Angle Prediction for Automotive Radar Using Complex-Valued Convolutional Neural Network.

In advanced driver-assistance systems (ADASs), the misalignment of the mounting angle of the automotive radar significantly affects the accuracy of object detection and tracking, impacting system safety and performance. This paper introduces the Automotive Radar Alignment Detection Network (AutoRAD-Net), a novel model that leverages complex-valued convolutional neural network (CV-CNN) to address azimuth misalignment challenges in automotive radars. By utilizing complex-valued inputs, AutoRAD-Net effectively learns the physical properties of the radar data, enabling precise azimuth alignment.

Obstacle-Aware Crowd Surveillance with Mobile Robots in Transportation Stations.

Recent transportation systems are operated by cooperative factors including mobile robots, smart vehicles, and intelligent management. It is highly anticipated that the surveillance using mobile robots can be utilized in complex transportation areas where the high accuracy is required. In this paper, we introduce a crowd surveillance system using mobile robots and intelligent vehicles to provide obstacle avoidance in transportation stations with a consideration of different moving strategies of the robots in an existing 2D area supported by line-based barriers and surveillance formations.

Abnormal Operation Detection of Automated Orchard Irrigation System Actuators by Power Consumption Level.

Information and communication technology (ICT) components, especially actuators in automated irrigation systems, are essential for managing precise irrigation and optimal soil moisture, enhancing orchard growth and yield. However, actuator malfunctions can lead to inefficient irrigation, resulting in water imbalances that impact crop health and reduce productivity. The objective of this study was to develop a signal processing technique to detect potential malfunctions based on the power consumption level and operating status of actuators for an automated orchard irrigation system.

Enhancing the Transformer Model with a Convolutional Feature Extractor Block and Vector-Based Relative Position Embedding for Human Activity Recognition.

The Transformer model has received significant attention in Human Activity Recognition (HAR) due to its self-attention mechanism that captures long dependencies in time series. However, for Inertial Measurement Unit (IMU) sensor time-series signals, the Transformer model does not effectively utilize the a priori information of strong complex temporal correlations. Therefore, we proposed using multi-layer convolutional layers as a Convolutional Feature Extractor Block (CFEB).

Reliability of Tracheal Temperature as a Measurement of Core Body Temperature During Cardiac Surgery Using Cardiopulmonary Bypass.

: To compare tracheal temperature (T) with nasopharyngeal temperature (T) in patients undergoing cardiac surgery using cardiopulmonary bypass (CPB). : T was measured using a thermistor in the cuff of an endotracheal tube and T was monitored using an esophageal stethoscope. Depending on the management of the CPB strategy, the operation was divided into four periods (pre-CPB, cooling, rewarming, and post-CPB).

Impact of Resistance Exercise and Nitrate Supplementation on Muscle Function and Clinical Outcomes After Knee Osteoarthritis Surgery in Middle-Aged Women with Sarcopenia: A Randomized, Double-Blind, Placebo-Controlled Clinical Trial.

Sarcopenia, characterized by reduced muscle mass and strength, is associated with osteoarthritis (OA), particularly in middle-aged women, and may worsen postoperatively. Resistance exercise (RE) can resolve sarcopenia; however, recovery is often suboptimal. Nitrate (NO) supplementation may enhance muscle recovery and complement RE.

Nonalcoholic Fatty Liver Disease Increases the Risk of Lung Abscess: Findings from a Nationwide Cohort Study.

This study aimed to investigate the association between nonalcoholic fatty liver disease (NAFLD), assessed by the Fatty Liver Index (FLI), and the occurrence of lung abscess within a large population-based cohort. We conducted a nationwide retrospective study using data from 367,930 subjects who underwent National Health check-ups between 2009 and 2018. Cox proportional hazards regression was performed to evaluate the association between the FLI and the incidence of lung abscess and community-acquired pneumonia (CAP) after adjusting for age, sex, and relevant covariates.

Impact of Uterine Leiomyomas on Cardiovascular Disease Risk in Young Reproductive-Aged Women: A Nationwide Population-Based Cohort Study.

Uterine leiomyomas are the most common benign gynecological tumors in women of reproductive age and are often associated with localized symptoms. However, emerging evidence suggests a link between uterine leiomyomas and systemic conditions such as cardiovascular disease (CVD), particularly myocardial infarction (MI) and ischemic stroke (IS). This study aimed to investigate the relationship between uterine leiomyomas and the risk of CVD events in young women aged 20-39 years using a large, nationwide, population-based cohort.

Comparative Efficacy of Extracorporeal Versus Conventional Cardiopulmonary Resuscitation in Adult Refractory Out-of-Hospital Cardiac Arrest: A Retrospective Study at a Single Center.

Extracorporeal cardiopulmonary resuscitation (ECPR) has the potential to improve neurological outcomes in patients with refractory out-of-hospital cardiac arrest (OHCA), offering an alternative to conventional cardiopulmonary resuscitation (CCPR). However, its effectiveness in OHCA remains controversial despite advancements in resuscitation techniques. This retrospective single-center study compared neurological outcomes and 30-day survival between ECPR and CCPR patients from January 2014 to January 2022.

Evaluation of a Novel Method for Temporary Eyebrow Lifting Using Polydioxanone Threads: Preliminary Findings.

Aging affects the face and eyebrow areas, with various resultant procedures for lifting the eyebrows. Recently, thread lifting using absorbable threads has become increasingly popular, with the advantages of a faster recovery and no visible scars, when compared with conventional facial rhytidectomy. Furthermore, polydioxanone (PDO) thread lifting is a favorable surgical method that has been used for eyebrow lifting.

Clinical Characteristics of COVID-19-Related Reversible Cerebral Vasoconstriction Syndrome: A Systematic Review of Case Series.

Although reversible cerebral vasoconstriction syndrome (RCVS) is a rare disease, the condition may occur with COVID-19 infection. We aimed to investigate the clinical characteristics of RCVS through a systematic review of case reports and case series that reported on COVID-19-related RCVS. : A literature search was performed in PubMed (MEDLINE), SCOPUS, and Web of Science.

Relationship Between Poor Sleep and Depressive Symptoms in Korean Elite Youth Athletes.

: Sleep and mental health are crucial to elite youth athletes, who face combined pressures of training, competition, school, and social commitments. We examined the association between sleep and depression in elite youth athletes. : We analyzed data of 248 Korean world-class youth athletes (aged 13-19 years).

Predictive Diagnostic Power of Anthropometric Indicators for Metabolic Syndrome: A Comparative Study in Korean Adults.

Metabolic syndrome (MetS) is a cluster of risk factors that significantly increase the risk of cardiovascular disease, including type 2 diabetes, etc. Assessing the predictive diagnostic power of anthropometric indicators for MetS is crucial for the early identification and prevention of related health issues. This study focuses on the Korean adult population while providing insights that may be applicable to broader global contexts.

Spontaneous Chest Wall Hematoma in a Hemodialysis Patient: A Case Report.

: Spontaneous chest wall hematomas are rare but potentially life-threatening complications, particularly in patients with multiple comorbidities such as those undergoing hemodialysis. This case report aims to highlight the significance of early diagnosis and appropriate management in preventing complications associated with this condition. : We report the case of a 79-year-old man with end-stage renal disease on hemodialysis, presenting with a large spontaneous hematoma (18.