• Journal of Positioning, Navigation, and Timing
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• v.12 no.4
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• pp.399-408
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• 2023

In this paper, we propose a solution that enables continuous indoor/outdoor positioning of smartphone users through the integration of Pedestrian Dead Reckoning (PDR) and GPS/WiFi signals. Considering that accurate step detection affects the accuracy of PDR, we propose a Deep Neural Network (DNN)-based technology to distinguish between walking and non-walking signals such as walking in place. Furthermore, in order to integrate PDR with GPS and WiFi signals, a technique is used to select a proper measurement by distinguishing between indoor/outdoor environments based on GPS Dilution of Precision (DOP) information. In addition, we propose a technology to adaptively change the measurement error covariance matrix by detecting measurement outliers that mainly occur in the indoor/outdoor transition section through a residual-based χ² test. It is verified through experiments on a testbed that these technologies significantly improve the performance of PDR and PDR/GPS/WiFi fingerprinting-based integrated pedestrian navigation.

• Food Science of Animal Resources
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• v.43 no.6
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• pp.949-960
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• 2023

This research aimed to validate a high-performance liquid chromatography method for the quantitative determination of bixin and norbixin in various foods. The Diode Array Detector (495 nm) technique was used. Method was validated for specificity, linearity, limit of detection (LOD), limit of quantification (LOQ), precision, and accuracy, and the measurement uncertainty was assessed. The calibration curve showed excellent linearity (r²≥0.9999) over the tested concentration range of 0.2-25 mg/L. The LOD and LOQ were 0.03-0.11 and 0.02-0.05 mg/L for bixin and norbixin, respectively. The intra-and inter-day accuracies and precisions were 88.0±1.3-97.0±0.5% and 0.2%-2.6% relative SD (RSD) for bixin and 88.2±0.8-105.8±0.8% and 0.3%-2.7% RSD for norbixin, respectively. Inter-laboratory validation for accuracy and precision was conducted in three laboratories, and these results all met the AOAC guidelines. In addition, the relative expanded uncertainty (<22%) satisfied the CODEX recommendation. Furthermore, products distributed in Korea were monitored for annatto extracts using the proposed method to demonstrate its application. The developed analytical method is reliable for quantifying bixin and norbixin in various foods.

• Journal of Information Processing Systems
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• v.19 no.6
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• pp.767-777
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• 2023

In modern society, user privacy is emerging as an important issue as closed-circuit television (CCTV) systems increase rapidly in various public and private spaces. If CCTV cameras monitor sensitive areas or personal spaces, they can infringe on personal privacy. Someone's behavior patterns, sensitive information, residence, etc. can be exposed, and if the image data collected from CCTV is not properly protected, there can be a risk of data leakage by hackers or illegal accessors. This paper presents an innovative approach to "machine learning based reversible chaotic masking method for user privacy protection in CCTV environment." The proposed method was developed to protect an individual's identity within CCTV images while maintaining the usefulness of the data for surveillance and analysis purposes. This method utilizes a two-step process for user privacy. First, machine learning models are trained to accurately detect and locate human subjects within the CCTV frame. This model is designed to identify individuals accurately and robustly by leveraging state-of-the-art object detection techniques. When an individual is detected, reversible chaos masking technology is applied. This masking technique uses chaos maps to create complex patterns to hide individual facial features and identifiable characteristics. Above all, the generated mask can be reversibly applied and removed, allowing authorized users to access the original unmasking image.

• Journal of the Korean Society of Systems Engineering
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• v.20 no.spc1
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• pp.90-96
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• 2024

In this paper, we propose a technique to model high resolution seafloor topography with 1m intervals using actual water depth data near the east coast of the Korea with 1.6km distance intervals. Using a feature point extraction algorithm that harris corner based on deep learning, the location of the center of seafloor mountain was calculated and the surrounding topology was modeled. The modeled high-resolution seafloor topography based on deep learning was verified within 1.1m mean error between the actual warder dept data. And average error that result of calculating based on deep learning was reduced by 54.4% compared to the case that deep learning was not applied. The proposed algorithm is expected to generate high resolution underwater topology for the entire Korean peninsula and be used to establish a path plan for autonomous navigation of underwater vehicle.

• Journal of Surface Science and Engineering
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• v.57 no.2
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• pp.71-85
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• 2024

This article provides an overview of Raman spectroscopy and its practical applications for surface analysis of semiconductor processes including real-time monitoring. Raman spectroscopy is a technique that uses the inelastic scattering of light to provide information on molecular structure and vibrations. Since its inception in 1928, Raman spectroscopy has undergone continuous development, and with the advent of SERS(Surface Enhanced Raman Spectroscopy), TERS(Tip Enhanced Raman Spectroscopy), and confocal Raman spectroscopy, it has proven to be highly advantageous in nano-scale analysis due to its high resolution, high sensitivity, and non-destructive nature. In the field of semiconductor processing, Raman spectroscopy is particularly useful for substrate stress and interface characterization, quality analysis of thin films, elucidation of etching process mechanisms, and detection of residues.

• Anatomy and Cell Biology
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• v.56 no.4
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• pp.469-473
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• 2023

Anomalies of anterior cerebral artery (ACA) include aplasia, hypoplasia and variations in number. Magnetic resonance angiography (MRA) is a non-invasive diagnostic technique for assessment of anomalies of cerebral arteries. The aim of the study was to determine the role of MRA in detection of variants of ACA in adults. This study is an observational retrospective study. This study included forty-nine adult cases (28 males and 21 females), mean age 48±12.9 SD with anomalies of ACA in MRA. Magnetic resonance imaging of the brain and MRA were done to all patients. Cerebral MRA and magnetic resonance images were evaluated for frequency and distribution of variants of anterior cerebral arteries, associated aneurysms and infarctions. Odds ratios (ORs) and relative risk were calculated to determine risk of occurrence of cerebral infarctions in patients with anomalies of ACA. Hypoplasia of ACA was the commonest anomaly of ACA (51% of cases). Risk of occurrence of cerebral infarctions was higher in cases with azygos variant (OR, 3.3; P=0.35) than in those with hypoplastic ACA (OR, 2; P=0.58). MRA was highly reliable in identification of different variants of ACA and concomitant vascular changes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.233-262
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• 2024

Due to the rapid evolution of vehicular ad hoc networks (VANETs), effective communication and security are now essential components in providing secure and reliable vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. However, due to their dynamic nature and potential threats, VANETs need to have strong security mechanisms. This paper presents a novel approach to improve VANET security by combining the Vehicular Delay-Tolerant Network (VDTN) protocol with the Deep Reinforcement Learning (DRL) technique known as the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. A store-carry-forward method is used by the VDTN protocol to resolve the problems caused by inconsistent connectivity and disturbances in VANETs. The TD3 algorithm is employed for capturing and detecting Worm Hole Attack (WHA) behaviors in VANETs, thereby enhancing security measures. By combining these components, it is possible to create trustworthy and effective communication channels as well as successfully detect and stop rushing attacks inside the VANET. Extensive evaluations and simulations demonstrate the effectiveness of the proposed approach, enhancing both security and communication efficiency.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.6
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• pp.1619-1637
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• 2024

In today's highly digitized landscape, securing digital communication is paramount due to threats like hacking, unauthorized data access, and network policy violations. The response to these challenges has been the development of cryptography applications, though many existing techniques face issues of complexity, efficiency, and limitations. Notably, sophisticated intruders can easily discern encrypted data during transmission, casting doubt on overall security. In contrast to encryption, steganography offers the unique advantage of concealing data without easy detection, although it, too, grapples with challenges. The primary hurdles in image steganography revolve around the quality and payload capacity of the cover image, which are persistently compromised. This article introduces a pioneering approach that integrates image steganography and encryption, presenting the BitPatternStego method. This novel technique addresses prevalent issues in image steganography, such as stego-image quality and payload, by concealing secret data within image pixels with identical bit patterns as their characters. Consequently, concerns regarding the quality and payload capacity of steganographic images become obsolete. Moreover, the BitPatternStego method boasts the capability to generate millions of keys for the same secret message, offering a robust and versatile solution to the evolving landscape of digital security challenges.

• Smart Structures and Systems
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• v.33 no.3
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• pp.227-241
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• 2024

Safety and structural integrity of civil structures, like bridges and buildings, can be substantially enhanced by employing appropriate structural health monitoring (SHM) techniques for timely diagnosis of incipient damages. The information gathered from health monitoring of important infrastructure helps in making informed decisions on their maintenance. This ensures smooth, uninterrupted operation of the civil infrastructure and also cuts down the overall maintenance cost. With an early warning system, SHM can protect human life during major structural failures. A real-time online damage localization technique is proposed using only the vibration measurements in this paper. The concept of the 'Degree of Scatter' (DoS) of the vibration measurements is used to generate a spatial profile, and fractal dimension theory is used for damage detection and localization in the proposed two-phase algorithm. Further, it ensures robustness against environmental and operational variability (EoV). The proposed method works only with output-only responses and does not require correlated finite element models. Investigations are carried out to test the presented algorithm, using the synthetic data generated from a simply supported beam, a 25-storey shear building model, and also experimental data obtained from the lab-level experiments on a steel I-beam and a ten-storey framed structure. The investigations suggest that the proposed damage localization algorithm is capable of isolating the influence of the confounding factors associated with EoV while detecting and localizing damage even with noisy measurements.

• Korean Journal of Construction Engineering and Management
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• v.25 no.2
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• pp.69-80
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• 2024

Currently rebar spacing inspection is carried out by human inspectors who heavily rely on their individual experience, lacking a guarantee of objectivity and accuracy in the inspection process. In addition, if incorrectly placed rebars are identified, the inspector need to correct them. Recently, laser scanning and AR technologies have been widely used because of their merits of measurement accuracy and visualization. This study proposes a technology for rebar spacing inspection and fixing by combining laser scanning and AR technology. First, scan data acquisition of rebar layers is performed and the raw scan data is processed. Second, AR-based visualization and fixing are performed by comparing the design model with the model generated from the scan data. To verify the developed technique, performance comparison test is conducted by comparing with existing drawing-based method in terms of inspection time, error detection rate, cognitive load, and situational awareness ability. It is found from the result of the experiment that the AR-based rebar inspection and fixing technology is faster than the drawing-based method, but there was no significant difference between the two groups in error identification rate, cognitive load, and situational awareness ability. Based on the experimental results, the proposed AR-based rebar spacing inspection and fixing technology is expected to be highly useful throughout the construction industry.