• Journal of Dental Anesthesia and Pain Medicine
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• v.21 no.6
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• pp.507-525
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• 2021

Local anesthesia is indispensable in dentistry. Worldwide, millions of local anesthetic injections are administered annually, and are generally considered safe invasive procedures. However, adverse effects are possible, of which dentists should be aware of. This scoping review aimed to provide an extensive overview of the reported literature on the adverse effects of dental local anesthesia. The types of papers, what is reported, and how they are reported were reviewed. Additionally, the incidence and duration of adverse effects and factors influencing their occurrence were also reviewed. An electronic search for relevant articles was performed in PubMed and Embase databases from inception to January 2, 2020. The titles and abstracts were independently screened by two reviewers. The analysis was narrative, and no meta-analysis was performed. This study included 78 articles. Ocular and neurological adverse effects, allergies, hematomas, needle breakage, tissue necrosis, blanching, jaw ankylosis, osteomyelitis, and isolated atrial fibrillation have been described. Multiple adverse effects of dental local anesthesia have been reported in the literature. The results were heterogeneous, and detailed descriptions of the related procedures were lacking. Vital information concerning adverse effects, such as the dosage or type of anesthetic solution, or the type of needle used, was frequently missing. Therefore, high-quality research on this topic is needed. Finally, the adverse effects that are rarely encountered in real-world general practice are overrepresented in the literature.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.6
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• pp.842-849
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• 2022

With the development of deep learning technology, there are many cases of using DNNs in embedded systems such as unmanned vehicles, drones, and robotics. Typically, in the case of an autonomous driving system, it is crucial to run several DNNs which have high accuracy results and large computation amount at the same time. However, running multiple DNNs simultaneously in an embedded system with relatively low performance increases the time required for the inference. This phenomenon may cause a problem of performing an abnormal function because the operation according to the inference result is not performed in time. To solve this problem, the solution proposed in this paper first reduces the computation by applying the Tucker decomposition to DNN models with big computation amount, and then, make DNN models run in parallel as much as possible in the unit of hidden layer inside the GPU. The experimental result shows that the DNN inference time decreases by up to 75.6% compared to the case before applying the proposed technique.

• Journal of IKEEE
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• v.26 no.2
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• pp.219-225
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• 2022

The textile industry in Korea, the dyeing sector is an energy-intensive sector and has low per-unit productivity due to its labor-intensive nature. If the defective rate of dyed fabrics is high, additional costs are incurred due to an increase in production cost due to re-dyeing. Therefore, the goal of the dyeing factory was to minimize the defect rate rather than to save energy. It was difficult to check the dyeing state of the fabric in real time due to the risk of accidents due to burns or pressure when dyeing in a high-temperature and high-pressure environment. In this paper, a complex sensor that can measure the exhaustion rate of dye solution in the dyeing machine using turbidity, pH, and conductivity sensors was proposed, and the experimental method and experimental results were analyzed.

• Journal of the Korean Recycled Construction Resources Institute
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• v.10 no.3
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• pp.307-313
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• 2022

In this study, the correlation between Half Cell Potential(HCP) and the corrosion influencing factors was analyzed with considering three levels of water-cement ratio, the concentration of chloride solution, and cover depth. As a result of long-term corrosion monitoring, HCP behavior was close to the critical corrosion potential(-350 mV) in all water-cement ratios in the case of 3.5 % and 7.0 % chloride concentration. Regarding the passed charge test in 548 curing days, the passed charge results were improved to 'Moderate' grade. Multiple regression analysis was performed to evaluate the correlation between corrosion influencing factors and HCP, and it was evaluated that the effects of influencing factors to HCP were in the order of chloride concentration, water-cement ratio, and cover depth. In the case of the relationship between HCP and the passed charge, the coefficient of determination showed a high level of 0.9, which yielded a close correlation between the passed charge and HCP.

• Advances in concrete construction
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• v.13 no.5
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• pp.385-394
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• 2022

Recently, an increasing number of cutting-edged studies have shown that designing a smart active control for real-time implementation requires piles of hard-work criteria in the design process, including performance controllers to reduce the tracking errors and tolerance to external interference and measure system disturbed perturbations. This article proposes an effective artificial-intelligence method using these rigorous criteria, which can be translated into general control plants for the management of civil engineering installations. To facilitate the calculation, an efficient solution process based on linear matrix (LMI) inequality has been introduced to verify the relevance of the proposed method, and extensive simulators have been carried out for the numerical constructive model in the seismic stimulation of the active rigidity. Additionally, a fuzzy model of the neural network based system (NN) is developed using an interconnected method for LDI (linear differential) representation determined for arbitrary dynamics. This expression is constructed with a nonlinear sector which converts the nonlinear model into a multiple linear deformation of the linear model and a new state sufficient to guarantee the asymptomatic stability of the Lyapunov function of the linear matrix inequality. In the control design, we incorporated H Infinity optimized development algorithm and performance analysis stability. Finally, there is a numerical practical example with simulations to show the results. The implication results in the RMS response with as well as without tuned mass damper (TMD) of the benchmark building under the external excitation, the El-Centro Earthquake, in which it also showed the simulation using evolved bat algorithmic LMI fuzzy controllers in term of RMS in acceleration and displacement of the building.

• Smart Structures and Systems
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• v.29 no.1
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• pp.181-193
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• 2022

Data-driven structural health monitoring (SHM) of civil infrastructure can be used to continuously assess the state of a structure, allowing preemptive safety measures to be carried out. Long-term monitoring of large-scale civil infrastructure often involves data-collection using a network of numerous sensors of various types. Malfunctioning sensors in the network are common, which can disrupt the condition assessment and even lead to false-negative indications of damage. The overwhelming size of the data collected renders manual approaches to ensure data quality intractable. The task of detecting and classifying an anomaly in the raw data is non-trivial. We propose an approach to automate this task, improving upon the previously developed technique of image-based pre-processing on one-dimensional (1D) data by enriching the features of the neural network input data with multiple channels. In particular, feature engineering is employed to convert the measured time histories into a 3-channel image comprised of (i) the time history, (ii) the spectrogram, and (iii) the probability density function representation of the signal. To demonstrate this approach, a CNN model is designed and trained on a dataset consisting of acceleration records of sensors installed on a long-span bridge, with the goal of fault detection and classification. The effect of imbalance in anomaly patterns observed is studied to better account for unseen test cases. The proposed framework achieves high overall accuracy and recall even when tested on an unseen dataset that is much larger than the samples used for training, offering a viable solution for implementation on full-scale structures where limited labeled-training data is available.

• Mass Spectrometry Letters
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• v.13 no.4
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• pp.177-183
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• 2022

The monomer and dimer structures of the amyloid fragment Aβ(1-16) sequence formed in H₂O were investigated using electrospray ionization mass spectrometry (MS) and tandem MS (MS/MS). Aβ16 monomers and dimers were indicated by signals representing multiple proton adduct forms, [monomer+zH]ⁿ⁺ (=M^z+, z = charge state) and [dimer+zH]^z+ (=D^z+), in the MS spectrum. Fragment ions of monomers and dimers were observed using collision-induced dissociation MS/MS. Peptide bond dissociation was mostly observed in the D1-D7 and V11-K16 regions of the MS/MS spectra for the monomer (or dimer), regardless of the monomer (or dimer) charge state. Both covalent and non-covalent bond dissociation processes were indicated by the MS/MS results for the dimers. During the non-covalent bond dissociation process, the D³⁺ dimer complex was separated into two components: the M¹⁺ and M²⁺ subunits. During the covalent bond dissociation of the D³⁺ dimer complex, the b and y fragment ions attached to the monomer, (M+b_10-15)^z+ and (M+y_9-15)^z+, were thought to originate from the dissociation of the M²⁺ monomer component of the (M¹⁺+M²⁺) complex. Two different D³⁺ complex geometries exist; two distinguished interaction geometries resulting from interactions between the M¹⁺ monomer and two different regions of M²⁺ (the N-terminus and C-terminus) are proposed. Intricate fragmentation patterns were observed in the MS/MS spectrum of the D⁵⁺ complex. The complicated nature of the MS/MS spectrum is attributable to the coexistence of two D⁵⁺ configurations, (M¹⁺+M⁴⁺) and (M²⁺M³⁺), in the Aβ16 solution.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1581-1587
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• 2021

As embedded system are used widely and variously, multi-edge system, which multiple edges gather and perform complex operations together, is actively operating. In a multi-edge system, it often occurs that an abnormal operation at one edge is transferred to another edge or the entire system goes down. It is necessary to determine and control edge anomalies in order to prevent system down, but this can be a heavy burden on the resource-limited edge. As a solution to this, we use power consumption data to check the state of the edge device and transmit it based on a QRcode to check and control errors at the server. The architecture proposed in this paper is implemented using 'chip-whisperer' to measure the power consumption of the edge and 'Raspberry Pi 3' to implement the server. As a result, the proposed architecture server showed successful data transmission and error determination without additional load appearing at the edge.

• Journal of the Korean Recycled Construction Resources Institute
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• v.10 no.1
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• pp.124-132
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• 2022

The corrosion potential in concrete varies greatly with exposure and concrete mix conditions. In this study, RC (Reinforcement Concrete) samples were prepared considering cover depth, chloride concentration, and W/C(water to cement) ratio as variables, and HCP(Half Cell Potential) was measured, which evaluated comparative potential between embedded steel and concrete surface. In addition, OCP(Open Circuit Potential) was measured using buried steel and CE(Counter Electrode). Agar and NaOH solution were used as ion exchange materials and Hg/HgO was used for RE(Reference Electrode), which was more sensitive to temperature than HCP. Among the influencing factors, the exposure period and chloride concentration had a relatively greater effect than cover depth and w/c ratio. Additionally, the entire measured HCP and OCP showed a clearly linear relationship with increasing cover depth and w/c ratio. Through multiple regression analysis, the relationship between HCP and OCP was quantified, and an improved correlation was obtained with temperature effect.

• Journal of Internet Computing and Services
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• v.24 no.1
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• pp.49-59
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• 2023

Recently, attention to the pandemic situation represented by COVID-19 emerged problems caused by unexpected shortage of medical personnel. In this paper, we present a method for diagnosing the presence or absence of lesional sign on PA chest X-ray images as computer vision solution to support diagnosis tasks. Method for visual anomaly detection based on feature modeling can be also applied to X-ray images. With extracting feature vectors from PA chest X-ray images and divide to patch unit, region-specific abnormality can be detected. As preliminary experiment, we created simulation data set containing multiple objects and present results of the comparative experiments in this paper. We present method to improve both efficiency and performance of the process through hard masking of patch features to aligned images. By summing up regional specificity and global anomaly detection results, it shows improved performance by 0.069 AUROC compared to previous studies. By aggregating region-specific and global anomaly detection results, it shows improved performance by 0.069 AUROC compared to our last study.