최근 광주 화정아이파크, 인천 검단 신도시 아파트 사고 등 국내에서 건축물 안전사고가 잇따라 발 생하고 있다. 시공 중 발생한 구조물 붕괴로 인해 인명·재산 피해가 수반된 대형 건설사고가 다수 발 생하였다. 건축물 안전사고의 발생 원인으로 무단 구조변경, 설계 및 시공 시 철근 누락 등이 제시되 면서 부실 감리에 대한 우려가 증가하고 있다. 하지만 현실적으로 건설 현장의 모든 장소에서 감리직 원이 상주하며 확인하는 것은 불가능하며 시공 정확도 검사 역시 감리자의 경험에 근거하여 육안 판 독 및 일부 수작업 계측으로 진행되고 있다. 감리 작업의 효율성을 높이기 위해 최근에는 3D 스캐너, Depth Camera 등을 구조 감리 기법 연구가 진행되고 있다. 철근 길이와 철근 배근 간격에 대한 연구 는 많이 진행되었지만 철근 직경의 검출 정확도는 아직 미흡한 상황이며, 특히 직경이 작은 D10과 D13의 구별에서는 한계를 나타내고 있다. 따라서 본 연구에서는 접근성이 용이한 스마트폰을 사용하 여 영상을 획득하고 이를 기반으로 3D 포인트 클라우드를 제가한 다음 철근 직경, 철근 길이, 철근 배근 간격 등의 자동 검측 기술을 개발하고 건설현장에서의 적용 가능성을 확인하고자 한다. 검증을 위한 실험체는 길이 2100mm, 폭 195mm, 높이 395mm의 철근 조립 상태의 보이다. 포인트 클라우드 제작을 위한 영상 촬영은 iPhone SE (3rd generation)을 사용하였다. 이후 MATLAB과 METASHAPE 를 사용하여 포인트 클라우드를 생성하고 Computer Vision과 Image Processing 기술을 활용하여 구 하고자 하는 철근 정보를 자동 검출하였다. 이후 실제 측정한 값과 자동 검출한 값을 비교하여 개발한 기법에 대한 적합성을 확인하였다.

Deep learning-based computer vision anomaly detection algorithms are widely utilized in various fields. Especially in the manufacturing industry, the difficulty in collecting abnormal data compared to normal data, and the challenge of defining all potential abnormalities in advance, have led to an increasing demand for unsupervised learning methods that rely on normal data. In this study, we conducted a comparative analysis of deep learning-based unsupervised learning algorithms that define and detect abnormalities that can occur when transparent contact lenses are immersed in liquid solution. We validated and applied the unsupervised learning algorithms used in this study to the existing anomaly detection benchmark dataset, MvTecAD. The existing anomaly detection benchmark dataset primarily consists of solid objects, whereas in our study, we compared unsupervised learning-based algorithms in experiments judging the shape and presence of lenses submerged in liquid. Among the algorithms analyzed, EfficientAD showed an AUROC and F1-score of 0.97 in image-level tests. However, the F1-score decreased to 0.18 in pixel-level tests, making it challenging to determine the locations where abnormalities occurred. Despite this, EfficientAD demonstrated excellent performance in image-level tests classifying normal and abnormal instances, suggesting that with the collection and training of large-scale data in real industrial settings, it is expected to exhibit even better performance.

This work involves the development of a novel waste-derived carbon dots (CDs) conjugated with silver (Ag) nanohybrid system-based Fluorescence Resonance Energy Transfer (FRET) sensor for the detection of melamine. CDs and Ag nanoparticles served as energy donors and energy acceptors, respectively. CDs were synthesized from orange peel waste through a combined hydrothermal and ultra-sonication route. The synthesized CDs had hydroxyl, amino, and carboxyl groups on their surface, explaining that waste-derived CDs can act as reducing and stabilizing agents and showed strong absorption and fluorescence emission at 305 and 460 nm, respectively. The bandgap, linear refractive index, conduction band, and valance band potential of CDs were observed to be 2.86, 1.849, 1.14, and 4.002 eV, respectively. No significant difference was observed in the fluorescence properties at different pH (acid and alkaline) and ionic concentrations. Given their fluorescent nature, the synthesized CDs were used for the detection of melamine. The fluorescence of CDs was found to be quenched by Ag+ due to the FRET energy transfer between CDs to Ag. Notably, the zeta potential of Ag@CDs was changed from − 28.7 mV to − 30.6 mV after the incorporation of Ag+. Ag@CDs showed excellent selectivity and sensitivity toward the sensing of melamine in the aqueous solutions with the limit of detection ~ 0.85 μM. Increasing the melamine level also raises the FL intensity of Ag@CDs. The substrate was effectively used in the detection of melamine in milk as a real application and the recovery percentage was found to be 98.03%. Moreover, other adulterants such as urea and formaldehyde can be detected selectively by Ag@CDs. Overall, the synthesized Ag@CDs can be used as an efficient material for sensing applications involving such food adulterants.

Black phosphorus (BP) is incorporated in the electrochemical detection of uric acid (UA) to form few layers of BP nanosheets (BPNS)-modified glassy carbon electrodes (BPNS/GCE), investigated by means of ultrasound-assisted liquid-phase exfoliation. We find a significant increase in the peak current magnitude and positive potential shift in the electrochemical response of BPNS/GCE, which may be attributed to the larger specific surface area and good charge transfer ability of BPNS. Further, the electrochemical response of BPNS/GCE is evaluated under different conditions to achieve the optimal conditions. UA detection using differential pulse voltammetry (DPV) shows linear response within the range of 1–1000 μM with a detection limit of 0.33 μM. This work reveals new applications of BP nanomaterials in the electrochemical sensing, thereby promoting further advancement in terms of practical applications of two-dimensional nanomaterials.

This study presents the fabrication and application of a graphene-assisted voltammetry platform for the sensitive detection of nitrate ions in PM2.5 (atmospheric aerosols with a maximum diameter of 2.5 μm). The MoS2/ reduced graphene oxide/ glassy carbon electrode ( MoS2/rGO/GCE) was prepared using a simple and efficient electrochemical deposition method. The rationale behind selecting MoS2/ rGO stems from their individual properties that, when combined, can enhance the electrode’s performance. MoS2 offers excellent electro-catalytic activity and selectivity for nitrate ion detection, while rGO provides high conductivity and a large surface area for enhanced sensitivity. The electrochemical performance of MoS2/ rGO/GCE was investigated and compared with MoS2/ GCE and bare GCE using cyclic voltammetry and electrochemical impedance spectroscopy. The results demonstrated that MoS2/ rGO/GCE exhibited enhanced electro-catalytic activity, high conductivity, and improved selectivity for nitrate ion detection. The optimal pH value for detecting nitrate ions was determined to be 8.0. Differential pulse voltammetry (DPV) was employed to investigate the linear range and detection limit of nitrate ions on MoS2/ rGO/GCE, resulting in a linear range from 1 to 300 μM and a detection limit of 0.35 μM. The reproducibility and the stability of MoS2/ rGO/GCE were assessed, showing satisfactory performance. Real sample analysis from Chengdu City showed a strong correlation between the results obtained using MoS2/ rGO/GCE and ion chromatography, highlighting its potential application in monitoring nitrate ions in PM2.5. The findings of this study contribute to the development of a graphene-assisted voltammetry platform for sensitive nitrate ion detection in PM2.5, offering potential benefits for real-time air pollution monitoring and environmental health assessments.

In the present study, an innovative electrochemical sensing platform was established for sensitive detection of NO2 —. This sensor was developed using CoFe alloy encapsulated in nitrogen-doped carbon nanocubes (named as CoFe@NC-NCS), synthesized through the calcination of polydopamine-coated CoFe Prussian-blue analogues (CoFe-PBA@PDA). The morphological and electrochemical characterization reveals that the CoFe@NC-NCS possesses high electrocatalytic activity for electrochemical quantitation of NO2 —, ascribed to the huge surface area and plentiful active positions, benefiting from the porous, hollow, and core–shell structure of CoFe@NC-NCS. Under the optimal conditions, CoFe@NC-NCS/GCE possessed remarkable sensing performance for NO2 — with wide liner ranges and a detection limit of 0.015 μM. NO2 — recovery experiments in real samples exhibited recoveries in the range of 98.8–103.5%. Hence, the CoFe@NC-NCS shows great promise for the construction of electrochemical sensor with more potential application.

In this research, reduced graphene oxide/polypyrrole (rGO/PPy) particles were synthesized and used to measure the amount of dopamine (DA) electrochemically. The obtained rGO/PPy particle was characterized by Fourier Transform Infrared Spectrophotometer (FTIR), UV–Visible Spectrophotometer (UV–Vis), and X-Ray Diffraction Diffractometry (XRD). To investigate the DA sensor performance, cyclic voltammetry (CV) and differential pulse voltammetry (DPV) were used to acquire electrochemical measurements of the sensor. Current values of 1.65 and 5.9 mA were observed in the CV at 0.2 mM and 1.2 mM concentrations of target molecule, respectively. Under optimized conditions, the linear calibration plots were found to exhibit significant sensitivity in the linear range of 0.2 and 1.2 mM, with a corresponding detection limit of 0.061 μM for DA. The results obtained were similar to the sensor results of DA made using precious metals. This work was a demonstration of the feasibility of high-sensitivity electrochemical analysis with conductive carbon materials without the use of precious metals. It was also observed that the cost-effective rGO/PPy exhibited a very high potential for DA detection.

Recently, there has been an increasing attempt to replace defect detection inspections in the manufacturing industry using deep learning techniques. However, obtaining substantial high-quality labeled data to enhance the performance of deep learning models entails economic and temporal constraints. As a solution for this problem, semi-supervised learning, using a limited amount of labeled data, has been gaining traction. This study assesses the effectiveness of semi-supervised learning in the defect detection process of manufacturing using the MixMatch algorithm. The MixMatch algorithm incorporates three dominant paradigms in the semi-supervised field: Consistency regularization, Entropy minimization, and Generic regularization. The performance of semi-supervised learning based on the MixMatch algorithm was compared with that of supervised learning using defect image data from the metal casting process. For the experiments, the ratio of labeled data was adjusted to 5%, 10%, 25%, and 50% of the total data. At a labeled data ratio of 5%, semi-supervised learning achieved a classification accuracy of 90.19%, outperforming supervised learning by approximately 22%p. At a 10% ratio, it surpassed supervised learning by around 8%p, achieving a 92.89% accuracy. These results demonstrate that semi-supervised learning can achieve significant outcomes even with a very limited amount of labeled data, suggesting its invaluable application in real-world research and industrial settings where labeled data is limited.

Background: Single nucleotide polymorphisms (SNPs) are widely used genetic markers with applications in human disease diagnostics, animal breeding, and evolutionary studies, but existing genotyping methods can be labor-intensive and costly. The aim of this study is to develop a simple and rapid method for identification of a single nucleotide change. Methods: A modified Polymerase Chain Reaction Amplification of Multiple Specific Alleles (PAMSA) and high resolution melt (HRM) analysis was performed to discriminate a bovine polymorphism in the NCAPG gene (rs109570900, 1326T > G). Results: The inclusion of tails in the primers enabled allele discrimination based on PCR product lengths, detected through agarose gel electrophoresis, successfully determining various genotypes, albeit with some time and labor intensity due to the use of relatively costly high-resolution agarose gels. Additionally, high-resolution melt (HRM) analysis with tailed primers effectively distinguished the GG genotype from the TT genotype in bovine muscle cell lines, offering a reliable way to distinguish SNP polymorphisms without the need for time-consuming AS-PCR. Conclusions: Our experiments demonstrated the importance of incorporating unique mismatched bases in the allele-specific primers to prevent cross-amplification by fragmented primers. This efficient and cost-effective method, as presented here, enables genotyping laboratories to analyze SNPs using standard real-time PCR.

Smart factory companies are installing various sensors in production facilities and collecting field data. However, there are relatively few companies that actively utilize collected data, academic research using field data is actively underway. This study seeks to develop a model that detects anomalies in the process by analyzing spindle power data from a company that processes shafts used in automobile throttle valves. Since the data collected during machining processing is time series data, the model was developed through unsupervised learning by applying the Holt Winters technique and various deep learning algorithms such as RNN, LSTM, GRU, BiRNN, BiLSTM, and BiGRU. To evaluate each model, the difference between predicted and actual values was compared using MSE and RMSE. The BiLSTM model showed the optimal results based on RMSE. In order to diagnose abnormalities in the developed model, the critical point was set using statistical techniques in consultation with experts in the field and verified. By collecting and preprocessing real-world data and developing a model, this study serves as a case study of utilizing time-series data in small and medium-sized enterprises.

본 논문에서는 대규모 실시간 매칭의 생존 게임에서 플레이를 위한 유저들의 소셜 관계에 대해 연구한다. 특 히 “사전 팀 구성”을 통한 자의적인 팀 구성이 어떤 방식으로 유저들을 연결하는 지 연구하고자 한다. 다수 의 사람 간 집단 역학에서 나타나는 특성이나 패턴에 대한 조사를 중심으로 하였으며, 개인의 특성은 보조적 인 수단으로만 사용된다. 이번 연구에서는 게임을 플레이하는 유저들의 익명화 된 대규모 데이터를 활용하며 이에 대한 간소화된 집계 방법을 제안한다. 데이터 세트에는 사전 팀 구성에 관한 11,259만 줄의 속성이 포 함되어 있으며, 데이터에서 우리는 250만개의 노드와 1,182만개의 무방향 에지가 있는 협업 네트워크를 구성 하여 대규모 게임 내 협동 네트워크를 만듭니다. 연결 정도, 경로 길이, 클러스터링 및 소속 하위 컴포넌트의 크기 등 네트워크에 관한 수치를 통해 게임내 소셜 활동에 대한 이해를 높이고자 한다. 본 논문에서는 다음 의 두가지 특성을 중심으로 결론을 제시한다. 첫째, 네트워크 내에는 대규모로 연결된 2개(전체의 44% 및 2%)와 나머지의 파편화된 하위 컴포넌트로 구성 되어있다. 이 대규모 컴포넌트 중 작은 쪽은 한국 유저로만 구성되어 있다. 둘째, 컴포넌트 크기 별 평균 연결 거리와 군집화 계수, k-core를 확인함으로써 기타 다른 네 트워크 대비 이웃 간 연결이 강하면서 전체적으로는 비교적 멀리 떨어져 있음을 확인한다.

본 연구는 네트워크 이상 감지 및 예측을 위해 벡터 자기회귀(VAR) 모델의 사용을 비교 분석한다. VAR 모 델에 대한 간략한 개요를 제공하고 네트워크 이상 체크로 사용 가능한 두 가지 버전을 검토하며 두 종류의 VAR 모델을 통한 경험적인 평가를 제시한다. VAR-Filtered moving-common-AR 모델이 단일 노드 이상 감지 성능에서 우수하며, VAR-Adaptive Learning 버전은 몇 개의 노드 간 이상을 효과적으로 식별하는 데 특히 효 과적이며 두 가지 주요VAR 모델의 전반적인 성능 차이에 대한 근본적인 이유도 분석한다. 각 기술의 장단점 을 개요로 제공하고 성능 향상을 위한 제안도 제시하고자 한다.

Efficiently detecting the nearest navigational dangers in Electronic Chart Display and Information Systems (ECDIS) remains pivotal for maritime safety. However, the software implementation of ADMAR(Automatic Distance measurement and Ranging) functionality faced challenges, necessitating extensive computations across ENC cells and impacting real-time performance. To address this, we present a novel method employing dynamic programming. Our proposed algorithm strategically organizes nodes into a tree structure, optimizing the search process towards nodes likely to contain navigational hazards. Implementation of this method resulted in a notable sevenfold reduction in computation time compared to the conventional Brute Force approach. Our study established a direct correlation between the ADMAR functionality and node count, achieving error margins deemed acceptable for practical navigation scenarios. Despite this theoretical progress, minor errors in results prompt further refinement. Consequently, future iterations will explore varying values for N, considering hierarchy and cell sizes to enhance algorithmic precision. This research signifies a potential advancement in optimizing navigational danger detection within ECDIS, offering a promising avenue for improved efficiency. By introducing a dynamic programming-based approach, we have streamlined the detection process while acknowledging the scope for algorithmic refinement in subsequent studies. Our findings underline the feasibility of employing dynamic programming to enhance navigational danger detection, emphasizing its potential in ensuring maritime safety. This work lays a foundation for future research endeavors, aiming to fine-tune algorithms and advance navigational safety measures in ECDIS.

해상교통관제센터(VTS)의 관제사는 구역 내 교통 상황을 빠르고 정확하게 파악하여 관제가 필요한 선박에게 정보를 제공하는 역할을 수행한다. 그러나 교통량이 급격히 증가하는 경우 관제사의 업무 부하로 인해 관제 공백이 발생하기도 한다. 이러한 이유에서 관 제사의 업무 부하를 줄이고, 일관성 있는 관제 정보를 제공할 수 있는 관제 지원 기술의 개발이 필요한 실정이며, 본 논문에서는 구역 내 이상 운항 선박을 자동으로 식별하는 모델을 제안하였다. 제안하는 이상 운항 식별 모델은 규칙 기반 모델, 위치 기반 모델, 맥락 기반 모 델로 구성되며, 대상 해역의 교통 특성에 최적화된 교통 네트워크 모델을 사용하는 특징이 있다. 구현된 모델은 시범센터(대산항 VTS)에 서 수집되는 실해역 데이터를 적용하여 실험을 수행하였다. 실험을 통해 실해역의 다양한 이상 운항 상황이 자동으로 식별됨을 확인하였 고, 전문가 평가를 통해 식별 결과를 검증하였다.

PURPOSES : Demonstrated performance degradation of LiDAR for vehicle and pedestrian dummy in rainy and foggy road conditions. METHODS : In real-scale rain and fog road conditions, adjust the distance between LiDAR and the measurement target from 10m to 70m (in 10m interval), measure LiDAR NPC (number of point cloud) and intensity, and compare the resulting numerical values. RESULTS : LiDAR's NPC and Intensity showed statistically significant differences by overall weather condition (normal, rain, fog), and the values were found to be larger in the order of normal>rainfall>fog. In the case of vehicles, sunder rain conditions, NPC and intensity are recognized even at 70m as in normal conditions, but under fog conditions, NPC and intensity are measured only up to 30m. In the case of pedestrians, the reflective area size is smaller than that of vehicles, so they are recognized only up to 30m in rainy conditions, and NPC and intensity are measured only up to 20m in fog conditions. CONCLUSIONS : It was confirmed that LiDAR performance deteriorates in rain and fog compared to normal.

Water utilities are making various efforts to reduce water losses from water networks, and an essential part of them is to recognize the moment when a pipe burst occurs during operation quickly. Several physics-based methods and data-driven analysis are applied using real-time flow and pressure data measured through a SCADA system or smart meters, and methodologies based on machining learning are currently widely studied. Water utilities should apply various approaches together to increase pipe burst detection. The most intuitive and explainable water balance method and its procedure were presented in this study, and the applicability and detection performance were evaluated by applying this approach to water supply pipelines. Based on these results, water utilities can establish a mass balance-based pipe burst detection system, give a guideline for installing new flow meters, and set the detection parameters with expected performance. The performance of the water balance analysis method is affected by the water network operation conditions, the characteristics of the installed flow meter, and event data, so there is a limit to the general use of the results in all sites. Therefore, water utilities should accumulate experience by applying the water balance method in more fields.