가로수의 수종에 따른 결함 요인을 분석하고자 인천광역시의 가로수 느티나무, 백합나무, 양버즘나무 와 왕벚나무 4수종을 대상으로 시각적 수목평가를 실시하고 위해 정도를 파악하였다. 느티나무는 변재 부의 부후 상처가 다른 수종에 비해 많고 상처면적이 큰 개체도 다수 발견되나 줄기에서 관찰되는 공동 의 수는 적고 균류의 자실체 발생률 또한 낮았다. 반면 타진음 검사에서 이상 소견이 가장 많이 관찰되었 고 동일세력 줄기의 발생률과 분기지점의 결함 수관에서 차지하는 고사지의 비율 등은 다른 수종에 비 해 높다. 또한 줄기를 옥죄는 뿌리의 발생률은 비교적 낮고 수관이 차도 또는 보행로 방향으로 편중된 경향을 보였다. 백합나무는 구조적으로 안정된 수형을 이루고 있으며, 변재부의 부후상처 발생률, 타진음 검사결과 동일세력 줄기의 결함 등은 다른 수종에 비해 건전한 것으로 분석되었다. 그러나 백합나무 는 줄기가 직립하는 성질이 강하여 수관이 편향된 개체의 비율은 비교적 낮은 편이다. 양버즘나무는 세장비가 비교적 높고 줄기가 약하게 기울어 자라는 특성이 관찰되나 활관비가 높아 활력을 유지하는 것으로 판단된다. 줄기에 발생하는 결함 요인들도 비교적 낮은 수준이어서 건강성이 높으나 위험하지 않을 정도로 줄기가 기울어 수관이 차도 방향으로 편중된 개체의 비율은 높다. 왕벚나무는 세장비가 4수종 중 가장 낮아 초살이 잘 발달하는 수종이며, 활관비와 줄기의 기울기 각도는 비교적 안정한 편이 다. 그러나 부후와 공동의 발생률은 다른 수종에 비해 유의적으로 높아 상처에 취약한 수종임을 확인할 수 있다. 수종별 위험도 등급을 구분한 결과 느티나무와 왕벚나무의 결함 발생률과 위험도가 비교적 높게 나타났으며, 백합나무와 양버즘나무는 상대적으로 도로환경의 적응도가 높은 것으로 판단된다. 시각적 수목평가는 조사할 나무의 수량이 많고 선형으로 식재된 가로수의 위험성 평가에 흔히 통용되 는 방법인데, 향후 가로수의 건강한 생육과 안전한 관리를 위해 평가 항목을 개발하고 평가 기준을 표준 화하는 등의 추가적인 연구가 필요하다.

Silicon carbide (SiC) has emerged as a promising material for next-generation power semiconductor materials, due to its high thermal conductivity and high critical electric field (~3 MV/cm) with a wide bandgap of 3.3 eV. This permits SiC devices to operate at lower on-resistance and higher breakdown voltage. However, to improve device performance, advanced research is still needed to reduce point defects in the SiC epitaxial layer. This work investigated the electrical characteristics and defect properties using DLTS analysis. Four deep level defects generated by the implantation process and during epitaxial layer growth were detected. Trap parameters such as energy level, capture-cross section, trap density were obtained from an Arrhenius plot. To investigate the impact of defects on the device, a 2D TCAD simulation was conducted using the same device structure, and the extracted defect parameters were added to confirm electrical characteristics. The degradation of device performance such as an increase in on-resistance by adding trap parameters was confirmed.

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.

The need for lightweight yet strong materials is being demanded in all industries. Carbon fiber-reinforced plastic is a material with increased strength by attaching carbon fiber to plastic, and is widely used in the aerospace industry, ships, automobiles, and civil engineering based on its low density. Carbon-reinforced fiber plastic is a material widely used in parts and manufactured products, and structural analysis simulation is required during design, and application of actual material properties is necessary for accurate structural analysis simulation. In the case of carbon-reinforced fiber plastics, it is reported that there is a porosity of around 0.5% to 6%, and it is necessary to check the change in material properties according to the porosity and pore shape. It was confirmed by applying the method. It was confirmed that the change in elastic modulus according to the porosity was 10.7% different from the base material when the porosity was 6.0%, and the Poisson's ratio was confirmed to be less than 3.0%. It was confirmed that the elliptical spherical pore derived different material properties from the spherical pore depending on the pore shape, and it was confirmed that the shape of the pore had to be confirmed to derive equivalent material properties.

This research proposes a novel approach to tackle the challenge of categorizing unstructured customer complaints in the automotive industry. The goal is to identify potential vehicle defects based on the findings of our algorithm, which can assist automakers in mitigating significant losses and reputational damage caused by mass claims. To achieve this goal, our model uses the Word2Vec method to analyze large volumes of unstructured customer complaint data from the National Highway Traffic Safety Administration (NHTSA). By developing a score dictionary for eight pre-selected criteria, our algorithm can efficiently categorize complaints and detect potential vehicle defects. By calculating the score of each complaint, our algorithm can identify patterns and correlations that can indicate potential defects in the vehicle. One of the key benefits of this approach is its ability to handle a large volume of unstructured data, which can be challenging for traditional methods. By using machine learning techniques, we can extract meaningful insights from customer complaints, which can help automakers prioritize and address potential defects before they become widespread issues. In conclusion, this research provides a promising approach to categorize unstructured customer complaints in the automotive industry and identify potential vehicle defects. By leveraging the power of machine learning, we can help automakers improve the quality of their products and enhance customer satisfaction. Further studies can build upon this approach to explore other potential applications and expand its scope to other industries.

To make semiconductor chips, a number of complex semiconductor manufacturing processes are required. Semiconductor chips that have undergone complex processes are subjected to EDS(Electrical Die Sorting) tests to check product quality, and a wafer bin map reflecting the information about the normal and defective chips is created. Defective chips found in the wafer bin map form various patterns, which are called defective patterns, and the defective patterns are a very important clue in determining the cause of defects in the process and design of semiconductors. Therefore, it is desired to automatically and quickly detect defective patterns in the field, and various methods have been proposed to detect defective patterns. Existing methods have considered simple, complex, and new defect patterns, but they had the disadvantage of being unable to provide field engineers the evidence of classification results through deep learning. It is necessary to supplement this and provide detailed information on the size, location, and patterns of the defects. In this paper, we propose an anomaly detection framework that can be explained through FCDD(Fully Convolutional Data Description) trained only with normal data to provide field engineers with details such as detection results of abnormal defect patterns, defect size, and location of defect patterns on wafer bin map. The results are analyzed using open dataset, providing prominent results of the proposed anomaly detection framework.

Zinc-ion Batteries (ZIBs) are currently considered to be effective energy storage devices for wearable electronics because of their low cost and high safety. Indeed, ZIBs show high power density and safety compared with conventional lithium ion batteries (LIBs) and exhibit high energy density in comparison with supercapacitors (SCs). However, in spite of their advantages, further current collector development is needed to enhance the electrochemical performance of ZIBs. To design the optimized current collector for high performance ZIBs, a high quality graphene film is suggested here, with improved electrical conductivity by controlling the defects in the graphene film. The graphene film showed improved electrical conductivity and good electron transfer between the current collector and active material, which led to a high specific capacity of 346.3 mAh g-1 at a current density of 100 mA g-1, a high-rate performance with 116.3 mAh g-1 at a current density of 2,000 mA g-1, and good cycling stability (68.0 % after 100 cycles at a current density of 1,000 mA g-1). The improved electrochemical performance is firmly because of the defects-controlled graphene film, leading to improved electrical conductivity and thus more efficient electron transfer between the current collector and active material.

In this study, the safety aspects were studied by comparing the charge control characteristics of the two vehicles when a failure occurs between the OBC including the charging port or the charging door module (CDM) during slow charging using the In Cable Control Box (ICCB) for a long time.When the AC terminal was momentarily disconnected during charging, the Model-3 vehicle was charged normally if the AC circuit was disconnected up to three times, and the charging control was stopped when the number of disconnects reached four times. However, in the Ioniq-5 vehicle, charging control was normally performed when the disconnected AC circuit was normally connected regardless of the number of disconnection.

In this paper, a study was conducted on the analysis of communication circuit faults using oscilloscope waveform analysis. Circuit resistance was calculated based on voltage and operating current values using a simple equation, and it was confirmed that the increase in resistance of the communication circuit could be analyzed by analyzing the voltage level during transmitter operation. By combining information of the controller ID, the location of the fault was identified and it was concluded that the location of the fault can be quickly found by analyzing the oscilloscope waveform and the controller ID information. Additionally, the value of communication line contact resistance can be calculated using a simple equation, and the location of the fault can be found by analyzing the communication voltage level and ID information.

Elevators are the main means of transport in buildings. A malfunction of an elevator in operation may cause in convenience to users. Furthermore, fatal accidents, such as injuries and death, may occur to the passengers also. Therefore, it is important to prevent failure before accidents happen. In related studies, preventive measures are proposed through analyzing failures, and the lifespan of elevator components. However, these methods are limited to existing an elevator model and its surroundings, including operating conditions and installed environments. Vibration occurs when the elevator is operated. Experts have classified types of faults, which are symptoms for malfunctions (failures), via analyzing vibration. This study proposes an artificial intelligent model for classifying faults automatically with deep learning algorithms through elevator vibration data, hereby preventing failures before they occur. In this study, the vibration data of six elevators are collected. The proposed methodology in this paper removes "the measurement error data" with incorrect measurements and extracts operating sections from the input datasets for proceeding deep learning models. As a result of comparing the performance of training five deep learning models, the maximum performance indicates Accuracy 97% and F1 Score 97%, respectively. This paper presents an artificial intelligent model for detecting elevator fault automatically. The users’ safety and convenience may increase by detecting fault prior to the fatal malfunctions. In addition, it is possible to reduce manpower and time by assisting experts who have previously classified faults.

PURPOSES : To efficiently manage pavements, a systematic pavement management system must be established based on regional characteristics. Suppose that the future conditions of a pavement section can be predicted based on data obtained at present. In this case, a more reasonable road maintenance strategy should be established. Hence, a prediction model of the annual surface distress (SD) change for national highway pavements in Gangwon-do, Korea is developed based on influencing factors. METHODS : To develop the model, pavement performance data and influencing factors were obtained. Exploratory data analysis was performed to analyze the data acquired, and the results show that the data were preprocessed. The variables used for model development were selected via correlation analysis, where variables such as surface distress, international roughness index, daily temperature range, and heat wave days were used. Best subset regression was performed, where the candidate model was selected from all possible subsets based on certain criteria. The final model was selected based on an algorithm developed for rational model selection. The sensitivity of the annual SD change was analyzed based on the variables of the final model. RESULTS : The result of the sensitivity analysis shows that the annual SD change is affected by the variables in the following order: surface distress ˃ heat wave days ˃ daily temperature range ˃ international roughness index. CONCLUSIONS : An annual SD change prediction model is developed by considering the present performance, traffic volume, and climatic conditions. The model can facilitate the establishment of a reasonable road maintenance strategy. The prediction accuracy can be improved by obtaining additional data, such as the construction quality, material properties, and pavement thickness.