• 대한기계학회논문집A
• /
• 제26권4호
• /
• pp.667-676
• /
• 2002

Many studies on the finite element modeling for bolted joints have proceeded, but the structures with bolted joints are complicated in shape and it is difficult to find out the characteristics according to joint condition. Usually, experimental methods have been used for bolted joint analysis. A reliable and practical finite element modeling technique for structure with bolted joints is very important for engineers in industry. In this study, three kinds of model are presented; a detailed model, a practical model and a simple model. The detailed model is modeled by using 3-D solid element and gap element, and the practical model is modeled by using shell element (a portion of bolt head) and beam element (a portion of bolt body), the simple model is modeled by simplifying practical model without using gap elements. Among these models, the simple model has the least degree of freedom and show the effect of memory reduction of 59%, when compared with the detailed model.

• 한국정보통신학회:학술대회논문집
• /
• 한국정보통신학회 2014년도 춘계학술대회
• /
• pp.271-272
• /
• 2014

범죄 수사에서 디지털 증거물이 증가됨에 따라, 법적으로 효용성이 큰 데이터를 추출할 수 있는 디지털 포렌식 도구에 대한 중요성이 높아지고 있다. 디지털 제품들은 빠르게 성장하고 있고, 포렌식 도구는 사용자와 사건에 맞도록 용이하게 구현 되어야 한다. 포렌식 업계나 정부에서는 소요 비용이 큰 포렌식 도구를 사용하고 있지만 메모리 한계, 사후 감사의 한계 등 한계성이 제시되고 있다. 이러한 문제를 해결하기 위하여 다양한 포렌식 도구가 빠르게 구현 할 수 있도록 오픈소스 포렌식 도구 개발이 필요하다. 본 논문에서는 현재 상용화 되고 있는 디지털 포렌식 기술들에 관해 연구하고, 이들의 한계성을 극복하기 위한 오픈 디지털 포렌식 기법들을 제시하고, 적용 방안에 대해 제안한다.

• 한국기계가공학회지
• /
• 제19권11호
• /
• pp.94-101
• /
• 2020

In the age of industry 4.0, artificial intelligence is being widely used to realize machinery condition monitoring. Due to their excellent performance and the ability to handle large volumes of data, machine learning techniques have been applied to realize the fault diagnosis of different equipment. In this study, we performed the failure mode effect analysis (FMEA) of an aluminum electrolytic capacitor by using deep learning and big data. Several tests were performed to identify the main failure mode of the aluminum electrolytic capacitor, and it was noted that the capacitance reduced significantly over time due to overheating. To reflect the capacitance degradation behavior over time, we employed the Vanilla long short-term memory (LSTM) neural network architecture. The LSTM neural network has been demonstrated to achieve excellent long-term predictions. The prediction results and metrics of the LSTM and Vanilla LSTM models were examined and compared. The Vanilla LSTM outperformed the conventional LSTM in terms of the computational resources and time required to predict the capacitance degradation.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제14권11호
• /
• pp.4522-4536
• /
• 2020

Nowadays, cloud is the fastest emerging technology in the IT industry. We can store and retrieve data from the cloud. The most frequently occurring problems in the cloud are security and privacy preservation of data. For improving its security, secret information must be protected from various illegal accesses. Numerous traditional cryptography algorithms have been used to increase the privacy in preserving cloud data. Still, there are some problems in privacy protection because of its reduced security. Thus, this article proposes an ElGamal Elliptic Curve (EGEC) Homomorphic encryption scheme for safeguarding the confidentiality of data stored in a cloud. The Users who hold a data can encipher the input data using the proposed EGEC encryption scheme. The homomorphic operations are computed on encrypted data. Whenever user sends data access permission requests to the cloud data storage. The Cloud Service Provider (CSP) validates the user access policy and provides the encrypted data to the user. ElGamal Elliptic Curve (EGEC) decryption was used to generate an original input data. The proposed EGEC homomorphic encryption scheme can be tested using different performance metrics such as execution time, encryption time, decryption time, memory usage, encryption throughput, and decryption throughput. However, efficacy of the ElGamal Elliptic Curve (EGEC) Homomorphic Encryption approach is explained by the comparison study of conventional approaches.

• 한국표면공학회지
• /
• 제54권1호
• /
• pp.18-24
• /
• 2021

KNN is widely used in the electronic industry such as memory devices, sensors, and capacitors due to various structural, electrical, and eco-friendly properties. In this study, Mn-doped KNN was prepared by adopting a sol-gel method with advantages of low cost and large area thin film fabrication. The Mn-doped KNN thin films were deposited by annealing in air for 1 hour and 700℃. The surface morphology characteristics and grain size of the heat-treated KNN were observed by SEM and AFM, and we used the X-ray diffraction for measuring the crystal phase of KNN. The XRD analysis results show that the fabrication of (K0.5Na0.5)(Nb1-xMnn)O3 thin films by sol-gel method in the thin film process of this experiment was stable in the perovskite phase of c-axis orientation. The SEM and AFM results show that the cracks were not confirmed from the fracture surface data of KNN thin films and were densely deposited with thin films with uniform thickness.

• 자동차안전학회지
• /
• 제14권3호
• /
• pp.60-64
• /
• 2022

In the automotive industry, the development of automobiles to meet safety requirements is becoming increasingly complex. This is because quality evaluation agencies in each country are continually strengthening new safety standards for vehicles. Among these various requirements, collision safety must be satisfied by controlling airbags, seat belts, etc., and can be defined as post-crash safety. Apart from this safety system, the Advanced Driver Assistance Systems (ADAS) use advanced detection sensors, GPS, communication, and video equipment to detect the hazard and notify driver before the collision. However, research to improve passenger safety in case of an accident by using the sensor of active safety represented by ADAS in the existing passive safety is limited to the level that utilizes the sudden braking level of the FCA (Forward Collision-avoidance Assist) system. Therefore, this study aims to develop logic that can improve passenger protection in case of an accident by using ADAS information and driving information secured before a collision. The proposed logic was constructed based on LSTM deep learning techniques and trained using crash test data.

• 대한임상독성학회지
• /
• 제2권1호
• /
• pp.31-36
• /
• 2004

Three workers, field operators in lubricating oil processing of petroleum refinery industry were found unconscious by other worker. One of them who were exposed to an high concentration of H2S was presented with Glasgow Coma Score of 5, severe hypoxemia on arterial blood gas analysis, normal chest radiography, and normal blood pressure. On hospital day 7, his mental state became clear, and neurologic examination showed quadriparesis, profound spasticity, increased tendon reflexes, abnormal Babinski response, and bradykinesia. He was also found to have decreased memory, attention deficits and blunted affect which suggest general cognitive dysfunction, which improved soon. MRI scan showed abnormal signals in both basal ganglia and motor cortex, compatible with clinical findings of motor dysfunction. Neuropsychologic testing showed deficits of cognitive functions. SPECT showed markedly decreased cortical perfusion in frontotemporoparietal area with deep white matter. Another case was recovered completely, but the other expired the next day.

• International journal of advanced smart convergence
• /
• 제12권2호
• /
• pp.159-166
• /
• 2023

Due to the aging workforce in the construction industry in South Korea, the accident rate has been increasing. The cognitive abilities of older workers are closely related to both safety incidents and labor productivity. Therefore, there is a need to improve cognitive abilities through personalized training based on cognitive assessment results, using cognitive training content, in order to enable safe performance in labor-intensive environments. The provided cognitive training content includes concentration, memory, oreintation, attention, and executive functions. Difficulty levels were applied to each content to enhance user engagement and interest. To stimulate interest and encourage active participation of the participants, the difficulty level was automatically adjusted based on feedback from the MMSE-DS results and content measurement data. Based on the accumulated data, individual training scenarios have been set differently to intensively improve insufficient cognitive skills, and cognitive training programs will be developed to reduce safety accidents at construction sites through measured data and research. Through such simple cognitive training, it is expected that the reduction of accidents in the aging construction workforce can lead to a decrease in the social costs associated with prolonged construction periods caused by accidents.

• Journal of Ginseng Research
• /
• 제48권1호
• /
• pp.98-102
• /
• 2024

Background: Ginseng (Panax ginseng Meyer) is a perennial plant belonging to the Araliaceae family that is known to have various beneficial effects including improving memory loss and spatial cognitive ability, and anti-cancer and anti-diabetes activity. Its functional benefits also include improving liver function, regulating blood pressure, stress, and providing antioxidant activity. Usually, various agrochemicals are used in cultivating ginseng preventing from many diseases. Methods: FCGP (field cultivated ginseng in pot) was implemented by imitating MCWG (mountain cultivated wild ginseng). Pesticide analysis of pot cultivation was carried out and the contents of bioactive components such as ginsenoside were also analyzed. Results: FCGP ginsenoside content was higher than that of FCG (field cultivated ginseng) and MCWG. FCGP has been shown to have a relatively high antioxidant effect compared with cultivated ginseng. Conclusion: It was confirmed that ginseng can be grown for 6 years without resorting to use of pesticides. In addition, it was confirmed that effective accumulation of physiologically active ingredients such as ginsenoside is possible. Our result represents FCGP is a novel method of pesticide-free ginseng cultivation

• Asia pacific journal of information systems
• /
• 제34권2호
• /
• pp.400-420
• /
• 2024

Nowadays, social media has evolved into a powerful networked ecosystem in which governments and citizens publicly debate economic and political issues. This holds true for the pros and cons of Indonesia's ore nickel export restriction to Europe, which we aim to investigate further in this paper. Using Twitter as a dependable channel for conducting sentiment analysis, we have gathered 7070 tweets data for further processing using two sentiment analysis approaches, namely Support Vector Machine (SVM) and Long Short Term Memory (LSTM). Model construction stage has shown that Bidirectional LSTM performed better than LSTM and SVM kernels, with accuracy of 91%. The LSTM comes second and The SVM Radial Basis Function comes third in terms of best model, with 88% and 83% accuracies, respectively. In terms of sentiments, most Indonesians believe that the nickel ore provision will have a positive impact on the mining industry in Indonesia. However, a small number of Indonesian citizens contradict this policy due to fears of a trade dispute that could potentially harm Indonesia's bilateral relations with the EU. Hence, this study contributes to the advancement of measuring public opinions through big data tools by identifying Bidirectional LSTM as the optimal model for the dataset.