• Smart Structures and Systems
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• v.28 no.6
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• pp.799-810
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• 2021

When monitoring the structural integrity of a bridge using data collected through accelerometers, identifying the profile of the load exerted on the bridge from the vehicles passing over it becomes a crucial task. In this study, the speed and location of vehicles on the deck of a bridge is reconfigured using real-time video to implicitly associate the load applied to the bridge with the response from the bridge sensors to develop an image-based deep learning network model. Instead of directly measuring the load that a moving vehicle exerts on the bridge, the intention in the proposed method is to replace the correlation between the movement of vehicles from CCTV images and the corresponding response by the bridge with a neural network model. Given the framework of an input-output-based system identification, CCTV images secured from the bridge and the acceleration measurements from a cantilevered beam are combined during the process of training the neural network model. Since in reality, structural damage cannot be induced in a bridge, the focus of the study is on identifying local changes in parameters by adding mass to a cantilevered beam in the laboratory. The study successfully identified the change in the material parameters in the beam by using the deep-learning neural network model. Also, the method correctly predicted the acceleration response of the beam. The proposed approach can be extended to the structural health monitoring of actual bridges, and its sensitivity to damage can also be improved through optimization of the network training.

• Geomechanics and Engineering
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• v.30 no.2
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• pp.107-121
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• 2022

Coal pillar assessment is of broad importance to underground engineering structure, as the pillar failure can lead to enormous disasters. Because of the highly non-linear correlation between the pillar failure and its influential attributes, conventional forecasting techniques cannot generate accurate outcomes. To approximate the complex behavior of coal pillar, this paper elucidates a new idea to forecast the underground coal pillar stability using combined unsupervised-supervised learning. In order to build a database of the study, a total of 90 patterns of pillar cases were collected from authentic engineering structures. A state-of-the art feature depletion method, t-distribution symmetric neighbor embedding (t-SNE) has been employed to reduce significance of actual data features. Consequently, an unsupervised machine learning technique K-mean clustering was followed to reassign the t-SNE dimensionality reduced data in order to compute the relative class of coal pillar cases. Following that, the reassign dataset was divided into two parts: 70 percent for training dataset and 30 percent for testing dataset, respectively. The accuracy of the predicted data was then examined using support vector classifier (SVC) model performance measures such as precision, recall, and f₁-score. As a result, the proposed model can be employed for properly predicting the pillar failure class in a variety of underground rock engineering projects.

• International Journal of Contents
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• v.17 no.4
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• pp.1-15
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• 2021

Named entity recognition (NER) is a crucial task for NLP, which aims to extract information from texts. To build NER systems, deep learning (DL) models are learned with dictionary features by mapping each word in the dataset to dictionary features and generating a unique index. However, this technique might generate noisy labels, which pose significant challenges for the NER task. In this paper, we proposed DL-dictionary features, and evaluated them on two datasets, including the OntoNotes 5.0 dataset and our new infectious disease outbreak dataset named GFID. We used (1) a Bidirectional Long Short-Term Memory (BiLSTM) character and (2) pre-trained embedding to concatenate with (3) our proposed features, named the Convolutional Neural Network (CNN), BiLSTM, and self-attention dictionaries, respectively. The combined features (1-3) were fed through BiLSTM - Conditional Random Field (CRF) to predict named entity classes as outputs. We compared these outputs with other predictions of the BiLSTM character, pre-trained embedding, and dictionary features from previous research, which used the exact matching and partial matching dictionary technique. The findings showed that the model employing our dictionary features outperformed other models that used existing dictionary features. We also computed the F1 score with the GFID dataset to apply this technique to extract medical or healthcare information.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.4184-4202
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• 2021

Text steganography combined with natural language generation has become increasingly popular. The existing methods usually embed secret information in the generated word by controlling the sampling in the process of text generation. A candidate pool will be constructed by greedy strategy, and only the words with high probability will be encoded, which damages the statistical law of the texts and seriously affects the security of steganography. In order to reduce the influence of the candidate pool on the statistical imperceptibility of steganography, we propose a steganography method based on a new sampling strategy. Instead of just consisting of words with high probability, we select words with relatively small difference from the actual sample of the language model to build a candidate pool, thus keeping consistency with the probability distribution of the language model. What's more, we encode the candidate words according to their probability similarity with the target word, which can further maintain the probability distribution. Experimental results show that the proposed method can outperform the state-of-the-art steganographic methods in terms of security performance.

• Journal of Sensor Science and Technology
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• v.31 no.6
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• pp.361-365
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• 2022

Advanced packaging demands are driven by the need for dense integration systems. Consequently, stacked packaging technology has been proposed instead of reducing the ultra-fine patterns to secure economic feasibility. This study proposed an effective packaging process technology for semiconductor devices using a 9-inch dicing die attach film (DDAF), wherein the die attach and dicing films were combined. The process involved three steps: tape lamination, dicing, and bonding. Following the grinding of a silicon wafer, the tape lamination process was conducted, and the DDAF was arranged. Subsequently, a silicon wafer attached to the DDAF was separated into dies employing a blade dicing process with a two-step cut. Thereafter, one separated die was bonded with the other die as a substrate at 130 ℃ for 2 s under a pressure of 2 kgf and the chip was hardened at 120 ℃ for 30 min under a pressure of 10 kPa to remove air bubbles within the DAF. Finally, a curing process was conducted at 175 ℃ for 2 h at atmospheric pressure. Upon completing the manufacturing processes, external inspections, cross-sectional analyses, and thermal stability evaluations were conducted to confirm the optimality of the proposed technology for application of the DDAF. In particular, the shear strength test was evaluated to obtain an average of 9,905 Pa from 17 samples. Consequently, a 3D integration packaging process using DDAF is expected to be utilized as an advanced packaging technology with high reliability.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.319-331
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• 2022

The latest global COVID-19 pandemic has made the use of facial masks an important aspect of our lives. People are advised to cover their faces in public spaces to discourage illness from spreading. Using these face masks posed a significant concern about the exactness of the face identification method used to search and unlock telephones at the school/office. Many companies have already built the requisite data in-house to incorporate such a scheme, using face recognition as an authentication. Unfortunately, veiled faces hinder the detection and acknowledgment of these facial identity schemes and seek to invalidate the internal data collection. Biometric systems that use the face as authentication cause problems with detection or recognition (face or persons). In this research, a novel model has been developed to detect and recognize faces and persons for authentication using scale invariant features (SIFT) for the whole segmented face with an efficient local binary texture features (DLBP) in region of eyes in the masked face. The Fuzzy C means is utilized to segment the image. These mixed features are trained significantly in a convolution neural network (CNN) model. The main advantage of this model is that can detect and recognizing faces by assigning weights to the selected features aimed to grant or provoke permissions with high accuracy.

• Tribology and Lubricants
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• v.38 no.3
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• pp.109-114
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• 2022

This paper reports the development of an oil flushing system combined with a microbubble generator. Oil flushing plays a crucial role in regulating the lubricant's performance during the lubricant replacement process. Moreover, harmful contaminants, such as sludge, wear particles, and rust, from piping systems or lubrication system can be removed by oil flushing. Oil flushing aims to increase the system's efficiency using a dedicated flushing oil, increasing of the supply pressure and generating a vortex. In addition, it helps the mechanical system or equipment achieve peak performance and reduces the potential for premature failure. However, the contaminant-removal applications of existing oil flushing system are limited. In this research, we aim to improve the performance of oil flushing system by incorporating a microbubble generator, which uses the venture effect to generate microbubbles and mixes them with lubricant. The microbubbles in the blended lubricant remove contaminants from the lubrication system more effectively. Structural mechanics and fluid dynamics are analyzed through fluid-structure interaction (FSI) analysis, and the numerical analysis results are used for the designing the system. The magnitude of the maximum stress is investigated based on the pressure results obtained by the CFD analysis; through the CFD analysis, the mixing ratio of air (bubble) and lubricant is evaluated using the volume of fluid (VOF) model according to the working conditions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.4
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• pp.1187-1208
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• 2022

One of the main goals of wireless sensor networks (WSNs) is to utilize the energy of sensor nodes effectively and maximize the network lifetime. Thus, this paper proposed a routing protocol for WSNs based on virtual force disturbing mobile Sink node (VFMSR). According to the number of sensor nodes in the cluster, the average energy and the centroid factor of the cluster, a new cluster head (CH) election fitness function was designed. At the same time, a hexagonal fixed-point moving trajectory model with the best radius was constructed, and the virtual force was introduced to interfere with it, so as to avoid the frequent propagation of sink node position information, and reduce the energy consumption of CH. Combined with the improved ant colony algorithm (ACA), the shortest transmission path to Sink node was constructed to reduce the energy consumption of long-distance data transmission of CHs. The simulation results showed that, compared with LEACH, EIP-LEACH, ANT-LEACH and MECA protocols, VFMSR protocol was superior to the existing routing protocols in terms of network energy consumption and network lifetime, and compared with LEACH protocol, the network lifetime was increased by more than three times.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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• pp.424-444
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• 2022

In hospitals and pharmacies, the distribution of medicines is an important part. Any mistakes, misses, fake medicines and expired medicines can cause medical accidents. With the widespread application of the Internet of Things technology (IoT), traditional drug distribution methods need to be upgraded. This article proposes a drug distribution management scheme based on the Internet of Things technology. In the production of drugs, a flexible RFID tag was printed on the packaging box, which stored a series of information such as drug name, dosage, raw materials, efficacy, production date, expiration date, and manufacturer. The use of a drug distribution management system combined with RFID readers can identify drug information and effectively prevent the occurrence of erroneous, missed, counterfeit, and expired drugs. It can also improve management efficiency, reduce management costs, and control management risks. Through the circuit design and software system development, the test results show that this solution is effective and feasible, the proposed method can achieve the expected results.

• Journal of Advanced Information Technology and Convergence
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• v.10 no.2
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• pp.111-123
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• 2020

The core competencies for the 21st century are creativity, critical thinking, collaboration, and communication. In recent classes where ICT (information, communication, and technology) is grafted, a lot of efforts are also being made to increase such competencies. According to a research work, ICT is most often used as a communication channel between teachers and students or as an online collaboration tool among students. However, ICT has only played a role as a guideline for instruction, but not included in the curriculum until now. The research on methods how to integrate technology into teaching and learning is in full swing due to the development of technology and the advent of Covid-19. In this paper, we propose a technology integration hierarchy model, namely K-TIHM that can be combined with STEAM education. Since only learning environments have been proposed in the existing research for technology-based STEAM education, our model proposes a series of technology integration hierarchy that can be applied by school age along with STEAM. Also, we analyze the differences in among the Korea's ICT education operation guidelines, the Korea's Software education guidelines, and ours. The proposed model can help developing the primary and secondary school curriculum integrated with technology.