• The Journal of Engineering Research
• /
• v.2 no.1
• /
• pp.49-56
• /
• 1997

In emission computed tomography, statistical reconstruction methods in the context of a Bayesian framework have been a topic of interest over the last decade. This was mainly due to the fact that Bayesian approaches can incorporate a priori information into the reconstruction algorithm. Although these approaches can exhibit good performance, their applications to the clinic is hindered mainly by their high computational cost. On the other hand, the speed and simplicity of the filtered backprojection (FBP) algorithm have led to its widespread use in most clinical applications. In this work, we use spline models, which have been quite useful in Bayesian reconstruction, as regularizers for high-frequency apodization in FBP algorithm and show that the effects of using spline models as priors in Bayesian reconstruction can also be achieved in FBP reconstruction.

• Electronics and Telecommunications Trends
• /
• v.34 no.4
• /
• pp.55-64
• /
• 2019

In this study, we introduce trends in neural-network-based deep learning research applied to dialogue systems. Recently, end-to-end trainable goal-oriented dialogue systems using long short-term memory, sequence-to-sequence models, among others, have been studied to overcome the difficulties of domain adaptation and error recognition and recovery in traditional pipeline goal-oriented dialogue systems. In addition, some research has been conducted on applying reinforcement learning to end-to-end trainable goal-oriented dialogue systems to learn dialogue strategies that do not appear in training corpora. Recent neural network models for end-to-end trainable chit-chat systems have been improved using dialogue context as well as personal and topic information to produce a more natural human conversation. Unlike previous studies that have applied different approaches to goal-oriented dialogue systems and chit-chat systems respectively, recent studies have attempted to apply end-to-end trainable approaches based on deep neural networks in common to them. Acquiring dialogue corpora for training is now necessary. Therefore, future research will focus on easily and cheaply acquiring dialogue corpora and training with small annotated dialogue corpora and/or large raw dialogues.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.10
• /
• pp.3793-3814
• /
• 2021

Most secure solutions like cryptography are software based and they are designed to mainly deal with the outside attacks for traditional networks, but such soft security is hard to be implemented in wireless sensor networks to counter the inside attacks from internal malicious nodes. To address this issue, reputation has been introduced to tackle the inside malicious nodes. Reputation is essentially a stimulating mechanism for nodes' cooperation and is employed to detect node misbehaviors and improve the trust-worthiness between individual nodes. Among the reputation models, binomial distribution based reputation has many advantages such as light weight and ease of implementation in resource-constraint sensor nodes, and accordingly researchers have proposed many insightful related methods. However, some of them either directly use the modelling results, apply the models through simple modifications, or only use the required components while ignoring the others as an integral part of the whole model, this topic still lacks a comprehensive and systematical review. Thus the motivation of this study is to provide a thorough survey concerning each detailed functional components of binomial distribution based reputation for wireless sensor networks. In addition, based on the survey results, we also argue some open research problems and suggest the directions that are worth future efforts. We believe that this study is helpful to better understanding the reputation modeling mechanism and its components for wireless sensor networks, and can further attract more related future studies.

• International journal of advanced smart convergence
• /
• v.9 no.4
• /
• pp.184-191
• /
• 2020

With the emergence of the 4th Industrial Revolution, core technologies that will lead the 4th Industrial Revolution such as AI (artificial intelligence), big data, and Internet of Things (IOT) are also at the center of the topic of the general public. In particular, there is a growing trend of attempts to present future visions by discovering new models by using them for big data analysis based on data collected in a specific field, and inferring and predicting new values with the models. In order to obtain the reliability and sophistication of statistics as a result of big data analysis, it is necessary to analyze the meaning of each variable, the correlation between the variables, and multicollinearity. If the data is classified differently from the hypothesis test from the beginning, even if the analysis is performed well, unreliable results will be obtained. In other words, prior to big data analysis, it is necessary to ensure that data is well classified according to the purpose of analysis. Therefore, in this study, data is classified using a decision tree technique and a random forest technique among classification analysis, which is a machine learning technique that implements AI technology. And by evaluating the degree of classification of the data, we try to find a way to improve the classification and analysis rate of the data.

• Wind and Structures
• /
• v.34 no.6
• /
• pp.499-510
• /
• 2022

Computational Wind Engineering has rapidly grown in the last decades and it is currently reaching a relatively mature state. The prediction of wind loading by means of numerical simulations has been proved effective in many research studies and applications to design practice are rapidly spreading. Despite such success, caution in the use of simulations for wind loading assessment is still advisable and, indeed, required. The computational burden and the know-how needed to run high-fidelity simulations is often unavailable and the possibility to use simplified models extremely attractive. In this paper, the applicability of some well-known 2D unsteady RANS models, particularly the k-ω SST, in the aerodynamic characterization of extruded bodies with bluff sections is investigated. The main focus of this paper is on the drag coefficient prediction. The topic is not new, but, in the authors' opinion, worth a careful revisitation. In fact, despite their great technical relevance, a systematic study focussing on sections which manifest a fully detached flow configuration has been overlooked. It is here shown that the considered 2D RANS exhibit a pathological behaviour, failing to reproduce the transition between reattached and fully detached flow regime.

• The Journal of Asian Finance, Economics and Business
• /
• v.9 no.7
• /
• pp.53-62
• /
• 2022

Commercial banks have a significant impact on the economy of Vietnam because they provide the majority of transactional capital. Therefore, the operational efficiency of commercial banks is a viral topic for the study of the Vietnamese banking system. The research aims to examine the efficiency and inefficiency of joint-stock commercial banks in Vietnam from 2016 to 2020 and then classify them into the efficient group and inefficient group. The study employs the time series data of 29 joint-stock commercial banks during the period 2016-2020. Based on the data collected from the annual audited financial statements of 29 Vietnamese joint-stock commercial banks, the authors select input and output variables for the standard DEA models and anti-efficient DEA models. This research uses two stages, first, by applying the standard DEA model, we investigate the efficient banks; second, by employing the anti-efficient DEA model, we find out the inefficient banks. The results reveal that the average efficiency score of 29 joint-stock commercial banks tends to increase in the period 2016-2018 and decrease gradually in the period 2019-2020. The findings of this study suggest that several small and medium-sized banks in the Vietnamese banking sector have both promising and risky performances and the efficiency of state-owned commercial banks has also improved significantly during the study period.

• Steel and Composite Structures
• /
• v.44 no.3
• /
• pp.389-406
• /
• 2022

Recycling concrete construction waste is an encouraging step toward green and sustainable building. A lot of research has been done on recycled aggregate concretes (RACs), but not nearly as much has been done on concrete made with recycled aggregate. Recycled aggregate concrete, on the other hand, has been found to have a lower mechanical productivity compared to conventional one. Accurately estimating the mechanical behavior of the concrete samples is a most important scientific topic in civil, structural, and construction engineering. This may prevent the need for excess time and effort and lead to economic considerations because experimental studies are often time-consuming, costly, and troublous. This study presents a comprehensive data-mining-based model for predicting the splitting tensile strength of recycled aggregate concrete modified with glass fiber and silica fume. For this purpose, first, 168 splitting tensile strength tests under different conditions have been performed in the laboratory, then based on the different conditions of each experiment, some variables are considered as input parameters to predict the splitting tensile strength. Then, three hybrid models as GWO-RF, GWO-MLP, and GWO-SVR, were utilized for this purpose. The results showed that all developed GWO-based hybrid predicting models have good agreement with measured experimental results. Significantly, the GWO-RF model has the best accuracy based on the model performance assessment criteria for training and testing data.

• International Journal of Internet, Broadcasting and Communication
• /
• v.14 no.1
• /
• pp.129-135
• /
• 2022

Real human digital models appear more and more frequently in VR/AR application scenarios, in which real-time markerless face capture animation of personalized virtual human faces is an important research topic. The traditional way to achieve personalized real human facial animation requires multiple mature animation staff, and in practice, the complex process and difficult technology may bring obstacles to inexperienced users. This paper proposes a new process to solve this kind of work, which has the advantages of low cost and less time than the traditional production method. For the personalized real human face model obtained by 3D reconstruction technology, first, use R3ds Wrap to topology the model, then use Avatary to make 52 Blend-Shape model files suitable for AR-Kit, and finally realize real-time markerless face capture 3D real human on the UE4 platform facial motion capture, this study makes rational use of the advantages of software and proposes a more efficient workflow for real-time markerless facial motion capture of personalized 3D real human models, The process ideas proposed in this paper can be helpful for other scholars who study this kind of work.

• Smart Media Journal
• /
• v.13 no.6
• /
• pp.35-43
• /
• 2024

During the post-pandemic period, the interest in foreign language learning surged, leading to increased usage of language-learning apps. With the rising demand for these apps, analyzing app reviews becomes essential, as they provide valuable insights into user experiences and suggestions for improvement. This research focuses on extracting insights into users' opinions, sentiments, and overall satisfaction from reviews of HelloTalk, one of the most renowned language-learning apps. We employed topic modeling and emotion analysis approaches to analyze reviews collected from the Google Play Store. Several experiments were conducted to evaluate the performance of sentiment classification models with different settings. In addition, we identified dominant emotions and topics within the app reviews using feature importance analysis. The experimental results show that the Random Forest model with topics and emotions outperforms other approaches in accuracy, recall, and F1 score. The findings reveal that topics emphasizing language learning and community interactions, as well as the use of language learning tools and the learning experience, are prominent. Moreover, the emotions of 'admiration' and 'annoyance' emerge as significant factors across all models. This research highlights that incorporating emotion scores into the model and utilizing a broader range of emotion labels enhances model performance.

• Journal of the Korean earth science society
• /
• v.29 no.4
• /
• pp.352-362
• /
• 2008

This study explored pre-service teachers' mental models about eclipse phenomena to investigate their understandings on the earth science concept needed f3r an integrated approach. We conducted in-depth interviews with two different contexts on 30 secondary and 36 primary pre-service teachers participants, and analyzed phenomenological primitives (p-prims) and facets of causal explanations about eclipses. Based on this study, we identified four different levels of mental models about eclipses. Four mental models were categorized as (1) Screening model, (2) Orbital plane model, (3) Hybrid model, and (4) Shadow cast model. Screening model is a flawed mental model, orbital plane model is an incomplete correct mental model, and shadow cast model is a scientifically correct mental model. Hybrid model, composite of two or more mental models, use multiple mental models simultaneously. Orbital plane model was the most widespread mental model in secondary pre-service teachers group, whereas screening model was used frequently in primary group. It was found that the level of mental model could be determined by the level of facet and p-prims. We confirmed context sensitivity of the mental models and perceived the necessity of integrated approaches to promote progression of mental models. Implications of our findings for enhancing pre-service science teachers' topic-specific pedagogical content knowledge (PCK) associated with eclipse phenomena are also discussed here.