• Nuclear Engineering and Technology
• /
• v.56 no.7
• /
• pp.2835-2841
• /
• 2024

Real-time analysis of metallic mineral grade and slurry concentration is significant for improving flotation efficiency and product quality. This study proposes an online detection method of ore slurry combining the Prompt Gamma Neutron Activation Analysis (PGNAA) technology and artificial neural network (ANN), which can provide mineral information rapidly and accurately. Firstly, a PGNAA analyzer based on a D-T neutron generator and a BGO detector was used to obtain a gamma-ray spectrum dataset of ore slurry samples, which was used to construct and optimize the ANN model for adaptive analysis. The evaluation metrics calculated by leave-one-out cross-validation indicated that, compared with the weighted library least squares (WLLS) approach, ANN obtained more precise and stable results, with mean absolute percentage errors of 4.66% and 2.80% for Fe grade and slurry concentration, respectively, and the highest average standard deviation of only 0.0119. Meanwhile, the analytical errors of the samples most affected by matrix effects was reduced to 0.61 times and 0.56 times of the WLLS method, respectively.

• Journal of Communications and Networks
• /
• v.16 no.1
• /
• pp.36-44
• /
• 2014

Exploiting the energy-delay tradeoff for energy saving is critical for developing green wireless communication systems. In this paper, we investigate the delay-constrained energy-efficient packet transmission. We aim to minimize the energy consumption of multiple randomly arrived packets in an additive white Gaussian noise channel subject to individual packet delay constraints, by taking into account the practical on-off circuit power consumption at the transmitter. First, we consider the offline case, by assuming that the full packet arrival information is known a priori at the transmitter, and formulate the energy minimization problem as a non-convex optimization problem. By exploiting the specific problem structure, we propose an efficient scheduling algorithm to obtain the globally optimal solution. It is shown that the optimal solution consists of two types of scheduling intervals, namely "selected-off" and "always-on" intervals, which correspond to bits-per-joule energy efficiency maximization and "lazy scheduling" rate allocation, respectively. Next, we consider the practical online case where only causal packet arrival information is available. Inspired by the optimal offline solution, we propose a new online scheme. It is shown by simulations that the proposed online scheme has a comparable performance with the optimal offline one and outperforms the design without considering on-off circuit power as well as the other heuristically designed online schemes.

• Journal of Korea Society of Industrial Information Systems
• /
• v.23 no.3
• /
• pp.97-116
• /
• 2018

Individuals depend considerably on gathering information from personal social networks rather than from commercial network channels or the mass media. Most academic journals that have examined this topic concentrate on online users' information-searching behaviours; however, this paper discusses online users' information-providing behaviour in the online community. The aim of this study is to investigate that online users' motivation to write reviews on Facebook and how the motivations affect users' information-providing behaviour. This study focusses on Facebook members' motivations that affect their review-writing behaviour. The fundamental theory for examining this topic is Vogt and Fesenmaier's (1998) 'information need'. This study modifies Vogt and Fesenmaier's (1998) theory for virtual communities through the development of each concept's measurement items, selecting the information need of four variables: functional, hedonic, innovation, and sign need. Among the four variables, sign need is the most important factor for Facebook users in the virtual environment. Through sign need, people indicate their status, personality form, and position, which significantly affects members' review-writing behaviour on Facebook.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.6
• /
• pp.2095-2110
• /
• 2015

This paper presents a design for a throughput-efficient online relay selection scheme for dual-hop multi-relay cooperative networks. Problems arise with these networks due to unpredictability of the relaying link quality and high time-consumption to probe the dual-hop link. In this paper, we firstly propose a novel probing and relaying protocol, which greatly reduces the overhead of the dual-hop link estimation by leveraging the wireless broadcasting nature of the network. We then formulate an opportunistic relay selection process for the online decision-making, which uses a tradeoff between obtaining more link information to establish better cooperative relaying and minimizing the time cost for dual-hop link estimation to achieve higher throughput. Dynamic programming is used to construct the throughput-optimal control policy for a typically heterogeneous Rayleigh fading environment, and determines which relay to probe and when to transmit the data. Additionally, we extend the main results to mixed Rayleigh/Rician link scenarios, i.e., where one side of the relaying link experiences Rayleigh fading while the other has Rician distribution. Numerical results validate the effectiveness and superiority of our proposed relaying scheme, e.g., it achieves at least 107% throughput gain compared with the state of the art solution.

• Journal of KIISE
• /
• v.44 no.7
• /
• pp.726-732
• /
• 2017

In practical distributed sensing and prediction applications over wireless sensor networks (WSN), environmental sensing activities are highly dynamic because of noisy sensory information from moving source signals. The recent distributed online convex optimization frameworks have been developed as promising approaches for solving approximately stochastic learning problems over network of sensors in a distributed manner. Negligence of mobility consequence in the original distributed saddle point algorithm (DSPA) could strongly affect the convergence rate and stability of learning results. In this paper, we propose an integrated sliding windows mechanism in order to stabilize predictions and achieve better convergence rates in cooperative detection of a moving source signal scenario.

• Journal of The Korean Association of Information Education
• /
• v.22 no.1
• /
• pp.159-167
• /
• 2018

In this study, we have developed an online learning community site using Humhub social network software and promote social constructive learning through the questions and answers in subject specific learning groups. By accumulating learning contents which consist of questions and answers about specific topics, learners can acquire knowledge by searching relevant topics and questions and can create and reconstruct knowledge as well as consuming knowledge by participating in self-regulated learning community. We have developed a mathematical editor feature which enables users to enter mathematical expression such as equations and greek characters. Online learning community sites can be used for inquiry based information education.

• International Journal of Computer Science & Network Security
• /
• v.21 no.5
• /
• pp.84-89
• /
• 2021

Medical tests are very important part of the health monitoring process. It is performed for various reasons like diagnosing diseases, determining medications effectiveness, etc. Due to that, patients should be able to read and understand the available online tests and results in order to take proper decisions regarding their health condition. In fact, people are varying in their educational level and health backgrounds that make providing such information in an easily readable format by the majority of people considered as a challenge in the health domain since ever. This paper describes the MTReadable corpus which constructed for evaluating the readability of online medical tests. It covered 32 basic periodic check-up tests with over 36k words. These tests information are annotated and labelled based on three readability levels which are easy, neutral and difficult by three non-specialists native Arabic speakers. This paper contributes to enriching the Arabic health research community with an investigation of the level of readability of online medical tests and to be a baseline for further complex health online reports and information.

• International Journal of Computer Science & Network Security
• /
• v.21 no.4
• /
• pp.123-130
• /
• 2021

A novel approach for the detection of cheating during e-Exams is presented here using convolutional neural networks (CNN) based systems. This system will help the proctors to identify any kind of uncertain event at the time of online exams, for which most of the government's across the globe are recommending due to the Covid-19 pandemic. Most of the institutions and students across the globe are badly affected by their academic programs and it is a challenging task for universities to conduct examinations using the traditional methods. Therefore, the students are attending most of their classes using different types of third party applications that are available online. However, to conduct online exams the universities cannot rely on these service providers for a long time. Therefore, in this work, a complete setup of the software tools is provided for the students, which can be used by students at their respective laptops/personal computers with strict guidelines from the university. The proposed approach helps most of the universities in Saudi Arabia to maintain their database of different events/activities of students at the time of E-Exams. This method proved to be more accurate and CNN based detection proved to be more sensitive with an accuracy of 97% to detect any kind of uncertain activity of the students at the time of e-Exam.

• International Journal of Computer Science & Network Security
• /
• v.22 no.3
• /
• pp.396-404
• /
• 2022

Online social networks contain a large amount of data that can be converted into valuable and insightful information. Text mining approaches allow exploring large-scale data efficiently. Therefore, this study reviews the recent literature on text mining in online social networks in a way that produces valid and valuable knowledge for further research. The review identifies text mining techniques used in social networking, the data used, tools, and the challenges. Research questions were formulated, then search strategy and selection criteria were defined, followed by the analysis of each paper to extract the data relevant to the research questions. The result shows that the most social media platforms used as a source of the data are Twitter and Facebook. The most common text mining technique were sentiment analysis and topic modeling. Classification and clustering were the most common approaches applied by the studies. The challenges include the need for processing with huge volumes of data, the noise, and the dynamic of the data. The study explores the recent development in text mining approaches in social networking by providing state and general view of work done in this research area.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
• /
• v.7 no.4
• /
• pp.395-402
• /
• 2017

Social network service provides an excellent platform for people seeking for advices. This research analyzed the effect of rational and emotional response and social distance on people's acceptance for advices online by the construal level theory. An experiment was completed to test the relationship between advisers and questioners and verify the content of online feedback would influence people's acceptance for advices on social network site. The online response included emotional advice and rational advice. Social distance between advisers and questioners was divided to close social distance condition and distant social distance condition. The study showed: (1) comparing with emotional advice, rational advice had a higher persuasive effect in both close and distant social distance conditions; (2) the impact of feedback from people with close social distance was stronger than the impact of the advices from people with distant social distance; (3) there existed an interaction effect between social distance and affective advice. The results revealed that the adviser's affective expression and relationship with the questioner would interactively impact the online persuasive effect.