• Advances in nano research
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• v.10 no.1
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• pp.91-99
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• 2021

Silicene is an emerging two-dimensional (2D) semiconductor material which has been envisaged to be compatible with conventional silicon technology. This paper presents a theoretical study of uniformly doped silicene with aluminium (AlSi3) Field-Effect Transistor (FET) along with the benchmark of device performance metrics with other 2D materials. The simulations are carried out by employing nearest neighbour tight-binding approach and top-of-the-barrier ballistic nanotransistor model. Further investigations on the effects of the operating temperature and oxide thickness to the device performance metrics of AlSi3 FET are also discussed. The simulation results demonstrate that the proposed AlSi3 FET can achieve on-to-off current ratio up to the order of seven and subthreshold swing of 67.6 mV/dec within the ballistic performance limit at room temperature. The simulation results of AlSi3 FET are benchmarked with FETs based on other competitive 2D materials such as silicene, graphene, phosphorene and molybdenum disulphide.

• Structural Engineering and Mechanics
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• v.89 no.3
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• pp.283-300
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• 2024

Machine learning (ML) models based on artificial neural network (ANN) and decision tree (DT) were developed for estimation of axial capacity of concrete columns reinforced with fiber reinforced polymer (FRP) bars. Between the design codes, the Canadian code provides better formulation compared to the Australian or American code. For empirical models based on elastic modulus of FRP, Hadhood et al. (2017) model performed best. Whereas for empirical models based on tensile strength of FRP, as well as all empirical models, Raza et al. (2021) was adjudged superior. However, compared to the empirical models, all ML models exhibited superior performance according to all five performance metrics considered. The performance of ANN and DT models were comparable in general. Under the present setup, inclusion of the transverse reinforcement information did not improve the accuracy of estimation with either ANN or DT. With selective use of inputs, and a much simpler ANN architecture (4-3-1) compared to that reported in literature (Raza et al. 2020: 6-11-11-1), marginal improvement in correlation could be achieved. The metrics for the best model from the study was a correlation of 0.94, absolute errors between 420 kN to 530 kN, and the range being 0.39 to 0.51 for relative errors. Though much superior performance could be obtained using ANN/DT models over empirical models, further work towards improving accuracy of the estimation is indicated before design of FRP reinforced concrete columns using ML may be considered for design codes.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.6
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• pp.79-86
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• 2009

In this paper, it has the performance metrics and the utility evaluation of the collusion codes about multimedia fingerprinting code based on BIBD and then the tracing algorithm of all colluders is proposed. Among the collusion codes, the bit stream of "all 0" or "all 1" are generated, also same collusion code and bit reversed code with user's fingerprinting code are generated. Thus there was occurred some problems, in which a colluder is deciding to anti-colluder or anti-colluder is deciding to colluder. In this paper, for the performance metrics and the utility evaluation of the collude codes, the experiment onto the total solution is processed by the logical collusion operation added with a partially processed averaging attack in the past papers. The proposed performance metrics and the utility evaluation about the collusion code generated from multimedia fingerprinting code based on BIBD is operated. Through the experiment, it confirmed that the ratio of colluder tracing is 100%.

• Journal of Broadcast Engineering
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• v.21 no.2
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• pp.157-168
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• 2016

In image processing and computer vision fields, mean squared error (MSE) has popularly been used as an objective metric in image quality optimization problems due to its desirable mathematical properties such as metricability, differentiability and convexity. However, as known that MSE is not highly correlated with perceived visual quality, much effort has been made to develop new image quality assessment (IQA) metrics having both the desirable mathematical properties aforementioned and high prediction performances for subjective visual quality scores. Although recent IQA metrics having the desirable mathematical properties have shown to give some promising results in prediction performance for visual quality scores, they also have high computation complexities. In order to alleviate this problem, we propose a new fast IQA metric using a simple Laplace operator. Since the Laplace operator used in our IQA metric can not only effectively mimic operations of receptive fields in retina for luminance stimulus but also be simply computed, our IQA metric can yield both very fast processing speed and high prediction performance. In order to verify the effectiveness of the proposed IQA metric, our method is compared to some state-of-the-art IQA metrics. The experimental results showed that the proposed IQA metric has the fastest running speed compared the IQA methods except MSE under comparison. Moreover, our IQA metric achieves the best prediction performance for subjective image quality scores among the state-of-the-art IQA metrics under test.

• Smart Media Journal
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• v.12 no.2
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• pp.66-75
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• 2023

Recently, the number of cloud web applications is increasing owing to the accelerated migration of enterprises and public sector information systems to the cloud. Traditional network attacks on cloud web applications are characterized by Denial of Service (DoS) attacks, which consume network resources with a large number of packets. However, HTTP DoS attacks, which consume application resources, are also increasing recently; as such, developing security technologies to prevent them is necessary. In particular, since low-bandwidth HTTP DoS attacks do not consume network resources, they are difficult to identify using traditional security solutions that monitor network metrics. In this paper, we propose a new detection model for detecting HTTP DoS attacks on cloud web applications by collecting the application metrics of web servers and learning them using machine learning. We collected 18 types of application metrics from an Apache web server and used five machine learning and two deep learning models to train the collected data. Further, we confirmed the superiority of the application metrics-based machine learning model by collecting and training 6 additional network metrics and comparing their performance with the proposed models. Among HTTP DoS attacks, we injected the RUDY and HULK attacks, which are low- and high-bandwidth attacks, respectively. As a result of detecting these two attacks using the proposed model, we found out that the F1 scores of the application metrics-based machine learning model were about 0.3 and 0.1 higher than that of the network metrics-based model, respectively.

• JSTS:Journal of Semiconductor Technology and Science
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• v.11 no.3
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• pp.169-181
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• 2011

In this present paper, a comprehensive drain current model incorporating the effects of channel length modulation has been presented for multi-layered gate material engineered trapezoidal recessed channel (MLGME-TRC) MOSFET and the expression for linearity performance metrics, i.e. higher order transconductance coefficients: $g_{m1}$, $g_{m2}$, $g_{m3}$, and figure-of-merit (FOM) metrics; $V_{IP2}$, $V_{IP3}$, IIP3 and 1-dB compression point, has been obtained. It is shown that, the incorporation of multi-layered architecture on gate material engineered trapezoidal recessed channel (GME-TRC) MOSFET leads to improved linearity performance in comparison to its conventional counterparts trapezoidal recessed channel (TRC) and rectangular recessed channel (RRC) MOSFETs, proving its efficiency for low-noise applications and future ULSI production. The impact of various structural parameters such as variation of work function, substrate doping and source/drain junction depth ($X_j$) or negative junction depth (NJD) have been examined for GME-TRC MOSFET and compared its effectiveness with MLGME-TRC MOSFET. The results obtained from proposed model are verified with simulated and experimental results. A good agreement between the results is obtained, thus validating the model.

• Journal of Communications and Networks
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• v.16 no.1
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• pp.10-17
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• 2014

This work investigates two important performance metrics of underlay cooperative cognitive radio (CR) networks: Interference cumulative distribution function of licensed users and outage probability of unlicensed users. These metrics are thoroughly analyzed in realistic operating conditions such as imperfect fading channel information and strict transmit power constraint, which satisfies interference power constraint and maximum transmit power constraint, over Nakagami-m fading channels. Novel closed-form expressions are derived and subsequently validated extensively through comparisons with respective results from computer simulations. The proposed expressions are rather long but straightforward to handle both analytically and numerically since they are expressed in terms of well known built-in functions. In addition, the offered results provide the following technical insights: i) Channel information imperfection degrades considerably the performance of both unlicensed network in terms of OP and licensed network in terms of interference levels; ii) underlay cooperative CR networks experience the outage saturation phenomenon; iii) the probability that the interference power constraint is satisfied is relatively low and depends significantly on the corresponding fading severity conditions as well as the channel estimation quality; iv) there exists a critical performance trade-off between unlicensed and licensed networks.

• Journal of Contemporary Eastern Asia
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• v.13 no.1
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• pp.19-37
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• 2014

In an attempt to develop comprehensive evidence-based methods for evaluation of the R&D performance of cross-disciplinary projects, a joint bibliometric analysis of patents and publications was performed for two industry-university-government collaborative projects aimed at commercialization: Hokkaido University Research & Business Park Project (2003-2007; 63 inventors; 176 patents; 853 papers), and Matching Program for Innovations in Future Drug Discovery and Medical Care - phase I (2006-2010; 46 inventors; 235 patents; 733 papers). Besides the simple output indicators (for five years period), and citations (from the publication date to the end of 2012), science maps based on the network analysis of words and co-authorship relations were generated to identify the prominent research themes and teams. Our joint analysis of publications and patents yields objective and mutually complementing information, which provides better insights on research and commercialization performance of the large-scale projects. Hence, such analysis has potential for use in the industry-university project's performance evaluation.

• Journal of Advanced Marine Engineering and Technology
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• v.39 no.6
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• pp.656-661
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• 2015

The goal of this paper is to analyze the performance of a word autocomplete system for mobile devices such as smartphones, tablets, and PCs. The proposed system automatically completes a partially typed string into a full word, reducing the time and effort required by a user to enter text on these devices. We collect a large amount of data from Twitter and develop both unigram and bigram dictionaries based on the frequency of words. Using these dictionaries, we analyze the performance of the word autocomplete system and devise a keystroke profit rate and recovery rate as new evaluation metrics that better describe the characteristics of the word autocomplete problem compared to previous measures such as the mean reciprocal rank or recall.

• Journal of Communications and Networks
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• v.8 no.2
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• pp.241-252
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• 2006

Traditional service-level agreements (SLAs), defined by average delay or packet loss, often camouflage the instantaneous performance perceived by end-users. We define a set of metrics for service availability to quantify the performance of Internet protocol (IP) backbone networks and capture the impact of routing dynamics on packet forwarding. Given a network topology and its link weights, we propose a novel technique to compute the associated service availability by taking into account transient routing dynamics and operational conditions, such as border gateway protocol (BGP) table size and traffic distributions. Even though there are numerous models for characterizing topologies, none of them provide insights on the expected performance perceived by end customers. Our simulations show that the amount of service disruption experienced by similar networks (i.e., with similar intrinsic properties such as average out-degree or network diameter) could be significantly different, making it imperative to use new metrics for characterizing networks. In the second part of the paper, we derive goodness factors based on service availability viewed from three perspectives: Ingress node (from one node to many destinations), link (traffic traversing a link), and network-wide (across all source-destination pairs). We show how goodness factors can be used in various applications and describe our numerical results.