• Journal of Information Technology Services
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• v.14 no.3
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• pp.183-196
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• 2015

The concept of energy-efficient networking has begun to spread in the past few years, gaining increasing popularity. A common opinion among networking researchers is that the sole introduction of low consumption silicon technologies may not be enough to effectively curb energy requirements. Thus, for disruptively boosting the network energy efficiency, these hardware enhancements must be integrated with ad-hoc mechanisms that explicitly manage energy saving, by exploiting network-specific features. The IEEE 802.3az Energy Efficient Ethernet (EEE) standard is one of such efforts. EEE introduces a low power mode for the most common Ethernet physical layer standards and is expected to provide large energy savings. However, it has been shown that EEE may not achieve good energy efficiency because mode transition overheads can be significant, leading to almost full energy consumption even at low utilization levels. Coalescing techniques such as packet coalescing and interrupt coalescing were proposed to improve energy efficiency of EEE, but their implementations typically adopt a simple policy that employs a few fixed values for coalescing parameters, thus it is difficult to achieve optimal energy efficiency. The paper proposes adaptive interrupt coalescing (AIC) that adopts an optimal policy that could not only improve energy efficiency but support performance. AIC has been implemented at the sender side with the Intel 82579 network interface card (NIC) and e1000e Linux device driver. The experiments were performed at 100 M bps transfer rate and show that energy efficiency of AIC is improved in most cases despite performance consideration and in the best case can be improved up to 37% compared to that of conventional interrupt coalescing techniques.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.1
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• pp.142-149
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• 2016

This paper proposes a method to detect LP(License Plate) of vehicles in indoor and outdoor parking lots. In restricted environment, there are many conventional methods for detecting LP. But, it is difficult to detect LP in natural and complex scenes with background clutters because several patterns similar with text or LP always exist in complicated backgrounds. To verify the performance of LP text detection in natural images, we apply MB-LGP feature by combining with ensemble machine learning algorithm in purpose of selecting optimal features of small number in huge pool. The feature selection is performed by adaptive boosting algorithm that shows great performance in minimum false positive detection ratio and in computing time when combined with cascade approach. MSER is used to provide initial text regions of vehicle LP. Throughout the experiment using real images, the proposed method functions robustly extracting LP in natural scene as well as the controlled environment.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.1
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• pp.665-673
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• 2021

Default has become an extreme concern in the current world due to the financial crisis. The previous prediction of companies' bankruptcy exhibits evidence of decision assistance for financial and regulatory bodies. Notwithstanding numerous advanced approaches, this area of study is not outmoded and requires additional research. The purpose of this research is to find the best classifier to detect a company's default risk and bankruptcy. This study used secondary data from the Pakistan Stock Exchange (PSX) and it is time-series data to examine the impact on the determinants. This research examined several different classifiers as per their competence to properly categorize default and non-default Pakistani companies listed on the PSX. Additionally, PSX has remained consistent for some years in terms of growth and has provided benefits to its stockholders. This paper utilizes machine learning techniques to predict financial distress in companies listed on the PSX. Our results indicate that most multi-stage mixture of classifiers provided noteworthy developments over the individual classifiers. This means that firms will have to work on the financial variables such as liquidity and profitability to not fall into the category of liquidation. Moreover, Adaptive Boosting (Adaboost) provides a significant boost in the performance of each classifier.

• Steel and Composite Structures
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• v.49 no.6
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• pp.645-666
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• 2023

Due to the steadily declining supply of natural coarse aggregates, the concrete industry has shifted to substituting coarse aggregates generated from byproducts and industrial waste. Oil palm shell is a substantial waste product created during the production of palm oil (OPS). When considering the usage of OPSC, building engineers must consider its uniaxial compressive strength (UCS). Obtaining UCS is expensive and time-consuming, machine learning may help. This research established five innovative hybrid AI algorithms to predict UCS. Aquila optimizer (AO) is used with methods to discover optimum model parameters. Considered models are artificial neural network (AO - ANN), adaptive neuro-fuzzy inference system (AO - ANFIS), support vector regression (AO - SVR), random forest (AO - RF), and extreme gradient boosting (AO - XGB). To achieve this goal, a dataset of OPS-produced concrete specimens was compiled. The outputs depict that all five developed models have justifiable accuracy in UCS estimation process, showing the remarkable correlation between measured and estimated UCS and models' usefulness. All in all, findings depict that the proposed AO - XGB model performed more suitable than others in predicting UCS of OPSC (with R², RMSE, MAE, VAF and A_15-index at 0.9678, 1.4595, 1.1527, 97.6469, and 0.9077). The proposed model could be utilized in construction engineering to ensure enough mechanical workability of lightweight concrete and permit its safe usage for construction aims.

• Journal of Microbiology and Biotechnology
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• v.30 no.9
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• pp.1420-1429
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• 2020

Corynebacterium glutamicum, an important industrial strain, has a relatively slower reproduction rate. To acquire a growth-boosted C. glutamicum, a descendant strain was isolated from a continuous culture after 600 generations. The isolated descendant C. glutamicum, JH41 strain, was able to double 58% faster (t_d=1.15 h) than the parental type strain (PT, t_d=1.82 h). To understand the factors boosting reproduction, the transcriptomes of JH41 and PT strains were compared. The mRNAs involved in respiration and TCA cycle were upregulated. The intracellular ATP of the JH41 strain was 50% greater than the PT strain. The upregulation of NCgl1610 operon (a putative dyp-type heme peroxidase, a putative copper chaperone, and a putative copper importer) that presumed to role in the assembly and redox control of cytochrome c oxidase was found in the JH41 transcriptome. Plasmid-driven expression of the operon enabled the PT strain to double 19% faster (t_d=1.82 h) than its control (t_d=2.17 h) with 14% greater activity of cytochrome c oxidase and 27% greater intracellular ATP under the oxidative stress conditions. Upregulations of genes those might enhance translation fitness were also found in the JH41 transcriptome. Plasmid-driven expressions of NCgl0171 (encoding a cold-shock protein) and NCgl2435 (encoding a putative peptidyl-tRNA hydrolase) enabled the PT to double 22% and 32% faster than its control, respectively (empty vector: t_d=1.93 h, CspA: t_d=1.58 h, and Pth: t_d=1.44 h). Based on the results, the factors boosting growth rate in C. gluctamicum were further discussed in the viewpoints of cellular energy state, oxidative stress management, and translation.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.4
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• pp.144-154
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• 2013

This paper proposes a new super-resolution algorithm where sharpness enhancement is merged in order to improve overall visual quality of up-scaled images. In the learning stage, multiple dictionaries are generated according to sharpness strength, and a proper dictionary among those dictionaries is selected to adapt to each patch in the inference stage. Also, additional post-processing suppresses boosting of artifacts in input low-resolution images during the inference stage. Experimental results that the proposed algorithm provides 0.3 higher CPBD than the bi-cubic and 0.1 higher CPBD than Song's and Fan's algorithms. Also, we can observe that the proposed algorithm shows better quality in textures and edges than the previous works. Finally, the proposed algorithm has a merit in terms of computational complexity because it requires the memory of only 17% in comparison with the previous work.

• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1714-1728
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• 2016

This paper presents improved harmonics extraction based on Adaptive Linear Neuron (ADALINE) algorithm for single phase photovoltaic (PV) shunt active power filter (SAPF). The proposed algorithm, named later as Improved ADALINE, contributes to better performance by removing cosine factor and sum of element that are considered as unnecessary features inside the existing algorithm, known as Modified Widrow-Hoff (W-H) ADALINE. A new updating technique, named as Fundamental Active Current, is introduced to replace the role of the weight factor inside the previous updating technique. For evaluation and comparison purposes, both proposed and existing algorithms have been developed. The PV SAPF with both algorithms was simulated in MATLAB-Simulink respectively, with and without operation or connection of PV. For hardware implementation, laboratory prototype has been developed and the proposed algorithm was programmed in TMS320F28335 DSP board. Steady state operation and three critical dynamic operations, which involve change of nonlinear loads, off-on operation between PV and SAPF, and change of irradiances, were carried out for performance evaluation. From the results and analysis, the Improved ADALINE algorithm shows the best performances with low total harmonic distortion, fast response time and high source power reduction. It performs well in both steady state and dynamic operations as compared to the Modified W-H ADALINE algorithm.

• Journal of the Korean Data and Information Science Society
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• v.22 no.3
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• pp.371-380
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• 2011

The noise caused by a variety of reasons worsens the quality of input image when we use the images reproducing device. The basic difficulty to solve this problem is that the noise and the signal are difficult to be distinguished. Contrast enhancement such as unsharp masking is one of the most important procedures to improve the quality of input images. The conventional unsharp masking enhances the images by adding their amplified high frequency components. The noise component of the input images, however, also tends to be amplified due to the nature of the unsharp masking. This paper considers the block approach for detecting niose and image feature of the input image so that the unsharp masking could be adaptively applied accordingly. Simulation results show that it is made possible to enhance contrast of the image without boosting up the noisy components by applying the proposed algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.12
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• pp.6069-6091
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• 2017

Augmented Reality (AR) overlays virtual information on real world data, such as displaying useful information on videos/images of a scene. This paper presents an Enhanced AR (EAR) system that displays useful statistical players' information on captured images of a sports game. We focus on the situation where the input image is degraded by strong sunlight. Proposed EAR system consists of an image enhancement technique to improve the accuracy of subsequent player and face detection. The image enhancement is followed by player and face detection, face recognition, and players' statistics display. First, an algorithm based on multi-scale retinex is proposed for image enhancement. Then, to detect players' and faces', we use adaptive boosting and Haar features for feature extraction and classification. The player face recognition algorithm uses boosted linear discriminant analysis to select features and nearest neighbor classifier for classification. The system can be adjusted to work in different types of sports where the input is an image and the desired output is display of information nearby the recognized players. Simulations are carried out on 2096 different images that contain players in diverse conditions. Proposed EAR system demonstrates the great potential of computer vision based approaches to develop AR applications.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.35 no.11
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• pp.155-162
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• 2019

The construction industry is the highest safety accident causing industry as 28.55% portion of all industries' accidents in Korea. In particular, falling is the highest accidents type composed of 60.16% among the construction field accidents. Therefore, we analyzed the factors of major disaster affecting the fall accident and then derived feature importances by considering various variables. We used data collected from Korea Occupational Safety & Health Agency (KOSHA) for learning and predicting in the proposed model. We have an effort to predict the degree of occupational fall accidents by using the machine learning model, i.e., Adaboost, short for Adaptive Boosting. Adaboost is a machine learning meta-algorithm which can be used in conjunction with many other types of learning algorithms to improve performance. Decision trees were combined with AdaBoost in this model to predict and classify the degree of occupational fall accidents. HyOperpt was also used to optimize hyperparameters and to combine k-fold cross validation by hierarchy. We extracted and analyzed feature importances and affecting fall disaster by permutation technique. In this study, we verified the degree of fall accidents with predictive accuracy. The machine learning model was also confirmed to be applicable to the safety accident analysis in construction site. In the future, if the safety accident data is accumulated automatically in the network system using IoT(Internet of things) technology in real time in the construction site, it will be possible to analyze the factors and types of accidents according to the site conditions from the real time data.