• Structural Engineering and Mechanics
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• v.39 no.2
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• pp.241-266
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• 2011

The Self Compacting Concrete, SCC is the new generation type of concrete which is not needed to be compacted by vibrator and it will be compacted by its own weight. Since SCC is a new innovation and also the high strength self compacting concrete, HSSCC behavior is like a brittle material, therefore, understanding the strength effect on the serviceability performance of reinforced self compacting concretes is critical. For this aim, first the normal and high strength self compacting concrete, NSSCC and HSSCC was designed. Then, the serviceability performance of reinforced connections consisting of NSSCC and HSSCC were investigated. Twelve reinforced concrete connections (L = 3 m, b = 0.15 m, h = 0.3 m) were simulated, by this concretes, the maximum and minimum reinforcement ratios ${\rho}$ and ${\rho}^{\prime}$ (percentage of tensile and compressive steel reinforcement) are in accordance with the provision of the ACI-05 for conventional RC structures. This study was limited to the case of bending without axial load, utilizing simple connections loaded at mid span through a stub (b = 0.15 m, h = 0.3 m, L = 0.3 m) to simulate a beam-column connection. During the test, concrete and steel strains, deflections and crack widths were measured at different locations along each member. Based on the experimental readings and observations, the cracked moment of inertia ($I_{cr}$) of members was determined and the results were compared with some selective theoretical methods. Also, the flexural crack widths of the members were measured and the applicability for conventional vibrated concrete, as for ACI, BS and CSA code, was verified for SCCs members tested. A comparison between two Codes (ACI and CSA) for the theoretical values cracking moment is indicate that, irrespective of the concrete strength, for the specimens reported, the prediction values of two codes are almost equale. The experimental cracked moment of inertia $(I_{cr})_{\exp}$ is lower than its theoretical $(I_{cr})_{th}$ values, and therefore theoretically it is overestimated. Also, a general conclusion is that, by increasing the percentage of ${\rho}$, the value of $I_{cr}$ is increased.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.6
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• pp.2895-2899
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• 2016

Background: Increasing breast cancer (BC) incidence rates have been reported from India; causal factors for this increased incidence are not understood and diagnosis is mostly in advanced stages. Trivandrum exhibits the highest BC incidence rates in India. This study aimed to estimate trends in incidence by age from 2005-2014, to predict rates through 2020 and to assess the stage at diagnosis of BC in Trivandrum. Materials and Methods: BC cases were obtained from the Population Based Cancer Registry, Trivandrum. Distribution of stage at diagnosis and incidence rates of BC [Age-specific (ASpR), crude (CR) and age-standardized (ASR)] are described and employed with a joinpoint regression model to estimate average annual percent changes (AAPC) and a Bayesian model to estimate predictive rates. Results: BC accounts for 31% (2681/8737) of all female cancers in Trivandrum. Thirty-five percent (944/2681) are <50 years of age and only 9% present with stage I disease. Average age increased from 53 to 56.4 years (p=0.0001), CR (per $10^5$ women) increased from 39 (ASR: 35.2) to 55.4 (ASR: 43.4), AAPC for CR was 5.0 (p=0.001) and ASR was 3.1 (p=0.001). Rates increased from 50 years. Predicted ASpR is 174 in 50-59 years, 231 in > 60 years and overall CR is 80 (ASR: 57) for 2019-20. Conclusions: BC, mostly diagnosed in advanced stages, is rising rapidly in South India with large increases likely in the future; particularly among post-menopausal women. This increase might be due to aging and/or changes in lifestyle factors. Reasons for the increased incidence and late stage diagnosis need to be studied.

• Journal of Ocean Engineering and Technology
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• v.7 no.2
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• pp.173-181
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• 1993

This work has been investigated the ralationship between single surface crack length and crack depth have influence on the fatigue life. The simulation based on experimental results of 2.25 Cr-1Mo steel at various crack configuration ratios has enabled successful prediction of fatigue life at room temperature. The effect of crack depth should be considered for predicting fatigue crack growth rates as well as that of surface crack length. It is also shwn that the crack growth mechanisms are in good agreement with expreimental data according to the interaction of crack length and crack depth.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.4
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• pp.475-490
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• 2010

Cognitive Radio is an advanced enabling technology for the efficient utilization of vacant spectrum due to its ability to sense the spectrum environment. It is important to determine accurate spectrum utilization of the primary system in a cognitive radio environment. In order to define the spectrum utilization state, many CR systems use what is known as the quiet period (QP) method. However, even when using a QP, interference can occur. This causes reduced system throughput and contrary to the basic condition of cognitive radio. In order to reduce the interference time, a frequency-hopping algorithm is proposed here. Additionally, to complement the loss of throughput in the FH, a HMM-based channel prediction algorithm and a channel allocation algorithm is proposed. Simulations were conducted while varying several parameters. The findings show that the proposed algorithm outperforms conventional channel allocation algorithms.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.4C
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• pp.384-391
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• 2003

Blind channel identification and equalization attempt to identify the communication channel and to remove the inter-symbol interference caused by a communication channel without using any known trainning sequences. In this paper, we propose a blind adaptive channel identification and equalization algorithm with phase offset compensation for single-input multiple-output (SIMO) channel. It is based on the one-step forward multichannel linear prediction error method and can be implemented by an RLS algorithm. Phase offset problem, we use a blind adaptive algorithm called the constant modulus derotator (CMD) algorithm based on condtant modulus algorithm (CMA). Moreover, unlike many known subspace (SS) methods or cross relation (CR) methods, our proposed algorithms do not require channel order estimation. Therefore, our algorithms are robust to channel order mismatch.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.247-253
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• 2021

The outbreak of the deadly virus COVID-19 is said to infect 17.3Cr people around the globe since 2019. This outbreak is continuously affecting a lot of new people till this day and, most of it is said to under control. However, vaccines introduced around the world can help mitigate the risk of the virus. Apart from medical professionals, prediction models are also said to combinedly help predict the risk of infection based on given datasets. This paper is based on publication of a machine learning approach using regression models to predict the output based on dataset which have indictors grouped based on active, tested, recovered and critical cases along with regions and cities covering most of it from Dubai. Hence, the active cases are tested based on the other indicators and other attributes. The coefficient of the determination (r²) is 0.96, which is considered promising. This model can be used as an frame work, among others, to predict the resources related to the dangerous outbreak.

• Computers and Concrete
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• v.19 no.6
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• pp.631-639
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• 2017

This study experimentally investigated the flexural capacity of a concrete beam reinforced with a newly developed GFRP bar that overcomes the lower modulus of elasticity and bond strength compared to a steel bar. The GFRP bar was fabricated by thermosetting a braided pultrusion process to form the outer fiber ribs. The mechanical properties of the modulus of elasticity and bond strength were enhanced compared with those of commercial GFRP bars. In the four-point bending test results, all specimens failed according to the intended failure mode due to flexural design in compliance with ACI 440.1R-15. The effects of the reinforcement ratio and concrete compressive strength were investigated. Equations from the code were used to predict the deflection, and they overestimated the deflection compared with the experimental results. A modified model using two coefficients was developed to provide much better predictive ability, even when the effective moment of inertia was less than the theoretical $I_{cr}$. The deformability of the test beams satisfied the specified value of 4.0 in compliance with CSA S6-10. A modified effective moment of inertia with two correction factors was proposed and it could provide much better predictability in prediction even at the effective moment of inertia less than that of theoretical cracked moment of inertia.

• Proceedings of the KSME Conference
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• 2000.04a
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• pp.188-193
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• 2000

A more accurate life prediction for gas turbine blade takes into account the material behavior under the complex thermo-mechanical fatigue(TMF) cycles normally encountered in turbine operation. An experimental program has been carried out to address the thermo-mechanical fatigue life of the IN738LC nickel-base superalloy. In the first phase of the study, out-of-phase and in-phase TMF experiments have been performed on uncoated and coated materials. In the temperature range investigated. the deposition of NiCrAlY air plasma sprayed coating did not affect the fatigue resistance. In the second phase of the study, a physically-base life prediction model that takes into account of the contribution of different damage mechanisms has been applied. This model was able to reflect the temperature and strain rate dependences of isothermal cycling fatigue lives, and the strain-temperature history effect on the thermo-mechanical fatigue lives.

• Korean Journal of Materials Research
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• v.29 no.12
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• pp.781-789
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• 2019

The prediction of Jominy hardness curves and the effect of alloying elements on the hardenability of boron steels (19 different steels) are investigated using multiple regression analysis. To evaluate the hardenability of boron steels, Jominy end quenching tests are performed. Regardless of the alloy type, lath martensite structure is observed at the quenching end, and ferrite and pearlite structures are detected in the core. Some bainite microstructure also appears in areas where hardness is sharply reduced. Through multiple regression analysis method, the average multiplying factor (regression coefficient) for each alloying element is derived. As a result, B is found to be 6308.6, C is 71.5, Si is 59.4, Mn is 25.5, Ti is 13.8, and Cr is 24.5. The valid concentration ranges of the main alloying elements are 19 ppm < B < 28 ppm, 0.17 < C < 0.27 wt%, 0.19 < Si < 0.30 wt%, 0.75 < Mn < 1.15 wt%, 0.15 < Cr < 0.82 wt%, and 3 < N < 7 ppm. It is possible to predict changes of hardenability and hardness curves based on the above method. In the validation results of the multiple regression analysis, it is confirmed that the measured hardness values are within the error range of the predicted curves, regardless of alloy type.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.11C
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• pp.1071-1076
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• 2006

In this paper, we develop face and hand tracking for sign language recognition system. The system is divided into two stages; the initial and tracking stages. In initial stage, we use the skin feature to localize face and hands of signer. The ellipse model on CbCr space is constructed and used to detect skin color. After the skin regions have been segmented, face and hand blobs are defined by using size and facial feature with the assumption that the movement of face is less than that of hands in this signing scenario. In tracking stage, the motion estimation is applied only hand blobs, in which first and second derivative are used to compute the position of prediction of hands. We observed that there are errors in the value of tracking position between two consecutive frames in which velocity has changed abruptly. To improve the tracking performance, our proposed algorithm compensates the error of tracking position by using adaptive search area to re-compute the hand blobs. The experimental results indicate that our proposed method is able to decrease the prediction error up to 96.87% with negligible increase in computational complexity of up to 4%.