• Proceedings of the KOR-BRONCHOESO Conference
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• 1993.05a
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• pp.105-105
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• 1993

Laryngeal cancer is the commonest of all head and neck malignancies and, in Korea, constitutes approximately 2.3% of all malignancies among males. Survival rate is rather higher than any other head and neck malignancies. Treatment results of 121 laryngeal cancer patients were analyzed. 1. Glottic region 44%, supraglottic 41%, subglottic 8%, transglottic 7%. 2. Rate of cervical metastasis according to T stage was 19% in T2 stage, 47% in T3 stage, 69% in T4 stage. 3. Overall 3-year NED survival rate was 65% and showed stage I 93%, stage II 76%, stage III 63%, stage IV 53%. 4. Glottic cancer showed much better prognosis(3-year NED 82%) than supraglottic cancer(3-year NED 58 %). 5. According to treatment modality, complication rates in surgery with postoperative radiation was higher than in surgery along and disproportionally higher in salvage surgery after failed curative radiation.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.340-346
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• 2022

Construction industry is considered as one of the oldest industries in the world since human came into being and the need of their own space is realized. All this led to make the world a space of many beautiful constructive ventures. As per the requirements of today's world, every industry is recognizing the need for use and adoption of modern as well as innovative technologies due to their benefits and timely production. Now construction industry has also started adopting the use of modern and innovative technologies during their projects but still the rate of adoption is so slow. From design to completion, construction projects take a lot to manage for which technology based solutions have continuously been proposed. These include Computer Aided Design (CAD), building information modeling (BIM) and cloud computing have been proved to be much successful until now. The construction projects are high budgeted, and direly require timely and successful completion with quality, resource and other constraints. So, the researchers observe the need of more clear and technology based communication between the construction projects and its constructors and other stakeholders is required before and during the construction to take timely precautions for expected issues. This study has analyzed the use of Augmented Reality (AR) technology adopting GammaAR, and ARki applications in construction industry. It has been found that both applications are light-weighted, upgradable, provide offline availability and collaborative environment as well as fulfil most of the requirements of the construction industry except the cost. These applications also support different screen size for better visualization and deep understanding. Both applications are analyzed, based on construction's application requirements, usability of AR and ratings of applications user collected from application's platform. The purpose of this research is to provide a detail insight of construction applications which are using AR to facilitate both the future developers and consumers.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.1-7
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.210-216
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• Journal of Navigation and Port Research
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• v.30 no.9
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• pp.729-734
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• 2006

The authors aim to establish the theory necessary for developing the free gyroscopic compass and focus on mainly two points. One is to suggest north-finding principle by the angular velocity of the earth's rotation, and the other is to suggest orthogonal coordinate transformations of the motion rate of the spin axis, which transforms the components of motion rate in the free gyro frame into those in the platform frame and that this transformed rate is, in turn, transformed into the NED(north-east-down) navigation frame. Subsequently, ship's heading is obtained by using the fore-aft and athwartship components of the motion rate of the spin axis in the NED frame. In addition it was found how to solve the transformation matrix necessary for transforming each frame.

• Communications for Statistical Applications and Methods
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• v.7 no.2
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• pp.371-383
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• 2000

Lindsay (1994) and Basu et al (1997) show that another density-based distance called the negative exponential disparity (NED) is an excellent competitor to the Hellinger distance (HD) in generating an asymptotically fully efficient and robust estimator. Bhattacharya and Basu (1996) consider estimation of the locations of several normal populations when an order relation between them is known to be true. They empirically show that the robust HD based weighted likelihood estimators compare favorably with the M-estimators based on Huber's $\psi$ function, the Gastworth estimator, and the trimmed mean estimator. In this paper we investigate the performance of the weighted likelihood estimator based on the NED as a robust alternative relative to that based on the HD. The NED based estimator is found to be quite competitive in the settings considered by Bhattacharya and Basu.

• Journal of Electrical Engineering and Technology
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• v.12 no.4
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• pp.1417-1426
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• 2017

Power system analysis, Non-Convex Economic Dispatch (NED) is considered as an open and demanding optimization problem. Despite the fact that realistic ED problems have non-convex cost functions with equality and inequality constraints, conventional search methods have not been able to effectively find the global answers. Considering the great potential of meta-heuristic optimization techniques, many researchers have started applying these techniques in order to solve NED problems. In this paper, a new and efficient approach is proposed based on imperialist competitive algorithm (ICA). The proposed algorithm which is named multi-operator ICA (MuICA) merges three operators with the original ICA in order to simultaneously avoid the premature convergence and achieve the global optimum answer. In this study, the proposed algorithm has been applied to different test systems and the results have been compared with other optimization methods, tending to study the performance of the MuICA. Simulation results are the confirmation of superior performance of MuICA in solving NED problems.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.9-14
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• 2006

The authors aim to establish the theory necessary for developing the free gyrocompass. The following considerations are taken. One is to suggest north-finding principle by the angular velocity of the earth's rotation, and the other is to suggest that the motion rate of the spin axis in the free gyro frame is transformed into the platform fame and this transformed rate is again transformed into the NED navigation frame. After transformation ship's heading is obtained using the fore-aft and athwartship components of the motion rate of the spin axis in the NED frame In addition it was suggested how to solve the transformation matrix necessary for transforming each frame.

• Journal of Navigation and Port Research
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• v.31 no.9
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• pp.751-757
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• 2007

The authors aim to establish the theory necessary for developing free gyro positioning system and focus on measuring the nadir angle by using the motion rate of a free gyro. The azimuth of a gyro vector from the North can be given by using the property of the free gyro. The motion rate of the spin axis in the gyro frame is transformed into the platform frame and again into the NED (north-east-down) navigation frame. The nadir angle of a gyro vector is obtained by using the North components of the motion rate of the spin axis in the NED frame. The component has to be transformed into the horizontal component of the gyro by using the azimuth of the gyro vector and then has to be integrated over the sampling interval.

• International Journal of Computer Science & Network Security
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• v.21 no.5
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• pp.129-132
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• 2021

Two feature selection approaches are analyzed in this study. First Approach used in this paper is Filter Approach which comprises of correlation technique. It provides two reduced feature sets using positive and negative correlation. Secondly Approach used in this paper is the wrapper approach which comprises of Sequential Forward Selection technique. The reduced feature set obtained by positive correlation results comprises of Rate of Acceleration, Intensity and Formant. The reduced feature set obtained by positive correlation results comprises of Rasta PLP, Log energy, Log power and Zero Crossing Rate. Pitch, Rate of Acceleration, Log Power, MFCC, LPCC is the reduced feature set yield as a result of Sequential Forwarding Selection.