• Smart Structures and Systems
• /
• v.5 no.6
• /
• pp.613-632
• /
• 2009

The present paper is concerned with the design of a proper patch actuator network in order to track a desired displacement of the sidewalls of a one-storey frame structure; both, for the static and the dynamic case. Weights for each patch of the actuator network found in our previous work were based on beam theory; in the present paper a refinement of these weights by modeling the sidewalls of the frame structure as thin plates is presented. For the sake of calculating the refined weights approximate solutions of the plate equations are calculated by an extended Galerkin method. The solutions based on the analytical plate model are compared with three-dimensional Finite Element results computed in the commercially available code ANSYS. The patch actuator network is put into practice by means of four piezoelectric patches attached to each of the two sidewalls of the frame structures, to which electric voltages proportional to the analytically refined patch weights are applied. Analytical and numerical results coincide very well over a broad frequency range.

• Geomechanics and Engineering
• /
• v.9 no.1
• /
• pp.57-81
• /
• 2015

Water inrush and mud outburst always restricts the tunnel constructions in mountain area, which becomes a major geological barrier against the development of underground engineering. In view of the complex disaster-causing mechanism and difficult quantitative predictions of water inrush and mud outburst, several theoretical methods are adopted to realize dynamic assessment of water inrush in the progressive process of tunnel construction. Concerning both the geological condition and construction situation, eleven risk factors are quantitatively described and an assessment system is developed to evaluate the water inrush risk. In the static assessment, the weights of eight risk factors about the geological condition are determined using Analytic Hierarchy Process (AHP). Each factor is scored by experts and the synthesis scores are weighted. The risk level is ultimately determined based on the scoring outcome which is derived from the sum of products of weights and comprehensive scores. In the secondary assessment, the eight risk factors in static assessment and three factors about construction situation are quantitatively analyzed using fuzzy evaluation method. Subordinate levels and weight of factors are prepared and then used to calculate the comprehensive subordinate degree and risk level. In the dynamic assessment, the classical field of the eleven risk factors is normalized by using the extension evaluation method. From the input of the matter-element, weights of risk factors are determined and correlation analysis is carried out to determine the risk level. This system has been applied to the dynamic assessment of water inrush during construction of the Yuanliangshan tunnel of Yuhuai Railway. The assessment results are consistent with the actual excavation, which verifies the rationality and feasibility of the software. The developed system is believed capable to be back-up and applied for risk assessment of water inrush in the underground engineering construction.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.49 no.9
• /
• pp.512-519
• /
• 2000

A Neural networks has been used for a expert system and fault diagnosis system. It is possible to nonlinear function mapping and parallel processing. Therefore It has been developing for a Diagnosis system of nuclear plower plant. In general Neural Networks is a static mapping but Dynamic Neural Network(DNN) is dynamic mapping.쪼두 a fault occur in system a state of system is changed with transient state. Because of a previous state signal is considered as a information DNN is better suited for diagnosis systems than static neural network. But a DNN has many weights so a real time implementation of diagnosis system is in need of a rapid network architecture. This paper presents a algorithm for RCP monitoring Alarm diagnosis system using Self Dynamic Neural Network(SDNN). SDNN has considerably fewer weights than a general DNN. Since there is no interlink among the hidden layer. The effectiveness of Alarm diagnosis system using the proposed algorithm is demonstrated by applying to RCP monitoring in Nuclear power plant.

• Proceedings of the Earthquake Engineering Society of Korea Conference
• /
• 1999.04a
• /
• pp.292-299
• /
• 1999

In order to investigate dynamic behaviors of marine cables under wave and earthquake forces a geometric nonlinear. F, E formulation of marine cables is presented and tangent stiffness and mass matrices for the isoparametric cable element are derived, The initial equilibrium state of cables subjected to self -weights and current forces is determined and free vibration and dynamic nonlinear analysis of cable structures under additional environmental loads are performed based on the initial configuration Challenging examples are presented and discussed in order to demonstrate the feasibility of the present finite element method and investigate dynamic nonlinear behaviors of marine cables.

• The KIPS Transactions:PartC
• /
• v.12C no.4 s.100
• /
• pp.519-524
• /
• 2005

Due to various types of errors added to dynamic weight measurement data, proper methods to reduce measurement errors are required to produce reliable weights. It is very difficult to reduce the measurement error due to excessive oscillation of the system. To cope with parasitic types of errors in real systems, information provided by the photo sensors are utilized and combined in such a way to reduce the measurement errors of load cells. In addition to four channels of load cells from a model trailer, photo sensors are used to obtain the information to compensate the error induced from vertical movement of the vehicle due to the variation of ground level. A model trailer system is run to verify the effectiveness of the proposed method to reduce noise of dynamic weight measurements.

• Proceedings of the KIEE Conference
• /
• 2000.07d
• /
• pp.2488-2491
• /
• 2000

A Neural network is possible to nonlinear function mapping and parallel processing. Therefore It has been developing for a Diagnosis system of nuclear plower plant. In general Neural Networks is a static mapping but Dynamic Neural Network(DNN) is dynamic mapping. When a fault occur in system, a state of system is changed with transient state. Because of a previous state signal is considered as a information. DNN is better suited for diagnosis systems than static neural network. But a DNN has many weights, so a real time implementation of diagnosis system is in need of a rapid network architecture. This paper presents a algorithm for RCP monitoring Alarm diagnosis system using Self Dynamic Neural Network(SDNN). SDNN has considerably fewer weights than a general DNN. Since there is no interlink among the hidden layer. The effectiveness of Alarm diagnosis system using the proposed algorithm is demonstrated by applying to RCP monitoring in Nuclear power plant.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.19 no.2
• /
• pp.450-454
• /
• 2015

In this paper, when points with weights are given in a plane, for an arbitrary constant r, we shall find a square area S such that the sum of weights of points belonging to S is maximized. If the length of the side of S is not given, the problem to find arbitrary rectangular area has been studied. In this paper, we will consider the problem to find a square area with a side of a length r when a constant r is given. We will solve the one dimensional problem in dynamic environment and propose an algorithm with the time complexity of O(nlogn+rn).

• International Journal of Computer Science & Network Security
• /
• v.21 no.9
• /
• pp.212-222
• /
• 2021

Multiple waves of COVID-19 highlighted one crucial aspect of this pandemic worldwide that factors affecting the spread of COVID-19 infection are evolving based on various regional and local practices and events. The introduction of vaccines since early 2021 is expected to significantly control and reduce the cases. However, virus mutations and its new variant has challenged these expectations. Several countries, which contained the COVID-19 pandemic successfully in the first wave, failed to repeat the same in the second and third waves. This work focuses on COVID-19 pandemic control and management in Saudi Arabia. This work aims to predict new cases using deep learning using various important factors. The proposed method is called Deep Learning and Dynamic Weighing-based (DLDW) COVID-19 cases prediction method. Special consideration has been given to the evolving factors that are responsible for recent surges in the pandemic. For this purpose, two weights are assigned to data instance which are based on feature importance and dynamic weight-based time. Older data is given fewer weights and vice-versa. Feature selection identifies the factors affecting the rate of new cases evolved over the period. The DLDW method produced 80.39% prediction accuracy, 6.54%, 9.15%, and 7.19% higher than the three other classifiers, Deep learning (DL), Random Forest (RF), and Gradient Boosting Machine (GBM). Further in Saudi Arabia, our study implicitly concluded that lockdowns, vaccination, and self-aware restricted mobility of residents are effective tools in controlling and managing the COVID-19 pandemic.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.10
• /
• pp.3534-3553
• /
• 2021

The low cost of Bluetooth technology has led to its wide usage in indoor positioning. However, some inherent shortcomings of Bluetooth technology have limited its further development in indoor positioning, such as the unstable positioning state caused by the fluctuation of Received Signal Strength Indicator (RSSI) and the low transmission frequency accompanied by a poor real-time performance in positioning and tracking moving targets. To address these problems, an indoor fusion positioning algorithm of Bluetooth technology and pedestrian dead reckoning (PDR) based on a particle filter with dynamic adjustment of weights calculation strategy (BPDW) will be proposed. First, an orderly statistical filter (OSF) sorts the RSSI values of a period and then eliminates outliers to obtain relatively stable RSSI values. Next, the Group-based Trilateration algorithm (GTP) enhances positioning accuracy. Finally, the particle filter algorithm with dynamic adjustment of weight calculation strategy fuses the results of Bluetooth positing and PDR to improve the performance of positioning moving targets. To evaluate the performance of BPDW, we compared BPDW with other representative indoor positioning algorithms, including fingerprint positioning, trilateral positioning (TP), multilateral positioning (MP), Kalman filter, and strong tracking filter. The results showed that BPDW has the best positioning performance on static and moving targets in simulation and actual scenes.

• Journal of Korea Multimedia Society
• /
• v.13 no.4
• /
• pp.526-534
• /
• 2010

This paper describes an algorithm which merges two HDR pictures taken under different exposure time to display on the LDR devices such as LCD or CRT. The proposed method does not generate the radiance map, but directly merges using the weights computed from the input images. The weights are firstly produced on the pixel basis, and then blended with a Gaussian function. This process prevents some possible sparkle noises caused by radical change of the weights and contributes to smooth connection between 2 image informations. The chrominance informations of the images are merged on the weighted averaging scheme using the deviations of RGB average and their differences. The algorithm is characterized by the feature that it represents well the unsaturated area of 2 original images and the connection of the image information is smooth. The proposed method uses only 2 input images and automatically tunes the whole internal process according to them, thus autonomous operation is possible when it is included in HDR cameras which use double shuttering scheme or double sensor cells.