• Geomechanics and Engineering
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• v.38 no.1
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• pp.69-77
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• 2024

The development of shield-driven cross-river tunnels in China is witnessing a notable shift towards larger diameters, longer distances, and higher water pressures due to the more complex excavation environment. Complex geological formations, such as fault and karst cavities, pose significant construction risks. Real-time adjustment of shield tunneling parameters based on parameter prediction is the key to ensuring the safety and efficiency of shield tunneling. In this study, prediction models for the torque and thrust of the cutter plate of ultra-large diameter slurry shield TBMs is established based on integrated learning algorithms, by analyzing the real data of Heyan Road cross-river tunnel. The influence of geological complexities at the excavation face, substantial burial depth, and high water level on the slurry shield tunneling parameters are considered in the models. The results reveal that the predictive models established by applying Random Forest and AdaBoost algorithms exhibit strong agreement with actual data, which indicates that the good adaptability and predictive accuracy of these two models. The models proposed in this study can be applied in the real-time prediction and adaptive adjustment of the tunneling parameters for shield tunneling under complex geological conditions.

• Journal of the Korean Society for information Management
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• v.16 no.1
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• pp.7-30
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• 1999

This study is to develop effective document ranking algorithms in the P-norm retrieval system which can be implemented to the Boolean retrieval system without major difficulties by using non-statistical term weights based on document structure. Also, it is to enhance the performance by introducing the rank adjustment process which rearranges the ranks of retrieved documents according to the similarity between the top ranked documents and the rest of them. Of the non-statistical term weight algorithms, this study uses field weight and term pair distance weight. In the rank adjustment process, five retrieval experiments were performed, ranging between the case of using one record for the similarity measurement and the case of using first five records. It is proved that non-statistical term weights are highly effective and the rank adjustment process enhance the performance further.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1371-1373
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• 1996

A clustering algorithms with dynamic adjustment of learning rate for GBFCM(Gradient Based FCM) is proposed in this paper. This algorithm combines two idea of dynamic K-means algorithms and GBFCM : learning rate variation with entropy concept and continuous membership grade. To evaluate dynamic GBFCM, we made comparisons with Kohonen's Self-Organizing Map over several tutorial examples and image compression. The results show that DGBFCM(Dynamic GBFCM) gives superior performance over Kohonen's algorithm in terms of signal-to-noise.

• Industrial Engineering and Management Systems
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• v.6 no.2
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• pp.146-157
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• 2007

This paper discusses two algorithms for recommending notebook computer (NBC) and workstation adjustments so that the user can assume an ergonomic seated posture during NBC operation. Required input data are the user's anthropometric data and physical dimensions of the NBC and the workstation. The first algorithm is based on an assumption that there are no workstation constraints while the second algorithm considers the actual seat height and work surface height. The results from the algorithms include recommendations for adjusting the NBC (tilt angle of the NBC base unit, angle between the base and screen units, and base support height) and the workstation (heights of seat support and footrest, and distance between the body and the NBC).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3534-3553
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• 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 Positioning, Navigation, and Timing
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• v.2 no.1
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• pp.81-89
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• 2013

Many of the current visual odometry algorithms suffer from some extreme limitations such as requiring a high amount of computation time, complex algorithms, and not working in urban environments. In this paper, we present an approach that can solve all the above problems using a single camera. Using a planar motion assumption and Ackermann's principle of motion, we construct the vehicle's motion model as a circular planar motion (2DOF). Then, we adopt a 1-point method to improve the Ransac algorithm and the relative motion estimation. In the Ransac algorithm, we use a 1-point method to generate the hypothesis and then adopt the Levenberg-Marquardt method to minimize the geometric error function and verify inliers. In motion estimation, we combine the 1-point method with a simple least-square minimization solution to handle cases in which only a few feature points are present. The 1-point method is the key to speed up our visual odometry application to real-time systems. Finally, a Bundle Adjustment algorithm is adopted to refine the pose estimation. The results on real datasets in urban dynamic environments demonstrate the effectiveness of our proposed algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.12
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• pp.2232-2238
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• 2008

Most questions made for remote examinations on E-learning databank based selection system use methods of making questions automatically using degrees of difficulty. This method is the kernel of a question selection that degrees of difficulty as make test questions, and then needs continuous management for degrees of difficulty. This paper presents improved algorithms for dynamically adjustment of degrees of difficulty based on examination result that is more efficient sot of questions. We identified this algorithm is more effective as compared with previous algorithms on web-based education systems.

• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.4
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• pp.372-378
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• 2010

This paper propose the fast motion estimation algorithm with adaptive search range adjustment using motion activities of temporal and spatial neighbor blocks. The existing fast motion estimation algorithms with adaptive search range adjustment use the maximum motion vector of all blocks in the reference frame. So these algorithms may not control a optimum search range for slow moving block in current frame. The proposed algorithm use the maximum motion vector of neighbor blocks in the reference frame to control a optimum search range for slow moving block. So the proposed algorithm can reduce computation time for motion estimation. The experiment results show that the proposed algorithm can reduce the number of search points about 15% more than Simple Dynamic Search Range(SDSR) algorithm while maintaining almost the same bit-rate and motion estimation error.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.9 no.1
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• pp.15-28
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• 1999

In Multilevel Secure Database Management System(MLS/DBMS), we assume that system has a security clearance level for each user and a classification level for each data item in system and the objective of these systems is to protect secure information from unauthorized user. Many algorithms which have been researched have focus on removing covert channel by modifying conventional lock-based algorithm or timestamp-based algorithm. but there is high-level starvation problem that high level transaction is aborted by low level transaction repeatedly. In order to solve this problem, we propose an algorithm to reduce high-level starvation using dynamic adjustment of serialization order, which is basically using orange lock. Because our algorithm is based on a single version unlike conventional secure algorithms which are performed on multiversion, it can get high degree of concurrency control. we also show that it guarantees the serializability of concurrent execution, and satisfies secure properties of MLS/DBMS.

• Proceedings of the KSRS Conference
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• v.1
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• pp.500-503
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• 2006

To adjust the discrepancy between Light Detection and Ranging (LIDAR) strips, previous researches generally have been conducted using conjugate features, which are called feature-based approaches. However, irrespective of the type of features used, the adjustment process relies upon the existence of suitable conjugate features within the overlapping area and the ability of employed methods to detect and extract the features. These limitations make the process complex and sometimes limit the applicability of developed methodologies because of a lack of suitable features in overlapping areas. To address these drawbacks, this paper presents a methodology using area-based algorithms. This approach is based on the scheme that discrepancies make complex the local height variations of LIDAR data whithin overlapping area. This scheme can be helpful to determine an appropriate transformation for adjustment in the way that minimizes the geographical complexity. During the process, the contour tree (CT) was used to represent the geological characteristics of LIDAR points in overlapping area and the Iterative Closest Points (ICP) algorithm was applied to automatically determine parameters of transformation. After transformation, discrepancies were measured again and the results were evaluated statistically. This research provides a robust methodology without restrictions involved in methods that employ conjugate features. Our method also makes the overall adjustment process generally applicable and automated.