• Computers and Concrete
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• v.6 no.3
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• pp.203-223
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• 2009

Regression analysis (RA) can establish an explicit formula to predict the strength of High-Performance Concrete (HPC); however, the accuracy of the formula is poor. Back-Propagation Networks (BPNs) can establish a highly accurate model to predict the strength of HPC, but cannot generate an explicit formula. Genetic Operation Trees (GOTs) can establish an explicit formula to predict the strength of HPC that achieves a level of accuracy in between the two aforementioned approaches. Although GOT can produce an explicit formula but the formula is often too complicated so that unable to explain the substantial meaning of the formula. This study developed a Backward Pruning Technique (BPT) to simplify the complexity of GOT formula by replacing each variable of the tip node of operation tree with the median of the variable in the training dataset belonging to the node, and then pruning the node with the most accurate test dataset. Such pruning reduces formula complexity while maintaining the accuracy. 404 experimental datasets were used to compare accuracy and complexity of three model building techniques, RA, BPN and GOT. Results show that the pruned GOT can generate simple and accurate formula for predicting the strength of HPC.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.21 no.45
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• pp.57-65
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• 1998

This paper addresses the data classification using the induction of decision trees. A weakness of other techniques of induction of decision trees is that decision trees are too large because they construct decision trees until leaf nodes have a single class. Our study include both overcoming this weakness and constructing decision trees which is small and accurate. First, we construct the decision trees using classification threshold and exception threshold in construction stage. Next, we present two stage pruning method using classification threshold and reduced error pruning in pruning stage. Empirical results show that our method obtain the decision trees which is accurate and small.

• Journal of Convergence for Information Technology
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• v.10 no.8
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• pp.23-34
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• 2020

Recently, the artificial intelligence deep learning field has been hard to commercialize due to the high computing power and the price problem of computing resources. In this paper, we apply a double pruning techniques to evaluate the performance of the in-depth neural network and various datasets. Double pruning combines basic Network-slimming and Parameter-prunning. Our proposed technique has the advantage of reducing the parameters that are not important to the existing learning and improving the speed without compromising the learning accuracy. After training various datasets, the pruning ratio was increased to reduce the size of the model.We confirmed that MobileNet-V3 showed the highest performance as a result of NetScore performance analysis. We confirmed that the performance after pruning was the highest in MobileNet-V3 consisting of depthwise seperable convolution neural networks in the Cifar 10 dataset, and VGGNet and ResNet in traditional convolutional neural networks also increased significantly.

• Journal of Internet Computing and Services
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• v.25 no.4
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• pp.7-21
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• 2024

There are various machine learning techniques such as Reinforcement Learning, Deep Learning, Neural Network Learning, and so on. In recent, Large Language Models (LLMs) are popularly used for Generative AI based on Reinforcement Learning. It makes decisions with the most optimal rewards through the fine tuning process in a particular situation. Unfortunately, LLMs can not provide any explanation for how they reach the goal because the training is based on learning of black-box AI. Reinforcement Learning as black-box AI is based on graph-evolving structure for deriving enhanced solution through adjustment by human feedback or reinforced data. In this research, for mutually exclusive decision-making, Mutually Exclusive Learning (MEL) is proposed to provide explanations of the chosen goals that are achieved by a decision on both ends with specified conditions. In MEL, decision-making process is based on the tree-based structure that can provide processes of pruning branches that are used as explanations of how to achieve the goals. The goal can be reached by trade-off among mutually exclusive alternatives according to the specific contextual conditions. Therefore, the tree-based structure is adopted to provide feasible solutions with the explanations based on the pruning branches. The sequence of pruning processes can be used to provide the explanations of the inferences and ways to reach the goals, as Explainable AI (XAI). The learning process is based on the pruning branches according to the multi-dimensional contextual conditions. To deep-dive the search, they are composed of time window to determine the temporal perspective, depth of phases for lookahead and decision criteria to prune branches. The goal depends on the policy of the pruning branches, which can be dynamically changed by configured situation with the specific multi-dimensional contextual conditions at a particular moment. The explanation is represented by the chosen episode among the decision alternatives according to configured situations. In this research, MEL adopts the tree-based learning model to provide explanation for the goal derived with specific conditions. Therefore, as an example of mutually exclusive problems, employment process is proposed to demonstrate the decision-making process of how to reach the goal and explanation by the pruning branches. Finally, further study is discussed to verify the effectiveness of MEL with experiments.

• Journal of the Korea Society of Computer and Information
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• v.10 no.1 s.33
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• pp.85-92
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• 2005

Recently, query processing techniques for the multi-dimensional data like images have been widely used to perform content-based retrieval of the data . Range query and Nearest neighbor query are widely used multi dimensional queries . This paper Proposes the efficient pruning strategies for k-nearest neighbor query in R-tree variants indexing structures. Pruning strategy is important for the multi-dimensional indexing query processing so that search space can be reduced. We analyzed the Pruning strategies and perform experiments to show overhead and the profit of the strategies. Finally, we propose best use of the strategies.

• Journal of the Institute of Convergence Signal Processing
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• v.22 no.3
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• pp.99-103
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• 2021

Recently, convolutional neural networks (CNNs) have been widely utilized due to their outstanding performance in various computer vision fields. However, due to their computational-intensive and high memory requirements, it is difficult to deploy CNNs on hardware platforms that have limited resources, such as mobile devices and IoT devices. To address these limitations, a neural network compression research is underway to reduce the size of neural networks while maintaining their performance. This paper proposes a CNN compression technique that dynamically adjusts the thresholds of pruning, one of the neural network compression techniques. Unlike the conventional pruning that experimentally or heuristically sets the thresholds that determine the weights to be pruned, the proposed technique can dynamically find the optimal thresholds that prevent accuracy degradation and output the light-weight neural network in less time. To validate the performance of the proposed technique, the LeNet was trained using the MNIST dataset and the light-weight LeNet could be automatically obtained 1.3 to 3 times faster without loss of accuracy.

• Journal of KIISE:Databases
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• v.35 no.2
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• pp.182-191
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• 2008

Most of previous works for skyline queries have focused only on static attributes of target objects. With the advance in mobile applications, however, the need of continuous skyline queries for moving objects has been increasing. Even though several techniques to process continuous skyline queries have been proposed recently, they cannot process subspace queries, which use only the subset of attribute dimensions. Therefore it is not feasible to utilize those methods for mobile applications which must consider moving objects and subspaces simultaneously. In this paper, we propose a dominant object-based pruning method to compute subspace skyline of moving objects efficiently at query time and present the experimental results to show the effectiveness of the proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.2 no.6
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• pp.280-298
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• 2008

For efficient content-based image retrieval, diverse visual features such as color, texture, and shape have been widely used. In the case of leaf images, further improvement can be achieved based on the following observations. Most plants have unique shape of leaves that consist of one or more blades. Hence, blade-based matching can be more efficient than whole shape-based matching since the number and shape of blades are very effective to filtering out dissimilar leaves. Guaranteeing rotational invariance is critical for matching accuracy. In this paper, we propose a new shape representation, indexing and matching scheme for leaf image retrieval. For leaf shape representation, we generated a distance curve that is a sequence of distances between the leaf’s center and all the contour points. For matching, we developed a blade-based matching algorithm called rotation invariant - partial dynamic time warping (RI-PDTW). To speed up the matching, we suggest two additional techniques: i) priority queue-based pruning of unnecessary blade sequences for rotational invariance, and ii) lower bound-based pruning of unnecessary partial dynamic time warping (PDTW) calculations. We implemented a prototype system on the GEMINI framework [1][2]. Using experimental results, we showed that our scheme achieves excellent performance compared to competitive schemes.

• Smart Structures and Systems
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• v.32 no.3
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• pp.179-193
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• 2023

This study explores an alternative to the existing centralized process for data anomaly detection in modern Internet of Things (IoT)-based structural health monitoring (SHM) systems. An edge intelligence framework is proposed for the early detection and classification of various data anomalies facilitating quality enhancement of acquired data before transmitting to a central system. State-of-the-art deep neural network pruning techniques are investigated and compared aiming to significantly reduce the network size so that it can run efficiently on resource-constrained edge devices such as wireless smart sensors. Further, depthwise separable convolution (DSC) is invoked, the integration of which with advanced structural pruning methods exhibited superior compression capability. Last but not least, quantization-aware training (QAT) is adopted for faster processing and lower memory and power consumption. The proposed edge intelligence framework will eventually lead to reduced network overload and latency. This will enable intelligent self-adaptation strategies to be employed to timely deal with a faulty sensor, minimizing the wasteful use of power, memory, and other resources in wireless smart sensors, increasing efficiency, and reducing maintenance costs for modern smart SHM systems. This study presents a theoretical foundation for the proposed framework, the validation of which through actual field trials is a scope for future work.

• Journal of Internet of Things and Convergence
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• v.10 no.5
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• pp.53-63
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• 2024

The rapid development of wireless sensor network technology has garnered significant attention from researchers. In extensive distributed networks, these applications often rely on battery power. Given the limited energy capacity of batteries, effective energy management is crucial for improving network performance. Wireless sensor networks consist of numerous sensor nodes, where energy consumption is primarily driven by these nodes. In clustering protocols, certain nodes repeatedly serve as cluster heads, resulting in increased energy consumption compared to other nodes. This energy-balancing algorithm employs pruning techniques to evaluate and analyze a node's position, its frequency of acting as a cluster head, and its remaining energy. Additionally, it includes a dynamic adjustment mechanism for selecting the cluster head node. Experimental results demonstrate that this algorithm extends the operational duration of sensor nodes, thereby effectively prolonging the lifespan of the wireless sensor network.