• Journal of the Korea Convergence Society
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• v.10 no.11
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• pp.71-77
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• 2019

This paper defines a multi-selection knapsack problem and presents an algorithm for seeking its optimal solution. Multi-selection means that all members of the particular group be selected or excluded. Our branch-and-bound algorithm introduces a simplex containing the feasible region of the original problem to exploit the fact that the most tightly underestimating function on the simplex is linear. In bounding operation, the subproblem defined over the candidate simplex is minimized. During the branching process the candidate simplex is splitted into two one-less dimensional subsimplices by being projected onto two hyperplanes. The approach of this paper can be applied to solving the global minimization problems under various types of the knapsack constraints.

• Journal of Physiology & Pathology in Korean Medicine
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• v.34 no.4
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• pp.159-169
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• 2020

Shanghanlun (Treatise on Cold Damage Diseases) is the oldest document in the literature on clinical records of Traditional Asian medicine (TAM), on which TAM theories about symptoms-herbs relationships are based. In this study, we aim to quantitatively explore the relationships between symptoms and herbs in Shanghanlun. The text in Shanghanlun was converted into structured data. Using the structured data, Term Frequency - Inverse Document Frequency (TF-IDF) scores of symptoms and herbs were calculated from each chapter to derive the major symptoms and herbs in each chapter. To understand the structure of the entire document, principal component analysis (PCA) was performed for the 6-dimensional chapter space. Bipartite network analysis was conducted focusing on Jaccard scores between symptoms and herbs and eigenvector centralities of nodes. TF-IDF scores showed the characteristics of each chapter through major symptoms and herbs. Principal components drawn by PCA suggested the entire structure of Shanghanlun. The network analysis revealed a 'multi herbs - multi symptoms' relationship. Common symptoms and herbs were drawn from high eigenvector centralities of their nodes, while specific symptoms and herbs were drawn from low centralities. Symptoms expected to be treated by herbs were derived, respectively. Using measurable metrics, we conducted a computational study on patterns of Shanghanlun. Quantitative researches on TAM theories will contribute to improving the clarity of TAM theories.

• Journal of Cadastre & Land InformatiX
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• v.46 no.1
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• pp.87-100
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• 2016

The acquisition of the forest resource information has depended on a partial sampling method or aerial photographs which demand a lot of effort and time because of the vast areas and the difficult approach. For the acquisition of the forest resource information, there have been the optical remote-sensing and the multi-spectrum image to offer only horizontal distributions of trees, but a new technological approach, such as Airborne LiDAR, is more necessary to acquire directly three dimensional information related to the forest terrains and trees' features. This paper proposes an algorithm for the forest information extraction such as trees' individual numbers and the heights of trees by using LiDAR data. Especially, this proposed algorithm adopts a region growing method for the extraction of the vegetation-point and extracts the forest information using morphological features of trees.

• Journal of Energy Engineering
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• v.11 no.4
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• pp.332-341
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• 2002

This Paper discusses a theoretical approach to assess the acceptability on nuclear energy facili-ties and policies, that is associated with derivation of indicators influencing perception on the nuclear energy. Facets of the public perception include the necessity of nuclear energy, expected benefits and costs, possi-bility of control, nuclear energy risk sentiment level, and equality between present and future generations. It also identifies indicators directly or indirectly affecting the perception facets and classifies them into the knowledge-based and the trust-based. Knowledge on nuclear energy facility is acquired on the foundation of the understanding of fact, through information, education, PR, and experience the media. Meanwhile, trust on nuclear energy Policies as value judgment on reality is built through legitimacy, communication, compensa-tion, participation, and the media. Multi-dimensional analysis on nuclear energy acceptability will provide a key to developing a more realistic and mutually agreeable policies and solving the imminent issues.

• Journal of Korean Society of Transportation
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• v.25 no.6
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• pp.171-183
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• 2007

Urban transport demand is derived from activity participation. A variety of individual daily activities based on the decisions on activity participation result in collective spatial behavior. The travel derived from the effort to overcome the spatially distributed locations of adjacent activities represents the detailed structural relationships among activities. An activity-based approach provides an important framework of analyzing contemporary urban daily life in the sense that it studies the interaction between individuals' daily decision making and social practice in time and space, on the one hand, and socio-spatial environment on the other. The current study identifies representative patterns of urban daily activity implementations and analyzes the correlation between representative patterns and individuals' characteristics and contextual characteristics. The study shows that urban daily activity patterns can be grouped in a limited number of representative patterns, which are systematically correlated with socio-spatial characteristics. The results provide related transportation policy implications.

• Journal of Korean Society of Rural Planning
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• v.10 no.2 s.23
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• pp.35-42
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• 2004

This study aimed at establishing the multi-ranged approach on data acquisition technique for rural landscape planning, which tried categorization, grading and transferring of landscape elements in the more detailed level. For the systematic management of database for the topographic informations in the village level, a kind of the aerial photographing techniques with UAV(Unmanned Aerial Vehicle) was used and its resultant data for the landscape simulation of the rural village, which in turn helped the convenient approach to understanding of its comprehensive spatial structure. The image data from aerial photography was systematically processed through; First, after revision of the distorted one, the image map was adjusted with the topographical and cadastral maps. Second, the farm houses and buildings, and other facilities difficult to find out in the topographical map was digitally read from the adjusted image. The topographical landscape view of rural village was simulated on the base of DEM(Digital Elevation Model) and the 3-dimensional shapes of farm houses and buildings were automatically modelled using the input system developed by the author. In conclusion, the aerial image information adjusted with the edited maps could give more intuitive and detailed villagescape than the ordinary one and through landscape simulation of the rural village, its topography, features of houses/buildings and spatial distribution of land uses were effectively reproduced. And, by the linkage between field survey and photographed/simulated results of the typical landscape elements using hyper-link method, it would be expected to develop as an effective visualization technique of rural landscape.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3834-3857
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• 2021

The design of cluster-based routing protocols is necessary for Wireless Sensor Networks (WSN). But, due to the lack of features, the traditional methods face issues, especially on unbalanced energy consumption of routing protocol. This work focuses on enhancing the security and energy efficiency of the system by proposing Energy Efficient Based Secure Routing Protocol (EESRP) which integrates trust management, optimization algorithm and key management. Initially, the locations of the deployed nodes are calculated along with their trust values. Here, packet transfer is maintained securely by compiling a Digital Signature Algorithm (DSA) and Elliptic Curve Cryptography (ECC) approach. Finally, trust, key, location and energy parameters are incorporated in Particle Swarm Optimization (PSO) and meta-heuristic based Harmony Search (HS) method to find the secure shortest path. Our results show that the energy consumption of the proposed approach is 1.06mJ during the transmission mode, and 8.69 mJ during the receive mode which is lower than the existing approaches. The average throughput and the average PDR for the attacks are also high with 72 and 62.5 respectively. The significance of the research is its ability to improve the performance metrics of existing work by combining the advantages of different approaches. After simulating the model, the results have been validated with conventional methods with respect to the number of live nodes, energy efficiency, network lifetime, packet loss rate, scalability, and energy consumption of routing protocol.

• Structural Engineering and Mechanics
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• v.78 no.4
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• pp.485-496
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• 2021

So far analytical methods on collapse assessment of three-dimensional (3-D) steel frames have mainly focused on a single-column-removal scenario. However, the collapse of the Federal Building in the US due to car bomb explosion indicated that the loss of multiple columns may occur in the real structures, wherein the structures are more vulnerable to collapse. Meanwhile, the General Services Administration (GSA) in the US suggested that the removal of side columns of the structure has a great possibility to cause collapse. Therefore, this paper analytically deals with the robustness of 3-D steel frames in a two-side-column-removal (TSCR) scenario. Analytical method is first proposed to determine the collapse resistance of the frame during this column-removal procedure. The reliability of the analytical method is verified by the finite element results. Moreover, a design-based methodology is proposed to quickly assess the robustness of the frame due to a TSCR scenario. It is found the analytical method can reasonably predict the resistance-displacement relationship of the frame in the TSCR scenario, with an error generally less than 10%. The parametric numerical analyses suggest that the slab thickness mainly affects the plastic bearing capacity of the frame. The rebar diameter mainly affects the capacity of the frame at large displacement. However, the steel beam section height affects both the plastic and ultimate bearing capacity of the frame. A case study on a six-storey steel frame shows that the design-based methodology provides a conservative prediction on the robustness of the frame.

• Smart Structures and Systems
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• v.29 no.1
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• pp.181-193
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• 2022

Data-driven structural health monitoring (SHM) of civil infrastructure can be used to continuously assess the state of a structure, allowing preemptive safety measures to be carried out. Long-term monitoring of large-scale civil infrastructure often involves data-collection using a network of numerous sensors of various types. Malfunctioning sensors in the network are common, which can disrupt the condition assessment and even lead to false-negative indications of damage. The overwhelming size of the data collected renders manual approaches to ensure data quality intractable. The task of detecting and classifying an anomaly in the raw data is non-trivial. We propose an approach to automate this task, improving upon the previously developed technique of image-based pre-processing on one-dimensional (1D) data by enriching the features of the neural network input data with multiple channels. In particular, feature engineering is employed to convert the measured time histories into a 3-channel image comprised of (i) the time history, (ii) the spectrogram, and (iii) the probability density function representation of the signal. To demonstrate this approach, a CNN model is designed and trained on a dataset consisting of acceleration records of sensors installed on a long-span bridge, with the goal of fault detection and classification. The effect of imbalance in anomaly patterns observed is studied to better account for unseen test cases. The proposed framework achieves high overall accuracy and recall even when tested on an unseen dataset that is much larger than the samples used for training, offering a viable solution for implementation on full-scale structures where limited labeled-training data is available.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.2
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• pp.719-727
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• 2013

Four-step travel-demand modeling based on a trip-level has been widely used over many decades. However, there has been a wide variance between forecasted- and real-travel demands, which leads less reliable on the model implications. A primary reason is that person's real travel behavior is not properly captured throughout the model developments. An activity-based modeling (ABM) approach was proposed and developed toward increasing the accuracy and reality of person's travel behavior in the U.S. since 1990', and stands as a good alternative to replace the existing trip-based approach. The paper contributes to the understanding of how the ABM approaches are dissimilar to the trip-based modeling approach in terms of estimation units, estimation process, their pros and cons and etc. We examined three activity-based travel demand model systems (DaySim, CT-Ramp, and CEMDAP) that are most commonly applied by many MPOs (Metropolitan Planning Organization). We found that the ABM approach can effectively explain multi-dimensional travel decision-makings and be expected to increase the predictive accuracy. Overall, the ABM approach can be a good substitute for the existing travel-demand methods having unreliable forecasts.