• Journal of the Korea Institute of Information Security & Cryptology
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• v.14 no.3
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• pp.41-48
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• 2004

Finite field multiplication and division are important arithmetic operation in error-correcting codes and cryptosystems. The elements of the finite field GF($2^m$) are represented by bases with a primitive polynomial of degree m over GF(2). We can be easily realized for multiplication or computing multiplicative inverse in GF($2^m$) based on a normal basis representation. The number of product terms of logic function determines a complexity of the Messay-Omura multiplier. A normal basis exists for every finite field. It is not easy to find the optimal normal element for a given primitive polynomial. In this paper, the generating method of normal basis is investigated. The normal bases whose product terms are less than other bases for multiplication in GF($2^m$) are found. For each primitive polynomial, a list of normal elements and number of product terms are presented.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.113-130
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• 2022

Sophisticated automation-based electronics world, more electrical and electronic devices are being used by people from different regions across the universe. Different manufacturers and vendors develop and market a wide variety of power generation and utilization devices under different operating parameters and conditions. People use a variety of appliances which use electrical energy as power source. These appliances or gadgets utilize the generated energy in different ratios. Night time the utilization will be less when compared with day time utilization of power. In industrial areas especially mechanical industries or Heavy machinery usage regions power utilization will be a diverse at different time intervals and it vary dynamically. This always causes a fluctuation in the grid lines because of the random and intermittent use of these apparatus while the power generating apparatus is made to operate to provide a steady output. Hence it necessitates designing and developing a method to optimize the power generated and the power utilized. Lot of methodologies has been proposed in the recent years for effective optimization and economical load dispatch. One such technique based on intelligent and evolutionary based is Black Widow Optimization BWO. To enhance the optimization level BWO is hybridized. In this research BWO based optimize the load for multi area is proposed to optimize the cost function. A three type of system was compared for economic loads of 16, 40, and 120 units. In this research work, BWO is used to improve the convergence rate and is proven statistically best in comparison to other algorithms such as HSLSO, CGBABC, SFS, ISFS. Also, BWO algorithm best optimize the cost parameter so that dynamically the load and the cost can be controlled simultaneously and hence effectively the generated power is maximum utilized at different time intervals with different load capacity in different regions of utilization.

• Smart Structures and Systems
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• v.30 no.6
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• pp.687-701
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• 2022

Inevitable response loss under complex operational conditions significantly affects the integrity and quality of measured data, leading the structural health monitoring (SHM) ineffective. To remedy the impact of data loss, a common way is to transfer the recorded response of available measure point to where the data loss occurred by establishing the response mapping from measured data. However, the current research has yet addressed the structural condition changes afterward and response mapping learning from a small sample. So, this paper proposes a novel data driven structural response reconstruction method based on a sophisticated designed generating adversarial network (UAGAN). Advanced deep learning techniques including U-shaped dense blocks, self-attention and a customized loss function are specialized and embedded in UAGAN to improve the universal and representative features extraction and generalized responses mapping establishment. In numerical validation, UAGAN efficiently and accurately captures the distinguished features of structural response from only 40 training samples of the intact structure. Besides, the established response mapping is universal, which effectively reconstructs responses of the structure suffered up to 10% random stiffness reduction or structural damage. In the experimental validation, UAGAN is trained with ambient response and applied to reconstruct response measured under earthquake. The reconstruction losses of response in the time and frequency domains reached 16% and 17%, that is better than the previous research, demonstrating the leading performance of the sophisticated designed network. In addition, the identified modal parameters from reconstructed and the corresponding true responses are highly consistent indicates that the proposed UAGAN is very potential to be applied to practical civil engineering.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.4
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• pp.53-60
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• 2022

In a group-testing method, instead of testing a sample, for example, blood individually, a batch of samples are pooled and tested simultaneously. If the pooled test is positive (or defective), each sample is tested individually. However, if negative (or good), the test is terminated at one pooled test because all samples in the batch are negative. This paper considers a queueing system with a two-stage group-testing policy. Samples arrive at the system according to a Poisson process. The system has a single server which starts a two-stage group test in a batch whenever the number of samples in the system reaches exactly a predetermined size. In the first stage, samples are pooled and tested simultaneously. If the pooled test is negative, the test is terminated. However, if positive, the samples are divided into two equally sized subgroups and each subgroup is applied to a group test in the second stage, respectively. The server performs pooled tests and individual tests sequentially. The testing time of a sample and a batch follow general distributions, respectively. In this paper, we derive the steady-state probability generating function of the system size at an arbitrary time, applying a bulk queuing model. In addition, we present queuing performance metrics such as the offered load, output rate, allowable input rate, and mean waiting time. In numerical examples with various prevalence rates, we show that the second-stage group-testing system can be more efficient than a one-stage group-testing system or an individual-testing system in terms of the allowable input rates and the waiting time. The two-stage group-testing system considered in this paper is very simple, so it is expected to be applicable in the field of COVID-19.

• International Journal of Advanced Culture Technology
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• v.12 no.2
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• pp.368-374
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• 2024

During an X-ray examination, the beam of radiation is dispersed in many directions. We believe that managing radiation dose is about providing transparency to users and patients in the accurate investigation and analysis of radiation dose. The purpose of measuring the radiation dose as a function of location is to ensure that medical personnel using the equipment or participating in the operating room are minimally harmed by the different radiation doses depending on their location. Four mobile diagnostic X-ray units were used to analyze the radiation dose depending on the spatial location. The image intensifier and the flat panel detector type that receives the image analyzed the dose by angle to measure the distribution of the exposure dose by location. The radiation equipment used was composed of four units, and measuring devices were installed according to the location. The X-ray (C-arm) was measured by varying the position from 0 to 360 degrees, and the highest dose was measured at the center position based on the abdominal position, and the highest dose was measured at the 90° position for the head position when using the image intensifier equipment. The operator or medical staff can see that the radiation dose varies depending on the position of the diagnostic radiation generator. In the image intensifier and flat panel detector type that accepts images, the dose by angle was analyzed for the distribution of exposed dose by position, and the measurement method should be changed according to the provision of dose information that is different from the dose output from the equipment according to the position.

• International Journal of Stem Cells
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• v.16 no.1
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• pp.108-116
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• 2023

γδ T cells are a rare and unique prototype of T cells that share properties with natural killer cells in secondary lymphoid organs. Although many studies have revealed the function and importance of adult-derived γδ T cells in cancer biology and regenerative medicine, the low numbers of these cells hamper their application as therapeutic cell sources in the clinic. To solve this problem, pluripotent stem cell-derived γδ T cells are considered alternative cell sources; however, few studies have reported the generation of human pluripotent stem cell-derived γδ T cells. In the present study, we investigated whether lymphoid lineage γδ T cells were successfully generated from human pluripotent stem cells via hemogenic endothelium under defined culture conditions. Our results revealed that pluripotent stem cells successfully generated γδ T cells with an overall increase in transcriptional activity of lymphoid lineage genes and cytolytic factors, indicating the importance of the optimization of culture conditions in generating lymphoid lineage γδ T cells. We uncovered an initial step in differentiating γδ T cells that could be applied to basic and translational investigations in the field of cancer biology. Based on our result, we will develop an appropriate method to purify γδ T cells with functionality and it helpful for the study of basic mechanism of γδ T cells in pathophysiologic condition as well as clinic application.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.9
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• pp.351-360
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• 2018

The LWR (Locally Weighted Regression) model, which is traditionally a lazy learning model, is designed to obtain the solution of the prediction according to the input variable, the query point, and it is a kind of the regression equation in the short interval obtained as a result of the learning that gives a higher weight value closer to the query point. We study on an incremental ensemble learning approach for LWR, a form of lazy learning and memory-based learning. The proposed incremental ensemble learning method of LWR is to sequentially generate and integrate LWR models over time using a genetic algorithm to obtain a solution of a specific query point. The weaknesses of existing LWR models are that multiple LWR models can be generated based on the indicator function and data sample selection, and the quality of the predictions can also vary depending on this model. However, no research has been conducted to solve the problem of selection or combination of multiple LWR models. In this study, after generating the initial LWR model according to the indicator function and the sample data set, we iterate evolution learning process to obtain the proper indicator function and assess the LWR models applied to the other sample data sets to overcome the data set bias. We adopt Eager learning method to generate and store LWR model gradually when data is generated for all sections. In order to obtain a prediction solution at a specific point in time, an LWR model is generated based on newly generated data within a predetermined interval and then combined with existing LWR models in a section using a genetic algorithm. The proposed method shows better results than the method of selecting multiple LWR models using the simple average method. The results of this study are compared with the predicted results using multiple regression analysis by applying the real data such as the amount of traffic per hour in a specific area and hourly sales of a resting place of the highway, etc.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.1
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• pp.32-40
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• 2008

Fuzzy rules are very useful and efficient to describe classification rules especially when the attribute values are continuous and fuzzy in nature. However, it is generally difficult to determine membership functions for generating efficient fuzzy classification rules. In this paper, we propose a method of automatic generation of efficient fuzzy classification rules using evolutionary algorithm. In our method we generate a set of initial membership functions for evolutionary algorithm by supervised clustering the training data set and we evolve the set of initial membership functions in order to generate fuzzy classification rules taking into consideration both classification accuracy and rule comprehensibility. To reduce time to evaluate an individual we also propose an evolutionary algorithm with data partition evaluation in which the training data set is partitioned into a number of subsets and individuals are evaluated using a randomly selected subset of data at a time instead of the whole training data set. We experimented our algorithm with the UCI learning data sets, the experiment results showed that our method was more efficient at average compared with the existing algorithms. For the evolutionary algorithm with data partition evaluation, we experimented with our method over the intrusion detection data of KDD'99 Cup, and confirmed that evaluation time was reduced by about 70%. Compared with the KDD'99 Cup winner, the accuracy was increased by 1.54% while the cost was reduced by 20.8%.

• Journal of the Korean Society for Railway
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• v.13 no.1
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• pp.71-77
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• 2010

It is increasing annually that the cost for bridge Maintenance Repair & Rehabilitation (MR&R) in developed countries. Based on Intelligent Technology, Bridge Management System (BMS) is developed for optimization of Life Cycle Cost (LCC) and reliability to predict long-term bridge deteriorations. However, such data are very limited amongst all the known bridge agencies, making it difficult to reliably predict future structural performances. To alleviate this problem, an Artificial Neural Network (ANN) based Backward Prediction Model (BPM) for generating missing historical condition ratings has been developed. Its reliability has been verified using existing condition ratings from the Maryland Department of Transportation, USA. The function of the BPM is to establish the correlations between the known condition ratings and such non-bridge factors as climate and traffic volumes, which can then be used to obtain the bridge condition ratings of the missing years. Since the non-bridge factors used in the BPM can influence the variation of the bridge condition ratings, well-selected non-bridge factors are critical for the BPM to function effectively based on the minimized discrepancy rate between the BPM prediction result and existing data (deck; 6.68%, superstructure; 6.61%, substructure; 7.52%). This research is on the generation of usable historical data using Artificial Intelligence techniques to reliably predict future bridge deterioration. The outcomes (Long-term Bridge deterioration Prediction) will help bridge authorities to effectively plan maintenance strategies for obtaining the maximum benefit with limited funds.

• Korean Journal of Construction Engineering and Management
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• v.23 no.2
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• pp.33-44
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• 2022

The simulation function by the 4D system is a representative BIM function in the construction stage. For the 4D simulation, schedule information for each activity must be created and then linked with the 3D model. Since the 3D model created in the design stage does not consider schedule information, there are practical difficulties in the process of creating schedule information for application to the construction stage and linking the 3D model. In this study, after extracting the schedule information of the construction stage using the HDBSCAN algorithm from the 3D model in the design stage, authors propose a methodology for automatically generating schedule information by identifying precedence and sequencing relationships by applying the topological alignment algorithm. Since the generated schedule information is created based on the 3D model, it can be used as information that is automatically linked by the common parameters between the schedule and the 3D model in the 4D system, and the practical utility of the 4D system can be increased. The proposed methodology was applied to the four bridge projects to confirm the schedule information generation, and applied to the 4D system to confirm the simplification of the link process between schedule and 3D model.