The Journal of The Korea Institute of Intelligent Transport Systems
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v.22
no.2
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pp.64-79
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2023
This study reviewed the process of applying the Monte Carlo simulation technique to the traffic allocation problem of metropolitan subways. The analysis applied the assumption of a normal distribution in which the travel time information of the inter-station sample is the basis of the probit model. From this, the average and standard deviation are calculated by separating the traffic between stations. A plan was proposed to apply the simulation with the weights of the in-vehicle time of individual links and the walking and dispatch interval of transfer. Long-distance traffic with a low number of samples of 50 or fewer was evaluated as a way to analyze the characteristics of similar traffic. The research results were reviewed in two directions by applying them to the Seoul Metropolitan Subway Network. The travel time between single stations on the Seolleung-Seongsu route was verified by applying random sampling to the in-vehicle time and transfer time. The assumption of a normal distribution was accepted for sample sizes of more than 50 stations according to the inter-station traffic sample of the entire Seoul Metropolitan Subway. For long-distance traffic with samples numbering less than 50, the minimum distance between stations was 122Km. Therefore, it was judged that the sample deviation equality was achieved and the inter-station mean and standard deviation of the transport card data for stations at this distance could be applied.
Yeong-Hyun Lim ;Hak-Jong, Joo ;Tae-Kyung Kim ;Kyung-Min Seo
Journal of the Korea Society for Simulation
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v.32
no.3
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pp.93-108
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2023
In manufacturing productivity analysis, understanding the intricate interplay among factors like facility performance, layout design, and workforce allocation within the production line is imperative. This paper introduces a simulation-based methodology tailored to food manufacturing, progressively expanding scenarios to analyze production enhancement. The target system is a food processing plant, encompassing production processes, including warehousing, processing, subdivision, packaging, inspection, loading, and storage. First, we analyze the target system and design a simulation model according to the actual layout arrangement of equipment and workers. Then, we validate the developed model reflecting the real data obtained from the target system, such as the workers' working time and the equipment's processing time. The proposed model aims to identify optimal factor values for productivity gains through incremental scenario comparisons. To this end, three stages of simulation experiments were conducted by extending the equipment and worker models of the subdivision and packaging processes. The simulation experiments have shown that productivity depends on the placement of skilled workers and the performance of the packaging machine. The proposed method in this study will offer combinations of factors for the specific production requirements and support optimal decision-making in the real-world field.
This study, presents the details of a propulsion system for a systematic approach to the remodeling of rural villages as part of the pilot project that will be implemented in 2013. Studies on remodeling rural villages have focused mainly on various concepts of remodeling purposes and functions, and on the size and allocation standards of the facilities, such as rural houses, village rest areas, or public facilities. Thus, research is required on the establishment of a plan to restructure rural spaces and suggestions regarding the standards applicable at the local sites. This study employed an upward access system to prepare for the remodeling project and encourage the local residents' participation. As a result, the local residents voluntarily participated in drawing up a comprehensive plan for the village maintenance settings and forms, and for making the arrangements, by discussing the pros and cons of the plan, or through the forum. Based on this result, the local government examined the project in terms of its specifics, size, timing, budgetary appropriation, and anticipated results in order to implement the project, and also provided aid from the field experts to review the plan for the space and facility plans, and the legal system to establish the remodeling plan for the rural villages. Thus, this study attempts to contribute toward improving the quality of the rural villager's lives through a comprehensive remodeling of the whole rural village, not through an individual remodeling method.
Objectives: The existing reductionist approach has not reached complete understanding of the cause of schizophrenia. The objective of this study was to investigate yin-yang and five-element characteristics reflected on four time pillars of birth of patients with schizophrenia through comparison with the general population in the perspective of consilience-based holistic approach. Methods: This study was conducted using a random sequential recruitment method for the general population and individuals with schizophrenia aged 18 to 64 based on the exact date and time of birth using structured questionnaires. Relative positional relations of yin-yang and five-element with day master were primarily examined. In addition, the strength of day master with a score range of 0~100 points was assessed through operational score allocation. Results: Of 591 participants, 576 (346: general population, 230: individuals with schizophrenia) were analyzed. Between-group analyses showed no significant difference in the distribution of types of day master (χ2=10.41, df=9, p=0.318). However, significant between-group differences were shown in the distribution of the strength of day master (t=2.14, p=0.032) and frequency of restraining month branch (χ2=5.23, df=1, p=0.022). In logistic regression analysis, 10-point increase on the strength of day master decreased the probability of onset of schizophrenia over the age of 30 by 29.6% (p=0.002; 95% confidence interval, 0.566~0.876). Conclusions: Findings in this study suggest that four time pillars of birth might be associated with schizophrenia through yin-yang and five-element theory and synchronicity principle, implicating the plausibility of consilience-based holistic approach in the determination of risk factors or cause of schizophrenia.
Kim, Gil Ho;Yi, Choong Sung;Lee, Sang Won;Shim, Myung Pil
KSCE Journal of Civil and Environmental Engineering Research
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v.29
no.2B
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pp.173-179
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2009
Industrial water supplied by water resource project is essential input materials along with labor, capital and land for companies. It is very important to stably secure these input materials in order for the industry to generate additional values. If the supply of industrial water is stopped, it is known damage for the industry is greater than domestic water or agriculture water based on same amount of supply. Like this, the actual value of industrial water has been highly acknowledged from the intuitive perspective, but study on the value and benefits of industrial water has been rarely conducted. Therefore, this study verified the value of industrial water supplied from water resource project, and used marginal production value as a measure to estimate the benefits of industrial water in the analysis of economic efficiency. As a result of empirical analysis using Cobb-Douglas production function and Translog production function, industries' average marginal production value was $5,427KRW/m^3$ and $5,583KRW/m^3$ respectively. The marginal production value for eleven industries were estimated by using same method. The marginal production value by industries presented by this study will be used as important data to calculate benefits of industrial water in the future. Moreover, the result of this study will provide reasonable criteria for decision making on the allocation of water in emergency situation, and problem of resource supply from water resource project.
The Journal of the Institute of Internet, Broadcasting and Communication
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v.23
no.5
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pp.151-158
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2023
The researchers entirely focused on meta-heuristic method for generalized assignment problem(GAP) that is known as NP-hard problem because of the optimal solution within polynomial time algorithm is unknown yet. On the other hand, this paper proposes a heuristic greedy algorithm with rules for finding solutions. Firstly, this paper reduces the weight matrix of original data to wij ≤ bi/l in order to n jobs(items) pack m machines(bins) with l = n/m. The maximum profit of each job was assigned to the machine for the reduced data. Secondly, the allocation was adjusted so that the sum of the weights assigned to each machine did not exceed the machine capacity. Finally, the k-opt swap optimization was performed to maximize the profit. The proposed algorithm is applied to 50 benchmarking data, and the best known solution for about 1/3 data is to solve the problem. The remaining 2/3 data showed comparable results to metaheuristic techniques. Therefore, the proposed algorithm shows the possibility that rules for finding solutions in polynomial time exist for GAP. Experiments demonstrate that it can be a P-problem from an NP-hard.
The Transactions of the Korea Information Processing Society
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v.13
no.3
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pp.91-101
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2024
The utilization of container virtualization technology ensures the consistency and portability of data-intensive and memory volatile workflows. Kubernetes serves as the de facto standard for orchestrating these container applications. Cloud users often overprovision container applications to avoid container restarts caused by resource shortages. However, overprovisioning results in decreased CPU and memory resource utilization. To address this issue, oversubscription of container resources is commonly employed, although excessive oversubscription of memory resources can lead to a cascade of container restarts due to node memory scarcity. Container restarts can reset operations and impose substantial overhead on containers with high memory volatility that include numerous stateful applications. This paper proposes a technique to mitigate container restarts in a memory oversubscription environment based on Kubernetes. The proposed technique involves identifying containers that are likely to request memory allocation on nodes experiencing high memory usage and temporarily pausing these containers. By significantly reducing the CPU usage of containers, an effect similar to a paused state is achieved. The suspension of the identified containers is released once it is determined that the corresponding node's memory usage has been reduced. The average number of container restarts was reduced by an average of 40% and a maximum of 58% when executing a high memory volatile workflow in a Kubernetes environment with the proposed method compared to its absence. Furthermore, the total execution time of a container workflow is decreased by an average of 7% and a maximum of 13% due to the reduced frequency of container restarts.
Proceedings of the Korea Contents Association Conference
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2003.05a
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pp.344-350
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2003
In this paper, we propose a novel sharing method ordering the events occurring between users collaborated with the common telecomputing environment. We realize the sharing method with multimedia data to improve the coworking effect using teleprocessing network. This sharing method advances the efficiency of communicating projects such as remote education, tele-conference, and co-authoring of multimedia contents by offering conveniences of presentation, group authoring, common management, and transient event productions of the users. As for the conventional sharing white board system, all the multimedia contents segments should be authored by the exclusive program, and we cannot use any existing contents or program. Moreover we suffer from the problem that ordering error occurs in the teleprocessing operation because we do not have any line-up technology for the input ordering of commands. Therefore we develop a method of retrieving input and output events from the windows system and the message hooking technology which transmits between programs in the operating system In addition, we realize the allocation technology of the processing results for all sharing users of the distributed computing environment without any error. Our sharing technology should contribute to improve the face-to-face coworking efficiency for multimedia contents authoring, common blackboard system in the area of remote educations, and presentation display in visual conference.
The aim of this research was to develop a climate change vulnerability index at the district level (Si, Gun, Gu) with respect to the health care sector in Korea. The climate change vulnerability index was esimated based on the four major causes of climate-related illnesses : vector, flood, heat waves, and air pollution/allergies. The vulnerability assessment framework consists of six layers, all of which are based on the IPCC vulnerability concepts (exposure, sensitivity, and adaptive capacity) and the pathway of direct and indirect impacts of climate change modulators on health. We collected proxy variables based on the conceptual framework of climate change vulnerability. Data were standardized using the min-max normalization method. We applied the analytic hierarchy process (AHP) weight and aggregated the variables using the non-compensatory multi-criteria approach. To verify the index, sensitivity analysis was conducted by using another aggregation method (geometric transformation method, which was applied to the index of multiple deprivation in the UK) and weight, calculated by the Budget Allocation method. The results showed that it would be possible to identify the vulnerable areas by applying the developed climate change vulnerability assessment index. The climate change vulnerability index could then be used as a valuable tool in setting climate change adaptation policies in the health care sector.
From January 2020 to October 2021, more than 500,000 academic studies related to COVID-19 (Coronavirus-2, a fatal respiratory syndrome) have been published. The rapid increase in the number of papers related to COVID-19 is putting time and technical constraints on healthcare professionals and policy makers to quickly find important research. Therefore, in this study, we propose a method of extracting useful information from text data of extensive literature using LDA and Word2vec algorithm. Papers related to keywords to be searched were extracted from papers related to COVID-19, and detailed topics were identified. The data used the CORD-19 data set on Kaggle, a free academic resource prepared by major research groups and the White House to respond to the COVID-19 pandemic, updated weekly. The research methods are divided into two main categories. First, 41,062 articles were collected through data filtering and pre-processing of the abstracts of 47,110 academic papers including full text. For this purpose, the number of publications related to COVID-19 by year was analyzed through exploratory data analysis using a Python program, and the top 10 journals under active research were identified. LDA and Word2vec algorithm were used to derive research topics related to COVID-19, and after analyzing related words, similarity was measured. Second, papers containing 'vaccine' and 'treatment' were extracted from among the topics derived from all papers, and a total of 4,555 papers related to 'vaccine' and 5,971 papers related to 'treatment' were extracted. did For each collected paper, detailed topics were analyzed using LDA and Word2vec algorithms, and a clustering method through PCA dimension reduction was applied to visualize groups of papers with similar themes using the t-SNE algorithm. A noteworthy point from the results of this study is that the topics that were not derived from the topics derived for all papers being researched in relation to COVID-19 (
) were the topic modeling results for each research topic (
) was found to be derived from For example, as a result of topic modeling for papers related to 'vaccine', a new topic titled Topic 05 'neutralizing antibodies' was extracted. A neutralizing antibody is an antibody that protects cells from infection when a virus enters the body, and is said to play an important role in the production of therapeutic agents and vaccine development. In addition, as a result of extracting topics from papers related to 'treatment', a new topic called Topic 05 'cytokine' was discovered. A cytokine storm is when the immune cells of our body do not defend against attacks, but attack normal cells. Hidden topics that could not be found for the entire thesis were classified according to keywords, and topic modeling was performed to find detailed topics. In this study, we proposed a method of extracting topics from a large amount of literature using the LDA algorithm and extracting similar words using the Skip-gram method that predicts the similar words as the central word among the Word2vec models. The combination of the LDA model and the Word2vec model tried to show better performance by identifying the relationship between the document and the LDA subject and the relationship between the Word2vec document. In addition, as a clustering method through PCA dimension reduction, a method for intuitively classifying documents by using the t-SNE technique to classify documents with similar themes and forming groups into a structured organization of documents was presented. In a situation where the efforts of many researchers to overcome COVID-19 cannot keep up with the rapid publication of academic papers related to COVID-19, it will reduce the precious time and effort of healthcare professionals and policy makers, and rapidly gain new insights. We hope to help you get It is also expected to be used as basic data for researchers to explore new research directions.
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