• Proceedings of the Korean Society of Medical Physics Conference
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• 한국의학물리학회 2002년도 Proceedings
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• pp.177-179
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• 2002

We have developed a scheduling system for heavy ion radiotherapy considering the condition of three treatment rooms and treatment planning for each patient. This system consists of a database (patient information, treatment method and machine schedule), a schedule for radiotherapy and WEB server. All operation of this system, such as data input, to change and to view the schedule, are performed by using a WEB browser. In order to protect personal information for the patients, access privilege to each information are limited by according to the occupational category. This system is connected with a hospital central information management system (AMIDAS) and an irradiation-managing computer for the heavy ion radiotherapy. A basic information for the patient is got from AMIDAS and the daily schedule sends to the treatment control computer at each treatment room through the irradiation-managing computer every morning. The daily, weekly, monthly schedules in the treatment room and the treatment condition of each patient are shared on the WEB browser with the all participants of the heavy ion therapy. This system could be useful to save a time to generate a treatment schedule and to inform us the most up-to-date treatment schedule and the related information at the same time.

• Korean Journal of Construction Engineering and Management
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• 제14권5호
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• pp.144-152
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• 2013

Recently construction projects become more bigger. So construction companies have being performed cost and schedule management task by using PMIS. In engineering and construction phase, progress management which occur the most frequent information I/O(input and output) should be managed effectively by PMIS. Although most information by PMIS manage efficiently, cost-schedule information for progress management is difficult to be managed due to recurrent information input. thus when changed by price fluctuation and change order, progress management could not connnect cost information with schedule information. So it caused inaccuracy of progress measurement and data increase in PMIS. To solve this problem, This study suggests improved module for progress management through integration of cost-schedule using quantity take-offs as progress measurement method.

• Journal of Communications and Networks
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• 제18권2호
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• pp.227-237
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• 2016

Data collection is a key function for wireless sensor networks. There has been numerous data collection scheduling algorithms, but they fail to consider the deep and complex relationship among network lifetime, sink aggregated information and sample cycle for wireless sensor networks. This paper gives the upper bound on the sample period under the given network topology. An optimal schedule algorithm focusing on aggregated information named OSFAI is proposed. In the schedule algorithm, the nodes in hotspots would hold on transmission and accumulate their data before sending them to sink at once. This could realize the dual goals of improving the network lifetime and increasing the amount of information aggregated to sink. We formulate the optimization problem as to achieve trade-off among sample cycle, sink aggregated information and network lifetime by controlling the sample cycle. The results of simulation on the random generated wireless sensor networks show that when choosing the optimized sample cycle, the sink aggregated information quantity can be increased by 30.5%, and the network lifetime can be increased by 27.78%.

• ETRI Journal
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• 제39권3호
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• pp.364-372
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• 2017

For the reliability of controllers in a software defined network (SDN), a dynamic and self-learning schedule method (DSL) is proposed. This method is original and easy to deploy, and optimizes the combination of multiple controllers. First, we summarize multiple controllers' combinations and schedule problems in an SDN and analyze its reliability. Then, we introduce the architecture of the schedule method and evaluate multi-controller reliability, the DSL method, and its optimized solution. By continually and statistically learning the information about controller reliability, this method treats it as a metric to schedule controllers. Finally, we compare and test the method using a given testing scenario based on an SDN network simulator. The experiment results show that the DSL method can significantly improve the total reliability of an SDN compared with a random schedule, and the proposed optimization algorithm has higher efficiency than an exhaustive search.

• ETRI Journal
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• 제46권3호
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• pp.473-484
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• 2024

We introduce a machine learning-based web application to help travel agents plan a package tour schedule. K-nearest neighbor (KNN) classification predicts the optimal tourists' dwelling time based on a variety of information to automatically generate a convenient tour schedule. A database collected in collaboration with an established travel agency is fed into the KNN algorithm implemented in the Python language, and the predicted dwelling times are sent to the web application via a RESTful application programming interface provided by the Flask framework. The web application displays a page in which the agents can configure the initial data and predict the optimal dwelling time and automatically update the tour schedule. After conducting a performance evaluation by simulating a scenario on a computer running the Windows operating system, the average response time was 1.762 s, and the prediction consistency was 100% over 100 iterations.

• Information Systems Review
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• 제6권2호
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• pp.195-208
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• 2004

In this paper we propose a new software project development management model incorporating quality management to schedule management. Though many efficient techniques such as code review and inspection are used to remove defects, the effect of defect removal time on project schedule hasn't been studied much. However, poor quality management has an important effect upon overall schedule and sometimes software projects fails due to it. Thus, quality management and schedule management should be considered together and we need to reflect the time to maintain software quality into the schedule management. For the proposed model we introduced "Quality Value" representing the needed time to remove software defects. We assume PSP/TSP to gather the needed data for quality value. The proposed model can be used to predict the effect of software defects on schedule in advance and to prevent schedule lag.

• Journal of the Korea Institute of Information and Communication Engineering
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• 제16권3호
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• pp.489-498
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• 2012

Smartphone schedule management applications are widely used in mobile environment with the help of their effective user interface and high user accessibility. Unfortunately, since the applications do not consider frequent context changes like user status or location, they can not support schedule management which reflects the changes. In this paper, we propose an intelligent notification service which gathers context information of users such as location and status, and actively provides appropriate services according to the context information. Based on the notification service, we also present a smartphone application for smart schedule management. Using the GPS sensor of a smartphone, the service can be aware of the status of the user through location and schedule information of the user. Also, according to the changed contexts, it provides proactive notifications to user.

• Journal of the Korea Society of Computer and Information
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• 제27권10호
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• pp.137-146
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• 2022

In relation to the travel itinerary recommendation service, which has recently become in high demand, our previous work introduces a method to quantify the popularity of places including tour spots, restaurants, and accommodations through social big data analysis, and to create a travel schedule based on the analysis results. On the other hand, the generated schedule was mainly composed of travel routes that connected tour spots with the shorted distance, and detailed schedule information including restaurants and accommodation information for each travel date was not provided. This paper presents an algorithm for constructing a detailed travel route using a scenario template in a travel schedule created based on social big data, and introduces a prototype system that implements it. The proposed system consists of modules such as place information collection, place-specific popularity score estimation, shortest travel rout generation, daily schedule organization, and UI visualization. Experiments conducted based on social reviews collected from 63,000 places in the Gyeongnam province proved effectiveness of the proposed system.

• International conference on construction engineering and project management
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• The 1th International Conference on Construction Engineering and Project Management
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• pp.1262-1267
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• 2005

It is still difficult to share and utilize the information generated at each phase of a steel box girder production process due to the spatial gap and different level of management information. The physical distance results in the inefficiency of the information transmission, the accidental omission and typos of the relative information, and so on. Various levels of management information make it difficult to embody a new management system. Eventually, these factors incur the loss of cost and schedule and interrupt development of a new management system. This paper analyzes a current process and presents graphical process flow by using IDEF0. Based this analysis, the research for new production process and work breakdown structure (WBS) is conducted. At the end of this paper, the conceptual design of this system is suggested. Through new management system, it is expected that the model proposed in this study will improve the management process in the steel box production, and the improved process will reduce the redundant cost and schedule information, transmission and deposit generated by manual paper.

• Journal of the Korean Society for Aviation and Aeronautics
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• 제29권2호
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• pp.84-93
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• 2021

Airline schedule is the most basic data for flight operations and has significant importance to an airline's management. It is crucial to know the airline's current schedule status in order to effectively manage the company and to be prepared for abnormal situations. In this study, machine learning techniques were applied to actual schedule data to examine the possibility of whether the airline's fleet state could be artificially learned without prior information. Given that the schedule is in categorical form, One Hot Encoding was applied and t-SNE was used to reduce the dimension of the data and visualize them to gain insights into the airline's overall fleet status. Interesting results were discovered from the experiments where the initial findings are expected to contribute to the fields of airline schedule health monitoring, anomaly detection, and disruption management.