• Journal of Advanced Navigation Technology
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• v.25 no.4
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• pp.273-279
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• 2021

A flight control computer(FLCC) applied to an unmanned aerial vehicle(UAV) is a safety-critical item, and which is designed in a multiple structure to increase the reliability of operation by securing fault tolerance. These FLCC of multiple structure should be designed so that each independent processing/control components can perform the same operation at the same time. And for this reason, a synchronization algorithm for synchronizing the operation between FLCCs should be included in an operational flight program. In this paper, we propose a software design method for synchronization between dual FLCCs applied to UAVs. The proposed synchronization method is designed to synchronize using only the minimum hardware resources to reduce a failure rate. In addition, the proposed synchronization method is designed to minimized synchronization errors due to a timer operation by designing in consideration of operation characteristics of the hardware timer used for the synchronization.

• Smart Structures and Systems
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• v.30 no.6
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• pp.613-626
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• 2022

Guaranteeing the quality and integrity of structural health monitoring (SHM) data is very important for an effective assessment of structural condition. However, sensory system may malfunction due to sensor fault or harsh operational environment, resulting in multiple types of data anomaly existing in the measured data. Efficiently and automatically identifying anomalies from the vast amounts of measured data is significant for assessing the structural conditions and early warning for structural failure in SHM. The major challenges of current automated data anomaly detection methods are the imbalance of dataset categories. In terms of the feature of actual anomalous data, this paper proposes a data anomaly detection method based on data-level and deep learning technique for SHM of civil engineering structures. The proposed method consists of a data balancing phase to prepare a comprehensive training dataset based on data-level technique, and an anomaly detection phase based on a sophisticatedly designed network. The advanced densely connected convolutional network (DenseNet) and Transformer encoder are embedded in the specific network to facilitate extraction of both detail and global features of response data, and to establish the mapping between the highest level of abstractive features and data anomaly class. Numerical studies on a steel frame model are conducted to evaluate the performance and noise immunity of using the proposed network for data anomaly detection. The applicability of the proposed method for data anomaly classification is validated with the measured data of a practical supertall structure. The proposed method presents a remarkable performance on data anomaly detection, which reaches a 95.7% overall accuracy with practical engineering structural monitoring data, which demonstrates the effectiveness of data balancing and the robust classification capability of the proposed network.

• Journal of Advanced Navigation Technology
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• v.27 no.6
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• pp.763-769
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• 2023

Flight control computers for unmanned aerial vehicles are avionics that require high reliability and are generally designed to be multiplexed for margins on failures. The multiplexed flight control computer should include an interface through discrete signals and CCDL for synchronization and fault separation between channels. With the development of unmanned aerial vehicle technology, various types of platforms such as AAM and LPI are being developed in the private and military, which require advanced control performance for high-performance flight control and SWaP optimization of onboard equipment. In this paper, we designed a optimized flight control computer architecture for unmanned aerial vehicles for multiplexing processing and performed a software design for input and output control. In addition, input/output processing performance was evaluated through the implemented flight control computer and input/output software.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.3
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• pp.493-500
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• 2017

Based on the data accumulated through EMU fault management, this paper examines the reliability of old railway car parts and proposes measurements to improve safety. Subway Line 7 of the Seoul Metropolitan Rapid Transit Corporation, auxiliary power unit (Static Inverter) of the EMU second version is a core equipment to supply power to various room-service units in cars and make an effect directly on passenger satisfaction. To analyze the pattern of failure throughout the field data over a long period of time, this analysis of statistics and reliability considers the operating environment and stress factors. This statistical analysis presents the correlation between failure and the temperature stress factors related to frequent failure occurring intensively in summer. In addition, throughout the analysis of the life of the IGBT inverter, the effect of the temperature stress factor was observed before and after the repair. As a result of an analysis of the optimal operating conditions considering two variations of EMU, such as variable load and outside temperature, a difference in the cooling capacity between the optimal operating conditions and frequent failure conditions was observed. Based on this analysis, this paper suggests a way to minimize cooling capacity difference for the optimal operational conditions.

• Journal of the Korean Society for Railway
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• v.20 no.5
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• pp.569-576
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• 2017

The electromagnetic valve of pneumatic doors of EMUs has a high failure rate due to air leakage because it supplies air on and off to operate the doors repeatedly. The electromagnetic valve is a very important safety component for which a very high reliability is required because failure makes it impossible to operate the passenger cars. However, domestic urban railway operators maintain electronic valves of the EMU door under a fixed cycle with a spare period according to the full overhaul cycle of the EMU. An improvement of the current maintenance cycle was suggested based on the reliability function and mission reliability. Using the statistical program MINITAB for the operational data of EMU line 6, we analyzed the characteristics of the fault distribution and derived the shape and scale parameters of the reliability function. If we limit the specific reliability probability to under a certain failure rate and calculate its statistical parameters, we can calculate the allowable inspection period with mission reliability. Through this study, we suggested a maintenance period based on RCM (reliability centered-maintenance) to improve the reliability of electromagnetic valves from 68% to 95%.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.99-120
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• 2010

Due to the wide spread of customers' frequent access of non face-to-face services, there have been many attempts to improve customer satisfaction using huge amounts of data accumulated throughnon face-to-face channels. Usually, a call center is regarded to be one of the most representative non-faced channels. Therefore, it is important that a call center has enough agents to offer high level customer satisfaction. However, managing too many agents would increase the operational costs of a call center by increasing labor costs. Therefore, predicting and calculating the appropriate size of human resources of a call center is one of the most critical success factors of call center management. For this reason, most call centers are currently establishing a department of WFM(Work Force Management) to estimate the appropriate number of agents and to direct much effort to predict the volume of inbound calls. In real world applications, inbound call prediction is usually performed based on the intuition and experience of a domain expert. In other words, a domain expert usually predicts the volume of calls by calculating the average call of some periods and adjusting the average according tohis/her subjective estimation. However, this kind of approach has radical limitations in that the result of prediction might be strongly affected by the expert's personal experience and competence. It is often the case that a domain expert may predict inbound calls quite differently from anotherif the two experts have mutually different opinions on selecting influential variables and priorities among the variables. Moreover, it is almost impossible to logically clarify the process of expert's subjective prediction. Currently, to overcome the limitations of subjective call prediction, most call centers are adopting a WFMS(Workforce Management System) package in which expert's best practices are systemized. With WFMS, a user can predict the volume of calls by calculating the average call of each day of the week, excluding some eventful days. However, WFMS costs too much capital during the early stage of system establishment. Moreover, it is hard to reflect new information ontothe system when some factors affecting the amount of calls have been changed. In this paper, we attempt to devise a new model for predicting inbound calls that is not only based on theoretical background but also easily applicable to real world applications. Our model was mainly developed by the interactive decision tree technique, one of the most popular techniques in data mining. Therefore, we expect that our model can predict inbound calls automatically based on historical data, and it can utilize expert's domain knowledge during the process of tree construction. To analyze the accuracy of our model, we performed intensive experiments on a real case of one of the largest car insurance companies in Korea. In the case study, the prediction accuracy of the devised two models and traditional WFMS are analyzed with respect to the various error rates allowable. The experiments reveal that our data mining-based two models outperform WFMS in terms of predicting the amount of accident calls and fault calls in most experimental situations examined.