• The Journal of Asian Finance, Economics and Business
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• v.9 no.4
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• pp.273-285
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• 2022

Using data from a departmental profit and loss management questionnaire survey conducted for a group of hospitals consisting of various establishment entities, this study evaluates the effectiveness of departmental profit and loss management practices, such as break-even analysis, based on objective performance data. The study also examines whether the implementation of departmental profit and loss accounting is still effective in improving profitability in the financial year 2021 and whether the effectiveness of the implementation of departmental profit and loss accounting is robust. This study reconfirmed that the implementation of departmental profit-and-loss accounting has a positive effect on objective financial performance in hospitals and that the effect of improving profitability can be enhanced by implementing it monthly with high frequency and regularity and by using the accounting results more actively. It was also found that the department's implementation of break-even analysis had a positive impact on financial performance, which was enhanced by more active use of the data. Given the current economic climate, a hospital organization's active participation in income statement management, not only for the hospital as a whole but also for each department, would be an effective management activity.

• Geomechanics and Engineering
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• v.37 no.3
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• pp.223-241
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• 2024

Evaluating the performance of Tunnel Boring Machines (TBMs) stands as a pivotal juncture in the domain of hard rock mechanized tunneling, essential for achieving both a dependable construction timeline and utilization rate. In this investigation, three advanced artificial neural networks namely, gated recurrent unit (GRU), back propagation neural network (BPNN), and simple recurrent neural network (SRNN) were crafted to prognosticate TBM-rate of penetration (ROP). Drawing from a dataset comprising 1125 data points amassed during the construction of the Alborze Service Tunnel, the study commenced. Initially, five geomechanical parameters were scrutinized for their impact on TBM-ROP efficiency. Subsequent statistical analyses narrowed down the effective parameters to three, including uniaxial compressive strength (UCS), peak slope index (PSI), and Brazilian tensile strength (BTS). Among the methodologies employed, GRU emerged as the most robust model, demonstrating exceptional predictive prowess for TBM-ROP with staggering accuracy metrics on the testing subset (R² = 0.87, NRMSE = 6.76E-04, MAD = 2.85E-05). The proposed models present viable solutions for analogous ground and TBM tunneling scenarios, particularly beneficial in routes predominantly composed of volcanic and sedimentary rock formations. Leveraging forecasted parameters holds the promise of enhancing both machine efficiency and construction safety within TBM tunneling endeavors.

• Geomechanics and Engineering
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• v.37 no.4
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• pp.307-321
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• 2024

This paper delves into the critical assessment of predicting sidewall displacement in underground caverns through the application of nine distinct machine learning techniques. The accurate prediction of sidewall displacement is essential for ensuring the structural safety and stability of underground caverns, which are prone to various geological challenges. The dataset utilized in this study comprises a total of 310 data points, each containing 13 relevant parameters extracted from 10 underground cavern projects located in Iran and other regions. To facilitate a comprehensive evaluation, the dataset is evenly divided into training and testing subset. The study employs a diverse array of machine learning models, including recurrent neural network, back-propagation neural network, K-nearest neighbors, normalized and ordinary radial basis function, support vector machine, weight estimation, feed-forward stepwise regression, and fuzzy inference system. These models are leveraged to develop predictive models that can accurately forecast sidewall displacement in underground caverns. The training phase involves utilizing 80% of the dataset (248 data points) to train the models, while the remaining 20% (62 data points) are used for testing and validation purposes. The findings of the study highlight the back-propagation neural network (BPNN) model as the most effective in providing accurate predictions. The BPNN model demonstrates a remarkably high correlation coefficient (R2 = 0.99) and a low error rate (RMSE = 4.27E-05), indicating its superior performance in predicting sidewall displacement in underground caverns. This research contributes valuable insights into the application of machine learning techniques for enhancing the safety and stability of underground structures.

• Proceedings of the IEEK Conference
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• 2004.08c
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• pp.486-490
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• 2004

Programmable logic controller (PLC) is widely used in manufacturing system or process control. This paper presents the design of a 32-bit microprocessor for a sequence control using an Application Specification Integrated Circuit (ASIC). The 32-bit microprocessor was designed by a VHDL with top down method; the program memory was separated from the data memory for high speed execution of 274 specified sequence instructions. Therefore it was possible that sequence instructions could be operated at the same time during the instruction fetch cycle. And in order to reduce the instruction decoding time and the interface time of the data memory interface, an instruction code size was implemented by 32-bits. And the real time debugging as single step run, break point run was implemented. Pulse instruction, step controller, master controllers, BIN and BCD type arithmetic instructions, barrel shit instructions were implemented for many used in PLC system. The designed microprocessor was synthesized by the S1L50000 series which contains 70,000 gates with 0.65um technology of SEIKO EPSON. Finally, the benchmark was performed to show that designed 32-bit microprocessor has better performance than Q4A PLC of Mitsubishi Corporation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.12
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• pp.4776-4794
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• 2020

We propose a rapid adaptive learning framework for streaming object detection, called EER-ASSL. The method combines the expected error reduction (EER) dependent rollback learning and the active semi-supervised learning (ASSL) for a rapid adaptive CNN detector. Most CNN object detectors are built on the assumption of static data distribution. However, images are often noisy and biased, and the data distribution is imbalanced in a real world environment. The proposed method consists of collaborative sampling and EER-ASSL. The EER-ASSL utilizes the active learning (AL) and rollback based semi-supervised learning (SSL). The AL allows us to select more informative and representative samples measuring uncertainty and diversity. The SSL divides the selected streaming image samples into the bins and each bin repeatedly transfers the discriminative knowledge of the EER and CNN models to the next bin until convergence and incorporation with the EER rollback learning algorithm is achieved. The EER models provide a rapid short-term myopic adaptation and the CNN models an incremental long-term performance improvement. EER-ASSL can overcome noisy and biased labels in varying data distribution. Extensive experiments shows that EER-ASSL obtained 70.9 mAP compared to state-of-the-art technology such as Faster RCNN, SSD300, and YOLOv2.

• The Journal of Korean Society for School & Community Health Education
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• v.7
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• pp.75-85
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• 2006

This study was conducted to prepare information about foodborne disease outbreaks by year, eating place, etiological agent, area, and type of school lunch program. In the study, the reported data was reviewed, but only the data during recent five years were mainly analyzed because of data shortage.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2443-2445
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• 2000

In this paper, the Intergrated SCADA is used to computer systems designed to perform the following functions for power plant. - to collect data from industrial plant devices or transducers - to process and perform calculations on the collected data - to present collected and derived data on displays on MMI - to accept commands entered by human operators and act on them such as sending control commands to plant devices. This system is characterised by open architectured that is based on the internationally recognized industrial standard for industrial automation control language, the IEC 1131-3

• Proceedings of the Korean Society of Crop Science Conference
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• 2019.09a
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• pp.103-103
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• 2019

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.11
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• pp.5839-5842
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• 2012

Breast cancer is the most commonly diagnosed cancer in women in the world and is one of the leading causes of death due to cancer. Health benefits have been linked to additive and synergistic combinations of phytochemicals in fruits and vegetables. Nigella sativa has been shown to possess anti-carcinogenic activity, inhibiting growth of several cancer cell lines in vitro. However, the molecular mechanisms of the anti-cancer properties of Nigella sativa phytochemical extracts have not been completely understood. Our data showed that Nigella sativa extracts significantly inhibited human breast cancer MDA-MB-231 cell proliferation at doses of $2.5-5{\mu}g/mL$ (P<0.05). Apoptotic induction in MDA-MB-231 cells was observed in a dose-dependent manner after exposure to Nigella sativa extracts for 48 h. Real time PCR and flow cytometry analyses suggested that Nigella sativa extracts possess the ability to suppress the proliferation of human breast cancer cells through induction of apoptosis.

• Journal of Satellite, Information and Communications
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• v.4 no.1
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• pp.14-20
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• 2009

This paper describes synchronization concept and algorithm of the reverse DS-SS/TDMA control channel to handle satellite terminals which are distributed through the mission area. Military satellite control channel should have ECCM capabilities and handle more than several hundreds satcom terminals simultaneously. DS-SS/TDMA control channel can satisfy these demand but it spend much synchronization time. Proposed algorithm insert the preamble which is divided with several short sub bins prior to control data and use the parallel matched filtering searcher for each sub bin. As a result of the test, proposed algorithm can acquire most of control channel packet successfully within several milliseconds in severe jamming environment.