• The Journal of Asian Finance, Economics and Business
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• 제9권4호
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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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• 제37권3호
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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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• 제37권4호
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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.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2004년도 학술대회지
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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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• 제14권12호
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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.

• 한국학교ㆍ지역보건교육학회지
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• 제7권
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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.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 D
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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

• 한국작물학회:학술대회논문집
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• 한국작물학회 2019년도 추계학술대회
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• pp.103-103
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• 2019

• Asian Pacific Journal of Cancer Prevention
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• 제13권11호
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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.

• 한국위성정보통신학회논문지
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• 제4권1호
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• pp.14-20
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• 2009

본 논문은 작전범위 내에 분포된 수백 대의 위성 단말의 상태를 제어, 감시하기 위한 DS-SS/TDMA 역방향 제어채널의 동기 구조 및 방법에 대하여 언급한다. 군용 위성은 전자전 기능 및 수백 대의 위성단말의 상태를 동시에 제어하기 위해 DS-SS/TDMA 방식의 제어채널이 필요하나 동기 시간이 긴 문제가 있다. 본 논문은 짧은 주기의 세부 Bin으로 구성된 프리앰블을 제어채널 데이터 앞부분에 삽입하고 각 세부 Bin을 병렬적으로 수신하는 정합 필터형태의 동기회로를 적용함으로서 심각한 재밍환경에서 수 ms이내에 대부분의 제어채널 패킷을 수신할 수 있는 제어채널 동기방법을 제안한다.