• Industrial Engineering and Management Systems
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• v.15 no.1
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• pp.77-91
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• 2016

Every service firm must find ways to attract new customers, retain existing customers, and remain competitive and profitable. As competition increases, delivering better service becomes more important. Service quality is considered as a vital aspect for the success of the firms. Restaurant cannot be separated from the service quality they have to deliver. The development of restaurant is supported with the reputation of the country where the food comes from. Recently, one of the most trending topic is Korean wave which affects the Korean cuisine. A fuzzy AHP was employed to evaluate the service quality. It is more preferable than traditional AHP which is criticized for its inability to handle the uncertainty of the decision maker's perception. Six attributes are used to evaluate five Korean restaurants in Semarang, Indonesia. The result shows that innovation is the most important attribute. It seems that decision makers viewed the food variation and new method service as main factors that the restaurants have to manage. This finding can provide the managers with valuable insights into the attribute that reflects customers' perceptions; also to position their service based on their competitors. Validating the scale in other culture-based restaurants is an interesting area to be pursued.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.1
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• pp.1-11
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• 1994

This paper presents an intelligent robotic control method for chamferless parts mating by integrating fuzzy control and neural network. The successful assembly task requires an extremely high position accuracy and a good knowledge of mating parts. However, conventional assembly method alone makes it difficult to achieve satisfactory assembly performance because of the complexity and the uncertainties of the process and its environments such as not only the limitation of the devices performing the assembly but also imperfect knowledge of the parts being assembled. To cope with these problems, an intelligent robotic assembly method is proposed, which is composed of fuzzy controller and learning mechanism based upon neural net. In this method, fuzzy controller copes with the complexity and the uncertainties of the assembly process, while neural network enhances the assembly scheme so as to learn fuzzy rules from experience and adapt to changes in environment of uncertainty and imprecision. The performance of the proposed assembly scheme is evaluted through a series of experiments using SCARA robot. The results show that the proposed control method can be effectively applied to chamferless precision parts mating.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.3121-3143
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• 2019

Segmentation plays an important role in the field of image processing and computer vision. Intuitionistic fuzzy C-means (IFCM) clustering algorithm emerged as an effective technique for image segmentation in recent years. However, standard fuzzy C-means (FCM) and IFCM algorithms are sensitive to noise and initial cluster centers, and they ignore the spatial relationship of pixels. In view of these shortcomings, an improved algorithm based on IFCM is proposed in this paper. Firstly, we propose a modified non-membership function to generate intuitionistic fuzzy set and a method of determining initial clustering centers based on grayscale features, they highlight the effect of uncertainty in intuitionistic fuzzy set and improve the robustness to noise. Secondly, an improved nonlinear kernel function is proposed to map data into kernel space to measure the distance between data and the cluster centers more accurately. Thirdly, the local spatial-gray information measure is introduced, which considers membership degree, gray features and spatial position information at the same time. Finally, we propose a new measure of intuitionistic fuzzy entropy, it takes into account fuzziness and intuition of intuitionistic fuzzy set. The experimental results show that compared with other IFCM based algorithms, the proposed algorithm has better segmentation and clustering performance.

• Nuclear Engineering and Technology
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• v.54 no.4
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• pp.1288-1294
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• 2022

Hydrogen as an environmentally friendly energy carrier has received special attention to solving uncertainty about the presence of renewable energy and its dependence on time and weather conditions. This material can be prepared from different sources and in various ways. In previous studies, fossil fuels have been used in hydrogen production, but due to several limitations, especially the limitation of the access to this material in the not-too-distant future and the great problem of greenhouse gas emissions during hydrogen production methods. New methods based on renewable and green energy sources as energy drivers of hydrogen production have been considered. In these methods, water or biomass materials are used as the raw material for hydrogen production. In this article, after a brief review of different hydrogen production methods concerning the required raw material, these methods are examined and ranked from different aspects of economic, social, environmental, and energy and exergy analysis sustainability. In the following, the current position of hydrogen production is discussed. Finally, according to the introduced methods, their advantages, and disadvantages, solar electrolysis as a method of hydrogen production on a small scale and hydrogen production by thermochemical method on a large scale are introduced as the preferred methods.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.201-212
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• 2022

Prior to the emergence of Covid-19, Saudi Arabia (SA) had never faced the challenge of dealing with a global pandemic. Significantly, the current crisis has impacted all industries and sectors in the country, including the healthcare system, and has led to an emphasis on human life being more precious and valuable than economic profit. This study focuses on the impact of Covid-19 on the health of nurses, including their quality of life, during 2020. Understanding the position of the nursing profession during the pandemic, including the most effective methods of preventing work-related stress is important. Information was acquired through an online survey method (i.e. self-completion), known as the Expanded Nursing Stress Scale (ENSS), which was distributed to nurses in all regions of SA. It was found that the main aspects impacting nurses' work-related stress include gender, employment type, training, and dealing with infected patients. In addition, they highlight that such stress plays a substantial role in patient safety and nurses' satisfaction at work, as well as the future survival of organizations. The emergence of Covid-19 as a novel infectious disease has increased nurses' uncertainty and work-related stress. The results of this research will provide insights into the views of both nurses and their managers, in order to identify the main indicators of stress.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.37.3-38
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• 2021

Radiation pressure noise of photon and photon shot noise are quantum noise limitation in interferometric gravita-tional wave detectors. Since relationship between the two noises is position and momentum of the Heisenberg uncertainty principle, quantum non-demolition (QND) technique is required to reduce the two noises at the same time. Frequency dependent squeezing using a filter cavity is one of realistic solutions for QND measurement and experimental results show that its cutting-edge performance is sufficient to apply to the current gravitational wave detectors. A 300m filter cavity is under construction at adv-LIGO. KAGRA (gravitational wave detector in Japan) has also started international collaboration to build a filter cavity. Recently we joined the filter cavity project for KAGRA. Current status of squeezing and filter cavity research at KASI and details of the KAGRA filter cavity project will be presented.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.1
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• pp.121-132
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• 2022

The uncertainty regarding inventory may impart dynamic impacts on corporate-level financial decisions. Among others, a decision about capital investment is a crucial decision that requires overall financial stability. Following these theoretical notions, the current study aims to identify possible consequences of inventory volatility relating to corporate capital investment decisions. We employed ten years of data (2010-2019) of non-financial sector firms to achieve the objective. The Driscoll-Kraay model was used to quantify the regression. The statistical results imply that inventory volatility negatively influences capital investment decisions due to information asymmetry about the current financial position. Additionally, more volatility brings discrepancies in managers' investing decisions to fulfill the possible demand options of capital investment that require processing the inventory. However, based upon the statistical findings, it is suggested to corporate managers that they should consider the financial sensitivity of enterprises regarding inventory volatility. Thus, the current study introduces new thoughts regarding inventory volatility and its empirical role in determining capital investment.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.204-206
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• 2022

The global economy and the air transport business have been affected since the spread of the COVID-19 pandemic. As countries tighten restrictions on international movements, the growing emphasis on air cargo puts pressure on airports to maintain and upgrade their cargo policies, facilities, and operations. Hence, ensuring the competitiveness of cargo airports becomes pivotal for airports survival under the volatile global demand. The study aims to evaluate the importance of the competitiveness factors for cargo airports and identify areas for further improvement. The study applies the Best-Worst Method (BWM) to assess the cargo airports' competitiveness factors: 'Transport Capacity,' 'Airport Operations and Facility Capacity,' 'Economic Growth,' 'Financial Performance,' and 'Airport Brand Value.' The selected airports include Heathrow Airport, Aéroport de Paris-Charles de Gaulle, Hong Kong International Airport, and Incheon International Airport. The results identify 'Transport Capacity' as the most significant competitiveness factor, and Hong Kong International Airport the best performing cargo airport. This research forms a reference framework for evaluating cargo airports' competitive position, which may help identify airports' relative strengths and weaknesses. Moreover, this framework can also serve as a tool facilitating the strategic design of airports that may accommodate both air cargo and passenger demand flexibly under the demand uncertainty.

• Steel and Composite Structures
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• v.51 no.5
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• pp.473-485
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• 2024

This paper proposes a composite form of fuzzy adaptive control plan based on a robust observer. The fuzzy 2D control gains are regulated by the parameters in the LMIs. Then, control and learning performance indices with weight matrices are constructed as the cost functions, which allows the regulation of the trade-off between the two performance by setting appropriate weight matrices. The design of 2D control gains is equivalent to the LMIs-constrained multi-objective optimization problem under dual performance indices. By using this proposed smart tracking design via fuzzy nonlinear criterion, the data link can be further extended. To evaluate the performance of the controller, the proposed controller was compared with other control technologies. This ensures the execution of the control program used to track position and trajectory in the presence of great model uncertainty and external disturbances. The performance of monitoring and control is verified by quantitative analysis. The goals of this paper are towards access to adequate, safe and affordable housing and basic services, promotion of inclusive and sustainable urbanization and participation, implementation of sustainable and disaster-resilient buildings, sustainable human settlement planning and manage. Therefore, the goal is believed to achieved in the near future by the ongoing development of AI and control theory.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.1
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• pp.58-63
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• 2014

Most of light duty AGVs(AGCs) using tape of magnetic for the guide path have digital guidance magnetic sensor. Digital guidance magnetic sensor using magnet-tape is on/off type and has positioning error of magnet-tape as 10~50 mm. AGC using this sensor doesn't induce accurate position of magnet-line which is magnet-tape because of magnetic field which motor in AGC creates, outer magnetic field, earth's magnetic field, etc. AGC when driving wobbles due to this error and this error can cause path deviation. In this paper, we propose fuzzy inference system for improvement of bipolar analog magnetic guidance sensor performance. Fuzzy is suitable in term of fault tolerance, uncertainty tolerance, real-time operation, and Nonlinearity as compared with other algorithms. In previous research, we produced bipolar magnetic guidance sensor and we set the threshold in order to calculate digital values of magnet position. Fuzzy inference system is designed using outputs of Analog hall sensors. Magnet position calculated by digital method is improved by outputs of this system. In result, proposed method was verified by improving performance of magnetic guidance sensor.