• International conference on construction engineering and project management
• /
• 2013.01a
• /
• pp.502-506
• /
• 2013

The cash flow forecasting is normally done by contractors in construction industry at early stages of the project for contractual decisions. The decision making in such situations involve uncertainty about future cash flows and assessment of working capital requirements gains more importance in projects constrained by cash. The traditional approach to assess the working capital requirements is deterministic in and neglects the uncertainty. This paper presents an alternate approach to assessment of working capital requirements for contractor based on fuzzy set theory by considering the uncertainty and ambiguity involved at payment periods. Statistical methods are used to deal with the uncertainty for working capital curves. Membership functions of the fuzzy sets are developed based on these statistical measures. Advantage of fuzzy peak working capital requirements is demonstrated using peak working capital requirements curves. Fuzzy peak working capital requirements curves are compared with deterministic curves and the results are analyzed. Fuzzy weighted average methodology is proposed for the assessment of peak working capital requirements.

• ETRI Journal
• /
• v.30 no.1
• /
• pp.113-128
• /
• 2008

Turbo codes are extensively used in current communications standards and have a promising outlook for future generations. The advantages of software defined radio, especially dynamic reconfiguration, make it very attractive in this multi-standard scenario. However, the complex and power consuming implementation of the maximum a posteriori (MAP) algorithm, employed by turbo decoders, sets hurdles to this goal. This work introduces an ASIP architecture for the MAP algorithm, based on a dual-clustered VLIW processor. It displays the good performance of application specific designs along with the versatility of processors, which makes it compliant with leading edge standards. The machine deals with multi-operand instructions in an innovative way, the fetching and assertion of data is serialized and the addressing is automatized and transparent for the programmer. The performance-area trade-off of the proposed architecture achieves a throughput of 8 cycles per symbol with very low power dissipation.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 2001.07a
• /
• pp.54-57
• /
• 2001

We studied the characteristics of polishing pad, which can apply STI-CMP process for global planarization of multilevel interconnection structure. Also, we investigated the effects of different sets of polishing pad, such as soft and hard pad. As an experimental result, hard pad showed center-fast type, and soft pad showed edge-fast type. Totally, the defect level has shown little difference, however, the counts of scratch was defected less than 2 on JRlll pad. Through the above results, we can select optimum polishing pad, so we can expect the improvements of throughput and devise yield.

• The Journal of Korean Academy of Orthopedic Manual Physical Therapy
• /
• v.26 no.1
• /
• pp.37-46
• /
• 2020

Background: The present study aimed to investigate the immediate effects of Soft Tissue Mobilization (STM) before Mobilization with Movement (MWM) on ankle ROM, pennation angle, balance in stroke patients. Methods: A total of 22 subjects were randomly assigned to one of two groups: the experimental group and the control group. The experimental group received intervention STM before MWM. STM was applied for one minute, MWM was applied one set of six times, in a total 3 sets. The passive ankle joint range of motion (ROM) was measured using a goniometer, the pennation angle was measured using RUSI, and the balance was measured using Timed Up & Go Test. Results: The ROM of the ankle dorsi-flexion, muscle tissue (pennation angle) and balance were significantly increased. Conclusion: In this study, it was confirmed that the ankle dorsi-flexion ROM, pennation angle of the medial gastrocnemius muscle, and balance were significantly improved in the group where STM was performed before the MWM intervention. Therefore, the physiotherapists should consider these results in their intervention. If MWM is applied to stroke patients, applying STM first is a better intervention.

• Journal of KIISE:Software and Applications
• /
• v.35 no.8
• /
• pp.493-500
• /
• 2008

This paper proposes an electronic colon cleansing method using a patient CT profile for a virtual colonoscopy. The proposed method extracts the colon using cubic seeded region growing, and removes tagged materials adjacent to the colon. Residuals produced by a partial volume effect at the boundary of air-tagged material are deleted, and the removed soft tissue pixels due to a partial volume effect at the boundary of tagged material-soft tissue are recovered using a patient CT profile. The proposed method was applied to 16 virtual colonoscopy patient data sets, and produced promising results by a subjective evaluation of a radiologist and by a quantitative evaluation of a computer-aided diagnosis system.

• Smart Structures and Systems
• /
• v.13 no.1
• /
• pp.81-98
• /
• 2014

This paper presents the elastic buckling of smart lightweight column structures integrated with a pair of surface piezoelectric layers using artificial intelligence. The finite element modeling of Smart lightweight columns is found using $ANSYS^{(R)}$ software. Then, the first buckling load of the structure is calculated using eigenvalue buckling analysis. To determine the accuracy of the present finite element analysis, a compression study is carried out with literature. Later, parametric studies for length variations, width, and thickness of the elastic core and of the piezoelectric outer layers are performed and the associated buckling load data sets for artificial intelligence are gathered. Finally, the application of soft computing-based methods including artificial neural network (ANN), fuzzy inference system (FIS), and adaptive neuro fuzzy inference system (ANFIS) were carried out. A comparative study is then made between the mentioned soft computing methods and the performance of the models is evaluated using statistic measurements. The comparison of the results reveal that, the ANFIS model with Gaussian membership function provides high accuracy on the prediction of the buckling load in smart lightweight columns, providing better predictions compared to other methods. However, the results obtained from the ANN model using the feed-forward algorithm are also accurate and reliable.

• Proceedings of the KSME Conference
• /
• 2007.05a
• /
• pp.372-377
• /
• 2007

For more than 2,500 years, surgical teaching has been based on the so called "see one, do one, teach one" paradigm, in which the surgical trainee learns by operating on patients under close supervision of peers and superiors. However, higher demands on the quality of patient care and rising malpractice costs have made it increasingly risky to train on patients. Minimally invasive surgery, in particular, has made it more difficult for an instructor to demonstrate the required manual skills. It has been recognized that, similar to flight simulators for pilots, virtual reality (VR) based surgical simulators promise a safer and more comprehensive way to train manual skills of medical personnel in general and surgeons in particular. One of the major challenges in the development of VR-based surgical trainers is the real-time and realistic simulation of interactions between surgical instruments and biological tissues. It involves multi-disciplinary research areas including soft tissue mechanical behavior, tool-tissue contact mechanics, computer haptics, computer graphics and robotics integrated into VR-based training systems. The research described in this paper addresses the problem of characterizing soft tissue properties for medical virtual environments. A system to measure in vivo mechanical properties of soft tissues was designed, and eleven sets of animal experiments were performed to measure in vivo and in vitro biomechanical properties of porcine intra-abdominal organs. Viscoelastic tissue parameters were then extracted by matching finite element model predictions with the empirical data. Finally, the tissue parameters were combined with geometric organ models segmented from the Visible Human Dataset and integrated into a minimally invasive surgical simulation system consisting of haptic interface devices and a graphic display.

• The Plant Pathology Journal
• /
• v.38 no.6
• /
• pp.656-664
• /
• 2022

Pectobacterium odoriferum is the primary causative agent in Kimchi cabbage soft-rot diseases. The pathogenic bacteria Pectobacterium genera are responsible for significant yield losses in crops. However, P. odoriferum shares a vast range of hosts with P. carotovorum, P. versatile, and P. brasiliense, and has similar biochemical, phenotypic, and genetic characteristics to these species. Therefore, it is essential to develop a P. odoriferumspecific diagnostic method for soft-rot disease because of the complicated diagnostic process and management as described above. Therefore, in this study, to select P. odoriferum-specific genes, species-specific genes were selected using the data of the P. odoriferum JK2.1 whole genome and similar bacterial species registered with NCBI. Thereafter, the specificity of the selected gene was tested through blast analysis. We identified novel species-specific genes to detect and quantify targeted P. odoriferum and designed specific primer sets targeting HAD family hydrolases. It was confirmed that the selected primer set formed a specific amplicon of 360 bp only in the DNA of P. odoriferum using 29 Pectobacterium species and related species. Furthermore, the population density of P. odoriferum can be estimated without genomic DNA extraction through SYBR Green-based real-time quantitative PCR using a primer set in plants. As a result, the newly developed diagnostic method enables rapid and accurate diagnosis and continuous monitoring of soft-rot disease in Kimchi cabbage without additional procedures from the plant tissue.

• Journal of Powder Materials
• /
• v.29 no.6
• /
• pp.459-467
• /
• 2022

Soft magnetic powder materials are used throughout industries such as motors and power converters. When manufacturing Fe-based soft magnetic composites, the size and shape of the soft magnetic powder and the microstructure in the powder are closely related to the magnetic properties. In this study, Fe-Si-Al-P alloy powders were manufactured using various manufacturing process parameter sets, and the process parameters of the vacuum induction melt gas atomization process were set as melt temperature, atomization gas pressure, and gas flow rate. Process variable data that records are converted into 6 types of data for each powder recovery section. Process variable data that recorded minute changes were converted into 6 types of data and used as input variables. As output variables, a total of 6 types were designated by measuring the particle size, flowability, apparent density, and sphericity of the manufactured powders according to the process variable conditions. The sensitivity of the input and output variables was analyzed through the Pearson correlation coefficient, and a total of 6 powder characteristics were analyzed by artificial neural network model. The prediction results were compared with the results through linear regression analysis and response surface methodology, respectively.

• ETRI Journal
• /
• v.39 no.2
• /
• pp.173-180
• /
• 2017

Multimodal medical image fusion is the process of retrieving valuable information from medical images. The primary goal of medical image fusion is to combine several images obtained from various sources into a distinct image suitable for improved diagnosis. Complexity in medical images is higher, and many soft computing methods are applied by researchers to process them. Intuitionistic fuzzy sets are more appropriate for medical images because the images have many uncertainties. In this paper, a new method, based on Sugeno's intuitionistic fuzzy set (SIFS), is proposed. First, medical images are converted into Sugeno's intuitionistic fuzzy image (SIFI). An exponential intuitionistic fuzzy entropy calculates the optimum values of membership, non-membership, and hesitation degree functions. Then, the two SIFIs are disintegrated into image blocks for calculating the count of blackness and whiteness of the blocks. Finally, the fused image is rebuilt from the recombination of SIFI image blocks. The efficiency of the use of SIFS in multimodal medical image fusion is demonstrated on several pairs of images and the results are compared with existing studies in recent literature.