• Computers and Concrete
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• v.24 no.4
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• pp.313-327
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• 2019

The purpose of the present study is to present a new approach to designing and selecting the details of multidimensional continuous RC beam by applying all strength, serviceability, ductility and other constraints based on ACI318-14 using Teaching Learning Based Optimization (TLBO) algorithm. The optimum reinforcement detailing of longitudinal bars is done in two steps. in the first stage, only the dimensions of the beam in each span are considered as the variables of the optimization algorithm. in the second stage, the optimal design of the longitudinal bars of the beam is made according to the first step inputs. In the optimum shear reinforcement, using gradient-based methods, the most optimal possible mode is selected based on the existing assumptions. The objective function in this study is a cost function that includes the cost of concrete, formwork and reinforcing steel bars. The steel used in the objective function is the sum of longitudinal and shear bars. The use of a catalog list consisting of all existing patterns of longitudinal bars based on the minimum rules of the regulation in the second stage, leads to a sharp reduction in the volume of calculations and the achievement of the best solution. Three example with varying degrees of complexity, have been selected in order to investigate the optimal design of the longitudinal and shear reinforcement of continuous beam.

• Advances in Energy Research
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• v.3 no.2
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• pp.81-95
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• 2015

Microgrid, which can be considered as an integration of various dispersed resources (DRs), is characterized by number of DRs interfaced through the power electronics converters. The microgrid comprising these DRs is often operated in an islanded mode. To minimize the cost, reduce complexity and increase reliability, it is preferred to avoid any communication channel between them. Consequently, the droop control method is traditionally adopted to distribute active and reactive power among the DRs operating in parallel. However, the accuracy of distribution of active and reactive power among the DRs controlled by the conventional droop control approach is highly dependent on the value of line impedance, R/X i.e., resistance to reactance ratio of the line, voltage setting of inverters etc. The limitations of the conventional droop control approach are demonstrated and a modified droop control approach to reduce the effect of impedance mis-match and improve the time response is proposed. The error in reactive power sharing is minimized by inserting virtual impedance in line with the inverters to remove the mis-match in impedance. The improved time response is achieved by modifying the real-power frequency droop using arctan function. Simulations results are presented to validate the effectiveness of the control approach.

• Journal of Magnetics
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• v.16 no.1
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• pp.71-73
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• 2011

Hybrid electric vehicles have attracted much attention of late, emphasizing the necessity of developing traction motors with a high input current and a wide speed range. Among such traction motors, various researches have been conducted on interior permanent-magnet synchronous motors (IPMSMs) with high power density and mechanical solidity. Due to the complexity of its parameters, however, with nonlinear motor characteristics and current vector control, it is actually difficult to accurately estimate the base speed within an actual operating speed range or a voltage limit. Moreover, it is impossible to construct an efficiency map as the efficiency differs according to the control mode. In this study, a simulation method for operation performance considering the nonlinearity of IPMSM was proposed. For this, datasets of various nonlinear parameters were made via the finite-element method and interpolation. Maximum torque-per-ampere and flux-weakening control were accurately simulated using the datasets, and an IPMSM efficiency map was accurately constructed based on the simulation. Lastly, the validity of the simulation was verified through tests.

• Current Optics and Photonics
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• v.2 no.2
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• pp.134-139
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• 2018

A novel approach for frequency-tripling vector signal generation via balanced detection without precoding and optical filter is proposed. The scheme is mainly utilizing an integrated dual-polarization quadrature phase shift keying (DPQPSK) modulator. In the DPQPSK modulator, one QPSK modulator is driven by an RF signal to generate high-order optical sidebands, while the other QPSK modulator is modulated by I/Q data streams to produce baseband vector signal as an optical carrier. After that, a frequency-tripling 16-quadrature-amplitude-modulation (16QAM) vector millimeter-wave (mm-wave) signal can be obtained by balanced detection. The proposed scheme can reduce the complexity of transmitter digital signal processing. The results show that, a 4 Gbaud baseband 16QAM vector signal can be generated at 30 GHz by frequency-tripling. After 10 km single-mode fiber (SMF) transmission, the constellation and eye diagrams of the generated vector signal perform well and a bit-error-rate (BER) below than 1e-3 can be achieved.

• The Journal of the Korea Contents Association
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• v.19 no.2
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• pp.469-476
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• 2019

Robotic motion is designed by the rotation and the translation of multiple joint coordinates in a three-dimensional space. Joint coordinates are generally modeled by homogeneous transform matrix. However, the complexity of three dimensional motions prefers the visualization methods based on simulation environments in which models and generated motions work properly. Many simulation environments have the limitations of usability and functional extension from platform dependency and interpretation of predefined commands. This paper proposes the web-based three dimensional simulation environment toward high user accessibility. Also, it covers the small size web server that is linked with Python script. The non linearities of robot control apply to verify the computing efficiency, the process management, and the extendability of user scripts.

• Structural Engineering and Mechanics
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• v.77 no.4
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• pp.495-508
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• 2021

This study devises a novel approach, namely quadruple 1D convolutional neural network, for detecting connection stiffness reduction in steel truss bridge structure using experimental and numerical modal data. The method is developed based on expertise in two domains: firstly, in Structural Health Monitoring, the mode shapes and its high-order derivatives, including second, third, and fourth derivatives, are accurate indicators in assessing damages. Secondly, in the Machine Learning literature, the deep convolutional neural networks are able to extract relevant features from input data, then perform classification tasks with high accuracy and reduced time complexity. The efficacy and effectiveness of the present method are supported through an extensive case study with the railway Nam O bridge. It delivers highly accurate results in assessing damage localization and damage severity for single as well as multiple damage scenarios. In addition, the robustness of this method is tested with the presence of white noise reflecting unavoidable uncertainties in signal processing and modeling in reality. The proposed approach is able to provide stable results with data corrupted by noise up to 10%.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.3
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• pp.169-177
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• 2021

Recently, the use of electric and electronic control systems is increasing in the automobile industry. This increase in the electric and electronic control system greatly increases the complexity of designing a vehicle, which leads to an increase in the malfunction of the system, and a safety problem due to the malfunction is becoming an issue. Based on IEC 61508 relating to the functional safety of electrical/electronic/programmable electronics, the ISO 26262 standard specific to the automotive sector was first established in 2011, and a revision was published in 2018. Malfunctions due to system failure are covered by ISO 26262, but ISO/PAS 21448 is proposed to deal with unintended malfunctions caused by changes in the surrounding environment. ISO 26262 sets out safety-related requirements for the entire life cycle. Functional safety analysis includes FTA (Fault Tree Analysis), FMEA (Failure Mode and Effect Analysis), and HAZOP (Hazard and Operability). These analysis have limitations in dealing with failures or errors caused by complex interrelationships because it is assumed that a failure or error affecting the risk occurs by a specific component. In order to overcome this limitation, it is necessary to apply the STPA (System Theoretic Process Analysis) technique.

• Clinical and Experimental Pediatrics
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• v.65 no.5
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• pp.239-249
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• 2022

Cells survive and proliferate through complex interactions among diverse molecules across multiomics layers. Conventional experimental approaches for identifying these interactions have built a firm foundation for molecular biology, but their scalability is gradually becoming inadequate compared to the rapid accumulation of multiomics data measured by high-throughput technologies. Therefore, the need for data-driven computational modeling of interactions within cells has been highlighted in recent years. The complexity of multiomics interactions is primarily due to their nonlinearity. That is, their accurate modeling requires intricate conditional dependencies, synergies, or antagonisms between considered genes or proteins, which retard experimental validations. Artificial intelligence (AI) technologies, including deep learning models, are optimal choices for handling complex nonlinear relationships between features that are scalable and produce large amounts of data. Thus, they have great potential for modeling multiomics interactions. Although there exist many AI-driven models for computational biology applications, relatively few explicitly incorporate the prior knowledge within model architectures or training procedures. Such guidance of models by domain knowledge will greatly reduce the amount of data needed to train models and constrain their vast expressive powers to focus on the biologically relevant space. Therefore, it can enhance a model's interpretability, reduce spurious interactions, and prove its validity and utility. Thus, to facilitate further development of knowledge-guided AI technologies for the modeling of multiomics interactions, here we review representative bioinformatics applications of deep learning models for multiomics interactions developed to date by categorizing them by guidance mode.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1162-1181
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• 2023

Tourism flow is not only the manifestation of tourists' special displacement change, but also an important driving mode of regional connection. It has been considered as one of significantly topics in many applications. The existing research on tourism flow prediction based on tourist number or statistical model is not in-depth enough or ignores the nonlinearity and complexity of tourism flow. In this paper, taking Nanjing as an example, we propose a prediction method of urban tourism flow based on deep learning methods using travel diaries of domestic tourists. Our proposed method can extract the spatio-temporal dependence relationship of tourism flow and further forecast the tourism flow to attractions for every day of the year or for every time period of the day. Experimental results show that our proposed method is slightly better than other benchmark models in terms of prediction accuracy, especially in predicting seasonal trends. The proposed method has practical significance in preventing tourists unnecessary crowding and saving a lot of queuing time.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.4
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• pp.172-179
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• 2014

HEVC (High Efficiency Video Coding) fine-tuned many existing coding tools and adopted also many new coding techniques. As a result, HEVC has accomplished about 2 times of compression efficiency enhancement compared to the existing video coding standard of H.264/AVC. One of the newly adopted tools in HEVC is the transform skip scheme which performs quantization without transform. This technique improves coding efficiency especially with computer-generated images. However, the unavailability of global or local properties of general video signals demands encoder to decide whether performing transform or not for each TU (Transform Unit). The necessity of computing rate-distortion costs for this decision is one reason to increase encoder complexity. In this paper, a fast transform skip mode decision method is proposed, which is based on the fast decision of rate-distortion cost calculation for transform skip mode, by considering frequency characteristics of residual signal. The proposed method can reduce $4{\times}4$ TU encoding time by about 27.1% with only about 0.03% consequential decrement in BDBR.