• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.2
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• pp.87-96
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• 2019

Low power issue is one of the most critical problems in the Internet of Things (IoT), which are powered by battery. To solve this problem, various approaches have been presented so far. In this paper, we propose a method to reduce the power consumption by reducing the numbers of accesses into the flash memory consuming a large amount of power for on-chip software execution. Our approach is based on using cooperative logging structure to distribute the sampling overhead in single sensor node to adjacent nodes in case of rare-event applications. The proposed algorithm to identify event occurrence is newly introduced with negative feedback method by observing difference between past data and recent data coming from the sensor. When an event with need of flash access is determined, the proposed approach only allows access to write the sampled data in flash memory. The proposed event detection algorithm (EDA) result in 30% reduction of power consumption compared to the conventional flash write scheme for all cases of event. The sampled data from the sensor is first traced into the random access memory (RAM), and write access to the flash memory is delayed until the page buffer of the on-chip flash memory controller in the micro controller unit (MCU) is full of the numbers of the traced data, thereby reducing the frequency of accessing flash memory. This technique additionally reduces power consumption by 40% compared to flash-write all data. By sharing the sampling information via LoRa channel, the overhead in sampling data is distributed, to reduce the sampling load on each node, so that the 66% reduction of total power consumption is achieved in several IoT edge nodes by removing the sampling operation of duplicated data.

• The Journal of Advanced Prosthodontics
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• v.13 no.1
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• pp.12-23
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• 2021

Purpose. The aim of the study was to compare the lingualized implant placement creating a buccal cantilever with prosthetic-driven implant placement exhibiting excessive crown-to-implant ratio. Materials and Methods. Based on patient's CT scan data, two finite element models were created. Both models were composed of the severely resorbed posterior mandible with first premolar and second molar and missing second premolar and first molar, a two-unit prosthesis supported by two implants. The differences were in implants position and crown-to-implant ratio; lingualized implants creating lingually overcontoured prosthesis (Model CP2) and prosthetic-driven implants creating an excessive crown-to-implant ratio (Model PD2). A screw preload of 466.4 N and a buccal occlusal load of 262 N were applied. The contacts between the implant components were set to a frictional contact with a friction coefficient of 0.3. The maximum von Mises stress and strain and maximum equivalent plastic strain were analyzed and compared, as well as volumes of the materials under specified stress and strain ranges. Results. The results revealed that the highest maximum von Mises stress in each model was 1091 MPa for CP2 and 1085 MPa for PD2. In the cortical bone, CP2 showed a lower peak stress and a similar peak strain. Besides, volume calculation confirmed that CP2 presented lower volumes undergoing stress and strain. The stresses in implant components were slightly lower in value in PD2. However, CP2 exhibited a noticeably higher plastic strain. CONCLUSION. Prosthetic-driven implant placement might biomechanically be more advantageous than bone quantity-based implant placement that creates a buccal cantilever.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.6
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• pp.1-6
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• 2020

Road movement pollutant air environment big data is a link between real-time traffic data such as vehicle type, speed, and load using AVC, VDS, WIM, and DTG, which are always traffic volume survey equipment, and road shape (uphill, downhill, turning section) data using GIS. It consists of traffic flow data. Also, unlike general data, a lot of data per unit time is generated and has various formats. In particular, since about 7.4 million cases/hour or more of large-scale real-time data collected as detailed traffic flow information are collected, stored and processed, a system that can efficiently process data is required. Therefore, in this study, an open source-based data parallel processing performance optimization study is conducted for the visualization of big data in the air environment of road transport pollution.

• CELLMED
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• v.11 no.1
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• pp.2.1-2.8
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• 2021

The clinical condition Amnesia causes difficulty in learning new information and the inability to recall past events. It is primarily concerned with recent memory loss. Majoon-e-Nisyan (MJN) is a polyherbal Unani formulation, present in a semi-solid form. It is widely used potent drug of the Unani System of Medicine (USM) for treating Nisyan (amnesia). In the present study polyherbal Unani formulation, MJN has been studied for its quality control and acute toxicity. Standardization (quality control) of drugs deals with drug identity, drug quality and purity determination. Standardization of MJN had been done as per the Unani pharmacopoeial parameters approved by World Health Organization (WHO) - Pharmacognostical parameters, Physico-chemical parameters, high-performance thin-layer chromatography (HPTLC), microbial load, aflatoxin, and heavy metals. Solvents and chemicals used in the study were of analytical grade and used instrument were calibrated. By conducting an acute oral toxicity study in rats, the safety of MJN was assessed. The limit test method of OECD guideline 425 was followed in the study. Results of standardization and standard operating procedures (SOPs) for preparation of MJN may serve as the standard reference in the future. The data generated in the study for the quality control of MJN proved the quality of formulation and shows that MJN is not toxic in rats following acute dosing up to 2000 mg/kg bw. The data obtained in the paper for MJN may be used as a standard guideline for preparation of the formulation which can save time, cost, and resources for future research endeavours.

• Journal of Digital Convergence
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• v.20 no.4
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• pp.459-468
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• 2022

In medical area, very high resolution images, which is loss sensitive data, are used. Therefore, the use of optical internet with high bandwidth and the transmission of high realiability is required. However, according to the nature of the Internet, various data use the same bandwidth and a new scheme is needed to differentiate effectively these data. In order to achieve the differentiation, optical delay line buffers are used. However, these buffers is constructed based on some optimal values such as the average offered load, measured data burst length, and basic delay unit. Once the buffers are installed, they are impossible to reinstall new buffers. So, the scheme changing burst length dynamically was considered. However, this method is highly unstable. Therefore, in this article, in order to guarantee the stable operation of the scheme, the analysis of operation conditions is performed. With the analysis together with the scheme, high resolution medical data with the higher class can transmit stably without loss.

• Journal of the Korea Convergence Society
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• v.13 no.4
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• pp.1-6
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• 2022

Real-time systems using web cralwing data must provide users with data from the same database as remote data. To do this, the web crawler repeatedly sends HTTP(HtypeText Transfer Protocol) requests to the remote server to see if the remote data has changed. This process causes network load on the crawling server and remote server, causing problems such as excessive traffic generation. To solve this problem, in this paper, based on user events, we propose a real-time web crawling technique that can reduce the overload of the network while securing the reliability of maintaining the sameness between the data of the crawling server and data from multiple remote locations. The proposed method performs a crawling process based on an event that requests unit data and list data. The results show that the proposed method can reduce the overhead of network traffic in existing web crawlers and secure data reliability. In the future, research on the convergence of event-based crawling and time-based crawling is required.

• Steel and Composite Structures
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• v.44 no.2
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• pp.241-254
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• 2022

The cables in a cable-stayed bridge are critical load-carrying parts. The potential damage to cables should be identified early to prevent disasters. In this study, an efficient deep learning model is proposed for the damage identification of cables using both a multi-layer perceptron (MLP) and a graph neural network (GNN). Datasets are first generated using the practical advanced analysis program (PAAP), which is a robust program for modeling and analyzing bridge structures with low computational costs. The model based on the MLP and GNN can capture complex nonlinear correlations between the vibration characteristics in the input data and the cable system damage in the output data. Multiple hidden layers with an activation function are used in the MLP to expand the original input vector of the limited measurement data to obtain a complete output data vector that preserves sufficient information for constructing the graph in the GNN. Using the gated recurrent unit and set2set model, the GNN maps the formed graph feature to the output cable damage through several updating times and provides the damage results to both the classification and regression outputs. The model is fine-tuned with the original input data using Adam optimization for the final objective function. A case study of an actual cable-stayed bridge was considered to evaluate the model performance. The results demonstrate that the proposed model provides high accuracy (over 90%) in classification and satisfactory correlation coefficients (over 0.98) in regression and is a robust approach to obtain effective identification results with a limited quantity of input data.

• Geomechanics and Engineering
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• v.34 no.6
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• pp.697-726
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• 2023

Brittleness as an important property of rock plays a crucial role both in the failure process of intact rock and rock mass response to excavation in engineering geological and geotechnical projects. Generally, rock brittleness indices are calculated from the mechanical properties of rocks such as uniaxial compressive strength, tensile strength and modulus of elasticity. These properties are generally determined from complicated, expensive and time-consuming tests in laboratory. For this reason, in the present research, an attempt has been made to predict the rock brittleness indices from simple, inexpensive, and quick laboratory test results namely dry unit weight, porosity, slake-durability index, P-wave velocity, Schmidt rebound hardness, and point load strength index using multiple linear regression, exponential regression, support vector machine (SVM) with various kernels, generating fuzzy inference system, and regression tree ensemble (RTE) with boosting framework. So, this could be considered as an innovation for the present research. For this purpose, the number of 39 rock samples including five igneous, twenty-six sedimentary, and eight metamorphic were collected from different regions of Iran. Mineralogical, physical and mechanical properties as well as five well known rock brittleness indices (i.e., B₁, B₂, B₃, B₄, and B₅) were measured for the selected rock samples before application of the above-mentioned machine learning techniques. The performance of the developed models was evaluated based on several statistical metrics such as mean square error, relative absolute error, root relative absolute error, determination coefficients, variance account for, mean absolute percentage error and standard deviation of the error. The comparison of the obtained results revealed that among the studied methods, SVM is the most suitable one for predicting B₁, B₂ and B₅, while RTE predicts B₃ and B₄ better than other methods.

• Plant Journal
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• v.18 no.4
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• pp.66-72
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• 2023

In order to respond to the demand for flexible operation of thermal power generation, development of natural sliding pressure operation that minimizes throttle loss by opening the turbine control valve 100% and maximize power generation efficiency in conjunction with ESS in order to quickly respond to fluctuations in the system frequency is required. The logic development of natural sliding pressure operation with ESS was developed to modify the existing logic at the power plant's top-level control logic such as the unit master, the boiler master and the turbine master. Cooperative control algorithms that complement the advantages and disadvantages of ESS operation (quick response, limited capacity) and power plant operation (slow response, continuous operation) not only improve efficiency when applied to actual power plants, but also respond quickly and flexibly to load demands to ensure system stability.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.2
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• pp.267-275
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• 2023

As aged infrastructures have been increased, the importance of accurate maintenance costs and proper budget allocation for infrastructure become prominent under limited resources. This study proposed a mapping algorithm between representative defects, repair methods, and the estimated maintenance costs for concrete bridges. In this regard, using BMS (Bridge Management System) data analysis, bridge repair methods were classified and matched with defects according to their locations, types, and sizes. In addition, the maintenance costs were estimated based on the amount of work-load and quantity per unit using CSPR (Cost Standard Production Rate). As a result, the level of accuracy was an average of 85.1 % compared with the actual bill of quantity for Seoul bridge maintenance. The accuracy of maintenance costs is expected to be enhanced by considering the various site conditions such as pier height, extra charge conditions, additional equipment, etc.