• Asian-Australasian Journal of Animal Sciences
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• v.32 no.5
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• pp.607-613
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• 2019

Objective: Intramuscular fat (IMF) content plays an important role in meat quality. Identification of single nucleotide polymorphisms (SNPs) and genes related to pig IMF, especially using pig populations with high IMF content variation, can help to establish novel molecular breeding tools for optimizing IMF in pork and unveil the mechanisms that underlie fat metabolism. Methods: We collected muscle samples of 453 Chinese Lulai black pigs, measured IMF content by Soxhlet petroleum-ether extraction method, and genotyped genome-wide SNPs using GeneSeek Genomic Profiler Porcine HD BeadChip. Then a genome-wide association study was performed using a linear mixed model implemented in the GEMMA software. Results: A total of 43 SNPs were identified to be significantly associated with IMF content by the cutoff p<0.001. Among these significant SNPs, the greatest number of SNPs (n = 19) were detected on Chr.9, and two linkage disequilibrium blocks were formed among them. Additionally, 17 significant SNPs are mapped to previously reported quantitative trait loci (QTLs) of IMF and confirmed previous QTLs studies. Forty-two annotated genes centering these significant SNPs were obtained from Ensembl database. Overrepresentation test of pathways and gene ontology (GO) terms revealed some enriched reactome pathways and GO terms, which mainly involved regulation of basic material transport, energy metabolic process and signaling pathway. Conclusion: These findings improve our understanding of the genetic architecture of IMF content in pork and facilitate the follow-up study of fine-mapping genes that influence fat deposition in muscle.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.125-140
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• 2018

As one of the processes in the manufacturing industry, quality inspection inspects the intermediate products or final products to separate the good-quality goods that meet the quality management standard and the defective goods that do not. The manual inspection of quality in a mass production system may result in low consistency and efficiency. Therefore, the quality inspection of mass-produced products involves automatic checking and classifying by the machines in many processes. Although there are many preceding studies on improving or optimizing the process using the data generated in the production process, there have been many constraints with regard to actual implementation due to the technical limitations of processing a large volume of data in real time. The recent research studies on big data have improved the data processing technology and enabled collecting, processing, and analyzing process data in real time. This paper aims to propose the process and details of applying big data for quality inspection and examine the applicability of the proposed method to the dairy industry. We review the previous studies and propose a big data analysis procedure that is applicable to the manufacturing sector. To assess the feasibility of the proposed method, we applied two methods to one of the quality inspection processes in the dairy industry: convolutional neural network and random forest. We collected, processed, and analyzed the images of caps and straws in real time, and then determined whether the products were defective or not. The result confirmed that there was a drastic increase in classification accuracy compared to the quality inspection performed in the past.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.12
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• pp.549-560
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• 2015

There is a wide variety of data quality attributes such as the ones proposed by the ISO/IEC organization and also by many other domestic and international institutions. However, it takes considerable time and costs to apply those criteria and guidelines to real environment. Therefore, it needs to define data quality evaluation attributes which are easily applicable and are not influenced by organizational environment limitations. The purpose of this paper is to derive data quality attributes and order of their priorities based on customer requirements for managing the process systematically and evaluating the data quantitatively. This study identifies the customer cognitive constructs of data quality attributes using the RGT(Repertory Grid Technique) based on a Korean quality standard model (DQC-M). Also the correlation analysis on the identified constructs is conducted, and the evaluation attributes is prioritized and ranked using the AHP. As the results of this paper, the consistent system, the accurate data, the efficient environment, the flexible management, and the continuous improvement are derived at the first level of the data quality evaluation attributes. Also, Control Compliance(13%), Regulatory Compliance(10%), Requirement Completeness(9.6%), Accuracy(8.4%), and Traceability(6.8%) are ranked on the top 5 of the 19 attributes in the second level.

• Journal of the Ergonomics Society of Korea
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• v.34 no.2
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• pp.85-101
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• 2015

Objective: The purpose of this study was to develop and evaluate smart secondary controls using iPad for the drivers with physical disabilities in the driving simulator. Background: The physically disabled drivers face problems in the operation of secondary control devices that accept a control input from a driver for the purpose of operating the subsystems of a motor vehicle. Many of conventional secondary controls consist of small knobs or switches that physically disabled drivers have difficulties in grasping, pulling or twisting. Therefore, their use while driving might increase distraction and workload because of longer operation time. Method: We examined the operation time of conventional and smart secondary controls, such as hazard warning, turn signal, window, windshield wiper, headlights, automatic transmission and horn. The hardware of smart secondary control system was composed of iPad, wireless router, digital input/output module and relay switch. We used the STISim Drive3 software for driving test, customized Labview and Xcode programs for interface control of smart secondary system. Nine subjects were involved in the study for measuring operation time of secondary controls. Results: When the driver was in the stationary condition, the average operation time of smart secondary devices decreased 32.5% in the normal subjects (p <0.01), 47.4% in the subjects with left hemiplegic disabilities (p <0.01) and 38.8% in the subjects with right hemiplegic disabilities (p <0.01) compared with conventional secondary devices. When the driver was driving for the test in the simulator, the average operation time of smart secondary devices decreased 36.1% in the normal subjects (p <0.01), 41.7% in the subjects with left hemiplegic disabilities (p <0.01) and 34.1% in the subjects with right hemiplegic disabilities (p <0.01) compared with conventional secondary devices. Conclusion: The smart secondary devices using iPad for people with hemiplegic disabilities showed significant reduction of operation time compared with conventional secondary controls. Application: This study can be used to design secondary controls for adaptive vehicles and to improve the quality of life of the people with disabilities.

• The KIPS Transactions:PartA
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• v.9A no.2
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• pp.207-216
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• 2002

This paper present a design and implementation of a distributed audio, Video stream service framework based on CORBA for efficient processing and control of audio/video stream. We design software components which support processing, control and transmission of audio/video streams as distributed objects. For optimization of stream transmission performance, we separate the transmission path of control data and media data. Distributed objects are defined by IDL and implemented using JAVA. And device dependent facilities like media capturing, playing and communication channels are implemented using JMF (Java Media Framework) components. We show a connection establishment and control procedure of streams communication. And for evaluation, we implement a test system and experiment a system performance. Our experiments show that test system has somewhat longer connection latency time compared to TCP connection establishment, but has optimized media transmission time compared to CORBA IIOP. Also test system show acceptable service quality of media transmission.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.2
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• pp.103-110
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• 2022

Degradation of handling qualities(HQs) due to bad weather or mechanical failure can pose a fatal risk to pilots unfamiliar with such situation. In particular, icing is an important issue to consider as it is a frequent cause of accidents. Most of the previous research works focuses on aerodynamic performance changes due to icing and the corresponding icing modeling or methods to prevent icing, whereas the present work attempts to actively compensate for HQ degradation due to icing on a full-scale helicopter through flight control law design. To this end, the present work first demonstrates HQ degradation due to icing using CONDUIT software, and subsequently presents a robust control design via the RS-LQR(Robust Servomechanism Linear Quadratic Regulation) procedure to compensate for the HQ degradation. Simulation results show that the proposed robust control maintains Level 1 HQ in the presence of icing.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.12 no.5
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• pp.29-37
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• 2017

In this study, real-time monitoring is carried out in the factory site for product quality control such as improvement of productivity through facility automation, improvement of product quality, improvement of factory environment, check of facility maintenance status and check of product defect, And to establish a smart factory for the purpose of protecting the personal environment of the worker. The company is an auto parts company located in the province. The main research content is development of automation and monitoring system of oil filter clipping necessary for smart factory. Smart factory oil filter clipping automation is divided into electric air parts, solenoid valve and other parts processing process. Smart factory quality inspection monitoring system is implemented by server PC and S / W, client software, Operator PC, operating program, and input terminal application. This research data is expected to be very useful data for the auto parts venture companies that are promoting smart factories.

• Ocean and Polar Research
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• v.31 no.4
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• pp.339-347
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• 2009

It is possible to obtain accurate temperature and salinity profiles of the oceans using a SBE 911plus CTD and accompanying data conversion packages. To obtain highly accurate results, CTD data needs to be carefully processed in addition to proper and regular maintenance of the CTD itself. Since the manufacturer of the CTD provides tools that are necessary for post processing, it is possible to conduct proper processing without too much effort. Some users, however, are not familiar with all of the processes and inadvertently ignore some of these processes at the expense of data quality. To draw attention to these and other similar issues, we show how it is possible to improve data quality by utilizing a few extra processes to the standard or default data process procedures with CTD data obtained from the equatorial Eastern Pacific between 2001 and 2005, and 2007. One easy step that is often ignored in the standard data process procedure is "wild edit", which removes abnormal values from the raw data. If those abnormal values are not removed, the abnormality could spread vertically during subsequent processes and produce abnormal salinity in a range much wider than that of the raw data. To remove spikes in salinity profiles the "align CTD" procedure must be carried out not with the default values included in the data processing software but with a proper time constant. Only when "cell thermal mass" correction is conducted with optimal parameters, we can reduce the difference between upcast and downcast, and obtain results that can satisfy the nominal accuracy of the CTD.

• Membrane and Water Treatment
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• v.9 no.4
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• pp.273-278
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• 2018

Due to the lack of water in arid and semi-arid areas, reuse of wastewater can be a suitable way to compensate for water scarcity. Therefore, in this research, evaluation of the quality of wastewater of Kashan Treatment Plant to use for irrigation was studied. This descriptive cross-sectional study was conducted in 2016. pH, TSS, TDS, turbidity, COD, BOD5, Total Kjeldahl Nitrogen, Total Phosphorus, Total Coliform, fecal coliform, nematode eggs of inlet and outlet of wastewater treatment plant in Kashan were studied. Mean and standard deviation and wastewater quality parameters before and after treatment were tested with SPSS 22 (2014) software. The mean wastewater output of COD, BOD5, TSS, TDS and turbidity were respectively 86.6, 41.2, 11.11, 1095 mgL-1 and 17.5 NTU and the pH was equal to 7.22. Also, the average of Total Kjeldahl Nitrogen and phosphorus were 22.4 and 2.2 mgL-1 respectively. The mean of Total Coliform and fecal coliform were 225, 161 MPN / 100 ml respectively. In addition, no nematode eggs were found in final effluent. The results indicated that the treatment plants had a significant role in the control of microbial and organic pollution load of wastewater. Also, it is concluded that all parameters were in accordance with the standards of Iran's Department of Environment, so, it can be used for unrestricted irrigation.

• Journal of Internet Computing and Services
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• v.25 no.2
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• pp.11-19
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• 2024

This paper was studied abouta technology for detecting damage to temporary works equipment used in construction sites with explainable artificial intelligence (XAI). Temporary works equipment is mostly composed of steel or aluminum, and it is reused several times due to the characters of the materials in temporary works equipment. However, it sometimes causes accidents at construction sites by using low or decreased quality of temporary works equipment because the regulation and restriction of reuse in them is not strict. Currently, safety rules such as related government laws, standards, and regulations for quality control of temporary works equipment have not been established. Additionally, the inspection results were often different according to the inspector's level of training. To overcome these limitations, a method based with AI and image processing technology was developed. In addition, it was devised by applying explainableartificial intelligence (XAI) technology so that the inspector makes more exact decision with resultsin damage detect with image analysis by the XAI which is a developed AI model for analysis of temporary works equipment. In the experiments, temporary works equipment was photographed with a 4k-quality camera, and the learned artificial intelligence model was trained with 610 labelingdata, and the accuracy was tested by analyzing the image recording data of temporary works equipment. As a result, the accuracy of damage detect by the XAI was 95.0% for the training dataset, 92.0% for the validation dataset, and 90.0% for the test dataset. This was shown aboutthe reliability of the performance of the developed artificial intelligence. It was verified for usability of explainable artificial intelligence to detect damage in temporary works equipment by the experiments. However, to improve the level of commercial software, the XAI need to be trained more by real data set and the ability to detect damage has to be kept or increased when the real data set is applied.