• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권1호
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• pp.46-63
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• 2024

One of the biggest dangers in the globe is water contamination. Water is a necessity for human survival. In most cities, the digging of borewells is restricted. In some cities, the borewell is allowed for only drinking water. Hence, the scarcity of drinking water is a vital issue for industries and villas. Most of the water sources in and around the cities are also polluted, and it will cause significant health issues. Real-time quality observation is necessary to guarantee a secure supply of drinking water. We offer a model of a low-cost system of monitoring real-time water quality using IoT to address this issue. The potential for supporting the real world has expanded with the introduction of IoT and other sensors. Multiple sensors make up the suggested system, which is utilized to identify the physical and chemical features of the water. Various sensors can measure the parameters such as temperature, pH, and turbidity. The core controller can process the values measured by sensors. An Arduino model is implemented in the core controller. The sensor data is forwarded to the cloud database using a WI-FI setup. The observed data will be transferred and stored in a cloud-based database for further processing. It wasn't easy to analyze the water quality every time. Hence, an Optimized Neural Network-based automation system identifies water quality from remote locations. The performance of the feed-forward neural network classifier is further enhanced with a hybrid GA- PSO algorithm. The optimized neural network outperforms water quality prediction applications and yields 91% accuracy. The accuracy of the developed model is increased by 20% because of optimizing network parameters compared to the traditional feed-forward neural network. Significant improvement in precision and recall is also evidenced in the proposed work.

• 한국물환경학회지
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• 제39권1호
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• pp.46-60
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• 2023

Climate change causes fluctuations in water quality in the aquatic environment, which can cause changes in water circulation patterns and severe adverse effects on aquatic ecosystems in the future. Therefore, research is needed to predict and respond to water quality changes caused by climate change in advance. In this study, we tried to predict the dissolved oxygen (DO), chlorophyll-a, and turbidity of the Paldang reservoir for about two weeks using long short-term memory (LSTM) and gated recurrent units (GRU), which are deep learning algorithms based on recurrent neural networks. The model was built based on real-time water quality data and meteorological data. The observation period was set from July to September in the summer of 2021 (Period 1) and from March to May in the spring of 2022 (Period 2). We tried to select an algorithm with optimal predictive power for each water quality parameter. In addition, to improve the predictive power of the model, an important variable extraction technique using random forest was used to select only the important variables as input variables. In both Periods 1 and 2, the predictive power after extracting important variables was further improved. Except for DO in Period 2, GRU was selected as the best model in all water quality parameters. This methodology can be useful for preventive water quality management by identifying the variability of water quality in advance and predicting water quality in a short period.

• 품질경영학회지
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• 제34권2호
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• pp.129-135
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• 2006

In the advance of computer technology, it is possible to keep all the related informations for monitoring equipments in control and huge amount of real time manufacturing data in a data base. Thus, the statistical analysis of large data sets with hundreds of thousands observations and hundred of independent variables whose some of values are missing at many observations is needed even though it is a formidable computational task. A tree structured approach to classification is capable of screening important independent variables and their interactions. In a Six Sigma project handling large amount of manufacturing data, one of the goals is to screen vital few variables among trivial many variables. In this paper we have reviewed and summarized CART, C4.5 and CHAID algorithms and proposed a simple method of screening vital few variables by selecting common variables screened by all the three algorithms. Also how to develop a logistics regression model on a large data set is discussed and illustrated through a large finance data set collected by a credit bureau for th purpose of predicting the bankruptcy of the company.

• 한국물환경학회지
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• 제29권1호
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• pp.36-44
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• 2013

HSPF model based on BASINS was applied to analyze effects of watershed management measures for water quality conservation in the Hwaseong Reservoir watershed. The model was calibrated against the field measurements of meteorological data, streamflow and water qualities ($BOD_5$, T-N, T-P) at each observatory for 4 years (2007-2010). The water quality characteristics of inflow streams were evaluated. The 4 scenarios for the water quality improvement were applied to inflow streams and critical area from water pollution based on previous researches. The reduction efficiency of point and non-point sources in inflow streams was evaluated with each scenario. The results demonstrate that the expansion of advanced treatment system within wastewater treatment plants (WWTPs) and construction of pond-wetlands would be great effective management measures. In order to satisfactory the target water quality of reservoir, the measures which can control both point source and non-point source pollutants should be implemented in the watershed.

• 유통과학연구
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• 제22권6호
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• pp.33-44
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• 2024

Purpose: This study aims to measure how information quality, system quality, security, usefulness, and ease of use of Augmented Reality (VR) and Virtual Reality (AR) influence user satisfaction, motivating intelligent travel technology developers to improve VR/AR quality to meet customer requirements. Research design, data and methodology: This study investigates users interested in travelling in Ho Chi Minh City and Nha Trang City, Vietnam. The research model was implemented using an online questionnaire and face-to-face from 405 valid samples. To evaluate the scale's reliability, the study used the software SPSS 20. Test research hypotheses and evaluate measurement and structural models. This research uses AMOS 20 software. The proposed model is firmly grounded in the Information System Success model (ISS) and the Technology Acceptance Model (TAM), providing a solid theoretical foundation for our research. Results: Results show that consumer perceptions of information quality, system quality, security, usefulness, and ease of use have a positive impact on the perceived quality of VR/AR, thereby influencing tourists' travel intention. Conclusions: The results of this research enrich the theoretical understanding of consumer behaviour toward intelligent technology products in tourism, providing management implications for manufacturers to improve the quality of tourism products and satisfy user requirements in experience before considering choosing a destination.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제4권12호
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• pp.549-560
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• 2015

데이터품질 속성으로는 ISO/IEC 기관 및 국내/외 여러 기관에서 제시한 속성이 존재하지만, 이러한 기준 및 가이드를 현실적으로 조직에 적용하기에는 시간과 비용이 상당히 소요된다. 따라서 조직환경의 제약사항이 존재하여도 적용 가능한 데이터품질 평가속성의 정의가 필요하다. 이 연구의 목적은 고객의 요구사항 기반하에 프로세스를 체계적으로 관리하고, 정량적으로 데이터를 평가하기 위한 데이터품질 평가속성과 우선순위 도출에 관한 연구이다. 본 연구에서는 데이터품질 표준(DQC-M)을 매개체로 RGT 기법을 사용하여 데이터품질 속성의 고객 인지구조(Construct)를 도출하고, 도출된 Construct 간의 상관분석을 수행하여 AHP기법으로 평가속성의 가중치 및 우선순위를 선별하였다. 그 결과 데이터품질 평가속성에서 1레벨에서는 일관된 체계, 정확한 데이터, 효율적 환경, 유연한 관리, 지속적 개선 순위가 결정되었다. 또한 2레벨의 19개 속성 중에서는 통제성(13%), 준거성(10%), 요구완전성(9.6%), 정확성(8.4%), 추적가능성(6.8%)이 상위 5순위로 결정되었다.

• 환경영향평가
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• 제21권6호
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• pp.893-901
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• 2012

Aircraft noise model such as FAA Integrated Noise Model(INM) has recently been used for forecasting the impact of noise in a residential area near an airport and quantifying the effect of various options for noise mitigation. The noise modeling should be reliable and precise in order to ensure the quality of the results provided. In this study, the validation of the noise levels simulated by the INM against measurement data recorded continuously at multiple monitoring sites was discussed. As a result of validation, the quality of the input data used as a fixed point profiles for the INM was enhanced. The noise contour maps were designed as a way to evaluate the aircraft noise of the vicinity of the airfield. The results of this study indicate that the validation of aircraft noise model by the measurement data would be required for the accurate assessment of the aircraft noise levels.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2012년도 학술발표회
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• pp.36-36
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• 2012

Drainage water in lowland paddy fields is quantitatively influenced recycle and/or repeated irrigation by irrigation facilities, i.e. pumps, check gates, small reservoirs and so on. In those drainage channels, nutrients accumulation and increasing organic matters are considered to be occurred, and water quality would be degraded not only environmental aspect but irrigation purpose. In general, Total Nitrogen (T-N) is interested water quality index in irrigation water, because high nitrogen concentration sometimes caused decreasing rice production by excess growth and fallen or degrading quality of taste, then, farmers would like to clear water less than 1mg/L of T-N concentration. In drainage channel, it is known that the nitrogen concentration change is influenced by physical, chemical and biological properties, i.e, stream or river bed condition, water temperature, other water quality index, and plant cover condition. In this study, discharge data (velocity and level) in a drainage channel was monitored by an Acoustic Doppler system and water quality was sampled at same time in 2011. So those data was analyzed by multi regression model to realize hydrological and environmental factors to influence with nitrogen concentration. The results showed the difference tendency between irrigation and non-irrigation period, and those influenced factors would be considered in water quality model developing in future.

• 산경연구논집
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• 제15권7호
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• pp.1-9
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• 2024

Purpose: This research adopts the SERVQUAL and LSQ frameworks to examine the correlation between return reverse logistics service quality of the JD platform and customer satisfaction, as well as the linkage between consumer satisfaction and repurchase intention. Research design, data and methodology: A comprehensive literature review on both domestic and international logistics service quality has been conducted. Considering the unique aspects of JD's return reverse logistics services, an evaluation framework with 5 dimensions and 21 indicators is formulated, including communication, information, return process, empathy, and convenience. A conceptual model exploring the influence of JD's reverse logistics service quality on customer repurchase intention is developed, proposing six hypotheses. For this investigation, 358 valid questionnaires were collected, processed, and analyzed using SPSS 22.0. The structural equation modeling was conducted and validated through AMOS 21.0 software. Results: Following a thorough analysis of data, it reveals that: (1) Information quality, return process quality, and empathy significantly enhance customer satisfaction. (2) Customer satisfaction positively impacts repurchase intention. Conclusion: Based on these findings, three strategic recommendations are offered for e-commerce platforms with in-house logistics systems. The research also discusses limitations and future research directions.

• 한국환경과학회:학술대회논문집
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• 한국환경과학회 2003년도 International Symposium on Clean Environment
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• pp.73-78
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• 2003

In this research, we implement Realtime Air Diffusion Prediction System which is a parallel Fortran model running on distributed-memory parallel computers. The system is designed for air diffusion simulations with four-dimensional data assimilation. For regional air quality forecasting a series of dynamic downscaling technique is adopted using the NCAR/Penn. State MM5 model which is an atmospheric model. The realtime initial data have been provided daily from the KMA (Korean Meteorological Administration) global spectral model output. It takes huge resources of computation to get 24 hour air quality forecast with this four step dynamic downscaling (27km, 9km, 3km, and lkm). Parallel implementation of the realtime system is imperative to achieve increased throughput since the realtime system have to be performed which correct timing behavior and the sequential code requires a large amount of CPU time for typical simulations. The parallel system uses MPI (Message Passing Interface), a standard library to support high-level routines for message passing. We validate the parallel model by comparing it with the sequential model. For realtime running, we implement a cluster computer which is a distributed-memory parallel computer that links high-performance PCs with high-speed interconnection networks. We use 32 2-CPU nodes and a Myrinet network for the cluster. Since cluster computers more cost effective than conventional distributed parallel computers, we can build a dedicated realtime computer. The system also includes web based Gill (Graphic User Interface) for convenient system management and performance monitoring so that end-users can restart the system easily when the system faults. Performance of the parallel model is analyzed by comparing its execution time with the sequential model, and by calculating communication overhead and load imbalance, which are common problems in parallel processing. Performance analysis is carried out on our cluster which has 32 2-CPU nodes.