• International conference on construction engineering and project management
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• 2009.05a
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• pp.525-536
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• 2009

The current residential process adheres to a traditional method of construction involving wood framing on-site on poured concrete foundations which has been widely applied in North America. A conventional residential construction process can include seventeen distinct stages ranging from stake-out to pre-occupancy inspection. The current practice possesses short comings including high construction material wastes, long scheduling timelines, adverse weather conditions, poor quality, low efficiencies and negative environmental impacts from transportation and equipment use. Over CAN $5 billion dollars was spent in the construction sector during 2007 in Canada. Previous findings in CO₂ emissions during the construction process of a conventional dwelling emphasize more than 45 tonnes of CO₂ emissions. Hence, in Alberta alone during 2007, almost 50,000 residential units would release more than two million tonnes of CO₂. These numbers demonstrate the economical and environmental impact in building construction and its relationship with CO₂ emissions. The aim of this paper is to quantify the CO₂ emissions from the current residential construction process in order to establish the baseline for CO₂ emission reduction opportunities. The quantification collection methodology will be approached by identifying the seventeen various stages of construction and quantifying the contributions of CO₂ from specific activities and their impacts of work for each stage. The approach of separating these into separate stages for collection will allow for independent opportunities for analysis from various independent contractors from the entire scope of work. The use of BIM will be implemented to efficiently quantify CO₂ emissions. Based on the CO₂ quantification baseline, emission reduction opportunities such as an industrialized construction process will be introduced that allows homebuilders to reduce the environmental and economical impact of home construction while enabling them to produce higher quality, more energy efficient homes in a safer and shorter period of time.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.131-145
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• 2010

The framework for making a coordinated decision for large-scale facilities has become an important issue in supply chain(SC) management research. The competitive business environment requires companies to continuously search for the ways to achieve high efficiency and lower operational costs. In the areas of production/distribution planning, many researchers and practitioners have developedand evaluated the deterministic models to coordinate important and interrelated logistic decisions such as capacity management, inventory allocation, and vehicle routing. They initially have investigated the various process of SC separately and later become more interested in such problems encompassing the whole SC system. The accurate quotation of ATP(Available-To-Promise) plays a very important role in enhancing customer satisfaction and fill rate maximization. The complexity for intelligent manufacturing system, which includes all the linkages among procurement, production, and distribution, makes the accurate quotation of ATP be a quite difficult job. In addition to, many researchers assumed ATP model with integer time. However, in industry practices, integer times are very rare and the model developed using integer times is therefore approximating the real system. Various alternative models for an ATP system with time lags have been developed and evaluated. In most cases, these models have assumed that the time lags are integer multiples of a unit time grid. However, integer time lags are very rare in practices, and therefore models developed using integer time lags only approximate real systems. The differences occurring by this approximation frequently result in significant accuracy degradations. To introduce the ATP model with time lags, we first introduce the dynamic production function. Hackman and Leachman's dynamic production function in initiated research directly related to the topic of this paper. They propose a modeling framework for a system with non-integer time lags and show how to apply the framework to a variety of systems including continues time series, manufacturing resource planning and critical path method. Their formulation requires no additional variables or constraints and is capable of representing real world systems more accurately. Previously, to cope with non-integer time lags, they usually model a concerned system either by rounding lags to the nearest integers or by subdividing the time grid to make the lags become integer multiples of the grid. But each approach has a critical weakness: the first approach underestimates, potentially leading to infeasibilities or overestimates lead times, potentially resulting in excessive work-inprocesses. The second approach drastically inflates the problem size. We consider an optimized ATP system with non-integer time lag in supply chain management. We focus on a worldwide headquarter, distribution centers, and manufacturing facilities are globally networked. We develop a mixed integer programming(MIP) model for ATP process, which has the definition of required data flow. The illustrative ATP module shows the proposed system is largely affected inSCM. The system we are concerned is composed of a multiple production facility with multiple products, multiple distribution centers and multiple customers. For the system, we consider an ATP scheduling and capacity allocationproblem. In this study, we proposed the model for the ATP system in SCM using the dynamic production function considering the non-integer time lags. The model is developed under the framework suitable for the non-integer lags and, therefore, is more accurate than the models we usually encounter. We developed intelligent ATP System for this model using genetic algorithm. We focus on a capacitated production planning and capacity allocation problem, develop a mixed integer programming model, and propose an efficient heuristic procedure using an evolutionary system to solve it efficiently. This method makes it possible for the population to reach the approximate solution easily. Moreover, we designed and utilized a representation scheme that allows the proposed models to represent real variables. The proposed regeneration procedures, which evaluate each infeasible chromosome, makes the solutions converge to the optimum quickly.

• Journal of Bio-Environment Control
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• v.31 no.4
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• pp.376-383
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• 2022

The current study, which consisted of two independent studies (laboratory and greenhouse), was carried out to project the hypothesis fungi-spray scheduling for leaf mold and gray leaf spot in tomato, as well as to evaluate the effect of temperature and leaf wet duration on the effectiveness of different fungicides against these diseases. In the first experiment, tomato leaves were infected with 1 × 10⁴ conidia·mL^-1 and put in a dew chamber for 0 to 18 hours at 10 to 25℃ (Fulvia fulva) and 10 to 30℃ (Stemphylium lycopersici). In farm study, tomato plants were treated for 240 hours with diluted (1,000 times) 30% trimidazole, 50% polyoxin B, and 40% iminoctadine tris (Belkut) for protection of leaf mold, and 10% etridiazole + 55% thiophanate-methyl (Gajiran), and 15% tribasic copper sulfate (Sebinna) for protection of gray leaf spot. In laboratory test, leaf condensation on the leaves of tomato plants were emerged after 9 hrs. of incubation. In conclusion, the incidence degree of leaf mold and gray leaf spot disease on tomato plants shows that it is very closely related to formation of leaf condensation, therefore the incidence of leaf mold was greater at 20 and 15℃, while 25 and 20℃ enhanced the incidence of gray leaf spot. The incidence of leaf mold and gray leaf spot developed 20 days after inoculation, and the latency period was estimated to be 14-15 days. Trihumin fungicide had the maximum effectiveness up to 168 hours of fungicides at 12 hours of wet duration in leaf mold, whereas Gajiran fungicide had the highest control (93%) against gray leaf spot up to 144 hours. All the chemicals showed an around 30-50% decrease in effectiveness after 240 hours of treatment. The model predictions in present study could be help in timely, effective and ecofriendly management of leaf mold disease in tomato.

• Agribusiness and Information Management
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• v.8 no.1
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• pp.17-26
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• 2016

India is largest producer of banana in the world producing 29.72 million tonnes from an area of 0.803 million ha with a productivity of 35.7 MT ha-1 and accounted for 15.48 and 27.01 per cent of the world's area and production respectively (www.nhb.gov.in). In India, Tamil Nadu leads other states both in terms of area and production followed by Maharashtra, Gujarat and Andhra Pradesh. In Rayalaseema region of Andhra Pradesh, Kurnool district had special reputation in the cultivation of banana in an area of 5765 hectares with an annual production of 2.01 lakh tonnes in the year 2012-13 and hence, it was purposively chosen for the study. On $23^{rd}$ November 2003, the Government of Andhra Pradesh has commenced a comprehensive project called 'Andhra Pradesh Micro Irrigation Project (APMIP)', first of its kind in the world so as to promote water use efficiency. APMIP is offering 100 per cent of subsidy in case of SC, ST and 90 per cent in case of other categories of farmers up to 5.0 acres of land. In case of acreage between 5-10 acres, 70 per cent subsidy and acreage above 10, 50 per cent of subsidy is given to the farmer beneficiaries. The sampling frame consists of Kurnool district, two mandals, four villages and 180 sample farmers comprising of 60 farmers each from Marginal (<1ha), Small (1-2ha) and Other (>2ha) categories. A well structured pre-tested schedule was employed to collect the requisite information pertaining to the performance of drip irrigation among the sample farmers and Data Envelopment Analysis (DEA) model was employed to analyze the performance of drip irrigation in banana farms. The performance of drip irrigation was assessed based on the parameters like: Land Development Works (LDW), Fertigation costs (FC), Volume of water supplied (VWS), Annual maintenance costs of drip irrigation (AMC), Economic Status of the farmer (ES), Crop Productivity (CP) etc. The first four parameters are considered as inputs and last two as outputs for DEA modelling purposes. The findings revealed that, the number of farms operating at CRS are more in number in other farms (46.66%) followed by marginal (45%) and small farms (28.33%). Similarly, regarding the number of farmers operating at VRS, the other farms are again more in number with 61.66 per cent followed by marginal (53.33%) and small farms (35%). With reference to scale efficiency, marginal farms dominate the scenario with 57 per cent followed by others (55%) and small farms (50%). At pooled level, 26.11 per cent of the farms are being operated at CRS with an average technical efficiency score of 0.6138 i.e., 47 out of 180 farms. Nearly 40 per cent of the farmers at pooled level are being operated at VRS with an average technical efficiency score of 0.7241. As regards to scale efficiency, nearly 52 per cent of the farmers (94 out of 180 farmers) at pooled level, either performed at the optimum scale or were close to the optimum scale (farms having scale efficiency values equal to or more than 0.90). Majority of the farms (39.44%) are operating at IRS and only 29 per cent of the farmers are operating at DRS. This signifies that, more resources should be provided to these farms operating at IRS and the same should be decreased towards the farms operating at DRS. Nearly 32 per cent of the farms are operating at CRS indicating efficient utilization of resources. Log linear regression model was used to analyze the major determinants of input use efficiency in banana farms. The input variables considered under DEA model were again considered as influential factors for the CRS obtained for the three categories of farmers. Volume of water supplied ($X_1$) and fertigation cost ($X_2$) are the major determinants of banana farms across all the farmer categories and even at pooled level. In view of their positive influence on the CRS, it is essential to strengthen modern irrigation infrastructure like drip irrigation and offer more fertilizer subsidies to the farmer to enhance the crop production on cost-effective basis in Kurnool district of Andhra Pradesh, India. This study further suggests that, the present era of Information Technology will help the irrigation management in the context of generating new techniques, extension, adoption and information. It will also guide the farmers in irrigation scheduling and quantifying the irrigation water requirements in accordance with the water availability in a particular season. So, it is high time for the Government of India to pay adequate attention towards the applications of 'Information and Communication Technology (ICT) and its applications in irrigation water management' for facilitating the deployment of Decision Supports Systems (DSSs) at various levels of planning and management of water resources in the country.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.620-626
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• 2004

For general nonlinear processes, it is difficult to control with a linear model-based control method and nonlinear controls are considered. Among the numerous approaches suggested, the most rigorous approach is to use dynamic optimization. Many general engineering problems like control, scheduling, planning etc. are expressed by functional optimization problem and most of them can be changed into dynamic programming (DP) problems. However the DP problems are used in just few cases because as the size of the problem grows, the dynamic programming approach is suffered from the burden of calculation which is called as 'curse of dimensionality'. In order to avoid this problem, the Neuro-Dynamic Programming (NDP) approach is proposed by Bertsekas and Tsitsiklis (1996). To get the solution of seriously nonlinear process control, the interest in NDP approach is enlarged and NDP algorithm is applied to diverse areas such as retailing, finance, inventory management, communication networks, etc. and it has been extended to chemical engineering parts. In the NDP approach, we select the optimal control input policy to minimize the value of cost which is calculated by the sum of current stage cost and future stages cost starting from the next state. The cost value is related with a weight square sum of error and input movement. During the calculation of optimal input policy, if the approximate cost function by using simulation data is utilized with Bellman iteration, the burden of calculation can be relieved and the curse of dimensionality problem of DP can be overcome. It is very important issue how to construct the cost-to-go function which has a good approximate performance. The neural network is one of the eager learning methods and it works as a global approximator to cost-to-go function. In this algorithm, the training of neural network is important and difficult part, and it gives significant effect on the performance of control. To avoid the difficulty in neural network training, the lazy learning method like k-nearest neighbor method can be exploited. The training is unnecessary for this method but requires more computation time and greater data storage. The pH neutralization process has long been taken as a representative benchmark problem of nonlin ar chemical process control due to its nonlinearity and time-varying nature. In this study, the NDP algorithm was applied to pH neutralization process. At first, the pH neutralization process control to use NDP algorithm was performed through simulations with various approximators. The global and local approximators are used for NDP calculation. After that, the verification of NDP in real system was made by pH neutralization experiment. The control results by NDP algorithm was compared with those by the PI controller which is traditionally used, in both simulations and experiments. From the comparison of results, the control by NDP algorithm showed faster and better control performance than PI controller. In addition to that, the control by NDP algorithm showed the good results when it applied to the cases with disturbances and multiple set point changes.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.3
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• pp.279-286
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• 2015

The rapid increase of the current marine accidents is mainly due to the human execution errors. In an effort to address this, various kinds of researches such as construction of the digital vessels and vessel information monitoring systems have been conducted. But for safe navigation of vessels, it lack on systems study which can efficiently store, utilize and manage the mass data accepted by the vessel. In this paper, we propose a VWS(Virtual World System) that is based on the architecture of intelligent systems RVC(Reactive Layer-Virtual World-Considerative Layer) model of intelligent autonomous navigation system. VWS is responsible to store all the necessary information for safe navigation of the vessel and the information services to the sub-system of intelligent autonomous navigation system. VWS uses topology database to express the specific problem area, and utilizes a scheduling to reflect the characteristics of the real-time processing environment. Also, Virtual World defines API for the system to reflect the characteristics of the distributed processing environment. As a case study, the VWS is applied to a intelligent ship autonomous navigation system, and simulation is done to prove the effectiveness of the proposed system.

• Journal of KIISE:Computer Systems and Theory
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• v.26 no.11
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• pp.1318-1332
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• 1999

본 논문에서는 입출력 응용을 위해 collective I/O 기법을 기반으로 한 실행시간 시스템의 설계, 구현 그리고 그 성능평가를 기술한다. 여기서는 모든 프로세서가 동시에 I/O 요구에 따라 스케쥴링하며 I/O를 수행하는 collective I/O 방안과 프로세서들이 여러 그룹으로 묶이어, 다음 그룹이 데이터를 재배열하는 통신을 수행하는 동안 오직 한 그룹만이 동시에 I/O를 수행하는 pipelined collective I/O 등의 두 가지 설계방안을 살펴본다. Pipelined collective I/O의 전체 과정은 I/O 노드 충돌을 동적으로 줄이기 위해 파이프라인된다. 이상의 설계 부분에서는 동적으로 충돌 관리를 위한 지원을 제공한다. 본 논문에서는 다른 노드의 메모리 영역에 이미 존재하는 데이터를 재 사용하여 I/O 비용을 줄이기 위해 collective I/O 방안에서의 소프트웨어 캐슁 방안과 두 가지 모형에서의 chunking과 온라인 압축방안을 기술한다. 그리고 이상에서 기술한 방안들이 입출력을 위해 높은 성능을 보임을 기술하는데, 이 성능결과는 Intel Paragon과 ASCI/Red teraflops 기계 상에서 실험한 것이다. 그 결과 응용 레벨에서의 bandwidth는 peak point가 55%까지 측정되었다.Abstract In this paper we present the design, implementation and evaluation of a runtime system based on collective I/O techniques for irregular applications. We present two designs, namely, "Collective I/O" and "Pipelined Collective I/O". In the first scheme, all processors participate in the I/O simultaneously, making scheduling of I/O requests simpler but creating a possibility of contention at the I/O nodes. In the second approach, processors are grouped into several groups, so that only one group performs I/O simultaneously, while the next group performs communication to rearrange data, and this entire process is pipelined to reduce I/O node contention dynamically. In other words, the design provides support for dynamic contention management. Then we present a software caching method using collective I/O to reduce I/O cost by reusing data already present in the memory of other nodes. Finally, chunking and on-line compression mechanisms are included in both models. We demonstrate that we can obtain significantly high-performance for I/O above what has been possible so far. The performance results are presented on an Intel Paragon and on the ASCI/Red teraflops machine. Application level I/O bandwidth up to 55% of the peak is observed.he peak is observed.

• Journal of The Korean Society of Agricultural Engineers
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• v.60 no.6
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• pp.43-54
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• 2018

The accurate estimation of reference crop evapotranspiration ($ET_o$) is essential in irrigation water management to assess the time-dependent status of crop water use and irrigation scheduling. The importance of $ET_o$ has resulted in many direct and indirect methods to approximate its value and include pan evaporation, meteorological-based estimations, lysimetry, soil moisture depletion, and soil water balance equations. Artificial neural networks (ANNs) have been intensively implemented for process-based hydrologic modeling due to their superior performance using nonlinear modeling, pattern recognition, and classification. This study adapted two well-known ANN algorithms, Backpropagation neural network (BPNN) and Generalized regression neural network (GRNN), to evaluate their capability to accurately predict $ET_o$ using daily meteorological data. All data were obtained from two automated weather stations (Chupungryeong and Jangsu) located in the Yeongdong-gun (2002-2017) and Jangsu-gun (1988-2017), respectively. Daily $ET_o$ was calculated using the Penman-Monteith equation as the benchmark method. These calculated values of $ET_o$ and corresponding meteorological data were separated into training, validation and test datasets. The performance of each ANN algorithm was evaluated against $ET_o$ calculated from the benchmark method and multiple linear regression (MLR) model. The overall results showed that the BPNN algorithm performed best followed by the MLR and GRNN in a statistical sense and this could contribute to provide valuable information to farmers, water managers and policy makers for effective agricultural water governance.

• Magazine of the Korean Society of Agricultural Engineers
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• v.28 no.2
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• pp.31-41
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• 1986

Knowledge of the degree of yield reduction due to water stress at different crop growth stages in rice production is important for rational scheduling of irrigation during periods of insufficient water supply. Previous studies to determine the degree of yield reduction duo to water stress suffered from interruptions by rain during experiment. Also the findings did rot relate the degree of water stress to the soil water potential and water deficit status of rice plants. In this study, two years experiments were conducted using the high yielding rice varieties, an Indica x Japonica (Nampoong) and a Japonica variety(Choochung). These were grown in 1/200$^{\circ}$ plastic pots placed under a rainfall autosensing, sliding clear plastic roof facility to control rainfall interruptions. The results obtained were as follows. 1.The two varieties differed in the growth stage most sensitive to water stress as well as the degree of yield reductions. When rice plants were stressed to the leaf rolling score 4 and soil water potential of about - 20 bar at major crop growth stages which included heading, booting, non-effective tillering, panicle initiation and early tillering stages, the yield reductions in the Indica x Japonica variety were 58%, 34%, 27%, 22%, and 21%, respectively, whereas in the Japonica vairety they were 23%, 36%, 1%, 13% and 22%, respectively. This result show that the recommended drainage during non-effective tillering is valid only for the Japonica variety. Sufficient irrigation at booting, heading and early tillering stages are necessary for both varieties. 2.The two varieties showed visible wilting symptoms when the soil water potential dropped to about - 3.0 bar. The Japonica variety showed more leaf rolling than the Indica X Japonica. However, it had a higher retention of leaf water content and greater stomatal diffusive resistance. When the soil water potential dropped, the Japonica variety showed leaf rolling score (LRS) 1 at 0 soil-5. 0 bar and LRS 2 at 0 soil -6.0 bar while the Indica X Japonica showed LRS 1 at 0 soil - 5.5 bar and LRS 2at 0 Soil - 9.0 bar. The stomatal diffusive resistance was maximum at the second top leaf blade in both varieties at intermediate water stress of 0 soil - 4.5 bar. 3.The number of days that was required for the soil water potential to drop to-3. 0 bar and to - 20.0 bar after drainage of irrigation water from the 20cm deep silty clay loam soil in the pots were 6 and 13 days, respectively for booting stage, and 7 and 11 days, respectively for heading stage, 9 and 12 days, respectively for panicle initiation stage, and 12 and 19 days, respectively for early tillering stage. 4.Water stress during the early tillering stage recorded the longest delay in beading time, the largest reduction in panicle numbers and a substantial yield decrease of 20%. This calls for better water management to ensure the availability of water at this stage, particularly during drought periods. In addition, a reexamination of the conventional inter-drainage practice during the non-effective tillering stage is necessary for the high yielding Indica X Japonica varieties.

• The Journal of the Korea Contents Association
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• v.18 no.9
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• pp.125-140
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• 2018

Due to rapid development of semiconductor technology, embedded systems have been developed from single-functional system to the multi-functional system. The system composed of software that has different criticality level is called Mixed-Criticality System. Currently, the project related to the Mixed-Criticality System is accelerating the efforts to seek the development direction and take technical initiatives led by EU and USA where the related industry has developed, but the movement in Korea is yet insignificant. Therefore, it is urgent to perform the research and project of various basic technologies to occupy the initiative for the related technology and market. In this paper, we analyze the trends of major project researches and developments related to the MCS. First, after defining the definition of the MCS and system model, we analyze the underlying technology constituting the MCS. In addition, we analyze the project trends of each country researching MCS and discuss the future research areas. Through this study, it is possible to grasp the research trends of the world in order to establish the research direction of the MCS and to lay the foundation for the integration into the military system.