• Journal of Multimedia Information System
• /
• v.6 no.4
• /
• pp.265-270
• /
• 2019

The technology to locate an individual has enabled various services, its utilization has increased. There were constraints such as the use of separate expensive equipment or the installation of specific devices on a facility, with most of the location technology studies focusing on the accuracy of location verification. These constraints can result in accuracy within a few tens of centimeters, but they are not technology that can be applied to a user's location in real-time in daily life. Therefore, this paper aims to track the locations of smartphones only using the basic components of smartphones. Based on smartphone sensor data, localization accuracy that can be used for verification of the users' locations is aimed at. Accelerometers, Wifi radio maps, and GPS sensor information are utilized to implement it. In forging the radio map, signal maps were built at each vertex based on the graph data structure This approach reduces traditional map-building efforts at the offline phase. Accelerometer data were made to determine the user's moving status, and the collected sensor data were fused using particle filters. Experiments have shown that the average user's location error is about 3.7 meters, which makes it reasonable for providing location-based services in everyday life.

• Journal of the Korea Society for Simulation
• /
• v.15 no.3
• /
• pp.39-47
• /
• 2006

In most of supply chain planning practices, the estimated demands, which are forecasted for each individual period in a forecasting window, are regarded as deterministic. But, in reality, the forecasted demands for the periods of a given horizon are stochastically distributed. Instead of using a safety stock, this study considers a direct control of service level by choosing the demand used in planning from the distributed forecasted demand values for the corresponding period. Using the demand quantile and echelon stock concept, we propose a simple but efficient heuristic algorithm for multi-echelon serial systems under service level constraints. Through a comprehensive simulation study, the proposed algorithm was shown to be very accurate compared with the optimal solutions.

• Journal of Communications and Networks
• /
• v.10 no.1
• /
• pp.28-37
• /
• 2008

In this paper, we investigate the problem of minimizing the average transmission power of users while guaranteeing the average delay constraints in time-varying uplink channels. We design a scheduler that selects a user for transmission and determines the transmission rate of the selected user based on the channel and backlog information of users. Since it requires prohibitively high computation complexity to determine an optimal scheduler for multi-user systems, we propose a low-complexity scheduling scheme that can achieve near-optimal performance. In this scheme, we reduce the complexity by decomposing the multiuser problem into multiple individual user problems. We arrange the probability of selecting each user such that it can be determined only by the information of the corresponding user and then optimize the transmission rate of each user independently. We solve the user problem by using a dynamic programming approach and analyze the upper and lower bounds of average transmission power and average delay, respectively. In addition, we investigate the effects of the user selection algorithm on the performance for different channel models. We show that a channel-adaptive user selection algorithm can improve the energy efficiency under uncorrelated channels but the gain is obtainable only for loose delay requirements in the case of correlated channels. Based on this, we propose a user selection algorithm that adapts itself to both the channel condition and the backlog level, which turns out to be energy-efficient over wide range of delay requirement regardless of the channel model.

• Safety and Health at Work
• /
• v.5 no.1
• /
• pp.1-6
• /
• 2014

Background: Workflow interruptions during surgery may cause a threat to patient's safety. Workflow interruptions were tested to predict failure in action regulation that in turn predicts near-accidents in surgery and related health care. Methods: One-hundred-and-thirty-three theater nurses and physicians from eight Swiss hospitals participated in a cross-sectional questionnaire survey. The study participation rate was 43%. Results: Structural equation modeling confirmed an indirect path from workflow interruptions through cognitive failure in action regulation on near-accidents (p < 0.05). The indirect path was stronger for workflow interruptions by malfunctions and task organizational blockages compared with workflow interruptions that were caused by persons. The indirect path remained meaningful when individual differences in conscientiousness and compliance with safety regulations were controlled. Conclusion: Task interruptions caused by malfunction and organizational constraints are likely to trigger errors in surgery. Work redesign is recommended to reduce workflow interruptions by malfunction and regulatory constraints.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.37 no.4
• /
• pp.63-71
• /
• 2014

As power consumption increases, more power utilities are required to satisfy the demand and consequently results in tremendous cost to build the utilities. Another issue in construction of power utilities to meet the peak demand is an inefficiency caused by surplus power during non-peak time. Therefore, most power company considers power demand management with time-based electricity rate policy which applies different rate over time. This paper considers an optimal machine operation problem under the time-based electricity rates. In TOC (Theory of Constraints), the production capacities of all machines are limited to one of the bottleneck machine to minimize the WIP (work in process). In the situation, other machines except the bottleneck are able to stop their operations without any throughput loss of the whole manufacturing line for saving power utility cost. To consider this problem three integer programming models are introduced. The three models include (1) line shutdown, (2) block shutdown, and (3) individual machine shutdown. We demonstrate the effectiveness of the proposed IP models through diverse experiments, by comparing with a TOC-based machine operation planning considered as a current model.

• Journal of Forest and Environmental Science
• /
• v.35 no.3
• /
• pp.189-196
• /
• 2019

Plants are consumed by a myriad of organisms that compete for resources. Direct interactions among multiple plant-feeding organisms in a single host can range for each species from positive to negative. Wood-boring beetle faces a number of biotic and abiotic constraints that interfere with the good prospects from the tree. Biotic factors, including arthropod pests and diseases, and abiotic factors, such as drought and water-logging, are the major constraints affecting the species. The present study aimed to provide basic data for analyzing forest health, identify the kinds of wood-boring beetles in the central part of Korea. Our second goal was to analyze the species composition and diversity of regional communities and to examine. A total of 10,461 individual wood-boring beetles belonging to 8 families and 50 species attracted to trap trees in the pine forests were recorded during the study period on study sites. The results of the analysis of collected species showed that the community structure on all study sites was similar. Seasonal occurrences of dominant wood-boring beetles (5 species) from each study site showed the highest number of all species, except for Siphalinus gigas in May, followed by a gradual decline, and the largest number of Siphalinus gigas appeared in June. The similarity index of species composition was relatively high, ranging from 0.75 to 0.90 for each study site.

• International conference on construction engineering and project management
• /
• 2022.06a
• /
• pp.196-203
• /
• 2022

The resource constrained scheduling problem (RCSP) constitutes one of the most challenging problems in Project Management, as it combines multiple parameters, contradicting objectives (project completion within certain deadlines, resource allocation within resource availability margins and with reduced fluctuations), strict constraints (precedence constraints between activities), while its complexity grows with the increase in the number of activities being executed. Due to the large solution space size, this work investigates the application of Genetic Algorithms to approximate the optimal resource alolocation and obtain optimal trade-offs between different project goals. This analysis uses the cost of exceeding the daily resource availability, the cost from the day-by-day resource movement in and out of the site and the cost for using resources day-by-day, to form the objective cost function. The model is applied in different case studies: 1 project consisting of 10 activities, 4 repetitive projects consisting of 40 activities in total and 16 repetitive projects consisting of 160 activities in total, in order to evaluate the effectiveness of the algorithm in different-size solution spaces and under alternative optimization criteria by examining the quality of the solution and the required computational time. The case studies 2 & 3 have been developed by building upon the recurrence of the unit/sub-project (10 activities), meaning that the initial problem is multiplied four and sixteen times respectively. The evaluation results indicate that the proposed model can efficiently provide reliable solutions with respect to the individual goals assigned in every case study regardless of the project scale.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.19 no.5
• /
• pp.632-639
• /
• 2018

Recently, a policy has been enforced to supply Plug-in Electric Vehicles (PEVs) but this may require reinforcement of the power system depending on its clustering because PEVs are charged directly from power systems. On the other hand, as the reinforcement of power system is limited by time and budget, it is important to supply the charging demand of PEVs efficiently using the existing power systems to increase the diffusion of PEVs. This paper establishes a charging schedule for Plug-in Electric Vehicles (PEVs) considering the power system constraints. For this, the required amount and time of the charging demand for an individual PEV was modeled to integrate into power systems based on the driving pattern and charging tariff of PEV. Furthermore, the charging schedule of PEVs was established to meet the power system constraints by calculating the operating conditions of the power systems with PEVs.

• Journal of Family Resource Management and Policy Review
• /
• v.9 no.1
• /
• pp.59-73
• /
• 2005

The purpose of this study was to understand which factors affect on the leisure constraint and leisure attitude of workers, to observe how the factors of leisure constraints affect on the leisure attitude. The subjects of this study were 267 urban workers who are older than 20 years old and resident in Seoul. The periods of this study was from November 15 to December 5. The modified versions of leisure constraint scale and leisure attitude scale were utilized for this study. The results of this study are as follows : First, as a result to compare the average of leisure constraint factors, intrapersonal constraint, which are individual mental status as interest, self-consciousness, uneasiness et at., was founded generally less than interpersonal constraints as personal relationship for leisure activity, or structural constraints as financial, time, information et at., Secondly, it was found that interpersonal constraint was statistically different in age, and that structural constraint was statistically different in sex, education, health status, marital status, income, and career. Thirdly, there were statistically different in cognitive leisure attitude, feeling leisure attitude, and behavioral leisure attitude according to the average monthly income and career. But, it was found that marital status and the five-day workweek system were not statistically different in 3 leisure attitude factors. Finally, 3 leisure constraint factors(intrapersonal, interpersonal, structural) had statistically significant effect on cognitive leisure attitude. Also, intrapersonal leisure constraint and interpersonal leisure constraint had statistically significant effect on feeling leisure attitude and behavioral leisure attitude

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.40 no.2
• /
• pp.111-120
• /
• 2017

This paper proposes an improved standard genetic algorithm (GA) of making a near optimal schedule for integrated process planning and scheduling problem (IPPS) considering tool flexibility and tool related constraints. Process planning involves the selection of operations and the allocation of resources. Scheduling, meanwhile, determines the sequence order in which operations are executed on each machine. Due to the high degree of complexity, traditionally, a sequential approach has been preferred, which determines process planning firstly and then performs scheduling independently based on the results. The two sub-problems, however, are complicatedly interrelated to each other, so the IPPS tend to solve the two problems simultaneously. Although many studies for IPPS have been conducted in the past, tool flexibility and capacity constraints are rarely considered. Various meta-heuristics, especially GA, have been applied for IPPS, but the performance is yet satisfactory. To improve solution quality against computation time in GA, we adopted three methods. First, we used a random circular queue during generation of an initial population. It can provide sufficient diversity of individuals at the beginning of GA. Second, we adopted an inferior selection to choose the parents for the crossover and mutation operations. It helps to maintain exploitation capability throughout the evolution process. Third, we employed a modification of the hybrid scheduling algorithm to decode the chromosome of the individual into a schedule, which can generate an active and non-delay schedule. The experimental results show that our proposed algorithm is superior to the current best evolutionary algorithms at most benchmark problems.