• International conference on construction engineering and project management
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• 2011.02a
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• pp.201-208
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• 2011

Today's highly competitive construction scenario forces all the major players in the field to take up multiple projects which have put an undue pressure on the resources available within the organization. Under such a situation, there are many instances where in the resource requirement exceeds its availability due to multiple activities (with same resource requirement) which are scheduled to start simultaneously and thus results in the constrained resource becoming a bottleneck of the project. As a consequence of sharing resources, this paper studies the impact on the completion date of two similar projects under two different conditions, the first one resulting in a postponed end date and the second without any postponement. The resource utilization, the possibility of substitution of a resource and its subsequent impact on the deadline of the project is analyzed under these two circumstances. The study is done on a Critical Chain Project Management (CCPM) platform instead of leaving the schedule with a traditional Critical Path Method (CPM) finish, which gives an added advantage of validating the robustness of the emerging CCPM trend in the field of resource management.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1994.10a
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• pp.74-74
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• 1994

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.6
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• pp.319-325
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• 2023

In this study, we proposed a method for hyperparameter optimization in the building and training of a deep learning model designed to process point cloud data collected by a millimeter-wave radar system. The primary aim of this study is to facilitate the deployment of a baseline model in resource-constrained IoT devices. We evaluated a RadHAR baseline deep learning model trained on a public dataset composed of point clouds representing five distinct human activities. Additionally, we introduced a coarse-to-fine hyperparameter optimization procedure, showing substantial potential to enhance model efficiency without compromising predictive performance. Experimental results show the feasibility of significantly reducing model size without adversely impacting performance. Specifically, the optimized model demonstrated a 3.3% improvement in classification accuracy despite a 16.8% reduction in number of parameters compared th the baseline model. In conclusion, this research offers valuable insights for the development of deep learning models for resource-constrained IoT devices, underscoring the potential of hyperparameter optimization and model size reduction strategies. This work contributes to enhancing the practicality and usability of deep learning models in real-world environments, where high levels of accuracy and efficiency in data processing and classification tasks are required.

• The KIPS Transactions:PartA
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• v.9A no.4
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• pp.467-478
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• 2002

With the rapid development of wired or wireless Internet, various kinds of resource constrained mobile devices, such as cellular phone, PDA, homepad, smart phone, handhold PC, and so on, have been emerging into personal or commercial usages. Most software to be embedded into those devices has been forced to have the characteristic of flexibility rather than the fixedness which was an inherent property of embedded system. It means that recent technologies require the flexible embedding into the variety of resource constrained mobile devices. A document processor for XML which has been positioned as a standard mark-up language for information representation on the Web, is one of the essential software to be embedded into those devices for browsing the information. In this paper, a framework for configurable XML processor called EmXJ is designed and implemented for flexible embedding into various types of resource constrained mobile devices, and its advantages are compared to conventional XML processors.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.12
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• pp.363-370
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• 2023

Object detection technology that accurately recognizes the road and surrounding conditions is a key technology in the field of autonomous driving. In the field of autonomous driving, object detection technology requires real-time performance as well as accuracy of inference services. Task offloading technology should be utilized to apply object detection technology for accuracy and real-time on resource-constrained devices rather than high-performance machines. In this paper, experiments such as performance comparison of task offloading, performance comparison according to input image resolution, and performance comparison according to camera object resolution were conducted and the results were analyzed in relation to the application of task offloading for real-time object detection of autonomous driving in resource-constrained devices. In this experiment, the low-resolution image could derive performance improvement through the application of the task offloading structure, which met the real-time requirements of autonomous driving. The high-resolution image did not meet the real-time requirements for autonomous driving due to the increase in communication time, although there was an improvement in performance. Through these experiments, it was confirmed that object recognition in autonomous driving affects various conditions such as input images and communication environments along with the object recognition model used.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.402-408
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• 2002

Multicast technology allows the transmission of data from one source node to a selected group of destination nodes. Multicast routes typically use trees, called multicast routing trees, to minimize resource usage such as cost and bandwidth by sharing links. Moreover, the quality of service (QoS) is satisfied by distributing data along a path haying no more than a given number of arcs between the root node of a session and a terminal node of it in the routing tree. Thus, a multicast routing tree for a session can be represented as a hop constrained Steiner tree. In this paper, we consider the hop-constrained multicast route packing problem with bandwidth reservation. Given a set of multicast sessions, each of which has a hop limit constraint and a required bandwidth, the problem is to determine a set of multicast routing trees in an arc-capacitated network to minimize cost. We propose an integer programming formulation of the problem and an algorithm to solve it. An efficient column generation technique to solve the linear programming relaxation is proposed, and a modified cover inequality is used to strengthen the integer programming formulation.

• The Journal of Information Systems
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• v.28 no.4
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• pp.175-197
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• 2019

Purpose This study develops a heuristic algorithm to solve the time-resource tradeoffs in project scheduling problems with a real basis. Design/methodology/approach Resource constrained project scheduling problem with time-resource tradeoff is well-known as one of the NP-hard problems. Previous researchers have proposed heuristic that minimize Makespan of project scheduling by deriving optimal combinations from finite combinations of time and resource. We studied to solve project scheduling problems by deriving optimal values from infinite combinations. Findings We developed heuristic algorithm named Push Algorithm that derives optimal combinations from infinite combinations of time and resources. Developed heuristic algorithm based on simulated annealing shows better improved results than genetic algorithm and further research suggestion was discussed as a project scheduling problem with multiple resources of real numbers.

• Korean Journal of Construction Engineering and Management
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• v.5 no.5 s.21
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• pp.135-142
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• 2004

This paper proposes the CPM bar chart (CBC), a hybrid of the bar chart and the critical pad method (CPM). The CBC overcomes shortages of the fenced bar chart, while still keeping advantages. The fence with direction is applied instead of the broken fence, which triggers considerable problems to identify and apply in the fenced bar chart. In addition, the notorious task to find dummy activities is no longer required. Upon the benefits of simplicity in the bar chart and logical work sequence in the CPM network. the CBC provides a relatively easy way to create and understand a schedule, thus improving communication quality between project participants. With the advantages, the CBC can also be effectively applied to various scheduling techniques such as resource constrained scheduling, resource leveling, scheduling with activity split, delay impact analysis, etc.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2004.11a
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• pp.575-578
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• 2004

Since a construction project is a series of works that utilizes resources to accomplish the project goal for a given time period, efficient resource management is a prerequisite for the success of the project. Two major areas of resource management are resource constrained scheduling focusing on the limited resource availability and resource leveling focusing on smoothing resource usage pattern on the fixed project completion time. It is not available, however, to apply both techniques to a project at the same time. This paper proposes a model to enhance the minimum moment algorithm of resource leveling, aiming to find an efficient usage of resources and an appropriate project completion time. A survey is performed to evaluate the major five factors using the AHP.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.10
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• pp.3989-4006
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• 2020

The intelligent agriculture monitoring is based on the perception and analysis of environmental data, which enables the monitoring of the production environment and the control of environmental regulation equipment. As the scale of the application continues to expand, a large amount of data will be generated from the perception layer and uploaded to the cloud service, which will bring challenges of insufficient bandwidth and processing capacity. A fog-based offline and real-time hybrid data analysis architecture was proposed in this paper, which combines offline and real-time analysis to enable real-time data processing on resource-constrained IoT devices. Furthermore, we propose a data process-ing algorithm based on the incremental principal component analysis, which can achieve data dimensionality reduction and update of principal components. We also introduce the concept of Squared Prediction Error (SPE) value and realize the abnormal detection of data through the combination of SPE value and data fusion algorithm. To ensure the accuracy and effectiveness of the algorithm, we design a regular-SPE hybrid model update strategy, which enables the principal component to be updated on demand when data anomalies are found. In addition, this strategy can significantly reduce resource consumption growth due to the data analysis architectures. Practical datasets-based simulations have confirmed that the proposed algorithm can perform data fusion and exception processing in real-time on resource-constrained devices; Our model update strategy can reduce the overall system resource consumption while ensuring the accuracy of the algorithm.