Automating the task of tracking the delivery and receipt of fabricated pipe spools in industrial projects

Song, Jong-Hwa; Haas, Carl T.; Caldas, Carlos H.; Ergen, Esin; Akinci, Burcu

doi:10.1016/j.autcon.2005.03.001

Cited by 218 publications

(99 citation statements)

References 13 publications

(25 reference statements)

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“…For Equations (1) and (2), in order to make the component part of , in the reasonable area, setting ∈ [− max , max ], ∈ [− max , max ], where the maximum value of the velocity is determined according to the length of the particle. Generally, if the scope of solution is [− max , max ], then max = * max , 0.1 ≤ ≤ 0.2.…”

Section: +1 = + +1mentioning

confidence: 99%

“…Ranky [9] proposed RFID as one of the most important ten technologies of this century. Li and Becerik-Gerber [10] presented a comparative study of eight indoor positioning sensing technologies taking into consideration the accuracy, power supply, affordability, wireless of the ith particle is updated according to Equations (1) and (2), respectively, until a fixed number of times is iterated or a predetermined minimum error is achieved. (2) where i = 1, 2, · · · , M, M is the number of particles in the population; x i and v i are the position and velocity of the ith particle, respectively; the t + 1 is the index of the current, new, iteration and t refers to the previous, old, iteration; w is a parameter that weighs the inertia of the particle; c 1 and c 2 are the parameters that weigh the cognitive and social constituents, respectively; rand 1 and rand 2 are random numbers which are uniformly distributed within the range [0, 1]; p g is the best position inferred by the swarm considering the experience of all the particles, and p i is the best position visited by the ith particle so far.…”

Section: Introductionmentioning

confidence: 99%

“…When it comes to indoor areas, due to the poor reception of satellite signals, GPS is unreliable. At the same time, GPS is expensive for deployment to automatically track individual material items [2]. To solve this problem, a variety range of technologies were used for indoor location sensing, such as wireless local area network (WLAN) [3], ultra-wideband (UWB) [4], indoor GPS-based solutions [5], and infrared positioning systems [6].…”

Section: Introductionmentioning

confidence: 99%

“…The swarm represents a set of the possible solutions. Every iterative process, the position and velocity of the th particle is updated according to Equations (1) and (2), respectively, until a fixed number of times is iterated or a predetermined minimum error is achieved.…”

Section: Introductionmentioning

confidence: 99%

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An Improved Particle Swarm Optimization-Based Feed-Forward Neural Network Combined with RFID Sensors to Indoor Localization

Wang

Shi

2017

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Location-based services (LBS) have long been recognized as a significant component of the emerging information services. However, the localization cost and the performance of algorithm still need to be optimized. In the study, an improved particle swarm optimization algorithm based on a feed-forward neural network (IMPSO-FNN) combined with RFID sensors is proposed, which can achieve the best indoor positioning location and overcome the problems effectively. In IMPSO-FNN, an improved PSO algorithm (IMPSO) is developed to determine the optimal connecting weights and markedly optimize the network parameters and structural parameters for the FNN, and then an optimal location prediction model is established by the IMPSO-FNN. To avoid the interference of environmental noise for the experimental data, some preprocessing methods are used during the positioning process. The computational results for learning two continuous functions show that the proposed positioning algorithm has a faster convergence rate and higher generalization performance. The model evaluation results also verify that the proposed positioning method really is superior to other algorithms in terms of the learning ability, efficiency, and positioning accuracy.

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Section: +1 = + +1mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Improved Particle Swarm Optimization-Based Feed-Forward Neural Network Combined with RFID Sensors to Indoor Localization

Wang

Shi

2017

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“…A wide range of technologies were used for indoor location sensing such as ultrasound solutions 12 ,…”

Section: Introductionmentioning

confidence: 99%

RFID Indoor Location Identification for Construction Projects

Montaser¹,

Moselhi²

2012

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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Purpose The purpose of this paper is to present an indoor location identification methodology using low cost passive Radio Frequency Identification (RFID) for construction projects. Method Location-aware information at construction sites is an emerging area, concerned with automating the delivery of spatial information on the location of materials, workforce, and equipment. This spatial information can provide knowledge on construction project status. Most RFID localization literature focuses on deploying active RFID tags, which are expensive and aimed at indoor localization. It has been experimented with in operating buildings but not on construction jobsites and with a different time span. For this paper low cost passive RFID-tags were used. Using this methodology, a number of passive RFID tags are distributed onsite where work is progressing and the user, such as the field superintendent, carries a mobile RFID-reader. The indoor construction work-active area is divided into exclusive zones for tracking. Each passive RFID-tag is used as a reference point with known location (LANDMARK) within a predefined zone. The reference tag's known location is used to estimate the location of the user. The methodology uses Received Signal Strength Indicator (RSSI) as the main attribute for signal measurement to process the reader captured data. Two localization algorithms (Trilateration and Proximity) were used to identify the user location. After identifying the user's location, the user can take snapshots with a camera and write comments about onsite activities. The collected data will be then attached to the as-planned project schedule and related CAD drawings automatically at the identified location. This data is used to represent actual progress, which is then compared to as-planned baseline progress using earned value analysis. Results & Discussion An actual construction jobsite was used to build 5 test beds at different locations and different construction time spans. Experiments were conducted on the test beds to compare the results obtained from Trilateration and Proximity algorithms. The results shows mean error equals to 1m for Trilateration method with standard deviation of 0.4m and for Proximity method mean error equals to 1.76m with standard deviation of 0.5m. Indoor location identification could be utilized for tracking the project status.

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