• Journal of Advanced Navigation Technology
• /
• v.28 no.4
• /
• pp.566-573
• /
• 2024

Applying autonomous driving technology to construction sites is very difficult due to safety issues. However, the application of various positioning and sensing devices, such as cameras and radars, to construction equipment is very active. Based on these technological trends, the government is making various efforts, including the Serious Accident Punishment Act and support for industrial safety management expenses, to reduce the incidence of accidents caused by construction equipment and industrial vehicles. And, related industries have been developing various safety equipment over the past few years and applying them to the field. In this paper, we investigate the current status of safety equipment-related technologies currently applied to construction equipment and industrial vehicles, and propose a direction for the development of safety technology in construction equipment based on artificial intelligence. Improving the safety and work efficiency of construction equipment based on the technology proposed in this paper should be reviewed through simulation in the future.

• Journal of the Health Care and Life Science
• /
• v.9 no.2
• /
• pp.297-302
• /
• 2021

As the price of LiDAR developed for autonomous driving has dropped dramatically, LiDAR has begun to be applied in various fields. Typical examples are vacuum cleaner robots, autonomous delivery robots, and autonomous obstacle avoidance drones. LiDAR is becoming the only means of figuring out the location of an object in real time while compensating for the weakness that 2D or 3D cameras are vulnerable to lighting. In this paper, we propose a method to monitor the movement of a patient by installing a 2D lidar in a hospital room. When a patient who needs intensive monitoring due to psychologically unstable, suicidal intention, or psychotic findings is alone in the ward, 2D LiDAR monitors the patient's movement and sends an appropriate alarm to the management room to effectively monitor the patient. devised a way to do it.

• Journal of Intelligence and Information Systems
• /
• v.19 no.2
• /
• pp.157-175
• /
• 2013

Since Korean government announced 'Smartwork promotion strategy' in 2010, Korean firms and government organizations have started to adopt smartwork. However, the smartwork has been implemented only in a few of large enterprises and government organizations rather than SMEs (small and medium enterprises). In USA, both Yahoo! and Best Buy have stopped their flexible work because of its reported low productivity and job loafing problems. In addition, according to the literature on smartwork, we could draw obstacles of smartwork adoption and categorize them into the three types: institutional, organizational, and technological. The first category of smartwork adoption obstacles, institutional, include the difficulties of smartwork performance evaluation metrics, the lack of readiness of organizational processes, limitation of smartwork types and models, lack of employee participation in smartwork adoption procedure, high cost of building smartwork system, and insufficiency of government support. The second category, organizational, includes limitation of the organization hierarchy, wrong perception of employees and employers, a difficulty in close collaboration, low productivity with remote coworkers, insufficient understanding on remote working, and lack of training about smartwork. The third category, technological, obstacles include security concern of mobile work, lack of specialized solution, and lack of adoption and operation know-how. To overcome the current problems of smartwork in reality and the reported obstacles in literature, we suggest a novel smartwork service model based on NFC(Near Field Communication). This paper suggests NFC-based Smartwork Service Model composed of NFC-based Smartworker networking service and NFC-based Smartwork space management service. NFC-based smartworker networking service is comprised of NFC-based communication/SNS service and NFC-based recruiting/job seeking service. NFC-based communication/SNS Service Model supplements the key shortcomings that existing smartwork service model has. By connecting to existing legacy system of a company through NFC tags and systems, the low productivity and the difficulty of collaboration and attendance management can be overcome since managers can get work processing information, work time information and work space information of employees and employees can do real-time communication with coworkers and get location information of coworkers. Shortly, this service model has features such as affordable system cost, provision of location-based information, and possibility of knowledge accumulation. NFC-based recruiting/job-seeking service provides new value by linking NFC tag service and sharing economy sites. This service model has features such as easiness of service attachment and removal, efficient space-based work provision, easy search of location-based recruiting/job-seeking information, and system flexibility. This service model combines advantages of sharing economy sites with the advantages of NFC. By cooperation with sharing economy sites, the model can provide recruiters with human resource who finds not only long-term works but also short-term works. Additionally, SMEs (Small Medium-sized Enterprises) can easily find job seeker by attaching NFC tags to any spaces at which human resource with qualification may be located. In short, this service model helps efficient human resource distribution by providing location of job hunters and job applicants. NFC-based smartwork space management service can promote smartwork by linking NFC tags attached to the work space and existing smartwork system. This service has features such as low cost, provision of indoor and outdoor location information, and customized service. In particular, this model can help small company adopt smartwork system because it is light-weight system and cost-effective compared to existing smartwork system. This paper proposes the scenarios of the service models, the roles and incentives of the participants, and the comparative analysis. The superiority of NFC-based smartwork service model is shown by comparing and analyzing the new service models and the existing service models. The service model can expand scope of enterprises and organizations that adopt smartwork and expand the scope of employees that take advantages of smartwork.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.9 no.4
• /
• pp.1-12
• /
• 2010

This paper deals with the transmission interval of vehicle data in smart highway where WAVE (Wireless Access for Vehicular Environments) systems have been installed for advanced road infrastructure. The vehicle data could be collected at every second, which is containing location information of the vehicle as well the vehicle speed, RPM, fuel consuming and safety data. The safety data such as DTC code, can be collected through OBD-II. These vehicle data can be used for valuable contents for processing and providing traffic information. In this paper, we propose a model to decide the collection interval of vehicle information in real time environment. This model can change the transmission interval along with special and time-variant traffic condition based on the 32 scenarios using microscopic traffic simulator, VISSIM. We have reviewed the transmission interval, communication transmission quantity and communication interval, tried to confirm about communication possibility and BPS, etc for each scenario. As results, in 2-lane from 1km highway segment, most appropriate transmission interval is 2 times over spatial basic segment considering to communication specification. In the future, if a variety of wireless technologies on the road is introduced, this paper considering not only traffic condition but also wireless network specification will be utilized the high value.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2011.05a
• /
• pp.336-339
• /
• 2011

In the RTLS(Real Time Location Based System), in case of existing a number of moving target, extremely complecated data flow is can be occurred. In the network where single gateway exists, various data which was collected from sensor node is transmitted along the simple route as time goes by. In case of multi-gateway configuration, the collected data is transmitted through diverse routes rather than simple route. This kind of data causes jams on nodes and this brings down the performance of the network. Different from existing studies, in this thesis, MAC (Media Access Control) protocol which minimizes data collision between nodes and guarantees QoS(Quality of Service) is suggested, in order to communicate efficiently in multi-gateway underwater sensor network environment. In the suggested protocol, source node which wants to transmit data makes a channel reservation to a number of destination node using a RTS packet. Source node reserves a channel without collision, by scheduling CTS response time using expected delay information from neighbor nodes. Once the reservation is made, source node transmit data packet without collision. This protocol analyzes/estimates the performance compared to a method provided from existing studies via simulation. As a results of the analysis, it was comfirmed that the suggested method has better performance, such as efficiency and delay.

• Information Systems Review
• /
• v.8 no.2
• /
• pp.29-49
• /
• 2006

The introduction of the Digital Economy has formed a new market producing and trading information. Depending on the current contexts, each user evaluates identical information differently. It is difficult, even though important, to create and deliver the information customized to individual users by using some factors as time, location, and their personal characteristics. Information value, therefore, could be influenced by the capability of information systems to delivery useful information based on individual contexts to the right user immediately at the right time. From this point of view, we argue mobile systems which are able to be aware of individual contexts and deliver contextual information in real time can improve information value easily than other systems can. This research presents the results of an empirical test about antecedents to mobile advertising value. Though context relevance doesn't influence directly mobile advertising value, it plays an important role enhancing information usefulness which has great influence on mobile advertising value. Moreover, to supply information connected with users' context overcomes the effect of irritation. Lastly, entertainment can improve mobile advertising value as satisfying user's hedonic desire beyond the information source supporting decision making.

• Nuclear Engineering and Technology
• /
• v.42 no.5
• /
• pp.582-589
• /
• 2010

As a part of the back-end fuel cycle, transportation of spent nuclear fuel (SNF) from nuclear power plants (NPPs) to a fuel storage facility is very important in establishing a nuclear fuel cycle. In Korea, the accumulated amount of SNF in the NPP pools is troublesome since the temporary storage facilities at these NPP pools are expected to be full of SNF within ten years. Therefore, Korea cannot help but plan for the construction of an interim storage facility to solve this problem in the near future. Especially, a decision on several factors, such as where the interim storage facility should be located, how many casks a transport ship can carry at a time and how many casks are initially required, affect the configuration of the transportation system. In order to analyze the various possible candidate scenarios, we assumed four cases for the interim storage facility location, three cases for the load capacity that a transport ship can carry and two cases for the total amount of casks used for transportation. First, this study considered the currently accumulated amount of SNF in Korea, and the amount of SNF generated from NPPs until all NPPs are shut down. Then, how much SNF per year must be transported from the NPPs to an interim storage facility was calculated during an assumed transportation period. Second, 24 candidate transportation scenarios were constructed by a combination of the decision factors. To construct viable yearly transportation schedules for the selected 24 scenarios, we created a spreadsheet program named TranScenario, which was developed by using MS EXCEL. TranScenario can help schedulers input shipping routes and allocate transportation casks. Also, TranScenario provides information on the cask distribution in the NPPs and in the interim storage facility automatically, by displaying it in real time according to the shipping routes, cask types and cask numbers that the user generates. Once a yearly transportation schedule is established, TranScenario provides some statistical information, such as the voyage time, the availability of the interim storage facility, the number of transported casks sent from the NPPs, and the number of transported casks received at the interim storage facility. By using this information, users can verify and validate a yearly transportation schedule. In this way, the 24 candidate scenarios could be constructed easily. Finally, these 24 scenarios were compared in terms of their operation cost.

• Korean Chemical Engineering Research
• /
• v.59 no.2
• /
• pp.209-218
• /
• 2021

First principle-based modeling studies have been performed to improve the heat exchange efficiency of ORV and optimize operation, but the heat transfer coefficient of ORV is an irregular system according to time and location, and it undergoes a complex modeling process. In this study, FNN, LSTM, and AutoML-based modeling were performed to confirm the effectiveness of data-based modeling for complex systems. The prediction accuracy indicated high performance in the order of LSTM > AutoML > FNN in MSE. The performance of AutoML, an automatic design method for machine learning models, was superior to developed FNN, and the total time required for model development was 1/15 compared to LSTM, showing the possibility of using AutoML. The prediction of NG and seawater discharged temperatures using LSTM and AutoML showed an error of less than 0.5K. Using the predictive model, real-time optimization of the amount of LNG vaporized that can be processed using ORV in winter is performed, confirming that up to 23.5% of LNG can be additionally processed, and an ORV optimal operation guideline based on the developed dynamic prediction model was presented.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2022.05a
• /
• pp.122-124
• /
• 2022

Handling Passenger Traffic is the main challenge for designing an elevator group-control algorithm. Advanced control systems such as Hyundai's Destination Selection System(DSS) lets passengers select the destination by pressing on a selecting screen, and the systems have shown great efficiency. However, the algorithm cannot be applied to the general elevator control system due to the expensive cost of the technology. Often many elevator systems use Nearest Car(NC) algorithms based on the SCAN algorithm, which results in time efficiency problems. In this paper, we designed an elevator group-control algorithm for specific buildings that have approximate timetable data for most of the passengers in the building. In that way, it is possible to predict the destination and the location of passenger calls. The algorithm consists of two parts; the waiting function and the assignment function. They evaluate elevators' actions with respect to the calls and the overall situation. 10 different timetables are created in reference to a real timetable following midday traffic and interfloor traffic. The specific coefficients in the function are set by going through the genetic algorithm process that represents the best algorithm. As result, the average waiting time has shortened by a noticeable amount and the efficiency was close to the known DSS result. Finally, we analyzed the algorithm by evaluating the meaning of each coefficient result from the genetic algorithm.

• International conference on construction engineering and project management
• /
• 2024.07a
• /
• pp.1307-1307
• /
• 2024

Recent research on heavy equipment has been conducted for the purposes of enhanced safety, productivity improvement, and carbon neutrality at construction sites. A sensor-based approach is being explored to monitor the location and movements of heavy equipment in real time. However, it poses significant challenges in terms of time and cost as multiple sensors should be installed on numerous heavy equipment at construction sites. In addition, there is a limitation in identifying the collaboration or interference between two or more heavy equipment. In light of this, a vision-based deep learning approach is being actively conducted to effectively respond to various working conditions and dynamic environments. To enhance the performance of a vision-based activity recognition model, it is essential to secure a sufficient amount of training datasets (i.e., video datasets collected from actual construction sites). However, due to safety and security issues at construction sites, there are limitations in adequately collecting training dataset under various situations and environmental conditions. In addition, the videos feature a sequence of multiple activities of heavy equipment, making it challenging to clearly distinguish the boundaries between preceding and subsequent activities. To address these challenges, this study proposed a domain adaptation in vision-based transfer learning for automated excavator activity recognition utilizing 3D ResNet (residual deep neural network). Particularly, this study aimed to identify the optimal depth of 3D ResNet (i.e., the number of layers of the feature extractor) suitable for domain adaptation via fine-tuning process. To achieve this, this study sought to evaluate the activity recognition performance of five 3D ResNet models with 18, 34, 50, 101, and 152 layers, which used two consecutive videos with multiple activities (5 mins, 33 secs and 10 mins, 6 secs) collected from actual construction sites. First, pretrained weights from large-scale datasets (i.e., Kinetic-700 and Moment in Time (MiT)) in other domains (e.g., humans, animals, natural phenomena) were utilized. Second, five 3D ResNet models were fine-tuned using a customized dataset (14,185 clips, 60,606 secs). As an evaluation index for activity recognition model, the F1 score showed 0.881, 0.689, 0.74, 0.684, and 0.569 for the five 3D ResNet models, with the 18-layer model performing the best. This result indicated that the activity recognition models with fewer layers could be advantageous in deriving the optimal weights for the target domain (i.e., excavator activities) when fine-tuning with a limited dataset. Consequently, this study identified the optimal depth of 3D ResNet that can maintain a reliable performance in dynamic and complex construction sites, even with a limited dataset. The proposed approach is expected to contribute to the development of decision-support systems capable of systematically managing enhanced safety, productivity improvement, and carbon neutrality in the construction industry.