• Korean Chemical Engineering Research
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• v.58 no.4
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• pp.565-572
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• 2020

This study developed a software platform using machine learning of artificial intelligence to optimize the distillation column system. The distillation column is representative and core process in the petrochemical industry. Process stabilization is difficult due to various operating conditions and continuous process characteristics, and differences in process efficiency occur depending on operator skill. The process control based on the theoretical simulation was used to overcome this problem, but it has a limitation which it can't apply to complex processes and real-time systems. This study aims to develop an empirical simulation model based on machine learning and to suggest an optimal process operation method. The development of empirical simulations involves collecting big data from the actual process, feature extraction through data mining, and representative algorithm for the chemical process. Finally, the platform for the distillation column was developed with verification through a developed model and field tests. Through the developed platform, it is possible to predict the operating parameters and provided optimal operating conditions to achieve efficient process control. This study is the basic study applying the artificial intelligence machine learning technique for the chemical process. After application on a wide variety of processes and it can be utilized to the cornerstone of the smart factory of the industry 4.0.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.2
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• pp.501-514
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• 2015

Earthwork is a basic operation for all forms of civil works and affects construction time, cost and productivity. It is a mechanized operation that needs various construction equipment as a group and uses a lot of fuel for construction equipment. But, the problem is that earthwork operation is usually performed by equipment operator's heuristic and intuition, which can cause low productivity, high fuel consumption, and high carbon dioxide emission. As one of solutions for this problem, the fleet management system for construction equipment is suggested for effective earthwork planning, optimal equipment allocation, efficient machine operation, fast information exchange, and so forth. The purpose of this research is to suggest core methods for developing the equipment fleet management system. The methods include 3D solid parametric model generation, soil distribution using Cctree data structure, equipment fleet construction and equipment fleet operation. A simulation test is performed to verify the effectiveness of the equipment fleet management system in terms of equipment operating ratio, fuel usage, and $CO_2$ emission.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.385-391
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• 2023

As the use of electric vehicles has increased to minimize carbon emissions, the analyzing the state and performance of lithium-ion batteries that is instrumental in electric vehicles have been important. Comprehensive analysis using not only the voltage, current and temperature of the battery pack, which can affect the condition and performance of the battery, but also the driving data and charging pattern data of the electric vehicle is required. Therefore, a thorough analysis is imperative, utilizing electric vehicle operation data, charging pattern data, as well as battery pack voltage, current, and temperature data, which collectively influence the condition and performance of the battery. Therefore, collection and preprocessing of battery data collected from electric vehicles, collection and preprocessing of data on driver driving habits in addition to simple battery data, detailed design and modification of artificial intelligence algorithm based on the analyzed influencing factors, and A battery analysis and evaluation model was designed. In this paper, we gathered operational data and battery data from real-time electric buses. These data sets were then utilized to train a Random Forest algorithm. Furthermore, a comprehensive assessment of battery status, operation, and charging patterns was conducted using the explainable Artificial Intelligence (XAI) algorithm. The study identified crucial influencing factors on battery status, including rapid acceleration, rapid deceleration, sudden stops in driving patterns, the number of drives per day in the charging and discharging pattern, daily accumulated Depth of Discharge (DOD), cell voltage differences during discharge, maximum cell temperature, and minimum cell temperature. These factors were confirmed to significantly impact the battery condition. Based on the identified influencing factors, a battery analysis and evaluation model was designed and assessed using the Random Forest algorithm. The results contribute to the understanding of battery health and lay the foundation for effective battery management in electric vehicles.

• Smart Media Journal
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• v.12 no.11
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• pp.57-66
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• 2023

In this paper, we present a study on an AI-based system utilizing the CCTV system within city buses to predict the intentions of boarding and alighting passengers, with the aim of preventing accidents. The proposed system employs the YOLOv7 Pose model to detect passengers, while utilizing an LSTM model to predict intentions of tracked passengers. The system can be installed on the bus's CCTV terminals, allowing for real-time visual confirmation of passengers' intentions throughout driving. It also provides alerts to the driver, mitigating potential accidents during passenger transitions. Test results show accuracy rates of 0.81 for analyzing boarding intentions and 0.79 for predicting alighting intentions onboard. To ensure real-time performance, we verified that a minimum of 5 frames per second analysis is achievable in a GPU environment. his algorithm enhance the safety of passenger transitions during bus operations. In the future, with improved hardware specifications and abundant data collection, the system's expansion into various safety-related metrics is promising. This algorithm is anticipated to play a pivotal role in ensuring safety when autonomous driving becomes commercialized. Additionally, its applicability could extend to other modes of public transportation, such as subways and all forms of mass transit, contributing to the overall safety of public transportation systems.

• The Journal of the Acoustical Society of Korea
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• v.43 no.1
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• pp.39-48
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• 2024

A car horn serves a crucial safety role as a means of communication between drivers and a part that alerts pedestrians in advance. While previous studies have utilized finite element method and electric circuit model to simulate and analyze characteristics of the car horns, there remains a lack of research on design methods of a trumpet horn. This paper presents a design approach that predicts the operating frequency based on the acoustic reactance at the throat of the horn, once the vibrating part is determined. We deal with a horn combining both an exponential horn and a waveguide in the acoustic section, and confirm that the acoustic reactance at the horn throat measured by impedance tube experiment agrees well compared with the numerical result obtained using the finite element method. The resonance frequency of the car horn is predicted using the COMSOL Multiphysics finite element numerical analysis model, and the proposed design method is validated by measuring the operating frequency of the designed horn in a sound pressure experiment. As a result, the resonance measured in a semi-anechoic chamber environment by applying a DC voltage of 12 [V] excluding the holder occurs accurately within a few [Hz] of the design operating frequency. This paper discuss the design method of a trumpet horn from the perspective of the horn's acoustic reactance, and is expected to be useful for designing horn systems.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.5
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• pp.581-589
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• 2021

With the recent increase in the introduction of micro-electric vehicles in Korea, interest in micro-electric vehicle user satisfaction is increasing to revitalize related markets. In this paper, a basic study was conducted on the development of public services using micro-electric vehicle based on the constituent factors of user satisfaction. The survey includes: ① 'Analytic Hierarchy Process (AHP) for selecting the priority factors of user satisfaction of micro-electric vehicles', ② 'A survey of micro-electric vehicles image' to collect data in advance for providing users' preferences and transportation services for micro-electric vehicles, ③ In order to investigate the user satisfaction level of users who actually operated micro-electric vehicles, the order of 'user satisfaction survey of micro-electric vehicle drivers' was conducted. In the Analytic Hierarchy Process (AHP) analysis, it was found that users regarded as important in the order of 'user utilization data', 'vehicle movement data', and 'charging service data'. In the micro-electric vehicle image survey, users perceived micro-electric vehicles more positively in terms of "safety", 'durability', 'Ride comfort', 'design', 'MOOE (Maintenance and other operating expense)', and 'environment-friendly' when comparing micro-electric vehicles with electric motorcycles. In the survey on the user satisfaction of micro-electric vehicle drivers, the use of micro-electric vehicle did not directly affect work performance efficiency, and there was an experience of being disadvantaged on the road due to the size of the micro-electric vehicle, and driving in a cluster of micro-electric vehicle for outdoor advertisements. The city's public relations effect was great, but it was concerned about safety. In the future, based on the results of this study, we plan to build a user satisfaction structural equation model, preemptively discover feedback R&D for micro-electric vehicle utilization services in the public field, and actively seek to discover new public mobility support services.

• Journal of Korean Society of Transportation
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• v.22 no.5
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• pp.123-137
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• 2004

The study on traffic assignment is actively being performed which reflect networks status using time. Its background is increasing social needs to use traffic assignment models in not only hardware area of road network plan but also software area of traffic management or control. In addition, multi-class traffic assignment model is receiving study in order to fill a gap between theory and practice of traffic assignment model. This model is made up of two, one of which is multi-driver class and the other multi-vehicle class. The latter is the more realistic because it can be combined with dynamic model. On this background, this study is to build multidynamic model combining the above-mentioned two areas. This has been a theoretic pillar of ITS in which dynamic user equilibrium assignment model is now made an issue, therefore more realistic dynamic model is expected to be built by combining it with multi-class model. In case of multi-vehicle, FIFO would be violated which is necessary to build the dynamic assignment model. This means that it is impossible to build multi-vehicle dynamic model with the existing dynamic assignment modelling method built under the conditions of FIFO. This study builds dynamic network model which could relieve the FIFO conditions. At the same time, simulation method, one of the existing network loading method, is modified to be applied to this study. Also, as a solution(algorithm) area, time dependent shortest path algorithm which has been modified from existing shortest path algorithm and the existing MSA modified algorithm are built. The convergence of the algorithm is examined which is built by calculating dynamic user equilibrium solution adopting the model and algorithm and grid network.

• The Transactions of the Korea Information Processing Society
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• v.7 no.9
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• pp.3037-3047
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• 2000

This paper describes a method of tracking a close leading vehicle by color image processing using the pairs of tail and brake lights. which emit red light and are housed on the rear of the vehicle in stop-and-go traffic condition. In the color image converted as an HSV color model. candidate regions of rear lights are identified using the color features of a pair of lights. Then. the pair of tailor brake lights are detected by means of the geometrical features and location features for the pattern of the tail and brake lights. The location of the leading vehicle can be estimated by the location of the detected lights and the vehicle can be tracked continuously. It is also possible to detect the braking status of the leading vehicle by measuring the change in HSV color components of the pair of lights detected. In the experiment. this method tracked a leading vehicle successfully from urban road images and was more useful at night than in the daylight. The KAV-Ill (Korea Autonomous Vehicle- Ill) equipped with a color vision system implementing this algorithm was able to follow a leading vehicle autonomously at speeds of up to 15km!h on a paved road at night. This method might be useful for developing an LSA (Low Speed Automation) system that can relieve driver's stress in the stop-and-go traffic conditions encountered on urban roads.

• Journal of Korean Society of Transportation
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• v.36 no.3
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• pp.169-183
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• 2018

Aggressive driving leads to a greater crash potential because it threatens surrounding vehicles. This study conducted traffic simulation experiments using driving behavior data obtained from multi-agent driving simulations. VISSIM traffic simulator and surrogate safety assessment model (SSAM) were used to identify the impacts of aggressive driving on traffic stream in terms of safety and operational efficiency. Market penetration rates (MPR) of aggressive driving vehicle, coupled with various traffic conditions, were taken into consideration in analyzing the impacts. As expected, it was identified that aggressive driving vehicles tended to deteriorate the traffic safety performance. From the perspective of operational efficiency, interesting results were observable. Under level of service (LOS) A, B, and C, it was observed that the average travel speed increased with greater MPRs. Conversely, the average travel speed decreased with under LOS D and E conditions. The outcome of this study would be effectively used for developing safety-related policies for reducing aggressive driving behavior.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.35 no.2
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• pp.161-168
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• 2011

The heating, ventilating, air conditioning (HVAC) system is a very important part of an automotive vehicle: it controls the microclimate inside the passenger's compartment and removes the frost or mist that is produced in cold/rainy weather. In this study, the numerical analysis of the defrost duct in an HVAC system and the de-icing pattern is carried out using commercial CFX-code. The mass flow distribution and flow structure at the outlet of the defrost duct satisfied the duct design specification. For analyzing the de-icing pattern, additional grid generation of solid domain of ice and glass is pre-defined for conductive heat transfer. The flow structure near the windshield, streakline, and temperature fields clearly indicate that the de-icing capacity of the given defrost duct configuration is excellent and that it can be operated in a stable manner. In this paper, the unsteady changes in temperature, water volume fraction, and static enthalpy at four monitoring points are discussed.