• Journal of Korean Society of Transportation
• /
• v.26 no.4
• /
• pp.205-215
• /
• 2008

As static traffic assignment has reached its limitation with ITS policy applications and due to the increase of interest in studies of ITS policies since the late 1980's, dynamic traffic assignment has been considered a tool to overcome such limitations. This study used the Dynameq program, which simulates route choice behavior by macroscopic modeling and dynamic network loading and traffic flow by microscopic modeling in consideration of the feasibility of the analysis of practical traffic policy. The essence of this study is to evaluate the feasibility for analysis in practical transportation policy of using the dynamic traffic assignment technique. The study involves the verification of the values estimated from the dynamic traffic assignment with South Korea's expressway network and dynamic O/D data by comparing results with observed link traffic volumes. This study used dynamic O/D data between each toll booth, which can be accurately obtained from the highway Toll Collection System. Then, as an example of its application, exclusive bus-lane policies were analyzed with the dynamic traffic assignment model while considering hourly variations.

• International conference on construction engineering and project management
• /
• 2022.06a
• /
• pp.814-822
• /
• 2022

The deployment of sensors for Structural Health Monitoring requires a complicated network arrangement, ground truthing, and calibration for validating sensor performance periodically. Any conventional sensor on a structural element is also subjected to static and dynamic vertical loadings in conjunction with other environmental factors, such as brightness, noise, temperature, and humidity. A structural model with strain gauges was built and tested to get realistic sensory information. This paper investigates different deep learning architectures and algorithms, including unsupervised, autoencoder, and supervised methods, to benchmark blind drift calibration methods using deep learning. It involves a fully connected neural network (FCNN), a long short-term memory (LSTM), and a gated recurrent unit (GRU) to address the blind drift calibration problem (i.e., performing calibrations of installed sensors when ground truth is not available). The results show that the supervised methods perform much better than unsupervised methods, such as an autoencoder, when ground truths are available. Furthermore, taking advantage of time-series information, the GRU model generates the most precise predictions to remove the drift overall.

• Smart Structures and Systems
• /
• v.6 no.5_6
• /
• pp.525-543
• /
• 2010

Testing and validation processes are critical tasks in developing a new hardware platform based on a new technology. This paper describes a series of experiments to evaluate the performance of a newly developed MEMS-based wireless sensor node as part of a wireless sensor network (WSN). The sensor node consists of a sensor board with four accelerometers, a thermometer and filtering and digitization units, and a MICAz mote for control, local computation and communication. The experiments include calibration and linearity tests for all sensor channels on the sensor boards, dynamic range tests to evaluate their performance when subjected to varying excitation, noise characteristic tests to quantify the noise floor of the sensor board, and temperature tests to study the behavior of the sensors under changing temperature profiles. The paper also describes a large-scale deployment of the WSN on a long-span suspension bridge, which lasted over three months and continuously collected ambient vibration and temperature data on the bridge. Statistical modal properties of a bridge tower are presented and compared with similar estimates from a previous deployment of sensors on the bridge and finite element models.

• Journal of Korean Society of Transportation
• /
• v.22 no.3 s.74
• /
• pp.179-201
• /
• 2004

Online traffic flow modeling is attracting more attention due to intelligent transport systems and technologies. The flow-density relation plays an important role in traffic flow modeling and provides a basic way to illustrate traffic flow behavior under different traffic flow and traffic density conditions. Until now the research effort has focused mainly on the shape of the relation. The time series of the relation has not been identified clearly, even though the time series of the relation reflects the upstream/downstream traffic conditions and should be considered in the traffic flow modeling. In this paper the flow-density relation is analyzed dynamically and interpreted as a states diagram. The dynamic flow-density relation is quantified by applying fuzzy logic. The quantified dynamic flow-density relation builds the basis for online application of a macroscopic traffic flow model. The new approach to online modeling of traffic flow applying the dynamic flow-density relation alleviates parameter calibration problems stemming from the static flow-density relation.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.10
• /
• pp.4117-4135
• /
• 2020

Due to the angle of view, illumination and scene diversity, real-time dynamic face detection and recognition is no small difficulty in those unrestricted environments. In this study, we used the intrinsic correlation between detection and calibration, using a multi-task cascaded convolutional neural network(MTCNN) to improve the efficiency of face recognition, and the output of each core network is mapped in parallel to a compact Euclidean space, where distance represents the similarity of facial features, so that the target face can be identified as quickly as possible, without waiting for all network iteration calculations to complete the recognition results. And after the angle of the target face and the illumination change, the correlation between the recognition results can be well obtained. In the actual application scenario, we use a multi-camera real-time monitoring system to perform face matching and recognition using successive frames acquired from different angles. The effectiveness of the method was verified by several real-time monitoring experiments, and good results were obtained.

• 제어로봇시스템학회:학술대회논문집
• /
• 1992.10b
• /
• pp.124-129
• /
• 1992

The goal of the proposed Intelligent Assisting System - IAS is to assist human operators in an intelligent way, while leaving decision and goal planning instances for the human. To realize the IAS the very important issue of manipulation skill identification and analysis has to be solved, which then is stored in a Skill Data Base. Using this data base the IAS is able to perform complex manipulations on the motion control level and to assist the human operator flexibly. We propose a model for manipulation skill based on the dynamics of the grip transformation matrix, which describes the dynamic transformation between object space and finger joint space. Interaction with a virtual world simulator allows the calculation and feedback of appropriate forces through controlled actuators of the sensor glove with 10 degrees-of-freedom. To solve the sensor glove calibration problem, we learn the nonlinear calibration mapping by an artificial neural network(ANN). In this paper we also describe the experimental system setup of the skill acquisition and transfer system as a first approach to the IAS. Some simple manipulation examples and simulation results show the feasibility of the proposed manipulation skill model.

• Korean Management Science Review
• /
• v.33 no.1
• /
• pp.49-58
• /
• 2016

Focusing on the drastic increase of smart devices, machine generated data expansion is a general phenomenon in network services and IoT (Internet of Things). Especially, built-in multi sensors in a smart device are used for collection of user status and moving data. Combining the internal sensor data and environmental information, we can determine landmarks that decide a pedestrian's locations. We use an ANOVA method to analyze data acquired from multi sensors and propose a landmark classification algorithm. We expect that the proposed algorithm can achieve higher accuracy of indoor-outdoor positioning system for pedestrians.

• Journal of Advanced Marine Engineering and Technology
• /
• v.31 no.5
• /
• pp.531-542
• /
• 2007

Simultaneous measurement technique for temperature and velocity fields near a heated solid body has been constructed. The measurement system consists of a 3-late CCD color camera, a color image grabber, a 1ighting system, a host computer and a software for the whole quantification process. Thermo Chromic Liquid Crystals (TCLC) was used as temperature sensors. A neural network was used to get a calibration curve between the temperature and the color change of the TCLC in order to enhance the dynamic range of temperature measurement. The velocity field measurement was attained by the use of the fray-level images taken for the flow field, and by introducing the cross-correlation technique. The temperature and the velocity fields of the forced and the natural convective flows neat the surface of a cartridge heater were measured simultaneously with the constructed measurement system.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.26 no.5D
• /
• pp.731-738
• /
• 2006

Facing congestion, people want to know traffic information about their routes, especially real-time link travel time (LTT). In this paper, as a sequel paper of the previous non-taxi based LTT generating study by Choi et al. (1998), taxi based GPS probes have been tried to produce LTT for urban arterials. Taxis in itself are good deployment mode of GPS probes although it by nature experiences boarding and alighting time noises which should be accounted. A heuristic real-time dynamic outlier filter algorithm for taxi GPS probe has been developed focusing on urban arterials. An actual traffic survey for dynamic link travel times has been conducted using license plate method for the test arterials of Seoul city transportation network. With the algorithm, it is estimated that 70% of outliers have been filtered and the relative error has been improved by 73.7%. The filtering algorithm developed here would be expected to be in use for other spatial sites with some calibration efforts. Some limitations and future research agenda have also been discussed.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.16 no.9
• /
• pp.1847-1855
• /
• 2012

A 1V 1.6-GS/s 6-bit flash analog-to-digital converter (ADC) with a clock calibration circuit is proposed. A single track/hold circuit with a bootstrapped analog switch is used as an input stage with a supply voltage of 1V for the high speed operation. Two preamplifier-arrays and each comparator composed of two-stage are implemented for the reduction of analog noises and high speed operation. The clock calibration circuit in the proposed flash ADC improves the dynamic performance of the entire flash ADC by optimizing the duty cycle and phase of the clock. It adjusts the reset and evaluation time of the clock for the comparator by controlling the duty cycle of the clock. The proposed 1.6-GS/s 6-bit flash ADC is fabricated in a 1V 90nm 1-poly 9-metal CMOS process. The measured SNDR is 32.8 dB for a 800 MHz analog input signal. The measured DNL and INL are +0.38/-0.37 LSB, +0.64/-0.64 LSB, respectively. The power consumption and chip area are $800{\times}500{\mu}m2$ and 193.02mW.