16 research outputs found
์ก์ ์๊ฒฐ ๋ฐ ํต์ ํ์ฑ ์๊ฒฐ ๋ฐฉ๋ฒ์ผ๋ก ์ ์ํ ์ฒ ๊ณ ํฉ๊ธ์ ๋ฏธ์ธ ๊ตฌ์กฐ ๊ฐ์ ๊ณผ ๊ธฐ๊ณ์ ํน์ฑ ํ๊ฐ
ํ์๋
ผ๋ฌธ(๋ฐ์ฌ) -- ์์ธ๋ํ๊ต๋ํ์ : ๊ณต๊ณผ๋ํ ์ฌ๋ฃ๊ณตํ๋ถ, 2021.8. ํ์ฑํ.๋ถ๋ง์ผ๊ธ๊ธฐ์ ์ ์์ฌ๋ฃ ๊ธ์ ๋ถ๋ง์ ์ฌ์ฉํ์ฌ ์๊ฒฐ ๋ถํ์ ์ต์ข
ํ์์ ๊ฐ๊น๊ฒ ์ ์กฐํ์ฌ ๋์ ์์ฐ์ฑ ๋ฐ ์ฌ๋ฃ ์์ค์ด ์ต์ํํ๋ ์ฅ์ ์ด ์๋ ๊ณต์ ๊ธฐ์ ์ด๋ค. ๋ํ, ์๋์ฐจ ์์ฌ, ๊ฐ์ ์ ํ, ์ ์์ ํ ๋ฑ์ ๋ฒ์ฉ ๊ธฐ๊ณ ์์ฌ์ ์ด๋ฅด๊ธฐ ๊น์ง ์ ์ฉ ์ฌ๋ฃ์ ๋ฒ์๋ ๊ด๋ฒ์ํ๋ค. ํ์ง๋ง ์ฒ ๊ณ ํฉ๊ธ์ ์๊ฒฐ ํ 5~15 %์ ๊ธฐ๊ณต์ด ์กด์ฌํ์ฌ ์๊ฒฐ ๋ถํ์ผ๋ก์ ์ ์ฉ์ด ์ ํ์ด ๋๋ค. ์๊ฒฐ์ฒด์ ๊ณ ๋ฐ๋ํ๋ฅผ ์ํด์ ์ก์์๊ฒฐ๋ฒ, ํซํ๋ ์ฑ, ํต์ ํ์ฑ์๊ฒฐ๊ณผ ๊ฐ์ ์๊ฒฐ ๊ธฐ์ ๋ค์ด ํ์ฌ ๋ง์ด ์ฐ๊ตฌ๊ฐ ์งํ๋์ด์๋ค. ํ์ง๋ง ์ฒ ๊ณ ํฉ๊ธ์ ๊ฐ๊ฐ์ ์๊ฒฐ ๋ฐฉ๋ฒ์์ ๋น๋กฏ๋๋ ๊ณ ์ ํ ๋ฏธ์ธ ๊ตฌ์กฐ๋ก ์ธํด ๊ธฐ๊ณ์ ํน์ฑ์ ํฅ์์ด ์ ํ์ ์ด๋ค. ๋ํ ์ด์ฒ๋ฆฌ ๋์ค ๋ฏธ์ธ๊ตฌ์กฐ์ ์๊ฒฐ ๊ฑฐ๋์ ๋ํ ์ด๋ก ์ ์ธ ์ฐ๊ตฌ๊ฐ ์ฒ ์ ํ๊ฒ ๋ถ์๋์ง ์์๋ค. ์ด ์ฐ๊ตฌ์์๋ ์๊ฒฐ ๋ฐฉ๋ฒ, ์๊ฒฐ ์กฐ๊ฑด, ์กฐ์ฑ, ์ด ์ฒ๋ฆฌ ๋ฑ์ ํตํ ๋ฏธ์ธ๊ตฌ์กฐ๋ฅผ ๊ฐ์ ์ด ๊ธฐ๊ณ์ ํน์ฑ์ ๋ผ์น๋ ์ํฅ์ ๋ํ ์ฃผ์ ๋ก ์ด ์ธ ๊ฐ์ง์ ๋ํด ๋
ผํ๊ณ ์ ํ๋ค.
์ฒซ ๋ฒ์งธ ์ฃผ์ ๋ก ๋์ผ (Ni)์ด Fe-B-C ํฉ๊ธ ์์คํ
์ ๋์
๋์ด ๊ณต์ต๋ฐ์ ์จ๋๋ฅผ ๋ฎ์ถฐ ๋ถ์ ํจ์ ํฉ๊ธ์ ์น๋ฐํ๋ฅผ ํฅ์์ํค๊ณ ์ ํ์๋ค. ํ์ง๋ง, ๋ค๋์ ์ก์์ด ์
๊ณ์์ ๊ณต์ต๊ฒฝ์ง์ (M23B6, M3B, M2B)์ ์ฐ์ ๋คํธ์ํฌ๋ฅผ ์์ฑํ๊ธฐ ๋๋ฌธ์ ์ก์์๊ฒฐ์์๋ ์ฐ์ ํน์ฑ์ ํฅ์์ด ๋งค์ฐ ์ ํ์ ์ด๋ค. ์ด๋ฅผ ๊ทน๋ณต ํ๊ธฐ ์ํด, ํฉ๊ธ์ ์กฐ์ฑ์ ์ ์ดํ์ฌ ์๊ณ ์์ ๋ถํผ ๋ถ์จ์ ์ต์ ํํ๊ณ ํ๋ผ์ดํธ ๋งคํธ๋ฆญ์ค๋ก ์๊ณ ๋ ฮฑ-Fe ์
์์ ์กฐ ๋ํ๋ฅผ ์ฌํ ์ด์ฒ๋ฆฌ์ ์ํด ๊ณต์ต์์ ์ฐ์ ๋คํธ์ํฌ๋ฅผ ์ค์ด๊ณ ์
์ ์ฐ์์ฑ์ ์ฆ๊ฐ์ํค๊ณ ์ ํ์๋ค. ์ต์ข
์ ์ผ๋ก ๋ฏธ์ธ ๊ตฌ์กฐ ๊ฐ์ ๊ฒฐ๊ณผ, ํ์ด์ฒ๋ฆฌ ๋ Fe-1Ni-0.4B-0.8C ํฉ๊ธ์ 5.2 %์ ๋์ ์ฐ์ ์จ ๊ฐ์ ๋ํ๋๋ค.
๋ ๋ฒ์งธ ์ฃผ์ ๋ก ๋ ๊ฐ์ Fe-Mo-B ๋ฐ Fe-B-C ๊ณต์ ์ ํ์ฑ์ ํตํด ์
๊ณ ๋ฏธ์ธ ๊ตฌ์กฐ๋ฅผ ์์ ํ๊ธฐ ์ํด Fe-B-C ํฉ๊ธ ์์คํ
์ ๋ชฐ๋ฆฌ๋ธ๋ด (Mo)์ด ๋์
๋์๋ค. ์ด๋ฅผ ์ํด Fe-xMo-0.4B-0.8C (x=1.0~5.0 in wt %) ํฉ๊ธ์ LPS๋ก ์ ์กฐํ๊ณ ๋ฏธ์ธ ๊ตฌ์กฐ ๋ฐ ๊ธฐ๊ณ์ ํน์ฑ์ ์กฐ์ฌํ์๋ค. Mo ์ฒจ๊ฐ๋ก ๋งคํธ๋ฆญ์ค๋ ํ๋ผ์ดํธ ๋ฐ ์ฌ ์นจ์ ๋ ํ๋ผ์ดํธ์์ ํ๋ผ์ดํธ (๋๋ ๋ฒ ์ด๋์ดํธ)๋ก ๋ณ๊ฒฝ๋๊ณ , ์
๊ณ๋ ์ฐ์ ๋คํธ์ํฌ์์ MoFe(C,B)๊ณผ (Fe,Mo)3(C,B) ๋ก ๊ตฌ์ฑ๋ ๋ผ๋ฉ๋ผ ๊ตฌ์กฐ๋ก ๋ณ๊ฒฝ๋์๋ค. ํฉ๊ธ ๋ด ๋ฏธ์ธ ๊ตฌ์กฐ์ ๊ฐ์ ์ ํตํด ํฉ๊ธ์ ๊ฒฝ๋, ์ธ์ฅ ๊ฐ๋, ํ๋จ ์ฐ์ ์จ๊ณผ ๊ฐ์ ๊ธฐ๊ณ์ ํน์ฑ์ด ํฌ๊ฒ ํฅ์๋์์ผ๋ฉฐ, ํนํ Fe-5Mo-0.4B-0.8C ํฉ๊ธ์ 674 MPa์ ๋์ ์ธ์ฅ ๊ฐ๋์ 4.92 %์ ๋์ ์ฐ์ ์จ์ ๋ํ๋๋ค.
์ธ ๋ฒ์งธ ์ฃผ์ ๋ก ์ผ๋ฐ์ ์ธ ์๊ฒฐ๋ฒ, ํซํ๋ ์ฑ, ํต์ ํ์ฑ์๊ฒฐ๋ก Fe-Ni ํฉ๊ธ (x=0.0~5.0 in wt %)์ ์ ์ํ์ฌ ํนํ, ํต์ ํ์ฑ ์๊ฒฐ๋ฒ์ ์ ๋ฅ์ ํจ๊ณผ์ ์ง์คํ์ฌ ์ํธ์ ์น๋ฐํ, ๋ฏธ์ธ๊ตฌ์กฐ, ์์ ๋ถํฌ๋, ๊ธฐ๊ณ์ ํน์ฑ ๋ฑ์ ๋น๊ต ์กฐ์ฌ ํ์๋ค. ๋์ผ์ ์ฒจ๊ฐ๋ ์ผ๋ฐ์ ์ธ ์๊ฒฐ๋ฒ๊ณผ ํต์ ํ์ฑ๋ฒ ๋ชจ๋์์ ์น๋ฐํ๋ฅผ ํฅ์์ํค๊ณ ์
์ ์ฑ์ฅ์ ์ต์ ํ์์ผ๋ฉฐ, ํนํ ํต์ ํ์ฑ๋ฒ์ผ๋ก ์์ ๊ฒฐ์ ํฌ๊ธฐ๋ฅผ ๊ฐ์ง๋ ๊ณ ๋ฐ๋ Fe-Ni ํฉ๊ธ์ ์ ์ํ๋๋ฐ ์ฑ๊ณตํ์๋ค. ๊ฒฐ๊ณผ์ ์ผ๋ก ํต์ ํ์ฑ๋ฒ์ผ๋ก ์ ์ํ Fe-Ni ํฉ๊ธ์ ๋ค๋ฅธ ์๊ฒฐ ๋ฒ์ผ๋ก ์ ์ํ ํฉ๊ธ์ ๋นํด์ ๋ฒํฌ ๊ฒฝ๋์์ ํฐ ํฅ์์ ๋ณด์๋ค. ์์ ๋ถํฌ๋ ๋ถ์์ ํตํด ๋์ผ์ ์ํธ์ ๊ฒฐ์ ๋ฆฝ๊ณ์ ๋ค์ ๋ถํฌํ๋ ๊ฒ์ ๊ด์ฐฐํ์์ผ๋ฉฐ, ํ์ฐ ์คํ์ ํตํด ํต์ ํ์ฑ์๊ฒฐ์ด ๊ฒฐ์ ๋ฆฝ๊ณ ํ์ฐ์ ํฅ์ํจ์ ๋ท๋ฐ์นจ ํ์๋ค. ์ ๊ฒฐ๊ณผ๋ก๋ถํฐ ํต์ ํ์ฑ์๊ฒฐ ๋ฒ์ ์๋ ฅ๊ณผ ๊ฐํด์ฃผ๋ ์ ๋ฅ๋ ๊ฒฐ์ ๋ฆฝ๊ณ์์ Ni์ ํ์ฐ์ ์ฉ์ดํ๊ฒ ํ์ฌ ๊ฒฐ๊ณผ์ ์ผ๋ก ์ํธ์ ์น๋ฐํ๋ฅผ ํฅ์์ํค๊ณ ์
๊ณ ์ฑ์ฅ์ ์ฑ๊ณต์ ์ผ๋ก ์ต์ ํ์๋ค.Powder metallurgy (P/M) is metal consolidation technique for the manufacture of parts from raw starting powders by compaction and heating. P/M technique has been used in various industries and applications, because of its efficiency and net-shaped capability. However, the P/M applications are very limited due to the presence of porosity ranging from 5 to 15 vol. %. To solve this problem, several sintering techniques, such as liquid phase sintering, hot pressing and field assisted sintering have been extensively investigated. However, the improvement of mechanical properties was still limited, and thus microstructure modification is required to enhance the mechanical properties. In addition, the microstructural development and densification mechanism during sintering have not been fully explored. In this thesis, the effects of sintering methods, sintering condition, composition and heat treatment on microstructure were intensively studied, and then the effect of microstructure modification on the mechanical properties of Fe-based alloys was thoroughly investigated. The mentioned above will be discussed in more detail on the three main topics. 1) Microstructure modification of liquid phase sintered Fe-Ni-B-C alloys for improved mechanical properties, 2) Effects of molybdenum addition on microstructure and mechanical properties of Fe-B-C sintered alloys, and 3) Effect of field assisted sintering on densification, microstructure and mechanical properties of Fe-Ni alloys.
First, Ni addition was employed to decrease the eutectic temperature and improve the densification and mechanical properties of Fe-B-C alloys. To solve the formation of continuous network of hard eutectics, the composition was controlled to achieve the system with the optimized hard phase fraction, and heat treatment was performed to induce the coarsening of solidified ฮฑ-Fe particles into the matrix. As a result of microstructure modification, the post annealed Fe-1Ni-0.4B-0.8C alloy resulted in the high elongation to failure of 5.2 %.
Second, molybdenum (Mo) was introduced in Fe-B-C alloy system to modify the grain boundary microstructure through the formation of two Fe-Mo-B and Fe-B-C solidified phases. For this, Fe-xMo-0.4B-0.8C (x=1.0~5.0 in wt%) alloys were prepared by LPS and their microstructure and mechanical properties were investigated. With Mo addition, the matrix grain changed from pearlite and re-precipitated ferrite to pearlite (or pearlite/bainite) and the grain boundary changed from a continuous network to a lamella structure composed of MoFe(C,B) (WCoB-type boride) and (Fe,Mo)3(C,B) (Fe3C-type carbide). As a result of microstructure modification, the mechanical properties such as hardness, tensile strength, and elongation to failure were significantly improved. In particular, Fe-5Mo-0.4B-0.8C alloy exhibited a high tensile strength of 674 MPa and a high elongation to failure of 4.92%.
Third, Fe-xNi alloys (x=0~5.0 in wt%) were consolidated by conventional sintering (CS), field assisted sintering (FAS), and hot pressing (HP) methods, and their densification, microstructure, mechanical properties were comparatively investigated, particularly focusing on the field or current effects of FAS technique. The Ni addition promoted the densification and suppressed the grain growth in Fe-Ni alloys consolidated by both CS and FAS, and the FAS produced the Fe-Ni alloys with higher apparent density and smaller grain size. Consequently, the Fe-Ni alloys fabricated by FAS exhibited a significant improvement in the bulk hardness compared to those consolidated by CS and HP. The obtained results indicated that the electric current together with mechanical pressure in FAS promoted the Ni diffusion along the grain boundaries, which resulted in the enhanced densification with suppressed grain growth.Chapter 1. General background 1
1.1. Powder metallurgy 1
1.2. Liquid phase sintering 5
1.3. Field assisted sintering 11
Chapter 2. Microstructure modification of liquid phase sintered Fe-Ni-B-C alloys for high mechanical properties 19
2.1. Introduction 19
2.2. Experimental 21
2.3. Results and discussion 23
2.4. Conclusions 28
Chapter 3. Effect of Molybdenum Addition on the Microstructure and Mechanical Properties of Fe-Mo-B-C Sintered Alloys 58
3.1. Introduction 58
3.2. Experimental 60
3.3. Results and discussion 61
3.4. Conclusions 68
Chapter4. Densification Mechanism and Microstructure Development of Fe-Ni Alloys onsolidated by Field Assisted Sintering 105
4.1. Introduction 105
4.2. Experimental 107
4.3. Results and discussion 109
4.4. Conclusions 114
Abstract in Korean 142๋ฐ
๊ต์ฐจ๋ก์์ ์์จ์ฃผํ ์ฐจ๋์ ์ ํ๋ ๊ฐ์์ฑ๊ณผ ๋ถํ์ค์ฑ์ ๊ณ ๋ คํ ์ข ๋ฐฉํฅ ๊ฑฐ๋๊ณํ
ํ์๋
ผ๋ฌธ(๋ฐ์ฌ) -- ์์ธ๋ํ๊ต๋ํ์ : ๊ณต๊ณผ๋ํ ๊ธฐ๊ณ๊ณตํ๋ถ, 2023. 2. ์ด๊ฒฝ์.This dissertation presents a novel longitudinal motion planning of autonomous vehicle at urban intersection to overcome the limited visibility due to complicated road structures and sensor specification, guaranteeing the safety from the potential collision with vehicles appearing from the occluded region.
The intersection autonomous driving requires high level of safety due to congested traffics and environmental complexities. Due to complicated road structures and the detection range of perception sensors, the occluded region is generated in urban autonomous driving. The virtual target is one of the motion planning methods to react the sudden appearance of vehicles from the blind spot. The Gaussian Process Regression (GPR) is implemented to train the virtual target model to generate various future driving trajectories interacting with the motion of the ego vehicle. The GPR model provides not only the predicted trajectories of the virtual target but also the uncertainty of the future motion. Therefore, prediction results from GPR can be utilized to a position constraint for the Model Predictive Control (MPC), and the uncertainties are taken into account as a chance constraint in the MPC.
In order to comprehend the surrounding environment including dynamic objects, a region of interest (ROI) is defined to determine targets of the interest. With the pre-determined driving route of the ego vehicle and the route information of the intersection, driving lanes intersecting with the ego driving lane can be determined, and the intersecting lanes are defined as ROI, reducing the computational load by eliminating targets of disinterest. Then the future motion of the selected target is predicted by a Long Short-Term Memory-Recurrent Neural Network (LSTM-RNN). Driving data for training are directly obtained with two different autonomous vehicles, providing their odometry information regardless to the limited field of view (FOV). For a widely known autonomous driving datasets such as Waymo and nuScenes, the vehicle odometry information are collected from the perceptive sensors mounted on the test vehicle. Thus, information of target that are out of the FOV of the test vehicle cant be obtained. The obtained training data are organized in the target centered coordinates for better input-domain adaptation and generalization. The mean squared error and the negative log likelihood loss functions are adapted to train and provide the uncertainty information of the target vehicle for the motion planning of the autonomous vehicle.
The MPC with a chance constraint is formulated to optimize the longitudinal motion of the autonomous vehicle. The dynamic and actuator constraints are designed to provide ride comfort and safety to drivers. The position constraint with the chance constraint guarantees the safety and prevent the potential collision with target vehicles. The position constraint for the travel distance over the prediction horizon time is determined based on the clearance between the predicted trajectories of the target and ego vehicle at every prediction sample time.
The performance and feasibility of the proposed algorithm are evaluated via computer simulation and test-data based simulation. The offline simulation validates the safety of the proposed algorithm, and the suggested motion planner has been implemented on an autonomous driving vehicle and tested in a real road. Through the implementation of the algorithm to an actual vehicle, the suggested algorithm is confirmed to be applicable in real life autonomous driving.๋ณธ ๋
ผ๋ฌธ์ ๋ณต์กํ ๋๋ก ๊ตฌ์กฐ์ ์ผ์ ์ฌ์์ผ๋ก ์ธํ ์์ผ ์ ํ์ ๊ทน๋ณตํ๋ฉฐ ์ฌ๊ฐ์ง๋์์ ๋ฑ์ฅํ๋ ์ฐจ๋๊ณผ์ ์ ์ฌ์ ์ธ ์ถฉ๋๋ก๋ถํฐ ์์ ์ ๋ณด์ฅํ๊ธฐ ์ํ ๋์ฌ ๊ต์ฐจ๋ก์์์ ์์จ์ฃผํ์ฐจ์ ์๋ก์ด ์ข
๋ฐฉํฅ ๊ฑฐ๋ ๊ณํ์ ์ ์ํ๋ค.
๋์ฌ ์์จ์ฃผํ์ ๊ตํต์ฒด์ฆ๊ณผ ํ๊ฒฝ์ ๋ณต์ก์ฑ์ผ๋ก ์ธํด ๋์ ์์ค์ ์์ ์ฑ์ด ์๊ตฌ๋ฉ๋๋ค. ๋ณต์กํ ๋๋ก ๊ตฌ์กฐ์ ์ธ์ง ์ผ์์ ์ธ์ง ๋ฒ์๋ก ์ธํด ๋์ฌ ์์จ์ฃผํ์์๋ ์ฌ๊ฐ์ง๋๊ฐ ๋ฐ์ํ๋ค. ๊ฐ์ ํ๊ฒ์ ์ฌ๊ฐ์ง๋์์ ์ฐจ๋์ ๊ฐ์์ค๋ฌ์ด ์ถํ์ ๋์ํ๊ธฐ ์ํ ๊ฑฐ๋ ๊ณํ ๋ฐฉ๋ฒ ์ค ํ๋์
๋๋ค. ์์ฐจ๋์ ๊ฑฐ๋๊ณผ ์ํธ์์ฉํ๋ ๋ค์ํ ๋ฏธ๋ ์ฃผํ ๊ถค์ ์ ์์ฑํ๋ ๊ฐ์ ํ๊ฒ ๋ชจ๋ธ์ ๊ตฌํํ๊ธฐ ์ํ์ฌ Gaussian Process Regression (GPR) ๋ฐฉ๋ฒ์ ์ฌ์ฉํฉ๋๋ค. GPR ๋ชจ๋ธ์ ๊ฐ์ ํ์ ์ ์์ธก๋ ๊ถค์ ๋ฟ๋ง ์๋๋ผ ๋ฏธ๋ ๊ถค์ ์ ๋ํ ๋ถํ์ค์ฑ๋ ์ ๊ณตํฉ๋๋ค. ๋ฐ๋ผ์ GPR์ ์์ธก ๊ฒฐ๊ณผ๋ Model Predictive Control (MPC)์ ๋ํ ์์น ์ ์ฝ ์กฐ๊ฑด์ผ๋ก ํ์ฉ๋ ์ ์์ผ๋ฉฐ ๋ถํ์ค์ฑ์ MPC์์ ๊ธฐํ ์ ์ฝ ์กฐ๊ฑด์ผ๋ก ๊ณ ๋ ค๋ฉ๋๋ค.
๋์ ๊ฐ์ฒด๋ฅผ ํฌํจํ ์ฃผ๋ณ ํ๊ฒฝ์ ํ์
ํ๊ธฐ ์ํด ๊ด์ฌ์์ญ์ ์ ์ํ์ฌ ๋ชฉํ ๋์์ ๊ฒฐ์ ํฉ๋๋ค. ๋ฏธ๋ฆฌ ๊ฒฐ์ ๋ ์์ฐจ๋์ ์ฃผํ๊ฒฝ๋ก์ ๊ต์ฐจ๋ก์ ๊ฒฝ๋ก์ ๋ณด๋ฅผ ํตํ์ฌ ์์ฐจ๋์ ์ฃผํ์ฐจ๋ก์ ๊ต์ฐจํ๋ ๋ค๋ฅธ ์ฐจ์ ์ ํ๋จํ์ฌ ๊ด์ฌ์์ญ์ผ๋ก ์ ์ํจ์ผ๋ก์จ ๊ด์ฌ์์ญ ๋ฐ์ ์ฐจ๋์ ์ ์ธํ์ฌ ์ฐ์ฐ๋์ ๊ฐ์์ํฌ ์ ์๋ค. ๋ค์์ผ๋ก ์ธ์ง๋ ์ฐจ๋์ ๋ฏธ๋ ์ด๋ ๊ถค์ ์ LSTM-RNN (Long Short-Term Memory Recurrent Neural Network)์ ์ํด ์์ธก๋ฉ๋๋ค. ํ๋ จ์ ์ํ ์ฃผํ ๋ฐ์ดํฐ๋ ๋ ๋์ ์์จ์ฃผํ ์ฐจ๋์์ ์ง์ ํ๋ํ์ฌ ์ ํ๋ ์์ผ์ ๊ด๊ณ์์ด ์ฐจ๋์ ์ํ ์ ๋ณด๋ฅผ ์ ๊ณตํฉ๋๋ค. ๊ตฌ๊ธ Waymo ๋ฐ nuScenes์ ๊ฐ์ด ๋๋ฆฌ ์๋ ค์ง ์์จ์ฃผํ ๋ฐ์ดํฐ์ ๊ฒฝ์ฐ ์ฐจ๋ ์ํ ์ ๋ณด๋ ํ
์คํธ ์ฐจ๋์ ์ฅ์ฐฉ๋ ์ธ์ง ์ผ์์์ ์์ง๋ฉ๋๋ค. ๋ฐ๋ผ์ ํ
์คํธ ์ฐจ๋์ ์์ผ์์ ๋ฒ์ด๋ ์๋ ์ฐจ๋ ์ ๋ณด๋ ์ป์ ์ ์์ต๋๋ค. ์ทจ๋ํ ์ฃผํ ๋ฐ์ดํฐ๋ ๋ ๋์ ์
๋ ฅ ๋ฐ์ดํฐ ์ ์ ๋ฐ ์ผ๋ฐํ๋ฅผ ์ํด ์์ฐจ๊ฐ ์๋ ํ๊ฒ์ฐจ๋ ์ค์ฌ ์ขํ๋ก ๊ตฌ์ฑ๋ฉ๋๋ค. ์์คํจ์๋ก ํ๊ท ์ ๊ณฑ ์ค์ฐจ ๋ฐ ์์ ๋ก๊ทธ ์ฐ๋ํจ์๋ฅผ ์ฌ์ฉํ์๊ณ ์์ ๋ก๊ทธ ์ฐ๋ํจ์๋ ์์จ์ฃผํ ์ฐจ๋์ ๊ฑฐ๋๊ณํ์ ์ฌ์ฉ๋ ์ ์๊ฒ ํ๊ฒ์ฐจ๋์ ๋ฏธ๋ ๊ถค์ ์ ๋ํ ๋ถํ์ค์ฑ ์ ๋ณด๋ฅผ ์ ๊ณตํ๋ค.
๊ธฐํ ์ ์ฝ ์กฐ๊ฑด์ด ์๋ MPC๋ ์์จ์ฐจ๋์ ์ข
๋ฐฉํฅ ๊ฑฐ๋์ ์ต์ ํํ๋๋ก ๊ตฌํ๋ฉ๋๋ค. ๋์ ์ ์ฝ ์กฐ๊ฑด ๋ฐ ๊ตฌ๋๊ธฐ ์ ์ฝ ์กฐ๊ฑด์ ์ด์ ์์๊ฒ ์น์ฐจ๊ฐ๊ณผ ์์ ์ ์ ๊ณตํ๋๋ก ์ค๊ณ๋์์ต๋๋ค. ๊ธฐํ ์ ์ฝ ์กฐ๊ฑด์ ์์น ์ ์ฝ ์กฐ๊ฑด์ ๊ฐ๊ฑดํ๊ฒ ํ์ฌ ์์ ์ ๋ณด์ฅํ๊ณ ๋์ ์ฐจ๋๊ณผ์ ์ ์ฌ์ ์ธ ์ถฉ๋์ ๋ฐฉ์งํฉ๋๋ค. ์์ธก ์๊ฐ๋์ ์ด๋ ๊ฑฐ๋ฆฌ์ ๋ํ ์์น ์ ์ฝ ์กฐ๊ฑด์ ๊ฐ ์์ธก์๊ฐ์ ํ๊ฒ๊ณผ ์์ฐจ๋์ ์์ธก๋ ๊ถค์ ๊ฐ์ ๊ฑฐ๋ฆฌ ์ฐจ์ด์ ์ํด ๊ฒฐ์ ๋๋ค.
์ ์ํ ์๊ณ ๋ฆฌ์ฆ์ ์ฑ๋ฅ๊ณผ ํ๋น์ฑ์ ์ปดํจํฐ ์๋ฎฌ๋ ์ด์
๊ณผ ํ
์คํธ ๋ฐ์ดํฐ ๊ธฐ๋ฐ ์๋ฎฌ๋ ์ด์
์ ํตํด ํ๊ฐ๋๋ค. ์คํ๋ผ์ธ ์๋ฎฌ๋ ์ด์
์ ํตํด ์ ์ํ ์๊ณ ๋ฆฌ์ฆ์ ์์ ์ฑ์ ๊ฒ์ฆํ์์ผ๋ฉฐ ์ ์ํ ๊ฑฐ๋๊ณํ ์๊ณ ๋ฆฌ์ฆ์ ์์จ์ฃผํ์ฐจ์ ๊ตฌํํ์ฌ ์ค์ ๋๋ก์์ ํ
์คํธํ์๋ค. ์ ์ํ ์๊ณ ๋ฆฌ์ฆ์ ์ค์ ์ฐจ๋์ ๊ตฌํํ์ฌ ์ค์ ์์จ์ฃผํ์ ์ ์ฉํ ์ ์์์ ํ์ธํ์๋ค.Chapter 1. Introduction 1
1.1. Research Background and Motivation of Intersection Autonomous Driving 1
1.2. Previous Researches on Intersection Autonomous Driving 9
1.2.1. Research on Trajectory Prediction and Intention Inference at Urban Intersection 10
1.2.2. Research on Intersection Motion Planning 11
1.3. Thesis Objectives 18
1.4. Thesis Outline 19
Chapter 2. Overall Architecture of Intersection Autonomous Driving System 22
2.1. Software Configuration of Intersection Autonomous Driving 22
2.2. Hardware Configuration of Autonomous Driving and Test Vehicle 24
2.3. Vehicle Test Environment for Intersection Autonomous Driving 25
Chapter 3. Virtual Target Modelling for Intersection Motion Planning 27
3.1. Limitation of Conventional Virtual Target Model for Intersection 27
3.2. Virtual Target Generation for Intersection Occlusion 31
3.3. Intersection Virtual Target Modeling 34
3.3.1. Gaussian Process Regression based Virtual Target Model at Intersection 35
3.3.2. Data Processing for Gaussian Process Regression based Virtual Target Model 38
3.3.3. Definition of Visibility Index of Virtual Target at Intersection 45
3.3.4. Long Short-Term Memory based Virtual Target Model at Intersection 51
Chapter 4. Surrounding Vehicle Motion Prediction at Intersection 54
4.1. Intersection Surrounding Vehicle Classification 54
4.2. Data-driven Vehicle State based Motion Prediction at Intersection 58
4.2.1. Network Architecture of Motion Predictor 58
4.2.2. Dataset Processing of the Network 65
Chapter 5. Intersection Longitudinal Motion Planning 68
5.1. Outlines of Longitudinal Motion Planning with Model Predictive Control 68
5.2. Stochastic Model Predictive Control of Intersection Motion Planner 69
5.2.1. Definition of System Dynamics Model 69
5.2.2. Ego Vehicle Prediction and Reference States Definition 70
5.2.3. Safety Clearance Decision for Intersection Collision Avoidance 71
5.2.4. Driving Mode Decision of Intersection Motion Planning 79
5.2.5. Formulation of Model Predictive Control with the Chance Constraint 83
Chapter 6. Performance Evaluation of Intersection Longitudinal Motion Planning 86
6.1. Performance Evaluation of Virtual Target Prediction at Intersection 86
6.1.1. GPR based Virtual Target Model Prediction Results 86
6.1.2. Intersection Autonomous Driving Computer Simulation Environment 90
6.1.2.1. Simulation Result of Effect of Virtual Target in Intersection Autonomous Driving 92
6.1.2.2. Virtual Target Simulation Result of the Right Turn Across Path Scenario in the Intersection 96
6.1.2.3. Virtual Target Simulation Result of the Straight Across Path Scenario in the Intersection 102
6.1.2.4. Virtual Target Simulation Result of the Left Turn Across Path Scenario in the Intersection 108
6.1.2.5. Virtual Target Simulation Result of Crooked T-shaped Intersection 113
6.2. Performance Evaluation of Data-driven Vehicle State based Motion Prediction at Intersection 124
6.2.1. Data-driven Motion Prediction Accuracy Analysis 124
6.2.2. Prediction Trajectory Accuracy Analysis 134
6.3. Vehicle Test for Intersection Autonomous Driving 146
6.3.1. Test Vehicle Configuration for Intersection Autonomous Driving 146
6.3.2. Software Configuration for Autonomous Vehicle Operation 147
6.3.3. Vehicle Test Environment for Intersection Autonomous Driving 148
6.3.4. Vehicle Test Result of Intersection Autonomous Driving 151
Chapter 7. Conclusion and Future Work 161
7.1. Conclusion 161
7.2. Future Work 164
Bibliography 166
Abstract in Korean 172๋ฐ
Target depth selection for minimization of detection probability
In the detection of underwater target, there exists an optimal depth for the sonar systems where the detection probability is maximized. In contrast, for the targets, there is a target depth where the detection probability by the sonar system is minimized. In this paper, we address this question from the view point of the target, that is to find the target depth where the detection probability of the target by sonar systems is minimized. The detection probability of the target is dependent on the depths of target and sonar, the submarine topography, the sound speed profile and the generated frequency of the target. In this paper, the detection probability of the target is calculated by the depth for the slope direction of the submarine slope based on the information about the sound speed profile and the submarine topography in the ocean.Abstract = I
๋ชฉ ์ฐจ = II
๊ทธ๋ฆผ๋ชฉ์ฐจ = IV
1. ์ ๋ก = 1
1.1 ์ฐ๊ตฌ์ ํ์์ฑ ๋ฐ ๋ชฉ์ = 1
1.2 ์ฐ๊ตฌ์ ๋ํฅ = 1
1.3 ์ฐ๊ตฌ๋ด์ฉ = 2
1.4 ๋
ผ๋ฌธ์ ๊ตฌ์ฑ = 3
2. ์๋๋ฐฉ์ ์ = 4
2.1 ์์์ค์(Source Level, SL) = 4
2.2 ์ ๋ฌ์์ค(Transmission Loss, TL) = 4
2.2.1 ํ๋๋ฐฉ์ ์ = 5
2.2.2 ํฌ๋ฌผ์ ๋ฐฉ์ ์ = 6
2.2.3 ๊ฑฐ๋ฆฌ์ข
์ ์ํฅ๋ชจ๋ธ = 14
2.2.4 ์์นํด์ ๊ธฐ๋ฒ = 16
2.3 ์์์ค์(Noise Level, NL) = 18
2.4 ์งํฅ์ง์(Directivity Index, DI) = 20
2.5 ํ์ง๋ฌธํฑ(Detection Threshold, DT) = 22
3. ํผํ์ง๊ฑฐ๋ฆฌ ๊ณ์ฐ์ ์ํ ์๊ณ ๋ฆฌ๋ฌ = 24
4. ์์น์คํ = 26
4.1 ์ผ์์ ๊น์ด๊ฐ ์๋ ค์ง ๊ฒฝ์ฐ = 26
4.1.1 Down slope ํ์งํ๊ฒฝ = 26
4.1.2 Up slope ํ๊ฒฝ = 40
4.2 ์ผ์์ ๊น์ด๋ฅผ ๋ชจ๋ฅผ ๊ฒฝ์ฐ = 50
5. ๊ฒฐ ๋ก = 53
์ฐธ๊ณ ๋ฌธํ = 5
Synthesis of Ta-doped TiO2 Nanorods and Ta:TiO2/Fe2O3 Heterostructure for PEC Application
ํ์๋
ผ๋ฌธ (์์ฌ)-- ์์ธ๋ํ๊ต ๋ํ์ : ์ฌ๋ฃ๊ณตํ๋ถ, 2014. 8. ํ์ฑํ.์ต๊ทผ ํ์์ฐ๋ฃ์ ๊ณ ๊ฐ๊ณผ ํ๊ฒฝ์ค์ผ์ ๋ฌธ์ ๊ฐ ๋๋๋จ์ ๋ฐ๋ผ ์นํ๊ฒฝ์ ์ธ ์๋์ง์ ๋ํ ์ฐ๊ตฌ์ ํ์์ฑ์ด ์ ๊ธฐ๋๊ณ ์๋ค. ๊ทธ ์ค์์๋ ํ์์๋์ง๋ฅผ ์์์๋์ง๋ก ์ ํํ ์ ์๋ ๊ด ์ด๋งค๋ฅผ ์ด์ฉํ ๊ธฐ์ ์ ์์ ํ๋ฉฐ ๊ฐ๋จํ๊ณ ๋ถ์ฐ๋ฌผ์ด ์๋ค๋ ์ ์์ ์ฌ์์๋์ง๋ก์จ ๋งค์ฐ ํจ๊ณผ์ ์ธ ๋ฐฉ๋ฒ์ด๋ค. [1]
ํ๋ฐํ๊ฒ ์ฐ๊ตฌ๋๋ ๊ด ์ด๋งค ๋ฌผ์ง์Fe2O3[2]ยฌ, WO3[3], CdS[4] ๋ฑ ์ด ์๋๋ฐ ๊ทธ ์ค์์๋ TiO2๋ 1972๋
๋จ ๊ฒฐ์ ์ ๊ทน์ ๋น์ ๋น์ถ๋ฉด ๋ฌผ์ด ๋ถํด๋๋ค๋ ๋ณด๊ณ ๊ฐ ๊ด ์ด๋งค ์ฐ๊ตฌ์ ์์์ด ๋์์ผ๋ฉฐ์์ง๊น์ง๋ ๋ค์ํ ๋ฐฉ๋ฉด์ผ๋ก ์ฐ๊ตฌ๊ฐ ์งํ๋๊ณ ์๋ค.[5, 6] TiO2 ์ ๊ฒฝ์ฐ morphology๋ฅผ ์กฐ์ ํ๊ธฐ ์ฝ๊ณ electron๊ณผ hole diffusion length๊ฐ ๋ค๋ฅธ ๋ฌผ์ง์ ๋นํด ๊ธด ํธ์ด๋ผ recombination์ ์ต์ ํ๋ ๋ฐฉ๋ฉด์์ ํฐ ์ฅ์ ์ด ์๋ค. ํ์ง๋ง TiO2 ๋ ์ ํญ์ด ๋งค์ฐ ์ปค์ ์์ฑ๋ electron์ด FTO๊ธฐํ์์ ๋น ์ ธ๋๊ฐ๊ธฐ ํ๋ค์ด recombination ์ด ์ผ์ด๋ photo-catalytic efficiency์ ์๋นํ ๋จ์ด๋จ๋ฆฌ๋ ๋จ์ ์ด ์๊ธฐ ๋๋ฌธ์ Ta์ด๋ Nb ๋ฑ ์ฌ๋ฌ atom์ TiO2 ์ doped์ ํ์ฌ ์ ํญ์ ๋ฎ์ถ๋ ๋ฑ์ ์ด๋ฅผ ๊ทน๋ณตํ๊ณ ์ ํ๋ ์ฐ๊ตฌ๊ฐ ํ์ํ ์์ ์ด๋ค.[7, 8]
๋ณธ ์ฐ๊ตฌ์์๋ FTO ๊ธฐํ ์์ TiO2๋ฅผ hydrothermal ๋ฐฉ์์ผ๋ก ์ฑ์ฅํ์๊ณ , ํ๋ฉด์ ์ ๋ํ ์ ์๋ 1D structure์ ๊ตฌ์กฐ๋ก ํฉ์ฑํ์ฌ ๋ฌผ๊ณผ ๋ฐ์ํ๋ ์์ญ์ ๋ํ water splitting์ ๋ ์ํ ์ ์๋๋ก ์ค๊ณ๋ฅผ ํ์๋ค. ํนํTantalum์ doped ์์ผ TiO2์ conductivity๋ฅผ ํฅ์ ์์ผ electron์ด ๋ณด๋ค ๋ ํ๋ฅผ ์ ์๊ฒ ํ์ฌ photo-catalytic property์ ํฅ์์ ํ๋๋ก ํ์๋ค. ๋ Tantalum ์ด doped ๋ TiO2 ์ ํฅ์์ ์ํด Fe2O3 ์ ์ด์ข
์ ํฉ์ ํ์ฌ ํน์ฑ์ ํฅ์์ ์ด๋์ด ๋ด์๋ค.
๋ณธ ์์ฌ ์กธ์
๋
ผ๋ฌธ์์๋ nanorod๋ก ์ฑ์ฅ์ํจTiO2์ Ta์ด doped๋ TiO2๋ฅผ ๋น๊ตํ์ฌ ๋ฏธ์ธ๊ตฌ์กฐ์ ๋ณํ ๋ฐ ํน์ฑ ํฅ์์ ๋ํด ๋
ผํ๊ณ ์ด๋ฅผ ๋ ํฅ์์ํฌ ์ ์๋ ๋ฐฉ์์ผ๋ก์ Fe2O3 ์ ์ด์ข
์ ํฉ์ ํ์ฌ ๋ ํฅ์๋ ๊ด์ด๋งค๋ฅผ ํฉ์ฑํ ์ ์์๋ค.์ด๋ก.................................................................................................................โ
ฐ
List of figures................................................................................................โ
ณ
1. ์๋ก ..............................................................................................................1
2. ๋ฌธํ์ฐ๊ตฌ.......................................................................................................4
2.1. ๋ฌผ ๋ถํด ์ฐ๊ตฌ...........................................................................................4
2.1.1. ๋ฌผ ๋ถํด์ ๊ธฐ๋ณธ ์๋ฆฌ..........................................................................5
2.1.2. ๊ด ์ด๋งค์ ์ข
๋ฅ...................................................................................6
2.1.3. ๊ด ์ด๋งค์ ํน์ฑ ์ธก์ ..........................................................................9
2.1.4. ๊ด ์ด๋งค์ ์ฑ๋ฅ ํ๊ฐ..........................................................................10
2.2. TiO2 ๊ด ์ด๋งค...........................................................................................11
2.2.1. ์ผ ์ฐจ์์ ๋๋
ธ ๊ตฌ์กฐ๋ฅผ ๊ฐ์ง๋ TiO2.................................................12
2.2.2. TiO2 ์ ํฉ์ฑ๋ฐฉ๋ฒ.............................................................................13
2.2.3. TiO2 ์ ํน์ฑ ํฅ์์ ์ํ ๋ํ ํจ๊ณผ..............................................15
2.2.4. TiO2 ์ ํน์ฑ ํฅ์์ ์ํ ์ด์ข
์ ํฉ..............................................16
3. ์คํ๋ฐฉ๋ฒ.....................................................................................................33
3.1. ์ํธ์ ์ค๋น...........................................................................................33
3.2. ํน์ฑ ํ๊ฐ..............................................................................................35
4. ๊ฒฐ๊ณผ ๋ฐ ๊ณ ์ฐฐ..............................................................................................38
4.1. HCl ๋๋์ ์ํ ์ํฅ...........................................................................38
4.2. ๊ณต์ ์๊ฐ์ ์ํ ์ํฅ.........................................................................42
4.3. ๋ํ ๋๋์ ์ํ ์ํฅ.........................................................................47
4.4. ์ด์ข
์ ํฉ์ ์ํ ์ํฅ.........................................................................69
5. ๊ฒฐ๋ก ...........................................................................................................74
6. ์ฐธ๊ณ ๋ฌธํ.....................................................................................................76
Abstract.........................................................................................................85Maste
The Analysis of Mathematical Tasks in KSAT-EBS linked Coursebooks: Focusing on Functions
ํ์๋
ผ๋ฌธ (์์ฌ)-- ์์ธ๋ํ๊ต ๋ํ์ : ์ฌ๋ฒ๋ํ ์ํ๊ต์ก๊ณผ, 2018. 2. ์ด๊ฒฝํ.์ํ ๊ณผ์ (Mathematical Tasks)๋ ํ์ต ๋ด์ฉ๋ฟ ์๋๋ผ ์ฌ๊ณ ๋ฐฉ์๊น์ง ๊ฒฐ์ ํ๊ธฐ ๋๋ฌธ์ ์๋ฏธ ์๋ ์ํ ๊ณผ์ ๋ฅผ ํ์ต์์๊ฒ ์ ์ํ๋ ๊ฒ์ ์ค์ํ ๋ฌธ์ ์ด๋ค. ์ด์ ๊ต๊ณผ์์ ํฌํจ๋ ์ํ ๊ณผ์ ์ ๋ํ ๋ถ์ ์ฐ๊ตฌ๋ ๊พธ์คํ ์ด๋ฃจ์ด์ ธ ์์ผ๋ EBSโ์๋ฅ ์ฐ๊ณ ์ ์ฑ
์ ์ํฅ์ผ๋ก EBS ์๋ฅ ์ฐ๊ณ๊ต์ฌ๊ฐ ๊ต๊ณผ์๋ฅผ ๋์ ํ์ฌ ํ๊ต ํ์ฅ์ ๊ต์ ํ์ต ๋ฐ ํ๊ฐ์ ํฐ ์ํฅ์ ๋ฏธ์น๊ณ ์์์๋ ๋ถ๊ตฌํ๊ณ ์ฐ๊ณ๊ต์ฌ์ ๋ํ ์ํ ๊ต์ก์ ๋ถ์์ ๋ฏธ๋นํ์ฌ ๊ณผ์ ์ฐ๊ตฌ์ ํ์์ฑ์ด ์ ๊ธฐ๋๋ค. ํจ์๋ ๊ต์ก๊ณผ์ ์ ์ฒด์ ๊ฑธ์น ๊ณตํต๋ ์ฃผ์ ์ด๊ณ ์ํ์ ์ฌ๋ฌ ์์ญ์ ํตํฉํ์ฌ ์ํ ํ์ต์์ ํ๋์ ์ฃผ์ ์ด์์ ์๋ฏธ๋ฅผ ๊ฐ์ง๋ฏ๋ก ์ฐ๊ณ๊ต์ฌ์ ํจ์ ๊ณผ์ ๋ถ์์ ๊ณผ์ ์์ ํ์๋ค์ด ๊ฒฝํํ๋ ์ํ์ ์ฌ๊ณ ์ ๋ํ์ฌ ๋ณด๋ค ํ์ฑํ ๋
ผ์๋ฅผ ์ด๋์ด๋ผ ์ ์๋ค.
๋ณธ ์ฐ๊ตฌ๋ EBS ์๋ฅ ์ฐ๊ณ๊ต์ฌ์ ํจ์์์ญ ์ํ ๊ณผ์ ๊ฐ ํ์๋ค์๊ฒ ์ ์ํ๋ ํจ์ ํ์ต์ ๊ธฐํ๋ฅผ ์ดํด๋ณธ๋ค. ์ด๋ฅผ ์ํด ๊ณผ์ ์ ์ธ์ง์ ์์ค๊ณผ ํจ์์ ํํ ๋ฐ ํํ ๊ฐ์ ๋ฒ์ญํ๋์ ๊ดํ ์ฐ๊ตฌ๋ฅผ ๋ถ์ํ๊ณ 2015 ๊ฐ์ ์ํ๊ณผ ๊ต์ก๊ณผ์ ์ ๋ฐ๋ฅธ ํจ์์์ญ์ ๋ด์ฉ์์์ ๊ธฐ๋ฅ์ ํ์ธํ์๋ค.
๋ณธ ์ฐ๊ตฌ์์๋ ๋ฌธํ ๋ถ์์ ๋ฐํ์ผ๋ก ๊ณผ์ ์ ์ธ์ง์ ์์ค, ๊ณผ์ ์ ๋ํ๋ ํจ์์ ํํ๊ณผ ํํ ๊ฐ ๋ฒ์ญ๊ณผ์ , ๊ณผ์ ์์ ๋ค๋ฃจ๊ณ ์๋ ํจ์์ ๋ด์ฉ์์์ ๊ธฐ๋ฅ์ ๋ฐ๋ฅธ ๋ถ์ํ์ ๋ง๋ค๊ณ ์ด์ ๋ฐ๋ผ EBS ์๋ฅ ์ฐ๊ณ๊ต์ฌ 4๊ถ์ผ๋ก๋ถํฐ 139๊ฐ์ ํจ์ ๊ณผ์ ๋ฅผ ๋ถ์ํ์๋ค. ํนํ ๊ณผ์ ์ ์ธ์ง์ ์์ค๊ณผ ํจ์์ ํํ ๋ฐ ๋ฒ์ญ์ ํ์ ๋ํ ๋ถ์์์ ์ดํด๋ณด์ง ๋ชปํ ๋ถ๋ถ์ 2015 ๊ฐ์ ์ํ๊ณผ ๊ต์ก๊ณผ์ ์์ ์ ์ํ๋ ๋ด์ฉ์์์ ๊ธฐ๋ฅ์ผ๋ก๋ถํฐ ์ธ๋ถ์ ์ผ๋ก ๋ถ์ํจ์ผ๋ก์จ ๊ณผ์ ์์ ๋ค๋ฃจ๊ณ ์๋ ํจ์์ ๊ดํ ์ํ์ ์ง์๊ณผ ์ด๋ฅผ ๋ค๋ฃจ๋ ๋ฐฉ์์ ํ์ธํ๋ ๊ฒ์ ์ค์ ์ ๋์๋ค.
๋ถ์ ๊ฒฐ๊ณผ EBS ์๋ฅ ์ฐ๊ณ๊ต์ฌ์ ํจ์ ๊ณผ์ ๋ ๊ต์ก๊ณผ์ ์ ๋ด์ฉ ์์์ ์ ์ฐจ์ ์ธ ์ง์์ ๊ณ ๋ฅด๊ฒ ํ์ตํ๊ฒ ํ๋ฉฐ ๊ธฐ๋ณธ ์ ํ์ ๊ทธ๋ํ์ ํ์ฉ์ ๋ํ ์ฐ์ต ๊ธฐํ๋ฅผ ์ถฉ๋ถํ ์ ๊ณตํ๊ณ ์์๋ค. ๋ค๋ง ์ํ์ ๋ฌธ์ ํด๊ฒฐ๊ณผ ์ถ๋ก ๊ณผ ๊ฐ์ ๊ณ ์ฐจ์์ ์ฌ๊ณ ์ ๋ค์ํ ํจ์ ๋งฅ๋ฝ๊ณผ ํํ์ ๊ฒฝํํ ๊ธฐํ๋ ์ ํ์ ์ผ๋ก ๋ํ๋ฌ๋ค. ๋ฐ๋ผ์ ํ์๋ค์๊ฒ ๋ณด๋ค ์๋ฏธ ์๋ ํจ์ ํ์ต ๊ธฐํ๋ฅผ ์ ๊ณตํ๊ธฐ ์ํด์ ๋์ ์ธ์ง์ ์์ค์ ๊ณผ์ ์ ๊ทธ๋ํ์ ๋ํ ๋ค์ํ ํ๋์ ํ์๋ก ํ๋ ๋ฌธํญ์ด ๋ ์ถ๊ฐ๋ ํ์๊ฐ ์๋ค. ๋ํ ํจ์ ๋งฅ๋ฝ์ ํฌํจํ๋ ๊ณผ์ ๋ฅผ ์ ์ํจ์ผ๋ก์จ ํ์๋ค์๊ฒ ์ค์ํ ์์ ์ฌ๋ฌ ํ์์ ์กฐ์งํ๊ณ ์ค๋ช
ํ๋ ๋๊ตฌ์ด์ ์ํ์ ์ฌ๋ฌ ์์ญ์ ํตํฉํ๋ ์์ด๋์ด๋ก์์ ํจ์๋ฅผ ๊ฒฝํํ๊ฒ ํ ๊ฒ์ ์ ์ํ๋ค.Exposure of students to meaningful and worthwhile mathematical tasks is an important issue since mathematical tasks determine not only the content to learn but also the way of thinking. Due to importance of mathematical tasks, many analytic studies of tasks in the textbook have been done. However, even though KSAT(Korean Scholastic Aptitude Test)-EBS linked coursebooks have had a great effect on the teaching-learning and the evaluation of high schools under the influence of the KSAT-EBS linked policy, there have been a lack of research on KSAT-EBS linked coursebooks from the mathematics educational point of view.
Function is the common theme throughout the mathematical curriculum and it has the meaning of more than just one theme in mathematical learning in that it integrates a number of areas of mathematics. Therefore, analysis of the tasks on functions in KSAT-EBS linked coursebooks would provide plentiful discussion about the mathematical thinking that students encounter from the tasks.
The purpose of this study is to explore how KSAT-EBS linked coursebooks tasks on functions provide students an opportunity-to-learn. For this purpose, this study investigates level of cognitive demand of tasks and studies about the use and translation of functional expressions. Also it investigates content elements and skills of the 2015 revised National Mathematics Curriculum. Based on the investigation, this study analyzes 139 tasks on function content dealt with in four KSAT-EBS linked coursebooks to identify the opportunity-to-learn functions. By analyzing tasks from the viewpoint of content elements and skills, this study focuses on identifying in detail the mathematical knowledge and the way it is dealt with in the tasks on functions.
The conclusion is follows: KSAT-EBS linked coursebooks deal with all content element and procedural knowledge without omission and provide sufficient opportunity to practice graphing the basic types of functions. However, the results also show that there are limited opportunities to experience higher-order thinking like mathematical problem solving or reasoning and to deal with diverse context of function and functional expressions.
From the results, the study suggests the necessity of additional high-level tasks and diverse graphing activities to provide students more meaningful opportunity to learn functions. Futhermore, it also suggests to provide tasks that include realistic context on functions in order that students can experience the function as a practical tool for organizing and explaining phenomena from daily life and as an idea for integrating multiple areas of mathematics.I. ์๋ก 1
๊ฐ. ์ฐ๊ตฌ์ ๋ชฉ์ ๋ฐ ํ์์ฑ 1
๋. ์ฐ๊ตฌ ๋ฌธ์ 4
II. ๋ฌธํ ๋ถ์ 5
๊ฐ. ์ธ์ง์ ๋
ธ๋ ฅ์์ค 5
1. ์ธ์ง์ ๋
ธ๋ ฅ์์ค์ ์๋ฏธ 5
2. ๊ต๊ณผ์ ๊ณผ์ ์ ์ธ์ง์ ๋
ธ๋ ฅ์์ค 6
๋. ํจ์ ์ง๋ 9
1. ํ๊ต ์ํ์ ํจ์์ ์ฌ๊ณ 9
2. ํจ์์ ํํ๊ณผ ํํ ๊ฐ ๋ฒ์ญํ๋ 11
3. ํจ์์ ํํ๊ณผ ํํ ๊ฐ ๋ฒ์ญ๋ฅ๋ ฅ์ ๊ดํ ์ฐ๊ตฌ 14
๋ค. 2015 ๊ฐ์ ์ํ๊ณผ ๊ต์ก๊ณผ์ 17
1. 2015 ๊ฐ์ ์ํ๊ณผ ๊ต์ก๊ณผ์ ์ ๋ด์ฉ์ฒด๊ณ ๋ฐ ์ฑ์ทจ๊ธฐ์ค 17
2. 2015 ๊ฐ์ ์ํ๊ณผ ๊ต์ก๊ณผ์ ์ ํจ์์์ญ ์ง๋๋ฐฉํฅ 20
โ
ข. ์ฐ๊ตฌ๋ฐฉ๋ฒ 23
๊ฐ. ๋ถ์ ๋์ 23
๋. ๋ถ์ ๋ฐฉ๋ฒ 25
1. ์ธ์ง์ ๋
ธ๋ ฅ์์ค ๋ถ์ํ 25
2. ํจ์์ ํํ๊ณผ ํํ ๊ฐ ๋ฒ์ญ ๋ถ์ํ 26
3. 2015 ๊ฐ์ ์ํ๊ณผ ๊ต์ก๊ณผ์ ์ ๋ด์ฉ์์์ ๊ธฐ๋ฅ์ ๋ฐ๋ฅธ ์ธ๋ถ ๋ถ์ํ 28
IV. ์ฐ๊ตฌ๊ฒฐ๊ณผ 32
๊ฐ. ๊ณผ์ ์ ์ธ์ง์ ๋
ธ๋ ฅ์์ค 32
๋. ํจ์์ ํํ๊ณผ ๋ฒ์ญํ๋ 34
1. ์ ์๋ ํจ์ ํํ 35
2. ํจ์ ํํ ๊ฐ์ ๋ฒ์ญํ๋ 40
๋ค. 2015 ๊ฐ์ ์ํ๊ณผ ๊ต์ก๊ณผ์ ์ ๋ด์ฉ์์์ ๊ธฐ๋ฅ์ ๋ฐ๋ฅธ ์ธ๋ถ ๋ถ์ ๊ฒฐ๊ณผ 43
1. M ๊ณผ์ ์ธ๋ถ ๋ถ์ ๊ฒฐ๊ณผ 43
2. PNC ๊ณผ์ ์ธ๋ถ ๋ถ์ ๊ฒฐ๊ณผ 45
2.1. ๊ณ์ฐํ๊ธฐ์ ์ดํดํ๊ธฐ 47
2.2. ํจ์ ๊ตฌํ๊ธฐ 48
2.3. ๊ทธ๋ํ ๊ทธ๋ฆฌ๊ธฐ์ ํํํ๊ธฐ 51
2.4. ํ์ฉํ๊ธฐ์ ๋ฌธ์ ํด๊ฒฐํ๊ธฐ 56
V. ๋
ผ์ ๋ฐ ๊ฒฐ๋ก 61
๊ฐ. ๋
ผ์ ๋ฐ ๊ฒฐ๋ก 61
์ฐธ๊ณ ๋ฌธํ 69Maste
PbTiOโ ๋ฏธ์ธ๋ถ๋ง ํฉ์ฑ์ ๊ดํ ์ฐ๊ตฌ
ํ์๋
ผ๋ฌธ(์์ฌ)--์์ธ๋ํ๊ต ๋ํ์ :์ฌ๋ฃ๊ณตํ๋ถ,2002.Maste
Effect on environmental pollution of population increases in seoul
๋ณด๊ฑดํ๊ณผ/์์ฌIn this paper environmental pollution is considered as a by - product of population increase and economic development.
The anticipated rise in environmental pollution is expressed as a result of urban expansion in size and functions.
Owing to the over-population and great density of the industrial area in Seoul environmental disruption has been brought out as a result of pollution. From this point of view, in order to determine the degree of environmental pollution, a time
series analysis, a principal component analysis, a multiple and partial correlation analysis have been undertaken with the data taken from the annual reports of Seoul metorpolitian Government statisitical Year book and the reports of the Seoul Institute of Hygiene. 1969 to 1974.
Eleven testing items were selected as variables, including population, concentration of sulfur dioxide, dust fall-out, total exhaust quantity of refuse and excrements, number of motor vehicles, use of Gasoline, Anthracite, Bunker c-oil, production of water and consumption level of electric power.
The results were as follows:
1. According to the time series analysis, as popolation increased, 10 variables environmental pollutants increased year by year.
2. Of the given variables from principal component analysis, three were classified as the causative factors to be identified as follows
1) Factor of Energy consumption
2) Factor of self-purificaton
3) Fcator of Environmental pollution
3. The cumulative percentage of eigen values from the three identified variables totaled 77.7%.
4. In the partial correlation between sulfur dioxide and population increase, it was shown that population increase would not give a direct influence to the increase of sulfur dioxide in the Environment.
5. There was no correlation between the quantity of dust fall-out and the population, but the increasement of population has some correlations with the refuse from the city (r=0.332), and with the use of anthracite for fuel (r=0.488).
6. As far as the population increase, the motor vehicles increase with the partial correlation of r=0.696.
7. the population in crease shows the increasement of anthracite use with r=0.361.restrictio
Performance Evaluation of Safety Envelop Based Path Generation and Tracking Algorithm for Autonomous Vehicle
This paper describes the tracking algorithm performance evaluation for autonomous vehicle using a safety envelope based path. As the level of autonomous vehicle technologies evolves along with the development of relevant supporting modules including sensors, more advanced methodologies for path generation and tracking are needed. A safety envelope zone, designated as the obstacle free regions between the roadway edges, would be introduced and refined for further application with more detailed specifications. In this paper, the performance of the path tracking algorithm based on the generated path would be evaluated under safety envelop environment. In this process, static obstacle map for safety envelope was created using Lidar based vehicle information such as current vehicle location, speed and yaw rate that were collected under various driving setups at Seoul National University roadways. A level of safety was evaluated through CarSim simulation based on paths generated with two different references: a safety envelope based path and a GPS data based one. A better performance was observed for tracking with the safety envelop based path than that with the GPS based one.N