Semiconducting Polymers and Block Copolymers Prepared by Chain-Growth Living Polymerization

Organic semiconducting polymers are the unique materials that considered a basis for the next generation of electronic and optoelectronic applications. However, high device performance of semiconducting polymers strongly depends on their molecular structure and nanoscale organization. Therefore, it is an essential task to develop robust and versatile synthetic approaches to build such well-defined semiconducting polymer materials. This Ph.D. study aimed at design of state-of-the-art synthetic approaches towards organic semiconducting polymers via chain-growth living polymerization as well as development of polymer architectures which can self-organize into supramolecular nanoassemblies or allow external control of the polymer’s properties. First, we prepared a series of temperature-responsive water-soluble poly(N-isopropylacrylamide)-functionalized polythiophenes, and showed that their supramolecular organization and temperature control of their conformation and conjugation length was strongly dependent on the extent of regioregularity of the polythiophene backbone. In order to improve the regioregularity, we developed a general approach to highly efficient external catalytic initiators for the synthesis of various semiconducting polymers. Extensive studies allowed better understanding of the unusual catalytic systems, and their behavior in chain-growth living polymerization reactions. Using this approach, we synthesized a variety of amphiphilic polythiophene block copolymers incorporating a low energy gap perylenedicarboximide (PDCI) unit to demonstrate the possibility to control supramolecular organization and photophysical properties of such systems by using external stimuli (such as solvent and temperature). As part of our general studies towards design of near-infrared (NIR) fluorescent conjugated polymers, we developed a synthetic approach to a novel class of such materials which are based on cyanine dyes as monomeric repeating units. The obtained polymers showed a variety of properties (thermal stability, solubility, absorption and fluorescence in the NIR range) that may make them a useful class of NIR fluorescent conjugated polymers

Optimal Parking Planning for Shared Autonomous Vehicles

Parking is a crucial element of the driving experience in urban transportation systems. Especially in the coming era of Shared Autonomous Vehicles (SAVs), parking operations in urban transportation networks will inevitably change. Parking stations will serve as storage places for unused vehicles and depots that control the level-of-service of SAVs. This study presents an Analytical Parking Planning Model (APPM) for the SAV environment to provide broader insights into parking planning decisions. Two specific planning scenarios are considered for the APPM: (i) Single-zone APPM (S-APPM), which considers the target area as a single homogeneous zone, and (ii) Two-zone APPM (T-APPM), which considers the target area as two different zones, such as city center and suburban area. S-APPM offers a closed-form solution to find the optimal density of parking stations and parking spaces and the optimal number of SAV fleets, which is beneficial for understanding the explicit relationship between planning decisions and the given environments, including demand density and cost factors. In addition, to incorporate different macroscopic characteristics across two zones, T-APPM accounts for inter- and intra-zonal passenger trips and the relocation of vehicles. We conduct a case study to demonstrate the proposed method with the actual data collected in Seoul Metropolitan Area, South Korea. Sensitivity analyses with respect to cost factors are performed to provide decision-makers with further insights. Also, we find that the optimal densities of parking stations and spaces in the target area are much lower than the current situations.Comment: 27 pages, 9 figures, 9 table

A Comparison of Image Aligning and Correcting Software with an Unmanned Aerial System

In the past few decades, many kinds of UAS for image acquisition have been developed. But software for image aligning and correcting is mostly high-cost commercial. This problem caused the cost-problem in using UAS. Nowadays, a variety of software, not only commercial but also open-source, provides powerful image processing tool. There are a number of software to support image processing. In this study,five popular programs are tested for comparison. The goal of this study is to compare popular open-source software based on the ease use, overall accuracy and processing time for chunk of images from UAS

Balancing Project Financing and Mezzanine Project Financing with Option Value to Mitigate Sponsor’s Risks for Overseas Investment Projects

Major steel-making companies in Korea have recently been trying to advance into international markets for better profitability and new market shares. Even with strategic partnerships with local organizations, the Korean steel companies are facing and incurring significant risks which impact their ability to achieve a sustainable profit. The objective of this research is to determine an optimum combination of financial models, specifically Project (PF) and Mezzanine Financing (MF) with an option (convertible bond and bond with warrant). The results of the proposed model can lower interest rates of financing, thereby increasing the profitability of the project investors. To analyze the MF method’s effectiveness and proper use, the following three steps are applied: (1) Monte-Carlo Simulations (MCS) using Excel and @Risk software are performed for the Net Present Value (NPV) of the project and its volatility; (2) the Black-Scholes model (BSM) is applied to evaluate MF based on project value; and (3) interest rate of MF is calculated from its option value and is reapplied back to the NPV calculation of the project to determine the effects of MF. Assuming a 50% debt/equity ratio, these simulations were performed on five cases (50% senior debt, 0% MF for a base case then increasing MF and decreasing senior debt by 10% four times). Through this process, using the 10%, MF lowered the borrowing size by 20% and using MF continued to lower the borrowing size up to 40% borrowing when using 40% MF. Based on this result, the researchers support the use of MF to optimize Korean steel international financial models. The resultant data will serve as an effective method to increase net cash flow in overseas steel-plant project investments. This research was performed for a steel plant located in Iran as a case-study, but this optimized financing method using MF with an option product can be applied sustainably not only for overseas investment of steel plants but also any other business, such as oil & gas, power generation, and transportation industries.11Ysciessciscopu

Shepherding Slots to Objects: Towards Stable and Robust Object-Centric Learning

Object-centric learning (OCL) aspires general and compositional understanding of scenes by representing a scene as a collection of object-centric representations. OCL has also been extended to multi-view image and video datasets to apply various data-driven inductive biases by utilizing geometric or temporal information in the multi-image data. Single-view images carry less information about how to disentangle a given scene than videos or multi-view images do. Hence, owing to the difficulty of applying inductive biases, OCL for single-view images remains challenging, resulting in inconsistent learning of object-centric representation. To this end, we introduce a novel OCL framework for single-view images, SLot Attention via SHepherding (SLASH), which consists of two simple-yet-effective modules on top of Slot Attention. The new modules, Attention Refining Kernel (ARK) and Intermediate Point Predictor and Encoder (IPPE), respectively, prevent slots from being distracted by the background noise and indicate locations for slots to focus on to facilitate learning of object-centric representation. We also propose a weak semi-supervision approach for OCL, whilst our proposed framework can be used without any assistant annotation during the inference. Experiments show that our proposed method enables consistent learning of object-centric representation and achieves strong performance across four datasets. Code is available at \url{https://github.com/object-understanding/SLASH}

Optimal Gait Families using Lagrange Multiplier Method

The robotic locomotion community is interested in optimal gaits for control. Based on the optimization criterion, however, there could be a number of possible optimal gaits. For example, the optimal gait for maximizing displacement with respect to cost is quite different from the maximum displacement optimal gait. Beyond these two general optimal gaits, we believe that the optimal gait should deal with various situations for high-resolution of motion planning, e.g., steering the robot or moving in "baby steps." As the step size or steering ratio increases or decreases, the optimal gaits will slightly vary by the geometric relationship and they will form the families of gaits. In this paper, we explored the geometrical framework across these optimal gaits having different step sizes in the family via the Lagrange multiplier method. Based on the structure, we suggest an optimal locus generator that solves all related optimal gaits in the family instead of optimizing each gait respectively. By applying the optimal locus generator to two simplified swimmers in drag-dominated environments, we verify the behavior of the optimal locus generator.Comment: 6 page