이 논문에서는 하나논리의 세계관인 천일, 지일, 인일이 지향하는 삼일 논리에 의한 소유의 본체와 그 질적 속성이 어떻게 변용되는 지를 설명 하고자 하였다. 하나논리에 의하면 만물은 합일과 하나리기심 혼연일체 로 귀결되듯이, 소유 또한 대립하고 갈등하는 것처럼 보이지만, 공존과 지속가능성을 지향한다. ‘소유’라는 본체는 경제행위자라고 할 수 있는 국가, 기업, 개인은 각자 소유했다는 인식 하에서 행동이 발현되는데, 이 과정에 행위자들이 가진 것의 교집합이 존재하기 때문이다. 결과 소유권 이 명시하는 배타적·독점적 권리의 행사는 제한적인 것이 된다. 국가는 영토 안의 기업, 개인을 소유한 것으로 간주하며, 법과 제도를 통하여 주 권을 행사하게 마련이다. 기업은 해당 국가의 법·제도가 허용하는 범위 안에서의 경영권 행사가 가능하며, 개인은 취득한 국적에 따라 잠재적 소득의 규모와 복지의 범위에 차이가 발생한다. 나아가서 소유의 질적속 성은 그 잠재성의 발현으로 나타나게 된다. 소유잠재성은 높은 확률의 부모-자녀 상속, 중간 확률의 청약, 경매, 낮은 확률의 복권 당첨 등으로 나타나게 된다. 이를 통하여 서구 중심적 현재 세대 중심의 자원집중 현 상 문제점을 지적하고, 저출산 현상의 대안을 도출하고자 하였다.

This paper is a study on the malfunction that occurred during the power supply logic of the Gunner Display Device during Mortar Functional Firing under low temperature conditions. As a result of the phenomenon reproduction test and its analysis, the cause of the malfunction of the Gunner Display Device was Glitch, which occurred in the process of converting the image signal, and the improved software was applied to the Gunner's Display System by ignoring some of the image signal conversion process that causes Glitch. The improved Gunner Display Device passed the validity test and applied the improvement to the mortars. As a result of this study, several suggestions for power supply and control logic were proposed. It is expected that this study will be used as a reference in the future design of similar weapons systems.

NFTs are touted as revolutionary to market and monetize digital assets. However, critics have questioned NFT value, labeling it as fraudulent. Despite mixed reviews, various firms are leveraging NFTs as a value proposition for customers. In this study, using service-dominant (S-D) logic, we explore how NFTs can help firms in value co-creation and service exchange. We propose and test a conceptual framework using a multi-method approach -1) we investigate NFTs popularity using historical news articles spanning ten years, 2) we use a case study to examine business NFT use in value co-creation and service exchange, subsequently, proposing a conceptual framework illustrating such value exchange, 3) we test our conceptual framework by analyzing data from multiple sources, including surveys, online forums, social media, and transactions. Results from our study, provide business valuable insight into using NFTs as value co-creation and service exchange tool.

In this study, the safety aspects were studied by comparing the charge control characteristics of the two vehicles when a failure occurs between the OBC including the charging port or the charging door module (CDM) during slow charging using the In Cable Control Box (ICCB) for a long time.When the AC terminal was momentarily disconnected during charging, the Model-3 vehicle was charged normally if the AC circuit was disconnected up to three times, and the charging control was stopped when the number of disconnects reached four times. However, in the Ioniq-5 vehicle, charging control was normally performed when the disconnected AC circuit was normally connected regardless of the number of disconnection.

An Ant Colony Optimization Algorithm(ACO) is one of the frequently used algorithms to solve the Traveling Salesman Problem(TSP). Since the ACO searches for the optimal value by updating the pheromone, it is difficult to consider the distance between the nodes and other variables other than the amount of the pheromone. In this study, fuzzy logic is added to ACO, which can help in making decision with multiple variables. The improved algorithm improves computation complexity and increases computation time when other variables besides distance and pheromone are added. Therefore, using the algorithm improved by the fuzzy logic, it is possible to solve TSP with many variables accurately and quickly. Existing ACO have been applied only to pheromone as a criterion for decision making, and other variables are excluded. However, when applying the fuzzy logic, it is possible to apply the algorithm to various situations because it is easy to judge which way is safe and fast by not only searching for the road but also adding other variables such as accident risk and road congestion. Adding a variable to an existing algorithm, it takes a long time to calculate each corresponding variable. However, when the improved algorithm is used, the result of calculating the fuzzy logic reduces the computation time to obtain the optimum value.

Devices with negative differential transconductance (NDT) and negative differential resistance (NDR) have shown a strong potential for digital electronics with high information density due to their N-shaped current–voltage (I–V) characteristics leading to multiple threshold voltages ( Vths). The 2D materials, such as graphene, hBN, MoS2, WS2, etc., offer an attractive platform to achieve NDT and NDR because of the absence of dangling bonds on the surface, leading to a high-quality interface between the layers. The 2D materials' unique property of the weak van der Waals (vdW) interactions without dangling bonds on the heterostructure devices shows the way for the applications more than-Moore devices. This review holds a well-timed overview of 2D materials-based devices to develop future multi-valued logic (MVL) circuits exhibiting high information density. Notably, the recent advances in emerging 2D materials are reviewed to support the directions for future research on MVL applications.

PURPOSES : A mechanistic-empirical (ME) predictive design logic that can compute the reflective cracking life of hot-mix asphalt (HMA) overlaid on top of a composite pavement is proposed herein. METHODS : The overlay thickness design and analysis logic of the HMA were formulated based on the ME concept of reflection crack propagation. Climate data, traffic load data, the pavement material properties, and the thickness of each layer of the pavement are the main inputs for the ME-Reflective Cracking Rate (RCR) prediction algorithm. An Microsoft Excel Virtual Basic for Application (VBA) program was created to aid designers in assessing the expected performance of an HMA overlay design. Calibration was done using data from the Long-Term Pavement Performance (LTPP) sections. Sensitivity analysis was conducted to compare the results yielded by the program and data from a report by the Texas Transportation Institute. RESULTS : The predictive model performance effectively generates the dynamic and relaxation modulus curves. The correlation value of the calibration factors, R2, is 0.79. The calibration factors used for the Asphalt Overlay Thickness Design (AOTD) program and the sensitivity analysis, i.e., k1, k2,, and k3,, are set to 5, 5, and 150, respectively. The sensitivity of the AOTD program affords reasonable results. Additionally, the program yields results similar to the trends presented in a report by the Federal Highway Administration. CONCLUSIONS : The proposed ME design logic is successfully translated into an Excel VBA program, AOTD, which can perform routine assessments of laboratory tests for HMA overlays. The program can effectively perform numerous iterations and computations to predict an HMA overlay. The predictive model can generate reasonable dynamic modulus and relaxation modulus curves for the characterization of HMA overlays. Under the same asphalt binder grade and HMA type, doubling the HMA overlay thickness yields three times the expected reflective cracking service life.

MDPS control has been a difficult problem for the past two decades. Though there are many ways to control steering feeling, the MDPS control logic is still being upgraded or developed for steering feel improvement. A new point of view in MDPS is proposed by evolution logic, which is a new driver friendly improvement based on the analysis of driver’s driving pattern. As a result of the application of evolution logic, this paper shows that drivers behaviour effecting factors among MDPS parameters will efficiently lead to customers’ satisfaction.

Today, conventional CVT equipped vehicle controls engine torque and gear ratio by using engine torque map and shifting map. But this control process is difficult to optimize the fuel economy when the driving mode is changed arbitrary. In this study, I propose the real-time CVT control with considering the power loss of transmission system to improve vehicle fuel economy and drivability. The driving performance and fuel economy of the proposed control logic is analyzed by backward simulation and the validity of new control logic is verified.

It is worth studying the Saemaul Development Undong as a science when it is increasingly gaining a global society interest. The purposes of this research are to frame the project design matrix(PDM) for the Saemaul development project and to suggest underpinning theoretical background which explains the causal relationship of the logical frame approach of the PDM. This research diagramed the driving process of Saemaul projects which indicates the stages of hierarchical logic structures in terms of inputs-activities-outputs-outcomes and goal of the projects. Then it presented governance theory, bottom-up approach, endogenous development and network theory as theoretical backgrounds of the logic model of Saemaul projects. Expected effects of this research suggest that it will be a basic study to give a standardized criteria to evaluate Saemaul and ODA projects to raise the effectiveness of ODA projects, even though it needs more studies to secure the validity of the PDM and to get a generalization of the theory.

태양광 패널로부터 출력을 최대로 얻기 위해서는 신뢰성이 높은 태양광 추적 장치가 설계되어야 한다. 본 논문에서는 LabVIEW 프로그램을 이용하여 퍼지 제어를 기반으로 구현한 2축 태양광 추적 장치 시스템을 제작하여 그 성능에 대해서 알아보았다. 태양광 패널의 움직임을 제어하기 위한 구현된 퍼지 의사결정 시스템의 사용자 인터페이스를 통하여 모든 파라미터를 제어하고 확인할 수 있는 지능제어기와 기계적인 구동부분의 설계가 연구의 중심이 되고 있다. 실제 태양광 추적시스템을 개발하여 환경, 날씨, 계절 및 빛 상태와 같은 영향에 대해서 분석하였다. 태양광 추적장치는 실제 상황에서 시험하였고 시스템 동작과 관련된 모든 변수들은 기록되고 분석되었다. 제안한 태양광 추적시스템을 활용할 경우 고정식 패널에 비해 날씨에 따라 다르지만 최대 약 38% 정도의 더 높은 효율을 얻을 수 있어 자동으로 추적할 때 매우 좋은 결과를 얻을 수 있었다.

Two key challenges in statistical relational learning are uncertainty and complexity. Standard frameworks for handling uncertainty are probability and first-order logic respectively. A Markov logic network (MLN) is a first-order knowledge base with weights attached to each formula and is suitable for classification of dataset which have variables correlated with each other. But we need domain knowledge to construct first-order logics and a computational complexity problem arises when calculating weights of first-order logics. To overcome these problems we suggest a method to generate first-order logics and learn weights using association analysis in this study.