<?xml version="1.0" encoding="UTF-8"?>
<rss xmlns:dc="http://purl.org/dc/elements/1.1/" version="2.0">
  <channel>
    <title>Repository Collection:</title>
    <link>http://repository.sungshin.ac.kr/handle/2025.oak/176</link>
    <description />
    <pubDate>Tue, 19 May 2026 15:12:28 GMT</pubDate>
    <dc:date>2026-05-19T15:12:28Z</dc:date>
    <item>
      <title>최근 30년간 국내 프랜차이즈 분쟁사례의 변화에 관한 연구: 분쟁조정 의결서 대상 토픽모델링을 중심으로</title>
      <link>http://repository.sungshin.ac.kr/handle/2025.oak/8865</link>
      <description>Title: 최근 30년간 국내 프랜차이즈 분쟁사례의 변화에 관한 연구: 분쟁조정 의결서 대상 토픽모델링을 중심으로
Author(s): 김준영; 김향미; 김재욱; 정소희
Abstract: 본 연구는 국내 프랜차이즈 산업에서 발생한 분쟁의 구조적 양상과 진화 과정을 탐색적으로 분석하고자 한다. 기존 연구들이 주로 특정 시점의 사례나 법적 쟁점에 국한되었던 것과 달리, 본 연구는 1996년부터 2024년까지 약 30년에 걸친 공정거래위원회의 가맹사업 분쟁 조정 및 심의 자료 757건을 수집하여, 시계열적·총체적으로 분쟁의 양상 변화를 조망하였다. 특히, 단순 빈도 분석이나 사례 열거에 그치지 않고, LDA(Latent Dirichlet Allocation) 기반 토픽 모델링과 연관어 네트워크 분석이라는 정량적 탐색 기법을 접목함으로써, 가맹사업 분쟁의 주요 주제와 이슈 군집이 시기별로 어떻게 형성되고 변화해 왔는지를 실증적으로 규명하였다.&#xD;
분석 결과, 산업 초기에는 정보공개서 미제공, 허위·과장 정보 등 계약 체결 단계에서의 정보 비대칭 문제가 핵심 갈등 요인으로 작용했으나, 산업이 성숙함에 따라 로열티, 필수 물품 강제, 영업 지역 침해 등 운영·수익 배분 단계의 구조적 갈등으로 쟁점이 확산되었음이 확인되었다. 최근에는 점주 단체의 결성, 집단 소송, 계약 해지 등을 둘러싼 갈등의 집단화 및 제도화 경향이 뚜렷해졌으며, 분쟁이 보다 복합적이고 구조적인 양상으로 진화하고 있음을 보여주었다. 이러한 결과는 프랜차이즈 갈등이 거래 효율성의 문제를 넘어, 통제와 자율성의 긴장, 권력과 자원의 불균형 등 보다 구조적 요인과 관련되어 있음을 시사한다.&#xD;
본 연구는 국내 프랜차이즈 분쟁에 대한 정책적 개입의 방향성을 도출함과 동시에, 정성·정량 자료를 통합하는 탐색적 방법론의 유용성을 실증적으로 제시했다는 점에서 학술적 기여가 있다. 특히, 갈등의 구조적 원인을 파악하고 장기적 관점에서의 규제 및 조정 장치를 설계할 수 있는 근거를 제공하며, 가맹본부와 가맹점 간 균형 잡힌 파트너십 구축, 사전적 분쟁 예방 시스템 설계, 정보공개서의 실효성 제고 등의 정책적 시사점을 함께 제시한다.</description>
      <pubDate>Wed, 30 Jul 2025 15:00:00 GMT</pubDate>
      <guid isPermaLink="false">http://repository.sungshin.ac.kr/handle/2025.oak/8865</guid>
      <dc:date>2025-07-30T15:00:00Z</dc:date>
    </item>
    <item>
      <title>Seamless Indoor-Outdoor Localization Through Transition Detection</title>
      <link>http://repository.sungshin.ac.kr/handle/2025.oak/8846</link>
      <description>Title: Seamless Indoor-Outdoor Localization Through Transition Detection
Author(s): 유재현
Abstract: Indoor localization techniques operate independently of Global Navigation Satellite Systems (GNSSs), which are primarily designed for outdoor environments. However, integrating indoor and outdoor positioning often leads to inconsistent and delayed location estimates, especially at transition zones such as building entrances. This paper develops a probabilistic transition detection algorithm to identify indoor, outdoor, and transition zones, aiming to enhance the continuity and accuracy of positioning. The algorithm leverages multi-source sensor data, including WiFi Received Signal Strength Indicator (RSSI), Bluetooth Low-Energy (BLE) RSSI, and GNSS metrics such as carrier-to-noise ratio. During transitions, the system incorporates Inertial Measurement Unit (IMU)-based tracking to ensure smooth switching between positioning engines. The outdoor engine utilizes a Kalman Filter (KF) to fuse IMU and GNSS data, while the indoor engine employs fingerprinting techniques using WiFi and BLE. This paper presents experimental results using three distinct devices across three separate buildings, demonstrating superior performance compared to both Google’s Fused Location Provider (FLP) algorithm and a GPS.</description>
      <pubDate>Thu, 26 Jun 2025 15:00:00 GMT</pubDate>
      <guid isPermaLink="false">http://repository.sungshin.ac.kr/handle/2025.oak/8846</guid>
      <dc:date>2025-06-26T15:00:00Z</dc:date>
    </item>
    <item>
      <title>Textual Analysis of Effectiveness on Digital Transformation based on Artificial Intelligence Reskilling: Small and Medium Enterprises (SMEs) Cases</title>
      <link>http://repository.sungshin.ac.kr/handle/2025.oak/8838</link>
      <description>Title: Textual Analysis of Effectiveness on Digital Transformation based on Artificial Intelligence Reskilling: Small and Medium Enterprises (SMEs) Cases
Author(s): 김준영; 송효진; 김향미
Abstract: Digital Transformation (DX) is the primary innovation strategy to enhance organizational flexibility and productivity in the workplace. Each company promotes DX within its unique organizational direction and situations, and employee competency becomes a critical factor for successful transformation. However, existing small and medium-sized enterprises (SMEs) have limited resources to hire AI experts or develop appropriate DX implementation strategies compared to large enterprises. As a result, many SMEs adopt a more practical and efficient approach by implementing internal AI reskilling programs for their employees.&#xD;
In this paper, we aim to examine whether such reskilling programs effectively drive DX adaptation within SMEs. For our investigation, we designed AI-based reskilling programs that were implemented across 39 SMEs from diverse industries, alongside Proof of Concept (PoC) projects that were aligned with each company’s DX goals. We extracted key terms from the reskilling program reports and categorized the companies by industry type and organizational scale. We also conducted text analysis to identify strategic directions and transformation intents regarding DX.</description>
      <pubDate>Sun, 29 Jun 2025 15:00:00 GMT</pubDate>
      <guid isPermaLink="false">http://repository.sungshin.ac.kr/handle/2025.oak/8838</guid>
      <dc:date>2025-06-29T15:00:00Z</dc:date>
    </item>
    <item>
      <title>초해상도를 위한 Depthwise 컨볼루션 활용 영역별 컨볼루션 신경망</title>
      <link>http://repository.sungshin.ac.kr/handle/2025.oak/8812</link>
      <description>Title: 초해상도를 위한 Depthwise 컨볼루션 활용 영역별 컨볼루션 신경망
Author(s): 이규중; 김세은; 이현지
Abstract: Super-resolution(SR) is the image processing that reconstructs high-resolution images from low- resolution inputs. Representative models such as ASCNN distinguish between high and low frequency regions and apply a Low Parameter Convolution(LPC) with reduced channel dimensions to the low frequency regions. Although various other lightweight models have also been proposed, balancing computational cost and reconstruction quality remains a significant challenge. In this paper, we propose DW-ASCNN, a lightweight variant of the original ASCNN, in which the LPC layers are replaced with depthwise and pointwise convolutions. The proposed structure identifies distinct convolutional paths by leveraging the frequency characteristics of each region in ASCNN, and achieves parameter and computational efficiency through structural simplification of these separated paths. Experimental results show that DW-ASCNN reduces the number of parameters by approximately 24% and lowers FLOPs by up to 7.93% compared to the original ASCNN, while keeping the PSNR degradation within an average of 0.02 dB. These results show that the proposed model is well-suited for deployment in resource-constrained environments.</description>
      <pubDate>Sun, 29 Jun 2025 15:00:00 GMT</pubDate>
      <guid isPermaLink="false">http://repository.sungshin.ac.kr/handle/2025.oak/8812</guid>
      <dc:date>2025-06-29T15:00:00Z</dc:date>
    </item>
  </channel>
</rss>

