An Apparel Brands Channel Strategy: The Case of Oliver in Korea

The purpose of this case study was to describe the development of a channel strategy for an apparel brand, BoKids, designed to distribute its brand, Oliver, efficiently to customers. Bokids launched its childrens apparel brand, Oliver, in Korea by signing a brand license contract with Oliver of USA. When the brand was launched in 2005, Oliver was positioned as a brand with a reasonable price and a high quality product, which was sold primarily through department stores. In 2007, Oliver was suffering from sluggish sales volumes, and switched its main distribution channel from department stores to discount stores, which are the number 1 retail format in Korea. Oliver was compelled to adjust the price range of its main products to $20 30 in order to satisfy the needs of discount store customers. However, Oliver has considered Internet shopping as another channel for the Oliver brand, as Internet shopping is rapidly gaining popularity in Korea. This case can be used in conjunction with discussions on marketing topics, such as the design of marketing channels (Chapter 6, Designing the Marketing Channel, Marketing Channels: A Management View, 7th Edition by Bert Rosenbloom, South-Western College Pub, 2007) for senior level marketing seminars

China Market Entry Strategy Of Paris Baguette

This case study analyzes the global strategy of Paris Baguette, a leading bakery franchise in Korea. Because of stricter regulations in the local market, Paris Baguette has encouraged franchises to target overseas markets. The company made first inroads into the Chinese market in 2004 with a bakery cafe in Shanghai. The main point of Paris Baguette’s global strategy is summarized by high quality, style, diversification, and localization. Also, Paris Baguette directly operates its flagship store from headquarters, due to the poor legal environment in China. In this study, we analyze strategies of China market and suggest considerations for future business expansion

A Revisiting Method Using a Covariance Traveling Salesman Problem Algorithm for Landmark-Based Simultaneous Localization and Mapping

This paper presents an efficient revisiting algorithm for landmark-based simultaneous localization and mapping (SLAM). To reduce SLAM uncertainty in terms of a robot’s pose and landmark positions, the method autonomously evaluates valuable landmarks for the data associations in the SLAM algorithm and selects positions to revisit by considering both landmark visibility and sensor measurement uncertainty. The optimal path among the selected positions is obtained by applying the traveling salesman problem (TSP) algorithm. To plan a path that reduces overall uncertainty, the cost matrix associated with the change in covariance between all selected positions of all pairs is applied for the TSP algorithm. From simulations, it is verified that the proposed method efficiently reduces and maintains SLAM uncertainty at the low level compared to the backtracking method

Senior Recitals

A senior recital presented at the UNT College of Music Voertman Hall

Graph Search-Based Exploration Method Using a Frontier-Graph Structure for Mobile Robots

This paper describes a graph search-based exploration method. Segmented frontier nodes and their relative transformations constitute a frontier-graph structure. Frontier detection and segmentation are performed using local grid maps of adjacent nodes. The proposed frontier-graph structure can systematically manage local information according to the exploration state and overcome the problem caused by updating a single global grid map. The robot selects the next target using breadth-first search (BFS) exploration of the frontier-graph. The BFS exploration is improved to generate an efficient loop-closing sequence between adjacent nodes. We verify that our BFS-based exploration method can gradually extend the frontier-graph structure and efficiently map the entire environment, regardless of the starting position

지역 지도 기반의 이동 로봇 탐사 기법

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Scene recognition with omnidirectional images in low-textured environments

A combined method involving global and local descriptors was developed to recognise scenes for loop closure detection in low-textured environments. An omnidirectional image is divided into background regions and salient regions according to the colour distribution. To represent a scene with features that are appropriate to its characteristics, global features for background regions are calculated and scale invariant feature transform features for salient regions are extracted. The proposed method can compute a more distinct scene similarity, and this was verified by an experiment involving loop closure detection.X111sciescopu

Local map-based exploration for mobile robots

For an accurate and efficient exploration, a local map-based exploration strategy is proposed. Segmented frontiers and relative transformations constitute a tree structure; using frontier segmentation and a local map management method, a robot can expand the mapped environment by moving along the tree structure. Although this local map-based exploration method uses only local maps and adjacent node information, mapping completion and efficiency can be greatly improved by merging and updating the frontier nodes. Simulation results demonstrate that the computational time does not increase during the exploration process, or when the resulting map becomes large. Additionally, the resulting path is effective in reducing the uncertainty in simultaneous localization and mapping or localization because of the loop-inducing characteristics from the child node to the parent node.X1144sciescopu