Color Matching Ondel-Ondel Gamification as Culture Edutainment for Children

Gamification can be one-approach children for playing while learning. Researchers proposed one gamification that using ondel-ondel; the giant Betawi dolls that famous, as Betawi icon that can be enjoyed in all Jakarta’s corners will be one of the media for children to learn how to color. Color Matching Ondel-Ondel is an offered coloring multimedia gamification, which inserted information on form elements of ondel-ondel’s attributes. This research used a merging method of gamification implementation in education and user-centered approach. To analyze the form elements of the ondel-ondel’s attribute, semiotic of William Morris had been used, that focus on form, content, context. Several children with positive responds have tried this gamification, they were not just excited and happy but also gave feedback to make this game more interesting and useful. It is expected to be more gamification using ondel-ondel as a main character and coloring gamification of other Betawi’s art and culture.

A Two-Stage Path Planning Method for the Rearrangement Problem in Modular Puzzle-Based Storage System

The modular puzzle-based storage system consists of multiple storage modules. The system has multiple input/output (IO) points which can simultaneously deal with multiple orders in one batch. When a batch of orders comes in advance, it is necessary to rearrange the items near each IO point to reduce the picking time of customers. To complete the rearrangement quickly, this paper proposes a two-stage path planning method considering the simultaneous movement of multiple items. This method includes two stages: planning single moves and merging single moves. In the first stage, the sequence of single moves of each module needs to be obtained so as to convert the system from an initial state to a target state. In the second stage, the single moves in the sequence are merged into block moves and parallel moves to reduce the steps of movement. The simulation results show that the single move planning method can be used to solve the rearrangement problem stably and effectively and that the single move merging method can greatly optimize the experimental results with the optimization rate more than 50% in different configurations.

A Meta-Mining Ontology Framework for Data Processing

Extracting knowledge from data streams received from observed objects through data mining is required in various domains. However, there is a lack of any kind of guidance on which techniques can or should be used in which contexts. Meta mining technology can help build processes of data processing based on knowledge models taking into account the specific features of the objects. This paper proposes a meta mining ontology framework that allows selecting algorithms for solving specific data mining tasks and build suitable processes. The proposed ontology is constructed using existing ontologies and is extended with an ontology of data characteristics and task requirements. Different from the existing ontologies, the proposed ontology describes the overall data mining process, used to build data processing processes in various domains, and has low computational complexity compared to others. The authors developed an ontology merging method and a sub-ontology extraction method, which are implemented based on OWL API via extracting and integrating the relevant axioms.

Map Merging with Suppositional Box for Multi-Robot Indoor Mapping

For the map building of unknown indoor environment, compared with single robot, multi-robot collaborative mapping has higher efficiency. Map merging is one of the fundamental problems in multi-robot collaborative mapping. However, in the process of grid map merging, image processing methods such as feature matching, as a basic method, are challenged by low feature matching rate. Driven by this challenge, a novel map merging method based on suppositional box that is constructed by right-angled points and vertical lines is proposed. The paper firstly extracts right-angled points of suppositional box selected from the vertical point which is the intersection of the vertical line. Secondly, based on the common edge characteristics between the right-angled points, suppositional box in the map is constructed. Then the transformation matrix is obtained according to the matching pair of suppositional boxes. Finally, for matching errors based on the length of pairs, Kalman filter is used to optimize the transformation matrix. Experimental results show that this method can effectively merge map in different scenes and the successful matching rate is greater than that of other features.

K nearest neighbour in merging satellite rainfall estimates from diverse sources in sparsely gauged basins

<p>Satellite based rainfall estimates (SBRE) are used as an alternative to gauge rainfall in hydrological studies particularly for basins with data issues. However, these data products exhibit errors which cannot always be corrected by bias correction methods such as Ratio Bias Correction (RBC). Data fusion or data merging can be a potentially good approach in merging various satellite rainfall products to obtain a fused dataset, which can benefit from all the data sources and may minimise the error in rainfall estimates. Data merging methods which are commonly applied in meteorology and hydrology are: Arithmetic merging method (AMM), Inverse error squared weighting (IESW) and Error variance (EV). Among these methods EV is popular, which merges can be used to merge bias corrected SBREs using the minimisation of variance principle.</p><p>In this research we investigated the possibility of using K nearest neighbour as a data merging method. Four satellite rainfall products were used in this study namely CMORPH, PERSIANN CDR, TRMM 3B42 and MSWEP. MSWEP was used as a reference dataset for comparing the merged rainfall dataset since it is also a merged product. All these products were downloaded at 0.25° x 0.25° spatial scale and daily temporal scale. Satellite products are known to behave differently at different temporal and spatial scales. Based on the climatic and physiographic features the Indus basin was divided into four zones. Rainfall products were compared at daily, weekly, fortnightly, monthly and seasonal scales whereas spatial scales were gauge location, zonal scales and basin scale. The RBC method was used to correct the biasness of satellite products by correcting the products at monthly and seasonal scale. Wth bias correction the daily normalised root mean square error (NRMSE) was reduced up to 20% for CMORPH, 22% for PERSIANN CDR and 14% for TRMM at the Indus basin scale for monthly scale which is why the monthly bias corrected data was used for merging. Merging of satellite products can be fruitful to benefit from the strength of each product and minimize the weakness of products. Four different merging methods i.e. Arithmetic merging method (AMM), Inverse error squared weighting (IESW), Error variance (EV) and K Nearest Neighbour method (KNN) were used and performance was checked in two seasons i.e. non-wet and wet season. AMM and EV methods performed similarly whereas IESW performed poorly at zonal scales. KNN merging method outperformed all other merging methods and gave lowest error across the basin. Daily NRMSE was reduced to 0.3 at Indus basin scale with KNN method, AMM and EV reduced the error to 0.45 in comparison to error produced by CMORPH, PERSIANN CDR and TRMM of 0.8, 0.65 and 0.53 respectively in the wet season. KNN merged product gave lowest error at daily scale in calibration and validation period which justifies that merging improves rainfall estimates in sparsely gauged basin.</p><p> </p><p><strong>Key words:</strong> Merging, data fusion, K nearest neighbour, KNN, error variance, Indus.</p>