Lauri Tuovinen

Smart archive: a component-based data mining application framework

Implementation of data mining applications is a challenging and complicated task, and the applications are often built from scratch. In this paper, a component-based application framework, called smart archive (SA) designed for implementing data mining applications, is presented. SA provides functionality common to most data mining applications and components for utilizing history information. Using SA, it is possible to build high-quality applications with shorter development times by configuring the framework to process application-specific data. The architecture, the components, the implementation and the design principles of the framework are presented. The advantages of a framework-based implementation are demonstrated by presenting a case study which compares the framework approach to implementing a real-world application with the option of building an equivalent application from scratch. In conclusion, the paper presents a lucid framework for creating data mining applications and illustrates the importance and advantages of using the presented approach.

Methods for classifying spot welding processes: a comparative study of performance

Resistance spot welding is an important and widely used method for joining metal objects. In this paper, various classification methods for identifying welding processes are evaluated. Using process identification, a similar process for a new welding experiment can be found among the previously run processes, and the process parameters leading to high-quality welding joints can be applied. With this approach, good welding results can be obtained right from the beginning, and the time needed for the set-up of a new process can be substantially reduced. In addition, previous quality control methods can also be used for the new process. Different classifiers are tested with several data sets consisting of statistical and geometrical features extracted from current and voltage signals recorded during welding. The best feature set - classifier combination for the data used in this study is selected. Finally, it is concluded that welding processes can be identified almost perfectly by certain features.

Application of the Extended

Resistance spot welding is used to join two or more metal objects, and the technique is widely used in, for example, the automotive and electrical industries. This paper introduces the use of the k-nearest-neighbor (knn) method to identify similar welding processes. The two main benefits achieved from knowing the most similar process are the following: 1) The time needed for the setup of a new process can be substantially reduced by restoring the process parameters leading to high-quality joints, and 2) the quality of new welding spots can be predicted and improved using the stored information of a similar process. In this paper, the basic knn method was found to be inadequate, and an extension of the knn method, which is called similarity measure, was developed. The similarity measure provides information of how similar the new process is by using the distance to the knns. Based on the results, processes can be classified, and the similarity measure proved to be a valuable addition to the existing methodology. Furthermore, process information can provide a major benefit to welding industry.

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Process identification in the field of resistance spot welding can be used to improve welding quality and to speed up the set-up of a new welding process. Previously, good classification results of welding processes have been obtained using a feature set consisting of

nn Method to Resistance Spot Welding Process Identification and the Benefits of Process Information

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Feature Selection for Identification of Spot Welding Processes

features extracted from current and voltage signals recorded during welding. In this study, the usability of the individual features is evaluated and various feature selection methods are tested to find an optimal feature subset to be used in classification. Ways are sought to further improve classification accuracy by discarding features containing less classification-relevant information. The use of a small feature set is profitable in that it facilitates both feature extraction and classification. It is discovered that the classification of welding processes can be performed using a substantially reduced feature set. In addition, careful selection of the features used also improves classification accuracy. In conclusion, selection of the feature subset to be used in classification notably improves the performance of the spot welding process identification system.

Resistance Spot Welding Process Identification Using an Extended knn Method

Resistance spot welding is used to join two or more metal objects together, and the technique is in widespread use in, for example, the automotive and electrical industries. This paper introduces the use of the k- nearest neighbours (knn) method to identify different welding processes. Process information can be used to find suitable initialisation parameters for welding machines or to predict the quality of welding spots using previously gathered data. In this study, the basic knn method was found to be inadequate, and an extension to the knn method was developed. The distance to the k-nearest neighbours was considered important information, and a similarity measure was formulated to provide this information to the user. According to the results, processes can be classified using the method and specific features. The similarity measure proved to be a valuable addition, which helps the user to decide whether the closest process is close enough to be classified as the same process.

Building a Database to Support Intelligent Computational Quality Assurance of Resistance Spot Welding Joints

A database system for storing information on resistance spot welding processes is outlined. Data stored in the database can be used for computationally estimating the quality of spot welding joints and for adaptively setting up new welding processes in order to ensure consistent high quality. This is achieved by storing current and voltage signals in the database, extracting features out of those signals and using the features as training input for classifier algorithms. Together the database and the associated data mining modules form an adaptive system that improves its performance over time. An entity-relationship model of the application domain is presented and then converted into a concrete database design. Software interfaces for accessing the database are described and the utility of the database and the access interfaces as components of a welding quality assurance system is evaluated. A relational database with tables for storing test sets, welds, signals, features and metadata is found suitable for the purpose. The constructed database has served well as a repository for research data and is ready to be transferred to production use at a manufacturing site.

Gamified Persuasion: User Experiences of Online Activation Service

The authors have empirically examined the persuasive properties of digital games from a multidisciplinary perspective. Besides the relevant cultural and psychological theories related to the game phenomenon, the authors have included a case study where a persuasive online activation service was tested among young men N=280, average 17.9 year old in the promotion of physical and social activity. The emphasis of the article is on qualitative material, which is based on in-depth interviews of 10 individuals, as well as participant observation considering the user experiences regarding the activation service and gaming in general. The authors have concluded that games contain persuasive characteristics based on human culture and psychology and that these characteristics could effectively be utilized in physically and socially activating games.

SE-155 DBSA: a device-based software architecture for data mining

In this paper a new architecture for a variety of data mining tasks is introduced. The Device-Based Software Architecture (DBSA) is a highly portable and generic data mining software framework where processing tasks are modeled as components linked together to form a data mining application. The name of the architecture comes from the analogy that each processing task in the framework can be thought of as a device. The framework handles all the devices in the same manner, regardless of whether they have a counterpart in the real world or whether they are just logical devices inside the framework. The DBSA offers many reusable devices, ready to be included in applications, and the application programmer can easily code new devices for the architecture. The framework is bundled with connections to several widely used external tools and languages, making prototyping new applications easy and fast. In the paper we compare DBSA to existing data mining frameworks, review its design and present a case study application implemented with the framework. The paper shows that the DBSA can act as a base for diverse data mining applications.