Penghasilan manual rjngkas penggunaan alat Total Station Sokkia Set5f dan Perisian Sdr Mapping & Design untuk automasi ukur topografi

Projek ini dilaksanakan untuk menghasilkan manual ringkas penggunaan alat Total Station Sokkia SET5F dan Perisian SDR Mapping & Design dalam menghasilkan pelan topografi yang lengkap mengikut konsep field to finish. Manual telah dihasilkan dalam dua bentuk iaitu buku dan CD-ROM. Manual ini telah dinilai berdasarkan data yang diperolehi daripada 7 orang responden melalui kaedah Borang Penilaian Manual. Analisis data dilakukan menggunakan perisian SPSS versi 11.0. Hasil analisis skor min menunjukkan kesemua responden bersetuju bahawa manual dalam bentuk buku ini menarik Min ( M ) ^ ^ dan Sisihan Piawai (SD) = .535 tetapi kurang interaktif (M) = 2.29 dan (SD) = 0.488. Berbanding dengan manual dalam format CD-ROM yang mencatat nilai (M) = 3.57 dan (SD) = 0.535 semua responden bersetuju bahawa manual ini mesra pengguna dan lebih interakti

Self-Learning Cloud Controllers: Fuzzy Q-Learning for Knowledge Evolution

Cloud controllers aim at responding to application demands by automatically scaling the compute resources at runtime to meet performance guarantees and minimize resource costs. Existing cloud controllers often resort to scaling strategies that are codified as a set of adaptation rules. However, for a cloud provider, applications running on top of the cloud infrastructure are more or less black-boxes, making it difficult at design time to define optimal or pre-emptive adaptation rules. Thus, the burden of taking adaptation decisions often is delegated to the cloud application. Yet, in most cases, application developers in turn have limited knowledge of the cloud infrastructure. In this paper, we propose learning adaptation rules during runtime. To this end, we introduce FQL4KE, a self-learning fuzzy cloud controller. In particular, FQL4KE learns and modifies fuzzy rules at runtime. The benefit is that for designing cloud controllers, we do not have to rely solely on precise design-time knowledge, which may be difficult to acquire. FQL4KE empowers users to specify cloud controllers by simply adjusting weights representing priorities in system goals instead of specifying complex adaptation rules. The applicability of FQL4KE has been experimentally assessed as part of the cloud application framework ElasticBench. The experimental results indicate that FQL4KE outperforms our previously developed fuzzy controller without learning mechanisms and the native Azure auto-scaling

Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS

Understanding requirements dependency in requirements prioritization: a systematic literature review

Requirement prioritization (RP) is a crucial task in managing requirements as it determines the order of implementation and, thus, the delivery of a software system. Improper RP may cause software project failures due to over budget and schedule as well as a low-quality product. Several factors influence RP. One of which is requirements dependency. Handling inappropriate handling of requirements dependencies can lead to software development failures. If a requirement that serves as a prerequisite for other requirements is given low priority, it affects the overall project completion time. Despite its importance, little is known about requirements dependency in RP, particularly its impacts, types, and techniques. This study, therefore, aims to understand the phenomenon by analyzing the existing literature. It addresses three objectives, namely, to investigate the impacts of requirements dependency on RP, to identify different types of requirements dependency, and to discover the techniques used for requirements dependency problems in RP. To fulfill the objectives, this study adopts the Systematic Literature Review (SLR) method. Applying the SLR protocol, this study selected forty primary articles, which comprise 58% journal papers, 32% conference proceedings, and 10% book sections. The results of data synthesis indicate that requirements dependency has significant impacts on RP, and there are a number of requirements dependency types as well as techniques for addressing requirements dependency problems in RP. This research discovered various techniques employed, including the use of Graphs for RD visualization, Machine Learning for handling large-scale RP, decision making for multi-criteria handling, and optimization techniques utilizing evolutionary algorithms. The study also reveals that the existing techniques have encountered serious limitations in terms of scalability, time consumption, interdependencies of requirements, and limited types of requirement dependencies

Applying Ragin's Crisp and Fuzzy Set QCA to Large Datasets: Social Class and Educational Achievement in the National Child Development Study

The paper explores the use of Charles Ragin's Qualitative Comparative Analysis (QCA) in both its crisp and fuzzy set versions in the study of the relations between social class origin, sex, 'ability' and subsequent educational achievement. The work reported is part of a larger ongoing project which is employing QCA to compare these relations within two birth cohorts. Here data are used from the British National Child Development Study, i.e. from children born in 1958. The paper has a methodological focus, bringing out the strengths but also the difficulties that arise when employing QCA with a large dataset of this type. In particular, the problem of calibrating membership in fuzzy sets in a context where detailed case knowledge is not available is illustrated. It is also shown how the use of gradually increasing thresholds with Ragin's fs/QCA software can bring out the relative importance of various factors in accounting for achievement. The QCA-based analysis suggests that the processes of educational attainment can, at best, only be seen as partly falling under a 'meritocratic' description. It is also hoped that this paper will serve as a useful introduction to the potential of QCA for readers not yet familiar with it.QCA, Social Class, Educational Attainment, Gender, Fuzzy Sets, Meritocracy.