Data-driven Soft Sensors in the Process Industry

In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work

Neural networks and support vector machines based bio-activity classification

Classification of various compounds into their respective biological activity classes is important in drug discovery applications from an early phase virtual compound filtering and screening point of view. In this work two types of neural networks, multi layer perceptron (MLP) and radial basis functions (RBF), and support vector machines (SVM) were employed for the classification of three types of biologically active enzyme inhibitors. Both of the networks were trained with back propagation learning method with chemical compounds whose active inhibition properties were previously known. A group of topological indices, selected with the help of principle component analysis (PCA) were used as descriptors. The results of all the three classification methods show that the performance of both the neural networks is better than the SVM

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

Karektor guru pendidikan khas aliran kemahiran berlandaskan nilai retorik dari perspektif pelajar pendidikan khas masalah pendengaran di Malaysia

Latar Belakang: Pendidikan Khas di Malaysia adalah satu usaha yang berterusan untuk melahirkan insan yang berkemahiran, berpandangan jauh, berupaya, beriman, berdikari, mampu merancang dan menguruskan kehidupan harian serta menyedari potensi diri sendiri yang selaras dengan Falsafah Pendidikan Kebangsaan. Aliran pendidikan teknikal dan vokasional juga tidak dikecualikan pelajar yang mempunyai keperluan khas. Oleh itu, guru pendidikan khas aliran kemahiran harus mempunyai karektor yang istimewa untuk mendidik pelajar golongan ini. Namun begitu, masih belum wujudnya satu model standard guru pendidikan khas terutamanya aliran kemahiran. Objektif: Kajian ini dijalankan untuk mengenal pasti tahap penerapan elemen dan dimensi nilai retorik dalam proses pengajaran dan pembelajaran guru aliran kemahiran bagi pelajar pendidikan khas masalah pendengaran. Keputusan: Dapatan kajian ini menunjukkan penerapan elemen nilai retorik ethos dan logos dalam kalangan guru berada pada tahap tinggi, diikuti dengan pathos pada tahap sederhana. Dapatan kajian juga menunjukkan penerapan nilai retorik bagi kebanyakan dimensi pada tahap tinggi, hanya dimensi perasaan dan visualisasi pada tahap sederhana. Kesimpulan: Umumnyaa, guru pendidikan khas aliran kemahiran telah menerapkan nilai retorik pada tahap yang tinggi. Setiap guru digalak untuk menguasai nilai retorik supaya dapat membantu para pelajar menerokai ilmu pengetahuan yang disampaikan oleh mereka dengan berkesan dan seterusnya memberi impak yang positif terhadap pencapaian pelajar

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