40,339 research outputs found

    Penguasaan kemahiran generik di kalangan graduan hospitaliti di politeknik : satu kajian berkenaan keperluan industri perhotelan, persepsi pensyarah dan pelajar

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    Kajian yang dijalankan ini bertujuan untuk mengenal pasti kepentingan kemahiran generik mengikut keperluan industri perhotelan di Malaysia dengan persepsi pensyarah dan persepsi pelajar Jabatan Hospitaliti. Oleh kerana matlamat kurikulum pendidikan kini adalah untuk melahirkan graduan yang dapat memenuhi keperluan pihak industri, maka kajian ini dijalankan untuk menilai hubungan di antara keperluan industri perhotelan di Malaysia dengan persepsi pensyarah dan pelajar Jabatan Hospitaliti di Politeknik. Terdapat 13 kemahiran generik yang diperolehi daripada Kementerian Pelajaran dan Latihan Ontario (1997) dijadikan sebagai skop kepada instrumen kajian. Responden kajian terdiri daripada tiga pihak utama iaitu industri perhotelan di Malaysia yang melibatkan 40 buah hotel yang diwakili oleh MAH Chapter dan jawatankuasa dalam Malaysian Associated of Hotel (MAH), pensyarah Unit Hotel dan Katering dan pelajar semester akhir Diploma Hotel dan Katering di Politeknik Johor Bahru, Johor dan Politeknik Merlimau, Melaka. Kajian rintis yang dijalankan menunjukkan nilai Alpha Cronbach pada 0.8781. Data yang diperolehi dianalisis secara deskriptif dan inferensi dengan menggunakan perisian Statistical Package for Social Science (SPSS) versi 11.5. Melalui dapatan kajian, satu senarai berkenaan kemahiran generik yang diperlukan oleh industri perhotelan telah dapat dihasilkan. Selain itu, senarai kemahiran generik menurut persepsi pensyarah dan juga persepsi pelajar turut dihasilkan. Hasil statistik dan graf garis yang diperolehi menunjukkan terdapat perbezaan di antara kemahiran generik yang diperlukan oleh industri perhotelan di Malaysia dengan kemahiran generik menurut persepsi pensyarah dan persepsi pelajar Politeknik. Dapatan analisis menggunakan korelasi Pearson mendapati bahawa tidak terdapat perhubungan yang signifikan di antara kemahiran generik yang diperlukan oleh industri perhotelan dengan persepsi pensyarah dan persepsi pelajar. Namun begitu, terdapat hubungan yang signifikan di antara persepsi pensyarah dengan persepsi pelajar berkenaan dengan amalan kemahiran generik di Politeknik

    Data-driven Soft Sensors in the Process Industry

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    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

    Applications of Soft Computing in Mobile and Wireless Communications

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    Soft computing is a synergistic combination of artificial intelligence methodologies to model and solve real world problems that are either impossible or too difficult to model mathematically. Furthermore, the use of conventional modeling techniques demands rigor, precision and certainty, which carry computational cost. On the other hand, soft computing utilizes computation, reasoning and inference to reduce computational cost by exploiting tolerance for imprecision, uncertainty, partial truth and approximation. In addition to computational cost savings, soft computing is an excellent platform for autonomic computing, owing to its roots in artificial intelligence. Wireless communication networks are associated with much uncertainty and imprecision due to a number of stochastic processes such as escalating number of access points, constantly changing propagation channels, sudden variations in network load and random mobility of users. This reality has fuelled numerous applications of soft computing techniques in mobile and wireless communications. This paper reviews various applications of the core soft computing methodologies in mobile and wireless communications

    A novel technique for load frequency control of multi-area power systems

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    In this paper, an adaptive type-2 fuzzy controller is proposed to control the load frequency of a two-area power system based on descending gradient training and error back-propagation. The dynamics of the system are completely uncertain. The multilayer perceptron (MLP) artificial neural network structure is used to extract Jacobian and estimate the system model, and then, the estimated model is applied to the controller, online. A proportional–derivative (PD) controller is added to the type-2 fuzzy controller, which increases the stability and robustness of the system against disturbances. The adaptation, being real-time and independency of the system parameters are new features of the proposed controller. Carrying out simulations on New England 39-bus power system, the performance of the proposed controller is compared with the conventional PI, PID and internal model control based on PID (IMC-PID) controllers. Simulation results indicate that our proposed controller method outperforms the conventional controllers in terms of transient response and stability

    Meta-heuristic algorithms in car engine design: a literature survey

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    Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system

    Intelligent systems in manufacturing: current developments and future prospects

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    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

    Theoretical Interpretations and Applications of Radial Basis Function Networks

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    Medical applications usually used Radial Basis Function Networks just as Artificial Neural Networks. However, RBFNs are Knowledge-Based Networks that can be interpreted in several way: Artificial Neural Networks, Regularization Networks, Support Vector Machines, Wavelet Networks, Fuzzy Controllers, Kernel Estimators, Instanced-Based Learners. A survey of their interpretations and of their corresponding learning algorithms is provided as well as a brief survey on dynamic learning algorithms. RBFNs' interpretations can suggest applications that are particularly interesting in medical domains
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