Hasil Pencarian  ::  Simpan CSV :: Kembali

"Cylindrical uranium dioxide pellets, which are the main components for nuclear fuel elements in light water reactors, should have a high density profile, a uniform shape, and a minimum standard quality for their safe use as a reactor fuel component. The quality of green pellets is conventionally monitored by laboratory measurement of the physical pellet characteristics; however, this conventional classification method shows some drawbacks, such as difficult usage, low accuracy, and high time consumption. In addition, the method does not address the non-linearity and complexity of the relationship between pellet quality variables and pellet quality. This paper presents the development and application of a modified Radial Basis Function neural network (RBF NN) as an automatic classification system for green pellet quality. The weight initialization of the neural networks in this modified RBF NN is calculated through an orthogonal least squared method, and in conjunction with the use of a sigmoid activation function on its output neurons. Experimental data confirm that the developed modified RBF NN shows higher recognition capability when compared with that of the conventional RBF NNs. Further experimental results show that optimizing the quality classification problem space through eigen decomposition method provides a higher recognition rate with up to 98% accuracy."

"Cylindrical uranium dioxide pellets, which are the main components for nuclear fuel elements in light water reactors, should have a high density profile, a uniform shape, and a minimum standard quality for their safe use as a reactor fuel component. The quality of green pellets is conventionally monitored by laboratory measurement of the physical pellet characteristics; however, this conventional classification method shows some drawbacks, such as difficult usage, low accuracy, and high time consumption. In addition, the method does not address the non-linearity and complexity of the relationship between pellet quality variables and pellet quality. This paper presents the development and application of a modified Radial Basis Function neural network (RBF NN) as an automatic classification system for green pellet quality. The weight initialization of the neural networks in this modified RBF NN is calculated through an orthogonal least squared method, and in conjunction with the use of a sigmoid activation function on its output neurons. Experimental data confirm that the developed modified RBF NN shows higher recognition capability when compared with that of the conventional RBF NNs. Further experimental results show that optimizing the quality classification problem space through eigen decomposition method provides a higher recognition rate with up to 98% accuracy."

"Diabetes is one of themost serious health challenges in both developed and developing countries. Early detection and accurate diagnosis ofdiabetes can reduce the risk of complications. In recent years, the use of machine learning in predicting disease hasgradually increased. A promising classification technique in machinelearning is the use of support vector machines in combination with radial basisfunction kernels (SVM-RBF). In this study, we used SVM-RBF to predict diabetes.The study used a Pima Indian diabetes dataset from the University ofCalifornia, Irvine (UCI) Machine Learning Repository. The subjects were female and ≥ 21 yearsof age at the time of the index examination. Our experiment design used 10-foldcross-validation. Confusion matrix and ROC were used to calculate performanceevaluation. Based on the experimental results, the study demonstrated that SVM-RBFshows promise in aiding diagnosis of Pima Indian diabetes disease in the earlystage."

"Ground moving radartarget classification is one of the recent research issues that has arisen inan airborne ground moving target indicator (GMTI) scenario. This work presentsa novel technique for classifying individual targets depending on their radarcross section (RCS) values. The RCS feature is evaluated using the Chebyshevpolynomial. The radar captured target usually provides an imbalanced solutionfor classes that have lower numbers of pixels and that have similarcharacteristics. In this classification technique, the Chebyshev polynomial?sfeatures have overcome the problem of confusion between target classes withsimilar characteristics. The Chebyshev polynomial highlights the RCS featureand is able to suppress the jammer signal. Classification has been performed byusing the probability neural network (PNN) model. Finally, the classifier withthe Chebyshev polynomial feature has been tested with an unknown RCS value. Theproposed classification method can be used for classifying targets in a GMTIsystem under the warfield condition."

"In any heterogeneous system, porting reconfigurable computing is often a highperformance platform for a broad range of computationally challenging issues.However, efficiently utilizing the maximum potential of these reconfigurablesystems is a difficult job without understanding their performancecharacteristics. This work proposes an analytic performance model using PetriNets (PN) for a Reconfigurable OR1200 (ROR1200) soft-core processor with modelvalidation and verifications. By modeling the ROR1200 system using Petri Nets,both behavioral and structural properties existing in parallel systems wereanalyzed. The Bound Level Analysis with respect to the dependency level of datais also performed on Soft Core Processors (SCP) like the ROR1200, the OR1200and the MicroBlaze."

"almari arsip merupakan sarana yang sangat penting dalam memenuhi kebutuhan pelayanan yang memuaskan. permasalahan yang akan menjadi pembahasan dalam penelitian ini diperoleh dari pengamatan sehari-hari dimana pengguna almari arsip sangatlah penting dan sering dilakukan. pemakaian almari arsip yang praktis merupakan suatu upaya penunjang peningkatan produktivitas kerja yang sangat diperlukan oleh anggota sebagai penggunanya."

"Pesatnya perkembangan sistem informasi menjadi pemicu dalam pengembangan aplikasi pendukung operasional organisasi. Tak terkecuali pemerintah Indonesia. Sebagai organisasi yang besar, pemerintah Indonesia memandang perlu adanya perbaikan layanan kepada masyarakat. Hal tersebut dilakukan dengan menetapkan penerapan e-government. E-government merupakan salah satu alat yang digunakan dalam perbaikan birokrasi dan tata kelola pemerintahan. Pusat Penelitian X sebagai lembaga pemerintahan melakukan perbaikan terus menerus untuk meningkatkan tata kelola organisasi. Dari beberapa area yang akan menjadi objek pengembangan e-government, pihak manajemen Pusat Penelitian X memutuskan untuk memprioritaskan perbaikan pada proses keuangan, khususnya proses penganggaran. Dalam rangka perbaikan proses anggaran tersebut, perlu kajian terkait pengembangan penganggaran elektronik. Berdasarkan hal tersebut penelitian ini bertujuan untuk pengembangan penganggaran elektronik di Pusat Penelitian X. Pengembangan dilakukan dengan menggunakan Software Quality Function Deployment (SQFD) yang merupakan pengembangan Quality Function Deployment (QFD) tradisional yang umum digunakan pada pengembangan produk manufaktur. Dari penelitian ini diperoleh hasil bahwa dalam pengembangan penganggaran elektronik di Pusat Penelitian X, programmer perlu memperhatikan delapan kriteria prioritas yang menurut pengguna perlu ada di dalam penganggaran elektronik. Dimana delapan kriteria prioritas tersebut dapat digunakan sebagai dasar pengetesan sistem untuk finalisasi pengembangan penganggaran elektronik."

"Wireless 3.5G is designed to deliver various kind of multimedia packages through IP network. Quality of Service (QoS) fulfilment is a crucial factor for multimedia applications...."

"Todevelop seismic design criteria for buildings, seismic hazard analysis is required to estimate the ground motionintensity with criteria such as peak groundacceleration (PGA). The seismic hazard can be analyzed by using two approaches: deterministic seismic hazard analysis (DSHA) andprobabilistic seismic hazard analysis (PSHA). In these two approaches, theseismic hazard is evaluated from past earthquake events and active faults data. In Thailand, seismic hazard is classified in the low lying regions; however, in recently years,earthquakes have occurred frequently in the North of Thailand. Toprevent and reduce damage due to earthquakes in the future, determinationof seismic hazard is needed. Thisresearch proposes a deterministic seismic hazardmap evaluated from nineteenactive faults affectingThailand. Two types of active faults are considered: first, an active fault in a subduction zone and second, a crustal fault. The seismic hazard is evaluated by using a groundmotion prediction equation (GMPEs). Four GMPEs are weighted equally forseismic crustal fault, and two GMPEs are weighted equally fora seismic subduction zone. The hypocentral distance isused to evaluate the seismic hazard for all ground motion prediction equations.The Northern part and the Western part of Thailand are high seismic hazardregions, because there are active faults with the large possibility of earthquakes of a maximum magnitude. The seismichazards in the North, West and Northeast of Thailand are about 0.60 g.The seismic hazard in Bangkok is about 0.25 g due to the Three Pagoda fault and Sri Sawat fault. The seismic hazard in the South of Thailand is about 0.40 g."

"The photocatalytic degradation of methylcyclohexane (MCH) in two phases (aqueous and vapor) was examined using modified titania that was immobilized on pumice and performed in the system of a specific condition. The photodegradation system that used a particular configuration reactor and modified catalyst could facilitate the two-phase photodegradation of MCH simultaneously. The photocatalyst was prepared by the mechanical mixing of urea and TiO2 P25 with mass ratios of 1:3 and 2:3, respectively and then calcined at 350 and 450oC. This modified photocatalyst was then immobilized on pumice and finally used for the photodegradation of MCH. The Infrared spectra studies revealed that modified titania with urea successfully incorporated a non-metal dopant within the TiO2 lattice. The catalyst that spread evenly across the surface of the pumice can be seen from Scanning Electron Microscope (SEM) characterization. The loading of 7.5% mass photocatalyst that immobilized on pumice degraded MCH in two-phases simultaneously during a 120 minute period and can be considered the optimum condition."