20 research outputs found

    Multiple convolutional neural network training for Bangla handwritten numeral recognition

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    Recognition of handwritten numerals has gained much interest in recent years due to its various application potentials. The progress of handwritten Bangla numeral is well behind Roman, Chinese and Arabic scripts although it is a major language in Indian subcontinent and is the first language of Bangladesh. Handwritten numeral classification is a high-dimensional complex task and existing methods use distinct feature extraction techniques and various classification tools in their recognition schemes. Recently, convolutional neural network (CNN) is found efficient for image classification with its distinct features. In this study, three different CNNs with same architecture are trained with different training sets and combined their decisions for Bangla handwritten numeral recognition. One CNN is trained with ordinary training set prepared from handwritten scan images; and training sets for other two CNNs are prepared with fixed (positive and negative, respectively) rotational angles of original images. The proposed multiple CNN based approach is shown to outperform other existing methods while tested on a popular Bangla benchmark handwritten dataset

    Flooding attacks detection of mobile agents in IP networks

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    A two-storage model for deteriorating items with holding cost under inflation and Genetic Algorithms

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    A deterministic inventory model has been developed for deteriorating items and Genetic Algorithms (GA) having a ramp type demands with the effects of inflation with two-storage facilities. The owned warehouse (OW) has a fixed capacity of W units; the rented warehouse (RW) has unlimited capacity. Here, we assumed that the inventory holding cost in RW is higher than those in OW. Shortages in inventory are allowed and partially backlogged and Genetic Algorithms (GA) it is assumed that the inventory deteriorates over time at a variable deterioration rate. The effect of inflation has also been considered for various costs associated with the inventory system and Genetic Algorithms (GA). Numerical example is also used to study the behaviour of the model. Cost minimization technique is used to get the expressions for total cost and other parameters

    Two stage Indian food grain supply chain network transportation-allocation model

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    This paper investigates the food grain supply chain, transportation allocation problem of Indian Public Distribution System (PDS). The different activities of Indian food grain supply chain are procurements, storage, movement, transportation and distribution. We have developed a mixed integer nonlinear programming model (MINLP) to minimize the transportation, inventory and operational cost of shipping food grains from the cluster of procurement centers of producing states to the consuming state warehouses. A recently developed chemical reaction optimization (CRO) algorithm is used for testing the model which gives the superior computational performance compared to other metaheuristics

    DETEKSI PENYAKIT GAGAL JANTUNG BERDASARKAN SINYAL EKG MENGGUNAKAN NAIVE BAYES

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    Abstrak Gagal jantung merupakan masalah kesehatan masayarakat yang banyak ditemukan dan menjadi salah satu penyakit yang sangat berbahaya. Resiko terkena penyakit ini sangat besar terjadi pada usia lanjut. Penelitian ini bertujuan untuk menganalisis seseorang terkena penyakit gagal jantung atau tidak terkena penyakit gagal jantung (normal). Sinyal EKG (ElektroKardioGram) merupakan data yang digunakan untuk mengtahui detak jantung seseorang yang akan diteliti. Sinyal EKG kemudian akan diperhalus menggunakan wavelet, kemudian akan diolah lagi dengan Metode Naive Bayes. Metode Naive Bayes digunakan dalam mengklasifikasi sinyal yang sudah di extraksi menjadi penentuan hasil akhir. Hasil prediksi dari Naive bayes mendapat akurasi 70% . Kata kunci : Gagal Jantung, Naive Bayes, EKG, wavele

    ๋น„์ •์ƒ์œ ๋™์ด ์กฐ๋ฅ˜๋ฐœ์ „๋‹จ์ง€ ๋ฐฐ์น˜์ตœ์ ํ™”์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ๊ฑด์„คํ™˜๊ฒฝ๊ณตํ•™๋ถ€, 2022.2. ํ™ฉ์ง„ํ™˜.Due to the global climate crisis and the air pollution, demand on the renewable energy is consequently increasing as one of the main efforts. Wind and solar energy are taking the lead on the renewable energy industry, and as of the next competitive resource, tidal power is estimated to have a huge potential, thanks to its high energy density and easily predictable characteristics. Tidal power has not reached the practical level yet, due to financial challenges. In terms of reducing the cost and reach the competitive level of LCOE, the power extraction should be maximized within the constraints by conducting layout optimization of the turbines deployed, hence, understanding, and predicting the algorithm for layout optimization is necessary. The layout optimization for the tidal turbine is somewhat sophisticated, due to the unsteady tidal current condition in the nature, hence previous studies have found the problem under the steady condition. However, since the unsteadiness is a critical feature of the tidal current, there needs a study on the distinctive optimization characteristics under the unsteady condition. This study aims to find the tidal turbine farm layout optimization problem under the simplified unsteady tidal current condition in the nature and identify if the tidal turbine farm layout optimization procedure under unsteady condition can converges to find the global optimum. A number of numerical experiments were handled during the study to find the general trend/pattern of convergence to the global optimum under the various unsteady condition, with variation in the amplitude and the direction. The study first demonstrated the difference in the wake profile and the energy production of a single turbine under steady & unsteady flow, to be used as the basic assumption when figuring out the characteristics of layout optimization procedure under unsteady condition. The study also demonstrated the insight of the optimized layout and the minimum velocity threshold that enables the optimization to converge to the globally optimized layout at a given tolerance under steady condition. Finally, generalization of the strategy for the tidal turbine farm layout optimization under the unsteady flow was presented by finding the difference in the optimization procedure between steady & unsteady flow. It has been discovered that optimal layout under unidirectional, unsteady flow condition is similar to the optimal layout under steady condition when it satisfies the minimum velocity threshold condition. However, optimal layout under bidirectional conditions was totally different to the optimal layout under unidirectional conditions, to consider the wake effect from both directions. Under the bidirectional flow condition, the turbines were found to be staggered with respect to each other in order to take advantage of local speedups between upwind turbines. The numerical experiments were performed with OpenTidalFarm, an open-source solver for specific PDE-constrained, gradient-based optimization problems, especially those related to tidal farm design. The simulation domain was described as a rectangular farm, PDE is given as two-dimensional nonlinear shallow water equations, total power output is the target functional to be maximized, and turbine was parameterized as a bump function. Adjoint method was used as to compute the gradient for the optimization problem.์„ธ๊ณ„์ ์ธ ๊ธฐํ›„ ์œ„๊ธฐ์™€ ๋Œ€๊ธฐ ์˜ค์—ผ์œผ๋กœ ์ธํ•ด, ์žฌ์ƒ ์—๋„ˆ์ง€์— ๋Œ€ํ•œ ์ˆ˜์š”๊ฐ€ ์ฆ๊ฐ€ํ•˜๊ณ  ์žˆ๋‹ค. ํ’๋ ฅ๊ณผ ํƒœ์–‘๊ด‘ ๋ฐœ์ „์ด ์‹ ์žฌ์ƒ์—๋„ˆ์ง€ ์‚ฐ์—…์„ ์„ ๋„ํ•˜๊ณ  ์žˆ์œผ๋ฉฐ, ์กฐ๋ฅ˜ ๋ฐœ์ „์€ ๋†’์€ ์—๋„ˆ์ง€ ๋ฐ€๋„์™€ ์˜ˆ์ธก ๊ฐ€๋Šฅํ•œ ํŠน์„ฑ์œผ๋กœ ์ธํ•ด ์•ž์œผ๋กœ์˜ ์ž ์žฌ๋ ฅ์ด ํด ๊ฒƒ์œผ๋กœ ์ถ”์ •๋œ๋‹ค. ์กฐ๋ฅ˜ ๋ฐœ์ „์€ ์•„์ง ๊ฐ€๊ฒฉ ๊ฒฝ์Ÿ์˜ ์ธก๋ฉด์—์„œ ์‹ค์šฉํ™” ์ˆ˜์ค€์— ์ด๋ฅด์ง€ ๋ชปํ–ˆ๋‹ค. ๊ฐ€๊ฒฉ ๊ฒฝ์Ÿ๋ ฅ์„ ํ™•๋ณดํ•˜๊ธฐ ์œ„ํ•œ ๋น„์šฉ์ ˆ๊ฐ์˜ ์ˆ˜๋‹จ์œผ๋กœ๋Š” ์ „๋ ฅ ์ถ”์ถœ ๊ทน๋Œ€ํ™”๋ฅผ ์œ„ํ•œ ๋…ธ๋ ฅ์ด ํ•„์š”ํ•˜๋ฉฐ, ์ด์— ์ œ์•ฝ ์กฐ๊ฑด ๋‚ด์—์„œ์˜ ํ„ฐ๋นˆ ๋ฐฐ์น˜ ์ตœ์ ํ™”๋ฅผ ์œ„ํ•œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์— ๋Œ€ํ•œ ์ดํ•ด๊ฐ€ ์„ ํ–‰๋˜์–ด์•ผ ํ•œ๋‹ค. ์กฐ๋ฅ˜๋ฐœ์ „ ๋‹จ์ง€ ๋ฐฐ์น˜ ์ตœ์ ํ™”์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋Š” ๊ธฐ์กด์— ๋‹ค์ˆ˜ ์ง„ํ–‰๋˜์–ด ์™”์œผ๋‚˜, ๋น„์ •์ƒ ์œ ๋™์—์„œ์˜ ์ตœ์ ํ™” ํ•ด๋ฅผ ๋„์ถœํ•˜๋Š” ๊ฒƒ์ด ๋‹ค์†Œ ๋ณต์žกํ•ด ์ •์ƒ์œ ๋™ ์ƒํƒœ์˜ ๊ฐ€์ •์ด ์ฃผ๋ฅผ ์ด๋ฃจ์—ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜, ์กฐ๋ฅ˜๋Š” ๋‹ฌ๊ณผ ํƒœ์–‘์˜ ๊ธฐ์กฐ๋ ฅ์— ์˜ํ•œ ์กฐ๋ฅ˜์— ์˜ํ•ด ๋ฐœ์ƒํ•˜๋Š” ํ˜„์ƒ์ž„์— ๋”ฐ๋ผ, ๋น„์ •์ƒ ์œ ๋™์ด๋ผ๋Š” ํŠน์„ฑ์ด ํฌ๊ฒŒ ์ž‘์šฉํ•˜๋Š” ๋ฐ”, ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ด๋Ÿฌํ•œ ํŠน์„ฑ์„ ๋ฐ˜์˜ํ•œ ๋ฐฐ์น˜ ์ตœ์ ํ™” ์—ฐ๊ตฌ๊ฐ€ ์ง„ํ–‰๋˜์—ˆ๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ๋Š” ๋น„์ •์ƒ ์œ ๋™ ํ•˜์—์„œ์˜ ์กฐ๋ฅ˜๋ฐœ์ „๋‹จ์ง€ ๋ฐฐ์น˜ ์ตœ์ ํ™”์— ๊ด€ํ•œ ์ดํ•ด๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ์ด ๋•Œ์˜ ๋ฏผ๊ฐ๋„ ๊ธฐ๋ฐ˜ ์ตœ์ ํ™”์˜ ํ•ด๊ฐ€ ์ „์—ญ ์ตœ์ ํ•ด์— ์ˆ˜๋ ดํ•˜๋Š”๊ฐ€๋ฅผ ์ˆ˜์น˜์‹คํ—˜์„ ํ†ตํ•ด ํ™•์ธํ•˜๋Š” ๊ฒƒ์„ ๋ชฉ์ ์œผ๋กœ ํ•˜์˜€๋‹ค. ๋‹ค์–‘ํ•œ ์ง„ํญ๊ณผ ๋ฐฉํ–ฅ ๋“ฑ์˜ ์กฐ๊ฑด์—์„œ ์ˆ˜๋ฐฑ๊ฐœ์˜ ์ˆ˜์น˜์‹คํ—˜์ด ์ด๋ฃจ์–ด์กŒ์œผ๋ฉฐ ์ด๋ฅผ ํ†ตํ•ด ๋น„์ •์ƒ ์œ ๋™ ํ•˜์—์„œ์˜ ์กฐ๋ฅ˜๋ฐœ์ „๋‹จ์ง€ ๋ฐฐ์น˜ ์ตœ์ ํ™”์— ๋Œ€ํ•œ ์ผ๋ฐ˜์ ์ธ ์ถ”์„ธ์™€ ์ตœ์ ํ•ด๋ฅผ ํ™•์ธํ•˜์˜€๋‹ค. ๋จผ์ €, ์ •์ƒ ์œ ๋™๊ณผ ๋น„์ •์ƒ ์œ ๋™ ๊ฐ๊ฐ์— ๋‹จ์ผ ํ„ฐ๋นˆ์„ ๋‘์–ด ๊ฐ ์กฐ๊ฑด ํ•˜์—์„œ์˜ ํ›„๋ฅ˜ ํ˜•ํƒœ์™€ ์—๋„ˆ์ง€ ์ƒ์‚ฐ๋Ÿ‰ ์ฐจ์ด๋ฅผ ํ™•์ธํ•˜์˜€๋‹ค. ๋˜ํ•œ ์ •์ƒ ์œ ๋™ ํ•˜์—์„œ์˜ ์ตœ์  ๋ฐฐ์น˜ ํ˜•ํƒœ์™€ ์ตœ์ ํ•ด์— ๋„๋‹ฌํ•  ์ˆ˜ ์žˆ๋Š” ์ž„๊ณ„ ์†๋„ ๊ฐ’์„ ๋„์ถœํ•˜์—ฌ ๋น„์ •์ƒ ์œ ๋™ ํ•˜์—์„œ์˜ ์ˆ˜์น˜์‹คํ—˜์— ์„ ํ–‰ ๊ฐ€์ •์œผ๋กœ ์‚ฌ์šฉ๋  ์ˆ˜ ์žˆ๋„๋ก ํ•˜์˜€๋‹ค. ์ด๋Ÿฌํ•œ ์‹คํ—˜ ๊ฒฐ๊ณผ๋“ค์„ ๋ฐ”ํƒ•์œผ๋กœ ๋น„์ •์ƒ ์œ ๋™ ํ•˜์—์„œ์˜ ์กฐ๋ฅ˜๋ฐœ์ „๋‹จ์ง€ ๋ฐฐ์น˜ ์ตœ์ ํ™”์— ๋Œ€ํ•œ ์ผ๋ฐ˜์ ์ธ ์ถ”์„ธ์™€ ์ตœ์ ํ•ด๋ฅผ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋น„์ •์ƒ ๋‹จ๋ฐฉํ–ฅ ์œ ๋™์˜ ๊ฒฝ์šฐ์—๋Š” ์ž„๊ณ„ ์†๋„ ์กฐ๊ฑด์„ ๋งŒ์กฑํ•˜์˜€์„ ๋•Œ ์ •์ƒ ์œ ๋™ ํ•˜์—์„œ์˜ ์ตœ์  ๋ฐฐ์น˜์™€ ๋น„์Šทํ•œ ํ˜•ํƒœ๋ฅผ ๋ณด์˜€์ง€๋งŒ, ๋น„์ •์ƒ ์–‘๋ฐฉํ–ฅ ์œ ๋™์˜ ๊ฒฝ์šฐ์—๋Š” ํ„ฐ๋นˆ์ด ๊ต์ฐจ ๋ฐฐ์น˜๋œ ํ˜•ํƒœ๋ฅผ ์ตœ์  ๋ฐฐ์น˜๋กœ ๊ฐ–๋Š” ๊ฒƒ์„ ์•Œ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ˆ˜์น˜์‹คํ—˜์—๋Š” ํŒŒ์ด์ฌ ๊ธฐ๋ฐ˜ ์˜คํ”ˆ์†Œ์Šค ์†Œํ”„ํŠธ์›จ์–ด์ธ OpenTidal Farm์ด ์‚ฌ์šฉ๋˜์—ˆ๋‹ค. ์ˆ˜์น˜์‹คํ—˜์€ ์ง์‚ฌ๊ฐํ˜• ์กฐ๋ฅ˜๋ฐœ์ „ ๋‹จ์ง€ ๋‚ด์—์„œ ์ด๋ฃจ์–ด์กŒ์œผ๋ฉฐ, ํŽธ๋ฏธ๋ถ„ ๋ฐฉ์ •์‹์€ 2์ฐจ์› ์ฒœ์ˆ˜๋ฐฉ์ •์‹์„ ์‚ฌ์šฉํ–ˆ๊ณ , ๋ชฉ์ ํ•จ์ˆ˜๋Š” ์ด์—๋„ˆ์ง€ ์ถ”์ถœ๋Ÿ‰์œผ๋กœ ๊ตฌ์„ฑ๋˜์—ˆ๋‹ค. ๋˜ํ•œ, ํ„ฐ๋นˆ์€ ๋ฏผ๊ฐ๋„ ๊ธฐ๋ฐ˜ ์ตœ์ ํ™”์— ์ ํ•ฉํ•˜๊ฒŒ๋” ๋ฒ”ํ”„ํ•จ์ˆ˜๋กœ ๋งค๊ฐœ๋ณ€์ˆ˜ํ™” ๋˜์–ด ์‹คํ—˜์— ์‚ฌ์šฉ๋˜์—ˆ๋‹ค.ABSTRACT i TABLE OF CONTENTS iv List of Figures vii List of Symbol xi CHAPTER 1. INTRODUCTION 1 1.1 General introduction 1 1.2 Objective 2 CHAPTER 2. THEORETICAL BACKGROUDS 7 2.1 General evolution of the renewable energy 7 2.2 Tidal turbine farm 10 2.2.1 The physics of tide 10 2.2.2 The physics of tidal currents - unsteadiness 13 2.2.3 Tidal turbine 15 2.2.4 Tidal energy resources in Korea 17 2.3 Shallow water equation (SWE) 19 2.4 Gradient-based optimization using adjoint method 21 2.4.1 Problem formulation 22 2.4.2 The adjoint method 22 CHAPTER 3. METHODOLOGY 25 3.1 Numerical model description 25 3.1.1 The design parameters 25 3.1.2 The PDE constraints 25 3.1.3 The turbine parameterization 26 3.1.4 The functional of interest 27 3.1.5 Box and inequality constraints 27 3.1.6 Optimization algorithm 28 3.2 Experiment overview 29 3.2.1 Experiment procedure 29 3.2.2 Experimental flow chart 31 3.3 Simulation set-up 31 3.3.1 Mesh domain 31 3.3.2 Boundary condition 33 3.3.3 Parameter settings 36 CHAPTER 4. Test cases, Results and Discussions 39 4.1 Pilot Test 1: Steady & Unsteady flow impact on a single turbine 39 4.1.1 Wake behavior 39 4.1.2 comparison criterion between steady and unsteady flow based on the energy production 46 4.1.3 Conclusion of Pilot test 1 48 4.2 Pilot Test 2: Minimum velocity threshold (MVT) to converge to QGO and the concept of the optimized layout 49 4.2.1 Test cases 49 4.2.2 Finding minimum threshold of velocity (MVT) 51 4.2.3 Insights on the optimized layout 55 4.2.4 Conclusion of Pilot test 2 57 4.3 Main test: Effect of unsteadiness in the optimization procedure compared to the steady condition 59 4.3.1 Test cases 59 4.3.2 Optimal layout for each flow conditions 62 4.3.3 Strategy to obtain QGO for bidirectional flow condition 70 4.3.4 Conclusion of Main test 71 CHAPTER 5. Conclusions 73 REFERENCES 76 ๊ตญ๋ฌธ์ดˆ๋ก 80์„
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