Articoli di riviste: "Blackbox optimization"

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Audet, Charles, Sébastien Le Digabel e Mathilde Peyrega. "Linear equalities in blackbox optimization". Computational Optimization and Applications 61, n. 1 (19 ottobre 2014): 1–23. http://dx.doi.org/10.1007/s10589-014-9708-2.

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Audet, Charles, J. E. Dennis e Sébastien Le Digabel. "Trade-off studies in blackbox optimization". Optimization Methods and Software 27, n. 4-5 (ottobre 2012): 613–24. http://dx.doi.org/10.1080/10556788.2011.571687.

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Audet, Charles, Alain Batailly e Solène Kojtych. "Escaping Unknown Discontinuous Regions in Blackbox Optimization". SIAM Journal on Optimization 32, n. 3 (4 agosto 2022): 1843–70. http://dx.doi.org/10.1137/21m1420915.

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Gramacy, Robert B., Genetha A. Gray, Sébastien Le Digabel, Herbert K. H. Lee, Pritam Ranjan, Garth Wells e Stefan M. Wild. "Modeling an Augmented Lagrangian for Blackbox Constrained Optimization". Technometrics 58, n. 1 (2 gennaio 2016): 1–11. http://dx.doi.org/10.1080/00401706.2015.1014065.

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Chen, Hao, e William J. Welch. "Comment: Expected Improvement for Efficient Blackbox Constrained Optimization". Technometrics 58, n. 1 (2 gennaio 2016): 12–15. http://dx.doi.org/10.1080/00401706.2015.1044119.

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Audet, Charles, Gilles Caporossi e Stéphane Jacquet. "Binary, unrelaxable and hidden constraints in blackbox optimization". Operations Research Letters 48, n. 4 (luglio 2020): 467–71. http://dx.doi.org/10.1016/j.orl.2020.05.011.

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Audet, Charles, Jean Bigeon, Romain Couderc e Michael Kokkolaras. "Sequential stochastic blackbox optimization with zeroth-order gradient estimators". AIMS Mathematics 8, n. 11 (2023): 25922–56. http://dx.doi.org/10.3934/math.20231321.

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Abstract (sommario):

<abstract><p>This work considers stochastic optimization problems in which the objective function values can only be computed by a blackbox corrupted by some random noise following an unknown distribution. The proposed method is based on sequential stochastic optimization (SSO), i.e., the original problem is decomposed into a sequence of subproblems. Each subproblem is solved by using a zeroth-order version of a sign stochastic gradient descent with momentum algorithm (i.e., ZO-signum) and with increasingly fine precision. This decomposition allows a good exploration of the space while maintaining the efficiency of the algorithm once it gets close to the solution. Under the Lipschitz continuity assumption on the blackbox, a convergence rate in mean is derived for the ZO-signum algorithm. Moreover, if the blackbox is smooth and convex or locally convex around its minima, the rate of convergence to an $ \epsilon $-optimal point of the problem may be obtained for the SSO algorithm. Numerical experiments are conducted to compare the SSO algorithm with other state-of-the-art algorithms and to demonstrate its competitiveness.</p></abstract>

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Audet, Charles, e Michael Kokkolaras. "Blackbox and derivative-free optimization: theory, algorithms and applications". Optimization and Engineering 17, n. 1 (1 febbraio 2016): 1–2. http://dx.doi.org/10.1007/s11081-016-9307-4.

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Herraz, Mahfoud, Jean-Max Redonnet, Mohammed Sbihi e Marcel Mongeau. "Blackbox optimization and surrogate models for machining free-form surfaces". Computers & Industrial Engineering 177 (marzo 2023): 109029. http://dx.doi.org/10.1016/j.cie.2023.109029.

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Sankaran, Anush, Olivier Mastropietro, Ehsan Saboori, Yasser Idris, Davis Sawyer, MohammadHossein AskariHemmat e Ghouthi Boukli Hacene. "Deeplite NeutrinoTM: A BlackBox Framework for Constrained Deep Learning Model Optimization". Proceedings of the AAAI Conference on Artificial Intelligence 35, n. 17 (18 maggio 2021): 15166–74. http://dx.doi.org/10.1609/aaai.v35i17.17780.

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Designing deep learning-based solutions is becoming a race for training deeper models with a greater number of layers. While a large-size deeper model could provide competitive accuracy, it creates a lot of logistical challenges and unreasonable resource requirements during development and deployment. This has been one of the key reasons for deep learning models not being excessively used in various production environments, especially in edge devices. There is an immediate requirement for optimizing and compressing these deep learning models, to enable on-device intelligence. In this research, we introduce a black-box framework, Deeplite Neutrino^{TM} for production-ready optimization of deep learning models. The framework provides an easy mechanism for the end-users to provide constraints such as a tolerable drop in accuracy or target size of the optimized models, to guide the whole optimization process. The framework is easy to include in an existing production pipeline and is available as a Python Package, supporting PyTorch and Tensorflow libraries. The optimization performance of the framework is shown across multiple benchmark datasets and popular deep learning models. Further, the framework is currently used in production and the results and testimonials from several clients are summarized.

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Audet, Charles, Kwassi Joseph Dzahini, Michael Kokkolaras e Sébastien Le Digabel. "Stochastic mesh adaptive direct search for blackbox optimization using probabilistic estimates". Computational Optimization and Applications 79, n. 1 (11 marzo 2021): 1–34. http://dx.doi.org/10.1007/s10589-020-00249-0.

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Bigeon, Jean, Sébastien Le Digabel e Ludovic Salomon. "DMulti-MADS: mesh adaptive direct multisearch for bound-constrained blackbox multiobjective optimization". Computational Optimization and Applications 79, n. 2 (27 marzo 2021): 301–38. http://dx.doi.org/10.1007/s10589-021-00272-9.

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Audet, Charles, Sébastien Le Digabel e Christophe Tribes. "Dynamic scaling in the mesh adaptive direct search algorithm for blackbox optimization". Optimization and Engineering 17, n. 2 (2 settembre 2015): 333–58. http://dx.doi.org/10.1007/s11081-015-9283-0.

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Audet, Charles, Amina Ihaddadene, Sébastien Le Digabel e Christophe Tribes. "Robust optimization of noisy blackbox problems using the Mesh Adaptive Direct Search algorithm". Optimization Letters 12, n. 4 (2 gennaio 2018): 675–89. http://dx.doi.org/10.1007/s11590-017-1226-6.

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Talbi, El-Ghazali. "Automated Design of Deep Neural Networks". ACM Computing Surveys 54, n. 2 (aprile 2021): 1–37. http://dx.doi.org/10.1145/3439730.

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In recent years, research in applying optimization approaches in the automatic design of deep neural networks has become increasingly popular. Although various approaches have been proposed, there is a lack of a comprehensive survey and taxonomy on this hot research topic. In this article, we propose a unified way to describe the various optimization algorithms that focus on common and important search components of optimization algorithms: representation, objective function, constraints, initial solution(s), and variation operators. In addition to large-scale search space, the problem is characterized by its variable mixed design space, it is very expensive, and it has multiple blackbox objective functions. Hence, this unified methodology has been extended to advanced optimization approaches, such as surrogate-based, multi-objective, and parallel optimization.

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Liang, Ziying, Ting Shu e Zuohua Ding. "A Novel Improved Whale Optimization Algorithm for Global Optimization and Engineering Applications". Mathematics 12, n. 5 (21 febbraio 2024): 636. http://dx.doi.org/10.3390/math12050636.

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The Whale Optimization Algorithm (WOA) is a swarm intelligence algorithm based on natural heuristics, which has gained considerable attention from researchers and engineers. However, WOA still has some limitations, including limited global search efficiency and a slow convergence rate. To address these issues, this paper presents an improved whale optimization algorithm with multiple strategies, called Dynamic Gain-Sharing Whale Optimization Algorithm (DGSWOA). Specifically, a Sine–Tent–Cosine map is first adopted to more effectively initialize the population, ensuring a more uniform distribution of individuals across the search space. Then, a gaining–sharing knowledge based algorithm is used to enhance global search capability and avoid falling into a local optimum. Finally, to increase the diversity of solutions, Dynamic Opposition-Based Learning is incorporated for population updating. The effectiveness of our approach is evaluated through comparative experiments on blackbox optimization benchmarking and two engineering application problems. The experimental results suggest that the proposed method is competitive in terms of solution quality and convergence speed in most cases.

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Bi, Sirui, Benjamin Stump, Jiaxin Zhang, Yousub Lee, John Coleman, Matt Bement e Guannan Zhang. "Blackbox optimization for approximating high-fidelity heat transfer calculations in metal additive manufacturing". Results in Materials 13 (marzo 2022): 100258. http://dx.doi.org/10.1016/j.rinma.2022.100258.

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Cheng, Yichen, e Faming Liang. "Comment: “Modeling an Augmented Lagrangian for Blackbox Constrained Optimization” by Gramacy et al." Technometrics 58, n. 1 (2 gennaio 2016): 15–17. http://dx.doi.org/10.1080/00401706.2015.1040927.

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Peters, Lukas, Rüdiger Kutzner, Marc Schäfer e Lutz Hofmann. "Ability of Black-Box Optimisation to Efficiently Perform Simulation Studies in Power Engineering". Acta Mechanica et Automatica 17, n. 2 (10 maggio 2023): 292–302. http://dx.doi.org/10.2478/ama-2023-0034.

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Abstract In this study, the potential of the so-called black-box optimisation (BBO) to increase the efficiency of simulation studies in power engineering is evaluated. Three algorithms (“Multilevel Coordinate Search” (MCS) and “Stable Noisy Optimization by Branch and Fit” (SNOBFIT) by Huyer and Neumaier and “blackbox: A Procedure for Parallel Optimization of Expensive Black-box Functions” (blackbox) by Knysh and Korkolis) are implemented in MATLAB and compared for solving two use cases: the analysis of the maximum rotational speed of a gas turbine after a load rejection and the identification of transfer function parameters by measurements. The first use case has a high computational cost, whereas the second use case is computationally cheap. For each run of the algorithms, the accuracy of the found solution and the number of simulations or function evaluations needed to determine the optimum and the overall runtime are used to identify the potential of the algorithms in comparison to currently used methods. All methods provide solutions for potential optima that are at least 99.8% accurate compared to the reference methods. The number of evaluations of the objective functions differs significantly but cannot be directly compared as only the SNOBFIT algorithm does stop when the found solution does not improve further, whereas the other algorithms use a predefined number of function evaluations. Therefore, SNOBFIT has the shortest runtime for both examples. For computationally expensive simulations, it is shown that parallelisation of the function evaluations (SNOBFIT and blackbox) and quantisation of the input variables (SNOBFIT) are essential for the algorithmic performance. For the gas turbine overspeed analysis, only SNOBFIT can compete with the reference procedure concerning the runtime. Further studies will have to investigate whether the quantisation of input variables can be applied to other algorithms and whether the BBO algorithms can outperform the reference methods for problems with a higher dimensionality.

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Belakaria, Syrine, Aryan Deshwal e Janardhan Rao Doppa. "Multi-Fidelity Multi-Objective Bayesian Optimization: An Output Space Entropy Search Approach". Proceedings of the AAAI Conference on Artificial Intelligence 34, n. 06 (3 aprile 2020): 10035–43. http://dx.doi.org/10.1609/aaai.v34i06.6560.

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We study the novel problem of blackbox optimization of multiple objectives via multi-fidelity function evaluations that vary in the amount of resources consumed and their accuracy. The overall goal is to appromixate the true Pareto set of solutions by minimizing the resources consumed for function evaluations. For example, in power system design optimization, we need to find designs that trade-off cost, size, efficiency, and thermal tolerance using multi-fidelity simulators for design evaluations. In this paper, we propose a novel approach referred as Multi-Fidelity Output Space Entropy Search for Multi-objective Optimization (MF-OSEMO) to solve this problem. The key idea is to select the sequence of candidate input and fidelity-vector pairs that maximize the information gained about the true Pareto front per unit resource cost. Our experiments on several synthetic and real-world benchmark problems show that MF-OSEMO, with both approximations, significantly improves over the state-of-the-art single-fidelity algorithms for multi-objective optimization.

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Belakaria, Syrine, Aryan Deshwal, Nitthilan Kannappan Jayakodi e Janardhan Rao Doppa. "Uncertainty-Aware Search Framework for Multi-Objective Bayesian Optimization". Proceedings of the AAAI Conference on Artificial Intelligence 34, n. 06 (3 aprile 2020): 10044–52. http://dx.doi.org/10.1609/aaai.v34i06.6561.

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We consider the problem of multi-objective (MO) blackbox optimization using expensive function evaluations, where the goal is to approximate the true Pareto set of solutions while minimizing the number of function evaluations. For example, in hardware design optimization, we need to find the designs that trade-off performance, energy, and area overhead using expensive simulations. We propose a novel uncertainty-aware search framework referred to as USeMO to efficiently select the sequence of inputs for evaluation to solve this problem. The selection method of USeMO consists of solving a cheap MO optimization problem via surrogate models of the true functions to identify the most promising candidates and picking the best candidate based on a measure of uncertainty. We also provide theoretical analysis to characterize the efficacy of our approach. Our experiments on several synthetic and six diverse real-world benchmark problems show that USeMO consistently outperforms the state-of-the-art algorithms.

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Afif, Mushlihul, Elin Haerani, Eka Pandu Cynthia, Fitri Wulandari e Siti Ramadhani. "Implementasi Metode Multi – Objective Optimization On The Basis Of Ratio Analysis (MOORA) Pada Sistem Pengukuran Tingkat Kepuasan Kualitas Kinerja Sekolah". Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) 5, n. 2 (28 aprile 2022): 216–24. http://dx.doi.org/10.32672/jnkti.v5i2.4186.

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Abstrak - SMK Telkom adalah sekolah kejuruan swasta di Pekanbaru yang mempunyai banyak prestasi, baik dari prestasi akademik maupun prestasi non akademik. Untuk mencapai prestasi tersebut dibutuhkan adanya proses evaluasi terhadap kinerja sekolah. Pengukuran dilakukan untuk memberikan evaluasi guna meningkatkan kualitas pendidikan dan kualitas pelayanan yang terbaik serta bisa bersaing dengan sekolah lainnya. Untuk menentukan proses pengukuran dibutuhkan enam kriteria, yaitu tata usaha, tenaga kependidikan, humas, sarana dan prasarana, pembelajaran dan tenaga pendidik dengan bobot yang sudah di tentukan pada setiap kriteria melalui pengukuran yang dilakukan kepada siswa, wali murid, pegawai, guru dan kepala sekolah. Pada penelitian yang dilakukan menggunakan metode Multi - Objective Optimization On The Basis Of Ratio Analysis (MOORA) untuk mendapatkan pengukuran terbaik berdasarkan kriteria yang sudah ditentukan. Berdasarkan hasil Analisa fungsionl keselurahan sistemdari pengujian Blackbox mendapatkan hasil “Valid” dan pengujian menggunakan User Acceptance Testing (UAT) mendapatkan hasil skor 4,4 dari 5,00 “Sangat Setuju”.Kata kunci: Abstract - Telkom Vocational School is a private vocational school in Pekanbaru that has many achievements, both academic and non-academic achievements. To achieve this achievement requires an evaluation process of school performance. Measurements are carried out to provide evaluations to improve the quality of education and the best quality of service and to be able to compete with other schools. To determine the measurement process, six criteria are needed, namely administration, education staff, public relations, facilities and infrastructure, learning and teaching staff with a predetermined weight on each criterion through measurements made to students, parents, staff, teachers, and school principals. . In the research conducted using the Multi-Objective Optimization On The Basis Of Ratio Analysis (MOORA) method to obtain the best measurement based on predetermined criteria. Based on the results of the functional analysis of the entire system from the Blackbox test, the results are "Valid" and testing using User Acceptance Testing (UAT) gets a score of 4.4 out of 5.00 "Strongly Agree".Keywords: A system of measurement, School performance quality, Multi - Objective Optimization On The Basis Of Ratio Analysis

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Elaalami, Ilham A., Sunday O. Olatunji e Rachid M. Zagrouba. "AT-BOD: An Adversarial Attack on Fool DNN-Based Blackbox Object Detection Models". Applied Sciences 12, n. 4 (15 febbraio 2022): 2003. http://dx.doi.org/10.3390/app12042003.

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Object recognition is a fundamental concept in computer vision. Object detection models have recently played a vital role in various applications, including real-time and safety-critical systems such as camera surveillance and self-driving cars. Scientific research has proven that object detection models are prone to adversarial attacks. Although several proposed methods exist throughout the literature, they either target white-box models or specific-task black-box models and do not generalize on other detectors. In this paper, we proposed a new adversarial attack against Blackbox-based object detectors called AT-BOD. The proposed AT-BOD model can fool the single-stage and multi-stage detectors, where we used an optimization algorithm to generate adversarial examples depending only on the detector predictions. AT-BOD model works in two diverse ways, reducing the confidence score and misleading the model to make the wrong decision or hide the object detection models. Our solution achieved a fooling rate of 97% and a false negative increase of 99% on the YOLOv3 detector, and a fooling rate of 61% false-negative increase of 57% on the Faster R-CNN detector. The detection accuracy of YOLOv3 and Faster R-CNN under AT-BOD was dramatically reduced and reached ≤1% and ≤3%, respectively.

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Sadiah, Halimah Tus, Delta Hadi Purnama e Muhamad Saad Nurul Ishlah. "Implementation of the First In First Out (FIFO) Algorithm in the Sandal and Shoe Product Inventory (Stock) Application". International Journal of Quantitative Research and Modeling 5, n. 1 (23 aprile 2024): 31–39. http://dx.doi.org/10.46336/ijqrm.v5i1.552.

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This study addresses the optimization of inventory management for sandal and shoe products, at CV Diva Karya Mandiri Warehouse, which covers of five key features: a dashboard, master data management, transaction data, reporting, and user management. The First In First Out (FIFO) algorithm is specifically applied to the transaction feature, ensuring timely disbursement in line with the order of receipt. It is implemented using Rapid Application Development (RAD) methodology, which consists of Planning Requirements, User Design, Construction, and Cutover phases. The developed inventory application offers two access levels: administrators with comprehensive access and warehouse managers with limited access for viewing, searching, and filtering item data. This study successfully implementing the FIFO algorithm, with 95% Blackbox testing result achieved through boundary value analysis approach.Top of Form

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Hoseinzade, Davood, Esmail Lakzian e Ali Hashemian. "A blackbox optimization of volumetric heating rate for reducing the wetness of the steam flow through turbine blades". Energy 220 (aprile 2021): 119751. http://dx.doi.org/10.1016/j.energy.2020.119751.

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Kokkolaras, Michael. "C. Audet and W. Hare: Derivative-free and blackbox optimization. Springer series in operations research and financial engineering". Optimization and Engineering 20, n. 3 (16 gennaio 2019): 955–57. http://dx.doi.org/10.1007/s11081-019-09422-9.

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P.P., Fathimathul Rajeena, Walaa N. Ismail e Mona A. S. Ali. "A Metaheuristic Harris Hawks Optimization Algorithm for Weed Detection Using Drone Images". Applied Sciences 13, n. 12 (13 giugno 2023): 7083. http://dx.doi.org/10.3390/app13127083.

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There are several major threats to crop production. As herbicide use has become overly reliant on weed control, herbicide-resistant weeds have evolved and pose an increasing threat to the environment, food safety, and human health. Convolutional neural networks (CNNs) have demonstrated exceptional results in the analysis of images for the identification of weeds from crop images that are captured by drones. Manually designing such neural architectures is, however, an error-prone and time-consuming process. Natural-inspired optimization algorithms have been widely used to design and optimize neural networks, since they can perform a blackbox optimization process without explicitly formulating mathematical formulations or providing gradient information to develop appropriate representations and search paradigms for solutions. Harris Hawk Optimization algorithms (HHO) have been developed in recent years to identify optimal or near-optimal solutions to difficult problems automatically, thus overcoming the limitations of human judgment. A new automated architecture based on DenseNet-121 and DenseNet-201 models is presented in this study, which is called “DenseHHO”. A novel CNN architecture design is devised to classify weed images captured by sprayer drones using the Harris Hawk Optimization algorithm (HHO) by selecting the most appropriate parameters. Based on the results of this study, the proposed method is capable of detecting weeds in unstructured field environments with an average accuracy of 98.44% using DenseNet-121 and 97.91% using DenseNet-201, the highest accuracy among optimization-based weed-detection strategies.

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Bazhrullah, Muhammad Rayda, Tina Tri Wulansari, Nariza Wanti Wulan Sari, Fahrullah Fahrullah e Dedy Mirwansyah. "Sistem Pendukung Keputusan Penentuan Promosi Produk Menggunakan Metode Multi-Objective Optimization On The Basis Of Ratio Analysis (MOORA)". LOFIAN: Jurnal Teknologi Informasi dan Komunikasi 1, n. 2 (26 marzo 2022): 59–64. http://dx.doi.org/10.58918/lofian.v1i2.178.

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Dalam suatu bisnis, diperlukan upaya memaksimalkan keuntungan diantaranya dengan melakukan promosi. Masa Coffee pada saat ini masih belum menggunakan data penjualan yang terdokumentasi untuk mengamati selera konsumen sebagai dasar melakukan promosi produk. Dengan demikian perlu adanya sebuah sistem yang dapat membantu menentukan rekomendasi promosi. Sistem pendukung keputusan tentunya akan lebih mempermudah dalam pengambilan keputusan dalam mengevaluasi rekomendasi promosi produk. Metode Multi-Objective Optimization on The Basis of Ratio Analysis (MOORA) ini dapat dimanfaatkan dalam proses penentuan rekomendasi promosi produk. Metode ini memiliki hasil yang berupa perankingan dimana rekomendasi promosi produk diambil dari ranking terendah dalam membantu pengambilan keputusan agar memudahkan pihak Kedai Masa Coffee dalam menentukan rekomendasi produk. Metode pengembangan sistem yang digunakan adalah metode SPK dan alat bantu analisis yang digunakan adalah flowchart, CD, DFD, dan ERD dengan metode pengujian blackbox. Dari hasil perhitungan pada sistem ini, didapat bahwa terdapat produk yang dipilih untuk dijadikan produk promosi yaitu produk Americano dengan nilai 0,045156.

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Zhang, Yang, Ruohan Zong, Lanyu Shang, Ziyi Kou, Huimin Zeng e Dong Wang. "CrowdOptim: A Crowd-driven Neural Network Hyperparameter Optimization Approach to AI-based Smart Urban Sensing". Proceedings of the ACM on Human-Computer Interaction 6, CSCW2 (7 novembre 2022): 1–27. http://dx.doi.org/10.1145/3555536.

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AI-based smart urban sensing (ASUS) has emerged as a scalable and pervasive application paradigm in smart city planning and management that aims to automatically assess the physical status of the urban environments by leveraging AI techniques and massive urban sensing data. In this paper, we focus on a crowd-driven neural network (NN) hyperparameter optimization problem in ASUS applications. Our goal is to utilize the human intelligence from crowdsourcing systems to identify the optimal NN hyperparameter configuration for an ASUS model. Our work is motivated by the observation that the hyperparameters of current ASUS models are often manually configured by the AI specialists, which is known to be both error-prone and suboptimal. Two key technical challenges exist in solving our problem: i) it is challenging to effectively translate the highly complex NN hyperparameter optimization problem in AI to a simplified problem that can be solved by crowd workers without extensive AI expertise; ii) it is difficult to identify the optimal hyperparameter configuration in the large hyperparameter search space given the blackbox nature of the AI model. To address the above challenges, we develop CrowdOptim, a crowd-AI collaborative learning framework that integrates the techniques from crowdsourcing, hyperparameter optimization, and estimation theory to address the crowd-driven NN hyperparameter optimization problem in ASUS applications. The evaluation results from two real-world ASUS applications (i.e., smart city infrastructure monitoring (SCIM) and urban environment cleanliness assessment (UECA)) show that CrowdOptim consistently outperforms the state-of-the-art deep convolutional networks, crowd-AI, and hyperparameter optimization baselines in achieving the ASUS application objectives under various evaluation scenarios.

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Febianti Putri, Elga, Muhammad Rafi Muttaqin e Mochzen Gito Resmi. "SISTEM PENDUKUNG KEPUTUSAN DALAM MENENTUKAN PRIORITAS PEMBANGUNAN DESA KAMOJING MENGGUNAKAN METODE MOORA (MULTI OBJECTIVE OPTIMIZATION OF RATIO ANALYSIS)". JATI (Jurnal Mahasiswa Teknik Informatika) 7, n. 4 (6 gennaio 2024): 2788–85. http://dx.doi.org/10.36040/jati.v7i4.7199.

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Banyaknya pembangunan yang tidak akurat dan tidak tepat sasaran, Desa Kamojing dituntut untuk melakukan pengambilan keputusan dengan seobjektif mungkin. Pembangunan yang kurang tepat ini dialami oleh Desa Kamojing karena di dalam musyawarah nya selalu ada selisih paham akibat dari perbedaan suatu pemikiran, sikap, pandangan dalam mengambil keputusan. Pembangunan yang dilakukan tidak melihat dari segi prioritas yang seharusnya lebih diutmakan dan didahulukan, sehingga seringkali terjadi penundaan pembangunan. Sistem Pendukung Keputusan (SPK) merupakan salah satu alternatif dalam menentukan sebuah keputusan, sehingga penelitian ini dilakukan bertujuan untuk memudahkan dalam memilih prioritas pembangunan yang dapat dibangun nantinya. Sistem pendukung keputusan ini menggunakan metode Multi Objective Optimization On The Basis Of Ratio Analysis (MOORA), sedangkan metode pengembangan perangkat lunak yang digunakan adalah model Waterfall. Aplikasi ini dibangun menggunakan bahasa pemrograman PHP Codeigniter dengan Database Management Sistem MySQL, kemudian aplikasi ini diuji coba menggunakan pengujian BlackBox Testing. Berdasarkan Hasil Pengujian yang telah dilakukan sistem pendukung keputusan dalm menentukan prioritas pembangunan di Desa Kamojing telah berhasil dibuat, dengan adanya sistem pendukung keputusan berbasis komputer ini diharapkan dapat mempermudah dalam pengambilan keputusan dengan baik, tepat dan juga cepat, serta dapat dipertanggungjawabkan.

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Ulhaq, Muhammad Dhiya Ulhaq, e Irawati. "Implementasi Metode Visekriterijumsko Kompromisno Rangiranje (VIKOR) Pada Seleksi Program Keluarga Harapan Komponen Pendidikan Berbasis Web". Indonesian Journal of Data and Science 2, n. 1 (31 maggio 2021): 38–49. http://dx.doi.org/10.33096/ijodas.v2i1.30.

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Program Keluarga Harapan adalah program pemberian bantuan sosial kepada keluarga miskin yang ditetapkan sebagai penerima. Dalam penelitian ini, sistem pendukung keputusan digunakan untuk mendapatkan hasil keputusan terbaik dengan menggunakan Metode Vikor Multi-Criteria Optimization and Compromise Solution yang merupakan salah satu dari sekian banyak teknik MCDM dalam menentukan hasil keputusan terbaik. Tahap dalam penelitian ini meliputi penentuan Alternatif dan Kriteria selanjutnya dibentuk kedalam matriks yang akan di normalisasi. Tahap berikutnya matriks hasil normalisasi akan dikalikan dengan bobot kriteria yang telah ditentukan sehingga dalam proses selanjutnya dapat dihitung nilai Utility Measure (S) dan Regret Measure (R). Tahap terakhir menghitung indeks Vikor untuk mendapatkan nilai indeks setiap Alternatif, lalu nilai tersebut akan di ranking berdasarkan indeks terbaik. Semakin kecil nilai indeks maka semakin baik hasil keputusan. Berdasarkan hasil pengujian yang telah dilakukan menggunakan teknik Blackbox Testing diperoleh hasil perancangan sistem telah berjalan sesuai perencanaan serta dapat menentukan solusi terbaik pada setiap alternatif.

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Dębski, Roman. "Gradient-Based Algorithms in the Brachistochrone Problem Having a Black-Box Represented Mathematical Model". Journal of Telecommunications and Information Technology, n. 1 (30 marzo 2014): 32–40. http://dx.doi.org/10.26636/jtit.2014.1.1003.

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Trajectory optimization problems with black-box represented objective functions are often solved with the use of some meta-heuristic algorithms. The aim of this paper is to show that gradient-based algorithms, when applied correctly, can be effective for such problems as well. One of the key aspects of successful application is choosing, in the search space, a basis appropriate for the problem. In an experiment to demonstrate this, three simple adaptations of gradient-based algorithms were executed in the forty-dimensional search space to solve the brachistochrone problem having a blackbox represented mathematical model. This experiment was repeated for two different bases spanning the search space. The best of the algorithms, despite its very basic implementation, needed only about 100 iterations to find very accurate solutions. 100 iterations means about 2000 objective functional evaluations (simulations). This corresponds to about 20 iterations of a typical evolutionary algorithm, e.g. ES(µ,,,λ).

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Ismail, Walaa N. "Snake-Efficient Feature Selection-Based Framework for Precise Early Detection of Chronic Kidney Disease". Diagnostics 13, n. 15 (27 luglio 2023): 2501. http://dx.doi.org/10.3390/diagnostics13152501.

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Chronic kidney disease (CKD) refers to impairment of the kidneys that may worsen over time. Early detection of CKD is crucial for saving millions of lives. As a result, several studies are currently focused on developing computer-aided systems to detect CKD in its early stages. Manual screening is time-consuming and subject to personal judgment. Therefore, methods based on machine learning (ML) and automatic feature selection are used to support graders. The goal of feature selection is to identify the most relevant and informative subset of features in a given dataset. This approach helps mitigate the curse of dimensionality, reduce dimensionality, and enhance model performance. The use of natural-inspired optimization algorithms has been widely adopted to develop appropriate representations of complex problems by conducting a blackbox optimization process without explicitly formulating mathematical formulations. Recently, snake optimization algorithms have been developed to identify optimal or near-optimal solutions to difficult problems by mimicking the behavior of snakes during hunting. The objective of this paper is to develop a novel snake-optimized framework named CKD-SO for CKD data analysis. To select and classify the most suitable medical data, five machine learning algorithms are deployed, along with the snake optimization (SO) algorithm, to create an extremely accurate prediction of kidney and liver disease. The end result is a model that can detect CKD with 99.7% accuracy. These results contribute to our understanding of the medical data preparation pipeline. Furthermore, implementing this method will enable health systems to achieve effective CKD prevention by providing early interventions that reduce the high burden of CKD-related diseases and mortality.

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Tu, Chun-Chen, Paishun Ting, Pin-Yu Chen, Sijia Liu, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh e Shin-Ming Cheng. "AutoZOOM: Autoencoder-Based Zeroth Order Optimization Method for Attacking Black-Box Neural Networks". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 luglio 2019): 742–49. http://dx.doi.org/10.1609/aaai.v33i01.3301742.

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Recent studies have shown that adversarial examples in state-of-the-art image classifiers trained by deep neural networks (DNN) can be easily generated when the target model is transparent to an attacker, known as the white-box setting. However, when attacking a deployed machine learning service, one can only acquire the input-output correspondences of the target model; this is the so-called black-box attack setting. The major drawback of existing black-box attacks is the need for excessive model queries, which may give a false sense of model robustness due to inefficient query designs. To bridge this gap, we propose a generic framework for query-efficient blackbox attacks. Our framework, AutoZOOM, which is short for Autoencoder-based Zeroth Order Optimization Method, has two novel building blocks towards efficient black-box attacks: (i) an adaptive random gradient estimation strategy to balance query counts and distortion, and (ii) an autoencoder that is either trained offline with unlabeled data or a bilinear resizing operation for attack acceleration. Experimental results suggest that, by applying AutoZOOM to a state-of-the-art black-box attack (ZOO), a significant reduction in model queries can be achieved without sacrificing the attack success rate and the visual quality of the resulting adversarial examples. In particular, when compared to the standard ZOO method, AutoZOOM can consistently reduce the mean query counts in finding successful adversarial examples (or reaching the same distortion level) by at least 93% on MNIST, CIFAR-10 and ImageNet datasets, leading to novel insights on adversarial robustness.

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Feng, Zunlei, Jiacong Hu, Sai Wu, XiaoTian Yu, Jie Song e Mingli Song. "Model Doctor: A Simple Gradient Aggregation Strategy for Diagnosing and Treating CNN Classifiers". Proceedings of the AAAI Conference on Artificial Intelligence 36, n. 1 (28 giugno 2022): 616–24. http://dx.doi.org/10.1609/aaai.v36i1.19941.

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Recently, Convolutional Neural Network (CNN) has achieved excellent performance in the classification task. It is widely known that CNN is deemed as a 'blackbox', which is hard for understanding the prediction mechanism and debugging the wrong prediction. Some model debugging and explanation works are developed for solving the above drawbacks. However, those methods focus on explanation and diagnosing possible causes for model prediction, based on which the researchers handle the following optimization of models manually. In this paper, we propose the first completely automatic model diagnosing and treating tool, termed as Model Doctor. Based on two discoveries that 1) each category is only correlated with sparse and specific convolution kernels, and 2) adversarial samples are isolated while normal samples are successive in the feature space, a simple aggregate gradient constraint is devised for effectively diagnosing and optimizing CNN classifiers. The aggregate gradient strategy is a versatile module for mainstream CNN classifiers. Extensive experiments demonstrate that the proposed Model Doctor applies to all existing CNN classifiers, and improves the accuracy of 16 mainstream CNN classifiers by 1%~5%.

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Romadhona, Agus, Ulfiah Ulfiah, Sukardi Sukardi e Nur Indah Sari. "IMPLEMENTASI SELEKSI PENERIMAAN BANTUAN SISWA MISKIN DENGAN METODE MOORA". JTIK (Jurnal Teknik Informatika Kaputama) 7, n. 1 (1 gennaio 2023): 128–35. http://dx.doi.org/10.59697/jtik.v7i1.54.

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Bantuan biaya belajar atau beasiswa yang diberikan kepada siswa adalah bentuk tunjangan pendidikan. Salah satu jenis beasiswa yang diberikan pemerintah kepada para pelajar yaitu Bantuan Siswa Miskin. Metode alternatif untuk pengambilan keputusan adalah metode MOORA (Multi Objective Optimization On The Basis Of Ratio Analysis). Pada penelitian ini yang akan dicapai dalam penelitian ini adalah membangun aplikasi sistem yang dapat menganalisa penerimaan Bantuan Siswa Miskin (BSM) berbasis website menggunakan metode MOORA sebagai alternatif seleksi terhadap data yang dipilih. Batasan masalah pada penelitian ini yaitu Metode MOORA difungsikan untuk alternatif pembanding beberapa data yang dihasilkan oleh sekolah terhadap data yang dihasilkan oleh aplikasi yang berbasis website, aplikasi sistem akan menampilkan berdasarkan hasil analisa data. Berdasarkan analisis kebutuhan dan perancangan serta implementasi pada seleksi Penerimaan Bantuan Siswa Miskin dengan Metode MOORA pada SMP 3 Bambapula, peneliti dapat menyimpulkan bahwa aplikasi sistem dapat dijadikan sebagai pilihan alternatif pada permasalahan dalam proses seleksi Penerimaan Bantuan Siswa Miskin. Pengujian yang dilakukan terhadap aplikasi sistem dengan metode Blackbox Testing Serta menggunakan algoritma MOORA yang digunakan menghasilkan 100% dengan demikian diharapakan dapat membantu pihak pihak yang terkait.

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Pawan, Elvis, e Patmawati Hasan. "Optimization of Hill Cipher Method for Encryption and Decryption of Prescription Drugs at Puskesmas Twano Jayapura City". International Journal of Computer and Information System (IJCIS) 2, n. 4 (30 novembre 2021): 149–54. http://dx.doi.org/10.29040/ijcis.v2i4.48.

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A drug prescription is a written request from a doctor to a pharmacist that must be kept secret because it contains certain doses of drugs and types of drugs that cannot be known by just anyone, especially those who are not interested. From time to time technological advances have a rapid impact on all sectors, both private and government agencies, including the health sector. One form of service in the health sector that can utilize information technology is the manufacture of electronic drug prescriptions that can be sent via an application from a doctor to a pharmacist. The frequent misuse of prescription drugs by unauthorized persons, as well as errors by officers at the pharmacy in reading prescriptions can be fatal for the community, so a solution is needed to overcome this problem. This application is designed using the Hill Cipher Algorithm which is one of the classic types of algorithms in the field of cryptography, but to get the maximum level of security, the algorithm key will be modified using a postal code pattern as a matrix key. Broadly speaking, the Encryption Stage is the first starting from the plaintext which is the type of drug and drug dose, the second key matrix using a POS code pattern, the three plaintexts are converted into blocks, the fourth is arranged into a 2x2 matrix, the fifth is multiplied between the key and the sixth plaintext is multiplied into mod 26 to generate an encrypted ciphertext or recipe. The success rate of system functionality testing using the blackbox method is 100%

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Susanto, Heri, Fitra Kurnia, Yusra Yusra e Lola Oktavia. "Implementasi Metode Moora Pada Sistem Pendukung Keputusan Penilaian Kinerja Karyawan". JURNAL MEDIA INFORMATIKA BUDIDARMA 6, n. 4 (25 ottobre 2022): 2222. http://dx.doi.org/10.30865/mib.v6i4.4750.

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Employee performance appraisal is needed by an agency or company with the aim of evaluating performance and improving the quality of competent human resources and high loyalty for each employee at work, then an agency or company can give awards to each of its employees such as contract extensions, salary increases , get special promotions, appointments, and allowances, which can motivate every employee. This study aims to facilitate a planner in a company PT. SUPRACO INDONESIA in providing performance appraisals of each employee uses a decision support system using the Multi Objective Optimization On The Basic Of Ratio Analysis (MOORA) method. This employee performance appraisal decision support system uses a sample of 3 employees from 11 employees using the MOORA method of calculation. the final results of the calculations carried out are: for the first rank in alternative 2 with a value of 5.7805, while the second rank in alternative 1 with a value of 5.7736, and third place in alternative 3 with a value of 5.7671. In the tests carried out using Blackbox Testing, for all the features on the system running 100% with very good information and testing using the UAT (User Acceptance Test) method, it showed that the results of system user acceptance were 92%.

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Hidayat, Ahmad Tri, Andi Muhammad Dirham Dewantara e Shoffan Saifullah. "The Development of Website on Management Information System for E-commerce and Services". Jurnal Sisfokom (Sistem Informasi dan Komputer) 9, n. 3 (20 ottobre 2020): 380–86. http://dx.doi.org/10.32736/sisfokom.v9i3.992.

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Currently, the sales system is overgrowing. The concept of selling that was done manually (which is still less than optimal) becomes electronic (e-commerce). System development requires a digital platform. This platform must be able to carry out all activities that were carried out before (manually), such as collecting documents, recording transactions, and reporting. Besides, the e-commerce platform can provide support and increased performance in the sales process, both in checking stock items, transaction reports, and services. Besides, this optimization can provide services precisely and quickly to consumers. A management information system concept is needed to carry out e-commerce and services with integrated data and be stored in its development database. This prototype concept requires a method for website development. The method used is a waterfall. Website design uses the Hypertext PreProcessor (PHP) programming language and MySQL database. The design model uses two concepts: entity-relationship diagrams (ERD) and data flow diagrams (DFD). The result is a website and e-commerce services that can be accepted by users and e-commerce organizers with tests that have been carried out. System testing uses Blackbox and Whitebox testing, each of which results can be used to implement e-commerce sites and services. The website can assist officers in service and e-commerce and make it easier for officers to determine the target and service status.

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Sudiatmika, I. Putu Gd Abdi, Komang Hari Santhi Dewi e Mafrul Zaenal Alfian. "Sistem Penjadwalan Maintenance AC Berbasis Mobile Menggunakan Algoritma Genetika Pada Hotel Nusa Dua Convention Center". Journal of Information System Research (JOSH) 3, n. 4 (31 luglio 2022): 515–23. http://dx.doi.org/10.47065/josh.v3i4.1859.

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Scheduling is the most important thing in Instasi, at the Bali Nusa Dua Convention Center hotel. In this system AC maintenance scheduling is intended for staff technicians at BNDCC hotels. In BNDCC, scheduling still uses a manual system using bookkeeping so that ac maintenance scheduling takes a long time and the resulting schedule information is less effective because the process tends to take a long time and the information generated is less accurate. The purpose of this study is to apply a genetic algorithm to solve optimization problems in scheduling ac maintenance. Genetic algorithms present candidate solutions randomly, then evaluate using the fitness function and then do a selection, then cross-move and mutase. After a few generations the genetic algorithm produces the best schedule. Scheduling In making this scheduling system uses the development of the waterfall method and is designed using the Unified Modeling Language (UML) with the Hypertext Prepprocessing (php) programming language and MySQL as a database. In the scheduling system it has a generate function that generates an AC maintenance schedule automatically. With the scheduling system, the scheduling process can be done quickly and effectively. Besides the scheduling system that is built to display the overall schedule, namely Day, Time, Room, Technician and Job. At the final stage of testing, the system is tested using the blackbox testing method and all tests performed have obtained the appropriate results.

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Alarie, Stéphane, Charles Audet, Pierre-Yves Bouchet e Sébastien Le Digabel. "Optimization of Stochastic Blackboxes with Adaptive Precision". SIAM Journal on Optimization 31, n. 4 (gennaio 2021): 3127–56. http://dx.doi.org/10.1137/20m1318894.

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Chahine, Khaled, Mark Ballico, John Reizes e Jafar Madadnia. "Optimization of a Graphite Tube Blackbody Heater for a Thermogage Furnace". International Journal of Thermophysics 29, n. 1 (25 gennaio 2008): 386–94. http://dx.doi.org/10.1007/s10765-008-0370-8.

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Lucas, Javier De. "Numerical optimization of the radial dependence of effective emissivity in blackbody cylindrical cavities". Metrologia 51, n. 5 (25 giugno 2014): 402–9. http://dx.doi.org/10.1088/0026-1394/51/5/402.

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Alfeld, Scott, Xiaojin Zhu e Paul Barford. "Machine Teaching as Search". Proceedings of the International Symposium on Combinatorial Search 7, n. 1 (1 settembre 2021): 117–18. http://dx.doi.org/10.1609/socs.v7i1.18404.

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Machine teaching (MT) studies the task of designing a training set. Specifically, given a learner (e.g., an artificial neural network or a human) and a target model, a teacher aims to create a training set which results in the target model being learned. MT applications include optimal education design for human learners and computer security where adversaries aim to attack learning-based systems. In this work, we formulate pool-based MT as a state space search problem. We discuss the properties and challenges of the resulting problem and highlight opportunities for novel search techniques. In our preliminary study we use a beam search approach, and find that training and evaluating empirical risk of models dominate the run time of the search. Toward the goal of better search techniques for future work, we develop optimizations ranging from implementation details for specific learners to algorithm changes applicable to general blackbox learners. We conclude with a discussion of open problems and research directions.

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De Oliveira Lorenzi, Jéssica, e Daniel Neves Micha. "Optimization of Multi-Junction Solar Cells for the Orbit of Mars". Journal of Integrated Circuits and Systems 18, n. 3 (28 dicembre 2023): 1–6. http://dx.doi.org/10.29292/jics.v18i3.783.

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Photovoltaic (PV) devices have been used in space applications since 1958 and need to be constantly optimized in terms of specific power (W/kg). For the next decade, Mars is one important pursued destination by NASA and, as such, optimized PV designs will be needed. This work presents optimal designs in terms of bandgap energy configuration for multijunction solar cells operating at the Mars’ orbit conditions. For this, a physical model using blackbody radiation was implemented to calculate the solar spectrum at different orbital points and the PV solar cell temperature. By means of a computer simulation based on the Schockley-Queisser detailed balance model, power conversion efficiencies are mapped and the optimal bandgap energy combination is obtained for each configuration. As results, we obtained for the optimized double junction solar cell an efficiency of 45.3% with bandgaps of 0.90/1.60 eV and 51.7% for the best triple junction solar cell with bandgaps of 0.75/1.22/1.84 eV.

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Wu, Yifei, Zuoru Dong, Yulu Chen, Bingbing Wang, Liming Wang, Xiaowan Dai, Junming Zhang e Xiaodong Wang. "Optimization of Pixel Size and Electrode Structure for Ge:Ga Terahertz Photoconductive Detectors". Sensors 22, n. 5 (1 marzo 2022): 1916. http://dx.doi.org/10.3390/s22051916.

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To investigate the effects of the pixel sizes and the electrode structures on the performance of Ge-based terahertz (THz) photoconductive detectors, vertical structure Ge:Ga detectors with different structure parameters were fabricated. The characteristics of the detectors were investigated at 4.2 K, including the spectral response, blackbody response (Rbb), dark current density-voltage characters, and noise equivalent power (NEP). The detector with the pixel radius of 400 μm and the top electrode of the ring structure showed the best performance. The spectral response band of this detector was about 20–180 μm. The Rbb of this detector reached as high as 0.92 A/W, and the NEP reached 5.4 × 10−13 W/Hz at 0.5 V. Compared with the detector with a pixel radius of 1000 μm and the top electrode of the spot structure, the Rbb increased nearly six times, and the NEP decreased nearly 12 times. This is due to the fact that the optimized parameters increased the equivalent electric field of the detector. This work provides a route for future research into large-scale array Ge-based THz detectors.

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Bai, Hai Cheng, Hong Ji Meng e Zhi Xie. "Development of an Embedded High-Temperature Field Measuring Instrument". Advanced Materials Research 508 (aprile 2012): 151–54. http://dx.doi.org/10.4028/www.scientific.net/amr.508.151.

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This paper describes the development of an embedded high-temperature measuring instrument, which is composed of lens, photoelectric converter based on area-array CCD, and data acquisition, processing, Ethernet communication module based on DSP. The device is a creation of imaging spectrum, CCD imaging technology, digital image processing method and Ethernet communication together effectively. The advantages of this approach are: First, the networked measuring platform provides the possibility for process parameters optimization. Second, direct digital signal communication improves the anti-interference ability. Third, by employing 4-stage pipeline data processing mechanism greatly improves the real-time requirement. Fourth, through the constitution of application-layer protocol, the reliability of high-speed data transmission via Ethernet is guaranteed. The experiment by blackbody furnace in the laboratory shows that the maximum absolute error is 3.2 ºC, and the maximum relative error is 0.40%.

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Wang, Junlin, Zhi Xie e Xunjian Che. "Development of a Novel Pyrometer by Eliminating the Uncertainty of Emissivity Using Reflector with Two Apertures in Medium Plate Rolling Process". Actuators 11, n. 7 (9 luglio 2022): 188. http://dx.doi.org/10.3390/act11070188.

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The uncertainty of emissivity has a major effect on the accuracy of a pyrometer in billet temperature measurement. In order to eliminate the influence of emissivity, we place a reflector with two apertures at the front of a pyrometer. The two apertures on the reflector are used to measure intrinsic radiation and approximate blackbody radiation of the billet. The radiation is collected by two infrared dual-band detectors in the pyrometer. Then, the real-time emissivity of the billet can be measured with no assumptions, so the influence of emissivity is eliminated. In addition, the measurement uncertainty is analyzed based on the ray-tracing method. The pyrometer is developed and the accuracy verification of emissivity is implemented. Compared with the reference material at the same temperature, the measurement errors of the emissivity are 0.021 and 0.005 at two wavelengths. Then, we install the pyrometer in the medium plate rolling process for measurement. Compared with a thermal imager used in the rolling process, the measurement fluctuation is reduced obviously. It indicates that the method of emissivity measurement is very effective for billet temperature measurement.

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Wright, Sean E., e Marc A. Rosen. "Exergetic Efficiencies and the Exergy Content of Terrestrial Solar Radiation". Journal of Solar Energy Engineering 126, n. 1 (1 febbraio 2004): 673–76. http://dx.doi.org/10.1115/1.1636796.

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In the field of solar engineering the practical performance of solar energy conversion devices is generally evaluated strictly on an energy (first law) basis. However, the second law of thermodynamics determines the maximum work potential or exergy content of radiative fluxes independent of any conceptual device. The work in this paper quantifies the effect of directional and spectral distribution of terrestrial solar radiation (SR) on its exergy content. This is particularly important as the thermodynamic character of terrestrial SR is very different from that of blackbody radiation (BR). Exergetic (second law) efficiencies compare the work output of a device to the exergy content of the radiative source flux rather than its energy flux. As a result, exergetic efficiencies reveal that the performance of devices in practice is always better than what is indicated by the corresponding energy efficiency. The results presented in this paper introduce the benefits of using exergy analysis for solar cell design, performance evaluation and optimization.

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Alarie, Stéphane, Charles Audet, Paulin Jacquot e Sébastien Le Digabel. "Hierarchically constrained blackbox optimization". Operations Research Letters, giugno 2022. http://dx.doi.org/10.1016/j.orl.2022.06.006.

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