Journal articles on the topic 'White-box Analysis'
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Chitic, Raluca, Ali Osman Topal, and Franck Leprévost. "Empirical Perturbation Analysis of Two Adversarial Attacks: Black Box versus White Box." Applied Sciences 12, no. 14 (July 21, 2022): 7339. http://dx.doi.org/10.3390/app12147339.
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Through the addition of humanly imperceptible noise to an image classified as belonging to a category ca, targeted adversarial attacks can lead convolutional neural networks (CNNs) to classify a modified image as belonging to any predefined target class ct≠ca. To achieve a better understanding of the inner workings of adversarial attacks, this study analyzes the adversarial images created by two completely opposite attacks against 10 ImageNet-trained CNNs. A total of 2×437 adversarial images are created by EAtarget,C, a black-box evolutionary algorithm (EA), and by the basic iterative method (BIM), a white-box, gradient-based attack. We inspect and compare these two sets of adversarial images from different perspectives: the behavior of CNNs at smaller image regions, the image noise frequency, the adversarial image transferability, the image texture change, and penultimate CNN layer activations. We find that texture change is a side effect rather than a means for the attacks and that ct-relevant features only build up significantly from image regions of size 56×56 onwards. In the penultimate CNN layers, both attacks increase the activation of units that are positively related to ct and units that are negatively related to ca. In contrast to EAtarget,C’s white noise nature, BIM predominantly introduces low-frequency noise. BIM affects the original ca features more than EAtarget,C, thus producing slightly more transferable adversarial images. However, the transferability with both attacks is low, since the attacks’ ct-related information is specific to the output layers of the targeted CNN. We find that the adversarial images are actually more transferable at regions with sizes of 56×56 than at full scale.
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Pintelas, Emmanuel, Ioannis E. Livieris, and Panagiotis Pintelas. "A Grey-Box Ensemble Model Exploiting Black-Box Accuracy and White-Box Intrinsic Interpretability." Algorithms 13, no. 1 (January 5, 2020): 17. http://dx.doi.org/10.3390/a13010017.
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Machine learning has emerged as a key factor in many technological and scientific advances and applications. Much research has been devoted to developing high performance machine learning models, which are able to make very accurate predictions and decisions on a wide range of applications. Nevertheless, we still seek to understand and explain how these models work and make decisions. Explainability and interpretability in machine learning is a significant issue, since in most of real-world problems it is considered essential to understand and explain the model’s prediction mechanism in order to trust it and make decisions on critical issues. In this study, we developed a Grey-Box model based on semi-supervised methodology utilizing a self-training framework. The main objective of this work is the development of a both interpretable and accurate machine learning model, although this is a complex and challenging task. The proposed model was evaluated on a variety of real world datasets from the crucial application domains of education, finance and medicine. Our results demonstrate the efficiency of the proposed model performing comparable to a Black-Box and considerably outperforming single White-Box models, while at the same time remains as interpretable as a White-Box model.
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Velez, Miguel, Pooyan Jamshidi, Florian Sattler, Norbert Siegmund, Sven Apel, and Christian Kästner. "ConfigCrusher: towards white-box performance analysis for configurable systems." Automated Software Engineering 27, no. 3-4 (August 5, 2020): 265–300. http://dx.doi.org/10.1007/s10515-020-00273-8.
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Pang, Siyu, Tingting Lin, Xuejia Lai, and Zheng Gong. "A White-Box Implementation of IDEA." Symmetry 13, no. 6 (June 15, 2021): 1066. http://dx.doi.org/10.3390/sym13061066.
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IDEA is a classic symmetric encryption algorithm proposed in 1991 and widely used in many applications. However, there is little research into white-box IDEA. In traditional white-box implementations of existing block ciphers, S-boxes are always converted into encoded lookup tables. However, the algebraic operations of IDEA without S-boxes, make the implementation not straight forward and challenging. We propose a white-box implementation of IDEA by applying a splitting symmetric encryption method, and verify its security against algebraic analysis and BGE-like attacks. Our white-box implementation requires an average of about 2800 ms to encrypt a 64-bit plaintext, about 60 times more than the original algorithm would take, which is acceptable for practical applications. Its storage requirements are only about 10 MB. To our knowledge, this is the first public white-box IDEA solution, and its design by splitting can be applied to similar algebraic encryption structures.
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Hasan, Md Moudud, Md Shariot Ullah, Ajoy Kumar Saha, and MG Mostofa Amin. "Comparing the performances of multiple rainfall-runoff models of a karst watershed." Asian-Australasian Journal of Bioscience and Biotechnology 6, no. 1 (July 18, 2021): 26–39. http://dx.doi.org/10.3329/aajbb.v6i1.54878.
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Different modeling concepts, a simple (black-box) to a fully distributed modeling (white-box), were used to develop a rainfall-runoff model based on the watershed characteristics to estimate runoff at the watershed outlet. A conceptual (grey-box) model is usually a balance between the black-box and white-box model. In this study, three grey-box models were developed by varying model structures for a karst watershed. The performance of the grey-box models was evaluated and compared with a semi-distributed type (white-box) model that was developed using the Soil and Water Assessment Tool in a previous study. The evaluation was carried out using goodness-of-fit statistics and extreme flow analysis using WETSPRO (Water Engineering Time Series Processing tool). Nash-Sutcliffe efficiencies (NSE) of the grey-box models were from 0.39 to 0.77 in the calibration period and from 0.30 to 0.61 in the validation period. However, the white-box model performed better in terms of NSE but has a higher bias. The best grey-box model performed better in simulating extreme flow, whereas the white-box (SWAT) model adequately simulated daily flows.
Asian Australas. J. Biosci. Biotechnol. 2021, 6 (1), 26-39
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LEE, Seungkwang. "A White-Box Cryptographic Implementation for Protecting against Power Analysis." IEICE Transactions on Information and Systems E101.D, no. 1 (2018): 249–52. http://dx.doi.org/10.1587/transinf.2017edl8186.
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Grujicic, M., V. Chenna, R. Galgalikar, J. S. Snipes, S. Ramaswami, and R. Yavari. "Computational analysis of gear-box roller-bearing white-etch cracking." International Journal of Structural Integrity 5, no. 4 (November 11, 2014): 290–327. http://dx.doi.org/10.1108/ijsi-10-2013-0028.
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Purpose – A simple economic analysis has revealed that in order for wind energy to be a viable alternative, wind-turbines (convertors of wind energy into electrical energy) must be able to operate for at least 20 years, with only regular maintenance. However, wind-turbines built nowadays do not generally possess this level of reliability and durability. Specifically, due to the malfunction and failure of drive-trains/gear-boxes, many wind-turbines require major repairs after only three to five years in service. The paper aims to discuss these issues. Design/methodology/approach – The subject of the present work is the so-called white etch cracking, one of the key processes responsible for the premature failure of gear-box roller-bearings. To address this problem, a multi-physics computational methodology is developed and used to analyze the problem of wind-turbine gear-box roller-bearing premature-failure. The main components of the proposed methodology include the analyses of: first, hydrogen dissolution and the accompanying grain-boundary embrittlement phenomena; second, hydrogen diffusion from the crack-wake into the adjacent unfractured material; third, the inter-granular fracture processes; and fourth, the kinematic and structural response of the bearing under service-loading conditions. Findings – The results obtained clearly revealed the operation of the white-etch cracking phenomenon in wind-turbine gear-box roller-bearings and its dependence on the attendant loading and environmental conditions. Originality/value – The present work attempts to make a contribution to the resolution of an important problem related to premature-failure and inferior reliability of wind-turbine gearboxes.
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Zhang, Chao, and Yu Wang. "Research on the Structure of Authentication Protocol Analysis Based on MSCs/Promela." Advanced Materials Research 989-994 (July 2014): 4698–703. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.4698.
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To discover the existing or possibly existing vulnerability of present authentication protocol, we proposed a structure of authentication protocol analysis, which consist of white box analysis, black box analysis and indicator system as the three main functional components. White box analysis makes use of the transformation from MSCs (Message Sequence Charts) to Promela (Process Meta Language), the input language of the remarkable model checker SPIN; black box analysis is based on the attack platform of authentication protocol analysis; indicator system is decided by deducibility restraint methods. Compared with the UML modeling methods based on Promela, the MSC2Promela methods we figured out on the context have more advantages than its disadvantages. Finally, we proposed a structure of authentication protocol analysis based on the transformation from MSC2Promela, which makes sense to the later work on the area of authentication protocol analysis.
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KRISHNA MOHAN, K., A. K. VERMA, A. SRIVIDYA, and LJUBISA PAPIC. "INTEGRATION OF BLACK-BOX AND WHITE-BOX MODELING APPROACHES FOR SOFTWARE RELIABILITY ESTIMATION." International Journal of Reliability, Quality and Safety Engineering 17, no. 03 (June 2010): 261–73. http://dx.doi.org/10.1142/s0218539310003792.
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Quality of a software product should be tracked during the software lifecycle right from the architectural phase to its operational phase. Heterogeneous systems consist of several globally distributed components, thus rendering their reliability evaluation more complex with respect to the conventional methods. The objective of our work is to expand the evaluation process for effective reliability analysis by using both white box and black box approaches at prototype and at module/component level before the actual development. In this paper the, Black box testing is based on non-functional requirements for early quantitative analysis for the reliability estimation of the application development based on the output results of the prototype development. White box testing is based on inter-component interactions which deal with probabilistic software behavior. It uses an internal perspective of the system to design test cases based on internal structure at requirements and design phases. This paper has been applied for evolution of effective reliability quantification analysis at prototype level of a financial application case study with both non functional test data of software Development Life cycle (SDLC) phases captured from defect consolidation table in the form orthogonal defect classification as well functional requirements at requirement and design phases captured through software architectural modeling paradigms.
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Xie, Li, Jiang Yu, Shuang Li, and Sha Sha Diao. "Analysis of Volatile Organic Compounds in Disposable Food Packages at Three types of Temperature." Advanced Materials Research 476-478 (February 2012): 2625–29. http://dx.doi.org/10.4028/www.scientific.net/amr.476-478.2625.
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Eight volatile organic compounds (VOCs) in the four disposable food packages at refrigeration temperature, room temperature and heating temperature have been analyzed by a headspace gas chromatography and mass spectrometry method. The results of external standard method show that the amount of VOCs in the four disposable packages at different temperatures are arranged in the following order: black plastic lunch box, white plastic lunch box, paper cup, paper bowl. Under the three types of temperature for an hour, the amount of VOCs in the black plastic lunch box at heating temperatures is higher (1.2382 mg/m2at 90 °C), room temperature has smaller content (0.6682 mg/m2and 0.9105 mg/m2at 20 °C and 30 °C, repectively). Content of VOCs in the white plastic lunch box increases gradually with temperature rising.
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Pasaribu, Johni Setiady. "Perbandingan Pengujian Boundary Value Analysis, Equivalence Partitioning dan Error Guessy (Studi Kasus Indeks Nilai)." Jurnal ICT : Information Communication & Technology 20, no. 2 (December 15, 2021): 210–17. http://dx.doi.org/10.36054/jict-ikmi.v20i2.388.
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Software testing is a critical and time-consuming element of the software development life cycle (SDLC). Software testing determines the quality of a software before it is used by end users. The main goal is to find software errors or defects so that these errors or defects can be corrected in this testing phase. There are two general techniques for detecting software errors, namely: Black Box and White Box Testing. Black Box testing technique is used for validation, namely whether you have built the right software and White Box testing is used for verification, namely whether the software made is correct. This study aims to test the value index software using three methods of Black Box testing techniques, namely Equivalence partitioning (EP), Boundary Value Analysis (BVA) and Error Guessing. The testing process is carried out to find out how much error occurs in the value index software. The research method is qualitative descriptive and analytical. The results of this study indicate that the value index application has no shortcomings (error of 0%) both with BVA, EP and Error Guessing techniques.
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Bao, Tao, Yunhui Zheng, and Xiangyu Zhang. "White box sampling in uncertain data processing enabled by program analysis." ACM SIGPLAN Notices 47, no. 10 (November 15, 2012): 897–914. http://dx.doi.org/10.1145/2398857.2384681.
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Wang, Bowen. "Modelling and simulation of solar thermal power generation network." Thermal Science 25, no. 4 Part B (2021): 2905–12. http://dx.doi.org/10.2298/tsci2104905w.
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In the smart grid context, the article combines SEGS-VI solar thermal power station parameters to establish a solar thermal power generation system model. The thesis is based on the First and Second laws of thermodynamics. It uses the white box model analysis method of the energy system to calculate the solar thermal power generation system-concentrating and collecting subsystem, heat exchange subsystem, and power subsystem to obtain the subsystems dissipation of each process. Finally, the article uses the white box model analysis of the total energy system to treat the subsystems as white boxes, and connects them to form a white box network, makes a reasonable evaluation of the energy consumption status of the total energy system, and finds the weak links in the energy use process of the system. Provide a basis for system energy saving.
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Wang, Yanan, Xuebing Han, Languang Lu, Yangquan Chen, and Minggao Ouyang. "Sensitivity of Fractional-Order Recurrent Neural Network with Encoded Physics-Informed Battery Knowledge." Fractal and Fractional 6, no. 11 (November 2, 2022): 640. http://dx.doi.org/10.3390/fractalfract6110640.
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In the field of state estimation for the lithium-ion battery (LIB), model-based methods (white box) have been developed to explain battery mechanism and data-driven methods (black box) have been designed to learn battery statistics. Both white box methods and black box methods have drawn much attention recently. As the combination of white box and black box, physics-informed machine learning has been investigated by embedding physic laws. For LIB state estimation, this work proposes a fractional-order recurrent neural network (FORNN) encoded with physics-informed battery knowledge. Three aspects of FORNN can be improved by learning certain physics-informed knowledge. Firstly, the fractional-order state feedback is achieved by introducing a fractional-order derivative in a forward propagation process. Secondly, the fractional-order constraint is constructed by a voltage partial derivative equation (PDE) deduced from the battery fractional-order model (FOM). Thirdly, both the fractional-order gradient descent (FOGD) and fractional-order gradient descent with momentum (FOGDm) methods are proposed by introducing a fractional-order gradient in the backpropagation process. For the proposed FORNN, the sensitivity of the added fractional-order parameters are analyzed by experiments under the federal urban driving schedule (FUDS) operation conditions. The experiment results demonstrate that a certain range of every fractional-order parameter can achieve better convergence speed and higher estimation accuracy. On the basis of the sensitivity analysis, the fractional-order parameter tuning rules have been concluded and listed in the discussion part to provide useful references to the parameter tuning of the proposed algorithm.
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Kong, Xian Chao, Jia Liu, Min Xu, and Jian Li. "The Impact of Growing Environment on the Organ Development of White Mice." Advanced Materials Research 183-185 (January 2011): 176–79. http://dx.doi.org/10.4028/www.scientific.net/amr.183-185.176.
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This essay has applied 6 kinds of different materials, studied the impact of white mice’s growth and their organ development in the feeding boxes which are made of 6 kinds of different materials, such as red-pine plain board, red-pine painted board, veneer particle board, ordinary particle board, formaldehyde painted particle board, and plastic. Discover the indoor microenvironment to be fit for the growth of white mice through dissection, determination and comparative analysis of organ weights. The result indicates that white mice are living in the feeding box made of red-pine plain board whose cardiac index is the highest, the index is in the middle for the one living in the veneer particle board and plastic feeding box, the index is lower for the one living in the red-pine box with painted alkyd varnish and ordinary particle board, and the lowest one is living in the particle board box brushed with formaldehyde; The measured value of white mice’s lungs is highest in the red-pine box. It takes second place in the veneer particle board box, others are in the middle, and the lowest one is in the box brushed with formaldehyde; white mice’s liver index is highest in the red-pine box with painted alkyd varnish, it takes second place in red-pine box without painting, and it is the lowest in the ordinary particle board box and particle board box brushed with formaldehyde; white mice’s thymus index is the highest in the plastic box, it is relatively higher in red-pine box and the red-pine box painted with alkyd varnish, and it is the lowest in the particle board box brushed with formaldehyde; white mice’s kidney index (two kidneys) is the highest in the red-pine box with painted alkyd varnish, and it is the lowest in the particle board box brushed with formaldehyde. This shows that wooden environment has the positive effect on feeding white mice. Rooms in homes are the major place for Man’s living and working. Human beings could spend 2/3 of the whole life in the indoor environment. Along with the social progress, economic development, enhanced living standard, and constantly pursuing the quality of life, people are more and more concerned with the structure of living and working space. Rooms are not merely a shelter from the storm, and the more important is to provide a better working and learning environment as well as a comfortable rest place. Therefore, the quality of the indoor environment is influencing Man’s work and study directly. It concerns Man’s survival and health. Research of the relationships between indoor environment and Man’s survival and health, especially studying the impacts of indoor environment on health of growth and breeding, is increasingly becoming a topic not to be ignored [1].
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Boodi, Abhinandana, Karim Beddiar, Yassine Amirat, and Mohamed Benbouzid. "Building Thermal-Network Models: A Comparative Analysis, Recommendations, and Perspectives." Energies 15, no. 4 (February 11, 2022): 1328. http://dx.doi.org/10.3390/en15041328.
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The development of smart buildings, as well as the great need for energy demand reduction, has renewed interest in building energy demand prediction. Intelligent controllers are a solution for optimizing building energy consumption while maintaining indoor comfort. The controller efficiency on the other hand, is mainly determined by the prediction of thermal behavior from building models. Due to the development complexity of the models, these intelligent controllers are not yet implemented on an industrial scale. There are primarily three types of building models studied in the literature: white-box, black-box, and gray-box. The gray-box models are found to be robust, efficient, of low cost computationally, and of moderate modeling complexity. Furthermore, there is no standard model configuration, development method, or operation conditions. These parameters have a significant influence on the model performance accuracy. This motivates the need for this review paper, in which we examined various gray-box models, their configurations, parametric identification techniques, and influential parameters.
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Morales, Manuel D., Javier M. Antelis, Claudia Moreno, and Alexander I. Nesterov. "Deep Learning for Gravitational-Wave Data Analysis: A Resampling White-Box Approach." Sensors 21, no. 9 (May 3, 2021): 3174. http://dx.doi.org/10.3390/s21093174.
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In this work, we apply Convolutional Neural Networks (CNNs) to detect gravitational wave (GW) signals of compact binary coalescences, using single-interferometer data from real LIGO detectors. Here, we adopted a resampling white-box approach to advance towards a statistical understanding of uncertainties intrinsic to CNNs in GW data analysis. We used Morlet wavelets to convert strain time series to time-frequency images. Moreover, we only worked with data of non-Gaussian noise and hardware injections, removing freedom to set signal-to-noise ratio (SNR) values in GW templates by hand, in order to reproduce more realistic experimental conditions. After hyperparameter adjustments, we found that resampling through repeated k-fold cross-validation smooths the stochasticity of mini-batch stochastic gradient descent present in accuracy perturbations by a factor of 3.6. CNNs are quite precise to detect noise, 0.952 for H1 data and 0.932 for L1 data; but, not sensitive enough to recall GW signals, 0.858 for H1 data and 0.768 for L1 data—although recall values are dependent on expected SNR. Our predictions are transparently understood by exploring tthe distribution of probabilistic scores outputted by the softmax layer, and they are strengthened by a receiving operating characteristic analysis and a paired-sample t-test to compare with a random classifier.
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Lee, Seungkwang, Taesung Kim, and Yousung Kang. "A Masked White-Box Cryptographic Implementation for Protecting Against Differential Computation Analysis." IEEE Transactions on Information Forensics and Security 13, no. 10 (October 2018): 2602–15. http://dx.doi.org/10.1109/tifs.2018.2825939.
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Juhász, László, and Irén Juhász Junger. "Spectral Analysis and Parameter Identification of Textile-Based Dye-Sensitized Solar Cells." Materials 11, no. 9 (September 5, 2018): 1623. http://dx.doi.org/10.3390/ma11091623.
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Linearized equivalent electrical-circuit representation of dye-sensitized solar cells is helpful both for the better understanding of the physical processes in the cell as well as for various optimizations of the cells. White-box and grey-box modelling approaches are well-known and they are widely used for standard cell types. However, in the case of new cell types or the lack of deep knowledge of the cell’s physic such approaches may not be applicable immediately. In this article a black-box approach for such cases is presented applied together with spectral analysis. The spectral analysis and the black-box approach were as first validated with a standard glass-based dye-sensitized solar cell and thereafter applied for the characterization of a new type of textile-based dye-sensitized solar cells. Although there are still improvement potentials, the results are encouraging and the authors believe that the black-box method with spectral analysis may be used particularly for new types of dye-sensitized solar cells.
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F. Ayala, Néstor, Paolo Gaiardelli, Giuditta Pezzotta, Marie Anne Le Dain, and Alejandro G. Frank. "Adopting service suppliers for servitisation: which type of supplier involvement is more effective?" Journal of Manufacturing Technology Management 32, no. 5 (February 25, 2021): 977–93. http://dx.doi.org/10.1108/jmtm-09-2020-0374.
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PurposeThe purpose of this study is to analyse the effect of different forms of service supplier involvement on the service business dimensions necessary for servitisation and on the resulting servitisation performance.Design/methodology/approachThree different configurations of service supplier involvement are considered in this study: black box (service design and execution driven by the service supplier), grey box (joint service design) and white box (service design driven by the product firm). The study analyses their contribution by means of a cross-sectional quantitative survey with 104 Brazilian and Italian firms using multivariate analysis of variance.FindingsCompanies that adopted the grey box configuration presented the best results in servitisation. White and black box may offer different benefits depending on the service business dimension that the company chooses to emphasise.Originality/valueThe results show which type of service supplier involvement is more effective for servitisation. The empirical data demonstrate that a joint service design (grey box involvement) is the best approach, but the paper discusses limitations for its implementation and alternatives regarding the two other types of service supplier involvement. The findings contribute to the discussion on the role of service suppliers in servitisation and provide empirical evidence to support operations managers in deciding on how to organise their service supply chain when aiming for servitisation.
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Zhu, Kang, Biao Deng, Pengxiang Zhang, Hee Seok Kim, Peng Jiang, and Weishu Liu. "System efficiency and power: the bridge between the device and system of a thermoelectric power generator." Energy & Environmental Science 13, no. 10 (2020): 3514–26. http://dx.doi.org/10.1039/d0ee01640c.
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Maryenko, Nataliia I., and Oleksandr Yu Stepanenko. "COMPARATIVE ANALYSIS OF FRACTAL DIMENSIONS OF HUMAN CEREBELLUM: IMPACT OF IMAGE PREPROCESSING AND FRACTAL ANALYSIS METHODS." Wiadomości Lekarskie 75, no. 2 (2022): 438–43. http://dx.doi.org/10.36740/wlek202202120.
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The aim: To compare the values of the fractal dimensions of human cerebellum obtained using different algorithms of image preprocessing and different methods of fractal analysis. Materials and methods: The study involved 120 people without structural changes in the brain (age 18-86 years, 55 men and 65 women). T1- and T2-weighted MR brain images were studied. Fractal analysis was performed using box counting and pixel dilatation methods. Fractal dimensions of cerebellar tissue as a whole, cerebellar cortex and its individual layers, cerebellar white matter were measured and compared to each other and to fractal dimension of cerebellar white matter determined in cadaveric cerebella. Results: It was no significant difference between fractal dimension values of cerebellar tissue as a whole measured on T1 and T2 weighted magnetic resonance images of cerebellum, and fractal dimension values measured on the same images using different methods of fractal analysis – pixel dilatation and box counting. T2 weighted images are preferable for fractal analysis of different components of cerebellar tissue. Segmentation according to pixel luminance is the preferable image preprocessing method for fractal analysis of cerebellar cortex as a whole, individual cortical layers and cerebellar tissue as a whole; skeletonizing of cerebellar magnetic resonance images is the preferable method of the image preprocessing for fractal analysis of cerebellar white matter. Conclusions: The algorithm of image preprocessing, magnetic resonance imaging sequence and method of fractal analysis should be chosen according to aim of quantitative study of cerebellar magnetic resonance images and features of the studied structure of cerebellum.
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Pramudita, Rully. "Pengujian Black Box pada Aplikasi Ecampus Menggunakan Metode Equivalence Partitioning." INFORMATICS FOR EDUCATORS AND PROFESSIONAL : Journal of Informatics 4, no. 2 (June 2, 2020): 193. http://dx.doi.org/10.51211/itbi.v4i2.1347.
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Salah satu tahapan penting dalam pengembangan sebuah perangkat lunak yaitu tahap pengujian. Teknik testing dibagi menjadi dua yaitu black box testing dan white box testing. Pada penelitian akan menerapkan teknik pengujian black box. Teknik pengujian black box terdiri dari, Equivalence Partitioning, Boundary Value Analysis / Limit Testing, Comparison Testing, Sample Testing, Robustness Testing, Behavior Testing, Requirement Testing, Performance Testing, Endurance Testing, Cause-Effect Relationship Testing. Salah satu yang akan diterapkan dalam penelitian ini yaitu teknik equivalence partitioning. Teknik ini ujicoba yang dilakukan berdasarkan tujuh tahapan, yaitu menentukan use case yang diuji, tentukan kriteria, definisikan partisi, buat data uji, buat kasus uji, lakukan pengujian dan evaluasi. Hasil pengujian nantinya akan menghasilkan feedback untuk masukan bagi pengelola ecampus.
Kata kunci: Aplikasi, Black box, Ecampus, Ekuivalence partitioning, Pengujian
Abstract: One important stage in the development of a software is the testing phase. Testing techniques are divided into two namely black box testing and white box testing. In the study will apply black box testing techniques. Black box testing techniques consist of, Equivalence Partitioning, Boundary Value Analysis / Limit Testing, Comparison Testing, Sample Testing, Robustness Testing, Behavior Testing, Requirement Testing, Performance Testing, Endurance Testing, Cause-Effect Relationship Testing. One that will be applied in this research is equivalence partitioning technique. This technique is based on seven stages of testing, namely determining the tested use case, determining criteria, defining partitions, creating test data, making test cases, testing and evaluating. The test results will later produce feedback for input for ecampus managers.
Keywords: Application, Black box, Ecampus, Ekuivalence partitioning, Testing
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Rácz, Levente, Bálint Németh, Gábor Göcsei, Dimitar Zarchev, and Valeri Mladenov. "Performance Analysis of a Dynamic Line Rating System Based on Project Experiences." Energies 15, no. 3 (January 29, 2022): 1003. http://dx.doi.org/10.3390/en15031003.
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This paper aims to demonstrate the performance and reliability analysis of a dynamic line rating (DLR) system at the Bulgarian demonstration site of the FLEXITRANSTORE project. As part of the project, various manufacturers’ different line monitoring DLR sensors and weather stations were installed on a 110 kV double-circuit overhead line (OHL). These devices provided input parameters to the DLR system based on objective measurements. This paper used statistical tools to examine the reliability and accuracy of installed devices, thus making products from different manufacturers comparable. In addition, two independent line monitoring and DLR models have been developed: the black-box and extended white-box models. The performances of the two models were analyzed for the same input parameters and compared to the field measurements. Based on the presented results, the reliability and accuracy of the applied weather stations of different companies were almost the same. This conclusion cannot be said for DLR line monitoring sensors, where the devices could be differentiated based on reliability and measurement accuracy results. In terms of models, the usability of the extended white-box model seemed to be limited in certain weather conditions, implicating a more significant role for soft-computing-based DLR models in the future. In addition to the results, root causes for the errors and future directions that may provide a framework for further research are also presented.
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Mateo Tudela, Francesc, Juan-Ramón Bermejo Higuera, Javier Bermejo Higuera, Juan-Antonio Sicilia Montalvo, and Michael I. Argyros. "On Combining Static, Dynamic and Interactive Analysis Security Testing Tools to Improve OWASP Top Ten Security Vulnerability Detection in Web Applications." Applied Sciences 10, no. 24 (December 20, 2020): 9119. http://dx.doi.org/10.3390/app10249119.
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The design of the techniques and algorithms used by the static, dynamic and interactive security testing tools differ. Therefore, each tool detects to a greater or lesser extent each type of vulnerability for which they are designed for. In addition, their different designs mean that they have different percentages of false positives. In order to take advantage of the possible synergies that different analysis tools types may have, this paper combines several static, dynamic and interactive analysis security testing tools—static white box security analysis (SAST), dynamic black box security analysis (DAST) and interactive white box security analysis (IAST), respectively. The aim is to investigate how to improve the effectiveness of security vulnerability detection while reducing the number of false positives. Specifically, two static, two dynamic and two interactive security analysis tools will be combined to study their behavior using a specific benchmark for OWASP Top Ten security vulnerabilities and taking into account various scenarios of different criticality in terms of the applications analyzed. Finally, this study analyzes and discuss the values of the selected metrics applied to the results for each n-tools combination.
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Park, Soojin. "Software Requirement Specification Based on a Gray Box for Embedded Systems: A Case Study of a Mobile Phone Camera Sensor Controller." Computers 8, no. 1 (March 2, 2019): 20. http://dx.doi.org/10.3390/computers8010020.
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One of the most widely used models for specifying functional requirements is a use case model. The viewpoint of the use case model that views a system as a black box focuses on descriptions of external interactions between the system and related environments. However, for embedded systems that do not disclose most implementation logics outside the system, black box-based use case models may experience the drawback that considerable information that must be defined for system developments is omitted. To solve this shortcoming, several studies have been proposed on the use of kind of white box technique in which the dynamic behaviors of embedded systems are defined first using a state diagram and the results are reflected in the requirement specifications. However, white box-based modeling has not been widely adopted by developers due to tasks that require a lot of time in the requirement analysis phase in the initial phase of the software development life cycle. This study proposes a gray box-based requirement specification method as a trade-off between two contradictory elements (the amount of information required to develop an embedded system and the cost of the effort required during the requirement analysis phase) in terms of the two approaches, the black and the white box-based models. The proposed method suggests that an appropriate depth level of embedded system modeling is required to define the requirements. This study also proposes a mechanism that automatically generates an application programming interface for each component based on the created model. The proposed method was applied to the development of a camera sensor controller in a mobile phone, and the case results proved the feasibility of the method through discussion of the application results.
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Melkamu Bayabl, Agernew, and A. Balasuadhakar. "Experimental Comparison Analysis of White Coated and Black Coated Inside Box Type Solar Cooker." International Journal of Advances in Scientific Research and Engineering 5, no. 5 (2019): 260–65. http://dx.doi.org/10.31695/ijasre.2019.33226.
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Chen, Bernard, Valentin Velchev, James Palmer, and Travis Atkison. "Wineinformatics: A Quantitative Analysis of Wine Reviewers." Fermentation 4, no. 4 (September 25, 2018): 82. http://dx.doi.org/10.3390/fermentation4040082.
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Data Science is a successful study that incorporates varying techniques and theories from distinct fields including Mathematics, Computer Science, Economics, Business and domain knowledge. Among all components in data science, domain knowledge is the key to create high quality data products by data scientists. Wineinformatics is a new data science application that uses wine as the domain knowledge and incorporates data science and wine related datasets, including physicochemical laboratory data and wine reviews. This paper produces a brand-new dataset that contains more than 100,000 wine reviews made available by the Computational Wine Wheel. This dataset is then used to quantitatively evaluate the consistency of the Wine Spectator and all of its major reviewers through both white-box and black-box classification algorithms. Wine Spectator reviewers receive more than 87% accuracy when evaluated with the SVM method. This result supports Wine Spectator’s prestigious standing in the wine industry.
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Jia, Yong Ying, and Zhi Guo Wang. "The Study of Coupled Transfer of Heat, Moisture and Air in Reservoir Porous Media." Applied Mechanics and Materials 271-272 (December 2012): 1195–200. http://dx.doi.org/10.4028/www.scientific.net/amm.271-272.1195.
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This paper studied on heat and mass transfer mechanism of steam in reservoir porous media, built coupled mathematical models for heat and mass transfer in reservoir porous media. “Black box model” and “white box model” are first proposed in heat and mass process analysis of reservoir porous media. Temperature field has been simulated by CMG-STARS software. Our work will give theoretical basis and technical support for enhanced oil recovery technology.
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Koga, Kazuki, and Kazuhiro Takemoto. "Simple Black-Box Universal Adversarial Attacks on Deep Neural Networks for Medical Image Classification." Algorithms 15, no. 5 (April 22, 2022): 144. http://dx.doi.org/10.3390/a15050144.
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Universal adversarial attacks, which hinder most deep neural network (DNN) tasks using only a single perturbation called universal adversarial perturbation (UAP), are a realistic security threat to the practical application of a DNN for medical imaging. Given that computer-based systems are generally operated under a black-box condition in which only input queries are allowed and outputs are accessible, the impact of UAPs seems to be limited because well-used algorithms for generating UAPs are limited to white-box conditions in which adversaries can access model parameters. Nevertheless, we propose a method for generating UAPs using a simple hill-climbing search based only on DNN outputs to demonstrate that UAPs are easily generatable using a relatively small dataset under black-box conditions with representative DNN-based medical image classifications. Black-box UAPs can be used to conduct both nontargeted and targeted attacks. Overall, the black-box UAPs showed high attack success rates (40–90%). The vulnerability of the black-box UAPs was observed in several model architectures. The results indicate that adversaries can also generate UAPs through a simple procedure under the black-box condition to foil or control diagnostic medical imaging systems based on DNNs, and that UAPs are a more serious security threat.
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Prince Benard Ikpabi and Oluwatoyin Olakunle Akinsete. "Correlation for predicting bubble point pressure for 22.3≤°API≥45 crude oils: A white-box machine learning approach." International Journal of Frontiers in Engineering and Technology Research 3, no. 2 (November 30, 2022): 015–27. http://dx.doi.org/10.53294/ijfetr.2022.3.2.0043.
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Bubble point pressure (BPP) is a key parameter for oil and gas reservoir identification, characterization, and management. An accurate correlation of this property with the evolving digital technology of machine learning, in the absence of experimental PVT analysis, serves as guidance in the development and recovery of reservoir fluids. In this study, a predictive BPP correlation was derived by intrinsically linearizing a nonlinear multiple regression, with the best coefficients (global minimum) extracted using White-box (Linear Regression, Ridge Regression, and Lasso Regression) Machine Learning models. The new correlation was developed, validated, and tested using 314 measured PVT data points from the Niger Delta Region. The data were subdivided into four classes: extra-light crude for API > 45, light crude for 31.1 < API ≤ 45, medium crude for 22.3 < API ≤ 31.1, and heavy crude for API ≤ 22.3. Statistical evaluation metrics such as root mean squared error, average absolute relative error, and average relative error were employed to compare the performance of the new correlation with the existing empirical ones. Results showed that the new BPP correlation developed by White-box Linear Regression outperformed the other White box (Ridge Regression and Lasso Regression) and other existing BPP empirical models. Taking advantage of emerging data-driven and machine learning as BPP predictive model is effective and efficient in reservoir fluids analysis.
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Zhao, Yin-He, Xue-Mei Zhang, and De-Zhu Li. "Development of the petaloid bracts of a paleoherb species, Saururus chinensis." PLOS ONE 16, no. 9 (September 2, 2021): e0255679. http://dx.doi.org/10.1371/journal.pone.0255679.
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Saururus chinensis is a core member of Saururaceae, an ancient, perianthless (lacking petals or sepals) family of the magnoliids in the Mesangiospermae, which is important for understanding the origin and evolution of early flowers due to its unusual floral composition and petaloid bracts. To compare their transcriptomes, RNA-seq abundance analysis identified 43,463 genes that were found to be differentially expressed in S. chinensis bracts. Of these, 5,797 showed significant differential expression, of which 1,770 were up-regulated and 4,027 down-regulated in green compared to white bracts. The expression profiles were also compared using cDNA microarrays, which identified 166 additional differentially expressed genes. Subsequently, qRT-PCR was used to verify and extend the cDNA microarray results, showing that the A and B class MADS-box genes were up-regulated in the white bracts. Phylogenetic analysis was performed on putative S. chinensis A and B-class of MADS-box genes to infer evolutionary relationships within the A and B-class of MADS-box gene family. In addition, nature selection and protein interactions of B class MADS-box proteins were inferred that B-class genes free from evolutionary pressures. The results indicate that petaloid bracts display anatomical and gene expression features normally associated with petals, as found in petaloid bracts of other species, and support an evolutionarily conserved developmental program for petaloid bracts.
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Petrick, Elizabeth R. "Building the Black Box: Cyberneticians and Complex Systems." Science, Technology, & Human Values 45, no. 4 (October 10, 2019): 575–95. http://dx.doi.org/10.1177/0162243919881212.
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In the 1950s and 1960s, cyberneticians defined and utilized a concept previously described by electronic engineers: the black box. They were interested in how it might aid them, as both a metaphor and as a physical or mathematical model, in their analysis of complex human-machine systems. The black box evolved as they applied it in new ways, across a range of scientific fields, from an unnamed concept involving inputs and outputs, to digital representations of the human brain, to white boxes that might be used to replicate black boxes. The diversity of understandings of the black box reflected the diversity of scientific perspectives and goals brought under the label of cybernetics. In this paper, I examine how cyberneticians drew upon the black box in their personal writings and publications. My goal is to unpack what the black box meant to these theorists as a starting framework from which we may understand the initial shape of the black box.
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Alharbi, Basma, Zhenwen Liang, Jana M. Aljindan, Ammar K. Agnia, and Xiangliang Zhang. "Explainable and Interpretable Anomaly Detection Models for Production Data." SPE Journal 27, no. 01 (November 30, 2021): 349–63. http://dx.doi.org/10.2118/208586-pa.
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Summary Trusting a machine-learning model is a critical factor that will speed the spread of the fourth industrial revolution. Trust can be achieved by understanding how a model is making decisions. For white-box models, it is easy to “see” the model and examine its prediction. For black-box models, the explanation of the decision process is not straightforward. In this work, we compare the performance of several white- and black-box models on two production data sets in an anomaly detection task. The presence of anomalies in production data can significantly influence business decisions and misrepresent the results of the analysis, if not identified. Therefore, identifying anomalies is a crucial and necessary step to maintain safety and ensure that the wells perform at full capacity. To achieve this, we compare the performance of K-nearest neighbor (KNN), logistic regression (Logit), support vector machines (SVMs), decision tree (DT), random forest (RF), and rule fit classifier (RFC). F1 and complexity are the two main metrics used to compare the prediction performance and interpretability of these models. In one data set, RFC outperformed the remaining models in both F1 and complexity, where F1 = 0.92, and complexity = 0.5. In the second data set, RF outperformed the rest in prediction performance with F1 = 0.84, yet it had the lowest complexity metric (0.04). We further analyzed the best performing models by explaining their predictions using local interpretable model-agnostic explanations, which provide justification for decisions made for each instance. Additionally, we evaluated the global rules learned from white-box models. Local and global analysis enable decision makers to understand how and why models are making certain decisions, which in turn allows trusting the models.
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NYIKOS, LAJOS, LÁSZLÓ BALÁZS, and ROBERT SCHILLER. "FRACTAL ANALYSIS OF ARTISTIC IMAGES: FROM CUBISM TO FRACTALISM." Fractals 02, no. 01 (March 1994): 143–52. http://dx.doi.org/10.1142/s0218348x94000144.
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Black-and-white graphic images of great artists: Durer, Rembrandt, Picasso and Munch were analyzed in terms of fractal geometry. The pictures were digitized and the spatial distributions of the resulting ensembles of black and white pixels were characterized by their: (a) box dimension Db, (b) information dimension Di, and (c) mass exponent Dm. Whereas Db and Di were seen to coincide, Dm was usually found to be somewhat higher. Plates, which can best be described as line drawings, have D = 1 at small lengths, and D = 2 at length scales commensurate with that of the plate, with a fairly narrow crossover region. Some textured images, however, are of definite fractal character: their dimensions are well-defined non-integer constants over more than two orders of magnitude of length. At present the authors refrain from discussing the role of dilatation symmetry in graphic art.
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Tarr, Eric. "Digital models of analog circuits for musical audio production: A review of techniques and library for automated circuit solving." Journal of the Acoustical Society of America 152, no. 4 (October 2022): A179. http://dx.doi.org/10.1121/10.0015958.
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In music production, many recording and mixing engineers prefer to use analog equipment as a matter of perceptual preference. Digital models of analog circuits have the potential to achieve similar perceptual qualities as hardware without the drawbacks of cost, maintenance, and availability. Many different techniques of system modeling are used in music production software, ranging on a spectrum from “black-box modeling” to “white-box modeling.” In black-box modeling, the analog system is modeled as a processing block which maps an input signal to an output signal. Examples include the linear impulse response, adaptive filters, Volterra series, and Weiner-Hammerstein models. In white-box modeling, each individual component of the analog circuit is modeled as part of the overall system. Examples include wave digital filters, state-space modeling, and modified nodal analysis. Various other techniques exist on the spectrum between these two types, using some combination of each. One example is Virtual Analog Filtering based on the Topology Preserving Transform. Machine Learning techniques have also had an important role in advancing the accuracy of digital modeling. Lastly, the “Point to Point Library,” developed by the author, will be demonstrated. This MATLAB and C + + library performs automated circuit solving for modeling audio effects.
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Asri, M. N. A. M., N. A. Z. Abdullah, and Mohd Shahrir Mohd Sani. "The effect of modal properties of crash box structures with trigger mechanisms towards the crashworthiness by using finite element analysis." Journal of Mechanical Engineering and Sciences 15, no. 3 (September 23, 2021): 8459–68. http://dx.doi.org/10.15282/jmes.15.3.2021.22.0666.
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In the automotive structure, there are different components that utilise aluminium alloy (AA) sheets and it is used widely in the car body-in-white which comprise bumpers and the crash box structure at the front end of the car which specifically designed to withstand the event of collision. As the structures are also experiencing dynamic loading, it were also a concern for the structures to show satisfied modal properties. In this study, the modal properties of the crash box structures are investigated along with the effect of the modal properties towards the crashworthiness behaviour of the structure itself with the approach of finite element analysis. Experimental modal analysis was also done to further validating the finite element analysis of the modal properties. Three different designs of trigger mechanisms are applied towards the crash box structure to observe on both findings. For the connector element, equivalent nodes of both parts of the crash box structures are used. For the results, the correlation from both findings did show that the presence of trigger mechanism did decreased the magnitude of natural frequencies as well as the mode shape as shown by crash box type 3 by 9.50% and for the crashworthiness output, the crashworthiness behaviour of the crash box with trigger mechanisms were better in term of the collisions phases indicated by the primary peak force and the secondary peak force from the force-displacement curve as also shown by crash box structure type 3 with the percentage of 22.59%. The study does shows that the stiffness and mass distribution due to the presence of trigger mechanism do affect the modal properties of a structure as well as its crashworthiness output.
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Jin, Hui, and Jong-Han Lee. "Analysis and research of The Chinese animation film 〈White Snake(2019)〉 the box office of investment." Cartoon and Animation Studies 55 (June 30, 2019): 59–81. http://dx.doi.org/10.7230/koscas.2019.55.059.
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Souza, Victor L. F., Adriano L. I. Oliveira, Rafael M. O. Cruz, and Robert Sabourin. "A white-box analysis on the writer-independent dichotomy transformation applied to offline handwritten signature verification." Expert Systems with Applications 154 (September 2020): 113397. http://dx.doi.org/10.1016/j.eswa.2020.113397.
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Cheng, Yong Chun, Yu Ping Shi, and Guo Jin Tan. "Research on Temperature Effect on the Natural Frequencies of Continuous Beams Based on Stochastic Subspace Identification." Advanced Materials Research 706-708 (June 2013): 1545–48. http://dx.doi.org/10.4028/www.scientific.net/amr.706-708.1545.
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Natural frequencies are of great value to bridge structural design, health monitoring and detection. Related research data show that the ambient temperature can affect the natural frequencies of the continuous box-girder bridges. In order to research the effect of temperature on the bridge structure and conclude the influence law, theoretical analysis of temperature effect on the natural frequencies of the continuous box girder bridges is conducted based on the stochastic subspace identification. First, the finite element model of the bridge is built to conduct thermal-structural coupling analysis. Then regard the analysis results as the original state, and exert white noise excitation on the structure to obtain the dynamic response of the structure. And then analyze the dynamic response based on the stochastic subspace identification and calculate the natural frequencies of the bridges under the temperature effect. At last, based on the practical project of one 3-span continuous box-girder bridge, the validity and the reliability of this method is verified.
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Chen, Jian, Zhi Guo Wang, Lei Zhang, and Wen Zhe Yang. "The Analysis Model of Coupling Heat Flux in Heavy Oil Reservoir and its Application." Advanced Materials Research 594-597 (November 2012): 2425–29. http://dx.doi.org/10.4028/www.scientific.net/amr.594-597.2425.
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There are heat-fluid-solid multi-field coupling in the thermal recovery reservoir. At present, the researches on oil reservoir porous media coupling are mostly on fluid-solid. According to the oil reservoir characteristics, this paper puts forward the “white box” analysis model on the heat and mass transfer processes in thermal recovery reservoir. On this basis, the analysis and stimulation of the various porosity effects on heat and mass transfer processes use CMG software. This result can provide theoretical basis for enhancing oil/gas recovery.
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Rose, K. A., J. K. Summers, R. A. Cummins, and D. G. Heimbuch. "Analysis of Long-Term Ecological Data Using Categorical Time Series Regression." Canadian Journal of Fisheries and Aquatic Sciences 43, no. 12 (December 1, 1986): 2418–26. http://dx.doi.org/10.1139/f86-300.
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We propose a time series analysis method based on the use of categorized variables and ordinary least squares regression. It has several advantages over Box–Jenkins models and time series regression with continuous variables, including model specification based on ecological information, parsimonious representations of the functional forms of model terms and interactions, robust treatment of the high uncertainty associated with long-term ecological data, and interpretive features based on linear combinations of the regression coefficients. Aspects of model building, significance testing, and interpretation of results are discussed and illustrated with a fisheries example involving an annual measure of white perch (Morone americana) stock size in the Delaware River/Bay from 1929 to 1974. Variation in white perch dynamics is analyzed using the following explanatory variables: lagged values of stock, hydrographic variables (freshwater flow and water temperature), and pollution-related variables (sewage loading, dredging activity, and dissolved oxygen). Potential statistical problems with the new method involving multicollinearity, autocorrelated errors, and other violations of ordinary least squares are identified.
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Huang, Yu-Mei, Hui-Yin Yan, and Tieyong Zeng. "Multiplicative Noise Removal Based on Unbiased Box-Cox Transformation." Communications in Computational Physics 22, no. 3 (July 6, 2017): 803–28. http://dx.doi.org/10.4208/cicp.oa-2016-0074.
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AbstractMultiplicative noise removal is a challenging problem in image restoration. In this paper, by applying Box-Cox transformation, we convert the multiplicative noise removal problem into the additive noise removal problem and the block matching three dimensional (BM3D) method is applied to get the final recovered image. Indeed, BM3D is an effective method to remove additive Gaussian white noise in images. A maximum likelihood method is designed to determine the parameter in the Box-Cox transformation. We also present the unbiased inverse transform for the Box-Cox transformation which is important. Both theoretical analysis and experimental results illustrate clearly that the proposed method can remove multiplicative noise very well especially when multiplicative noise is heavy. The proposed method is superior to the existing methods for multiplicative noise removal in the literature.
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Das, Debayan, and Shreyas Sen. "Electromagnetic and Power Side-Channel Analysis: Advanced Attacks and Low-Overhead Generic Countermeasures through White-Box Approach." Cryptography 4, no. 4 (October 31, 2020): 30. http://dx.doi.org/10.3390/cryptography4040030.
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Electromagnetic and power side-channel analysis (SCA) provides attackers a prominent tool to extract the secret key from the cryptographic engine. In this article, we present our cross-device deep learning (DL)-based side-channel attack (X-DeepSCA) which reduces the time to attack on embedded devices, thereby increasing the threat surface significantly. Consequently, with the knowledge of such advanced attacks, we performed a ground-up white-box analysis of the crypto IC to root-cause the source of the electromagnetic (EM) side-channel leakage. Equipped with the understanding that the higher-level metals significantly contribute to the EM leakage, we present STELLAR, which proposes to route the crypto core within the lower metals and then embed it within a current-domain signature attenuation (CDSA) hardware to ensure that the critical correlated signature gets suppressed before it reaches the top-level metal layers. CDSA-AES256 with local lower metal routing was fabricated in a TSMC 65 nm process and evaluated against different profiled and non-profiled attacks, showing protection beyond 1B encryptions, compared to ∼10K for the unprotected AES. Overall, the presented countermeasure achieved a 100× improvement over the state-of-the-art countermeasures available, with comparable power/area overheads and without any performance degradation. Moreover, it is a generic countermeasure and can be used to protect any crypto cores while preserving the legacy of the existing implementations.
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Wang, Li-Jun, Yang-Zhou Lai, and Zhi-Wei Wang. "Fatigue failure and Grms–N curve of corrugated paperboard box." Journal of Vibration and Control 26, no. 11-12 (January 13, 2020): 1028–41. http://dx.doi.org/10.1177/1077546319891322.
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As a kind of transport packaging, the corrugated paperboard box is widely used in logistics. During transport, the stacked packaging units are subjected to random vibration induced by vehicles. Thus, the random dynamic force will be exerted to corrugated paperboard box continuously. As the shipping distance increases, there will be cumulative fatigue damage within the corrugated paperboard box, which will weaken the mechanical property (stiffness) and the protective value of the corrugated paperboard box. Therefore, the dynamic damage and fatigue failure of corrugated paperboard box are crucial for the design of corrugated paperboard box. This article aims to develop the acceleration root mean square-life [Formula: see text] curve of corrugated paperboard box. At first, the accelerated random vibration test method was developed by the theory analysis according to the damage equivalent principle of a specified point within the corrugated paperboard box. Then, the random vibration experiments were conducted on the loaded corrugated paperboard box by taking white noise as excitation power spectral density. In the experiments, the acceleration transmissibility curve was recorded periodically. Relative stiffness was taken as the indicator to detect the cumulative damage and assess the structural integrity of the corrugated paperboard box. The stiffness reduction 20%, 30%, and 40% were taken as fatigue failure criteria to develop the [Formula: see text] curves. Results show that both the Basquin type and exponential function type are in good fitting with the [Formula: see text] curve of the corrugated paperboard box. The index b of the Basquin type is between 8.16 and 11.01, and b′ of the exponential function type between 21.19 and 26.84. The study provides reference for the accelerated random vibration test of corrugated paperboard box.
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Wang, Xisen. "White Noise Tests on the LSTM Model Trained with Double Pendulum." Journal of Physics: Conference Series 2137, no. 1 (December 1, 2021): 012032. http://dx.doi.org/10.1088/1742-6596/2137/1/012032.
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Abstract This paper describes the intrinsic qualities of a simple double pendulum (DP), with a visual representation, a rigorous deduction of the Lagrangian equation, and a concrete factor analysis. LSTM model was utilized to simulate the double pendulum’s periodic and chaotic behaviors and evaluates the effectiveness of the model. The auto-correlation coefficients was calculated. Meanwhile, Box-Pierce test and Ljung-Box tests for various state-dependent time series were conducted to give various initial conditions to explore the DP system’s random characteristics. The research results are as follows: 1) Chaos did not lead to direct randomness; 2) seasonality could coexist with chaos; 3) the highly auto-regressive nature of DP’s time series data are found. Therefore, it can be concluded that the chaos in a double pendulum has particular patterns (such as the positive relationship with the likelihood of being a random white noise series) that could be further explored.
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Riyanto, Rifki Diva, and Mahmuddin Yunus. "Sistem Pendukung Keputusan Pemberian Kredit Berbasis Web Menggunakan Kombinasi Metode Analytical Hierarchy Process (AHP) dan Simple Additive Weighting (SAW)." Jurnal Manajemen Informatika (JAMIKA) 11, no. 2 (October 1, 2021): 102–17. http://dx.doi.org/10.34010/jamika.v11i2.4936.
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Abstrak
Produk layanan yang diberikan oleh pihak bank diantaranya ialah kredit. Namun, ketidakakuratan analisis pemberian kredit dapat menyebabkan terjadinya kredit macet. Maka dari itu perlu diterapkan sistem pendukung keputusan terutama pada KSU DARMA Cabang Srono, Kabupaten Banyuwangi yang proses analisisnya masih manual. Pembangunan sistem tersebut memakai bahasa pemograman PHP dan MySQL yang dijadikan basis data dan mengombinasikan metode AHP dan SAW. Kombinasi metode ini lebih baik daripada metode AHP atau SAW. Sistem dikembangkan dengan mengimplementasikan metode waterfall. Setelah sistem berhasil dibangun, dilakukan empat pengujian yaitu pengujian akurasi yang menghasilkan tingkat keakuratan 100%, pengujian black box testing yang menghasilkan sistem baik secara fungsional, pengujian white box testing menghasilkan sistem baik secara alur dan logika program, serta pengujian respon pengguna menghasilkan respon pengguna sangat baik terhadap sistem yang telah dibangun.
Kata kunci: Sistem Pendukung Keputusan, Kredit, Web, Analytical Hierarchy Process, Simple Additive Weighting
Abstract
The service products provided by the bank include the credit. However, inaccurate analysis of credit disbursement can lead to bad credit. Therefore it is necessary to apply a decision support system, especially in the KSU DARMA Srono Branch, Banyuwangi Regency, where the analysis process is still manual. The system development uses the programming language PHP and MySQL which is used as a database and combines the AHP and SAW methods. This combination of methods is better than the AHP or SAW method. The system is developed by implementing the waterfall method. After the system was successfully built, four tests were carried out, namely accuracy testing which resulted in 100% accuracy, black-box testing which resulted in a functionally good system, white box testing producing a system both inflow and program logic, and testing user responses which resulted in very user responses good against the system that has been built.
Keywords: Decision Support System, Credit, Web, Analytical Hierarchy Process, Simple Additive Weighting
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Guo, An Fu, Jian Zhao, Jian Feng Li, Fang Yi Li, and Ai Qing Wei. "Study on the Environmental Friendliness of Biomass Dishware Based on Life Cycle Assessment." Advanced Materials Research 690-693 (May 2013): 1024–28. http://dx.doi.org/10.4028/www.scientific.net/amr.690-693.1024.
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Biomass materials are the best substitute for those made of plastic packaging materials all over the world. Widespread application of these materials can control or eliminate the increase of white pollution problem effectively. In this paper, the analysis model of biomass 450 ml lunch box was established based on Life Cycle Assessment, and then the environment Friendliness of the box, such as resource consumption, environment quality and human health, was analyzed. The results have shown that the resource consumption, environment quality and human health of biomass products were 26.6mPt, 6.23mPt, 16mPt, and these parameters of plastic products were 291mPt, 12.1mPt, 74.1mPt. Therefore, the biomass products have better environmental Friendliness than plastic products.
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De Rosa, Mattia, Marcus Brennenstuhl, Carlos Andrade Cabrera, Ursula Eicker, and Donal P. Finn. "An Iterative Methodology for Model Complexity Reduction in Residential Building Simulation." Energies 12, no. 12 (June 25, 2019): 2448. http://dx.doi.org/10.3390/en12122448.
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The present paper introduces an iterative methodology to progressively reduce building simulation model complexity with the aim of identifying potential trade-offs between computational requirements (i.e., model complexity) and energy estimation accuracy. Different levels of model complexity are analysed, from commercial building energy simulation tools to low order calibrated thermal networks models. Experimental data from a residential building in Germany were collected and used to validate two detailed white-box models and a simplified white-box model. The validation process was performed in terms of internal temperature profiles and building thermal energy demand predictions. Synthetic profiles were generated from the validated models and used for calibrating high order models. A reduction (trimming) procedure was applied to reduce the model complexity using an energy performance criterion prior to model trimming. The proposed methodology has the advantage of keeping the physical structure of the original RC model, thus enabling the use of the trimmed lumped parameter building model for other applications. The analysis showed that it is possible to reduce the model complexity by half, while keeping the accuracy above 90% for the targeted building.
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Adamczyk-Gruszka, Olga, Agata Horecka-Lewitowicz, Jakub Gruszka, Monika Wawszczak-Kasza, Agnieszka Strzelecka, and Piotr Lewitowicz. "Endometrial Cancer in Aspect of Forkhead Box Protein Contribution." International Journal of Environmental Research and Public Health 19, no. 16 (August 21, 2022): 10403. http://dx.doi.org/10.3390/ijerph191610403.
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(1) Background: The present study aimed to investigate the influence of forkhead box (FOX) on endometrial cancer (EC) progression. For a better understanding, the driving mechanisms are vital to identifying correlations between genes and their regulators. (2) Methods: The study enrolled one hundred and three white female patients with confirmed EC. For the analysis, we used next-generation sequencing with the Hot Spot Cancer Panel provided by Illumina Inc., San Diego, CA, USA, and an immunohistochemical analysis of FOXA1, FOXP1, and estrogen receptors. (3) Results: FOXA1 silencing led to a worse outcome based on the correlation with FOXA1 (test log-rank p = 0.04220 and HR 2.66, p = 0.033). Moreover, FOX proteins were closely correlated with TP53 and KRAS mutation. (4) Conclusions: Our study confirmed previous reports about FOX box protein in the regulation of tumor growth. A remarkable observation about the unclear crosstalk with crucial genes, as TP53 and KRAS need deeper investigation.