Predicting the necessary power to maintain a balanced supply and demand is crucial for the efficient operation of power-providing companies. This study employs a load forecasting model utilizing Multinomial Naive Baye...
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ISBN:
(纸本)9798350370058;9798350370164
Predicting the necessary power to maintain a balanced supply and demand is crucial for the efficient operation of power-providing companies. This study employs a load forecasting model utilizing Multinomial Naive Bayes in conjunction with a Support Vector Machine. The objective is to investigate and evaluate the performance of these algorithms on a designated dataset, thereby advancing the knowledge base of load forecasting models. The study compares three approaches: Multinomial Naive Bayes, Support Vector Machine, and a hybrid approach combining both. Results indicate that the Support Vector Machine algorithm demonstrates remarkable accuracy, achieving an average Mean Absolute Percentage Error (MAPE) of 1.48%. Multinomial Naive Bayes also exhibit commendable precision with an average MAPE of 3.93%. Furthermore, the hybrid approach yields promising results, attaining an average MAPE of 1.95%. Statistical analysis proves a significant difference between the hybrid algorithm and the individual approaches.
The usage of propellers is increasingly as an alternative to traditional aircraft and have become the primary option for new urban mobility vehicles. However, noise emissions from rotating blades can be a challenge fo...
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ISBN:
(数字)9781624107047
ISBN:
(纸本)9781624107047
The usage of propellers is increasingly as an alternative to traditional aircraft and have become the primary option for new urban mobility vehicles. However, noise emissions from rotating blades can be a challenge for the certification of these new vehicles. The acoustic sources from a propeller can be decomposed into deterministic and random components. Therefore, academic research has focused on developing methods to separate these components. This study compares the performance of several literature methods to decompose propeller noise signals into tonal and broadband components. Initially, the methods were applied to a synthetic data that represents the main characteristics of rotating blade noise. Later, the methods were tested on realistic acoustic data collected within an isolated propeller test rig. The results showed that methods based on phase-averaging the recorded signals satisfactorily detected harmonic components. The method based on cross-correlation between data blocks had good ability to extract the broadband spectra, while poor performance on harmonic detection was observed. Additionally, the wavelet transform and Proper Orthogonal Decomposition methods were capable of reconstructing the time series related to both deterministic and random parts. However, further investigation is required regarding the POD modes separation, as unexpected trends were observed with the wavelet-based method.
It has been shown that digitally manipulated face images can pose a security threat to automated authentication systems (e.g., when such systems are used for border control). In such scenarios, a malicious actor can, ...
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ISBN:
(纸本)9798400706370
It has been shown that digitally manipulated face images can pose a security threat to automated authentication systems (e.g., when such systems are used for border control). In such scenarios, a malicious actor can, in many countries, apply for an identity document using a manipulated face image, which can then be used to gain fraudulent access to a system. Research has shown that humans and algorithms struggle to detect digitally manipulated face images, especially when the type of manipulation is unknown or when evaluated across multiple types of manipulations. In this work, we consider the detection performance of algorithms and humans on datasets consisting of retouched, face swapped and morphed images. Specifically, we investigate the joint performance of algorithms and humans in a differential detection scenario where both a trusted and suspected image are presented simultaneously. To this end, we propose a conditional face image manipulation detection approach where the human decision is only considered when the algorithm is unsure about the decision outcome. The results show that the automated algorithm performs better than the human detectors and that combining the decisions of algorithms and humans, in general, leads to an increased detection performance. To our knowledge, this is the first study to explore the joint detection performance of algorithms and humans in a differential face manipulation detection scenario and when using a variety of face image manipulations.
Y Random mechanisms including mutations are an internal part of evolutionary algorithms, which are based on the fundamental ideas of Darwin's theory of evolution as well as Mendel's theory of genetic heritage....
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Y Random mechanisms including mutations are an internal part of evolutionary algorithms, which are based on the fundamental ideas of Darwin's theory of evolution as well as Mendel's theory of genetic heritage. In this paper, we debate whether pseudo-random processes are needed for evolutionary algorithms or whether deterministic chaos, which is not a random process, can be suitably used instead. Specifically, we compare the performance of 10 evolutionary algorithms driven by chaotic dynamics and pseudo-random number generators using chaotic processes as a comparative study. In this study, the logistic equation is employed for generating periodical sequences of different lengths, which are used in evolutionary algorithms instead of randomness. We suggest that, instead of pseudorandom number generators, a specific class of deterministic processes (based on deterministic chaos) can be used to improve the performance of evolutionary algorithms. Finally, based on our findings, we propose new research questions. (C) 2021 The Author(s). Published by Elsevier Inc.
Optimal power flow (OPF), which aims to find the safest and most economical operating point while satisfying a variety of constraints, is a significant optimization problem in power systems. In this paper, a novel app...
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The Medical Imaging and Data Resource Center (MIDRC) is a multi-institutional effort to accelerate medical imaging machine intelligence research and create a publicly available data commons as well as a sequestered co...
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ISBN:
(纸本)9781510660434;9781510660441
The Medical Imaging and Data Resource Center (MIDRC) is a multi-institutional effort to accelerate medical imaging machine intelligence research and create a publicly available data commons as well as a sequestered commons for performance evaluation of algorithms. This work sought to evaluate the currently implemented methodology for apportioning data to the public and sequestered data commons by investigating the resulting distributions of joint demographic characteristics between the public and sequestered commons. 54,185 patients whose de-identified imaging studies and metadata had been submitted to MIDRC were previously separated into public and sequestered commons using a multi-dimensional stratified sampling method, resulting in 41,556 patients (77%) in the public commons and 12,629 patients (23%) in the sequestered commons. To compare the balance obtained in the joint distributions of patient characteristics from use of the developed sequestration method, patients from each commons were separated into bins, representing a unique combination of the demographic variables of COVID-19 status, age, race, and sex assigned at birth. The joint distributions of patients were visualized, and the absolute and percent difference in each bin from an exact 77:23 split of the data were calculated. Results indicated 75.9% of bins obtained differences of less than 15 patients, with a median difference of 3.6 from the total data for both public and sequestered commons. Joint distributions of patient characteristics in both the public and sequestered commons closely matched each other as well as that of the total data, indicating the sequestration by stratified sampling method has operated as intended.
Source-Separation Non-Negative Matrix Factorization (SSNMF) is a mathematical algorithm recently developed to extract scalp-recorded frequency-following responses (FFRs) from noise. Despite its initial success, the ef...
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Source-Separation Non-Negative Matrix Factorization (SSNMF) is a mathematical algorithm recently developed to extract scalp-recorded frequency-following responses (FFRs) from noise. Despite its initial success, the effects of silent intervals on algorithm performance remain undetermined. Our purpose in this study was to determine the effects of silent intervals on the extraction of FFRs, which are electrophysiological responses that are commonly used to evaluate auditory processing and neuroplasticity in the human brain. We used an English vowel /i/ with a rising frequency contour to evoke FFRs in 23 normal-hearing adults. The stimulus had a duration of 150 ms, while the silent interval between the onset of one stimulus and the offset of the next one was also 150 ms. We computed FFR Enhancement and Noise Residue to estimate algorithm performance, while silent intervals were either included (i.e., the WithSI condition) or excluded (i.e., the WithoutSI condition) in our analysis. The FFR Enhancements and Noise Residues obtained in the WithoutSI condition were significantly better (p < .05) than those obtained in the WithSI condition. On average, the exclusion of silent intervals produced a 11.78% increment in FFR Enhancement and a 20.69% decrement in Noise Residue. These results not only quantify the effects of silent intervals on the extraction of human FFRs, but also provide recommendations for designing and improving the SSNMF algorithm in future research.
In-orbit relative navigation between a networked swarm of centimeter-scale femto-spacecraft would add considerable value to a range of space mission concepts and applications, such as for multipoint sensing and distri...
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In-orbit relative navigation between a networked swarm of centimeter-scale femto-spacecraft would add considerable value to a range of space mission concepts and applications, such as for multipoint sensing and distributed sparse aperture interferometry. For a swarm of networked femto-spacecraft, relative position determination would be possible by inferring coarse range estimates from the received signal strength indication associated with the communication link between swarm members. This is particularly advantageous for highly resource-constrained devices. In this paper, algorithms for swarm relative positioning using interspacecraft range estimates are presented that can be applied to centralized, decentralized, and distributed network configurations. Relative navigation filters for initial relative orbit determination and state estimation are presented for femto-spacecraft swarm deployment and dispersal scenarios. The algorithms presented could also find use in terrestrial applications, in static and dynamic wireless sensor networks.
In this paper, a path planning algorithm is proposed for dynamic obstacle avoidance by improving a rapidly exploring random tree (RRT) algorithm, a sampling-based path-planning algorithm. Although guaranteeing a globa...
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In this paper, a path planning algorithm is proposed for dynamic obstacle avoidance by improving a rapidly exploring random tree (RRT) algorithm, a sampling-based path-planning algorithm. Although guaranteeing a globally optimal path is impossible when using RRT algorithm, it is advantageous because it can generate the path quickly. Considering this advantage of the RRT algorithm, the directed RRT (DRRT) algorithm was developed, which reduces the generation time and supplements optimality by improving node generation and tree expansion process. It is also added the path smoothing method for the flight. The anytime DRRT algorithm is based on the DRRT algorithm and repeats path generation within the time limit considering the direction of movement of the obstacle and selects the shortest path for the flight. Air collision avoidance between unmanned aerial vehicles (UAVs) considering the intruders as a dynamic obstacle was simulated using the Gazebo simulator. Based on the simulation, the performance of the proposed anytime DRRT algorithm was verified with varying the host UAV and intruder conditions, and its applicability to UAVs was affirmed.
The Minimum Weighted Connected Vertex Cover problem(MWCVC) is to find a subset F subset of V(G) with minimum weight in a node-weighted graph G, such that when removing the set F, the inducing graph of remaining vertic...
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The Minimum Weighted Connected Vertex Cover problem(MWCVC) is to find a subset F subset of V(G) with minimum weight in a node-weighted graph G, such that when removing the set F, the inducing graph of remaining vertices holds no edges, and the graph induced from set F in G is required to be connected. This problem comes from the classical combinatorial problem in graph theory, i.e., the Vertex Cover Problem. A large number of results on algorithms for the MWCVC problem have been reported. In this paper, we proposed two heuristic algorithms, denoted as VCC and LCVCC, to find a connected vertex cover set in a general weighted graph. The time complexity of both two algorithms are less than O(n(4)). We compare these two algorithms with two known heuristic algorithms GR and GD (proposed by Dagdeviren in 2021) on connected graphs, and draw a conclusion that both of VCC and LCVCC perform better than GR or GD. Relatively speaking, LCVCC is expected to have better performance in dense graphs than VCC.
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