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Articles 7651 - 7680 of 63342
Full-Text Articles in Entire DC Network
Deep Learning-Based Breast Cancer Diagnosis With Multiview Of Mammography Screening To Reduce False Positive Recall Rate, Meryem Altın Karagöz, Özkan Ufuk Nalbantoğlu, Derviş Karaboğa, Bahriye Akay, Alper Baştürk, Halil Ulutabanca, Serap Doğan, Damla Coşkun, Osman Demi̇r
Deep Learning-Based Breast Cancer Diagnosis With Multiview Of Mammography Screening To Reduce False Positive Recall Rate, Meryem Altın Karagöz, Özkan Ufuk Nalbantoğlu, Derviş Karaboğa, Bahriye Akay, Alper Baştürk, Halil Ulutabanca, Serap Doğan, Damla Coşkun, Osman Demi̇r
Turkish Journal of Electrical Engineering and Computer Sciences
Breast cancer is the most prevalent and crucial cancer type that should be diagnosed early to reduce mortality. Therefore, mammography is essential for early diagnosis owing to high-resolution imaging and appropriate visualization. However, the major problem of mammography screening is the high false positive recall rate for breast cancer diagnosis. High false positive recall rates psychologically affect patients, leading to anxiety, depression, and stress. Moreover, false positive recalls increase costs and create an unnecessary expert workload. Thus, this study proposes a deep learning based breast cancer diagnosis model to reduce false positive and false negative rates. The proposed model has …
Signer-Independent Sign Language Recognition With Feature Disentanglement, İnci̇ Meli̇ha Baytaş, İpek Erdoğan
Signer-Independent Sign Language Recognition With Feature Disentanglement, İnci̇ Meli̇ha Baytaş, İpek Erdoğan
Turkish Journal of Electrical Engineering and Computer Sciences
Learning a robust and invariant representation of various unwanted factors in sign language recognition (SLR) applications is essential. One of the factors that might degrade the sign recognition performance is the lack of signer diversity in the training datasets, causing a dependence on the singer’s identity during representation learning. Consequently, capturing signer-specific features hinders the generalizability of SLR systems. This study proposes a feature disentanglement framework comprising a convolutional neural network (CNN) and a long short-term memory (LSTM) network based on adversarial training to learn a signer-independent sign language representation that might enhance the recognition of signs. We aim to …
Brain Science And Brain-Inspired Intelligence In Intelligent Era, Xu Zhang
Brain Science And Brain-Inspired Intelligence In Intelligent Era, Xu Zhang
Bulletin of Chinese Academy of Sciences (Chinese Version)
With intelligence technology as the core technology and intelligent computing power as the productive force, the intelligent era has once again pushed brain science to the forefront of world science and technology. Brain science is the science that studies the nature and rule of cognition and intelligence of human, animal, and machine. A comprehensive analysis of the structure and functional connection rule of the nervous system will eventually draw the functional connectivity map of the brain. In the past decade, neuroscience research has been committed to systematically analyzing the types of neurons and neural structural connections of the nervous system, …
Advances In Data-Driven Life Sciences Research, Haiping Jiang, Chunchun Gao, Wenhao Liu, Yungui Yang, Xin Li
Advances In Data-Driven Life Sciences Research, Haiping Jiang, Chunchun Gao, Wenhao Liu, Yungui Yang, Xin Li
Bulletin of Chinese Academy of Sciences (Chinese Version)
The field of life sciences is rapidly evolving, driven by advancements in experimental techniques and vast biological big data which gradually arise and play an increasingly important role in life science research. First of all, biological big data has diversity and complexity, including genomic data, epigenomic data, proteomic data and other types. These data provide researchers with more comprehensive information and help reveal the laws behind life phenomena. Second, new data-driven developments and applications in life sciences cover many fields such as gene editing, precision medicine, drug development, etc., providing unprecedented possibilities for human health and quality of life. However, …
Analysing An Imbalanced Stroke Prediction Dataset Using Machine Learning Techniques, Viswapriya Subramaniyam Elangovan, Rajeswari Devarajan, Osamah I. Khalaf, Mhd Saeed Sharif, Wael Elmedany
Analysing An Imbalanced Stroke Prediction Dataset Using Machine Learning Techniques, Viswapriya Subramaniyam Elangovan, Rajeswari Devarajan, Osamah I. Khalaf, Mhd Saeed Sharif, Wael Elmedany
Karbala International Journal of Modern Science
A stroke is a medical condition characterized by the rupture of blood vessels within the brain which can lead to brain damage. various symptoms may be exhibited when the brain's supply of blood and essential nutrients is disrupted. To forecast the possibility of brain stroke occurring at an early stage using Machine Learning and Deep Learning is the main objective of this study. Timely detection of the various warning signs of a stroke can significantly reduce its severity. This paper performed a comprehensive analysis of features to enhance stroke prediction effectiveness. A reliable dataset for stroke prediction is taken from …
Potential Enhancement Of Microbial Disinfection Using Oxygen Enriched Cold Atmospheric-Pressure Argon (Ar/O2) Plasma Jet, Waleed O. Younis, Mahmoud M. Berekaa, Mostafa A. Ellbban, Abdel-Sattar S. Gadallah, Jamal Q. Almarashi, Abdel-Aleam H. Mohamed
Potential Enhancement Of Microbial Disinfection Using Oxygen Enriched Cold Atmospheric-Pressure Argon (Ar/O2) Plasma Jet, Waleed O. Younis, Mahmoud M. Berekaa, Mostafa A. Ellbban, Abdel-Sattar S. Gadallah, Jamal Q. Almarashi, Abdel-Aleam H. Mohamed
Karbala International Journal of Modern Science
Oxygen activated cold-atmospheric-pressure-argon plasma jet (APPJ) has gained prominence over the regular argon plasma especially in disinfection and decontamination. As an objective of the current research, an oxygen-enriched argon system was built, where plasma produced through a vessel metallic tube that is introduced into alumina one. A sinusoidal high voltage signal of 25 kHz was used to generate plasma jet. Potential impact of oxygen enriched APP jet (Ar/O2) in decontamination of different microbial cells was observed. For examination, suspension of each tested microbe was placed in contact with plasma jet nearly 10 mm away from the jet nozzle …
A Potential Of Watercress Nasturtium Officinale Bioactive Compounds In Inhibiting Infectious Myonecrosis Virus (Imnv) By Targeting Rna-Dependent Rna Polymerase (Rdrp) Virus From Several Countries: In Silico Approach, Qurrota A’Yunin, Fatchiyah Fatchiyah, Maftuch Maftuch, Feri Eko Hermanto, Muhammad Hermawan Widyananda, Narendra Santika Hartana, Muhaimin Rifa’I, Yoga Dwi Jatmiko
A Potential Of Watercress Nasturtium Officinale Bioactive Compounds In Inhibiting Infectious Myonecrosis Virus (Imnv) By Targeting Rna-Dependent Rna Polymerase (Rdrp) Virus From Several Countries: In Silico Approach, Qurrota A’Yunin, Fatchiyah Fatchiyah, Maftuch Maftuch, Feri Eko Hermanto, Muhammad Hermawan Widyananda, Narendra Santika Hartana, Muhaimin Rifa’I, Yoga Dwi Jatmiko
Karbala International Journal of Modern Science
Infectious myonecrosis virus (IMNV) disease causes mass mortality and decreased shrimp production. The RdRp region projects to the interior, where it may function in transcription. The focus of this study was to determine the effect of amino acid polymorphisms from several countries on the structure of RdRp and identify the potential of watercress in inhibiting IMNV by targeting the RdRp protein of IMNV through an in silico approach. The results showed that the structure of the IMNV RdRp protein from Indonesia was similar to Mexico, and the protein structure from India_QDN was identical to India_QIL. Ligand binding affinity values showed …
Extraction Of Morphometric Features The Shape Of Mangrove Leaves Based On Digital Images And Classification Using The Support Vector Machine, Ishak Ariawan, Della Ayu Lestari, Luthfi Anzani, Tri Yanti, Cakra Rahardjo, M. Saleh, Sahril Angga Permana, Dea Aisyah Rusmawati
Extraction Of Morphometric Features The Shape Of Mangrove Leaves Based On Digital Images And Classification Using The Support Vector Machine, Ishak Ariawan, Della Ayu Lestari, Luthfi Anzani, Tri Yanti, Cakra Rahardjo, M. Saleh, Sahril Angga Permana, Dea Aisyah Rusmawati
Karbala International Journal of Modern Science
At present, several botanists still rely on the use of manual estimating methods to assess the carbon content in mangrove. However, these methods have been reported to be extremely time-consuming, showing the need to develop a system for prediction. An effective solution lies in the creation of an artificial intelligence application, which can provide rapid and cost-effective results. In constructing this application, careful consideration must be given to the selection of parameters or attributes. Species is an essential parameter for the assessment of carbon content, but its determination has proven to be challenging due to the similarities of mangrove. The …
Helping The Home Cook: How Unsupervised Machine Learning Can Prevent Food Waste, Ryan B. Watson
Helping The Home Cook: How Unsupervised Machine Learning Can Prevent Food Waste, Ryan B. Watson
Honors Projects
A common problem for the home cook is having too much of one food ingredient leftover, then not knowing what to do with it. To alleviate this problem, I propose using an unsupervised machine learning model to recommend recipes based on what ingredients the home cook wants to use. This model is built with FastText and trained on the recipe ingredients in the RecipeNLG dataset. Recipes are recommended based on which recipe ingredient set is most similar to the recipe ingredients provided in the user input. This solution will reduce consumer food waste by giving the home cook the information …
Making The Most Of Artificial Intelligence And Large Language Models: A Novel Approach For Book Recommendation And Discovery In Medical Libraries, Ivan Portillo, David Carson
Making The Most Of Artificial Intelligence And Large Language Models: A Novel Approach For Book Recommendation And Discovery In Medical Libraries, Ivan Portillo, David Carson
Library Presentations, Posters, and Audiovisual Materials
This poster presentation evaluates the use of Artificial Intelligence and large language models (LLMs) to assist health science libraries in recommending and discovering book titles as part of their collection development. Using pre-determined prompts, the researchers evaluated ChatGPT 4.0, Bing Chat, and Google Bard as recommender systems for book discovery and ranking existing titles.
Supporting South Korea’S Aging Population: How Ai And Iot Acceptance Connects The Young And Old, Bobby Im
Supporting South Korea’S Aging Population: How Ai And Iot Acceptance Connects The Young And Old, Bobby Im
Master's Projects and Capstones
In 2024, South Korea surpassed every other nation by becoming the country with the lowest fertility rate (below 0.7%). Population decline will hinder future ability to care for their aging population and although the government and private corporations are investing millions of dollars on developing Artificial Intelligence-Internet of Things (AI-IoT) devices to support the aging, the acceptance levels and the amount of family support required is undervalued. By examining AI-IoT’s current use and role in South Korea’s public health system this paper shows how intergenerational support helps optimize existing procedures and equipment, increases the level of acceptance and use, and …
Fusing Classic Motion Energy Models And Deep Learning For Coarse-To-Fine Moving Object Segmentation, Matthias Tangemann, Matthias Kümmerer, Matthias Bethge
Fusing Classic Motion Energy Models And Deep Learning For Coarse-To-Fine Moving Object Segmentation, Matthias Tangemann, Matthias Kümmerer, Matthias Bethge
MODVIS Workshop
Classic motion energy models are able to predict a wide range of physiological and behavioral aspects of motion perception in humans. Whether these models can be used as a basis for higher-level tasks, such as moving object segmentation, has however hardly been explored yet. Here, we present a model that combines a motion energy representation with recent computer vision approaches for figure-ground segmentation of naturalistic stimuli. We find that unlike established motion segmentation models but similar to humans, our model generalizes to random-dot stimuli when only trained on RGB videos.
Synthetic Realities And Artificial Intelligence-Generated Contents, Daniel Moreira, Sebastien Marcel, Anderson Rocha
Synthetic Realities And Artificial Intelligence-Generated Contents, Daniel Moreira, Sebastien Marcel, Anderson Rocha
Computer Science: Faculty Publications and Other Works
Welcome to the IEEE Security & Privacy Special Issue on Synthetic Realities and Artificial Intelligence-Generated Contents! In this edition, we delve into the topic of synthetic realities, where generative artificial intelligence (GAI) is revolutionizing the construction of narratives, blurring the boundaries between fact and fiction, for the good and the bad. Indeed, content created or enabled by GAI spans a wide spectrum of usage and intentions, from fostering positive experiences, such as entertainment, training, and education, to more questionable utilization, such as deception, propaganda, and manipulation.
Missing Wedge Completion Via Unsupervised Learning With Coordinate Networks, Dave Van Veen, Jesús G Galaz-Montoya, Liyue Shen, Philip Baldwin, Akshay S Chaudhari, Dmitry Lyumkis, Michael F Schmid, Wah Chiu, John Pauly
Missing Wedge Completion Via Unsupervised Learning With Coordinate Networks, Dave Van Veen, Jesús G Galaz-Montoya, Liyue Shen, Philip Baldwin, Akshay S Chaudhari, Dmitry Lyumkis, Michael F Schmid, Wah Chiu, John Pauly
Faculty, Staff and Students Publications
Cryogenic electron tomography (cryoET) is a powerful tool in structural biology, enabling detailed 3D imaging of biological specimens at a resolution of nanometers. Despite its potential, cryoET faces challenges such as the missing wedge problem, which limits reconstruction quality due to incomplete data collection angles. Recently, supervised deep learning methods leveraging convolutional neural networks (CNNs) have considerably addressed this issue; however, their pretraining requirements render them susceptible to inaccuracies and artifacts, particularly when representative training data is scarce. To overcome these limitations, we introduce a proof-of-concept unsupervised learning approach using coordinate networks (CNs) that optimizes network weights directly against input …
Re-Instatement Of Pell Grants For Incarcerated Students: Implications For Cs Education, E. Hogan, D. J. Mir, A. Cencini, Keith O'Hara, A. G. S. Raj, W. G. Griswold, L. Porter
Re-Instatement Of Pell Grants For Incarcerated Students: Implications For Cs Education, E. Hogan, D. J. Mir, A. Cencini, Keith O'Hara, A. G. S. Raj, W. G. Griswold, L. Porter
Computer Science Faculty Works
The US incarcerates more people, and at a higher rate, than any other country in the world—predominantly low-income people of color. Higher education in prison (HEP) has a powerful impact on justice-impacted people, improving their quality of life during incarceration and reducing their likelihood of returning to prison post-release. HEP nearly disappeared in 1994 after Pell Grant eligibility was eliminated for incarcerated students, which was recently reinstated. More computing educators can reach incarcerated students with digital literacy skills, core computing content, and critical analyses of the impacts of computing on society. CS education researchers can also support the growth of …
High-Dimensional Data Analysis Using Parameter Free Algorithm Data Point Positioning Analysis, S. M. F. D. Syed Mustapha
High-Dimensional Data Analysis Using Parameter Free Algorithm Data Point Positioning Analysis, S. M. F. D. Syed Mustapha
All Works
Clustering is an effective statistical data analysis technique; it has several applications, including data mining, pattern recognition, image analysis, bioinformatics, and machine learning. Clustering helps to partition data into groups of objects with distinct characteristics. Most of the methods for clustering use manually selected parameters to find the clusters from the dataset. Consequently, it can be very challenging and time-consuming to extract the optimal parameters for clustering a dataset. Moreover, some clustering methods are inadequate for locating clusters in high-dimensional data. To address these concerns systematically, this paper introduces a novel selection-free clustering technique named data point positioning analysis (DPPA). …
Prevention Of Web Scraping And Copy And Paste Of Content By Font Obfuscation, Mingkui Wei, Yao Liu, Zhuo Lu, Junjie Xiong
Prevention Of Web Scraping And Copy And Paste Of Content By Font Obfuscation, Mingkui Wei, Yao Liu, Zhuo Lu, Junjie Xiong
Computer Science Faculty Research & Creative Works
A method and system provide and utilize obfuscated fonts for displayable content. Responsive to a request for displayable content having text, a text portion of the requested displayable content to be obfuscated is determined. For that text portion, obfuscated fonts are provided, by retrieving obfuscated fonts or by generating obfuscated fonts from a set of obfuscated glyphs created from a plurality of glyphs representative of a plurality of characters of the text portion of the displayable content. The obfuscated fonts can be created by assigning obfuscated glyphs of the set into the obfuscated fonts in accordance with one or more …
A Comparative Analysis Of Source Identification Algorithms, Pablo A. Curiel
A Comparative Analysis Of Source Identification Algorithms, Pablo A. Curiel
Biology and Medicine Through Mathematics Conference
No abstract provided.
A Robust Decision-Making Model For Medical Supplies Via Selecting Appropriate Unmanned Aerial Vehicle, Amira Salam, Mai Mohamed, Rui Yong, Jun Ye
A Robust Decision-Making Model For Medical Supplies Via Selecting Appropriate Unmanned Aerial Vehicle, Amira Salam, Mai Mohamed, Rui Yong, Jun Ye
Neutrosophic Systems with Applications
Recently, Unmanned Aerial Vehicles (UAVs) have been used in many fields, including the field of health care, especially in delivering the necessary medical equipment and supplies, due to the many advantages they have compared to other traditional methods and the presence of different types of UAVs, to improve healthcare and provide it with the medical supplies and equipment necessary to save the lives of patients. Choosing the appropriate UAV for a specific situation represents a problem facing decision-makers, which is considered a multi-criteria decision-making problem. Since the decision-making process is cumbersome and complex, and deals with uncertainty and ambiguity. In …
An Innovative Approach On Yao’S Three-Way Decision Model Using Intuitionistic Fuzzy Sets For Medical Diagnosis, Wajid Ali, Tanzeela Shaheen, Iftikhar Ul Haq, Florentin Smarandache, Hamza Ghazanfar Toor, Faiza Asif
An Innovative Approach On Yao’S Three-Way Decision Model Using Intuitionistic Fuzzy Sets For Medical Diagnosis, Wajid Ali, Tanzeela Shaheen, Iftikhar Ul Haq, Florentin Smarandache, Hamza Ghazanfar Toor, Faiza Asif
Neutrosophic Systems with Applications
In the realm of medical diagnosis, intuitionistic fuzzy data serves as a valuable tool for representing information that is uncertain and imprecise. Nevertheless, decision-making based on this kind of knowledge can be quite challenging due to the inherent vagueness of the data. To address this issue, we employ power aggregation operators, which prove effective in combining several sources of data, such as expert thoughts and patient information. This allows for a more correct diagnosis; a particularly crucial aspect of medical practice where precise and timely diagnoses can significantly impact medication policy and patient results. In our research, we introduce a …
Dense Video Description Method Based On Multi-Modal Fusion In Transformer Network, Xiang Li, Haifeng Sang
Dense Video Description Method Based On Multi-Modal Fusion In Transformer Network, Xiang Li, Haifeng Sang
Journal of System Simulation
Abstract: In order to solve the problems that most of the current dense video description models use twostage methods, which have low efficiency, ignore audio and semantic information, and have incomplete description results, a multi-modal and semantic information fusion dense video description method was proposed. An adaptive R(2+1)D network was proposed to extract visual features, a semantic detector was designed to generate semantic information, audio features were added to supplement it, a multi-scale deformable attention module was established, and a parallel prediction head was applied to accelerate the convergence rate and improve the accuracy of the model. The experimental results …
Research On Simulation Model Of Double-Layer Expansion Design Of Expressway, Jiandong Qiu, Yi Tang, Yuxiong Ji, Heng Liu, Junsha Luo
Research On Simulation Model Of Double-Layer Expansion Design Of Expressway, Jiandong Qiu, Yi Tang, Yuxiong Ji, Heng Liu, Junsha Luo
Journal of System Simulation
Abstract: Aiming at the problems that the traditional traffic simulation technology has insufficient evaluation accuracy and little application effect in the three-dimensional composite expansion scenario of expressway, a simulation model construction method for double-layer expansion design of expressway was proposed. The reconstruction and expansion project of Shenzhen Jihe Expressway is selected as the research object, the three simulation model modeling elements, including road network facilities, traffic demand data, and driving behavior model parameters, are sorted out, and the technical process of simulation modeling is proposed. The whole road network including key infrastructure such as interchange, toll station, ramp up and …
A Graph Neural Network Visual Slam Algorithm For Large-Angle View Motion, Jinhui Liu, Mengyuan Chen, Pengpeng Han, Hebao Chen, Yukun Zhang
A Graph Neural Network Visual Slam Algorithm For Large-Angle View Motion, Jinhui Liu, Mengyuan Chen, Pengpeng Han, Hebao Chen, Yukun Zhang
Journal of System Simulation
Abstract: Aimed at the difficulty of feature point extraction in mobile robots with drastic changes in illumination or sparse texture scenes under large-angle view motion, difficulty in matching features at extreme angles leads to large errors in Epipolar Geometry calculations, a fusion of an improved graph neural network based visual SLAM algorithm (GNN-SLAM) is proposed. The priori location estimation feature extraction network is proposed to achieve fast and uniform detection and description of image feature points by a priori location estimation and to construct real and accurate feature point information. The graph attention mechanism feature matching network is proposed to …
Implementation And Numerical Simulation On Object-Oriented Elastic-Plastic Finite Element Method Based On Python, Henghui Li, Yingxiong Xiao
Implementation And Numerical Simulation On Object-Oriented Elastic-Plastic Finite Element Method Based On Python, Henghui Li, Yingxiong Xiao
Journal of System Simulation
Abstract: With the continuous expansion of the application fields of finite element methods, higher requirements are put forward for the scalability of finite element methods. In order to overcome the defects of the traditional finite element methods, a simple and easily extensible object-oriented elasticplastic finite element program framework is proposed based on Python. Combined with the characteristics of Python, we design some finite element classes such as the pre-processing class, the post-processing class, the linear solution class, the stress integration class and the analysis class. By applying the resulting framework to several typical elastic-plastic mechanical problems and comparing the results …
Optimization Of Highway Emergency Lane Control Based On Kriging Genetic Algorithm, Jinjun Tang, Lipeng Hu, Mingyang Li, Xuan Zhang
Optimization Of Highway Emergency Lane Control Based On Kriging Genetic Algorithm, Jinjun Tang, Lipeng Hu, Mingyang Li, Xuan Zhang
Journal of System Simulation
Abstract: To address the issue of how to effectively improve the highway operational efficiency and reduce the safety risks under different traffic flow conditions, this study proposed a genetic algorithm based on Kriging agent model is proposed to optimize the emergency lane control strategy. A mathematical optimization model is designed by combining the spatial and temporal characteristics of the emergency lane opening strategy. By introducing Kriging agent model, combining the genetic algorithm to build the optimization framework, and using simulation software to obtain data to train the agent model, the problem of minimizing the total travel time and the total …
Research On Forest Fire Spread Simulation System Based On Osg, Lei Shao, Xiaotian Yan, Jian Liu, Yuming Liu
Research On Forest Fire Spread Simulation System Based On Osg, Lei Shao, Xiaotian Yan, Jian Liu, Yuming Liu
Journal of System Simulation
Abstract: A novel expended lattice structure of DEM-Cell model incorporating DEM elevation information is proposed, in response to the requirements of extrapolation of trends and hazards of forest fire spread, as well as the need of route selection and real-time path optimization for rescuers. Combining vegetation attributes of meta cells with geographic elevation information, we develop a simulation and fire fighting exercise system that computing the resultant data in real time and converting it into renderable resource, which is directly used by the OSG engines. With the help of the same data structure and the corresponding key algorithms and technologies, …
Hierarchical Guided Enhanced Multi-Objective Firefly Algorithm, Jia Zhao, Zhizhen Lai, Runxiu Wu, Zhihua Cui, Hui Wang
Hierarchical Guided Enhanced Multi-Objective Firefly Algorithm, Jia Zhao, Zhizhen Lai, Runxiu Wu, Zhihua Cui, Hui Wang
Journal of System Simulation
Abstract: The multi-objective firefly algorithm is easy to produce oscillation and aggregation phenomenon in the solution process, which leads to weak development ability and poor solution accuracy. This paper proposes a hierarchical guided enhanced multi-objective firefly algorithm (HGEMOFA). HGEMOFA builds a hierarchical guidance model, uses non-dominated sorting to obtain different levels of individuals. The individuals in the dominant layer are used to guide the evolution of the individuals in the inferior layer, the guidance direction is clear, the oscillation in the evolution process is solved, the aggregation phenomenon is reduced, and the convergence of the algorithm is enhanced. The Lévy …
Embodied Visions: Interactive Installations That Reimagine Bodily Presence In Digital Imaging Apparatuses As Shadows, Yunzi Shi
Dartmouth College Master’s Theses
Contextualized within a history of technological development, the evolution of imaging devices and technologies is accompanied by the abstraction of spatial relationships between the body of the observer, the apparatus, and physical reality, which leads to disembodying experiences for the observing subject. Compared with devices and interactive experiences, critical reflection on the epistemological impact of digital imaging devices has less priority in computational imaging and human-computer interaction research. Taking an artistic approach, this thesis describes Embodied Visions, an exhibition featuring three interactive installations exploring the technical infrastructure for imaging and reflecting on the (dis)embodied experiences in the digital age. …
Research On Path Planning Of Warehouse Robot With Improved Harris Hawks Algorithm, Xu Lei, Jingyi Chen, Xiaoyang Chen
Research On Path Planning Of Warehouse Robot With Improved Harris Hawks Algorithm, Xu Lei, Jingyi Chen, Xiaoyang Chen
Journal of System Simulation
Abstract: To improve the path planning efficiency of warehouse mobile robots in static environments, and to solve the problems of slow convergence and local optimum of traditional Harris Hawk (HHO) algorithm in path planning, a Harris Hawk optimization algorithm based on Tent chaotic mapping fused with Cauchy's back-learning variant (TCLHHO) is proposed. The population diversity is increased by Tent Chaotic mapping to speed up convergence. An exponential prey escape energy updating strategy is proposed to balance the global search and local exploitation capabilities of the algorithm. The optimal individual is disturbed by Cauchy mutation operator and inverse learning strategy to …
Image Self-Enhancement De-Hazing Algorithm Combined With Generative Adversarial Network, Wanjun Liu, Yuqian Cheng, Haicheng Qu
Image Self-Enhancement De-Hazing Algorithm Combined With Generative Adversarial Network, Wanjun Liu, Yuqian Cheng, Haicheng Qu
Journal of System Simulation
Abstract: To solve the problem that existing dehazing models are prone to over fitting after training with synthetic hazy image data sets, an image self-enhancement dehazing algorithm is proposed in combination with generative adversarial network. The depth information of an image is estimated while combining two Generative Adversarial Networks. The first GAN uses a clear image to learn the process of image hazing, and then adopts the hazed image generated by it as the input of the second GAN to guide the second GAN to correct dehazing. In order to reduce the difference before and after image processing, the consistency …