Motion Simulation And Performance Analysis Of 2d Variable Stiffness Snake-Like Robot,
2022
1.School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China;
Motion Simulation And Performance Analysis Of 2d Variable Stiffness Snake-Like Robot, Yanqin Long, Guifang Qiao, Guangming Song, Ying Zhang, Linlin Cheng
Journal of System Simulation
Abstract: Mutual interference of vehicular laser radar causes serious performance reduction of target detection and tracking. From spatial, time and modulation frequency, various possible interference of lidars of pulse and frequency modulated continuous wave method in road environments are studied by simulation. The possibility of the interference is calculated, the features and performance reduction of the interference are analyzed. For the possible interference, using a method of pseudo random noise code to modulate the output amplitude of the continuous wave laser can effectively reduce the probability of interference and ensure the reliable operation of lidar in road environments.
Optimization Of Dynamic Post-Disaster Emergency Distribution Network Under Perspective Of Rescue Efficiency,
2022
University of Shanghai for Science and Technology, Shanghai 200082, China;
Optimization Of Dynamic Post-Disaster Emergency Distribution Network Under Perspective Of Rescue Efficiency, Xinyu Gao, Jing Ni
Journal of System Simulation
Abstract: Aiming at the emergency rescue, a dynamic directed rescue network is established with the dynamic changes of the location, demand, and affected population of disaster site, and a mathematical model is constructed with the maximum rescue efficiency. A data envelope analysis model is applied to evaluate the efficiency of each rescue route segment. An efficiency-based dynamic routing model is established to transform the dynamic routes into the multi-stage static routes through the time slice division. An improved hybrid greedy-ant colony optimization algorithm is designed to solve the model, and the proposed algorithm is compared with the genetic algorithm, particle …
Idea Of Infinitesimal Method-Introduced Hybrid Tt&C Resources Joint Scheduling,
2022
1.Graduate School, Space Engineering University, Beijing 101416, China;
Idea Of Infinitesimal Method-Introduced Hybrid Tt&C Resources Joint Scheduling, Naiyang Xue, Dan Ding, Hongmin Wang, Yile Fan, Zhongqian Liu
Journal of System Simulation
Abstract: Aiming at the study on the motion performance of a two-dimensional variable stiffness snake-robot, a Simulink/Adams co-simulation model is established, and the effects of the damping coefficient, the stiffness parameters and the angular frequency of the variable stiffness actuator on the motion energy consumption are analyzed. To realize the synchronous control of joint trajectory and stiffness, a motion controller based on two-layer CPG is proposed. The results show that the damping coefficient can effectively reduce the proportion of starting energy consumption of the variable stiffness snake-like robot, and the ratio of starting energy consumption increases with the stiffness parameter …
Research On Multi-Depot Half-Open Vehicle Routing Problem With Time-Varying Speed,
2022
School of Information and Control Engineering, Xi'an University of Architecture and Technology, Xi'an 710399, China;
Research On Multi-Depot Half-Open Vehicle Routing Problem With Time-Varying Speed, Kaiqing Zhang, Qichun Ji
Journal of System Simulation
Abstract: Based on the analysis of operation concept and its elements, this paper proposes a framework of operation effectiveness simulation evaluation under the guidance of operation concept. Based on this framework, the basic idea of operation effectiveness simulation evaluation index construction under the guidance of operation concept is put forward. The specific methods of operation task modeling based on description, operation activity modeling based on decomposition, and operation effectiveness index selection based on effect are studied. The operation effectiveness simulation evaluation index system is constructed by taking the ground assault unit mountain key point capture and attack operation task guided …
Simulation On Critical Conditions Of Delay Diffusion In Scale-Free Logistics Network,
2022
Shanghai University of Engineering Science, Shanghai 201620, China;
Simulation On Critical Conditions Of Delay Diffusion In Scale-Free Logistics Network, Hongguang Yao, Huihui Xiao, Hang Zhang
Journal of System Simulation
Abstract: Aiming at the critical condition of delay diffusion in scale-free logistics network, a susceptible-infected-susceptible(SIS) model based on the density of delay nodes is established by mean field theory, and the critical value is solved. By designing the delay diffusion simulation system in logistics network, a virtual network with scale-free attribute is generated, and the critical condition is verified by simulation. The results show that there are a few key nodes with high value in scale-free logistics network. Once these nodes have service obstades, compared with other nodes, it will lead to a larger range of delay and faster …
Designing A Digital Interactive Emotion Measure (Diem) For Digital Media: Theoretical Foundations And Validation Protocols,
2022
Rochester Institute of Technology
Designing A Digital Interactive Emotion Measure (Diem) For Digital Media: Theoretical Foundations And Validation Protocols, Celeste Sangiorgio, Cassandra Berbary, Cory Crane, Caroline Easton
Frameless
Awareness of emotions is often a treatment target in psychotherapy, but it is difficult to assess emotions due to ambiguity in measurement or scale design. Lack of clarity in scale design may increase risk that participant interpretations of scale items may not align with emotion constructs those scales were designed to capture. Furthermore, emphasis on verbal or written cues leads to low scientific representation of patients who cannot read emotion scales (e.g., low literacy). Touch-screen applications provide a unique opportunity to create a visual emotion measure which has low barriers but can be used to assess a high level of …
Novel 360-Degree Camera,
2022
Circle Optics
Novel 360-Degree Camera, Ian Gauger, Andrew Kurtz, Zakariya Niazi
Frameless
Circle Optics is developing novel technology for low-parallax, real time, panoramic image capture using an integrated array of multiple adjacent polygonal-edged cameras. This technology can be optimized and deployed for a variety of markets, including cinematic VR. Circle Optics’ existing prototype, Hydra Alpha, will be demonstrated.
Warehouse And Logistics: Smart Picking With Vuzix Smart Glasses,
2022
Vuzix Corporation
Warehouse And Logistics: Smart Picking With Vuzix Smart Glasses, Elise Hemink
Frameless
Vuzix is an industry leader in augmented reality (AR) technology. We provide innovative products to an array of industries, a few being defense, security, enterprise, and consumers. Our AR technology provides a perfect balance of engagement in the digital and real worlds thanks to their innovative optics, AI apps and 5G capability.
Creating A Virtual Reality Experience In Service To A Non-Profit Agency,
2022
Rochester Institute of Technology
Creating A Virtual Reality Experience In Service To A Non-Profit Agency, Frank Deese, Susan Lakin, Isabelle Anderson
Frameless
In the summer of 2018, RIT Professors Susan Lakin and Frank Deese discussed with the principal officers of the Society for the Protection and Care of Children (SPCC) in Rochester how the new technology of Virtual Reality might be used to not only impart information to viewers, but generate empathy for those receiving services from the organization as well as those performing those services. Their ultimate goal was to create an experience that could be viewed with VR headsets at fundraising events and on a website using low-cost Google Cardboard.
Can We Walk In Our Patients’ Shoes? Immersive Virtual Reality As An Empathy Training Tool For Medical Students,
2022
University of Rochester
Can We Walk In Our Patients’ Shoes? Immersive Virtual Reality As An Empathy Training Tool For Medical Students, Riham Alieldin, Raffaella Borasi, Anne Nofziger, Karen Deangelis, Sarah Peyre
Frameless
Empathy is arguably the “backbone” of the patient-physician relationship. It has been shown to have numerous positive clinical outcomes especially in a patient-centered careservice. Nevertheless, studies have shown a disintegration of empathy and compassion in physicians during medical school and residency training due to the lack of standardization of empathy training in medical education.
Visual Attention Methods In Deep Learning: An In-Depth Survey,
2022
UniSA (University of South Australia), Faculty of Computers and Information, Fayoum University, Egypt
Visual Attention Methods In Deep Learning: An In-Depth Survey, Mohammed Hassanin, Anwar Saeed, Ibrahim Radwan, Fahad Shahbaz Khan, Ajmal Mian
Computer Vision Faculty Publications
Inspired by the human cognitive system, attention is a mechanism that imitates the human cognitive awareness about specific information, amplifying critical details to focus more on the essential aspects of data. Deep learning has employed attention to boost performance for many applications. Interestingly, the same attention design can suit processing different data modalities and can easily be incorporated into large networks. Furthermore, multiple complementary attention mechanisms can be incorporated in one network. Hence, attention techniques have become extremely attractive. However, the literature lacks a comprehensive survey specific to attention techniques to guide researchers in employing attention in their deep models. …
Iot Clusters Platform For Data Collection, Analysis, And Visualization Use Case,
2022
Kennesaw State University
Iot Clusters Platform For Data Collection, Analysis, And Visualization Use Case, Soin Abdoul Kassif Baba M Traore
Symposium of Student Scholars
Climate change is happening, and many countries are already facing devastating consequences. Populations worldwide are adapting to the season's unpredictability they relay to lands for agriculture. Our first research was to develop an IoT Clusters Platform for Data Collection, analysis, and visualization. The platform comprises hardware parts with Raspberry Pi and Arduino's clusters connected to multiple sensors. The clusters transmit data collected in real-time to microservices-based servers where the data can be accessed and processed. Our objectives in developing this platform were to create an efficient data collection system, relatively cheap to implement and easy to deploy in any part …
Machine Learning-Oriented Predictive Maintenance (Pdm) Framework For Autonomous Vehicles (Avs): Adopting Blockchain For Pdm Solution,
2022
Kennesaw State University
Machine Learning-Oriented Predictive Maintenance (Pdm) Framework For Autonomous Vehicles (Avs): Adopting Blockchain For Pdm Solution, Md Jobair Hossain Faruk, Hossain Shahriar, Maria Valero
Symposium of Student Scholars
Autonomous Vehicles (AVs) refers to smart, connected and multimedia cars with technological megatrends of the fourth industrial revolution (Industry 4.0) and have gained huge strive in today's world. AVs adopt automated driving systems (ADS) technique that permits the vehicle to manage and control driving points without human drivers by utilizing advanced equipment including a combination of sensors, controllers, onboard computers, actuators, algorithms, and advanced software embedded in the different parts of the vehicle. These advanced sensors provide unique inputs to the ADS to generate a path from point A to point B. Ensuring the safety of sensors by limiting maintenance …
Deep Learning: The Many Approaches Of Intrusion Detection System Can Be Implemented And Improved Upon,
2022
Norfolk State University
Deep Learning: The Many Approaches Of Intrusion Detection System Can Be Implemented And Improved Upon, Trinity Taylor
Cybersecurity Undergraduate Research Showcase
For my research topic I decided to look at Deep learning. Deep learning can be used in many ways for example in web searching. Deep learning can also can improve new businesses and products. Deep learning could lead to amazing discoveries. Deep learning is making a neural network learn something. In my research I talk about Intrusion detection system, traditional approach for intrusion detection, existing intrusion detection, machine learning and deep learning based intrusion detection system, and future work.
Internet Of Things Device Capabilities, Architectures, Protocols, And Smart Applications In Healthcare Domain: A Review,
2022
University of Waterloo, Canada
Internet Of Things Device Capabilities, Architectures, Protocols, And Smart Applications In Healthcare Domain: A Review, Md. Milon Islam, Sheikh Noorduddin, Fakhreddine (Fakhri) Karray, Ghulam Muhammad
Machine Learning Faculty Publications
Nowadays, the Internet has spread to practically every country around the world and is having unprecedented effects on people's lives. The Internet of Things (IoT) is getting more popular and has a high level of interest in both practitioners and academicians in the age of wireless communication due to its diverse applications. The IoT is a technology that enables everyday things to become savvier, everyday computation towards becoming intellectual, and everyday communication to become a little more insightful. In this paper, the most common and popular IoT device capabilities, architectures, and protocols are demonstrated in brief to provide a clear …
Mucot: Multilingual Contrastive Training For Question-Answering In Low-Resource Languages,
2022
Mohamed bin Zayed University of Artificial Intelligence
Mucot: Multilingual Contrastive Training For Question-Answering In Low-Resource Languages, Gokul Karthik Kumar, Abhishek Singh Gehlot, Sahal Shaji Mullappilly, Karthik Nandakumar
Computer Vision Faculty Publications
Accuracy of English-language Question Answering (QA) systems has improved significantly in recent years with the advent of Transformer-based models (e.g., BERT). These models are pre-trained in a self-supervised fashion with a large English text corpus and further fine-tuned with a massive English QA dataset (e.g., SQuAD). However, QA datasets on such a scale are not available for most of the other languages. Multi-lingual BERT-based models (mBERT) are often used to transfer knowledge from high-resource languages to low-resource languages. Since these models are pre-trained with huge text corpora containing multiple languages, they typically learn language-agnostic embeddings for tokens from different languages. …
Practical Considerations And Applications For Autonomous Robot Swarms,
2022
Louisiana State University
Practical Considerations And Applications For Autonomous Robot Swarms, Rory Alan Hector
LSU Doctoral Dissertations
In recent years, the study of autonomous entities such as unmanned vehicles has begun to revolutionize both military and civilian devices. One important research focus of autonomous entities has been coordination problems for autonomous robot swarms. Traditionally, robot models are used for algorithms that account for the minimum specifications needed to operate the swarm. However, these theoretical models also gloss over important practical details. Some of these details, such as time, have been considered before (as epochs of execution). In this dissertation, we examine these details in the context of several problems and introduce new performance measures to capture practical …
Multimodal Multi-Head Convolutional Attention With Various Kernel Sizes For Medical Image Super-Resolution,
2022
University of Bucharest, Romania
Multimodal Multi-Head Convolutional Attention With Various Kernel Sizes For Medical Image Super-Resolution, Mariana-Iuliana Georgescu, Radu Tudor Ionescu, Andreea-Iuliana Miron, Olivian Savencu, Nicolae Verga, Nicolae-Cătălin Ristea, Fahad Shabaz Khan
Computer Vision Faculty Publications
Super-resolving medical images can help physicians in providing more accurate diagnostics. In many situations, computed tomography (CT) or magnetic resonance imaging (MRI) techniques output several scans (modes) during a single investigation, which can jointly be used (in a multimodal fashion) to further boost the quality of super-resolution results. To this end, we propose a novel multimodal multi-head convolutional attention module to super-resolve CT and MRI scans. Our attention module uses the convolution operation to perform joint spatial-channel attention on multiple concatenated input tensors, where the kernel (receptive field) size controls the reduction rate of the spatial attention and the number …
Improving Negation Detection With Negation-Focused Pre-Training,
2022
The University of Melbourne, Australia
Improving Negation Detection With Negation-Focused Pre-Training, Hung Thinh Truong, Timothy Baldwin, Trevor Cohn, Karin Verspoor
Natural Language Processing Faculty Publications
Negation is a common linguistic feature that is crucial in many language understanding tasks, yet it remains a hard problem due to diversity in its expression in different types of text. Recent work has shown that state-of-the-art NLP models underperform on samples containing negation in various tasks, and that negation detection models do not transfer well across domains. We propose a new negation-focused pre-training strategy, involving targeted data augmentation and negation masking, to better incorporate negation information into language models. Extensive experiments on common benchmarks show that our proposed approach improves negation detection performance and generalizability over the strong baseline …
Wrapper And Hybrid Feature Selection Methods Using Metaheuristic Algorithms For English Text Classification: A Systematic Review,
2022
Universiti Sains Malaysia
Wrapper And Hybrid Feature Selection Methods Using Metaheuristic Algorithms For English Text Classification: A Systematic Review, Osamah Mohammed Alyasiri, Yu N. Cheah, Ammar Kamal Abasi, Omar Mustafa Al-Janabi
Machine Learning Faculty Publications
Feature selection (FS) constitutes a series of processes used to decide which relevant features/attributes to include and which irrelevant features to exclude for predictive modeling. It is a crucial task that aids machine learning classifiers in reducing error rates, computation time, overfitting, and improving classification accuracy. It has demonstrated its efficacy in myriads of domains, ranging from its use for text classification (TC), text mining, and image recognition. While there are many traditional FS methods, recent research efforts have been devoted to applying metaheuristic algorithms as FS techniques for the TC task. However, there are few literature reviews concerning TC. …
